diff --git a/.github/workflows/unittest-workflow.yml b/.github/workflows/unittest-workflow.yml
index c380b32..cbbbee3 100644
--- a/.github/workflows/unittest-workflow.yml
+++ b/.github/workflows/unittest-workflow.yml
@@ -9,17 +9,33 @@ jobs:
   unittest:
     runs-on: ubuntu-latest
     steps:
+      - name: checkout xcube
+        uses: actions/checkout@v4
+        with:
+          repository: xcube-dev/xcube
+          path: xcube
+
       - name: checkout xcube-stac
         uses: actions/checkout@v4
+        with:
+          path: xcube-stac
 
       - name: Set up MicroMamba
         uses: mamba-org/setup-micromamba@v1
         with:
-          environment-file: environment.yml
+          environment-file: xcube-stac/environment_workflow.yml
+
+      - name: Install xcube and start xcube server
+        shell: bash -l {0}
+        run: |
+          cd /home/runner/work/xcube-stac/xcube-stac/xcube
+          ls
+          pip install .
+          xcube serve -c examples/serve/demo/config.yml &
 
       - name: Run unit tests
         shell: bash -l {0}
         run: |
-          cd /home/runner/work/xcube-stac/xcube-stac
+          cd /home/runner/work/xcube-stac/xcube-stac/xcube-stac
           ls
           pytest 
diff --git a/README.md b/README.md
index 3d54532..540fddf 100644
--- a/README.md
+++ b/README.md
@@ -59,5 +59,9 @@ HTTP request. During development run
 pytest -v -s --record-mode new_episodes
 ```
 
-which saves the responses to `cassettes/**.yaml`. The testing can be then
-performed as usual.
+which saves the responses to `cassettes/**.yaml`. Note that `--record-mode new_episodes`
+overwrites all cassettes. If the user only wants to write cassettes which are not saved
+already, `--record-mode once` can be used.
+[pytest-recording](https://pypi.org/project/pytest-recording/) supports all records modes
+given by [VCR.py](https://vcrpy.readthedocs.io/en/latest/usage.html#record-modes).
+After recording the cassettes, testing can be then performed as usual.
diff --git a/environment_workflow.yml b/environment_workflow.yml
new file mode 100644
index 0000000..f06fe7d
--- /dev/null
+++ b/environment_workflow.yml
@@ -0,0 +1,54 @@
+name: xcube-stac
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  # Python
+  - python >=3.9
+  # Required xcube
+  - affine >=2.2
+  - botocore >=1.34.51
+  - cftime >=1.6.3
+  - click >=8.0
+  - cmocean >=2.0
+  - dask >=2021.6
+  - dask-image >=0.6
+  - deprecated >=1.2
+  - distributed >=2021.6
+  - fiona >=1.8
+  - fsspec >=2021.6
+  - gdal >=3.0
+  - geopandas >=0.8
+  - jdcal >=1.4
+  - jsonschema >=3.2
+  - mashumaro
+  - matplotlib-base >=3.8.3
+  - netcdf4 >=1.5
+  - numba >=0.52
+  - numcodecs >=0.12.1
+  - numpy >=1.16
+  - pandas >=1.3
+  - pillow >=6.0
+  - pyjwt >=1.7
+  - pyproj >=3.0
+  - pyyaml >=5.4
+  - rasterio >=1.2
+  - requests >=2.25
+  - rfc3339-validator >=0.1  # for python-jsonschema date-time format validation
+  - rioxarray >=0.11
+  - s3fs >=2021.6
+  - setuptools >=41.0
+  - shapely >=1.6
+  - tornado >=6.0
+  - urllib3 >=1.26
+  - xarray >=2022.6
+  - zarr >=2.11
+# Required xcube-stac
+  - pystac
+  - pystac-client
+  # for testing
+  - black
+  - flake8
+  - pytest
+  - pytest-cov
+  - pytest-recording
diff --git a/examples/geotiff_nonsearchable_catalog.ipynb b/examples/geotiff_nonsearchable_catalog.ipynb
new file mode 100644
index 0000000..dbf71de
--- /dev/null
+++ b/examples/geotiff_nonsearchable_catalog.ipynb
@@ -0,0 +1,2405 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load GeoTiff file from a non-seachable STAC catalog\n",
+    "\n",
+    "This notebook shows an example how to load a GeoTIFF file from a non-searchable STAC catalog. \n",
+    "\n",
+    "A non-searchable catalog does not implement the [STAC API - Item Search](https://github.com/radiantearth/stac-api-spec/tree/release/v1.0.0/item-search) conformance class. When searching in such type of catalog, the catalog needs to be crawled through and the items properties needs to be matched to the search parameters. This process can be slow, especially for large catalogs.\n",
+    "\n",
+    "### Setup\n",
+    "In order to run this notebook you need to install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac). You may install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) directly from the git repository by cloning the repository, directing into `xcube-stac`, and following the steps below:\n",
+    "\n",
+    "```bash\n",
+    "conda env create -f environment.yml\n",
+    "conda activate xcube-stac\n",
+    "pip install .\n",
+    "```\n",
+    "\n",
+    "Note that [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) is a plugin of [`xcube`](https://xcube.readthedocs.io/en/latest/), where `xcube` is included in the `environment.yml`.  \n",
+    "\n",
+    "Now, we first import everything we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3.43 s, sys: 244 ms, total: 3.67 s\n",
+      "Wall time: 1.9 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from xcube.core.store import new_data_store, get_data_store_params_schema\n",
+    "import itertools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we get the store parameters needed to initialize a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "description": "Describes the parameters of the xcube data store 'stac'.",
+       "properties": {
+        "storage_options_s3": {
+         "additionalProperties": true,
+         "properties": {
+          "anon": {
+           "title": "Whether to anonymously connect to AWS S3.",
+           "type": "boolean"
+          },
+          "asynchronous": {
+           "type": "boolean"
+          },
+          "client_kwargs": {
+           "additionalProperties": true,
+           "description": "Parameters for the botocore client.",
+           "properties": {
+            "endpoint_url": {
+             "format": "uri",
+             "minLength": 1,
+             "title": "Alternative endpoint URL.",
+             "type": "string"
+            },
+            "profile_name": {
+             "description": "Section name with within ~/.aws/config file, which provides AWS configurations and credentials.",
+             "minLength": 1,
+             "title": "Name of the AWS configuration profile",
+             "type": "string"
+            },
+            "region_name": {
+             "minLength": 1,
+             "title": "AWS storage region name",
+             "type": "string"
+            }
+           },
+           "type": "object"
+          },
+          "key": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_ACCESS_KEY_ID.",
+           "minLength": 1,
+           "title": "AWS access key identifier.",
+           "type": "string"
+          },
+          "listings_expiry_time": {
+           "type": "number"
+          },
+          "max_paths": {
+           "type": "integer"
+          },
+          "requester_pays": {
+           "default": false,
+           "description": "If \"RequesterPays\" buckets are supported.",
+           "type": "boolean"
+          },
+          "s3_additional_kwargs": {
+           "additionalProperties": true,
+           "description": "parameters that are used when calling S3 API methods. Typically used for things like \"ServerSideEncryption\".",
+           "properties": {},
+           "type": "object"
+          },
+          "secret": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_SECRET_ACCESS_KEY.",
+           "minLength": 1,
+           "title": "AWS secret access key.",
+           "type": "string"
+          },
+          "skip_instance_cache": {
+           "type": "boolean"
+          },
+          "token": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_SESSION_TOKEN.",
+           "minLength": 1,
+           "title": "Session token.",
+           "type": "string"
+          },
+          "use_listings_cache": {
+           "type": "boolean"
+          },
+          "use_ssl": {
+           "default": true,
+           "description": "Whether to use SSL in connections to S3; may be faster without, but insecure.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "url": {
+         "title": "URL to STAC catalog",
+         "type": "string"
+        }
+       },
+       "required": [
+        "url"
+       ],
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7d3df24432c0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_data_store_params_schema(\"stac\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We determine the url of the [EcoDataCube.eu](https://stac.ecodatacube.eu/) STAC catalog and initiate a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework) where the `xcube-stac` plugin is recognized by setting the first argument to `\"stac\"` in the `new_data_store` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 57.2 ms, sys: 5.28 ms, total: 62.5 ms\n",
+      "Wall time: 311 ms\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/konstantin/micromamba/envs/xcube-stac/lib/python3.12/site-packages/pystac_client/client.py:190: NoConformsTo: Server does not advertise any conformance classes.\n",
+      "  warnings.warn(NoConformsTo())\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "url = \"https://s3.eu-central-1.wasabisys.com/stac/odse/catalog.json\"\n",
+    "store = new_data_store(\"stac\", url=url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data IDs point to a [STAC item's JSON](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md) and are specified by the segment of the URL that follows the catalog's URL. The data IDs can be streamed using the following code where we show the first 10 data IDs as an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.66 s, sys: 275 ms, total: 1.93 s\n",
+      "Wall time: 18.3 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['lcv_land.mask_eumap/lcv_land.mask_eumap_2014.01.01..2016.12.31/lcv_land.mask_eumap_2014.01.01..2016.12.31.json',\n",
+       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20.json',\n",
+       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.03.21..2000.06.24/lcv_blue_landsat.glad.ard_2000.03.21..2000.06.24.json',\n",
+       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.06.25..2000.09.12/lcv_blue_landsat.glad.ard_2000.06.25..2000.09.12.json',\n",
+       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.09.13..2000.12.01/lcv_blue_landsat.glad.ard_2000.09.13..2000.12.01.json',\n",
+       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.12.02..2001.03.20/lcv_blue_landsat.glad.ard_2000.12.02..2001.03.20.json',\n",
+       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2001.03.21..2001.06.24/lcv_blue_landsat.glad.ard_2001.03.21..2001.06.24.json',\n",
+       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2001.06.25..2001.09.12/lcv_blue_landsat.glad.ard_2001.06.25..2001.09.12.json',\n",
+       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2001.09.13..2001.12.01/lcv_blue_landsat.glad.ard_2001.09.13..2001.12.01.json',\n",
+       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2001.12.02..2002.03.20/lcv_blue_landsat.glad.ard_2001.12.02..2002.03.20.json']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "data_ids = store.get_data_ids()\n",
+    "list(itertools.islice(data_ids, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next step, we can search for items using search parameters. The following code shows which search parameters are available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 37 µs, sys: 7 µs, total: 44 µs\n",
+      "Wall time: 47.9 µs\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "properties": {
+        "bbox": {
+         "items": [
+          {
+           "type": "number"
+          },
+          {
+           "type": "number"
+          },
+          {
+           "type": "number"
+          },
+          {
+           "type": "number"
+          }
+         ],
+         "title": "Bounding box [x1,y1,x2,y2] in geographical coordinates",
+         "type": "array"
+        },
+        "collections": {
+         "description": "Collection IDs to be included in the search request.",
+         "items": {
+          "minLength": 0,
+          "type": "string"
+         },
+         "title": "Collection IDs",
+         "type": "array",
+         "uniqueItems": true
+        },
+        "time_range": {
+         "description": "Time range given as pair of start and stop dates. Dates must be given using format 'YYYY-MM-DD'. Start and stop are inclusive.",
+         "items": [
+          {
+           "format": "date",
+           "type": [
+            "string",
+            "null"
+           ]
+          },
+          {
+           "format": "date",
+           "type": [
+            "string",
+            "null"
+           ]
+          }
+         ],
+         "title": "Time Range",
+         "type": "array"
+        }
+       },
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7d3df23c8cb0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "search_params = store.get_search_params_schema()\n",
+    "search_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's search for Landsat Thematic Mapper data for the European region during the first quarter of 2000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.58 s, sys: 143 ms, total: 1.73 s\n",
+      "Wall time: 15 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'data_id': 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20.json',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [-23.550818268711048,\n",
+       "   24.399543432891665,\n",
+       "   63.352379098951936,\n",
+       "   77.69295185585888],\n",
+       "  'time_range': ('1999-12-02', '2000-03-20'),\n",
+       "  'num_levels': 9},\n",
+       " {'data_id': 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.03.21..2000.06.24/lcv_blue_landsat.glad.ard_2000.03.21..2000.06.24.json',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [-23.550818268711048,\n",
+       "   24.399543432891665,\n",
+       "   63.352379098951936,\n",
+       "   77.69295185585888],\n",
+       "  'time_range': ('2000-03-21', '2000-06-24'),\n",
+       "  'num_levels': 9}]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "descriptors = list(\n",
+    "    store.search_data(\n",
+    "        collections=[\"lcv_blue_landsat.glad.ard\"],\n",
+    "        bbox=[-10, 40, 40, 70],\n",
+    "        time_range=[\"2000-01-01\", \"2000-04-01\"],\n",
+    "    )\n",
+    ")\n",
+    "[d.to_dict() for d in descriptors]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next step, we can open the data for each data ID. The following code shows which parameters are available for opening the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 921 µs, sys: 168 µs, total: 1.09 ms\n",
+      "Wall time: 1.09 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "properties": {
+        "asset_names": {
+         "description": "Names of assets which will be included in the data cube.",
+         "items": {
+          "minLength": 0,
+          "type": "string"
+         },
+         "title": "Names of assets",
+         "type": "array",
+         "uniqueItems": true
+        },
+        "open_params_dataset_geotiff": {
+         "additionalProperties": false,
+         "properties": {
+          "overview_level": {
+           "default": null,
+           "description": "GeoTIFF overview level. 0 is the first overview.",
+           "type": [
+            "integer",
+            "null"
+           ]
+          },
+          "tile_size": {
+           "default": [
+            512,
+            512
+           ],
+           "items": [
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            },
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            }
+           ],
+           "type": "array"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_dataset_levels": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_dataset_netcdf": {
+         "additionalProperties": true,
+         "properties": {},
+         "type": "object"
+        },
+        "open_params_dataset_zarr": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_mldataset_geotiff": {
+         "additionalProperties": false,
+         "properties": {
+          "tile_size": {
+           "default": [
+            512,
+            512
+           ],
+           "items": [
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            },
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            }
+           ],
+           "type": "array"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_mldataset_levels": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        }
+       },
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7d3df15b3590>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "open_params = store.get_open_data_params_schema()\n",
+    "open_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we open the data for a given data ID, where we select all available assets, which can be opened by the data store. Note that we assign `data_type` to get dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.47 s, sys: 123 ms, total: 1.6 s\n",
+      "Wall time: 2.44 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
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+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
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+       "\n",
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+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
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+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
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+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
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+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 114GB\n",
+       "Dimensions:      (x: 188000, y: 151000)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 2MB 9e+05 9e+05 9.001e+05 ... 6.54e+06 6.54e+06\n",
+       "  * y            (y) float64 1MB 5.46e+06 5.46e+06 ... 9.301e+05 9.3e+05\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    blue_p50     (y, x) uint8 28GB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    blue_p25     (y, x) uint8 28GB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    blue_p75     (y, x) uint8 28GB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    qa_f         (y, x) uint8 28GB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    source:   https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsa...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e5eac2f1-c76a-4a4d-81b9-39a85241c565' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e5eac2f1-c76a-4a4d-81b9-39a85241c565' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 188000</li><li><span class='xr-has-index'>y</span>: 151000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-10178ec0-8f02-468f-a5ea-5e03b7bef507' class='xr-section-summary-in' type='checkbox'  checked><label for='section-10178ec0-8f02-468f-a5ea-5e03b7bef507' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9e+05 9e+05 ... 6.54e+06 6.54e+06</div><input id='attrs-f5adf467-348f-41ae-b2ef-85c12c52d440' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5adf467-348f-41ae-b2ef-85c12c52d440' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb61f815-b08a-4d69-8b67-5b11f9845e1c' class='xr-var-data-in' type='checkbox'><label for='data-eb61f815-b08a-4d69-8b67-5b11f9845e1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 900015.,  900045.,  900075., ..., 6539925., 6539955., 6539985.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.46e+06 5.46e+06 ... 9.3e+05</div><input id='attrs-ed749550-685d-427f-abfd-b898cbe64933' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed749550-685d-427f-abfd-b898cbe64933' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad94424c-b6aa-4300-b3c8-5d66d8b06cef' class='xr-var-data-in' type='checkbox'><label for='data-ad94424c-b6aa-4300-b3c8-5d66d8b06cef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([5459995., 5459965., 5459935., ...,  930085.,  930055.,  930025.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-7b219a36-2930-4f9a-8bf6-c7ee4789d69c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b219a36-2930-4f9a-8bf6-c7ee4789d69c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b4bc53e-7214-4380-b6a0-749fc091c6a4' class='xr-var-data-in' type='checkbox'><label for='data-8b4bc53e-7214-4380-b6a0-749fc091c6a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;unknown&quot;,GEOGCS[&quot;unknown&quot;,DATUM[&quot;Unknown_based_on_GRS80_ellipsoid&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101004,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101004</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>Unknown_based_on_GRS80_ellipsoid</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;unknown&quot;,GEOGCS[&quot;unknown&quot;,DATUM[&quot;Unknown_based_on_GRS80_ellipsoid&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101004,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>900000.0 30.0 0.0 5460010.0 0.0 -30.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c630ef4-39f0-445d-bfd4-0917e006e451' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c630ef4-39f0-445d-bfd4-0917e006e451' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue_p50</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-0b9a1cb5-27f6-45f4-9d5c-75e4f58796e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b9a1cb5-27f6-45f4-9d5c-75e4f58796e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3aef5a9-fc5e-4685-8e00-e17c0efc4ea2' class='xr-var-data-in' type='checkbox'><label for='data-e3aef5a9-fc5e-4685-8e00-e17c0efc4ea2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.44 GiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (151000, 188000) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 108560 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
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+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
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+       "  <line x1=\"0\" y1=\"50\" x2=\"120\" y2=\"50\" />\n",
+       "  <line x1=\"0\" y1=\"55\" x2=\"120\" y2=\"55\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"116.382979\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >188000</text>\n",
+       "  <text x=\"140.000000\" y=\"48.191489\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,48.191489)\">151000</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blue_p25</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-b99161b9-d60f-46d3-81ea-b59e887c17e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b99161b9-d60f-46d3-81ea-b59e887c17e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5099e39c-93be-48c1-a5fa-d6dc381d0f62' class='xr-var-data-in' type='checkbox'><label for='data-5099e39c-93be-48c1-a5fa-d6dc381d0f62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.44 GiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (151000, 188000) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 108560 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
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+       "  <line x1=\"0\" y1=\"65\" x2=\"120\" y2=\"65\" />\n",
+       "  <line x1=\"0\" y1=\"70\" x2=\"120\" y2=\"70\" />\n",
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+       "  <line x1=\"0\" y1=\"96\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"96\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"96\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"116.382979\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >188000</text>\n",
+       "  <text x=\"140.000000\" y=\"48.191489\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,48.191489)\">151000</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blue_p75</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-5418904e-c6f2-419b-b890-f9e38a638f09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5418904e-c6f2-419b-b890-f9e38a638f09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0eadaed6-8399-4d88-8bee-116d7a35fc47' class='xr-var-data-in' type='checkbox'><label for='data-0eadaed6-8399-4d88-8bee-116d7a35fc47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.44 GiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (151000, 188000) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 108560 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
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+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
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+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
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+       "  <line x1=\"0\" y1=\"50\" x2=\"120\" y2=\"50\" />\n",
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+       "  <line x1=\"0\" y1=\"96\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"96\" />\n",
+       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"96\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"116.382979\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >188000</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qa_f</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-2c6aca45-7789-450c-bba1-a6fa47201f28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c6aca45-7789-450c-bba1-a6fa47201f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1575338-a04a-4b4b-a7a4-e1b9b35d16c0' class='xr-var-data-in' type='checkbox'><label for='data-e1575338-a04a-4b4b-a7a4-e1b9b35d16c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.44 GiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (151000, 188000) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 108560 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"35\" x2=\"120\" y2=\"35\" />\n",
+       "  <line x1=\"0\" y1=\"40\" x2=\"120\" y2=\"40\" />\n",
+       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
+       "  <line x1=\"0\" y1=\"50\" x2=\"120\" y2=\"50\" />\n",
+       "  <line x1=\"0\" y1=\"55\" x2=\"120\" y2=\"55\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"65\" x2=\"120\" y2=\"65\" />\n",
+       "  <line x1=\"0\" y1=\"70\" x2=\"120\" y2=\"70\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"81\" x2=\"120\" y2=\"81\" />\n",
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+       "  <line x1=\"0\" y1=\"91\" x2=\"120\" y2=\"91\" />\n",
+       "  <line x1=\"0\" y1=\"96\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"96\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"96\" />\n",
+       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"96\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"96\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"96\" />\n",
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+       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"96\" />\n",
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+       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"96\" />\n",
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+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"96\" />\n",
+       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"96\" />\n",
+       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"96\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"96\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"96\" />\n",
+       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"96\" />\n",
+       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"96\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,96.38297872340426 0.0,96.38297872340426\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"116.382979\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >188000</text>\n",
+       "  <text x=\"140.000000\" y=\"48.191489\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,48.191489)\">151000</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b797ea1a-258b-4c21-b88f-d4036390934d' class='xr-section-summary-in' type='checkbox'  ><label for='section-b797ea1a-258b-4c21-b88f-d4036390934d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5f34b438-cfbf-4e03-a0c5-3bad1610a800' class='xr-index-data-in' type='checkbox'/><label for='index-5f34b438-cfbf-4e03-a0c5-3bad1610a800' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 900015.0,  900045.0,  900075.0,  900105.0,  900135.0,  900165.0,\n",
+       "        900195.0,  900225.0,  900255.0,  900285.0,\n",
+       "       ...\n",
+       "       6539715.0, 6539745.0, 6539775.0, 6539805.0, 6539835.0, 6539865.0,\n",
+       "       6539895.0, 6539925.0, 6539955.0, 6539985.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=188000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7c5e2e3e-c9b2-40ed-88de-3b38190d860a' class='xr-index-data-in' type='checkbox'/><label for='index-7c5e2e3e-c9b2-40ed-88de-3b38190d860a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([5459995.0, 5459965.0, 5459935.0, 5459905.0, 5459875.0, 5459845.0,\n",
+       "       5459815.0, 5459785.0, 5459755.0, 5459725.0,\n",
+       "       ...\n",
+       "        930295.0,  930265.0,  930235.0,  930205.0,  930175.0,  930145.0,\n",
+       "        930115.0,  930085.0,  930055.0,  930025.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=151000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b581b071-5d3b-482c-8147-a5a95fc121e1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b581b071-5d3b-482c-8147-a5a95fc121e1' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsat.glad.ard_p50_30m_0..0cm_1999.12.02..2000.03.20_eumap_epsg3035_v1.1.tif</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 114GB\n",
+       "Dimensions:      (x: 188000, y: 151000)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 2MB 9e+05 9e+05 9.001e+05 ... 6.54e+06 6.54e+06\n",
+       "  * y            (y) float64 1MB 5.46e+06 5.46e+06 ... 9.301e+05 9.3e+05\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    blue_p50     (y, x) uint8 28GB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    blue_p25     (y, x) uint8 28GB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    blue_p75     (y, x) uint8 28GB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    qa_f         (y, x) uint8 28GB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    source:   https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsa..."
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = store.open_data(descriptors[0].data_id, data_type=\"dataset\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We plot the loaded data as an example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.26 s, sys: 2.03 s, total: 6.29 s\n",
+      "Wall time: 56.1 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7d3ddc10e120>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.blue_p50[100000:120000:20, 100000:120000:20].plot(vmax=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we open a GeoTIFF, the default opener represents the data as a [xcube's multi-resolution  dataset](https://xcube.readthedocs.io/en/latest/mldatasets.html#xcube-multi-resolution-datasets), where we can select the level of resolution, shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.98 s, sys: 107 ms, total: 2.09 s\n",
+      "Wall time: 2.35 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "mlds = store.open_data(descriptors[0].data_id)\n",
+    "mlds.num_levels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 111MB\n",
+       "Dimensions:      (x: 5875, y: 4719)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 47kB 9.005e+05 9.014e+05 ... 6.539e+06 6.54e+06\n",
+       "  * y            (y) float64 38kB 5.46e+06 5.459e+06 ... 9.314e+05 9.305e+05\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    blue_p50     (y, x) uint8 28MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    blue_p25     (y, x) uint8 28MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    blue_p75     (y, x) uint8 28MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    qa_f         (y, x) uint8 28MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    source:   https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsa...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-46620f9b-25c3-4688-851b-dd8cc5ad3464' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46620f9b-25c3-4688-851b-dd8cc5ad3464' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 5875</li><li><span class='xr-has-index'>y</span>: 4719</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cdafc797-c91a-444b-b4bc-e0dba7f9560f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cdafc797-c91a-444b-b4bc-e0dba7f9560f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.005e+05 9.014e+05 ... 6.54e+06</div><input id='attrs-4a6aa965-1132-45e8-a771-5e69ad62ea2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a6aa965-1132-45e8-a771-5e69ad62ea2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95671ca8-f861-4da3-81a2-bf23d85aeca5' class='xr-var-data-in' type='checkbox'><label for='data-95671ca8-f861-4da3-81a2-bf23d85aeca5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 900480.,  901440.,  902400., ..., 6537600., 6538560., 6539520.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.46e+06 5.459e+06 ... 9.305e+05</div><input id='attrs-33591b6e-61cd-421b-86bd-5ea5df04f823' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33591b6e-61cd-421b-86bd-5ea5df04f823' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f7bb66-5a16-44e2-a8ba-055c7ed0ccd4' class='xr-var-data-in' type='checkbox'><label for='data-85f7bb66-5a16-44e2-a8ba-055c7ed0ccd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([5459530.025429, 5458570.076287, 5457610.127146, ...,  932409.872854,\n",
+       "        931449.923713,  930489.974571])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c7606df9-569a-46fc-b918-f7d253970ccf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7606df9-569a-46fc-b918-f7d253970ccf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-222d6b7a-73cf-46e3-a348-ebc2dbdb7f10' class='xr-var-data-in' type='checkbox'><label for='data-222d6b7a-73cf-46e3-a348-ebc2dbdb7f10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;unknown&quot;,GEOGCS[&quot;unknown&quot;,DATUM[&quot;Unknown_based_on_GRS80_ellipsoid&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101004,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101004</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>unknown</dd><dt><span>horizontal_datum_name :</span></dt><dd>Unknown_based_on_GRS80_ellipsoid</dd><dt><span>projected_crs_name :</span></dt><dd>unknown</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_azimuthal_equal_area</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>52.0</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>10.0</dd><dt><span>false_easting :</span></dt><dd>4321000.0</dd><dt><span>false_northing :</span></dt><dd>3210000.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;unknown&quot;,GEOGCS[&quot;unknown&quot;,DATUM[&quot;Unknown_based_on_GRS80_ellipsoid&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101004,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,52],PARAMETER[&quot;longitude_of_center&quot;,10],PARAMETER[&quot;false_easting&quot;,4321000],PARAMETER[&quot;false_northing&quot;,3210000],UNIT[&quot;metre&quot;,1],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>900000.0 960.0 0.0 5460010.0 0.0 -959.9491417673236</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6910203-4cc1-4c9b-80ab-ab590cf3f664' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6910203-4cc1-4c9b-80ab-ab590cf3f664' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue_p50</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-3ea0b671-e78b-4bd1-97b2-7b71f157c7ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ea0b671-e78b-4bd1-97b2-7b71f157c7ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bbd464a-b33f-49e6-9194-3bba94e0b685' class='xr-var-data-in' type='checkbox'><label for='data-8bbd464a-b33f-49e6-9194-3bba94e0b685' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.44 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4719, 5875) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 120 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" />\n",
+       "  <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" />\n",
+       "  <line x1=\"0\" y1=\"62\" x2=\"120\" y2=\"62\" />\n",
+       "  <line x1=\"0\" y1=\"73\" x2=\"120\" y2=\"73\" />\n",
+       "  <line x1=\"0\" y1=\"83\" x2=\"120\" y2=\"83\" />\n",
+       "  <line x1=\"0\" y1=\"94\" x2=\"120\" y2=\"94\" />\n",
+       "  <line x1=\"0\" y1=\"96\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"96\" />\n",
+       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"96\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"96\" />\n",
+       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"96\" />\n",
+       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"96\" />\n",
+       "  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"96\" />\n",
+       "  <line x1=\"73\" y1=\"0\" x2=\"73\" y2=\"96\" />\n",
+       "  <line x1=\"83\" y1=\"0\" x2=\"83\" y2=\"96\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"96\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"96\" />\n",
+       "  <line x1=\"115\" y1=\"0\" x2=\"115\" y2=\"96\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,96.38808510638297 0.0,96.38808510638297\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"116.388085\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5875</text>\n",
+       "  <text x=\"140.000000\" y=\"48.194043\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,48.194043)\">4719</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blue_p25</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-cd26ae34-60c6-4b53-a88b-aabbfb3c72d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd26ae34-60c6-4b53-a88b-aabbfb3c72d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d176969c-6a9a-4352-9a42-1d96cb440145' class='xr-var-data-in' type='checkbox'><label for='data-d176969c-6a9a-4352-9a42-1d96cb440145' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.44 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4719, 5875) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 120 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" />\n",
+       "  <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" />\n",
+       "  <line x1=\"0\" y1=\"62\" x2=\"120\" y2=\"62\" />\n",
+       "  <line x1=\"0\" y1=\"73\" x2=\"120\" y2=\"73\" />\n",
+       "  <line x1=\"0\" y1=\"83\" x2=\"120\" y2=\"83\" />\n",
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+       "  <line x1=\"0\" y1=\"96\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"96\" />\n",
+       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"96\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"96\" />\n",
+       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"96\" />\n",
+       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"96\" />\n",
+       "  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"96\" />\n",
+       "  <line x1=\"73\" y1=\"0\" x2=\"73\" y2=\"96\" />\n",
+       "  <line x1=\"83\" y1=\"0\" x2=\"83\" y2=\"96\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"96\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"96\" />\n",
+       "  <line x1=\"115\" y1=\"0\" x2=\"115\" y2=\"96\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,96.38808510638297 0.0,96.38808510638297\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"116.388085\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5875</text>\n",
+       "  <text x=\"140.000000\" y=\"48.194043\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,48.194043)\">4719</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blue_p75</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-f9d33d4d-5be5-4728-b51f-3d185e082022' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9d33d4d-5be5-4728-b51f-3d185e082022' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5431878e-01cc-480d-94d9-c895c1739d90' class='xr-var-data-in' type='checkbox'><label for='data-5431878e-01cc-480d-94d9-c895c1739d90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.44 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4719, 5875) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 120 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" />\n",
+       "  <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" />\n",
+       "  <line x1=\"0\" y1=\"62\" x2=\"120\" y2=\"62\" />\n",
+       "  <line x1=\"0\" y1=\"73\" x2=\"120\" y2=\"73\" />\n",
+       "  <line x1=\"0\" y1=\"83\" x2=\"120\" y2=\"83\" />\n",
+       "  <line x1=\"0\" y1=\"94\" x2=\"120\" y2=\"94\" />\n",
+       "  <line x1=\"0\" y1=\"96\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"96\" />\n",
+       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"96\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"96\" />\n",
+       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"96\" />\n",
+       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"96\" />\n",
+       "  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"96\" />\n",
+       "  <line x1=\"73\" y1=\"0\" x2=\"73\" y2=\"96\" />\n",
+       "  <line x1=\"83\" y1=\"0\" x2=\"83\" y2=\"96\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"96\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"96\" />\n",
+       "  <line x1=\"115\" y1=\"0\" x2=\"115\" y2=\"96\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,96.38808510638297 0.0,96.38808510638297\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"116.388085\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5875</text>\n",
+       "  <text x=\"140.000000\" y=\"48.194043\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,48.194043)\">4719</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qa_f</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-3656ea50-bba6-4d6b-a2fd-3072adb23cf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3656ea50-bba6-4d6b-a2fd-3072adb23cf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d5efa1f-c1fd-44b8-a8ae-8e237d91deda' class='xr-var-data-in' type='checkbox'><label for='data-0d5efa1f-c1fd-44b8-a8ae-8e237d91deda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 26.44 MiB </td>\n",
+       "                        <td> 256.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4719, 5875) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 120 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" />\n",
+       "  <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" />\n",
+       "  <line x1=\"0\" y1=\"62\" x2=\"120\" y2=\"62\" />\n",
+       "  <line x1=\"0\" y1=\"73\" x2=\"120\" y2=\"73\" />\n",
+       "  <line x1=\"0\" y1=\"83\" x2=\"120\" y2=\"83\" />\n",
+       "  <line x1=\"0\" y1=\"94\" x2=\"120\" y2=\"94\" />\n",
+       "  <line x1=\"0\" y1=\"96\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"96\" />\n",
+       "  <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"96\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"96\" />\n",
+       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"96\" />\n",
+       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"96\" />\n",
+       "  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"96\" />\n",
+       "  <line x1=\"73\" y1=\"0\" x2=\"73\" y2=\"96\" />\n",
+       "  <line x1=\"83\" y1=\"0\" x2=\"83\" y2=\"96\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"96\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"96\" />\n",
+       "  <line x1=\"115\" y1=\"0\" x2=\"115\" y2=\"96\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,96.38808510638297 0.0,96.38808510638297\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"116.388085\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5875</text>\n",
+       "  <text x=\"140.000000\" y=\"48.194043\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,48.194043)\">4719</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-244a4505-db7a-4087-ada9-f31691069164' class='xr-section-summary-in' type='checkbox'  ><label for='section-244a4505-db7a-4087-ada9-f31691069164' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-64756f34-c25f-455a-ac21-d8e57e384374' class='xr-index-data-in' type='checkbox'/><label for='index-64756f34-c25f-455a-ac21-d8e57e384374' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 900480.0,  901440.0,  902400.0,  903360.0,  904320.0,  905280.0,\n",
+       "        906240.0,  907200.0,  908160.0,  909120.0,\n",
+       "       ...\n",
+       "       6530880.0, 6531840.0, 6532800.0, 6533760.0, 6534720.0, 6535680.0,\n",
+       "       6536640.0, 6537600.0, 6538560.0, 6539520.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=5875))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ac047a1-f08b-4c7c-a8ea-92422cf1cbfa' class='xr-index-data-in' type='checkbox'/><label for='index-6ac047a1-f08b-4c7c-a8ea-92422cf1cbfa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 5459530.025429117,   5458570.07628735,  5457610.127145582,\n",
+       "        5456650.178003815,  5455690.228862047,   5454730.27972028,\n",
+       "       5453770.3305785125,  5452810.381436746,  5451850.432294978,\n",
+       "        5450890.483153211,\n",
+       "       ...\n",
+       "          939129.51684679,  938169.5677050222,  937209.6185632553,\n",
+       "        936249.6694214875,  935289.7202797206,  934329.7711379528,\n",
+       "        933369.8219961859,   932409.872854419,  931449.9237126512,\n",
+       "        930489.9745708844],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=4719))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34ca2a1b-c4b3-4d77-82ea-f8f4d1c4d37e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-34ca2a1b-c4b3-4d77-82ea-f8f4d1c4d37e' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsat.glad.ard_p50_30m_0..0cm_1999.12.02..2000.03.20_eumap_epsg3035_v1.1.tif</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 111MB\n",
+       "Dimensions:      (x: 5875, y: 4719)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 47kB 9.005e+05 9.014e+05 ... 6.539e+06 6.54e+06\n",
+       "  * y            (y) float64 38kB 5.46e+06 5.459e+06 ... 9.314e+05 9.305e+05\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    blue_p50     (y, x) uint8 28MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    blue_p25     (y, x) uint8 28MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    blue_p75     (y, x) uint8 28MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    qa_f         (y, x) uint8 28MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    source:   https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsa..."
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = mlds.get_dataset(5)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 109 ms, sys: 7.45 ms, total: 116 ms\n",
+      "Wall time: 315 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7d3d86f53110>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.blue_p50[3125:3750, 3125:3750].plot(vmax=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/geotiff_searchable_catalog.ipynb b/examples/geotiff_searchable_catalog.ipynb
new file mode 100644
index 0000000..b295015
--- /dev/null
+++ b/examples/geotiff_searchable_catalog.ipynb
@@ -0,0 +1,2298 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load GeoTiff file from a seachable STAC catalog\n",
+    "\n",
+    "This notebook shows an example how to load a GeoTIFF file from a searchable STAC catalog. \n",
+    "\n",
+    "A searchable catalog implements the [STAC API - Item Search](https://github.com/radiantearth/stac-api-spec/tree/release/v1.0.0/item-search) conformance class, which provides the ability to search for STAC Item objects across collections.\n",
+    "\n",
+    "### Setup\n",
+    "In order to run this notebook you need to install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac). You may install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) directly from the git repository by cloning the repository, directing into `xcube-stac`, and following the steps below:\n",
+    "\n",
+    "```bash\n",
+    "conda env create -f environment.yml\n",
+    "conda activate xcube-stac\n",
+    "pip install .\n",
+    "```\n",
+    "\n",
+    "Note that [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) is a plugin of [`xcube`](https://xcube.readthedocs.io/en/latest/), where `xcube` is included in the `environment.yml`.  \n",
+    "\n",
+    "Now, we first import everything we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.23 s, sys: 495 ms, total: 4.73 s\n",
+      "Wall time: 2.96 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from xcube.core.store import new_data_store, get_data_store_params_schema\n",
+    "import itertools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we get the store parameters needed to initialize a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 322 ms, sys: 48.2 ms, total: 370 ms\n",
+      "Wall time: 367 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "description": "Describes the parameters of the xcube data store 'stac'.",
+       "properties": {
+        "storage_options_s3": {
+         "additionalProperties": true,
+         "properties": {
+          "anon": {
+           "title": "Whether to anonymously connect to AWS S3.",
+           "type": "boolean"
+          },
+          "asynchronous": {
+           "type": "boolean"
+          },
+          "client_kwargs": {
+           "additionalProperties": true,
+           "description": "Parameters for the botocore client.",
+           "properties": {
+            "endpoint_url": {
+             "format": "uri",
+             "minLength": 1,
+             "title": "Alternative endpoint URL.",
+             "type": "string"
+            },
+            "profile_name": {
+             "description": "Section name with within ~/.aws/config file, which provides AWS configurations and credentials.",
+             "minLength": 1,
+             "title": "Name of the AWS configuration profile",
+             "type": "string"
+            },
+            "region_name": {
+             "minLength": 1,
+             "title": "AWS storage region name",
+             "type": "string"
+            }
+           },
+           "type": "object"
+          },
+          "key": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_ACCESS_KEY_ID.",
+           "minLength": 1,
+           "title": "AWS access key identifier.",
+           "type": "string"
+          },
+          "listings_expiry_time": {
+           "type": "number"
+          },
+          "max_paths": {
+           "type": "integer"
+          },
+          "requester_pays": {
+           "default": false,
+           "description": "If \"RequesterPays\" buckets are supported.",
+           "type": "boolean"
+          },
+          "s3_additional_kwargs": {
+           "additionalProperties": true,
+           "description": "parameters that are used when calling S3 API methods. Typically used for things like \"ServerSideEncryption\".",
+           "properties": {},
+           "type": "object"
+          },
+          "secret": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_SECRET_ACCESS_KEY.",
+           "minLength": 1,
+           "title": "AWS secret access key.",
+           "type": "string"
+          },
+          "skip_instance_cache": {
+           "type": "boolean"
+          },
+          "token": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_SESSION_TOKEN.",
+           "minLength": 1,
+           "title": "Session token.",
+           "type": "string"
+          },
+          "use_listings_cache": {
+           "type": "boolean"
+          },
+          "use_ssl": {
+           "default": true,
+           "description": "Whether to use SSL in connections to S3; may be faster without, but insecure.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "url": {
+         "title": "URL to STAC catalog",
+         "type": "string"
+        }
+       },
+       "required": [
+        "url"
+       ],
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7128f51b3890>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "store_params = get_data_store_params_schema(\"stac\")\n",
+    "store_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We determine the url of the [Earth Search](https://element84.com/earth-search/) STAC catalog and initiate a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework), where the `xcube-stac` plugin is recognized by setting the first argument to `\"stac\"` in the `new_data_store` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 15.7 ms, sys: 11.9 ms, total: 27.6 ms\n",
+      "Wall time: 855 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "url = \"https://earth-search.aws.element84.com/v1\"\n",
+    "store = new_data_store(\"stac\", url=url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data IDs point to a [STAC item's JSON](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md) and are specified by the segment of the URL that follows the catalog's URL. The data IDs can be streamed using the following code where we show the first 10 data IDs as an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 23.5 ms, sys: 324 µs, total: 23.9 ms\n",
+      "Wall time: 1.1 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['collections/sentinel-1-grd/items/S1A_EW_GRDM_1SDH_20240711T122338_20240711T122433_054713_06A944',\n",
+       " 'collections/sentinel-1-grd/items/S1A_EW_GRDM_1SDH_20240711T122238_20240711T122338_054713_06A944',\n",
+       " 'collections/sentinel-1-grd/items/S1A_EW_GRDM_1SDH_20240711T122138_20240711T122238_054713_06A944',\n",
+       " 'collections/sentinel-1-grd/items/S1A_EW_GRDM_1SDH_20240711T122038_20240711T122138_054713_06A944',\n",
+       " 'collections/sentinel-1-grd/items/S1A_EW_GRDM_1SDH_20240711T121938_20240711T122038_054713_06A944',\n",
+       " 'collections/sentinel-1-grd/items/S1A_EW_GRDM_1SDH_20240711T121838_20240711T121938_054713_06A944',\n",
+       " 'collections/sentinel-1-grd/items/S1A_EW_GRDM_1SDH_20240711T121734_20240711T121838_054713_06A944',\n",
+       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240711T110311_20240711T110333_054712_06A93F',\n",
+       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240711T110221_20240711T110246_054712_06A93F',\n",
+       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240711T105615_20240711T105640_054712_06A93E']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "data_ids = store.get_data_ids()\n",
+    "list(itertools.islice(data_ids, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next step, we can search for items using search parameters. The following code shows which search parameters are available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 31 µs, sys: 4 µs, total: 35 µs\n",
+      "Wall time: 37.7 µs\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "properties": {
+        "bbox": {
+         "items": [
+          {
+           "type": "number"
+          },
+          {
+           "type": "number"
+          },
+          {
+           "type": "number"
+          },
+          {
+           "type": "number"
+          }
+         ],
+         "title": "Bounding box [x1,y1,x2,y2] in geographical coordinates",
+         "type": "array"
+        },
+        "collections": {
+         "description": "Collection IDs to be included in the search request.",
+         "items": {
+          "minLength": 0,
+          "type": "string"
+         },
+         "title": "Collection IDs",
+         "type": "array",
+         "uniqueItems": true
+        },
+        "time_range": {
+         "description": "Time range given as pair of start and stop dates. Dates must be given using format 'YYYY-MM-DD'. Start and stop are inclusive.",
+         "items": [
+          {
+           "format": "date",
+           "type": [
+            "string",
+            "null"
+           ]
+          },
+          {
+           "format": "date",
+           "type": [
+            "string",
+            "null"
+           ]
+          }
+         ],
+         "title": "Time Range",
+         "type": "array"
+        }
+       },
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7128f51d1130>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "search_params = store.get_search_params_schema()\n",
+    "search_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's search for Sentinel-2 L2a data in the region of Lake Constance for the period from June 1st to June 5th, 2020."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.52 s, sys: 139 ms, total: 1.66 s\n",
+      "Wall time: 1min 3s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'data_id': 'collections/sentinel-2-l2a/items/S2B_32TMT_20200705_1_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [7.769228745834018,\n",
+       "   46.85909198885185,\n",
+       "   9.130456971519783,\n",
+       "   47.85361872923358],\n",
+       "  'time_range': ('2020-07-05T10:27:51.180000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2B_32TMT_20200705_0_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [7.762146064135137,\n",
+       "   46.85901560719437,\n",
+       "   9.130456971519783,\n",
+       "   47.85361872923358],\n",
+       "  'time_range': ('2020-07-05T10:27:51.177000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2B_32TNT_20200705_1_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.999746010269408,\n",
+       "   46.85664904260366,\n",
+       "   10.467263761969436,\n",
+       "   47.85369284462768],\n",
+       "  'time_range': ('2020-07-05T10:27:47.411000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2B_32TNT_20200705_0_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.999746010269408,\n",
+       "   46.85664904260366,\n",
+       "   10.467263761969436,\n",
+       "   47.85369284462768],\n",
+       "  'time_range': ('2020-07-05T10:27:47.409000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2B_32UMU_20200705_1_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.097183381629907,\n",
+       "   47.76162472180052,\n",
+       "   9.132768981529203,\n",
+       "   48.752927528985374],\n",
+       "  'time_range': ('2020-07-05T10:27:36.382000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2B_32UMU_20200705_0_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.089444803260868,\n",
+       "   47.76156358517808,\n",
+       "   9.132768981529203,\n",
+       "   48.752927528985374],\n",
+       "  'time_range': ('2020-07-05T10:27:36.380000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2B_32UNU_20200705_1_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.999741508947045,\n",
+       "   47.755827810229526,\n",
+       "   10.493255611539013,\n",
+       "   48.75300400802843],\n",
+       "  'time_range': ('2020-07-05T10:27:33.029000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2B_32UNU_20200705_0_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.999741508947045,\n",
+       "   47.755827810229526,\n",
+       "   10.493255611539013,\n",
+       "   48.75300400802843],\n",
+       "  'time_range': ('2020-07-05T10:27:33.027000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32TMT_20200703_0_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [7.662878883910047,\n",
+       "   46.85818510451771,\n",
+       "   9.130456971519783,\n",
+       "   47.85361872923358],\n",
+       "  'time_range': ('2020-07-03T10:37:50.578000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32TMT_20200703_1_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [7.662878883910047,\n",
+       "   46.85818510451771,\n",
+       "   9.130456971519783,\n",
+       "   47.85361872923358],\n",
+       "  'time_range': ('2020-07-03T10:37:50.577000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32TNT_20200703_0_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.999746010269408,\n",
+       "   46.86545413497558,\n",
+       "   9.683667661181989,\n",
+       "   47.85369284462768],\n",
+       "  'time_range': ('2020-07-03T10:37:45.862000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32TNT_20200703_1_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.999746010269408,\n",
+       "   46.86660884670375,\n",
+       "   9.683264107355098,\n",
+       "   47.85369284462768],\n",
+       "  'time_range': ('2020-07-03T10:37:45.861000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32UMU_20200703_0_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [7.639190643894996,\n",
+       "   47.75741268155721,\n",
+       "   9.132768981529203,\n",
+       "   48.752927528985374],\n",
+       "  'time_range': ('2020-07-03T10:37:36.335000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32UMU_20200703_1_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [7.639190643894996,\n",
+       "   47.75741268155721,\n",
+       "   9.132768981529203,\n",
+       "   48.752927528985374],\n",
+       "  'time_range': ('2020-07-03T10:37:36.334000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32UNU_20200703_1_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.999741508947045,\n",
+       "   47.764447765603194,\n",
+       "   10.093645911255322,\n",
+       "   48.75300400802843],\n",
+       "  'time_range': ('2020-07-03T10:37:31.959000Z', None),\n",
+       "  'num_levels': 5},\n",
+       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32UNU_20200703_0_L2A',\n",
+       "  'data_type': 'mldataset',\n",
+       "  'bbox': [8.999741508947045,\n",
+       "   47.76336985243686,\n",
+       "   10.094053887015043,\n",
+       "   48.75300400802843],\n",
+       "  'time_range': ('2020-07-03T10:37:31.959000Z', None),\n",
+       "  'num_levels': 5}]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "descriptors = list(\n",
+    "    store.search_data(\n",
+    "        collections=[\"sentinel-2-l2a\"],\n",
+    "        bbox=[9, 47, 10, 48],\n",
+    "        time_range=[\"2020-07-01\", \"2020-07-05\"],\n",
+    "    )\n",
+    ")\n",
+    "[d.to_dict() for d in descriptors]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next step, we can open the data for each data ID. The following code shows which parameters are available for opening the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.03 ms, sys: 0 ns, total: 1.03 ms\n",
+      "Wall time: 1.04 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "properties": {
+        "asset_names": {
+         "description": "Names of assets which will be included in the data cube.",
+         "items": {
+          "minLength": 0,
+          "type": "string"
+         },
+         "title": "Names of assets",
+         "type": "array",
+         "uniqueItems": true
+        },
+        "open_params_dataset_geotiff": {
+         "additionalProperties": false,
+         "properties": {
+          "overview_level": {
+           "default": null,
+           "description": "GeoTIFF overview level. 0 is the first overview.",
+           "type": [
+            "integer",
+            "null"
+           ]
+          },
+          "tile_size": {
+           "default": [
+            512,
+            512
+           ],
+           "items": [
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            },
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            }
+           ],
+           "type": "array"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_dataset_levels": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_dataset_netcdf": {
+         "additionalProperties": true,
+         "properties": {},
+         "type": "object"
+        },
+        "open_params_dataset_zarr": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_mldataset_geotiff": {
+         "additionalProperties": false,
+         "properties": {
+          "tile_size": {
+           "default": [
+            512,
+            512
+           ],
+           "items": [
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            },
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            }
+           ],
+           "type": "array"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_mldataset_levels": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        }
+       },
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7128f4ff80b0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "open_params = store.get_open_data_params_schema()\n",
+    "open_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We select the band red (B04), green (B03), and blue (B02) and lazily load the corresponding data. Note that we assign the `data_type` to get dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 77.9 ms, sys: 7.75 ms, total: 85.7 ms\n",
+      "Wall time: 2.34 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       " */\n",
+       "\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
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+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
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+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 724MB\n",
+       "Dimensions:      (x: 10980, y: 10980)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 88kB 5e+05 5e+05 5e+05 ... 6.098e+05 6.098e+05\n",
+       "  * y            (y) float64 88kB 5.3e+06 5.3e+06 5.3e+06 ... 5.19e+06 5.19e+06\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    blue         (y, x) uint16 241MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    green        (y, x) uint16 241MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    nir          (y, x) uint16 241MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    source:   s3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NT/2020/7/S2B_32TN...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b995a213-2a2e-4978-9997-a76bbabd96eb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b995a213-2a2e-4978-9997-a76bbabd96eb' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 10980</li><li><span class='xr-has-index'>y</span>: 10980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2a083ef8-5932-47ab-922e-5349082f4b1d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a083ef8-5932-47ab-922e-5349082f4b1d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5e+05 5e+05 ... 6.098e+05 6.098e+05</div><input id='attrs-913ca96e-1df9-469f-afcc-6f0a90fd03e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-913ca96e-1df9-469f-afcc-6f0a90fd03e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f17e600-f923-4ccb-a825-7339b36b96f7' class='xr-var-data-in' type='checkbox'><label for='data-2f17e600-f923-4ccb-a825-7339b36b96f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([499985., 499995., 500005., ..., 609755., 609765., 609775.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.3e+06 5.3e+06 ... 5.19e+06</div><input id='attrs-ac7bfaf5-c67b-47b4-a327-8ee654abf319' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac7bfaf5-c67b-47b4-a327-8ee654abf319' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbb3e37b-53c9-4c84-8f2c-f2602d26968b' class='xr-var-data-in' type='checkbox'><label for='data-dbb3e37b-53c9-4c84-8f2c-f2602d26968b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([5300035., 5300025., 5300015., ..., 5190265., 5190255., 5190245.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-9a8113a3-8545-4874-b1e8-25f927960018' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a8113a3-8545-4874-b1e8-25f927960018' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b72c2ab5-19f0-45eb-862a-7a7fd63bb8b9' class='xr-var-data-in' type='checkbox'><label for='data-b72c2ab5-19f0-45eb-862a-7a7fd63bb8b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 32N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>499980.0 10.0 0.0 5300040.0 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ac669dd-f4f1-41c3-87a7-7d4f914eef3a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ac669dd-f4f1-41c3-87a7-7d4f914eef3a' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-30edbc02-68df-4686-a664-638f47838715' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30edbc02-68df-4686-a664-638f47838715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-736782ec-3c1a-426b-9f28-915f88ff8cbf' class='xr-var-data-in' type='checkbox'><label for='data-736782ec-3c1a-426b-9f28-915f88ff8cbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 229.95 MiB </td>\n",
+       "                        <td> 512.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (10980, 10980) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 484 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-23d7811b-cffc-4725-820e-f252fd0a2694' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23d7811b-cffc-4725-820e-f252fd0a2694' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-225ab2d9-8dd8-4710-b4f7-8cef02611c62' class='xr-var-data-in' type='checkbox'><label for='data-225ab2d9-8dd8-4710-b4f7-8cef02611c62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 229.95 MiB </td>\n",
+       "                        <td> 512.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (10980, 10980) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 484 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nir</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-6d8a646a-4753-4175-af9f-f31a20b0de07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d8a646a-4753-4175-af9f-f31a20b0de07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41bbac51-c515-410f-85f5-e515d9fa5a16' class='xr-var-data-in' type='checkbox'><label for='data-41bbac51-c515-410f-85f5-e515d9fa5a16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 229.95 MiB </td>\n",
+       "                        <td> 512.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (10980, 10980) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 484 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
+       "  <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" />\n",
+       "  <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
+       "  <line x1=\"0\" y1=\"44\" x2=\"120\" y2=\"44\" />\n",
+       "  <line x1=\"0\" y1=\"50\" x2=\"120\" y2=\"50\" />\n",
+       "  <line x1=\"0\" y1=\"55\" x2=\"120\" y2=\"55\" />\n",
+       "  <line x1=\"0\" y1=\"61\" x2=\"120\" y2=\"61\" />\n",
+       "  <line x1=\"0\" y1=\"67\" x2=\"120\" y2=\"67\" />\n",
+       "  <line x1=\"0\" y1=\"72\" x2=\"120\" y2=\"72\" />\n",
+       "  <line x1=\"0\" y1=\"83\" x2=\"120\" y2=\"83\" />\n",
+       "  <line x1=\"0\" y1=\"89\" x2=\"120\" y2=\"89\" />\n",
+       "  <line x1=\"0\" y1=\"95\" x2=\"120\" y2=\"95\" />\n",
+       "  <line x1=\"0\" y1=\"100\" x2=\"120\" y2=\"100\" />\n",
+       "  <line x1=\"0\" y1=\"106\" x2=\"120\" y2=\"106\" />\n",
+       "  <line x1=\"0\" y1=\"111\" x2=\"120\" y2=\"111\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"120\" />\n",
+       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"120\" />\n",
+       "  <line x1=\"16\" y1=\"0\" x2=\"16\" y2=\"120\" />\n",
+       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"120\" />\n",
+       "  <line x1=\"27\" y1=\"0\" x2=\"27\" y2=\"120\" />\n",
+       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"120\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"120\" />\n",
+       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"120\" />\n",
+       "  <line x1=\"55\" y1=\"0\" x2=\"55\" y2=\"120\" />\n",
+       "  <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"120\" />\n",
+       "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"120\" />\n",
+       "  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"120\" />\n",
+       "  <line x1=\"83\" y1=\"0\" x2=\"83\" y2=\"120\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"120\" />\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"120\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"120\" />\n",
+       "  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"120\" />\n",
+       "  <line x1=\"111\" y1=\"0\" x2=\"111\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >10980</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">10980</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8247db75-4200-4210-b754-c89f5a2478e2' class='xr-section-summary-in' type='checkbox'  ><label for='section-8247db75-4200-4210-b754-c89f5a2478e2' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1839fe86-46be-4540-a4e1-c4896f159af1' class='xr-index-data-in' type='checkbox'/><label for='index-1839fe86-46be-4540-a4e1-c4896f159af1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([499985.0, 499995.0, 500005.0, 500015.0, 500025.0, 500035.0, 500045.0,\n",
+       "       500055.0, 500065.0, 500075.0,\n",
+       "       ...\n",
+       "       609685.0, 609695.0, 609705.0, 609715.0, 609725.0, 609735.0, 609745.0,\n",
+       "       609755.0, 609765.0, 609775.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=10980))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f5503caa-a4dd-4051-8a67-a06dbbc6baa9' class='xr-index-data-in' type='checkbox'/><label for='index-f5503caa-a4dd-4051-8a67-a06dbbc6baa9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([5300035.0, 5300025.0, 5300015.0, 5300005.0, 5299995.0, 5299985.0,\n",
+       "       5299975.0, 5299965.0, 5299955.0, 5299945.0,\n",
+       "       ...\n",
+       "       5190335.0, 5190325.0, 5190315.0, 5190305.0, 5190295.0, 5190285.0,\n",
+       "       5190275.0, 5190265.0, 5190255.0, 5190245.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=10980))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-627fae41-327e-4990-9735-0bca038a4c9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-627fae41-327e-4990-9735-0bca038a4c9d' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>s3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NT/2020/7/S2B_32TNT_20200705_1_L2A/B02.tif</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 724MB\n",
+       "Dimensions:      (x: 10980, y: 10980)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 88kB 5e+05 5e+05 5e+05 ... 6.098e+05 6.098e+05\n",
+       "  * y            (y) float64 88kB 5.3e+06 5.3e+06 5.3e+06 ... 5.19e+06 5.19e+06\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    blue         (y, x) uint16 241MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    green        (y, x) uint16 241MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    nir          (y, x) uint16 241MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    source:   s3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NT/2020/7/S2B_32TN..."
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = store.open_data(descriptors[2].data_id, asset_names=[\"blue\", \"green\", \"nir\"], data_type=\"dataset\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We plot the loaded data as an example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.11 s, sys: 552 ms, total: 1.66 s\n",
+      "Wall time: 1min 1s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7128f521ebd0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.green[:5000:10, :5000:10].plot(vmax=2000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we open a GeoTIFF, the default opener represents the data in the [xcube's multi-resolution  dataset](https://xcube.readthedocs.io/en/latest/mldatasets.html#xcube-multi-resolution-datasets), where we can select the level of resolution, shown below.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 101 ms, sys: 12.8 ms, total: 114 ms\n",
+      "Wall time: 2.78 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "mlds = store.open_data(descriptors[2].data_id, asset_names=[\"blue\", \"green\", \"nir\"])\n",
+    "mlds.num_levels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
+       "Dimensions:      (x: 687, y: 687)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 5kB 5.001e+05 5.002e+05 ... 6.095e+05 6.097e+05\n",
+       "  * y            (y) float64 5kB 5.3e+06 5.3e+06 5.3e+06 ... 5.19e+06 5.19e+06\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    blue         (y, x) uint16 944kB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    green        (y, x) uint16 944kB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "    nir          (y, x) uint16 944kB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    source:   s3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NT/2020/7/S2B_32TN...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c9d6d90d-9df8-439a-ad64-e3b101769769' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c9d6d90d-9df8-439a-ad64-e3b101769769' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 687</li><li><span class='xr-has-index'>y</span>: 687</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eb807173-dabc-493a-87e6-823a82d62809' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb807173-dabc-493a-87e6-823a82d62809' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.001e+05 5.002e+05 ... 6.097e+05</div><input id='attrs-37aca9e8-0682-4985-a3b8-0d968eed90eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37aca9e8-0682-4985-a3b8-0d968eed90eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0d0e114-42e3-4c90-bb56-dc57106995cf' class='xr-var-data-in' type='checkbox'><label for='data-d0d0e114-42e3-4c90-bb56-dc57106995cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([500059.912664, 500219.737991, 500379.563319, ..., 609380.436681,\n",
+       "       609540.262009, 609700.087336])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.3e+06 5.3e+06 ... 5.19e+06</div><input id='attrs-358b8137-0d4c-42a8-ac38-e188b63c1d2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-358b8137-0d4c-42a8-ac38-e188b63c1d2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd1bccf7-c201-47c2-991e-e6fd9c91c68a' class='xr-var-data-in' type='checkbox'><label for='data-cd1bccf7-c201-47c2-991e-e6fd9c91c68a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([5299960.087336, 5299800.262009, 5299640.436681, ..., 5190639.563319,\n",
+       "       5190479.737991, 5190319.912664])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-af2d1893-267f-4979-bea0-2c81ad02c6b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af2d1893-267f-4979-bea0-2c81ad02c6b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79a4695d-d97f-4dd6-8b03-340c8698c76d' class='xr-var-data-in' type='checkbox'><label for='data-79a4695d-d97f-4dd6-8b03-340c8698c76d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 32N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>499980.0 159.82532751091702 0.0 5300040.0 0.0 -159.82532751091702</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-78532a2f-8a8f-4861-bf36-12e6787e210a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-78532a2f-8a8f-4861-bf36-12e6787e210a' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-63868e5c-6c62-41f7-af51-83fba40cc8a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63868e5c-6c62-41f7-af51-83fba40cc8a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d379e216-790f-4d39-89a7-4961ea90314e' class='xr-var-data-in' type='checkbox'><label for='data-d379e216-790f-4d39-89a7-4961ea90314e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 0.90 MiB </td>\n",
+       "                        <td> 512.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (687, 687) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"89\" x2=\"120\" y2=\"89\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >687</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">687</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>green</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-af248ddf-180e-41f2-ac9b-f6ca3ac47b55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af248ddf-180e-41f2-ac9b-f6ca3ac47b55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daebd925-e7ad-44ea-82bc-2f41782dbe4f' class='xr-var-data-in' type='checkbox'><label for='data-daebd925-e7ad-44ea-82bc-2f41782dbe4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 0.90 MiB </td>\n",
+       "                        <td> 512.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (687, 687) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"89\" x2=\"120\" y2=\"89\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >687</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">687</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nir</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-5a8e01f3-be4b-45c7-84d8-24d6a83ef56e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a8e01f3-be4b-45c7-84d8-24d6a83ef56e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b859d8c-619a-44e5-b455-6d5364c8422f' class='xr-var-data-in' type='checkbox'><label for='data-9b859d8c-619a-44e5-b455-6d5364c8422f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>_FillValue :</span></dt><dd>0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 0.90 MiB </td>\n",
+       "                        <td> 512.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (687, 687) </td>\n",
+       "                        <td> (512, 512) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"89\" x2=\"120\" y2=\"89\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"120\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >687</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">687</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0945b08-1c3b-49b9-84d6-1791d7b255ac' class='xr-section-summary-in' type='checkbox'  ><label for='section-b0945b08-1c3b-49b9-84d6-1791d7b255ac' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db3e5812-9b60-4b99-9506-7546d4b3a3f1' class='xr-index-data-in' type='checkbox'/><label for='index-db3e5812-9b60-4b99-9506-7546d4b3a3f1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 500059.9126637555,  500219.7379912664,  500379.5633187773,\n",
+       "       500539.38864628825, 500699.21397379914,  500859.0393013101,\n",
+       "         501018.864628821,  501178.6899563319,  501338.5152838428,\n",
+       "       501498.34061135375,\n",
+       "       ...\n",
+       "        608261.6593886463,  608421.4847161572,  608581.3100436681,\n",
+       "        608741.1353711791,  608900.9606986899,  609060.7860262009,\n",
+       "        609220.6113537118,  609380.4366812228,  609540.2620087336,\n",
+       "        609700.0873362445],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=687))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-39c5f2a3-4393-4d00-82d2-651d77659cf3' class='xr-index-data-in' type='checkbox'/><label for='index-39c5f2a3-4393-4d00-82d2-651d77659cf3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 5299960.087336245,  5299800.262008734,  5299640.436681223,\n",
+       "        5299480.611353712,  5299320.786026201,   5299160.96069869,\n",
+       "        5299001.135371179,  5298841.310043668,  5298681.484716157,\n",
+       "       5298521.6593886465,\n",
+       "       ...\n",
+       "        5191758.340611354, 5191598.5152838435,  5191438.689956333,\n",
+       "        5191278.864628822,  5191119.039301311,    5190959.2139738,\n",
+       "        5190799.388646289,  5190639.563318778,  5190479.737991267,\n",
+       "        5190319.912663756],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=687))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-420b0e36-f047-49ed-8cc3-7be71ff4959c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-420b0e36-f047-49ed-8cc3-7be71ff4959c' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>s3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NT/2020/7/S2B_32TNT_20200705_1_L2A/B02.tif</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 3MB\n",
+       "Dimensions:      (x: 687, y: 687)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 5kB 5.001e+05 5.002e+05 ... 6.095e+05 6.097e+05\n",
+       "  * y            (y) float64 5kB 5.3e+06 5.3e+06 5.3e+06 ... 5.19e+06 5.19e+06\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    blue         (y, x) uint16 944kB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    green        (y, x) uint16 944kB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "    nir          (y, x) uint16 944kB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    source:   s3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/NT/2020/7/S2B_32TN..."
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = mlds.get_dataset(4)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 61.3 ms, sys: 8.21 ms, total: 69.5 ms\n",
+      "Wall time: 267 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7128acc7ad50>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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PPvlkHvKQh3DkkUfy4Q9/mFe84hU/83ff/va36XQ6XHrppbzmNa/hqKOOWlOI8qejP8aYg0aEXvva16653tLSElu3buXoc19PfHjAOQ+5kh29Ma67bos9ITgDjdrc49CpeQBWopCVQUB6XRuA2iz4HUPcVCib0Mwd+ay+L0Vna7OccVseRTSqGYxB7oPbk++iMXAG4ESgsuImobknZ/542bUYD3Qk1/W6pmynTg1uL0cn8lk422fp6BbLh0p/GA15AG4H3IH8LPMgbYDKq/aF8zB+bZ/u5pClw6u+VHnVTqNhsN4wctw8/++IbwKwPZrioz86k8ZtLqF0FTozRCOK3I4+J4Jw0ZC7kAVybpXC4m/1cXbX2PA9EeJc2ephNMQt258+eMuAlmsX5xqMg85g7IbM9q2mt16R2coetQMwfv0A79odmF5V+0z5HrguWNq78nzwPajJDzvHT7HnEYoX/9bXOHdkFz/LtqVdPrJwOj+ek/GyY+8UJlF4Cy6t7XJ/I7dFqNSgcsOes2rS73VDbZ9iMGn7KQGnD2kd/CX5zOtD7mL7Sj5LRiD3DP6C7btMfjuyLcWJ5bkPJlzpIwNxq3r2TiyfARgH/BVDc/eAtO7azxROP8NbGhBN1QGYPUmeg2vbAqBT+W39gPR5MNtHZQbjaqIJKdS7/wEuKpdrlr+LQRnbDsAoeUe8jllz7rityT05HiBYyolGNKl0HWkN/A6kISTN6lno1F7DvjfRmPTl6K2p/dthZYvCW6nGv3Gq8aStBqzXM+jY4A6ks6IRTdKUMVyM/3A+I5xPcHoJKpGXJ2v5GEfR3SgPa+UQRWuPwennJC25SOYrohFpp2Ovl4byDrpd+bux35D50FuvUFaVwe/Ks9OJ9Ie0IcbpJuSBYDvThkd/0iVuq/J9TkN5b51I7quwtKYwSu6/sNpMNQm4UY4T5WS+Rtn5y+skrBxWoz9RnV9lcv7i3EbJmHNieR4A/Qm5jo7lXQWYuC5GRxlZzT54YzBKkTYcdGps/zkkLekbx7664RygILXvQzQu523tNngr0qjabIxRgFZEI1Icee5EB+OA17H92YF135mFJCGbkEmmv75GUlc09sUYLe/N/PEBy8dk+HPSUe3tBtMfcOVn/oZWq8V9Zd/+9rf57g8HHHGoy99fcCivffPsfdaWXye7z9Ntq63RaHDyySdz8803H/S4gpZ88skns3//ft7whjeUTtKGDRtuFzWamZk5aPgxCII7rI2VrQtpn9Dn/cdcyQt3n8kNdZnsjZdj/Bzt19hxkxQHNa5BrxuQb/ABUH2NOzDoVCZBkMU9aULkGfwVeemDxRQdZ3gr1tkZ9VnZIoueu18+q3dk8iwWIgCVG6J1muRwmXWcroN3QGHa0C02Mjk09kJ9boB/i1SgN70eIyNtoi22wnwK3qJMasXEN6grTAhp3eD27MIbgVNThKlLtuKU9xNNQmInudw3mHbGYmeKL3bPkHNlLo25JmPTOc0dfdtXirRRDb2FYz26xwCnLvPBB3wYgMv7h+GpjLf+6NFkt8iqV4ugP6VQdh4KV4CadT7tYmJCSI5ICfe5qJbcjw4VZhxMIH+PX5FS39PFGBfjyHNXjgO+hwoCyO1sH/iY0RZzp44CMPeoARed+S+cEbjA7QkGM5nMth+f/S0u7RzJTE8a6nVrGNdgRiHZIM9Uzbi4nRSV5UzdJmNm9hSNDiGw/Wk8qC1A4kBfSruhZ+2C70F9WT4b1MBkoG2T8hokdfB2pTh2sY5qHmhI64qV4+Sz8cMX6P1wgtYOu5gZqMU5rmPKBS73HbJ6gE5cwljaPjIX0J9QEEKRWUhHoLcRXM+Oz0YNlYO/EBPGMoaCLCRpgR5AkZDQBpI2mKLLF8DThkYnIa1XaYuk4WICuU+A3hRkIXgr9p4bshiH/crZUTlEo9LG0dvkAs7OHH8xpmiBGQ0YmVEYrXDsQhyHiqQpi3hip4V8BYJFUMtynsBA7mnypmwyALzlDF3zUF6OsZuyZEwcw6b9Xf0WRRpo0lGFaaqyDwIDWQOitn32jjxn3zoCPjmxr6CtSkcRLQu8owyZDby7y1rIL0bO7a8Y8vEAgy6HrHIAB9xEzguQOwqlZJOS+bYNCvIJUzqKUaBwYkOwmOFEdnxM1jBtFyeA5l7bx5Fh7kQHrPPoL4GugXIrx8b1rZPfgMzuufY/PCR3oL3NtjOT5+evQG3O9nuu8ZchBnLP9ruSjReeHWdzgAEfQxjL/KjrLoMxD50a4vXSEU4gY0RbRxsF0487hKwOjT32/jwl9zrqMxiX36m6otYFZa+fj0Nmx/h9Bc8wxvAXr3wUr/iTUc58UI2nvXAfL/rzRUZHR++T9vw62a+UClwURVx//fVs3LjxLv/GGLMGT/SQhzyEr371q2uO+cpXvsKZZ575C2vn0IY2tKENbWi/KvblL3+ZG25JePm5YzzirDoPODngb99w17C9Qzu43aeRpFe+8pU84QlPYOvWrczMzHD++eezvLzMH/3RHwGSBtuzZw8f+chHAHjve9/L1q1bOe644wDRTXrHO97BeeedV57zpS99KWeddRZve9vb+J3f+R0+97nP8bWvfY3vfOc7d7t9eWhYnm1y/uxxXLLtKFRidwm5xgBZqvEG8lk4p2B7vQwdF7u9pK5IJUshKawMOjVFbdbuehYBrVCmCF/ntG9x6G1UaOv7jW7LwBiSplOmIIoQfzAm2+Y4q1Hfp+ltUGWqwB2A1zeQG8yozUGMNFg4LizTFPX9EC7k+EtpGUnK/YCsBm5PlaH3LIDpBwf0jo3RC3KBvJahYk1tr/wdjyoJg3RcbvqfI+25oLEPwrkUHcmWNG7WyUKNM5DdYWtnBgo6p6c2SgNnBLsBeNBZ7+dZ1wgQf/LalLjlgt1tx6OStglnobm3Sq35s7J7j0akj9NQ0pWN3XZ3vZyA66AadUmxASZJUUqD50JdOieZbLD/gXU2PHEnAD847osc7JX5+PJJAPxw9lD27h7Hm5VzeytIqF7ByDa5Z28pxjiatO6W/e4vyj2ldRuNyRReDeoHDK4dZ27fsLJVzuXtK1IlisEE1ZZHQVYzdDa65Y5/MKHwutBfBxNHSN5z/pZxNl6fEyzIbnsw6ZL5CpUadD+xbcgxWmG8KsXS2jFA5aH076pUsnGgP1mk/IrG+LhdeTYql9Ra3JLIq9yzIq0Z3L78zulDGirSusbtS1+5Kwlxy6F/pCpTI3kRyLPX91Yk7RQ3JcIE8g7mvqQF/WW5R6MVad3F7dkIQwq1+RxnkJM05aT9cWnL6pRm2jDoTOEkVaRAGTAupDby0t3gEAYKt2+Im3L/SVMiEdGojVzZFGfuwcKJ0i/OQNPcKWm0IjVvAH8Zwnn5IKkrstCm7osMmKn+Gy7ktt8NOKuiGUqekTKUKffcVeU8VYwP5UBa06hsbZo/91T53NN60XZdNmIw7pD5RVrTtrWhbarfNs+RZ29cwDYtWJS0/mBSnhEAR3YJ/JR+Z8S2XdrS3pGQhbq8vzxUklqtVef3ls2alHuwnJGGmv6kWx4Tt1SZCgSoz0DSqFKqaQj9+/dZN75CN5JG9fo+4RV1olG3HAu5K31eXC9plhn6+8TyPOcvXvUkXnPeGC077v7mNRM86ql7+NPX7h+CuO+h3adO0u7du3n605/O7OwsU1NTnHHGGVx66aVlTbN9+/axc+fO8vg8z3nta1/Ltm3bcF2XI488kre+9a2ce+655TFnnnkmF110EX/1V3/F6173Oo488kg+8YlP/FwaSblncOsJP5g/DMfJSYu5xzWoRKMSVU4ExURUTmAKkoZi6YQM6jIj+bt9VCophcZ0MUGmGEdjXLug1zQj2xKCZRd/RY5xexnRiEuwJC8+SLrNX8lJIxsCjhV+xzBI1mJ9VG5AK5IJAfHd+gyX/370O3jtDhHX3PeBI3CiHOMo8qDASMj96LiaULIAshO7XPyQ9/Llrjip77jskYTX1sqFSyeQ9gL8JQiWjD2XIljO0WlOb4s4anMnuXgr4MTS9mAxp7FnQO8L43Da2mewJx1dg1cxq/BH3grEbVkUi8/8lZz2bQ65B8tH2sl9NKV5s8fU5ZKbcZb6MIggy8C36dEiTK4U/a0ySe95qM/vPfa7XLDu4IKKANvTFW7rTwEws9LEWXbLxcbrCrbEjQzhrAyYtO4Sj7jETVXiXPyOpG6yAmfTzMlCjU4N7W12oDmKuOWS1sDrWieir4hGdZkmCRahvk8cqWjUOi2r8CK97wvoaXwawvkIf3rF9mdI0vLFYS8wOf0EL8kkZ2Ud+bQVkLs2o1O8E1qev83ykIaKke0xbqfC50xeo1je6tHdDLk90BmAr1SZNmvuSzEavG6Gsukvlea4/Zx4TBOPynHhrDgVxQKrcsiNOJlZaNOHLjh9WRgLByga0YTzWeVsuYKdSRpVUL1+wNCfUuS+pHqkb5RgxAqnIhOH1esokoZNmyVVar1wdgAyT5UYRJXJGI2buuzj3DPoWOEYys8KByCpVw5b0rKp1mKoZhaD1VCkoWPPFRAspngLssNKxgL6U+K0BIt2I9Y1pKHCSUzpAIWLKU4iDntiF1plNG7flGmtXIMTC1asu946H9r2oR3fAIMxRX1/hW1KmuIwDcbFMQJo7jW05lP6Sy7LR8gNDRYDsl6doNiLehAsGLyVhKRhPRQlnzt9GTtF3/TWrU6KGKK2QxZQpj2L9GXurcKbWbxa0edODP6NNfZtddn22H8BJIV+RvRSwtv8cl4NFuQ3xd+ZD6nHfWb/+Z//yYG5jD/5o5HyszNOq/Hbv1XnzX95HH//Lwv3XeN+Dew+dZIuuuiig35/4YUXrvn7vPPOWxM1+ln2lKc8hac85Sn3pGliozFHrF/m9zZczifMA7lxpggJKdRIguNlpJF8pnLBChS7ksSzL5E2uPtkJne7EI/A6C05jdvs7Ksha/jknjwKt5/jDDL7IhZYJpf+pENrV4LXKSImDv5Kzqb/lHMbB/Y/SJOOZDhdeet1pvA6Oe7CgGRcZt0nnHYlx3kNNtYECTy3kqMyQ1rX5QSP3SHnbgUYBhj0Xd4189v8ZEEUlr1ba4IxsD9zIomW9TZCx2JoRm+C+nTMytaAAw+yO9l6grPklpGd7kZNZ2OdLIRv2ojJQ+1C96qf/B41izfqTzgoI8BrgNFbEhaP9Mh8SlCljnPChZykoajtk8+i2KO+3+AsFFtk6yClmQC1AWohuC7JxhF2PUpmvH998vv5rfBgA6Syldxl70Amqe5cHX9QRVlUDuGiROu6Gz17z4q0AfXV8Dkjjp+3bPEsbYnkZb7CWLxF7ijcnjitxT2HByKCyRpdm6XOXVDGkNRVCWI2WhwYr0PZruXDobVLV8CizIjTrlTp+xjPgcyQtjyMJ8d1Nvv0xxXNfTm5de6TljjntWI+VrB8qE+44BLOipfrLce0dxiSlk/3kCJqqahPQ3uHHOPPD8hqLnqQoTIZ62k7ZHmrQ9ZKKHzZtOPh9qsojtHyftX2U7ZpMGUjCCOwfKis2M09OfU9vdLha+wp3rGAaETuL5zP8TqqdPhAFtTeRnEcAOr7M4yj8Lo5y4c7ZRtGbxGnqHCSVCafV9EKxWBUU5vP2Pz1amHPAkNSq8gFmQVXr1mIa2uB9oWtdsiSuiKcN2VkOq2Ls+BElO9pY1ef3NdE437pHGaBJgsUbi8v5wGdmHKzB+JgRCMat2ckKoR1jN3KsQdozKQkdc1gvIim2chRUDk2gzFF7rm0dsVkFg9qlIsTg7J95QwkQpbWXdKaLvsvDVgTETKO7adyHlIklnhSPMPBuCW/xBVJIHcENF44c9qC2d1FlxsSmXQGxiVoRrg9v3QW+xsM/qIq78W4Ffj83rY0TXndX/whr3vFOLXaWvTM37x6gjMeu4s/e92OYTH1e2C/UsDtXzV79imXcsBdx3PaM+yMt3FztLX8Lk81Jx66hyuXZfAFiy5un3I3n4UygbRucstoU9KQELo7MKXT4s+s4GaGLLROUi8n9zTTZ3i4drc7dnPG0sN6dG+qM3GNzAxpqHD7EmUCWZDaJ83xwHW7+Pol9wNg9KacxlV7MZ0ufrYOgG99+IE84FFHsHTbKADtLRqvowmWTTnJJE2ZhJxVoffcg2BHwH8tPQBtUw6+3YVldpJJGzDYmKFihcqqHXDadMkCVYI2zUiO21P4FnjcOSxn7PRZ/u64T5TptsL6+xrkFoiuEyVMliIloSVVMhjTDMbsJFrX1GYNTmzwV2y0Iob6dCqOEUCSCjg78OUfgOcSbRphx+N8/vv33wHA0d4qmtRBbDrr8ILrnsPMzjEAglkXZ1Axk3IHVg7R1H2P3nrrGB6bwEDj9B3qxWSrZcJ2bT85czKevE7O4pHSL4NJy+gz1eIFAYNxSQmBMNJyV5HVq9SdpHmkz4sx6i+DM8hKhwFApYbc1yUqNQtdonGP3pQuUzq5C/EYJMuKsRukseGcx/zxXpnqcgYyjgZjmnBMPmzuTnB7Kc4A8jFZCXuBg9dxcPr2JcnzNQ4SSDTU68ritdpxQVE5opk4C26/ii4V6Uu3X0UPgsUUFSWlY6jjnKzmkgZqVbomxx0oMr+KFOvEUJupmGU6NeQIYDick8/SGkRthdcz5PZcRlkA+Zgq/859RTTmlots/UDOYEyuX7wjxb2tjnr4i/arYvy7lqlmqvSj6SuJ3JkqkivPFWILFA9rLm4vQRlILDi+s0Xe9dYuReZV0Zc8q9JmKBiMQaBVObbTuk0NdkwZMfR6KSr16G4s+ljuw+tAbUZ+l7SEqecvu4xfJ5NMY79PGlabtcyXa65s8apNUApuZKPKbtUuZSjhCU5s8LqGwagmsan53DpouVf1n06x7MTKYc8CaO5QPPGiP5M2bIxg0cdfBaLPfbv5WLU5LBy7e9s+9PebyXN4ztPat/vu5OMDnvzYJm94zSl86ONLd/Drod0VGzpJB7G20+dFG74ONPn8zpOpTdvJQ0M88Lky3YruWzxOSya4YlfiDGRySOvVy6sycSRWNjs0nGK3VicPnDId4AwkxRKtT4mm5JjmPs3fPeiTXHzM8Vwy82AA6vslAlTk6tM6BCrnxOYevhndX86d5ph2AzWIiKZkwe+c2eOo5gqjJ8gbv/nBS1z+xRPQmVCY5dyK/jqZCIpIRFqXewvmdLkIuX2JNJWLSww6daQf7GfRKEw/yMXtQjJuqfWpZvx6Q21GZhbj+Owfb9/OQQLQYzFJLIus21UkdVmg5XoeC8cbnEG1q5Q0nsHr5SUuJJw3BLMDiFfNZK0meSskD8QZSEZ8tj0Ftj3uH4G75hwV9ujLn8/K9hFqB6p0ZTRuyO1i40SySK3G8OhllzzMyWpCvQaZ9HsbK4elsVvR3Qwqd+gcZm9wKiL4cQ2vW10/bilyD7JmsbXW1CKhXA8mCzakRFiSerXoBQuweFSIPlx28u3bBhhPl3IUAHFTc+CBBn8RlF35nciQtAzLhylGbpPPgtkB7R2axSNlHLv9wiGA3gZsG1xGbkkZ2ZYQty3m49CMlcMMwaJsGkZvylFJBqtYQk4vobXbJWl4ZDaYW6SDS1NFetiUzkdr0RCt6DK1DeB2EozjYIIifCDX8bqmHEPRqFNGGQrqeRYq0lVRxe4GSU0FSznhgj2mC4NRSb+5BZckl74oJAgUMh6MUzlzhYPvDCrOpNe3Uhw2QpP74nTr1REULZ+rvIpU+R2DTg1pW55p1JY0bNKE5u7iXBoTaXKHUiLCiSQt6cSQ2ihuWlMiSWCvl3vQ35zhXe9UacdcobSVHbHPLJoIGIzq8p7DBWHZqX2mGutaMZgw7D7SsOnr0rH1/THekimdmKThEo279CdVGf3xVwyZkXYVTEA3qqJOAF4vR0c5nU1++d54Sxo3knsonqNO5XdpvcIu1g6A1zFlCErtDMtUZ/G8gnlYOnEVCGnOhUpJ5F6zwWDAm945z9teN4nn3TGr7g1/Ps7JD93Jq66/nuOPP/5ebuGvh/1KsduGNrShDW1oQxvandv7376F8VHNU5/4szd1Rx7m85yntXndqx58L7bs18uGkaSD2Ht++Ag+fthZzG8fpbHLkbw+smtz+xD3vXLXmjYkV13oCukYOodnjGxdov+TcUD0O6KtGU7fYe4k8U/3nyFbxfGr5TwjsxHxqM/YlW65o0pDeMWnn014QDGx00ZjEoOT5BhX9p7hgmGQuswmrXIHl9YUc6eNEzwz5lsn/euaezv1x08DYPvNG9h0o4jEFdfzV0TskVWRpHgsBz8n3OuWu9baAYPfqTAMaaKozRpyV5Usp9SKvqZQpuCmvuMwes1iCapshprompAT688sdZI2OH0u7h1FeE2t3ME5PdGL8i3upbsR3vU7H+HPr/g98luloW4PvJUMnRmau6Wh/lKMMz1fYbyO2Mhg0sft5wwm5BXYf7rhknPeAdw9MbjfufkcOttGaN+m8ayGVdJS9FEkIzY91dN4HYkwFRo+9d2KaNzB6Cpa4K+Av6TobSnYYA7+ov1dR8ZL7Yg+TrdGc19K3LKCgYFEhZTNudSnBdSdBarU30raGf6KRLSKaKfRMH9aWka3Wjs05FVKCGAwrjBeTuZXIp5hIqw048DyYRKxaO1S1GYitAUJJU1HsFROxRAbjCvqbQ+3mzFxvSUl9B3iUUoMjXE0ZAaVZNVnrjAhdQqFBqvKLYC3iCatAj2XqZnE0NqdEsxFpE2vOs5R5L5Tnsfpp9T35/Q2SEMLFlQh1AhyD2mzisL5KwadGHJf0dlox3pdGFO9FmUkKWlLO4vnjpF/jb15mS4tRBxVbokWSNQvrck1QaJPOpV/ZdS0K+9aFkB9xjIPt/eJ2x7djW7ZphLovTpS5quScQmSChNNtyqN218v46pIubtdUKmInRYp9wKHGTc1y1urPXchVinHCPNM5avEYjPI1iVs2TRHZ3KjfV4eKIU7KCAFWkRng9Vio4rcgdqKKZ9zGgJ+Bar3F2KWjqoJFspGd91ekbqu5rRi/GSrsEZJV9HuQ2O/tME49v5ait5UgQsE3XNwBlWk2FTBynvFVlZWeMvfL/Av71qH1nccRSrsL182zrFnbueyyy7jtNNOO+ixQ7u9DZ2kg1iw0yPe1mKsVzCw5K33DnTJRmrMndKgL1AfO6kqwYsg4dmzH3g9NSfmK7dKfmjztxJ07qMTWDhOznXsodPM9+rMrpMVKK3XMVom5YKNsnAcpGMpwa0eOipeXoWOc/zYMpw6GYOLx/jo/c8gtC9ssJyz4zGK20767O3ubXCFOG6HXJlZXEqVq1fGTvJzlVCerEB6TUqxt07RW+eUk2+wZPC6uYBBVTUZJu2cxpxm8mqZsFo3zqMGCTufKnmYtzzvw/zDzocx89mtvOAHAszXMUxeE7Mx7hNNyMLr9HPcXko0Ln9Pn+7wP4snES8HjOyVNozsSPG6KUaBa/tG9xNMq0E6KR5bHjgYR7GyxWPxLImTf+43389W9647SO9bFKDU1dccytiNGpVWYfvcAnWLVKwzEMyCMlXfuX3QBypgLsiiJ4rXBV3cUN9v8LoZSUN+uN8fIxyF5j5KCYWk5uB1DDWLjQnnZCXPPVUyCGdPcTAKmvvzUhohdxTrv+VQ3y+egLc0EOckq9FfLw9+MIE4C1kFTnUjSTlkQbWgdjf6NHcOqO0Xb8Dt+ejEJakreusqPE7cdqzDY4HT+3PaO0z5O5QiDx0cY8idYgwZdJLR2J+zbNPLOpF/xZgVh8G+F/bcXj8XhpwBt2s3F6ZgzBWD3X6WGYJFyzwMvVJ1u9gAGC2LY3E9JxKaf+5V/ZLWBbOjE6gdsCKeY5pozAjbEFmsCyp8ATA3zlrcD0iaT2eq3PB4HWHhOXHFahyMivPd2p1T2x/ZdiqiMWdtqjerHBYQR8U4jiU82MtalfbBeMUg1OnaPnZiqO/RpUNVtDNcyMkdebZyfkALeQWEgFDbD0FkSoe8AKDvnRll3GKCBCNVMfVUbohb8o4U10trkvbKvApLFI0q8hTiQsE8DIVVu1DJQeRWX7OADYBlDNZZg3HMaobMV9RmM3uMoj/prAXfN2QzU2yQVQqscK/au/7mMI4+wuNxj7jz0iObN7q8+Hmj/MUrz+J/vtG90+OHttaGTtJBrLnX4Fo9l+auAe51O+SLOMHtT1DbFKJTu1NviINU7Bh1Ct+86jiC0QHBXJWHb+7OUDmkV8oidNzJ+3nXsZfx9b68uefOPx9vUZOMGLKwoknTSBmMe0z9yKpWew4YU+6Is7rD8lE5tfYAd1mciDTUTF6u4Mm3v7frX/B+AE5+14to7xQaekEHTupCd64vV/Tf3jpVarzEFgvQ35ijUoXbqe4PIwy0Ai8iLBRFVoPWDaLPow4s0DnjcK55yfvL9jzhuC/yrKefzWX/fQIgYPXOZo9ozGfsBruIdxKclbhkWTV3OawkIU95wGVcfKkofPsLMVnNJQt0ueDErbqlJVuHMpLoV2cLvOP0T0o/+KvoOXdi3x3k/PPNvwlAcEAmUEVVDsZxBEOR2fkoC2Q8hAumLAmS1qRvGtOGYMnKQfRFKqGIsKUNB68jbLMCe9beVu3Ww7nE3o+pmInIYuAkOSqvwPHBnEvmw/JWXUb32tsMrZ0DnOUC8aowrrZlQuReagcgGZXxYBUOyALBQA3W5ZiGtL15vYfb82nslrCDtxjhLUZkdRdtV6r+hKI/rnD7TsnSrO3PcAYpg3VyTHe9Q202w+t6JfPKjQQU7PVy/JWCjSWLaloughYw7a16DoOM3FFkDRenV+DhcvLAxbiV7o6U4lCrojiUTkZhOrGaZnZBjVtaFu+EUsJBpcJYjEcoQcvkIh9QOClCqVdCvLC/cxJhxBWaTiDvmVmixJ65PVPKXxTOQH9KtK+MQ+nUpjWLiSpKgjgSwZX3uBp7SUvaXkgcqFwcHqMrOQbHAq6LPg6WDMqI7lPJamwoVKapz6TUbdQmasu9lOMlNDT2iMNZaDVlWuHt83GOWWHlMEsEuUbhr+QMrKZU3NZkNcvkVFWbBpP2ukVErWOZd4XcRaYIlsVpTu0c2t9gaO6SqFsxf8XrUwgyjN1IqFTh73fQiaFxo+w44k0jLB3mCOZtVUmoAmsG4C8b9PyqGk6/ZJubm+Od/7jIZy/ceJcVvl/14jGOOmM7l1xyCWefffYvuYW/XjZ0kg5i4VyGrzPZYYQuXsvGafsDSBLq+wa4kUxO3XUO0agqI0s6gXCfi7OtWTJWtj/eQY3G1K8KCSzZ4KaVdbARHl6TN/CWp/4T71ncykfe+biqNtchkPVd4lFD7lsW3G17wfcZ3H8zAMtbXXRq6C/WmCh2NcZQmzM87baH84kjvn6H95g8qEM3aTKyTSjNIDtWt28IFyqNGycOKzE2u6vzOpqkVdF6sxp0tipJ2RTllxwBXOsEspY4IjrwmH5mxE/bRw+9BF50CQBHf+xPcI/o8PenXsS5//M8ALb8T4BTd5k5TZzAaNzw471baNWiSvSy5hK3HOK2prPZTuQnd1EK0h+JdzB5dUZ3g+Lo39rG7zbu3s7qH5c28287H8zSfok61XJZdLQLrk0D+v2cfKDI7amjsYKJaErmUNKwk66BzLcRp74wG7WNgHkrUrIm9zSe1cwa6VpQs6nYiL5NJ/XWi0frDHJyV9Nb71aREA+iMYNzaJckkodT3xeShQ79dbJq+MspTj8lCx16k075/IxjIJfacgC9lsFb1BjPoLrWSQ9h+TAHf6Wgc4PbS9FxTmuHDJDcCclCRdzUpbijTuT+iuv1NkAWOITzuowIdUc0OnVo7o4Zu1F+190UsLJZraVeG9Efq03b6/kOxtPkSlVFZIzUyzMFo8/2Y+5r+lPSfwXrCbX23CqvmIGOrTmXNCmdWp0JScNfqoDa/rLVGLObDR0KeD+tKxLrl3vdCoDt2/IlWaCoH8jwCiHO1BCNe8yd6JTpWZ3Ke2YcXb6TylBqMYGcM61rBqEqtXzikSqNVEaSUqn/ZhSlc6pTQ2+dLuchY8HxSUMRja5qg6NIa15Vq62Iutk+qO9VOLEQKYrITloTksVgV5NglTyCTg21ucz2i0anmsH42hSxv2wduMJxyuR6RVTW78vfXreKRrJHgPe9zTm+FcPFywmbMUlsU5NdF5U5NPemmBmpe6anWnh9SGrV9QpR1IrpyBqG6C/b3vr6oznjtJCzz6zf+cHWJsYd/uxPRvmLP3803/lB726VT3nLW97Cpz/9aW644QZqtRpnnnkmb3vb2zj22GPLY4wxvPGNb+Sf//mfWVhY4PTTT+e9730vJ554YnlMFEW88pWv5OMf/zj9fp+HP/zhvO9971tTEHhhYYE//dM/5fOf/zwAT3ziE3nPe95zn5ZXGTpJBzEnNmBf6qTpkJ8kufNdj3D40O/8Iy094FnvfzkA49enpDW3DFVHLUM6ljL6E4/Rmy3deZPLzb/3IZ511Nnc+AFhGtz6rcPgmLXXPW90J//SrsK7GKjf6hEegHjM6iIdewje9DKDMZk9uodAc7vC7XvULEvN62Rkoea6A+vhiDu+xzR2pP6TZdUVv3MGGU43wZm1oQg1AZt8+lPVhBwsyI6yxFt0rYJuCLmtk6YS0cEZuyWlc5jM7m++4J9LHaSfZcYFdVWLd4yfw+gW8Sj7E2N4XcX4w/ZJPx32Df7ic0+nv69NraDx2kkxd1cV9Oz6HLJlll3HSd/5Kx6dkyPefcR/cneYbOfc8Dhuum0j7qJbvji5a4ttDlYtOLnoXRW1nvwlQ2M6FWq9tXAeimKzK1ulzSO3uYRzaRnRKEwnVXqqsNxVZUQtDx3ShlMxleKc3FF011csK5WAM1BkOxqYunRM3IbOJo/BWMGidPGXZRFPbOYxGrVYMkWpO6UyRTwCwYxDTUoCSnHjKVg8yi//bky71GYisqB0UfCXc1uwVnrQXxQWZnunhB7Tms9gSiIi9ZnqfjNfkTYdglm5odqBhM5Gv9zN1w7keP2c4EBf2HFQUuHFcbBpas8h93SJZykkD6JRr4w6FCKZOqEUgXS7BjdSDMYrvJMs1JXDYCxeyOtUYqrdDUoW5kUbcR4RnE8RzQHptyw0tG9dxeiLjDDZ7PV1lOH2HNyuQ3KSeN/RwMXfFeD0AOu85b44MoNjpc9rM1a3qOYSj9ixYAVtjaqkA1w718QtXd7j+A0p/Qldpac8EYZNWpqkVYw10e0KlnL8Zen3zmZPNLOsnlmwJGKdWU30iuR30k/uDlVingrHzrGQgqShSUPW6s81KNXDC3wTFh7Q2WKPaUn9t8FYVQA592QTZ9opsX176zf5eD2fIgibNsRx8+eKCQ2clQGtHR7zJ3il1EmhSl7gllRm6K8SI/1l2t69e3nvvy5yyWcPufODf8pe+sIx3vPBJf77v/+bxz3ucXf5d5dccgkvfvGLedCDHkSapvzlX/4lj3rUo7juuutoNGTj+fa3v52/+7u/48ILL+SYY47h/PPP55GPfCQ33nhjWfT3ZS97GV/4whe46KKLmJiY4M/+7M94/OMfz2WXXSb1BoFnPOMZ7N69my9/+csAvPCFL+RZz3oWX/jCF+72/f6ibOgkDW1oQxva0Ib2v8D+5i9O4DEPb3Da/e6iyu0qazU1r/3TMf7y1U/mMY/po/Vdc+wKh6WwD33oQ6xbt47LLruMs846C2MM7373u/nLv/xLnvxkwXZ8+MMfZv369fz7v/875557LktLS3zwgx/kox/9KI94xCMA+NjHPsaWLVv42te+xjnnnMP111/Pl7/8ZS699NKyQsYHPvABHvKQh3DjjTeuiVzdmzZ0kg5iaU2jLEDajXL2PFS2a7c8rcDShHCGRDnCb3v0pqpSFCpVgEtah8WjrDDfuohLoxSNKXea6y7POeKrz+O2R1bss7m8y8oxGa2bxLsevVk0XGoHUjqb5Fz9x/UYqYM2sl3L5lqYAzXGbhyUEYss0KSB5v4b9pTnvjbpcaJXZ9pWrM9t6iVbJaa3xuyOO/crkbcCp5QiKuOlKnAogoa1aSWREqxY20AwJc5PqWkfzMIZxeS1Kf2rNxM0Kn2e3FPs2i5gh/+sP5BsIqGfe6jc7vBTV1hHHvS2ys52asMicepSH5Owg3riEi857Mcc6d61KNLpV4h6+9yNk9Tn1e1UxpOGpAMKLIy/lEpV+WahzpwSTHdJx8IyWlEAUQdjVQqis1HjL6lSu8lbiksMTFa3ZSccZcP9Bh1VuSavk+LaELq7FKPynLFbHLobbDTL4k+MVvTsZ0lTIlL1A6sYVXUHfzktIwzOAOq7BXRVpIhBIn1ZUDGFJG0Ii8fL7tqf1wRLitzVDGz9rMwXgUZ3FaZFZQ7BQo67IqGCsZsVvSWXuF0xJKUcCHQ2uKX4YX1fxNRVcZmadIpac0lWRl+MU6RFckyxIBQRpGIIKshCh6ityshENCoA39FbJO0szznHKI23Uj0/t2+sKnWlj+X2WYPvc2KFylfVU+xKXyUNiDdKm50FV+okupSq3419NqJoo4o6TsF46AzSIl3aHtCbckibTqXDlEkpliL6E49AuKhxB6ZKpxup6WhU1Q+DUUVa06wcuuq3bYcshN7mzD4HB68rrLtsnUT+8iUPf1HT2aip21Rya5chDSuWoYC6kTR8EY1JBMuzurZlFhRsO5t6XSfRp3COsi5c7kvq2jjVuXSM0Get6RScRDSZCmX3rJbTus2h9gO/wjL1pLRTkV7sOspqQdXx6xJ6XzksZO4kyMPczukSUdUJdDdUGnndUQMf55dqt956Kx+6aJnLvrL1zg/+GfYnfzTCu/55kf/4j//gD/7gD36ucywtyUQwPi5hwW3btjE9Pc2jHvWo8pggCDj77LP53ve+x7nnnstll11GkiRrjtm0aRMnnXQS3/ve9zjnnHP4/ve/z8jIyJoSYmeccQYjIyN873vfGzpJv4rmxAbtGZw4JwudO3Qirj793wE44tkvYOLHaynDKpXUxYqtIXb8Yft4xiXn0rzGZzySt9qJcjZ+yeN+P3kRAMvHJwSjEa2NK6woS2v/iRRs7W508S2zZevUAT515FfXtOXofX8Cl0MwLQ7Q9FnjhI+f4XETV5XH/Mvsb/Gl204oa74Ra3obZLEqMAXeisHfv0K8rsnsqVKCZOlYg0otS2WpWDiE1r26NIROFOGCYer7ktMnScmbNQYbG6xsrdIud2an/c61XNU7EX/JVIrldZlE29fJsL1i/hgYTclCw/KxljZ8Vofe3iZOz+CMSmqmHQ649brN+BvEg33k5ht5RvtqVqfaCqcxw7BiRROvjTdw0f7TObBTJoP6jABwdUapqCw1vKRwbAE49XqazFPUDthn3E8xoZTaqO+3i4tN+WSBIpivnM94xMHrWemAwMFJC8CxTV9qKf5aYFUA9CBDJ/matJbRGrdXrUoqs8WOTbV4FemXAkCcO0qkIFKzhvUULEl6qPjMKKjNGJYPV3iFULZlIGmbVgrnoL4/QScZbt+1xxhxRoxaBYAWYUInKuoUprS3p2SBQ3+d3RBMKKJxcdjiduGQhIzc2kelWXm/Kl0Lns1rLrmrBcReyAkg4PSir3SaC27JrZhkWNxLfTrB6xSFccHtuwSrisdG4y5JraqVuHx8ijfnUptRJBagn9n0V8lS64pz0N2swNLonb6iuVOuW5A1+lNy0sa+6nk6kUgxHLtVatksDWoM+m28ZUVgNyXBkiEaqaQ3+lOQu1ocjQI0HUqKVyVSLxAqkHbSNDiRtGHpcBGEbOwsgM3yvmPA3W9LLVnSRh5U48Pt5YAmd+z1fHESvT742+39uJWMQ2Lb2pvS1A9UqeVCdNMZmNIhakznhHOSui7rBNY0UVuXat6DcZg/TgserF4oYSritqTTCscwGlGlQGo1aKA35bB4pOTpGo+c4eb7fYpX7T+Vjp3oLt5+DINtDUZOkE7/vS3Xovsd3sQv197+xgfw1Cc2Of4Y/84P/hkWhpq/evk457/hj3jsYx+75rsgCAiC4Gf8UswYwyte8Qp+8zd/k5NOOgmA6WkZjz9dSHf9+vXs2LGjPMb3fcbGxm53TPH76elp1q1bd7trrlu3rjzmvrChk3QQc3spTpCVi0p7m3z+lrljeO3ETWuOve2JH+AI/49p3CgDWKcC4GzvzOkJtppPHPUZXlp7ODd8+aQSW7F8pGyjSsXcAx5J3yGheplzB4KOAEybOyVffvNnj4I/W+skZeti9p0ZMnmNPNaH/fEPeOeGy9cc89Kpb/L57z6QcMYyZLak9DdkeJ2Kyq+MIa95pC2Pvh33P/yDv2NM1znpH15E7ba8bJfbq5zH3AOMLGrxOnFA/H3L6E4ff9ln5fC7Ptw+fOi3OP2cdRyYbePulRc3nKvYOADhAUj7Uuw1HRHnY7zZ44m/eQ2fuvH+pNMy0e1Q43grmhjp60tHD+NJI5exwZHIGsB/rdyfS+ePYNfyKEvLcly26KP7muZeef7hgiFpyi6yeHMyH6IJSEYzlo6Wjlg53CU8AKEFiNZmFXre4HQinE4hl2wwrkvuNUnqVbFQFKS20LBKjeBNTKWfo6MMP8oEfOwWUuc5RlfsLOM7qDTDiTK8rpw7GlGlzo5jx5pxQfcpoyVeJ8FdickDt+znpCFRlULnCSrV53C+opb310Hum7IMSjxpmLw6R8cZ/mJ1QWegiVtVUViUIhp1yhqBrpWjcLsJ4WzxpD1S64yUi79RQI1w0eLvlhIcY8BojCoiY5os0IIXK5hddbdUtwcIFjIyTxGNVWylcA7a22K8Tko8YhXZGw7hQkKwbLFTdQ+dGNKJymkK97joTPqpWMCzuiFr5LgFwN23RZ/noLFbno3XMxRlZgqQdDQiFHN3UDiYinhUznHDrbZ24pxLY79Ec1cXgdZJpU2ljIwplVdA/2hEoh/t7ZXj5C8b3EFOOOuuKrC8ln5f32tryhnKiBrGsjfjVU60VtaptOdeyQmWVCXJQIX7cmKHnnVUvK6xWCOLtfNsLbVVkcdwNi0LcRdzqG579Nb5ZamUQEupo2jSoAeWCXvUIvFGl2y+VSmIu4IZK5zcpGkVx5tCcgD4yf0+BcBb11/GS/Y8RP7//T/N7/5GlytjmYsv7R/J2y49C/gav0w70Hk4Z5367Xt8ngecHLBtl8vIyMiaz//6r/+aN7zhDQf97Ute8hKuuuoqvvOd79zuu58Ggxtj7hQg/tPH3NHxd+U8v0wbOkkHsdzVOLIpgtxQ3y8T8hff9DAGf+Xxxqlr1xz/yjP+h7/f+wQAglkBUS4cqzFNWSS+E7W5+KrjOWbnAKcns5px6kTtSnY/nIVsWQto1E5O4aKhPyl1o9w52fod8pUBp/AisjMFWN1fDmld5ZOFsOUvxIH7aQcJ4COLD0alirhtd2tLDjpVazRTuht98kN8Fk6A9olChR3T4jgUNGSQxbI2nxHYgqwrhwjbLW3CznNkpo2nPNxWjOPkHDm56271/yHtJeLMob9TZnxlF6CiX1QsC1taE3A2wMzOjXz9NBd1Y4OpW+Qel45skNZNKYC30K9xa7KO4719/KB/OACf3P4A5neMggZ32eo57VPU5k25s/WXM5KupruhSpGpTIqTGj8n3CTP5m9O+hx//tlnCQgaSVvkXoBfc8r0EMYwmApksViV+olGdAlAre/VjN1scPopac0usjUHfylBxTlZraCxO6isKquBMRitRW/Gzi1JE7ye7MwLSnfSMLgdhdO3UZyVWKrAN9xKX8YHd0nAqavp4iqXiEO8qoBu7sPW42THt/eyTTj9vqS5bBTHX0wk3aV8Bta5yMKK3Qfg9jNhm3la9LuAYEkTtV2isVVaNTXobFalGOjoLWC6mrThlGml3pT0Cwr8JSuX0ElkkS0A2csD4lGvjAgCUkh6JSEe9VnZWpV3d2IHdym2fWBKBlwhd1E7UAka9qzAZBYYRjYvEU2Pl884rVnafwkrUcLE8isH1uvKsUWR2MGYTXkugLlJ2uSvQG02J/dUKS0RWbmG0sE0FT2+mGN0Ihs4fzkv21ubiUjrLsEiDIoNQCDpwSIdtnRsjsqkcHRB3lhtBWkisaKbBYPRn48wnkblpgTVG63BUag8oG/vsT+pSGtOGdnMPDlPGlQLZFrXdDe6hHM5nnWcvE6K1/HWCFUGswLaLqPQqUN/McQJbZFnqnss2MfKQDRh5P9kaxflbWmPm5clzX/NwiP5ytg+vvLd+wMQTms2X9Fh5+275BduOYac/M4PvJNzNBoN9u3bt+bzO4sinXfeeXz+85/nW9/61hpG2oYNonc3PT3Nxo0by89nZmbK6NKGDRuI45iFhYU10aSZmRnOPPPM8pj9+/ff7roHDhy4XZTq3rShk3QQ60+6ZMoT+rWpismqHL5+/m/y2WeeQrdrFYdbfZb2tmnZyak2Z+hPCM5Ar8gC99Wlk1B+zs1/7IFVyh67VBEuVhgGnSjZ9We2hhASvg4WNE4CxuJVVJzS3pkzO2rp28DKsSnrti4Ilf5n2Ke33Y88zDFW/0kPNKon+i69UqkYepsMtzz9H9f89qRLn0FrR7Wr83oiIufb9FB7h6G1SybsAw+Rvtr2+A/crg3/urye57Vv/zL8tF15+RGEM5pmwXLSsLropzKWBZRUE1pvS05nENDcI4wbgGDeIWlZKjuwvFTnvdseSnbYJXx36SgAFm8ZI1jSpPVK+M9fktpclWMD/lJGFiiWD6uwKY29inTRg5tkZ/bK7c+isUewN2An+0QxGKsW3GhUEzehPmtKRfHMl51sUcjUHRhWDvHwO26pK5Q7QpHXUVYKJOa+LsVFi3aiFW4/K1MQbs9B5YZoRJcLR21G8E1di3PTiaS9uhvd0m8LFmUcOrEpF1SULIiNfRmBxQh11yucSDH7DYlyHPLjWLBAgVtGuIooQjibkNTlprsbRVOnrLPnaYlk+rqMcjiDjOY+RbCsS9XjqKCF27HQW+fgdTXLh+pyUVdG0kFx26O902KZ9vZxBlkpbaHSHK+T4kTOmuuBpD6LVJCkYVxq+y3ua3mAM+FBTumc5p7Q4Z3YlAtxMK9ZYpR6Up0n9ySKYQpZACtEupo2n4WSNlqt1O11wE0rR1HS21KnMFpVbw8liuAgUV1hg1UYHscKRCZ1LZE7JPrjDDJGtsU4kTQsGlUEi4a4bdNvxxm8jT2ypeYaJqczEKfOsf2XBtIHqRVA9ZY1KsmlaHExREOH5cNr9KZUGbkq+pBCay63jmSH0gnMXVcwYzVNWpcfpmHFZAUryZFAsKqmY/+2FvVFJX1onbC4LTpThWivMgpnoEjrlVbUEV94AUcetY9dc2Mk0zKwatOabycb2bBdTl6bGdC762z8e2QGQ2buqZNk5+t2+65d0xjOO+88PvOZz/DNb36Tww8/fM33hx9+OBs2bOCrX/0qp556KgBxHHPJJZfwtre9DYDTTjsNz/P46le/ylOf+lQA9u3bxzXXXMPb3/52AB7ykIewtLTED3/4Qx78YCmj8oMf/IClpaXSkbovbOgkHcSWjtA0OhrfvoS+TZU4A6G7Nj/eZnRQaIP4tDODcWTSyQJFa7fsYvdZJ/idGy7nnRsu55OdEY72xUn45xPO5rrFDez9sSwu7e3Q2G8XJLsDd/s5xlEMxjWZXVxmzh7jkc+7lLevv+Ju3dPydAt3pdq566igQq8CZDcha2X80Y6zWB/KbPE/Hz6Dddcn5H5G31Lbe1MuTiQUY4DG7h7OnjmSw9cRjdhZ4/G3b8NdcZAA3K6W8i/2Xc5C2T0X4HgnFk2V3AEnsW3apMiua2MUpfaOTsDtqZK2zLzP9PR6Xr//d9CeBRrPadyeaDrVZuW4YMk6xzbSUoBo6/shKtIwTYkkOFRYrclr0jVA92RUU5vLiFu6BObGI7Yq+ao30DjyDIrUkxMLoDYaUWW0rrk7wRlk9DaG+CvFWBO8ih5UOCWVZrhRitOX3wXzkDZ8wC2BxkXqsBDh87oe7kDUmgusik4MTmJQmegggaRmlo6S3zYsJ8CJoH7A0Ngj4YoibScLeTWpG6VwBintbUVuNyQaVeUzXjrcZ/S2CLeXVmU0xn16UyIy2Zgu0mbWGSoWXS2gW5VT6RtlFV27kEco2pbV5PmlozWcfoYTV4unjqQkyppoiaqcABkLoiOkTLUQF2U9RAagwnl5y9X7loUwmDK4fUVtunrOGFA1cf6kn6yDVHTdqohjqZ6tJMLkr5jSMUyaVZQIIHcLgaEqVWkUFrS9ShRyxCM4EKFTQ31/sQmSsVCA+INZh6zTIFysHK60Ju0JF/JSgFMZiJuKzEpUJPU6jWkZFwtHF3lAq3ZNJZiZeeLcJVa5vrce4vUJ8T6P1o611zNQShUMJiUV2LNBjGQkpzat0XFVHkbvVoTzBq9nSsV5f1meWXG9tCbvsksVgartctk5t0XEdUtSjmCwimK9WeiQu/dyXZJ70V784hfz7//+73zuc5+j1WqV+KCRkRFqtRpKKV72spdxwQUXcPTRR3P00UdzwQUXUK/XecYznlEe+8d//Mf82Z/9GRMTE4yPj/PKV76Sk08+uWS7HX/88Tz60Y/mBS94Af/0T/8EiATA4x//+PsMtA1DJ2loQxva0IY2tP8VZpB02T2xu/v7979f2NwPfehD13z+oQ99iOc85zkAvOpVr6Lf7/P//t//K8Ukv/KVr5QaSQDvete7cF2Xpz71qaWY5IUXXlhqJAH827/9G3/6p39asuCe+MQn8g//8A8/x13+4mzoJB3MlOzsix1DUT8L4+B3DeFsUuIfVrZ4Apa046+xPyeck+83fke6+bhNz+K7D/lHfr8JhUrl+zb/ADbDC0cknHjxd06htU22emUpg0jUo3MPGuvld1e8tirpcVfspXsfBEDrRsGbFFEjbWs76YRyB54aaN7qcvktJ5a7vJE9GUZJ+YSCmj2YKu7XUpJviWAQ4e2aY7Imnz34L/+Ez7zpb9ns3L3CsYf/1wsYnZZdY7GTLdpc9LHXyUkDRTiflIrUbt9j9n5ScqRouwgpVqUmvBWp65TuD8vdNcjO3ejq/Co1DCbcMpKk05zcc+itc8s2qdwCmaOqKGwWSqqo2I06sSENNWmoSkZZPAK6DmkBYMYCYBNwyqycoj6bi3BkgZP1NImvSWuWvYbghZw1USQrnKhUmcbNai5OlBHO5rg9C+YeFdE/z+6QM18wISqrIi9paFO/UY6/VKUpklZusUkWdLtkwbg2aJI1PDAGZ5CW7cl9t4yIFNGlYDFnMOaUJIWsBp1NPq0dA/RAOtDtZrgDSRfWZlP7O03SdCoszoikeVfXKFudEkqtUGqQGXSUVBEo30H3Y0Zuc8u+UnmOcTT+SobXLUD00NiXlJiavOETtx10KvcA0FsvtdxMpEo2XRYKo6p4t9w+BEtSNqS9rapX15/y0Zkq1btBmGdFpLGIhqX1Ctztr6yqoVZgmVYkMuX2C8VtTW+D0PYLZXzjyPn6E4rOFvtOLGvWLxTVgqvx0J9S5bPRsaQvk+aq++lQKVAXwcFMyu8UGCGp/+ajMkNk4Si5hwWms0bJWqeULNvWTsX8pEIdv0JqFe6LcipGVXUlc9cSDJqmPE8ayrxm7C05kYxl46ytmdcfr9rt9KTP+5MV425wRIwTpugbq3xa2qjaACI07M/dO5Gk3P7vnpi5m06SuQtq4kop3vCGNxwU+B2GIe95z3t4z3ve8zOPGR8f52Mf+9jdat8v24ZO0kEs9yAO5MUMFqA+Y2nmoQJjyGqVB5z5oq9SLMxOLJo+OjOE8zKDjXyxwe+PPp2LT/z8muv81tVPYnpOVs/zzvky31k4iit+cFSJjWnemOH2NN2Nmr1/cPtyHnfFdvdHgYr5osoFXMLzg/GKIVPfV4E1/WWbgsgEwKxTU9YDM3ZyKibMvBWgnUluOG+Eosruof+R8YnlU3jF2La71M7fu/WRAKz7nkOwlJUTEYDf0aQ1Vcog6ExwYirNywxLOK9wu76URVn1bscjhg2nSJrv+LH9XPy9k2nfrEs8UNyC5TGDv6Ro7qmcj3A2LfErWegStx2WD1fl73QsNGt/sVItBqv4XWjlJOJoZ0GVVoqnElSk8ZadEitVlHApLE0BrXF7plyo4pYu9YYK/aFwISMLHNQqAHQ04uJ1c/xFGS+551jcUobXkYb6yx6DiWohHowp4lEZG/VCSdsRPIjbVzT3yqBZ9jxatwmQuMTHOLLYFBuJ3pRLfTYjmDPo1SmjnzInEWXpssREJADe3A1p7bbtnOvTzHI6W0M6m6WxtQO5sLGsdk3mO3Q3y6JdLuA9Wbz760Db45p5Tl7zSgfMZDJ+gukViqLMxnNEUiA3VWmgMQgXXQKrshyN+8LiHKnKeBglTlFRC67oF6hA4V5XFMfdQV6q52e+SC+EC1IHDaoUaHEvypgSg+TaFKpRkvIr04xICqmxPy2LH6c1xeCEPo1mRP+GUcCmBW2JlaJ9rQWDjjOM65SbAuOISrdzf8n/JjeMUJuB/uZqkVaZprZH0nZJrcDpVc6KtB0wsmkosD7GlhHJ9ap6lwlgIFxVBy3/iYs7aJabkqQp41MpgQUU9wPQ2GU3sy1FPCr9VG4G7bzXG1UlqL23wd5/8XKNAPsV4Vw1/8ULHlnoEHZXsY1dwYcVTngWKFj17g3t18uGTtKdmHGE/eBE1e7aX0oZTEiF88Dm/v2OyOS3dtoFPDUYT2PyvKQc99YpBt21VZtP+N4f0vqvJg07KX5i7DTeduyneMVxY0R7J+XciwmDw0Nqj53hCktJvbt2xc0iQDbZEeetKIlgtGAx/I4qJfyVrcBuXErQZu45Monn1SQ4eouRwr5WRDFt+dz23Dq3PeGfy+ueetmL+NSuU++yk3TLpwVIPWLxQE5S4SYKIcaCWux2M9xeUfFeJikdZUxcK+DqgmaucqlEfsyooJj39QSzlAXiPAFlsVJvGYIlq02UGcgNxpFzpw2H5UMdkrZBWbC4NjYaZyj7L2pr0YCxb1fBOEKvKtcSaXANyUhWRa5yRWrAX6wW2MwDatVk7/YgHhHasmNB2ZmnSEO3wv4oRXejQxo6TF4tJx9MeqxsUdRmDHVb4TyxFO9C5kFlEI/mGM/g9YrwnbDWpH6h9IM7MCKHUBeQK4C2zLmiFEvuCSYsGqnRvk08BB1nwmrSqhI89RReR6QVSlMI+03JTbdTg9tL8Do5nc2FqKbGSXQpMuh3DPGyEBCKaFLxzro9iQIVfWMcXcknKIXxFGRGgMWr2pDW9Fqh1JoiHQlt38l30VED+pYO1tgmUdoskE0VyNiQQqvydxpKtAmlS8dJnB+9xqkvo7vWkoZaEwkCweCYaWHCFhioeESRhppgXnY84YJhZV9I1w1pWfxYFsj96AwCO4Ya+1IBoWc5eNWC7y3BypIMbD+VTWB9ty6dfeOIkxKNVBsAb0WiT6U0iCPvbn+yioI5A/Bi+a4Amdf3S+S92Fx0N4p4Zu6qMrq0OAW1/YpgyUaygdwzNDrVPOStKPwl0b0qnKvMl02d04fBhH0WzRw1GfFPZ3wUgFGnzx988k9p91UlkqrAn3VsfTb5KJgXrFOlaQX5hAv/wy/djIHsHtaJu6e//79mQyfpIDZ2oyERdiNez9C4dRGArBkye4pvQX92F+lAa3dObbYQLBEwKZpyV1c7oOn9YJRjrn+RrK6AvyCVr+sHZIKOZqb44yc+G99PC3FgKVa6TvHYDbf8XPfxkr0PRnVsEceaWjMZpzVxhOr7s1KduT+lyrRTEXZO6pKKqc/k5YKWu4rmdIq2tZbSusPRx1bq3gCPed53uXjvTxWn+xl2wvf+kImdVRRHZ0BuyOxOvbfBKkDbiV2nOanVGCrZgakhmI/tDrsQbjE0dzvcfJkUW4xbmnVGxA2LCIPbs6DpFVOmPJTtqELtev5Yh97mHBPk+AestpEj6Sq3Z6pdv9VwLBzMLJTFpajxBeCuOOShIXerh2EcI2lWuxg6kThJZhXryWihX3uDCpzrRALsL2pe6SRHbXLIA1jZ4tt7lt1xf1JhHDsWQklv5Z51FOuSdnH7VWpGZVDbI0VjK0fUxe2leC2P+WOsjk8TdFaBfP2OLEqDlqI+LccEvQSlDLnnlM8rnItxIrdUkjcukgrTVVpk4diAxrSLMjBxXVr1sQZlwdTxqIPOpA5YsRDHIzB6a059V69MA+aBC6aiopPnmNDHBBpVkDBsfbfBuFNGFLwVYVJmoZVi8ER3qnZ9WNUyXIYoswBp+1jDeXHUinemACsXzlRh/lIBIpa/xTlgTSQ1C2RMFM5IdEhCbcbD6+alE6GMRMCKSJbbz2nukvfWt87/ylaPyKarCiewSBWzSsm9Pp2QeT6tq2w9PiXjwV+uItFpA7rrZJ4ri0wHNlK0qoBv3FZkYTWuwgWD28/JAlUC5p0oIwsd3K54hzp1K6hD4USv7zPIQnS6Co7QE4eo2ESBFfA0t0/R9zflPPnsHwKwrTvB1vo86xxhKbxp9xPIN0QsTGoee/LVACwlNX7yqRMkapoVz1TUyvtWxqK/3pDW1qYqf1lmMPcYk3R3023/123oJB3EeuuUiDIbmewKbMXsqQ26W3PcFV0ON51DGivRpgGcJCfz9Rpl2GApR6eauK3KBSALobNZ41jRMyc21C5t0F8PNTsRzZ0csPlxO3jr+p/crfZf1Bnj/Gsew2B7G7VeZqfBVG0N7sZJZELJwmp3qDI7Ybcqxd/RWyJyV9Pd4Ja7YieWVIOfFvgVzcw3t8JxVRsuWHcVn23ceru2Hf+9P8TcIOGs2rQ4oWPditGnctBxjspNKbZYOAr9CafsKxHPqxSbVWasVMDash2ri8bmnlQbz11VygSA0MiNotTe8ZcynCSv8EeZDbnHetW5LKNopZIcyF0lujbW3B6gZNEuFICdri7j92Ul+1Sj06qyfe4izoICU2jqaMumiwy9qUI1WtPanRDeJppWebtG7oSCp7NOrs6q9FMRCfS6iqRNKaHg9oQlhK762usbvJUMHcs/AKeXSlmU1KAT6aveZkMwWzl4skgZVKDWFPYlzUV2rFiTHYW/GNMotI3WOWhf1KALJyINQeVC0W9MW+2dRSnZkrQrWQV/yWB0FQXIPehscvBWAvwFaZhKExljxW5aa3GYcoXxpD+NUkQTvhT+LTCG04bagXiV82EIF0AfoDyX0Yq4KWVritS1yo1ljNmUbaDoT2jyoHIYgiUpUxQ3VelYJzV5BsGiPU8s/TCYrIQOnTkXZyDPtGBy9jZCOOegjDg2ziDHGTh01+tSj0gZceiyQBZ8AG9ZnqkwArOyP/1xl8gqyWc1GDiSHitSZPL/Jaq42iFxBlUf5NaBKpwo6St5x2uzVUFno6z8wipcllEQj0oBYIB8yceP1solNPYa6zzaObWAE+SrNxdyvXBas5iI9/bJI77GpVGKZ/OCP9m9CcfLcGsJV8yKDtD0jVO0B2ujgYNxiQxn1qENFhTe7p9fBfvuWI5UBbgndk9//3/NhonUoQ1taEMb2tCGNrQ7sGEk6SCWNMCMAAbiRUX/0FEAFk7K+K0H3EAv87niR4Khqe+WXaTZaLtUWQVaXWFVMh+Ls6GU0HdiiJvQFZkkdCxhfOMZEqvn0d+ScMWx/3232//Omx9BdEsbr6dgWRphNKR1g2drLrkDiMZEIbjA5wRzisZ+g5kTXR6A4JYZ8skReutapYZOUpNUYMGycqKc1g7F3y0cvgaD9LuN7pp2farbwrm8VQod6lR0obxuXhUezQzRqEsWqDL3HyxIRKTAOgxGHfyVDKefkluMSdZ2pDAskBf1ueLM4nUkClHbL2U9oomgjBLFLU3SkGdW3B9IVKEQGRy9LcPrOfQ2VN/7i5Qg8SJCU5RkKKIOSU1wC1ndlGlW44JKFRqDt1SlbMlXp0CEmadMVRLE7cm5k6Yqd7aNaQhv2g+xPKt0i1CI3K7U4QPB7PQntYBe7e7aiSCrmbL+VmuHIVzILcutYCYZ0rpGp37ZxzrJUZkw/fxC8LQreKBCGNA4ECzaAshekYdU5DWXMo9c9LGj8Fbk2XhNYQGuBnkXeK/WbC4RJAQ7o5IMz5II0oZD3NAlfgokopDWBNviLdsSFt0YtK6iFcbI/1eKtCWd3FvvS7THrXBB4WKGuzTAWLpys5MItklTniutOSQNn7hVRU0KxlYxFvyOALfjrpJaekBSU3TXa4KlSoQyGq2AzyCMK7CijRa4nYzI/Q0mNYMJOVdzt+DVFo6SBkxeZwjnc7ob9ZrisiqD9vaUcEb6ZTUjssBrqTynvi9GpGpFUT+tVeQUuR8pEut1FB2vKC8ikeECL9bdqEl9GY9l+ZQiNRfqUnHdX5bC0N0NtiByICm13F2bNlO5LSS8UPSJ6HgV5aOKNHLmV8/PsbhBlcDXrj0egB9NXcKzf/DHHL9RWApp5OLXEwb76xy4WaLczRkZR3lQvYPy7lZQBLcP4QL3iv3i0m13wKIY2h3a0Ek6iD3v977KR/ecRXrtCGlIyax5/cM/zXPaM1wapTzjihcDAuITcOsqVosFvRZpA3cgaYjBeCEAUACnIbMgznjMpneUIZuydOf6KgTn3bC5HWOMbVOEC3kJdo5bmv6koj5jw+ydjKUjXBJbd60wJzL4S1WKJdkywdKRNQEhFyJ7SiaOuCUzXriY0diX8K//9mhe8ZJKouD/WziMF47eSE1JR/z79BmotFrM4qaiuZzj9jPilvRxMubQm1Rrao25kaG7sQKIhnMK4zi4/bxMXzq22KvQz23qLskgqyq6G1eT1T10lONZsLPXUcRtX4pg2kUgHnXIPFUC9gWwnFdYJ2xtqb4hXMxKRWkvllpq8yfYxWw0Q6UK45kSZJLXMkgVTl+X4pi5K+NjdZHkdERSF0VJkMa+XOY3BcqWTvB6OaZeK4u95p6Ag1u7csJ5eVjdDZ7QpmtVqiRYymnfoiucyHyGziRFU7KQXFHyVmmOY3FEGFGIT8YrCn5tRhavuFB6sEwvSbsU7D1FVnMxrirVwqVsiCrb7qxSKS/i3M4AavOGYCEVYDHYVIpBx4XjG+EMPNK6Q3+iSg8ZJD0Sj9r6f/1EnIDCAdGQBx79DSF9m7KK25VzU1g04uAManiWLaj6adkXBZg7HnEFX2MqoHF9JiGtO6WwaO4I+UE2R9LOhRONEAaWq2vmnhxTbJ6yQEqNOH3KyaMoh6IyCGeLjYTQ7ws177ilqU/H5H5QYmjSuqXz93NUbjGASQaOAldXxYAdjTNIS5XxpOaTe4L/McVeMCtStxmuhQxEIzB/ihEGJJaFF1tHpeh3I6l6Vb0S6FSeXWTTZq4tcOutULFJR2OSNKB9W+VIF2KQBfu4PgMrW3RJTAALjo/lPMEOOdnTP30eWTvlqmUhtSgDqZOjE7UqXVrNU+V7YtlvBZYvM/eew5H/QoDbv6DG/B+xoZN0EHvR6A4eMPkfvPQnLyBtCkMK4DltCYH8+U2/T8OWO/BXBC9QqDrrRKEjiMcNTs8u4H2hjtYOGJp7ZCUsWEMDW7pkJddEUzmMJKVz1KhVtP/3LUqu/P+N7v6Z7X74dVI/buQGh9wRPaECPO70M8IFvWaRGrvR0N3g0tlqadJ7DI29Ed3NQYkX2P4kh9t+R8qUHPn15wIQ3ByiY8oSHb2+SzhnwMCeTFbZSR1yVWczH9MRF8/LDu6WTx2FYyqFXLcjbCGUt0r9VuEkaxfZolZWWQbClwXCKHDi6v4Kpyi3qsom9GyBUFtuYURqmnldg7YSB3lgWUKrdslRW1t6tQVyp+KoOX3KCdqJTNm/BftL5YrelCZeb6Nw7Yj4QE3qQRVA7TBDLXvoWJXgYJD7LSKP/jJEVoU7s4veYFxbbJQp+2Vls0MWjOOvjMqz+h046shdzH52C82d0obavMY4Dkmjwjy5A8HKeFZTJ3cQZ7KblLtyZWSM5J4msQ6sM8il+HNkypIOuSvg33Bezm20BR47lA6CcR1yX5cLq/SVASqwfDAXk7uBRHKK6IEWGQJn4JYRmdxVBLODEj+j4gzw0EkuDDrsolgXp6e3wZ5MtYjGnApz1cnorXdF+6uAKa2SJCiec2eTIm4EjF9v9bjitIxAFUrn9ekIlRmywCnHgtFSGy+ckeeQ1V0yTxONuOX9TVwp49eJpB4eCL7POJX6uterxl+hbaRTcVa8rtRwK/rdiWXuAeiPa4J5TbCUEY0UHoPco2xuxHl0u6mswIpVbEsBuLs2GtnarUhr2jrDcn+5p/CWE5Qxpf7W/P1z3vjbn+bvbny43PN3xwlnoT6blY5hWlNlJKYYj4NxZ40Cfe5ZPJqhjHwn0yHBnMJfyYjbRQRWESxWpJm04eJEmtwBvaoOXe5VjiVA7hvq432MfdjrRlYYJB4zy37ZB7nt3zyqZAHSRsE+tM5ctxonv2wzcA9Vkhgiku6mDZ2kO7H/WjiVYEEiJgVtHqBvIvZsn6RlX3CdysS6Oiwcjwkg1I8rQKETy4RXyALUZiLcHvg25dLY5zAYd1g8KiSalB1PRIOjFp+DdnJec/+1PNM/3vUbAHzvS6fgRPJCF8wTBXS2gspd2rusU5QYnF4FaDZa0km1+RzfTkTt6+ZRaU5yVMjy/aUNrzz7C+VvXnba1wH415GH0L9qnMy3wO26wusqxm7MeNzlLwDgygddxAe3fJdLo5S//dbvArBuTw4GeqmllPeM7PZb1UIVj4gTFB6odteDcQH0FjpNKrP6TVkF0s4CR6JKSpVMJBBHNGqv1aAZjClyz6ZY7t+HWc3IjbpMNYFEdUo6sJEoTG0+X7OIupHUGkuDagGIxqhmIwXUhepvCodr4OB21kICVSZjrEg5DmoynuQ+bRMcSYH5K6YU60sa0J/QvO7NHwLg0bWYmazDo/Sfl+d2VxK8miZpKLrrbRtyh2DJlCw5v5NLSY58VRkRK3/g9JM14HeVZnhLhhGb2kybHr0NXiVeGShUrmydsoKmafCWYoyrKqHHtEh32f6Oc+ts+HQ3WrblOkkRO5GIaIJEQQJHldt84zviqAZuGcUJlqE3JWnAsvaX4xKtAmSH80r0oUaqcjfukvR7AfaVH9r/FOBuV5w9Eey0UbBO4bA5ZTo9GQlKAgJAf0rYaKtrEGaBSCkUDoFcQP5TCH0W0Rtn2VA7UNyLpHqMht46Gz3zrVNdVP8wUhQ5PBAxGhUSEUjZHE+VulYqlULKqApoX5ZnsffnLUT48wbjir4XiF4UGsgo5VDaNzi8efIxRAsS8ppYkHG7cohTRnuUWQX+tun64nkXfWCULUGiq3nVXyhK6eg16UidGoqyQbkjGyC3V6XgolFxcrqHZuVG5fSTbuWq6Y0MOjIHLLh10kxDkKOy4jlLdNTtV9IS0ZhNHRYR2QUkEj+0X0sbOkl3Yl/7zANpz8kOa8XuiAvHxFmpdj5dGxWp2dpSXkco8SpjzQ41nBe6diFAF9qdcZHW0lGGtwSNPVomIGQSjdo1ehvgUxseAMDz2l8C4NvbjgRg6gZJqWVexZCRgqxCvV06XBoat6QQbbhgUzOuKtk+JV4g8FD9LoNxxdUvu72y93mjO6XtRye8bfkcmJHJsL4bmntT3F7G3uXamt98r3c0gY0yBAupjYTIRNTZ7KJSKTTZtwVMB+vFEfGXNJHFtAj+o9rlKwTP4dUd8nZBaxdKsbeSlbv5YpFuTNvI3LTUO+tsdumN2ns2Ch3L4l1EGVQmtbS6h1u8zLyLTkW1t2iDG8FgzCGpVQKTRtsF1ilme/sv1ij7Q6ejS/G8UmiwIw7hikT/CU9cZGV/k3CvW1aMN7paVFfrAS0faXj0T9GQJdJQ4Yjq+yNQAb0p6yyOSZRHpzLDN3cOQCuM0mVaS2W5pLaSrEy3GVcYYUobodQXD8NU6TKUEvkCLcKJIBgXleaQqPJ3xtYWKxwGHWeQWn2sopbgMT3Srkfc8srIlZOIg9LbIuEIt58TzPZx+inBQlGzL8eoGlGqaO22aeO6or/KSRtMCIbHW6mqw5c12Ezl5+aeRN6cVYxJ4+rSWSr/drSIRNrP/GUbMXYKOQqrlbSqDdEY9E4cMDLWY/lmwZM1d4icQYHdMZ447E6U41nBUVEYN6xsUQw22vljoHF7qnTSyOWefU/jWhHR3LeaTJmI3QK4vUTa7CicdNU9rioZkddc2VTFWXk/gnF01mDyxm9ISHfWSgdFp7ZuYVtVY6FjZKNowF+W6y0f6tDbaMrUoRPZfl/FFkzq8lnuiWo5gD/IRf6idIgc+hNqTbq01J3SlE7SZbsOIV0KCKxERW/G508f/9988OYzyW4cK3/rWjxT6VjbdFvxbHIXnLWwy1+a5ZhfELttiEm6qzZ0kg5iD/rg82kuw+yp4PRUmYdvOjE/OrCVcL8q8TJAKawG4hDVDhiL2ylAm4bagZQ80GsSw0YBxWIWpagoE3G5QgHYVQQLCrTPTT86FIC3TByDpzLSeVk9ZUIyeJ1sVQpEUZtTdDa5dCVLR3xITNL0mbhG3nCvlwMKt5uVAOW0HTDzesNVDz546ZMMTdbzCFeqsLPby3C6KYd+TDyGk696EWc/7TL+YdMPef8DZFs82Bni9XKyoAB6GnRiiEY1g3HbJ3YXn9ZWTUY/hZtKta2e7rtl9MwdGJx+hnEqmQWMIWloVg6RNrk9meTidrU4m2UPlcmCadnT5L6NIiU2mjKwO3VXrUoLiBq2zgyZnaSd2Eb0brL6Mq5PqBD6flL+TIrjrho/KpO+aO6W8yw226IsXKPEodRmJFKY+RUlOauJM3zcv7wIgN845yq5hKKMphklpT/C+QSjqzxWWofOxmIxCyq5CutEB7MDwag4itwvhCI1birOfjQh9zh7skt9nyEsK6rnUkpjsBpsbaNGeV5ikIzrVM8J60woeXZFZGdqtMOCVyN3vXJuN1oRj/rlb91eikoyNFQ4mzQnnPekcryNInY3ybMrxB4HkzKu6tPVQgzy/NzIlA6zOzD4SxUmKq/5JE2XtO7gW9B50nSJW9oKJBZhDg8nydFxJQ+R1MXRLovZnrzCi477Pu+//Cwae2zU2YexW6pVXqUyhlGqAt7HMmZGb8nJt1dOS29Dlaarz2REI5rFo0NaO+UGjadRqcFfjNGFXlSBQ0Kv5Tw7irQp4yXzNDjigMWjFlwdatx+ClqV85yTGYKFqmRTNCoRxvqMiNQCpE0tJV2W0lIXKRrzSUczzILV3rJK4sFS5a1Go6pMsxfRQSdW1Kfj0lmNRqrIYNFXmXWu6jsdBjbqlvYcvJUKE+hEipYeMNXsMBNXTlI0Kg50qWulJFpVKHeHi4b+Ks2rX6YZ7jmmKL+Hv/+/ZkMJgKENbWhDG9rQhja0O7BhJOkgFq3PqP/mPG89/r/5xtLxfOm7pwLwhatOwd/tU4sqHAMWhFgxtiDIRE27LKA4yHGiDH9hgO7amlp1n7TplzgAo1wcpSQyVLAYcoVSksoK5+W4T+x+OIMpCEuAppQmcKK8KvlQ06wc4tCfqnZU5JCOZBT+sU6NsJmWIpwlyfsYz+Glx13yM/vlslh2pO+57qGoSJdgSCcRbJA7u4JxhYLWPcHwD5tE4fZ5J30fgH/unkVth8fYzZaNMi0MoOVDIdoou0qn6xDuF/BquYMztohlwTbpK4yRnV4oOoqoXCjrxqkEHTNfInu9TTbC5oOZiGDBp7bPRol6WpSEs4oRk4VYJtmq3/UrjIJcTyJdRqvys8y37Bd7nrQux61OvRptMQ6KsuitygSEWpRNANCRpNWKyIdKYen4DH/eoblLPhtYenIh/PmNa48DbaiNwPLWSuQu94WJV+z4wRY6tWVJlg6T/tarmFf9dS7hQkY4MygjmyoX1Wy0Yukwucn+hpz6dFWItzfpECzn1PdF6H6R5nElipGoMs2moxQVKzIbrcgDp4y6FNii6VsmhREWrIoeRApTd8qIjdOJpOaaW0VpjZZxELdVGbXJXVtWwjIfg3kb5V025fNyEqxQptDSAZGZCBx6m6yUhpJnHo1qUqu6nzsQjUmZFTeqCAFJy6G2X15UJzasHCqq6WndHhO5vPfShzFyjVeyA5O2wf1hJbSokpzcD1je4pYA4mCxKtFTRFv9JZFmKH4nRXpF2bpIraWBzY66CpMWuevbp19UkoHWJd7P6aZSYLnhVgWXFXQ3WEC9HdvBkkLlplSAz13oT+mSZAE2qj6b4HbTMkLZ3AXNPS7hnPyuPynstzRUZb8U0XwpGl2A3AWLaAqGayzpU+NRYckcyAKDjhT+QjWG5KSU/73gc0+GXBHYOScLhIzjdaoIXlHWpb3TpuG7GdGm23XfL8Xy6pbu0TmGdtdt6CQdxB5w0jY+ffK3iUzCO2/ZgrEAZfpSy0elFfMKrASAXZOysMqdVyBfcX5KHAcIq0epMgyttCOlNpwqfO32hNJeFLQEaO/IcOJKs6fAb+SeKsGt3fUO0ZhMPr6tazTr++S13KbZwF+IcRd6qJWeVL0ElOfxtk89mec99/bptqO+8Rz0bpvnyRWuNqU2TtKwGiuuQ3ezHOM3elwd9znZr/HK8esA+OT6UxnsGq8WuIGk+lq7NU4kN+J1BaCcNKToKthUVzND9wvAt4A4B5OmlHB2B/IcBqO6ZM9kYeWIgGharTRcVFDR+VWm6G+Q6u+FfpPKJSXm2OshmUmS1lrQbaGoXZZwsTo5acMugp5BZQrjmIpOHCnqexXk4K4q5ZH5EE3YZ7OocbtYllMFjg9mHfyFauy1dtg6dNaBCLf5RJM58ZgpGTjxCMTrEtwlF8+mYlaDUcHSrBWYrGLyLB+pqO9zmex4OIOiJIiAe1UuNdwAgh8LBqQYe75NoWaBUzotyhhIcrK6WxULtc5WiduLJVUaN3WJrWvsVuXMXrD8nNhYlp3FGo3WcKKskghA0kq9KUcU0e09FziwApxfOLlZoMp3K3cECK8MtFOr7t6XQsfFwmy0OAJxk7J+Xa4t7nAhL0v1eEspSbNC9Xq9HB1pkhby/AFnW43ctUrRBe5lRpG0XbwlW6Ijz3E7Gb2NLvEmq4e1U0BfcQs6h8v1Rq93GLsxKlNdyhjcboq3QllQN27qcvNW1rAr2IxpVrIDVZ4BeYkt6m8Oq/qJ1smMW8JSUzklbq67WeMM/HLTUEiFqMymzoDmngS3k5A2PFa2Sv/EbQGqFyVS3EGVnivGaZpKOjzzVFnzrXCmi5SqjmXcJ3WgHC9SNkeID7ZdOWtKl2ShMNacAWuIGf6Sko2WzX4294hCuteVsTcY98jCe6ssiSK7h3iifIhHuls2dJIOYsVgCpTHSNjnb88RCvwLr3gWalcbf8VUdOqe6NcsHFswnAotDVUWnxSnSRaX1Y5SFuhyUst8LfRnpyoG6XU1jZnMiqHJuXRiaO806LjABki5jnBRlZGr2mxOuADhXFpqn4QbA+IxjcqqGlgA+D6Lp60DYPkwzdmP/Mkd9ol/Y60slTAYB5QqnYGkoQRMe9go+x4lHbOu2eeJXzuPc065lm9uF+HN2reaTO2sar6hFbmnCJYy/JUKpJ0FFldUYIENhHtXO5jY4qAVaNrr5iR1LYVOi3nLACPiVIGUYlDXu6VzB+IwBAsSTSoWgKQpzKiyqvzAnkuJuByIQ5S7ECxVjlPuCb04q1latmdQjsFkCsdKPajMLiqrAKFpKOcYv9o6Gp28lEQoxplOob1NQPaFQ16IHvpWziCtKXQkOLbyWa0zBCMRepdX4eYSiST5NkqVNuwiravFun2b/f2ER23Wjr0oQxkZZ+1bJJRqXEVvY1gKRzb2RBKpcATHIhewoOtBWi7EvU0hKEU4t6rmoavXMMuU1coKFk1ZxDSYTxhM+uX1olGJKqnMlL/rrXOI2uI0Fzg9EIzg6kKrsX1XvZXKcUrr4jT2ugWwPMDrZDT2WaFSV9Pd5GGcqt6a05Pz5Y4S3KE9v9dJyja5Kwlu3xVWpI1CJ00ZR05EqU+lUyEEeMuFcJL0sROBYzE7ScuwcphghGr7Czo8dDf55f3ljhAZdFqxIb2uwe1n5VwElDIPgw1BGRlTmaG5a1AWTi6YcxhKLSqj5dmseV72v4WemRNVLLVwoZDqkAhSVtOlExbOi0hqZ5P83dqVk3swGFXlO+h17NwQgm/HoxMb+lNu+a5gLG3fr8ry5K7t3x6lgGwxZxQiujqx5UZCq0eFONdFodzWHml7bSZGZTm9DeIVdjbp1ZWKfqlmzD3HFA0xSXfPhk7SQezaS47krzfMsbM/zo3XH8L3p44GoL9Yo5nKIlpWnu4a4pZm08MkB3LK2F6+vO14VnY1qe+WNyhYEhG+3NUYO2HNnuSL8N9ueQHdvogedjarUnJAZxJVUpmpKMKA08to75DfRaMu3Q2ihdPaZbVxpiOKYp7JqHgE7Z052bQqlXbdA8vguaycPMXC7wtF4/WnfJE/aC7ccaeoSrRtMC403LJqvRZBvaXDHV75EJEM+PbCMQy+MsWPLrs/o50qBaCsGGB5WgPeSqWcreOMeMwnWgWujkdzdKSozdiduy+LQm3/qoiIESdHpVUtqbQpC1LtgPwdLBvCxbyMBoA4uTrNGYx59NbLBRNbqDSYLXa2Ak5PY1ZR1uW/g4lqJ61yyAMj4pGF9Ry8ri4jGmlDFhG3V9XnMkoWzEKrSRY4iBtV4dhwPi8XpGIxSurKRvHs9TMR2gznKnB3uF8xMA38XkVdrs3lFnRuHfSesKUG44q8ALTXivvT+MuFNo7o/HidrEzh6CQnbko75F4CGtNxGX2S5+Wgspys7pWaS1mgSENV1oALbaqtNpexUquYc5lvmXOmSH3qUqEZoDaTkjYcFo90yijAYJ0ptcmKVIm2C17xbkWjFSC/CKo09gupIPariFpfOQxGdems1venNPYlpIFfVqOXFJbc02DM3k9d09zRLSNhbmRo7fFZPMIpyRRp3RDOSGX72mwhCCoA76KfjFZ0NnulhlZh8biwOP0l60wnkp4qnCSjAS3K58WmIZxNcLsxWehWDLQoJR3zWT7UKcex21OMXBeXTq7RqnS0ivFYzH/R6KrCzLZuWykSaevwxW3KFHgwJ1H1qO2UKUOvm6MzTWQrDSR1SZXmXvWOBIs5Mw9SpBtisivlJusHDL0pVUoK1Gck8r58pMNDf1s2e1+/4VjUtlAyAAX0wDpbxZzqxALGXv1uZbbodbiYE+639f/ynOWjmiweWRAewFlkaL+mNnSSDmLNPfCpj59F7kMth3/68iMBCDqy48gd0e4BWDrSwThwYFrqB6yrdWiGEf3RgKgnL/NgURPMWyxAQVcdl7SMbzVznEgwBK1dFXW9SAOsNtEOqXAGwVKKcVzSUJXFPJ3t07KoNBpkG2RLpXIYvW4Jvcd6DLWQwRET7HlywhVnfACAtq7d7nqFBQuUbVd5USZDvnMG0Fvv4HXgHRc/Vo7JFBbeQrBkacqxOHtF9MwoSUmKInMhoCNFN+uqUvQ2C/p2lb1VKpNU/YBNUa2kGKXwm16FXxlIO4sJHURE0BlUVGeV5oI5WadZ+U3Z4uvbVqk8Ys/Rk1BSGbnqiGPTW2fKIpzGlfSf0yk0aCyGxqkcKR1BWjNEE4Zwzj77RO67YPjpFJp7UpK6Vy7WkdGlTEFR1kKUjEX+AWQXbFyssKF1gOqy6KW1tYw6YepVz9OJBX9RYHi8FTleGXGAQRTS50+C0Zs8GtOWpaYc8lURmt56RbDk4C0MKrq/o0A5ZL4ui/MapajNZeVCldlSMk6UV0w2K6jZW6fLFIsbiebSYFIuaHQVWSwiNBu+L07gyiFOmRJTqaSgdIFHCYTiXZutJDBq+wcYHbLk67INSVPGclV016O9I6G9M8Hv2shOTYm0RVRtZkSxWVfaU8YQzgzw1jfKSGZtvyJcNKQB5fPSqbxnRT9lW1wGExKhLNqejFj9ohVVOt+SQlurk5Q64OaqFA11ogxto3mmUI/PJYWlU1CFXlQfOke1S8d3MK4sY7JyIkSd2ohjX6ZQraOx6vlloYzTItqzclgoqfRmpSbv9qs2yDO1Ucl9poQHdDY5thSNXzq62SKEC2YVziwhCzRZXdGwuyc174tQ6yoWWjFmiv70F6G+PyetV/ICWahwBoZw/wAdWTkHT4otu/3qWdV3VXPJL9Ny+AWk24Z2d2zoJA1taEMb2tCG9r/A8iEm6V63oZN0EIvbkmLxekgap1sNLrcHaEgLpX8l0YHgetkefq97NPWJHm6YEhfifXOeaAM5CmXz/Ot/nDIYq0QpdWYVlZOM1ZpA2EhLqevjaAFzFxuYTGqtNXfGOHuE6mXSFDavZ/7UMRaPtviqBWjeBjQlspSuG2HlEI9aY5kVI1u4vUmX47zG7frj/+05HSeiDIcbbUPNNtWlU0PUEh2a8cMWpVm54pyzbuCzt5xC+J8SDqkX9a8KppcSVpPRFYBda4U7SPBchb9SCQ4ar4rG5J4wlXRctclfVrgrCcp45U7T7VdpFpASECqV1I1aFaUbTAiWY92YbG3312uQV3o2OrHAe7cqqVI7YHftRlU3VMvQC16508xdSakY1+AMbOquq3D7EsUpdv2ZrdeVF2nWVKKFTmyIm1WfuwMB8adlSkUJINhGiLxuzmBM4/UNxqZhuvcbYHouwXTFTOpPaLxuVd4kaSj6k6KdVLSpSJUUIogg70Rjt0RlCrBsFqg1qaDcFZxQeMCphClzq7mj1Cq8mMHtZWVU0YkEK2OMKmvvORbIm9Yo69V1Nrg0V42h5a0OOrYRDl31g6RTnVLs1YmEFVlEKwqGoRtBMF+V6qkdiHH7Lstb5cVcOSYnnNEly1BIBZpwLqG1XTo+aboiYKoq0UR/MRYBzQKzYhRJ20dlMHGtvLz+Qkx3c0BniyZYrCJeWUA5horCrt5KJSKajErbnX4VPTNamLWdw+T64YyUd8k3wJSFGao4k8LNUYIp6WIyFlfXV3MHUhS5IIcMtiaYTOHNudT2258lghNbrdLe3eCsYYAWlQZEcBL7LBULD44JWxF8vWXbrvC7OXphVcS3r9GxYeHoQuFbcELj11Ygfjei1HgDyOoOg1GHcL/ic5eeJn28IuO6ZJViMUl59XfmC2g8rVVMNp0aSWNrVbE7M0M4n4huFzLOvD097g0zQH4Pa8Xlq2vPDO1ObegkHcQKgGBuxAEqXnCVi3igMapU6XW79ljbo7VdLn0vlMnNovq6h+ZkNY/Jq7OSkZHWLbDUSvrrOK+AyqsRdkqVVPOibVABGpWy6ZXQxQ18+5nilqePc+ZvX4O2M9+3vn0yaTsk2Sy1NnY9SvP9J72DdU6TP9n9KAC+8dX7k7ZyzEiCWpSJoL7H4lEmK8AjCG6nEHKsdSUVNZiE2KYYjVEsW2BMQSuva6l3pX6qUGNad/GXKsq1qD9XE6DKFdGIIW3LvYxsXmJpvsn4932wbUiaDioVZlWRRuqvk7RRseAY7eI1NeG8XqW87IqCcQzdLwknvuZDNGnK+x1MSprLHUjaQ+5flVXJVWYdhtSTxcw6AlkgKVW3o8ktmDs+ZEA+cPH2e6Xzhgtev1J+ljItTlkEGfvIpSq6KhcJlOCEguWinyTNljRUKe448Y2AcCHHOFWJjKgtGJsydWKL66Z1SJrV+Mw9Q2NXVbTVX5ZUQxYgNc+gVCQu7jl3qMaxrTAfj9cYTLh4vbxUfM98xWDSI1goHJQcZYHP4aIFvmtHHDBNSXWP2xB3NDVbE83ryMKchZQAc5UZBpNuWeJGPgMVVAu4TqU/O5sUUVs6NFxwCOdT/MUYiz3GuC7eSlVzMRoXhyhturgr8pk/HwlbTKsSHZuHDsmoW2KzjKvprXNxI4NvmWtOL0WnAfGoKZ3AIn1W4vFGpE8FCC6ftW4R/JDOVpXOAZI2tLfKCZZqTTZ/2RGcnX0eg401/IUYpxuTtG3po1GXzBeHvLimvyxEivn7y9+3PeZf+Nfl9bz1M08u50KvY/D6Of1VdddynzUiu7kvOLvGdFbWfHMSAz0HZzQvwdy12Zi04ZbH9MelSK3bVyTt6nz1/Yb2bX0GU3aOsRvIYhz3xx06W2RedXoWXzgibD49qN55lRXpV/k7bYiwqnFAWWmE5p50TRkduaDBm+0zYmENRuti+hnar6ENnaSDmBNDMiUUbn9RYUo9INkJraGTIpNY8aJ6K+Bc79M9vMpV52FOf5NBXWnwFmWWVllAWg9x+8VuG4kQrdq5FPT/ovq7HGhxPIWjYQAUxlVkUzKj7DmryY3PWUvjf/j9xzmw6xDOftaPAfjmph8BTW5OOvzPT04EoLkIjb2azA9K/I9OZPLNAjC23IZRUF9S5cKVhkqAsBGYm2TWnjhthpqTkPRdlo+Q49wooLW9D0X5j9yAViQtp6T/FhE3ciO1thBGmb+kSsbKZKPLZKPL3pu3ULeVyuOWJqn7xC1FPGZxBc0M41TKzrkjSsHKUOJ4ohGZZL2uKenhTgxOUmm0xOOGaFyhZyBrWi2XOrRudQjnKI8r8C/OqrpbIHi0vgWF+/UYt9Wnv3e8wimllZNd9G8aKtmtFxgJT4DOq6vUp7Uiolew8hz668TpqVuHob4/lbIUhkpHKwiIm7oqJWLN6wgWQ+5F6NRet2LPeZ2MLHRZ2arKyEptzqwB4icNYU4ZR5PWLcV7RFhdaagrjZuaXcwii0UaeKQNcQy9TlFKRJfvQlmKZSDOc6GN43WFyYmRKJDcn8NgTJMF1XuqM6FwF/fsDnLSQNNbX0lGxG1N7nm0b0lKR2byygRnkNLZKgfNnaAJFqE35eLbOmb12Qynn+N2kjKykjakdlshA6AzUd3HUaXzCMKKNZMDgltlMKhcwOWFhlW6LibY5aP6q5iOPUsGKTS4kLG3PJ5z/glfBOA923+bzGzAXzFl1C93FcEcpO2Q7oaCfq8Ilgxxq4qioJRIITQqnYhtgynBVdp3vrtB4ySaaLT6zO3Jv3JcJ0KWcKKc1GKs0ppi9Frg2hGCBVv81xcHppRZcOR5S0TVjv+NEdwUkIVuCQJPQ9ksNvZZjFYP6vugt0HBIXaezRV57KD7fkXyUPKOFg60m67dAIIwE6Vosij7y7lylDJVsWXfIfHuHcntX1y6bRhJuqs2dJIOYvWZnCwXsKHXq4TbnMiQe4q4WTGowNb4KSawxBCPKJyuLtMbzpIiPADBXJfV4m1uT6rIl6bWDmGjFTir9JagdJqKyJKE4w0YmDlNHJSrX357naOlQY3lYzLu19y55vPHfvcl5f8vdm1OXLG3vJ4FKbeqa/bXGeKRikKOgubugoZs783J8FTGOcdfx5eikwEYTHs09jk4Xbu7VuIMBYtpWW8tHpHJNKmvSuMo2fnVpm3o/RiXU8b3sn30kLKelenL8XEb8sBq1cy5eB3KCvVFcdHBqC5TTfUDOf5Shk4NiQWKxy1tF+OCpqNKZlR9p00B2ohJ5lMCcQeHJdBzcK2+UhFhzHzKNMVKs4Uaj8inUgilna0rBVy6uuZU0gD6q1JPiRFHUlVRBp3IuTsbdfncULLQFlpU3nKMUYqk7ZVgXaMKGQHrmNoizt2tGd6SLVuzrMqUmykjBYrepGKwKcVbcW0bJN3r9i07yxdqd9z26G6sgNsFmDwas+fyxOEpJAHSmk8aKJrTKd6yDL7Q18RtV6rCWwB77oBxnKo+nyM1Af2luEzv9dcFpaxCsXkpwN1FvTAd5wRzMU7ss3yoBWA3hXbubQgJFmUBV6kgkQMb6Wxv98gdZQunSv/1phxy16V+QArKFud3u+mq6IVZGyEGjKeJRw1bNs6z6G2y1ysA6/a5z3nUZqQYdLE49ycL4UZKAdLUav3cNJD84mKvhjemmXtAhrLjeMvXclSWkzfcMqKnjEMaqtIxAVg42iH3oXmleDvH1Z4lzyOunE7jyDVVWhEqypp0o9XfvSlFZ5NXOtWNGYEZ6BQ8G4lbOTQkaVTOau7Z8zoCbgdYWadZPEaRO/4qCr881/YOU7apPpvTn3RJV2xU3c1x6wnpJoOxRa3DGZnXo1Vitcqm34IlG9m35A6VGXLLtsyVRseVaG9nS0inHcHF/NJNdJLumd7APXWy/q/Z0Ek6iOnYoPoGf9ngdyonpsCxeJ1Kbbc/IYyIMkWgwO8oKaZaYCpCaEynuPsWGRwlvOED9/OpzZmy/hOAinPcJC9rJuVBFSJ2rYBZVnPWVKvHVJilmlWsfdPs8bx+8noAjv/eHwIQ721AM+Xa3mb5XXs/C3mPdz/4Is77zjPtuQ3ZUX3Uzhq1fQWDw1hBtkq8ze1LrbVi8TRaHJSkIcwtgE4U8IXbTuJTD/xnrjxEOM9LO9axdFhA/UCxq9TETS0MFus8xk0ltdVW1cMzjoTGtU2V7No5ycxSq6Q/gzipwaIUP4Wi0KkNrZeMHPleMB/SzrglisD+clo6rI6v8BKDG1U78CKKVc4zRlI37sCgrKBlf6PG7erSEdCpHJ+FVVV3PVCwL8TNAeuQuJEw1vKyPyVypFJKBefMUzbdVt2PE0HmVM9BDSosUX/cRmh6Hu5KDEpV6YwpLRIBneqehL3olCmd3BapNa68DwCDcQcFjF/ulmPBOIq04ZRsS39+QDQRktWrcZo0IVhQFqOFHUNV/wCkgRLm2nIiis+Io+FENoVo00raFj8tJQ7mc1y7oCUjsup5nYzxGzJ661y6m6sxYjQls9EZpKhBiu/rsgONI5iztOHg22fjdwxeJy/f07HresSjPmivTDc7sWygnNjId8hY01EKNrKUBx7GkQLCWWgjE1lO+xbFyIMG7DpNUjj+bSEqhfq+KoLm9g1OAn27iekdnqJvEuexd3SR51TUb/H42E0PkmPm6rRb0N68zNKMdN5g1EXlITrKK/p7ZBiMCSap2JToFMZvTJk5Vdo51e6wobHCtSNHs+7yol6dZjAmUdmSjajkWcfj1kFf0nQPETxee7t1PqKc3HHwOrmI54IVEF11HksudCJo7pPfBYsecdvqStkUdPfwhJOO3s3N4eGAbNQ6h0Bw8iLYe67d6tPf4OL0FVmriNpLgd1o3DqiA9kQ6AiCxUJHTuQEjONUkT9XGjaYlGfcn1KkQ7/j19aGTtLQhja0oQ1taP8LzBh1j4Hb5h7+/v+aDZ2kg1gWapSWHaLKK90i48nOy+1XaTKVO0QjugQnu30p/ZE7oG0VeZ1aEHLo0/+zRQCuut+nOOri55BdItu35nRK/bYV1GIHZ0xALvFUk3jExUkMwX4JRSTjNdLQq8L4ueSZ80CX+KaPXfcgXn+WRJIGi7LlnrhWk/k+F0+KMOYnm7fy+014XH3Any7LcPAXFP0JFxWaMrJjlKI/CY19VUStCIuvVoweTMh9FjWxBj8eBwNPTF5Evk/CB2EuWItKKK/QnKrE+gaTkLRy/EW9imkC5JBa7E+w1yOfc/FWldYowMNrdFsCi/GwO2RlMT0qt3gXaytbNf6yz8g2OWGwCP5iVO74k1pIPCJRmiLao3IYvTUm2L1MMtWw91NjMEWpm6QGinDRKgDbNgQLFZOoKJmSu9KHBcPPOPLPjSoQetyWEgmrRfByRyIyVWkaaG83DEYVnUOKCdGntVNAuaVG10gxJuUIJzEESzmtPYbBmERQ+pOSYswd0agBq8ydgR9ZfA2AVji9rAQo575DWnfIfFXq1+SeAl2J9oHFU8VVmjppSqpEx1kVVWy75K6NqBU4r9ym2GzktjY9ELxT06uwZ56y5W5i6laRejDh4g4Mad1GGeNMgOKuLjE1oowvoZHi/UrqiszTGEc6uRZlBPMD4lGnYvh52HJFhu4mGSC1+VzSY25BX1UkTbdMmRUP2mi4+ppDy9Ru7kJtobpfr2tkLPRygkJwteegM3l/WmNy4MpSTfSArpBwUyOT59u7ZoxRm+rNAouf1FVU3Dj2velUqUC3n7N0mIu633L52ZW7NlNfFuFXeV4+aSCEg+IZOgNYORSyujyb8asUy/8/e38ebVtW1gfDv9msZrdnn/721QFF3yqdCthRlIox+qZRP6PoC1/yOWIy0GTELzYkGn0TXzLi6/tqlM8gAgOMSRBJgxEyEAmCWPRUUVTVrarb33NPt9vVzeb745nN2lXFNUUVheKZY9xxz9ln7bXXnmuuOZ/5PL/mZgadWZROqdukdJ8WWwLFZtQTSycImSQSEAV6VxU5GrgLZYajGkWgNlMct299Fg88k2ioe8e6uPmmq1jLC3x8mrt7KpHvOugD8xRhABzIr8VsnagcMUK2Mo9CkAq9K+N6nzqf3U0ndqlE/uVsj4dO0lG57dG1oyDpOo28qQxEoR2ewv+BAihemwBWTaexJEDHMFjmQJXGp3Pp98WNI3zoOb/hTsbxvpf9Cm5LfhQAYP6oh+79DFAKbIdANDJPCdQsgPI4LcQ6547R4YHbhHVgBgH8nH+sh68bfTf+7k0fxOkztCrNPn4cgwsai/9ME8rPfOL78O+/+R7kooEoWmWRc2mQ6AeAGh5bwGOJhQPNSqS154cW+SGVHn05JZlSwJLe08Fiy51/QJN/8D9zvk7MRpp+tkfeZV5V238eYy2WoQK0ZVB9ILkaj/GlukDzLp3gog/A1h3jJs79VLI4RoB5OWvhUIBg8Dm40IAZiWrEgn1EMrdI90uwRQGTDt29AaptFViNnradH0TAsEloop3czFC5skT/HFHTvc1FM3D06aq9oJLHmymXveK4iv2kUwKi8yaW5IpNBmYzojS703mDYS/wp3KGZMZgulE5WziV8WaA4KGXTlz5pyQ7CAAoVxnyA4HBOVe+XEkwOyFgZbzP6ZhKtvWQhXFFzLx4r2RJJcXF8U4o3QXZA94qd4LGkLclaVbS4BvnxVd5TWbHxaZEOvFioxrFhkA1pOuWhQYvG8iFQjJ35ZMtgCtXFnRBbTr11ijOS63WBNjtMBROcTvfZxATApP7wJArZ0Fk/XdhUF2Bco2HUi+zwPQmC+QG1nnF6Q6pxkdvSCA71EgnCsxFw7zmWBwn9qXZo44RBxLFNj0/9Pku0LgYAd+issgOGjTDJGwSVEb2ScP7C9QrdP75cYnDZzfI3YC59PktDM5ybHy6wuI49dX8GFkAWRFFIZmx6F1i6F715URLNjwdi8r58eX7VDqsuhHXaQUZVft+AWNgFk6cM/aVyun5Fl6momK4UK9h4Ny+t26aYqszxycunoRVUZwWTh4imcTnqVqJzFFZAN1rGnKuKUhHxMoxZcLPVnAwpZHu0+fpJEeRPzFAaAMGvUS1+xLO8Rjf/1etHQVJ12np1CCtGzBloDsSVseJznRYAKICDjexsJBz5+2USdqdSoZ0HMGsTV/iygsFeAt8d6McoCmdm/o20Kx1kR7OoE6Rene9Tn5KlgHMLaB1j3a+3mCUO4d5y6JqLzPAwYe38c/u+t+CxpO3BvH2B509hrNXngymgVU3IRcbcYfflj1IJwTb8ErWyRwAZ2FyJBVaA6Y5Flsxe5bvGSQzjabndtypW/gctsgyOi+ryUKEzkV/T+aR3qzzGAj4a7ICqFc1Vu6N/Wk5seB8hoZpZx7qFokATmUtsCkjqnG+Z8G8katSUKvdkHlJxjUGxkKWEsnc09MZJk/uI93shOxBPQTkgQwAWKYoM2YmkQ02O0VWETAItO9qxJAfLFvPcEV4GE/396w2k0SsmwdqeyndbGzD34J32zowO8mWdHaysXXsMs/ScWOndS7GiJHFaxYs2FJ3/nrAQ7BjRexfgOjbVpKGj78P9ZACjxAguffVwygdwBtixnnzUcB57RlHLS/jcVwhjLNqhSju3V2N9LAO96bYzFGtMNR9EfoqWcRgFYaypR6ECzi5iNJl9Vy/1wNiZCVOXTs5JLp/WwE+3zdIJwT+D15xNoK+AUDnAsncEQTc85CONQb3J9CXEszPeOkAg3JToH/ZPa9XSzBtwWuNcp0ClGyfAoziWOz4ZMJQr1gUjjjRO+eCyB6C32A6ZqhHCeqBCPpbZD6rwWoN7wprOUN+KUG+TQ94vVpDFjnKjQTzY67fV52e0zwq2hebzD2/MUvVuUqbnt4VvyFYYHGiA50JeF6EZXRvgrSFcbi6TYlk5gN67tT6gXLd9dVA457ZFgSnY15/4x/iqlrBnz14Bph5YgEgZ1hSI7fSbbT8s5VSQJdM6xDUUt9oGo/uoWDaEInDESCuvlDATo+yM1+t7ShIuk5Lpg2YFG7nYCNWVwC2cYuZm/wMB9K5Ai/cTrNoYLop6lGG5PKY3pcn0MkQ21+787DPkpfoye29YA8X7DrES2/A63/oPwEAfvfyC3Dlgzdg8ICFdAvc7osUVran0L87AoAlfRPPhBo+SBkwYmB57Rg6xu8qYQ36l+ySFqIsGRabnEpKntjl7BwMj5MB01TWyfeiCB/p+LBQwiGzUAZm+NLkJGeRsluuCcr86Fhe8JpEfiH3zba0ciwD6pFGeiAC60hUFvPjpEOj67hAtCnJnhWjs7jophPqr+7laD9gsgRNPwnZOptw8EqjuxPp7uV6inKVY/fZHF/zCiptvmr9s/jZ//49yHddaSED6pFFecwgOfSsMSdwaQFs00XU0w6SRdxZJ3MKVFVGMgBA1IwyDjwKENgaNgLqOzsK9Yjo715wsnbZO5EsG/gyTeUM6lsSFCzWY/ADQwFDOrVYuIxTuU76YG3qeT6x6F+oQrktKVI0Pbak30N6UXQdnjXpx5gvv1UjCqKzw9a5DzRMwgIbyrdqFSErlh/QOExmKhiyzs500fTILd7Typm2ISNK3zlDrzEkBOnGRecajXVeRz+8cp2CJ/95JqX72L/oDGtBRtKiVKhHKUlYwOtoWehOjO6TiUI2ryHcxogrg+E5hmJDYHaje077DdSixYyd11ArOar1LFw7r2j8mkRAhYAL6F5hQezUcrp21UcIfI0Epqck9YMTikzHwOrdFarNHOW6Y3G5cfiyE2cBAB8RN6Ds5rCcBy2qpsegnlxgwXNwh152eOgA/vfzUn5AYwQAWKnQuVoBNgvZQCscON1nRDOGcpVjdjwKynLlwPqWMlMAkK+U+PGT78UPfORHAAC/1ft6fP7aFsTZDmRgG9M4zg9N6D+TuNK5+56iJmKMZXHzy4wLcCULJdIgQOvnVBMz4F/uZsFgHiO77Uhx+9G1oyDpOk1nAkgFRG3AGw05LsPfmvUuqtUEzNWu5UKDVwp6QKtLtZ6i6XLI0qI5RmWYejWFZcCF+7bw/916NgDgF7Y+DSBOKHuXVpAJwHzNFD88pBrSDw//K37r+Bb+zb/9HohrjrJ+TmKxv4p+wETFMkrIRBjgi0lihIyrpUmctfAXorIYXNCo+zx40+kUgRHWzshk+wpy7hWHE9Qj8j8LDJnGLRRrApNnugByLpDeHfFHTJNfWL5vQjCwLwRUD2jmEXsAS87nXuhtcBnoXRZQOWmsAEB2UCHfZ1BdHsqAvnzVzkJZSf0Tzu3nDRYnQzXKyXm98IxFBt2VsEkLD2EsujsGs1cUePuNHwjn/5lcBwmARgCdKwxWisCEyg4IA2QPAOY6S5YUJISFsSTtFiuBJvEsQ1eCsctZvmQWg1W5UOT6niYxU2CcmrdEKHkkCyoveKf5Ziix2BJQWewOOIZd95oiNhAiHovrWO4SNUJwAgDdKzWYToNOERBd3on5Sfe5GlImcnQvRU3FVhI8zLw3XbZXwQqGaqUTyoA6BYpti+F98fuZlErQ85PUn17oE7aFIdPLHnrFOg8MMt+f9QqQzBmG51XLpFWiu2OQ7zoNplyCWQs5V+jWrWeQMyQzhXTsSuwrCcqNJCyoydxQ6cjYJfNfnTJiMnoDX8tgpUXt7lUz6mB8SxYWdoDKWz6LI2dx4ROVjYa3NVAngOobZNfi4loPXHDg5obsALCSo+mJkCm2jDKu//l/Pp8+74Ajd5fs+1POgfpiDptaNO59qktK+H4QJQXJVuT7GmLhBasAXinkuxEjZwVHsZ2ELGhnR4ErYJpHqY7s0KAaCVQbGuk2paKH3RLvmz4Tak7Rzic+9iSk+wxIyBQbAAYPMqx/Ygw+LcGeuwWAspDda6b1fan/rcO/tZtJRPg+TFPg5Mu6vYuAavCEtCNbkie+HQVJf07jjQVTBrwxYHuUEUKaQiYCvNLgrYcejGF2hmbfyy+3GJ4YY/rgCqykVeLrnnc3/ufHb8XaxwXe97GXAgB+/9jXQSyAkUtDr97NYYXBpe1lk9kfGu7gXwsEnMbGpzVdm1k6zInrucwHo7nZSLYUQBF4cTk74xWT/TEAlRt9Hb5eIXq/yaiEAgDQQLkhw+ThqdbVKOKURE3BiepGNeF8x0ka+IWjsCgzhtlJjtV7aXbqXbGY3EB6Ov566hX6oX+Rfs/GBrKwqEYc8xNud61TyIVZ0tQRDhjssxe2T5P86t02lAR0yrD3DIHJDV2s3E/3a+8ZlLkYuVJeOlEAA4H1fV+5gK37oR5ec8M3AAD+7PJp9O5PohZQSlmI9ABBXdhyhnKVZCO6Vx2OZ0gluGD2mjh9IhYVjK0DUfsgj66BQLY+42WcSWxb9iDbp3NUq2TkCZBURLbfBMwVMzGDEERSOZXD0jkPJUaTMlRDjnRmWkEEZWiM9PpRDPluA1FLzI+56zEEhlY5iU8CQH5IOCEfaGdjDr0hkM4sulccNqwhMEl+YKBdBqfpAex4gWLadX3F0N3l0EmK+XFPnnB9xmPmVJZE0bfC3dMpWdOUqyKawrbKhtk+DZpkrsG0hepRH6mOK7VJFjSXkmlDi3+pwvNleYKmhfHK9xnSiYJNOKpVWtSrEeENmQWSQ1fGSiSSQx7LfSsSRlLA72UkSGmaxrXPMFtOAaYf6ysPKsBY7KlkScconbpnwuO3GuDwSTlpjLnyaHFTg97dCVa+EIHOqkP/fHDcvWaRLBgmN8XPXD1nkY11ILXIeYOe18hyWkP1KIUsNOS4JpVyADYB8l2FapWOEY1BLcQSnlB1OV0fA2qHIZtyg3/38Zeic46+oFwgyIX4YWwZA7+yD2iNzg59QZ3lJMzqM5ZjDV7pANCma3I7KxaxpTCA7smAW1r/bAEzn+GJaNY+dkzSY33/X7V21FtH7agdtaN21I7aUTtqj9COMknXacxaMFgCaM5r2JLKbWzYB6yFvLQPO6UdBEsSoNPB7ATtUm55ynn8+I3/Hbc8dx8PqBEA4Fs7Cr+ycj/+f/d+OwbnI31UlpRCBoBmwJAfGvTvW741f1hIdHZtEPTzmZ+25AUzlrILkRwCODZYMMYNwKp4nvAae8gxiIrNXHMsNohJ5nFDhA9gKNfoWmenmGMqLTNWeAPIBUO2T8f1LhPLxdPMg71GD+G1/sUaTTcj5Wx/rsQi2+PBjyyZG3ANdPY0lNuhTk8KyFIQq8hjG3KAC4QtAVGpaXfqLSzmx1NUT6lw+zM+hf/2rhcBoKxZ1bFYuS/2L2/IhNUL4HlbjPU7Kzzwc08FAIwyBisMZidduQ1UOhtcUMh3aAyZhCOZpWj6IlxnfrBsC8FrBzJtMfo85qtdQjUpsR19RkMuFFitIUYyqIyLmspPoqCyF0BMLd6YUOLhFTE0MWDR68xQ5qBY4+ju0FjQKbEX5xlveaARuUEUdKG80JRlGTchW2EZgvCgqJw44AFR5L3Mglho5AeUrUsPfF8JAvmmMTPGa0BNU0i/uU+IYQfLwviUlQ0ZmtJlFdMpkCwMqpEvzyo0wwSzk/E7JzP6zntPkxjdS/e3d7EggdKcbtbhLQK8AYbnDfktgsp9qiPAlUS1Qt+nGvHgAQkQPq8ekgSAlwuZH2MBq+WB9swIpJNIyeeKGGLt7KDHe4k64hB9dss3OW2gBgk9q62yM7Mug+wyocxGzJhnbmbnExpbLXyhF3f1hJF0ZlAPxJKUhSwYRBX3380gp/tuIqlkcQxIxhIrD8Z5jlTbGySudFhsJATIB2KZNSOiBroacGrvBTIkF7Nw3c2QMsXZPjCYRnZb8ezTzpQ7zh/jm0V43nqXSXZC5xKi8uw2hnolBVcO0A2HTWxiNpkXCqaVmf9yNsIkPbbPemJ4eF897ShIuk4jV3qa5BJtgZNkpHTh5zg2+jMc/ofTOPaBawCA4oYRLXhuQV80KR6oN/GOnRfj506Sj9L7ixF+89e/HSf/xz6aVSqnqZ4kBowro8mKFpD+BYab3/U6AMA//qb/gl+7+2UYTk14wEGViwgmtG7wt58ATouptysBAOY839rP2SOV4GAJmB7A3IXF8JyB2qVgCaDgJ1mQUq9v6RiBEuyvU5T0eWHxKiyaHgtYgHRqIUsLM4mASZ0Qrkn1iBYMAHJOkgDBSFMyp8Njgq8YVxbFOmGp/OSns1hiANwiYImFkyycqnnCwJjFu/7ka7DpaOzFBkMyYcj3atdPNvSbn0SN5mDaQlQawllysL4LBgX9v9ikwE7OYh3HSo7soEEy11AdXx6zMEIG3zRZECbFCoTSmqhBMi829rEWROPnI18uY2AmgZGxTOfxJ7IgFhMAkjrQFp62JucNhg9aMJNisU2v1SNHtU+BYsPRoT1gm7VYkjsVTMojddoRHnTGA9CVG+tYeeQv5881OyHDuO5e00jHCibhRJ0H3TtRG6iOCLIHVgLpNRmA97Ki76byaBadTohRqTNGvm6I/nbdHdcHc4Xxk3KoHmFLaAwRZkdnpChNY0iA1wbayQxkh0BvRyOZKSyc/9l8m2N6qwYyjd7n6bjOLgXWnl2qE/J7yw8j/g4g7atkGrGJckF948HkvLHIJoYYVf5ZDuQLxOfZl2pd6bDYytD0yX+wXUas1oBqSyE5oPvgbT+aFdImAwjArlqMUs8qa3Jg3PeYJwoWmw2Fp916AQDwhT+7AQAHd4bPTZc+j9dYql00A2B8UyxzpmMgWQhUKw4zN3PX3JKR8M1qBuY8JPlBGvoLQLChMUnsT2Ys9p6eQHURZCRUz0DOHEsXgOoKJJMGJpUBX6dziXJd0gbJjW1RaUDHzYUVDNXgifRue4zltqMC0qNqR0HS9Zp1C6MlMG9xilQMP/XCN9Hff8b9a7V/Oya7j4VJ8cZPfgvyj3fxTSefBgAY3cWxeXcJtj+ByGhiVV0Jrm3I2NDCT4N4/eM0O/2b/e90mj46sJuIScbipGOWqeMAYI1bAFvBDmDBWAss6QImCrLaKajl5gOtZGbQtXF3Vw+iGz1hUrDk48SbiJkI/mOVAVMc1s0rTY8Fwc4gsicYCeW1wNZ+wfdgcljanXLNlgxTVYdA0Mb1jWeJBR86Tbtw1aHME/U7wC90cOwOg86OE5McJxCNDQFR0LqR3NmeUAaFKfLC8n9vCx4CgE4S5AcktBi0l2pDNicdEYMIRS7z3lut7nP0LwLVauxjruNi12Z6FZsxiOldYVhskhZR0C3qMyRzC1awcH+ZMtDdJNDfxVxBzBXyAxko88xJSzAVP483hC9jxgbdIMvhvkvEXPFGQ3cF4L4fMxbZ2KIasYBXaXocxWbUgmq6EsPzdO5qlIf3kW+aRrHhhD0HLnjyXApDmRLbIiqkByWSCYfqJ+F9lpN+kgerW8FhEobh/RaDc43rS4m6Twa2flzNT+VIJzpkaYYP0mAa35ihceKmi5MWg+NTTHZ7wcev2GQkndFiZIraEgA8aCflEDVz4HqH1donH7zaCa42PY5qxFFsxfElqtZ5/fPMEcRGAQInl+uAeP4Yxf1EIOlcpqxZ/z4ZggheE5EAloeMmkmpX4PdjXLaXd2IRcuvceRXLao9iQf2SHute5H+WA3duduWQO4ZbPouCNTxfqluzBgBAPoU5HDVEq4F0L9foBmKINYqZ7SRqNZcXzg5imo14tLKVYZmCDzp5ffjPU95bzjXze/9EeAyfWGdE3Cb6bip1BlHscHADEN24LOdimQBHF4TBig3j5bSr9Z2dGev07w6sWVAcaqPC3/7z6cw/N0V2o4aGPy7T78K63cpsM/QE7fzAo7Dl1lsvPeGoISc7wMrZ+sw+aZTcmsvt3KkLu2czikIMAmDrX2WQS/vIP01CxYWcEo12fAjHQCXOfC54mVQNx3Tylb51so+JU79livSyvETdTKlYwyLi1cypwlLlBY9t3s3kpHD+Wb8XN4QDdzvIkVF5rLZfuu7cRKs9IytYpPKekzHSKpaBcAt+GUWFi+TYklY0e9qq02D2qklJ3OgWW+gswhE9wEAHGCTKUNA5lGK6RmKWpimoKF/qUEybkt/t5lfLjvWlUFbBTwGwx6MLypNIOUgs5BisUE79aDlouh4y2NgIWpLHmh+eDKgfF6B255yJz7y6y8A4CjyhzqwqACg2uhgsS1DJrB/MUH3ShmzlXALjnIBbKtcSV5+FsVmGl4rVzkG510GasCDCbRnOo7O6sCY8v5x7V0/QBmQ/Z6AkdGQOJ1aNB0GUbOQWVFdIgf4YASlC3SbWNJphllUzHYU+e4OMdZ89q4eCfSu0Ljcewad3HsG5ntRcqEaUrDjS5rlusRii6MZUAYEAIZnGXB2hJGMC3a94pScvb6TonJlM5QhUMsOFOp+AlFZ9M9TFCGnNbHnnLBpsZ2j7rugKGQzfXktil76OcEHI8U2UB1T+MMX/AZeOf37dO6zObIxMQy9krXOaI6RJWLQ4rSV/L2hDLHF4F4e/RQ5bUzkAsDHh2HczE63CCAHFBy2pyrqB6AaRc9DXtOzKl1mxxtDy7KVPXYaa+WJBnzhtK+cCK4fG2CAdJsu7+0nCwBToH4IV190FJTL/KZTQwBvZcBr792WkOQLj6bbVnaQXyvjhiAV4fq+3M08DsDtL0VM8oMf/CB+6Zd+CXfccQcuX76Md73rXfiu7/qu8Hf2RcqN/+pf/Sv8o3/0jwAAr3jFK/BHf/RHS3//W3/rb+Gd73xn+P3g4AA/9mM/ht///d8HAHznd34nfuVXfgWj0ehRX/Pj1Y6CpOs0KzmMYNA5x/hGgX/5wnf++W9y7RWf/W70LrnF0aXoy6eWuGFrHxefdhI//J1/CAD4jY9/A1bui1Yi2YMHgNLAZh7sDmRJdhGWM1Qr3k7BkElnK5CxwmU4fNCjLbglM9f2c2GBmC2yDwE2fdHOaP3sDpelhSwUTOLKPLKV0q/9NdB702mUKKj7VDIIYosWtGMVcTIs1zxmxIZdMrOkgO4xNfNticVxoDyjIWaur+YMzFIWKmg8uWzWsnYTGW9KX94ogN4lCVFZ1E6NWVQWXJmI1XIBjkk5Koe/oEmboR6mWCOZJDQDActZULLu7JJpLjM2nMNyBptQqc6n9q1g0FkC4zR2xIIyJyaL6uAmoZ25bVGQ4XAonn1TrDMc3zjEbaPP4n8cpyBp+2MK2V4JkwoU27RyLLYkCfi5RanpM8xP5UTBbg2JxOG3/EIcFjXVtoagPm4HJV4k1O/mVc6gcwaVRZVv5rOFPmavY7YsWGS4hV91WAjIRcGWrFhk4bSVWmzPalUinWjorPW+ktzbvY6Ryqgsu9iKUgXMxOvu7GnXpyLgqKjveAjCw/NQWCdwGZlevj/aKvXVCsOMS6y4Z77pC+iUGH9ejBYAdEcGpmK1IiBqMm8NpbRtGhedvZYOVCuYBQA7B3BZ4rvueB06d7mIwWmNiZJEXgGg2EgpyFSx31UvYgv9vUoKFvBI/p6On0Rl4I6Tf+MN0BTxnpqU+sgkCJlvOQMSS/e0XqUDmzUNVvHwvKdjGodeyBagMmjxnALbazPsnFuF/0LpOOqfwQA2oWCr55ij6dRgsSFw6b1ncMtn/t90WNeVx1v9xawFVMwK0wbIQOUs6CvpUxyrPEf3Aj04JuHoXX5iNAC+UjpJ8/kcz3nOc/Ca17wG3/M93/Owv1++fHnp9//23/4bfuRHfuRhx772ta/FP//n/zz83ukss7i/7/u+DxcuXMB730vZvte97nX4gR/4AbznPe951Nf8eLWjIOmoHbWjdtSO2lH7S9DIluSxAbe/lCDp9ttvx+233/5F/37s2LGl39/97nfjG7/xG3HzzTcvvd7tdh92rG933XUX3vve9+IjH/kIXvQiIs+86U1vwkte8hLcfffduPXWWx/1dT8e7ShIuk7jtYHtM1QrhH35DztfCwD4npv+x5/73gsX1zHqMyQLjmRKO5ZTvytxcONJsK+f4bsHnwQAvGvjueDNANrJzKqNAeS1SShDAKSBwxuLpi/CjrgeSqQtZVgAVF1rAbCZtgT4FTH1bQSjcluoyLV+tsvnaWsneVHKpdesBddkxwAA01O8ZbhL/zcJpctluSzoxJtYTvEZcGYilqLpkEdZNWIhEzG4QFo1voxFIopAXXCYY07J994cvGqVH0BlE6bjucsbajzj5ou4f38N6jOUElq9RxPbq9JQfdo+l2sS6RiQXnFbcJiUoxzxgAsxDWWlqjXg4NY09KNOSS0cIKFR1ZfQCUMalMEVmBLQXQHlGFNNX6DpsVDKyLV1WAsbGD8EKKayiK+K+tKFctmZ4rjFtlD4pbOvDPdDdQXyHQtRqHAvinUZymYA4c1MSuVXX6YQFTA8p6AyhnLNayA5s2ALpEUcf5ZRxgWgrEHt2VfuGooNUnEvNwGTus8cE67OZyEGFxRl4Xo8MDmpHMmhOiywv5I5Yc78PdWuTMOMDWWXpkulYMsQSmqi1KiHERyfzC1Ul7SbfPZHu++WjQ2SuXb9x+ncvlzKos2FFx60DGg6ZAjssT7ZAd2fUHJN6N7Vw5jx6VyrkUwFklkTAPoAZbKLDY9dZEEh3q+RljtmY8LQ2fPMQ8du804AAshLoPz0ChJfPebOJNqKwB6bH6dncnDBorPjSnzrKcywhX2z1Mdc22DCzLUFswz5HrB2Jz2DvNHID3IcPsll0LcNjLTgTTSrVjlD5ypl/7jTT7P70vnxuS7WCAbMPsOsesANx/YwTCvsH26G43Sn3Z8AjAOnOzFO/4zl+0B24LK5ggdlfwBgSsNyBqYNmdrC30ONDIA57eaFTYfhdPpO1WoCXbdQ8X/F29WrV/Ff/st/wVve8paH/e3tb3873va2t2F7exu33347fvZnfxaDAaVk/+RP/gQrKyshQAKAF7/4xVhZWcGHP/zhoyDpL2KTswZIDLIxgywZPv1f6SY952u3cfsNd+L/2P7Uw97z/oIeruHaHLPTKazgIa3OG4v+BQ31sT5e/Xmq047uteD1IlDJrWT4/D/Yxhtvfxve8P/8HQBAvmehOpwWApe2F42FznkAwMISuLVtL+LPZ3kst1nJlijC4TiGgIWxDw2a3Pm5of/9+ZkBYCxS56uUzjjKtWUzWSNpoq4HPCjplmssLF4A4R6kxy61yhTMWBhJzByA5AFEHU2ErSQQa/ciw1zQyqi6FoIRENyLXi6OAzq10Nt08m952uexV/XwN5/0CXxs7QYAwPn9m7C1V0P1Euw8l84lXPkocaU8XmtU2xmqVRYVnI2TWNCRkm2kLzG477uROkAtg1y4xcVJCZD9RSy7tMtmVjL0rlhU661zCzo3KUlHLIZJImsHBrjv8yfQvSCC3xqshUkEyq0M9TAK5JWrkQXYdAXSqUUytxg6wHcyp7Lu7jOywDLsX1KoB4JEIF25xqQc45sSzIm3QGWbKfVFuRb7ShaAkRbJNGLyeA2sfoFAO+l9V4GmgV0b4drXkXehZcDgfIP5CRnGLm8A0RovTY/+ccWCmjfXlkrWFug4pexmmASlZYCeo4Yz9C+ZEHyDM4gSyPcbNA6vIkoKDLx5NJWBqUTqbV6MjJihUN6bU7k4sOouVSRy2JckPunGlVgwKoWejOKYViLIBOgUFJTODGTp6+kMkydpmBfOwd9DD4ksrfMmjLubdGrR2QXmjrGYTeiaTEIlVsBZxSiygPGtGVIwy/w4yyzSQwbTMta2jKF3AVi7u0JySA+cFQLJTIM3rjwtqETLKwbVc/NV18DsC2QTi/7FCPavRlGtHywGhbWDO1WnG8yqDA9eWUfmbU8EABWfd7kgkdhyo1USnVI51KQs4MzyXYNswsOzrDOGyTN7GD5YIz2s3H1X0N1s6RoAYLEhsNikh9IkDOzwiWGM2ceB3ebLdZPJZOn1LMuQZY+dpfeWt7wFg8EA3/3d3730+vd///fjpptuwrFjx/DZz34WP/mTP4lPfepT+MM/JOjJlStXsLW19bDzbW1t4cqVK4/5ur7UdhQkXadNb+xAZAl6V2rolCPfp8FVfGKEf7/3tfg/bn94kPQbl18BAHjG5hV8+OoAzUQimbrsQamQWYvhp2YREyQFrGAQbtGot/v41hd/Gt/Vm+P1z6GJPTtICP9TRs8wogJbMDdps4aCCmaXcRmEC2EhW2OFm+TaIG2HWWrT9oP1SAvUTeDLVvZKu4DJ+Lo/yQEYScBJAEgXtIvWKQuL0OxGA1HSpAkAqmdRjwDWMJgzNNOZaQLxGUmg7pYVgseSAE552uu2OMuFxRkFtWGRXE1Qb7h+7zW46dgu/ulNJMXwyxe+FQ8cruJT507CjCkgSraBxbEMsjCY3+zo4ROO4YNRndlKBNd0711V90kziFSvXbe4oCXIQWxy5IcG2bjFTlQW4AzNQAZQscdq+VYPOLEJL0ctJZu73XzTyrr13OcFnR+GzlWB3tWWVUlBGbL5cRlA4LKggDZYTJQW+b6CqE0A/1tBuj7dHdvCH1l0L0fsDF3DsjyDx5stTkUmVPcSZRo7O9Fk1ySk/J1eOKDPm8ZnI/hngYDisrDhunROFhUev0J2MiBgvVv4k4WlhbaiDQXdG9psePmJ4YMNuBIo1kQAtKdj8vgykgffwHxXoVyXrbEYiQEPxfQxHbOjHnvlr1OOKzBrIRYiMDmt5DAJR7OSYrEZs3WysEHbqB5QMJnMDZQbg7xhYJqhn1eAdUFSYaEyFhTGk7khzaCJQdP12EFGv3cY/COfuEyxyjngNiG9i6S0779Lp2JIx/TMi3D7LfJDg2S/CEBm1mhk14DuVS9JwUMWrLPjrz2y9DwuShYaTS+HtPEeq65jwvlsnQF6aY1rh2mYr1QOZCVlrKmvadB44DcADM6ROr/qsKWsusoR7vFiU2JxykJWScCfmYSjWiHrIx8k1asGhykPY7h3+SGsvC9jM5Z9ScDrh55jPp9jZWVl6fWf/dmfxRve8IbHdG4A+Hf/7t/h+7//+5Hn+dLrr33ta8PPz3zmM/HkJz8ZX/M1X4OPf/zjeP7znw/gkQHg1tovCgx/ItpRkHSdVq1y2BWGcj0jJ3I/8UwAfSV52PGv/sKr8LnPnQEApJtFACQLZ7nAqwaLG4boTivAp2q3+zAZR3rgdrp9if/+0WfDnPqf+NwrfxUA8OJP/UOs39ksZYgUo4xQeF4kAMe0Cv5PgsDBNGl88UG2FCABS6Bdv2NkJgZhge3lfvdrRDLTEDUZhXb2/TXQ+VTG0HFludFdHPNTQHWC+kV0FHQjgKkMeyTWU6iHpIPjAdeqQ6UobyViJO22i61Y3uhclNBPn2Pr1B5uO05I6v9+5am4/P7TeN3g79Ln1UB1usbm9hh7lnaDdtLBpW+ykBOB3gNxd205MD2ThT7QmU//0zGiot+JCRn7jTdxIdE5MDvGMbgApGNntWEtoC1UznHlJXScKC16F1i4AVZQkNLWROINZQJETaUdwGWZWAxsKUsFZIca+RUHLs0ScKaRjSXKVR7exxtgdL8XoSQigFiopVKC5QzdaxrzLQciXpWQC0Ng5jpmUQBntuvOXT21gFUc8ipFb+mY+lBUNsg5FOsM2b6KtUPBaXwl0cyWGaLlkyaWK2FWFHiU6y5bt07BY3YYS60qp6yS5UDpxFp1SmPGB+w65ShXBVQ3sjzBAN6QSKK/z94Yd/fZMbOT7y3T8OGyDSHTBxoTsnoEboS1RAMFWVw0A/p+8xPxHnavxoW5GVhk+xT8pId0v+oeR+88R33PJjbPFuHUciGCrEM15FhsczRd3jJHJluZtq2R6jBUa8QYHd3nntN7Cuw9q4Nm3V1y4oDuBmiGMaBcu6uBTSXKE3FRTA9q9C84n7uMWIbZxCxJnagOCWrKBd1okwgndOnmF81QbQHpcw6x0qXvt801fuj0n+BfHN4OdoWeXVm4jJN7/uoBC+D/znNpQKp7R0hnBskCIaPtvf18mZWlgJwyND1gegM9816yQXViUNS5TOX2wGhrbeS+3O3xyiT1er2Hga0fjyzSH//xH+Puu+/G7/zO7/y5xz7/+c9HkiS455578PznPx/Hjh3D1atXH3bctWvXsL29/Ziv7UttR0HSdZrKANMH5msGs40K2mUdRp8RyPaBp37oB/C8k0T5/+g9NyG5mKI3pvcWZQ9MWugOUJygWXv/aQOiBD9/A+VJ91QlBqgB7na2r37qHWh2bsSuXuAe58za2adsj0kYkokLLEoNnYnAnDOSQffI4NN7bHnTWx/gAHAmjkDUaHGB1kMyUGCOVORLaw6PROdqvWZsKNOJymJw3mB6kmOx5XbzM1pEqhWEjIzOKdD0rDjV58iuCQq49qmvmHbZEQlYL0LpJsJgSClocly5z4YJrB4wHNwscfHSGv7jf/5G1zfAymWD3kU6qFxPcWmQYEetgheeFUe7RjUwEKVX/ba48lIOvUGzqLiWYuvPLFbuLVGv0lgo1ji5xYs4SZu0xfID7YJ11+NXWuVRQ3gUOctdP5gQCAK04/Pn7V2hG1ENKROYVmYpq+gDHoAUtWXpynhen6bRsJLj8Ekcxc30fbr3phjdY5DtUb94/SfWGOeXBmQu06g6Iqhi10Ogs8eRzk3ApjCXffTZAZ0DWadB9cAAAxd0dq6ZMGYyJ9DJFU3MzYkRjZc0AcZTNMMs3GfuzpuNTRD2tJzh8El5DCL6lGXK9xCCfJJOYEECAqDskknj5mKxJYPpbmBHwWVcUxYWdRpHLIpXLpypb0vNXlREZ2cmZj6Yw/EkhVk6t+U8ML1UR2B8o0Qytyg3Xekp12AmQXGSBlK6XsAe9AmH5HBRIuhV0ZgGgOyggZxGphVrEpRrKco1BEkRT43nLaNm3oAW+zriB03qxHFdf5ZbBrAMcsYwegGJ6H7/jX+K9+48A7tvviFkZIwE0g2B3hXqhNE9JSxnKLaSwNhNZ4YYiQdNUGmvV9OlLDdXLkgRBjcNCMB4prOPHxjs4o1pA+V1u9qimqCxZxnJh4zPUbZkgwHZbkUlza00vM8yQGobfhcVZc68FAlvCLeWTiMWUpT0WrHhy5AAltU//lK04XD45x/0KNtv/uZv4gUveAGe85zn/LnHfu5zn0PTNDh+/DgA4CUveQnG4zH+9E//FC984QsBAB/96EcxHo/x0pe+9HG/1v/VdhQkXaeZxOEophxPf+4lXHDpSfPpDWRjoP58H5/4AuGU+odR4A6gkofqkAv1/tOom+e31rjrtn+LjC1noT5WxUntuSnDs9/zQrz8T/9RmNi3rjUEIjcs7rhd85gkzhmY5ahWGIp1uoj8gBS6KRsRKchL52hlIJYA3L6K8ZDSHTiigCUImxADMCCZavSvAIe3+ODN4WU6cRHy2RiPS0kPRcjE8FY2BKAdu9+pm4SCDV9mYhZYuc+QTYaboLs7GofjBGLOsfEZWtGavkS5JjA7TW+sVhj4iTn0NEW25/pqj3ATOo82BcU6g841UDghQgFMznBUw06gdDd9Wmja6tbCZQ58qYTD2WHMTcjQMGMJUK8tBg/ScYttTga3Po5iBP7ODnUo2aqn5EvfH6A+4zUB3QGgd0VBFAq6I6GHcUesehLlpsFrv/aPAQC/deWbyb7F0cx5pWETAd1PlrKFvFDQa2koH05vMdAZpzKDK23BkhaTB1uLhqP60yFWd4H+JeoYk3JwZZHuVRALFyRtpCg2E4jaqZQrC95JML2xg3IdoXHNkMwEmLsIrkHZyDXqLDnj4Jr63N8/WRE1XhbRyDidaOS7Fo0TaawHzGUD7ZIwYzXkSBYRL1atkYJ5/2LMkJqESsu+7CZqh1VrbySMC2YaH42A+riTRMNUF6CZmiHfdZmxYxbzUwa8dA/b3X10r1rohEF1PMAQQQV7coMDJg84+pfqUN5TXacl1BJTZdZCGsoclZt0DZ3LDMmUNmTclZqSaY31zwFTB1heVBzVmkW1rTBZuGfJJNjI5riwyoLxbn5AZdtk7O5x1cAKgTTnmB/zop4CycIgmTZQfZoYZsclmm40601mhIc63Ovjj/eeDAB40ZPvx7M++n3An6xAuCBJuzJvx9kVVS5wFw2QOYhE0wNUL4GcN0FupekJKr+1LX+SWK7z988k9Px4QUvS44olTVEBVi/Py1+u9niw276U989mM9x7773h9/vvvx+f/OQnsba2hjNnqHoymUzwu7/7u3jjG9/4sPffd999ePvb345v+7Zvw8bGBu688078+I//OJ73vOfh677u6wAAT3va0/CqV70Kr33ta/Hrv/7rAEgC4Du+4zu+YqBtAEf65EftqB21o3bUjtpfhuZ1kh7LP/slSAD82Z/9GZ73vOfhec97HgDg9a9/PZ73vOfhZ37mZ8Ix73znO2Gtxfd+7/c+7P1pmuL9738/brvtNtx66634sR/7Mbzyla/E+973PogWk/Dtb387nvWsZ+GVr3wlXvnKV+LZz3423vrWt34JPfX4taNM0nWaSYFEAfWKxef/xy3Y+jhtOXr3U+o331/BwuE0koVFM2Nhl1+PKJukFQtCaO/85l9BxroP+5yvzRJ8852vBgDszvpYu8sgHZMtAUA7W2YsxEwFbJEVDKLSQfG42BTE3pCIaeeUamZc2yUANjNtQDb9LQBgffOlNP+rA21bhjhqlKXsg2fFpWTcKRcGfWdNsP90QI00eMHB9+IOuF3D540rSXViBok3VF5rulE8rx4SNkOv00HZ+ZREAPOIQyk2BdhaARR5YCYxCwwerDA7TSc6eJaGONeFRNzx1yP6nDaQuRkA3QsiKnWv0s67XmXI9vxOnc6RjhHsG8hEsyVeaV0WLGEw0vcBAcKtZAHXU48AgAVxx7SwyHcVZBH7uOkTiy0bx/d5HzW/052eSpA4NWLr7BI8HmXzDoa3jL+ZDhQgwLLDaOT7bgwoQAR7EZAAprVR9VszgFFGzuOuOnskTChKGrPzYznkHBjdW0JOKKOnVnLIaQ1+ZR/o0IOS79YotrKWjxmp2195mcbrv/4PAABrYob/695vwt496yjX6ft0rrodvpMSUH0DNuEBAwcAxRqV2lbvMUFZWs6bJfubpidDVpAvPBifskRcGSy2XEm4Q/0dSqHClZ5tzB7rlDI2sojPl+WEjVI9uh/pvoCVgkgXLrskakMyDnlUmGdaoNowYC2NwqbH0PREACjL0iKdWCd4664hYyg2kpZvIT2zvInPs0loXhAlwjjOxuTDl+9WwSaHNRrpfom1Cd3kzm6O898OPP0pF7A7p8H2q3e8HL07M3QPbOh3UVuI0kANXNYv5RALhexaCWboQotNYiqWmxlKZzasunSKkAksLZoBwIQBl9RXO0UP9Z0ryFr2PMxlvoM0iKVMo85ovqAbwTC+OcXgXCy1lquM/OHcIfWAnqF0Gu+fzhDMkANGzpVYfWk5nRo0rYz7V2N7xSteAWuvny173eteh9e97nWP+LfTp08/TG37kdra2hre9ra3fUnX+OVqR0HSdVp1Qw1dZJBzhnSMoLDKdvaBLIVcDMCbCAjt7EUDWrkQKDbpgfJ03JuleMTPeft0DQ9+irjT/XMMTZc0RTzdmMzWLJI5g5g7uXw44LKb7OsBgoO8X5zTqQ3gbr8I8drGshlcAAZQYNV+Bjz4uyUX4LFMnmnAGND0k3CdPpgDCD8CAMOzAodP4zAdEwIE3tAE3VY47l6jAK8aueCj40CrLJbbqjUDsV4hTZxSrkqhuo5V1gp2TCXBRCxF0mcadK86PNcsQX6NPqdyJR01UpAH0jmqu+vSFCh5vBMZ5DKwZrksaIXDjbk+K9c4ZGHR3XG/r3LMTgPlusCxP6XXtCHgrCgV+hfpuhabaVBDBnyJTACCBVadZ+xUQ4Zs7O8VvWdxwp27awBpkV+SyBxtP5tE/ExQUeYW9cDhYwCISiAdqyC9EO65X/zcuOo96MoZNdC/TOMxv1rAch5Kd9mEPNosZ+A7hwCApBk4Q10OFNTJouwByAKzrNzOcf6VwEdv/9fYEv1w//7HxmX88bSLlafQyrRz/xrSAxEMWi0j7A+xwug9JqXAe35MgNf+ugSywxih5wfkx9Z0eVhkk4UFVyQ1cfiUiH8bfSGOKZVFDaZ2MMycT5l/agzo2RSVY0VKx0q1LGD5eG2Q71kCobtnMN8jPR+/MDc9uu9B5gEOtyfZklJ93Qcs40hceVE7EHmyiPe06ZPOVDIHelfp+3QvVc64tTUJaJoUmDHh+w3vkjh75UbYp7nabinIqLbHQj+IimH/aVlQic8PLbpXFDoXp0gP/DGJw2YxSFcqZ3sU1HnJD51yyNMzdPMa47MjAMDZ+TYSSRCCxivAK3oevS5XsgBgLURBBtUAbWJEZSELjWtPpUBt/oICoz/uAC1M0+g+G1TRgYiL1GkMhpkFEreBAQCTcTQtPNuXsz0etiSP9f1/1dpXNEh6wxvegH/2z/7Z0mvb29tfVBPhP/2n/4Rf+7Vfwyc/+UlUVYVnPOMZeMMb3oDbbrstHNM0DX7xF38Rb3nLW3Dx4kXceuut+Jf/8l/iVa961aO+vuRiCpG5XfM6sPt8wiQNNrsoNiWuvsgiPaRjh2ctkoUJ9fzhwqDpJs5biR645/3mP8Tn//dfe9jnvLp3ET89ogduoSTqFYZiS6Aa0d9FTb5mo3stpAs+bMKhekl8cHWspftFttjgSKfW7VL9QmiJxRSK7m5edgBdwE+49L3bGSZmLJhiS+7XJou6S95nybK4E+vuaKRTjqYno+FrhqVgzki3S+63WEDcAZ01gpHl8Sdfw8GsC/ZJmkVF4RYlDcAFXPk1IJ0kWJwwaAbUEZ0rJXQuA0D02EcE4ToYYP3uU0ukh8Tg8Y1rlyHywNya/hkJjJ/ivkxi0H1Qoh4wDC54CwuOasgwOO++oJUAKOMVqPwJp8WHsXBdq/cILLYE8gOHCZlpND2ByRkZM38KkE4Ys1ExoJw+tcGJU5SGmFcpGLM4FAPUK07n526XPagsuld8v7MlYG7T5xAVXwL685oA4slUI9+LQpHE3ouWMVaKZSyHdhpJ64m/NbS56OT0b0bBTjPMUK5y9B3Id74tceYpl5YCpMJW2C374MJiXjnJhkOB9LAlOVDS4pjMI7aoWmHBTsW4081Pcax9TgbdJJNw1BuSzuMB34qMm3ljke+6vkrp3NJteGoLwADcIghc+mze0hrEKHj1Pnsm5RCVBl80Qc6AcSCbaMp8euiStsSidOMxmfnNTsygeOFPfz/o2un3JtgOkX+c5QyFIwj5TUq6sCEoswmHrQ0Yj0GSHqQAY5ifoDtYDQkzN3jAQu1Qh2YpMLvJABboXHUkiAIoTpgw56geQzIXyDkPAXm9koDXBulhDekwVhIUGC2OxcyS+EwftQJSF4RUowZqSOQK7/EG6zTN/Fd2eLF0blE7sLqoqG+YsSGI5hc6mNwEqBXq0PySAD/vdNg8XMwFvV7LDXAZJAtIlzVdrAjwqhVcfhkbDbvHhkn6Usptf5XbVzyT9IxnPAPve9/7wu/t+uRD2wc/+EF867d+K37hF34Bo9EIb37zm/HqV78aH/3oR0Ot9Kd+6qfwtre9DW9605vw1Kc+FX/wB3+Av/7X/zo+/OEPh2P+V1syo4dPd2jimdxMr89fVeI5xy/hh9fuwi/9/l8DQGBDI8LmEMxYrN6rSGfGCcLZWxeP+DlD3gE/dKUh5QxBGdAey/UQOLwlwar2mSoF8MiEYpY+X7Q8k1SXJlJRtATvegxGilB+EQ09MoYxmJBdQshAtZlsAAOkhWlNwIxFw9lyjWFw3ixJFTBtkU00+a21A662iSp3/lUZR01xKAVcMzdBuSDs8tUR2GGC1C+MLOoD+YVJlgBKgCmO8U30Gm8yZLuRfpLv1lBdUpu2DkQvCxuByacj2DMbR3NZ37gCOidm8f79UR8mYZgdpwsrN1x5zS0S6VSjGnF0r8bMlixIpNGkPJitemV3f2/ScYN03KAedgJbUJYUKLV1kVQXkL0GNw4pSLrvcAM7l0fILidLBsFN3wta0u/ViG5pLM/SIp4e1LFUUmmYhIOrmInTHU6bAWcG6u+hlTyafnICCjc9Bn2S0nXiygHM2gC6l0KmniknML0RCFMRBy5cXcV7buri1V260DePb8FnPnMDupcECgc0HlwBRmcVpiciqJ4r0sHxi6XPCjITT08q1XEcWkZlnXoQ1aBF5cgA3ajnlB3S4jkbyHAepuke+Kxpvq8wP56gHkSxUSvIg8xLYhRbGbpXK4hGh80GDJAeNujbWAZUHcqyeBV1H+CaJJr8ejkMMbbI97V7zUB1RdismYwDTKK7o8Fc9Fiu03fu7KrweTrlMCJxQpdOAb5HQHtfDmPWZVQkCwEDV0D3AocsgWTqwf5A7wEeRf0NETq8OTRAQHPLBfoXRTTMrg0KZ+hM98HdLxHlNLIHshCA++dVOAPckEGUpJXVu2rAG5eta+g5tCxmsJv1BtnlBJ0L9H3lgrLATMfSuZcW4DWW5jUw8rvzP7MnDLjNH4dM0lGQ9GjaVzxIklJ+US+Xh7Z/82/+zdLvv/ALv4B3v/vdeM973hMCoLe+9a34p//0n+Lbvu3bAAB/7+/9PfzBH/wB3vjGNz7qWmdx3CBzQYhcxIfr+GiMdzhrkt96DvkpLM5vE93djT+vUyJKg+6CJrCJfGTp+saqYDuhM8ByojN7LFN2QBNkucqw90y3k56lYDoyh2BjgBEcuj1OxSJkiUiQEkGQzpaRnRb+ty5Aeuhz/9CatMs0+Z27n6TawZXloHQ2b+nQAFTu86ezlDUZnLdYOPr94oTL6jRRe0dUGayMAZE35GQN7fQAYHKzRfcKQ3cnUnRnxyV4YwPbphkmmB2XGJ5rsHLvIlwDOAOMxfw41TRmT2mQfC5ZdkHndD3qbqLPZvtA72IBZiyavg+4JIoNFkQiWU4SCNkhgi0JrzXAGLQU4TidUjbNM5KqUQedXY3+RQWVJ+4YwOQUKPmsgxVA+rkuPvnZp4Xr7E4p+5hN6KDFFikLiwoh+FY9LFnBJAdOSiKP+kC6I6l0URnIuZefYMGiJZgPG4BVOkoHjDXyAxLEPLyVsg7dUYZqlbAosqAbNjslcONLzuG+3ik65jwHv5jjpz/3nTj2bAJs/vYDL8bgrEA6AWqnz1NuAPwuE8p9TY+jGjlTXR8wFxb5HmUVPIMpO6AFtHmSw0QdGPDGLEk4qJyYS9UgZm24ZtCtLK0VwPRGi/waQ8dlm/w48pg0ANCM9I68qKTkRPlnSkbMn7bglUJ+RYUyuO4mqFaToO8kape5lVE0sVyjILR7DSg2HFZrVyHbq2PwY+k7ZodNgAKkE0HzgAHqFZ/9scj2KqhegsLZzyhnDeThApYzJD5767PJYKhWAb4Ty7FNl4VNIwD0Lxtku55p6s6d0bibH+NYv9MF3ylH04sBphGAGlAfi9bc4uECQb7ABU3VKv3ugyZmbDDDpmdLYL4tUb5sCgC48yW/iWf+hx/D6sdiJpyCsqhCL2obMsieQSsap7bu4/oGaDqPLXA5an9x21f8zt5zzz04ceIEbrrpJvztv/23cfbs2f/l9xpjMJ1Osba2Fl6rquphSp+dTgcf+tCHHrdrPmpH7agdtaN21J7o5sUkH8s/85Vf9v9Sta9oJulFL3oRfvu3fxtPecpTcPXqVfz8z/88XvrSl+Jzn/sc1tfX/9z3v/GNb8R8Psff/Jt/M7x222234V//63+Nl73sZbjlllvw/ve/H+9+97uh9Rc3IKyqClUVyzHe08Z0DJS25LdlEPBH5z92Cj+98Uz83NZnsdGhvPMDkkDR3KVdxaSBzgWmp9OwQ+3+1xQvG3wXPvjM3wufNTMlnvsf/gG2Pk/boukpDt6QKnCQvb+ikcw0epdY2In5kkHh2Es6Q1CE9hmaZAHU2TJGouk5vIXPkBU2aim5xjRCec6/zgzhF5hpnc+VatKpBzb4/5f71z6kdBiOCRgCFn72qtxJwaByUr/1GTIPLg9Cfd67y8R0PNMEIK2HkXklS4vJmQSdPY91IJsE2wKlW8mgnG3D4JzTUeknS2n8KgfqNQNZ8KA5YxlQjxKkYxXufedaA7AkZA+IGQV0diqI0gHvlYHJE/BKIzvwINGE2EjuqVxsMRTrkkp+LrNvUgKnM0MML4B2zly1sh4uy0Qinu47d6IGjGcCBuuZoA9EwFmdJcgOIrjZZBzJpIlKyMaSLoy1ITvBlIYVHM0oC32czAw611RgpO09I8XiOBEQMge4Hj+/xmuPfQr/5z0n3H3gyPYZJodd/NudV1C/K4HJ0xsM70zQu+DuxTrQDAS65ykTyDZzTE9RuZIA9kBnTzvV7JgFpn6O2TPV5USSSFrg5wGDcHgg0fIglIuoUL3YZth++g4OPrwd+qBakU4lHUvPYDqLzMDONQWdc+hOHp4XrgE5V8QU9c+bthCFQccxDo1gKNfoufUA5WLbIp0w6HFkbHElwFUUA2WKrIyq1SRkLFXHlfEsgrijERa8TlCtyjg+LOG6QmYncV57KhrcKsfIyw9sUIBXHQdudvNC91IBXjUgUVKPzUKwN/FaVMVmsqSnVm9o/O2XfgTv/NCLsf5Jl4FywHVexeNsCaQTUt4HAGRA9yoZN3euOaznMYlylaHYBKoZDYav/ehr0H8g+l92DxWKTfr+iSNF6ISyot3LGrNTaRw/CQs+mkyTaPAT0awl8PZjaY8V0/RXrX1Fg6Tbb789/PysZz0LL3nJS3DLLbfgLW95C17/+tdf973veMc78IY3vAHvfve7l0zxfvmXfxmvfe1r8dSnPhWMMdxyyy14zWtegze/+c1f9Fy/+Iu/+DAAOQBklyW4wyWQYSm93tkBfu/t34DfXfkG5CQ8i7UvNEhmzZKXFLMkaLdwZZ90BrD/exMvXP+7KBzGpHfJ4tSBjj5VhkQHmY24HV4TCLIaRbq2zhjR491kb5zoopWx9KQssDitwWqGwf2uvMaJseUxBZ1dUr21gkH1ZDi3b7KlFGzEQx4uG1Pe4SVHjfZihEENt4V98erdATTaMrv1jTcWWUWAV/+d/XdsM8t8ijwESV4BmcVgygrCdGnHvNI5MLpPI9stY7nI+dDpnAfn97U7XZkqgGcBZqmkExbZnkV9iTBe9YqPNhxoec0FsltkKaG6MiwIcNdtBQ9SD9mYQ2UyBKidXYVyTWCxxUPgZJIIHH2ooW47cGNwZRYX7MxPJHQ9vu/hWHw8svnqPuFduHEXCKB3qQIzDuQOHzAborGzWGptBjlUlwcWXtNnKBzLLz90QecQYGcWKMcZaldG4lLj/7nr5cgu0+fJOTA/bSF2Unxg/Ez6vIZBCAIye2xP02eY3CCQuAWPAn16xnwJzkgKtKtRLAVxTd/f417AEQgSbZV0XhNo3/fxfA3gNQug9/5Fi+bfb2FgLObbriRWeQPVtpQFYfa8zEA9EGj6FOyollGzqMSSFIcsCcuXOPPo2XGJekgBoPIsrhlD7yJR0H15NJ0ZyHFNsg0AFpsZAdhNS3V8bgNQPUhgDBnqYbKEx6mGkWRBn+dwQDZ+H7IlWjbWTie06eq5+8DLJpT7vRvA4BxHZ1+jHnCUDttjOW0mfJ+DAe+699no3y/C/NC5BpSrxMwLga8FsqkJ7gPVihMSzRjyfbqG4QMEjO/scSz2nYF1mYKrFtzAEhHBssicAwBeGchFg+5Vuojp6dTN7S4QbSzM6IkJPMzjYktyFCQ9mvYVxyS1W6/Xw7Oe9Szcc8891z3ud37nd/AjP/Ij+N3f/V18y7d8y9LfNjc38Xu/93soyxJ7e3s4ceIE/sk/+Se46aabvuj5fvInf3IpKJtMJjh9+nRYfHuXCbTpLRCIUksTst9NmJRjdiY+WfluA95YdK82aLrOob5D7105W2HFVxVbrDKAFmvSGtLBfoBZoq5awVAPeLgGZhgqt0jVqxaipICu6ywsZGFRbHHoFU3GlXDU3ymCFo/OCCDKtIX08gIN2ZtYGXFEgd7PWMDCtJWhAQRcx5JKN6frXAqCtHXK3a0XPdPHB1eN243OLUb3wfUfx2KbBSXrsKtUWMIxcLUEw8Jim2F2i4Ice4BmDLx8a4YpuDKQC41yNXF9pTE8bzA9RScvtmI20Wf5RMEwOwU03YjuFrVdsrBY/TwFPKIxEcvBeVDf9gtjMm6wMtNExQbAtAFXCWQpMT1N116PaBctKqBejdcviriz9oHQ9DRHuUYvMkWvt413raT+8kGC3w2LAhDOsDMdSKSHjcsk+uwip4CTRexdsSaWGGIeD0cA6EjTt4bjzM07uDahlbC43EMxS9Bb+L4jzFN6QNlEwOHqDMPorGoB9Mmo1wfu6bhG73JCLvZuw7E4JmElBUaD88Ydp1APBcpVd00c6F0ylGF1eCdRUwZBFvH75LtAtdZWXWaAAxWvPKDDfd97mkQyI90ogPA5XNmg01RsimD26p8TI52GVotpqHoMTT+yGst1oB5FawyAAsp0aiCLlultZQDBgoWMZQgMLb/hMWlUiQ/BjrFYbHCyz/G4qBwwicukw4Pj6Xv6c3HNsNhkYIaFIL27p5Ht14G1SWONwyYC6cTZrEw06hWJ+TEesEvZmAIxn8myBwLsyoD81lxARNdLmbMgvWDIxzCZ+ftOc1o9ZJCFUyK/MoeccrBj3ZA9o/vPMDvpgOp98rmsVuOcxiywOJagZ20IApMFAfs9dqseMDTXqVQctb/c7S9UkFRVFe666y58wzd8wxc95h3veAd++Id/GO94xzvw7d/+7V/0uDzPcfLkSTRNg//4H//jUknuoS3Lskc09xM1gks2r7FURhLlclaDaUo3eyuFup+Sw3htQmag6TGUqwJyrsEb/0YGixgoiVJD9SRMypf81nijkR7WECXdssV2EiX0QQuLnNFOs38hKtBlhylwIKOdQkLA1NxN4jonXy5Z6PD9gnihjYy0pk8GnKQLs8xMC9EI8z/H7xOAtC3QN2MMaJW6lrJMJr4Eaymr4Q4VjYaoeWDbFFtAcUIDHY3kchrezxUFEr5MplNAzAXyHbdLr/yuXyxJHDQ9geygQTpxwaIF0Ngw+YuariUbR+sEufDjIJYgaAK3yPwxhUXqgLNLmUZNpZBoAWJc6p7usUkleKXRuaKgOhQZlmtOAM8g6NAwTaDwKChIjLdqw+ApzzoPALhwOEKeNti/dy1QtX0WTnsmT4UQYPkNwWFXYnCeEQvSZTbznQLSEqjb+JJiTsKNgbiQUBCmBVBuxb6SX+jgvGaA283nexzlto6g6Q6QHlA2K5RxDQtjPd+hyLM76AHWIvVg/EEC0VAQIVyQpLp0b/J9GzzfxKKBXAhYQZ2VzMgEWPezID6qM45i3QWBbgJgqcu++vWVO+JAS+zRs7CyiUV+jW5+moklPzKTMExPkddevhfvH7cU9PsNQLWpIScCw/ti36keW8r+iMoFLAsTNlT1ikQ9FKEEyJVFMqfAyAdYdY8jPzRLpVeAhSy0L+3KhQO9+02PpVIf6zKA0eeVI4biGNC7hBYwXEEsmvjMG0CtZig20sDWK9cc4LuOfej1kULGa+I2dbNY5tSpE3sUcaOSzAjmoFveajr1MhUmXANAGbdyLX5O/3IMPFWHhXnIW/z4MTg/loZSoU7dvO9OXa0DdUs64cvZLDjMY2S3Pdb3/1VrX9Eg6Sd+4ifw6le/GmfOnMHOzg5+/ud/HpPJBD/4gz8IgDI8Fy9exG//9m8DoADp7/ydv4Nf/uVfxotf/OKgp9TpdLDifNU++tGP4uLFi3juc5+Lixcv4g1veAOMMfjH//gfP+rr61+ygNsNB6dvOGp9SxMIoIm1s0+LOECTQNMXkCULaXVRO0xIX0S6rCZch2ndCZ1xlGtxIKdTjfTQwCY8YJJEbdG/FMUrLQOSmYIcl7BORqE42UU6BlbvrsO56qEARjw4wScLS7tQhlD2gbUOe9TetWowxcMuFHDlIh4XACiAwVJp5qHPoW0tFO67hve5XVqbTWcZYZW4skuvi8qgs+cyZSVHMheY3MKgO+61OYPqWuh1G3zZeEXeVN4kNp0ZpGPlzH1d4DRvwo45cQahzSABGIIw3/As0NknGrH3zzIJQ+daAzlXYZEFY4SFSWN2zKRUhwypfW3BlIEVDLrn6PaNiQa4AFQuICoGYWIQoTsWyWVia/lsoOrCeaLR702HkVKxYpjVFPx/3cn7cd90HXt9DXk2ll5NGnfp5HhOQaYveTQSsExAVpTVAwCucvDGoFqVsTTrggYfqLUxZ34xA6iE1ftQHoL0agR0dsQS7iXIWrhgJ5tQJtdIFgLI/rkiYMgAEt2s+xwJM5BzH3H7jFD0YGPaglU1Bme9dDb1OdNxIa5WuGOoWaTTmEVRy3wQJDM6t8p94MshSsoeLY63Nl0MwSi3GnJU64DObMCi8ZqC7M4OMDtDr/3w138Q7z7/LMjP0K5L5yx4AvrF2bPcRKkg3HisBwJNl4fPM5KFfvAG08xYt2GL2MTOrkF2AEye04BNXR8/wIPUAUDBZrFBmdxF6gMLCkpEAXSvuYxaoUgOwmVE9SBFsUHq+D4I9GPOpHF8tBljAGU202nMlgHA+KYk0P9DeVkAvLAhuMoOWZAlSeZ0wuSAAYwhPWywfieNj3KNyuRelJVpi9plk/y9ZoYMb0UF7D/LyVuMGnTvykLmtbqxgrn6xARJBoB+jOWyo3Lbo2tf0SDpwoUL+N7v/V7s7u5ic3MTL37xi/GRj3wEN9xwAwDg8uXLOHfuXDj+13/916GUwo/+6I/iR3/0R8PrP/iDP4jf+q3fAgCUZYmf+qmfwtmzZ9Hv9/Ft3/ZteOtb34rRaPSor298I8NwHwGs7B+muk+4Hp1HSrLOKcsSLCXGCHTVgEeQBAjVKUfpLDK8uq/XNZGFJuPMJE46vDYwGdXlPYUcILAx96UZY5FcnQLjCez2uruGBkNDqs4Be+OiPQ8yZq4uZWXcRVHJDyHb4T9LNAZcsZDaB2POSdu90WWVLIBHeg7/V/CG4RiXlTKPMEI9uFXUFr0rBF7196HpOxfwoULhdq2Dzyfo7tiwi5Wl+74mgkZ5YwBryXjUl/4slZOSWcz6yZkC0wbJNEaBomgIW7RQ4VyiEEF9ulqRaPoC2aFZwkCBMTBlg5WHkRw2E/GalIXOOOGGHA27d54jmVmIymJ2ykkHZISX8UKHsqQSyfZLL+Pv30hSFd/Tm+L9hcD/Jb8Fd127GQAJb/IqYrdM5jI5UwT5CbhMnpHx3jQ9Aa45ylURFjtZWkjLYinPacuIKi6yOidckqgo8Kd7SfexWHeEBCeK6rNc/j6QzUskGDDlMp6+ZMlo4awHHOnMbQCmFoYzlGscXZc944UhEU+fDfXg6bKBd4xJ5gI6Z9AZW8IpWRZLnPkuCaVyRfII9J0ZLJdOxdyfy0CnHJXDq+1/XY2fe8m78LZLL8bZiua5/jkEAdG5ouN+577ng//xCnrueU+nwLyWmJ1qaQaNDYp1AZ3mwaJGOwB6W29J1LRx8RkiUTEstry4Ir1WjTiqEZBeTANo2c9nmcOUkaI4ldV8cJU2Tg08iWrkrNEuC03HTG/sYHaC+sQHSZ6Ykh7GIMlrvfnrTBuyJTp8ksDsFvfaxgz1lS56F3iYY6oRbUrCeHH6UZNbGETjBWVpIuG1RubIIdVKh4Ikh9vzoPW20K3K6XqaPnDD0y8DAF5z5sP4zRNfh0t7tDFP7+khvVDiqH11tq9okPTOd77zun/3gY9vH/jAB/7cc7785S/HnXfe+Riu6qgdtaN21I7aUfuL16xlj0O57SiT9GjaXyhM0l+0plYMag0MzpOQo6feelxAG4PhQZzFcbdLWaO6feca0Lvi8BBVgmqFo+mygAVQHdqtisbtfmccqsOgExbA1qnLLFjBUA7jLUunOpQfeE2MI9btwjo1Y9UhzE21Fo2FdMqWBAWTmXagbARcA+AyKx4zAwApiGrfKOjcAdFzhmSuA3jXAqF01gajt0GQ7dfCs24JlORZMuEYsXyeh0oQ+N1ysrABO9B0GRYVQ92ksIJeEzV9b48X0wlHOUrR2dMhS5RoC6Y0mDYhuyBnDeq1rIUB0eC1pvJZG3MlOMAiWBaMMgyOJgaTMsxOcoyURTJpgVhAGbpI+zbgFV0H/W5hEh6A9b7pjDImfgx5fFxbGVwWpFz9/tHTAQAfmihwZnG6d4DPnSKwlt3vEO7OgabltSjAGfzqJnHs+b/VKwK9ywbJwgSbCYAyX8xZpcg6Aur9rtxK2pkfPluBN3Sx/SsKybgBcxc/35YwjlEVAO1TGqO8ibivZp383ppeLIcZCUAiMOwoO0MCheUanb+3aNzxsX7uMx+8pn6XlYFlZCPTtv1JFgjPTe8ylWxJqNJlWxM4RpVB96rDSvUlPSM1vbF7d4p/ln8HpDTQmQfCU7aHWWK7AkDnt/oQRR0lRQoNnXLMT/BgestrG0gCnohQrVJ5yJfivLhl06MSLECU/ewgApABej66Vy34BQIiA3TfsomB9H51OSdbnlZJNDu0yA7JS3B6mvp4bVqD1QrFKZowZydciZ4B1Qm6eD4X6OzwoGQOUEYxn1sU654hCay/8hL+0U1/gPeNnwEA+C/3PAO2o8EbHjKgSCjb48tfvALqEVsqmy1O5EinGslBBSud8Oa+RnpQh+et6QvIgp4vXzYWFTEjVQ48cC+JHt+/vYnj3SmufYBkK9Y+bzAb4AlpxG47Krc9ke0oSLpOO/0HCjJpiCFiRNC90ZlT8GUtCr4EkCAAGq20mJ2iNLZXF07HNpQuPL7DSKrxWzeJNo4J5DVKAECnZLxaDRlmp/2CCqzfGXEU8+MS+XAVyUzh0tfT54kSWL1boxkIlKu+Rm/RvaYDM8NyRiU8TmVAwC0gTgMnLhIOYMwitqgeMMiKRVp7QnV/0kVqAbXh4qBWYNHGLXlX9rb3V6BDU/wU+sTyCAonVd3wCQCo7NO/DBQ1w4LmMJRrgGzhSVSPyj7NQARkbvdagv7FClawoDHDGxO/GyK2iNdRsgEc9FpjIivNs9iMB5Ia1EOBwydJrN3lFr3DCpYRHsnTt+VcUcnPBVtcWYh5g2Y1w2Ir+qY1PQqy2/YN/nUg6tQMPtrBBz/3fADA4oSBzTVWtmaw+zQ+vNGpL9XwxunuZBGwrDOitFcrDJNnOcXtfYmmz5EexnKNtRTEBP2tkpSKm17UfQryA7kJwHD+ICle+7FnEqdsLmLpenpKwnKgs8cgC0+CIGmG2Un/TJKyNTOxvCdKA1lS2ax04100HWS7FQXDAI0ld59MJt19plJWBR7BxA5j1XPYk3yvAVduY+IJFpwA0bKM2DKdccBaZK5sNnxQQF3poukxrBzapfEhKhssTnhtoDsCYuyDOuZKey0175xDJwycR9By03f4qXksGdddtoQtqleAzg5ZmXilbiOB7hWFZiCQuVJrtUKaasE2Z8jQ3TWQBUPjMD+qyyBKC1kBjQuuys0cyUyhGjrW2PAh7EsQXs4yp5btMVYFlfn9nDo/Y3BTWuJXL3wj7rmySf1wrovEEkO3rU2UzIFs7MkpDLpBkGcBgHKV/Bq7FsHTMZ0osFqjWc3cfbcQjQXXcRMrC9pkMQ2s3Env+63spUAhcPy+1jzRsln6crbHJ5N0BNx+NO0oSLpOK7YSJNwxXhwrDKDMRf+SQXpQB/bX/HiK8c08TgROzK8ZIOzg6gFDduhMOJmfZNzOxWE7iNrvjj/mJr4ekB24jJPbIaYTmlw908yucUxPCRSbAn//b74HAPB/fuQ2DB+UKNajsGLvMvHjK6frMblJYPvPKMPBffCRcaiejEBu+GsjdpbH0DBDVOrs0P8OpzWyjD/ywc4S3gjxe9IxwJKhrstItace0v+JJ2YGywe4JirCKsmF04o5Tv0bAKICYA39Xm7Qa1eerrH9xxn6l+I9NQmHzniknS8MIAEDEbFgIJNfzUX0MZO08HqtGlkYMCMwu8HCgnbb63cRk5Epi9RLL2jCLJnURSjcgFvCLPnFU2fkLcUjNI3A93kE1JuExpwaGOQ7dA2D+zhMwlFeXkXXMXe4ou/vxyLPGdIZ4X58kNT0CNzbdIHhBr1xbPpIDxPUIwQQsaiAZEGLKACkhxomZagHEnMXrOZ7tLh1P54G/7h6KN24iefRKVBtIGSudMdninhYCJkh+n/QaVoxqBccyaIVLHYlKicq6o+bnJHoC4bcefkxZWEzSQGc90WcNmCNgE6TgHUzkrAuvUv0PlEo6FxCZ5yyrgCy/RJyIdEMEygHxvc+iL51r9Zo+hIr9zdRRHE7w2KVQ1Qs6AjpTJCwZ+JYm+76hvebIANSrVDw00gWAkpek1SBt+jwgqu8jtkl7aQGRKWRuGek6QnYhDkbDgdsXhVhowZQANbZo/cGq54cSBlzz5yXWWjAK4VsTHNneuDkCATQOUdvzHcdoL6xyEPgS7pWPvsjCoY7P3YTdMcg3fNaVO7zU4Rnv7NHkgQxK0YYrrofgx2/SWsGAosNp5eWMaQJx9x5LpqENLWMl3/wzc3Jfv4afjp1mc4YsIvyiQmSjtoT346CpOs0nTI0ToBPLrC0S853SsiDBfSQnujeZQBIMd92LJNVEuZr+ziZhB7yzl58oME40okNC7iRtLPXKQtBmWQUZGSHQB4UqU2kt4IYWIvjDPUtJS7UjuPKyJF+SSWbU6akf9GXBTn2b5VYvZch8dT3xjjQNA+6PjrhEA2DKDSspffmBxr7T5WoXAmwu2uQzAyp/Hp3eA5K+T9SB7deZJqut11WstwCgn1xwHeLHdd27Qaoz3s7LpCxgjIv7j40LS0Y7wtnhISojcsC0Ws6E5ickWFnu/oFBV4bMM7Cwmg59RdXNlD5TS5gGAvBhygN0ilQbVrMT7vPSxKs3U3lSi9y6QUn/KLKrAcpG/QuUZSrOhl0ypb0ZIykQMbvZmXJSDdrq4CekLBPMgN0F9BdC+Gylv1LFra122YusGc6VAppMXSlkvJTtPLmLluj8xZzjdGC6Q1FkxkDry0G5xWUKwk3PVqshw+owNKcH+OYnUrRv2DcNTWoViWqNYZqw2dayJSXIRIlpDMe9aKooiQVdFFgSe/IZyyjea0rP3kDX0XMN15FBiH1PQkSJvNYflw5WxG1HZR1Un1JwYZ7jWnKNJarEUSsU+ayx6m77hiE1EO6hnrAXYbIBpp5tUIZPf/5nV2DZEHZQ5+FZhYY38xQnNAY3u21CpazaUxTnyfz+Lmisg6UbpEd0LVXQ75UtvPvtSJuzHqXnYDrgMVnjxF1f3Sfiern1lIJ07VsHOc+2cY3M8peCrfRq/sCi22gPObKnhOB/gXACBGCMp0jOAv4QKZ3qUKxkWJ2nDpdODC5zmjcA3Rs0yNG7PBB9yx1BXTOQ1bMCHdNdey/ahSNzj0QXSxojp4H02kGTJ+YEpYBe8wGt0eYpEfXjoKk67RyBGAIeAXl7k5cwJthSoFEKFExdHYVkoVLMe9w1D2y1Qi18oaCHNpB0vuyMdFbk6mXh7ZkHpowKCdCWRuaYLKxCWrQpGqN6CQOV/I7SPF79z6bPi/RgQ3j7RxgKEOTHdLn9c+nmJ0CDm8WyA4dA0iR+CEzAHMTmIAJi74PZNKxAqxE5WIyURPeSpZRWRdol9UiBqMa8jBpZ2MdynQ+QPGYJQMAXn/IZ6NaJTmfhQiLgisJtjFP/csKMJEZVQ846j4POjrUD7R4WMHDBF+tks2Ez/IVGwK9yxQMefo5s6QfpLoi2nZoC6ZNYPsAQL6fotjiMIkvbRGmBAB0N+JjeB1lAgDKZnnqNkDjoBo5K5JQbqNMkt/1ixJIDxkWGymw6QxghxxMMcg5Q89pZi02GTq7NrAv5VwTlX4QhQH9rj2dImR/SHKA+q5ao/eqkULnQhLMXuWc8F024ehfpOufnuIQDV2vF7BUPYeRceMxmdYo1yV0boOsAwwjTSEbMxh1f3nR5QoQpSVj2rVYdkynLnhzj5doXBnT4/wYiaQyZWAcls9nADvXGiRzxwatDOS0RlvsWOXOUFe6zESHo1qVIZPlr8Gzwug6LZouh+XRSFbnCIyxiNMjDJQXSExnBqyxKNZkKGuJgvo/ORAxk9Q4k+xWidNKotF7RiHZp9AzImY0iEZ3a0xv7mKxFRl9XvzWM9JE7ViJDAGLVjvbnuxAEWsQpO9lBmmwQbGc7k1WRebaYpMysMWmQO7GTFDO9/PFzXPU8x4GD9qAk/JZeWaB3EuBTGskHQGueOgDLyIaRF9r62QUoraWqCSxdp2pNktofPh5hV6krJycR3Yzb0hCoSFyG0wCMPPEBB4W7DFjio4wSY+uHQVJR+2oHbWjdtSO2l+CZu3jkEk6Mrh9VO0oSLpO0x1Ar1jYxMIecMwcCyMdU1o66fEgKlhs0u67d5W2IJ2dBl3nBaSzOCitYKiHIuxUfInEH+NBzFwZdK86/RwhkB9YpJPIJEsmCryJ7DaVOX+mkqFaODzEOIVUtDPymkuy0LR7dsDVzq5GuSpg0ohTagaAFRKD83XMlDEEaxSEjInB6D6N2Qknzz8kvaimBwjnl7JyljR9jGwBvvsMs9NA/7wrJeyD0Jst8UovJglYj6sNRrhtthwzdknLhg6M2SSAylFMxwxYdqiRTg2aHg/4DligWBeoB5wyW4jmsMKl9WenGMrVBL2rUVsFlgE5C2VFgEQ9reDQLgMkSoV0opHtxyxD95pFMlcoNtOQUUumyuE9vAimJuwNTNBcqgeEm+hfIOYRQFgpncZHuV6ha+7dnWB+2oFLK4buRYZ0SsavAMAVlTE8m6inrCcaLuHGvBVHe25OZiQueOBKFd21AvZcgtR5jYlKA8aiyUXUeLpi0XQpc+PtfETJMDpbQbrdvZUc3asNhmdTjJ/qMB8bDcZ9ATmL2jiiApQGmj4d0z/HMHxAQXU5TBI99NIpMe7a+k065UERmzUmZED8s2iYhU2pHJUe0HUxZSlT6MZa05co1sko2aSUKs72FeoBaXsFJX7HyPQlJevUt1XOook0J7XyRZ8Rg87fxwHCjVAzDttzvopunJWbgO5rrHxBBj2xct2VTH0GpSEslahbzMA+gdnVIIFYeOVOBCNg1cp8J7MIpG66wOEtCWQZhRtlBfSukLeZ7lGaaHY6h/Fsw1Zj2gaAvs4pi9h56iGmnx4BAFbuI4/DygnpDk6X2D+Ro3cp4gJ9xoypOI5hzBK+MZAC6pZQqqSSbXaoIgav0rCGB1PtpsdQDRnKzZhhTqaUKSo3LZXVQAzaZiWWIfMDQLcEU4/aV1c7CpKu00QBoGSwitLE6sm0WqpEo/5En1SVzzg69W6GzTtYKJt5zAprAXJMwrDYElhss0BfzcY0gSVOAC+dKKKFGxZ8jropCyBHWM+o0AE8ChA+CBBQXYZiSrOayWiCzncBufATigUSFspo6Vihs8sxPxmFG3njJoKeCKDGpueUdetoe5KOa2RjFYQAVY8mRpNZqBFd57QRSOYkMBlKJatAc6yGveRctUXEwDwU3E32KO4lBxz3wZZfgNrYJv/3dsmKJthItfefIUsL2fKXqocCiw2GauTA+imCxxV9Pn3H6WmGYt1R2C9qJHMDWSgoh3Mp19NAmwZIkFJUFtlBtEDQKVBupKiHPJbNSgNe6yXPK9WTSMYVkgMaZyP0IcsE2aEJeBLLGfiaAHf0e/cfkjlIdA/UR6pP9yCb8NB/AAL4nxlSX5dlZE02XQowKMiM/QxX4uw4TFNTDNG9EgUFA86KMWSHdJ1ywdHcmFJp0vjAybgSo/tABaR7BVYYUI1ofFTrbnMyiffCJABELGN19oxTSk/AnYCgTulfNjVYbLjFWQKzkxyioWeke35GyueMgTkJAA4KVI3ksGncvIiSoVmha5qclqgHVNbxFPamk8CkVJ70X8c4e4zK4aSYIQVxZloK3jyOs+kZ1w19z3JzpcrTnEpnIpY95RzoXJBUDvMA+hqwLVC/XNB9qoctnFRCeMnpSQmdenVH5zNXAfDSEg4kHcRGEwpuZAGkzouuu2uR7cWAE6BgsemLsMnwpAMjowyHqMg25xWn7sUffPZrAETcVO88vW9/OMLamUPML62HMh049aecx/Gr+56dRr+nE3q2q1EEgXMdx7lvTBmYXASB3s5VhemNHQoiPeBbuM/S0RtudorwYsE+ZWGDX+CXuxk8dkyReWIu9aumHQVJ12kr92t07m6gc47JGYGxm7Sz7RkWT6oBYeEdZlfu4ehdrgPrCQDhW1r6KwDpFoki1tYb5xieONl9ZoCqz4lOvO8m7Yb0lJKJhWiZjJbHukGjJZlqx6KyyA5psZ7cSDvv3pUI8mYWSw80Mxa9HQ1mxRI7JFlYND2Oxs2h9QqgjlmIiiFzRrHpmDA0kULOwFIgmTKwwxg4+Yl2ccp/H478/jQwqIp1gWxsnJO7j47ofeEfgLb8AB0TF/O2xcnS764Z0WLOuWwJMzZgCQinpZFOELAUTY+jHsbJNxlT3xgZWXGiFJCVRbmRhs+shgxMs6DETDR6woL53We5RkwbuUDQ31pspuhdNSHzojNGlgxSwErfdyZoF7UNZ2VpMXWWFqpLKs5L8gk6AnGnJ933Gzr82Y47HWeOMLCMyUim1D8ep+QxTNppZwEu+9Ceu13GRi50eCZ4qcFVgslpEcDHo3sKUngPZr8crCG2Ze9y4u4Nc2zOqOPkv1saQMU1xLSESTlkkYR+qIcM1Shmknxwt9ikwZftp5AHCzDOY39au5Sx9J+nOzJoMOmMnmFRxb5q+hRAyCLe02ZIqtI+G8ob6rfONY3KA7f7xNDKDi3GLijLDjjhrFqEjmpEMgftrJg3yvXDXVT0s2fZiorGnZFR1sE65QsugPm2CPfPJDGo8tdKnnHuvo+JkZYf6rDSitKgWs+gchaZtoKkA3xGJ5tQFml2kmPrmy4CAB64uIHkUob3fPR52LzHbdimmhh9Lujs3ydQnl9HZw9YHHPX1CLQeIJDvZKgGonQV/2LDSAYmq4Eb2GzkgVltH12nLmMrZfuEPMaK/cYdHZSTG6kDmzcxo8roNjyN9E9E+E6ADltpbK+jM2CQT/GctlRue3RtaMg6Tpt+GcXwLe2sfPSEaoRgot8WQzBhEUy4Rg8QMeG1K9rzFgwa5bAyES5pYm+OEPbkHoiIRoWJt/EWMiSgLjB8dsS2DPNeFz8JdB0ODq7vhxgMN/OkR1o5HsqHGQShmSqYdwixI0OXlXUDOS0Qb8yMK7k13S5Y+VEWrmoXNnKxEVWp110rzahtJDMEawHZAtI2gzIq8qmDmi5T4uen+yrEYPOBTq7reviceftVwCbWHdN7dSR698WgB2MwTx0HmAxhe4XSv+6ewnWlQR9ySibGBSVwGIrAoGFM871gGHVA3afTuD1dOp6VBCI3U/2ltPOVidRioE0n/xCTq/pnOQXUgcQzQ8py2IFg+mkrs/pvKRv5SQGFgr5HtC/4IKDnKjxVoBo+qBJ3TMtE1c+bFbgfOjo9/kxgXRK2ToP1k2cmSgxpvwiiMDCkq0yQ7UK4AG3eC4UREFlXV8+FIsGvUsK0zNJzL70E+TXinD/TE8SmU4ZiCCaSGUzUVkUm8tBdBxDCazowXIWmJuWcfQvGuw/naO8mTq++4UUw3MmClxyRrpgHCFQA+j55Y0J5ShmLUwmg64QAKi+BbMMxt3T8phF9xJDOkO4du2CqAAcdwBt1eFIXaCouuRLB9sGGrv/Kx+MUDaDqxjYm8SxzSRapV5avD2JwIvVJoUNS6MS7t7P4jNhne2M6i1fa35ooBeexWWR7TeQ8zqQCVQvwfyYQDWMBJXONSKo+Gfk0isseMUxvBc497njAICXvPBu/MniKRh8QWBwjr60XDRQ3aRl3cOJ5ZcgWKUACFpK05MOVN9EcDkAmJQ82vqXWaD7+6BxeiZBvu+00S6X4HXcmFkhwGoFWUSDYFkwVGv0jFrpy6MkTzC6xx9joduit0ftq6odBUnXadMXnILa7GJ+nIxFzzyPdkG7sz6qz4yW2DbM2IfsPFn434btPEMy0+BKItn1FGRgfjKqBI/O0kOYLEzIRPjsg0lZMHq0DFTScadWXYlqhcTlwiJ/qKEzDtUTYRHKDix4E/Px3tiTGQXuqOHJmBbKZpigdKUnplyanwHzG1wmocPQ3UEQ2QzsMxsnpWrVthbZmAVQ3fb3IpaMZREf0HZXDxO5K6U9koL3wxS+W/eiLVLpP5/OycKOmLlFl76DwyzAorOvg99duUZpdsuXrw+Z24G7bk1KH6i4zJyjK4vGwvgFzi1sZDxK71Md65Sl6fd5xrHYSiHntFhRvxh0rikwbSEXy+rdfoFtui7wsUD3ivujARYnqPSahz5mmJ1kmDzJZzEZRp9n6F7VqEaOvdT3VHTAdPxi4qUqEILRdEy4jdlJGterdyuYlFSRfenZ5Amy3RJyoTC5ieogqieAqyaoxFerCfJrFZgx6F10mkRlAq4sFpsyBKLswLPU6HdZkHyDqE0cH65Gs3aXweV1VzbuE2utbXhrOgmYMWClC4hawZLJY9lQLJrwbBWbHOkhQ7bfDioI96LyaBDshQhDoO80xMpVHrwESRHe6QS5rI3qUrk/O4z31yQAyoid4jWLsg0+2zmjTI9nzjV9r4sUy9ZG0iYmmcfrlJOomxWYje5ee6Zqtl9DVNpl+xwebt4gmSWYnWZQPTfH7FNG0s+NYrWGmiXgWkLMHayAa+RbC+jzA6i+D3Z0FPkEwRC616IiN0BzjRVAOkNU2e8sl7+qFSqhJRMF5vB29YA2oiYD8v3W9+MImX5eNTRf84dIIbiMmqh85soz/fz3bTB/ghS3SUzysZbbjgK6R9OOgqSjdtSO2lE7akftL0EjCYDHVi6zRxIAj6odBUnXaTtfy2FHFsnxOZ557Cr+2tYnAQBveuDrUUjKPnggYu+qyxo4thnpb2jAxJ0fZSCIjeV3iEw5rZgJ/S7nGiYjHR/W9fgEYkepLg8sOFloWG0xO0WgxcUWQ9MHulcYujsuA1UoyDlgnf8XAGelwFt+YdaV3+L3pl2pRT2M+j2yBExD31m69Ht2aFGuyQiqZJS2rzYM8mt0nd0rDJ1rlE2a3BRVcz1LBXBgxAwoc4Q0/uCCdpm66KgO+CxRLLnYxGfCEF4LJTTxkPe1G3NYjhZOyZdGQ8nPAjA2KIoDAtPTDItTBr0H6aJMQrv+7ABLwHdm4g7Y6wp1dqNa8uIYC6J/3jsthb9H8ZoPn9VAzAW2Puruw9wimdbkc+f1kyyVhnxmzQPomQWsxxEVQHOygklTZAcuYzgnCwabu+ykZpgfFxg8aDB4kC5ifjIN3lv+PogS4NxlMNxnyhJgOyww4crNDHKhIbQN12kSDsYps+TvTTni6OZJYO8V6wLZAYc1AtplcdKpRtMX0FncvfcuN5CzmEljhqxAVD9FtSpDPyy2OVbua3D8Q44d2LeUcfIebpJ0qHQqYVboWTLOAoSEBV3G5LAEMwaZY8XZm0mlO51GXBz5frHQ/wDAK1dic5fq9Z1MwlCvuHszoWMWx1skAYdf8zgirsl2o7Orw3NZ94lNqzosPIP5PumbFQ7sXq0Rjkmny89DOgHSmQ1gf2YIr2NZLEUbCdR9jtGlwo29BiaT0JlYIhdwZQmH6M4lGpf1cmNj690ZOrsNeN3AcqrJ/fF9t8A0HGLdYr7tsogrwrF0XaZMUfaVq6ivRLgil+112XECZbcyyRwotlKoDgv9oDtUshucs+heLlvHslAOZpwDgqFekQEDlUxpbq5bc0yw43GfLwqFTvHE0NtITPKxBTmP9f1/1dpRkHSdNnz6Pn7vRW/HGUm51Kd9+P9Ff/jcANK40oybeKohRzLTIQ3NShIUtIKHyZ4sJSzyfRvo9unEgu0tq+gyTZNXOfIA4gTdaxpyYbDYdkagx4j264O0atMAhiE7YGSLAaAZJgS8nahwp+uVBOVqlCDIDzVEqcmWwAVOcmGgc47ZCRbKZp5Km0xigGMZQ9ON1z53i6mcRcZW/5JB/4ECqp8EthIslRLmp9y131BBXEshFxHsWawLZIcmYo7c+5YsTywAUBDFH4FdEplyy6rdvmRHInA2ngoAFMB9Cc6VIr3dSL5nkY0ZJhMZ+qNzzaJSDLKI2J6m56jT7hgj6V/T54H2LUoGk1GpMQSZoLLV7DQds/WsHUzObkBOeLCISeaK2FiZXAK560yE4E6nDM3QQpQsCPXlBxr8IMHoaXsY1xvus4jNlF5zbL7MoneR7DXq1TxcuyxoQc0mrrzRJaq0BwRTX1FfeDDt9KSALDj6FxFJA4qeB6Y1Onv0Wt3n0LkMY1Z1oyGzxz3lu7QgpzPrWJyEw+KNjgKnTvZBdSWKDS/wSmDnckOif4EWsc4OSTJ4wVDLGJjV4BDRUkgwWEsYQjnzmD8FmyXhOrvXLLIxqXKXq/R5s1OAvWUO/oVeEES0LdNhOpFjea1GHA0F2haqb9C/388VPkiIb01mBulhDXKbBlQuQpnOM8jSwxqL43kQkwxBbBGfkcW2801rYinNCipZiZZ9CTOEz5NjD5RiUD0KDn0/6FxCFgaDcyQWCkSRVI9VzA4Ukn0KtFbOen+8DmAJ61OQLRsWT62R9Wqk/3Pgvi/1F9PRaiadEl6zGQBm4iU2rAuU6Dw6ZQGL5ZsvwfOmRfbQBrojodz87EkwqhOxn8qVI62I90IWdC4vzsmUgU2PAo+v1nYUJF2nfePxe0KA9DfOfgvqS7RNzlSUxPeLxPgWBssTDB9wW3cPJLZ2GWjMGXpXFCk3gx5ak7AAmmaGAKpMW9ReLl8yzLclZGED/qfYcgDJPXcdz5uDMwt9aYjpqXhbafcvo1Fn4x54DzZdlRC1RLEZd0qiEhDlQzzlXIDSDOkfAMgZ0L8cF7Gml6DcIAVlr/dCisSk++Spyzp14G73e1mLcKxv9YAUjTt7JgQWOnU7SB8btDJM2oM9LWLE4497KEbJvZcyUBFPxQwglG3hm5YDL9KhMhicV5id8NILBr0di8WGCAFCXhB+zC+6JqHFgCuGThXPyZ1ZqfC0ducvZxzAfS1fYPYF0sjyWSmdCbDGBMVkgBzOr76whaEwFvk1Wkh8AKa6HCt3M+ytDoAVz5QTtFh6DIol/arubjdkvCY3MfTP0WLqZSRESdYbRgCLZ9GKmmQK4o/6S2zDZsBQrkv0LketIYCA3KmXBZhzZ2MRAcrzYwJNJwLhdUIeaunEItv35yJnVNbC/umOBAQLGkz1gKG4UUHOJbpXvIyz/97t1CkDrxTSuQc4uUxrK1tgswQ2ETEj25D/Ii3I9LZ0DMwPM4g8gqubHmWL2157xnmnBSV6BaiRRrIbMVfKaRT5wLceEuM1O4jjTJaEbTIpBaiAC3ZyHuYnz6bLD2yUetAO28dZ1BFy15PMgO5VvyloljJGAPnaATFIEqWCKAFZSDROJ8mrqvtrT6aE9SGHgjhOs4nB4JyGcBvLg4MM0xuz4GzgTZtFGbXQeEO2OjVHYN4aSffKmx8bycAaCpy9thHXjlgiGKp1F2RmhAvz92HQWCT7BTo7Eost6phii57LZEYb2nAPJUM99JsLDjtrCVx9GZu1jx1TZI8ySY+qHQVJ12nvft+L8XurLwMAZHsc3nfUSnpomh7CjpHr6Bnlm0kEgRF9yt7AiclFYTdmaRcknG9aclBA91KYhENWrmzAaJGcbMeF1wcxXrujebAP0zHopggBkSwItDl9ksbmTYRWHN+xiXQSDTCl8zZiepla3PQRdpaAE2bLXUbkmJsoLyVoxgxslTommTvvKQfeBmgnajIByxi6zkut6XNMz0StqN79Miw0bQNf1QOqEYfYifIIbQsSovUvA7OXdo+tIKoN8vZBk2UtfL2PaUVrErKe1hwDKXCyNgg0+Jyh2BAhoxf6KovXRPRxoFqhwA+gIDKdWnBlg9+f6lPJ5WtecC8A4GP33oj1KY0tHyTNTiZYuU+Btxav+bEOzFoNzGi89B/kGFwwOLyFY/Jkl7G5xpHvAWsfyqI5aQeY3aQhp44Of0jg62vPiXRqXtN44pqj49ltPHphWU3XvjWa4uqJPjIHihUVjU1a3GnB6e40EKWGzltZy7kCVxb1imdAxPtZrvl7wyAqWjDnJyjD1dmpIBsd7DGspJJyMq6xMvWA6BzltlgKSERlCKwv4rNqBYfuyJCV0l2BZNKQbYdjt9mEAidvWWIkBaNNn4cgMzuwkKWEyhEyOcaDqm3sF4DAv57FCAYkexK9SwjyDtkh+dppN17yfRJMLLYTqCxunqj8jSBqOz+ZQ2XxObIdBzouTQhQOtdsYDp6kUtRWdSKgysbNL78XBX6StsIbg5sCleuVxaydCK2DOhcqyM4vjEweQLdEUFMMpmTIKXJYpZ0dG8NrtKw8fTCnJbFYK7p85AF8/dUue84P+ZLh8DggnUs03CbISrbYv4CtkfEBC/HIssEclqDaROy47gaS80ezO2ZnvE6BUz/oSnDL08j4PaRBMAT2Y6CpOu02UZdwQABAABJREFUzhVAHFDqVbRKzk2PHpp0BrSeQYgaqB0bTC5oUkqmNjJbXM3fMh4mLCsYRGkgD2lWY7OCtJAGeSsbRbup7JCh2KKXimMGdckgSl+2AzARRP9tYVq4BrI9gb0R5Y3zBZWNChHxL8RgQVjUdUrUcSviRESlKYehca7eJrWOvhy1Y8Cp9u/LKcmEVKQhgGTmFMQlIwaMz1JpOne77GRSYk6ZDMG3SZaWslAdj4kyDlOAEHTCadzQ94+B6FJOiLkyy0MzRbG7w72xlPygvyvaHesOD+KAXBG2xE/c/trbvnCidIFTJ6oXd3dI6mFymodJWuWA7hpMGuoYvkfihHUaGW9GwvnLRR+x0VmFYjsNvnByQf3RvQJ0r9JB5RrtivNrMWNoEhobfrzwCuhdWMaA5XsW5TpDucbAtMv4cRrrqhP7ZlGn0D0DVbqSi6LAru4D5arDx6ykyMYOU+I+o7dD2D1Pra/7lCHJxhblmi//0qJqEoaZK+kwmyETjrkJwvKJSgdBSADIJhr5jnRSC+64wkthu7GhNVhpYJIMzl8YOmHguQBTLf0mQQrcOomdI2rnZO8mAdUhjalkFllj3NI4aFqZ3M418lKc3uIxLQydHeoznyUyCbA4YTG8z5X3rjSYn0yDPx9dOz3rzCBolemULbHBmCGjZyMQA7UGmJxh6F2xqH0Gu2PJGLbLA7ORN0D/Mkd+merrNhHUX6qlCcQYsd2sReKYfzpnFDi5e8EaDWYMdCbCs6tyhqZH82Ey9PeUxoUfL9pJbnANVA7rVq1ZpAeMMkNug+e9Nb0EQTKhkh9HHGdNh97DtI3BVU4bOu7m9mrA0U84VE+G6xQNBbrVWsSRJjOaY3w/qQ4gr+IJaQYM+jF7tx21R9OOgqTrNFEDyAFP1Q74nNxplLSpsswvlDQTlevS7bIE5DyqcBvJAWYhyggOFoUC36NtpR1PwMwa9FYvTOTCpcwJJBprTbwVENWrFrxiSFQsdfWualJMlhz6As0g6ZQm49Uv+NKJQTUSaHrRcJbe4zJR7js3Q0D1CKCZjGMKW6ctBWCXFs8PY2nGCwm2weMeNO0XfmZoV23S+H1UxwVkVQxami5D02UoHKhSLsjawYqWdtIjzB/MLJfyCOf0kOMNgsVJGwPVjqP8ZzRdHk19C/r+bdG9ximdB6NOQwEkswgYIa6cFlYWz5+Ogc69HOd2bgAA5K6PvCAgQOOwGUjwKgnfIT2oMTwrsdimL1OPqNyX78cAXVQMOqcMoseUJTNfgnDHlC3QeAvkKwvKgvmgpX/RwKSkPMymNIXslyPIkqNZcdkmyYg0IIHavWacvpMVCKDlZiDBdAT/+89t+ixKPziAePeaxsQFO4c3c6SbGVbub5buTXm8A+mC8WSi0L8oUK2wsKAlc4GkbmGZOAeUgZzVIbsVtJG0CSuKlQL1KILCaSzSOPJlGJ0xlJskuOrLXapLmyo1pBN1L3DwxhKG7YovWdH3U51WoC0Y8h0ypqV+cvijflzUE3dsOjPBJoeCRh5EN5kh3JdJEMr3zRAoNyzyvaiH1fQZsgN6poPoJHc/+3SrcCk5rcNrVnKH67JBAb5aS1CPUuTXXDpLA7CWHAJSp6I+ct9hHrOkXFFmVVbxOo2bX6pV18dDBX4tWRK9JJX/+GwNz2mkhw14pYOEQzZOkMw1eGNQbjjNsYRKuL6vZq+coXutg3SqAvmlHHG3wYtzEwWlgHEYJqbYUeTxVdyOgqSjdtSO2lE7akftL0GzONJJeqLbUZB0nWYF+Yzle6CsgC+7NC4FvBZxPMnMeX053AavLQ6fxCG3ONY/50GpCkKZYGILkAoxv7QLc0iZJN7t4uptJ1ENgeE5D6DUaHqET/AUay9S59P6vtyjM6KtAuTXZoQg0KF7jTcWnV0Vyw7GOnYUW9p9WkHn91mHag3YeOoudu/aQMfJHXR2LXTKkB84zFVhCFjZwsuYTIBpomezUOKwSyagltGO28jYnyaNlgtBhNLRzvvnXZ/PLbimTJJPoYdS2UPMctsilMGWpJUpWvo5UJAj+NSf03LK7HiRRmYI1KwzFlL05bpFOmbBZJQr+j4eBwIA5RoH0w/JRvJlLFPTp3vZ3dGhr4xwRricBb2Tai0lmYE69l25TqWXoCwtnExBErOD3UMSY6wdc8ikrsJr4zHVilMJZ5FJObrPoE4Jt5QeLuMbXKWQ2Fk19St3mYFkHMeVv1/FFpAeuHIxiIGlOpTJ9LgarigLIuqWaXGKJazR7HSGaoXuz+CCK8fuN8j3NYwUoRwbxoAfL4YBiWc3maX/6Z54xpaGXPBQtqt7DDZxEhVuXKdTUtyuh/F5y3cp8+PLnr4MKUsbyBsqY6G05JllvDbo7EWvv6ZHGZuHiqwStseiGdB1LTbFEoNLFnTPSXzW3aM+0DvP0Nk3MO46J07NXztQOfVDzIxT37GQTYo+iA63pC3aqvf1gIPXzsZpzwR8U/u5zA5ICdyX10hI1QZ8opf2YAZB6NZUnEpkLTB8MrcoEhbGS/fcFDbxdisOpN3lEKUB47EcKRrCg/oKwQ3r+yjYSYiFQpJGMVVmWbA0ASjL15mqYH5sMoH9k09M4PG4YJIe4/v/qrWjIOk6zTPBCKSJYMLKC5r8kwmgXbo6ndDkv/d0WuXTKb02u8HCJPQU9i9KdK804G1bEGuBXhdcOFXnpx7H3tc0YIlFsqD3dXYadOYNVFdivu1SxY4Jpbqu5DKhmrvOEUpBY54Si0sBg/M0uyaOnRIMSBMGWWr0LgOVA88uTlBEIecItG9RM+yNexAVawF/yfTRTzqypEWDjvGLi3GpeRvUrfP9hsoADotQDemcllPZEKAUtpzTRBhZdxbJLFo1yNIAjPBffjHTCZbKeoALblq/L5XbEF8DfMnNXYNd/husdf54QOZLjooAxeWISi3+fUxHT7SksNAJve6pzPUKleqsQAi+0ymx5bgDQzddZ6h7SmJ4vmV62xVID3SLYUQ6V8F5vopjV7esWPz1hvPkDLKM9h9NnzA1xTbh3+j+EVZK9YD6OB1YrkqoLgHvVccxmOZuDEw9noRkLHjTYmC2bFK86nE9pHHsx6csNSY3ZoCN5TZmaEHTabTJYZrKU5Mbnf/ZgALt7hUyiQYAnQuI0iAbRw/Ccl1CFhq8aqnOWxdou2eQ1QrgPIDBAYBXCmLegK84CrvlsC6Y9OUpH2irLlCP3BidM3R2I6bRj3MjEADYOiMFbDIW9uVKgDXx+5qEQVYWmLCgu8Y0kI010rFC6QyXq1UqrQbgvaJ+E5WNG7o5BXTJVAVMl3+u9DBuONKJhShNDIgkg4WA0PEZCQw9awOoXeUMKmeo1qivst2HkB/gcGYLg2xiwBunHbbFUa7F+SXfi5jJiAeisqPXqAKoLwmrR8dYIcAXNdRqN2DdTAKwkYBlggJBwPVJHGf3f+w0Ttak+l2NvBRDADhGrB5z+ER3H/SAP0z9/qh99bSjIOk6jSuLzg7tvDw+BoiLdjKP7AnVoQc1ezHN/pN5Bv6FHkTBwsI0uYFjsZWhf9EEzzXLE5hUwkri1V98XY2PvuTX8FOXX4kPnX0Ove+mlITeRJwIeE0ZrcH9bqfZd15Le1ii9fKaXLj95GskA28MuNeJ4QxgDKJQGDj37WYgUA8tdM5gZ35XDmQf6ZJ7eSdeA9cR6yAqDlERQNOzchJFO0zTlRBFNDrtXNFIHZ5F5RmUo0on07gj84BdzzRRuQN9dn0QFHFUS7R/hw9bDooecnPZI7zOEDAzAGBY3MnSC9aZscbFyzK/2wTkzC8m9M9n/fIDSyBzwTA943a4XSB34OewA94xyA4VeCNdfzLUQwo0apcp0ClN7lZmYWfbdB3jp00zF0DdjbiNZOYwZEnM1oEBxVoEjjMNnPz2B/H/Of0B/IP3kibYyt0CydzhqnK6rtlphu5li3QWKd31yEKtaOSX6ZhkSjo1bYd6WXlH9ag5k47p59ovShUnqYteFKaEY3CJJu7mVR+wRexjrh1OpEUySqYNBbYt0UlwRljB4EPHYLgLjrx/XJZADRKUazJkctKpQbEWda4sA6Y30JzgxxCzNA6YBUzXsQq7ADMCXe/Z57JoorGBkKC6DqCsbNgAAASADj8ndK2itkETKZkowk05Kw0gWgf5wE3l3konmkN3d0hWQ5Q6BDIABQQWWMKHMW1g0qgjxOxyxogZAysFdCYwvpEmnnINROB40PVVImAFh0l40GoikVhGUhMBNweYFkBaFhTEBNYsEEx4+5d1CIarUQJRxvEyvrWP/vkS1Xoa+ti4rJ8sbJgXdMbQ2dXQqSfbMIiFhuqlwSbKa4FVvRgQ5vtOzNdLMSw0hHzoBPPlaQYE3n4s7Uhx+9G1oyDpOk2UFsZQMGBFDD443K7dxkVIlDT/Tr6wCgAwuUGndu915zMZMHlqhfnpFP1zRHPK9wmsePkb6KizX/8bAPr4jVMfxs03Pc29M6XMUQtYmx0AvSskMAkAxZZEPXCUdeOB1VRKk6UJAGpm3G7VT+yuhGU5QzZ2on1jAZMwpFOERaHpsZDm9qyS7jXtJj23G526icNaYrX5xh0DztP8Cx0CjtDXDqCtPJtOUn+rTiyxiIYCkpK0ENG/4L6DjjtwZuKOb0mFGw/JHj3CnGYZfRXdooczE93NOeNoBGnj+Gtv+lQClcVydsTyuGCXa5xKRTz2XTqmbI9JY7qfa1qE/OKYTgzyAyq1+JIEb2jx8N5c1FcU2PlzZ2ODxRZHOolAYF+K5Qqw7tbojAC01XF6Ib8gcf5ghPcNnoF0i5DH6lwf2QGVVn1GYXFSIzsgjSVfVrKSwXKBgVsYdUoK8LyJgnzePb27q0OfFhsC9YBhdsILA9JYKNcQaPSJW/iTuQFzZnHcBSfMCwjmtHh2duNYh7GxhGZ8CiqWxwAqp/nFzkin+r2REgh7NR6nMoFiM2bMpjdaZDdOoT83DNfgx1S+C4hChOtKx3Ge0M7XzXIe5hNYCpp8xgygDCwzPFDmRQ1YTtkgHzDwmiOZatSjJAT2nV2Duh+JBUwhbBwCQ5cBXqXdCz9SEOzYl54R1iXj6WTiwPHKhGfIZ27BKGs8vSHD/rOdfMEuRzaOGd3Z6S6ysYKcK/Qv0lgrNiXAXKnRZ9RyGi8+Q5RODKoRXyr71ytOLfxQQTivvcwAA56ELC0A1KspinUR4Aht82B/L/JDjWTSYGUeGZHJpEK1nkdWr3BBI4uZ2nRuIGZNAIUzY1GvPDElLPu4KG4/+vd88IMfxC/90i/hjjvuwOXLl/Gud70L3/Vd3xX+/kM/9EN4y1vesvSeF73oRfjIRz4Sfq+qCj/xEz+Bd7zjHSiKAt/8zd+MX/3VX8WpU6fCMQcHB/ixH/sx/P7v/z4A4Du/8zvxK7/yKxiNRo/+oh+ndhQkXadVI47iJE2++X7cYekugOIhWQhLi/jwrC/DiKC07BdLOQcGn85gJDAnM2wKAgTD2g1Uk9jRM2wJerKzAT3VySxFMnUTpXs+8gODdKKhO56ua8Frt6P0mjALWgiLDRF0VNKxQjJTLbVmsmlgDEHPoHclQTpl6F9sQvBRbGSoh7QA+EnMp/29aq+oDDSjEgVXThOp53ZpRZyILAcg4gKgMxcUOSYh4EpVzGU+vFFs45iFfpfsVHVF/fBMEWkqPSTL1PpbGw8VrgnL9HcyvbUR6MjgbDVsSMcX6yws5tFYlRZxjwEhOQDaEfsgVznsUTKJ4pyTMwLdazzibhJGmCtGJS/qGMowdfZ0CNRUV5Abvct6VG7C7l2ObvfMlYbIssLJAmxQcGd36AukE6C8cwXvmTwbqOgY6ZhoxTag19zJhMFiWyA9jBmh/nlnt+N0fop1QYteHYNV1XXBYxp1mLwcQMCUCSrtyUXcEPCGmEiLzbjoeTkKFuMhwi4NOBbHqbOyA1pImbaAx2ZJTgrJHh9nDHSWolqR4RlpZ1L8dSYLgwLLWbfq3ABZFTOBxNQCqkHMMKdTEMbL3b9q5DYErSA6mVOgWw1jQC5Ku4TP8firtCUpojocohbQGQ/3XjupDo/x0in1X/v5YBrQKUexnQfRUMAFBJ3IVu3uGiQztUz5BwBtwax/gDiagUS1wpBfo2voXbLID0y479UKR3YIsFoHvKQvg8nKho1YIbmz9PGnpjHtS5SAw91Z0rLy2Md6RaIccSpHAg7TRkGoZ++JGhj9bxdw7hMn0XGswvGTJI59xAYIAhiDlRyi1Ohf9gK5nNi+abR+aToM9XqOZEw3WfUlmGkJtH0Z2+OBSbJfwvvn8zme85zn4DWveQ2+53u+5xGPedWrXoU3v/nN4fc0TZf+/g//4T/Ee97zHrzzne/E+vo6fvzHfxzf8R3fgTvuuAPClbq/7/u+DxcuXMB73/teAMDrXvc6/MAP/ADe8573POprfrzaUZB01I7aUTtqR+2oHbUv2m6//Xbcfvvt1z0myzIcO3bsEf82Ho/xm7/5m3jrW9+Kb/mWbwEAvO1tb8Pp06fxvve9D7fddhvuuusuvPe978VHPvIRvOhFLwIAvOlNb8JLXvIS3H333bj11lsf3y/1v9iOgqTrNQb0LrqyT07ikYADcyfLGj5wO8Og+GwJT6MTFna/ukPp73bpIjugv80+QdShV+r/HZ/82ncCAKoxbT9Zj3ZXzSCW/MpNDmYkrryUfn/Ssy7gwQ+dQf8CCfIBQO+yBma0Aw9Gj2kCnXMk86hj1MbYAEDnaoWuofS/6jrw5YEl/EiLjcUUscv8blv1SIBPVAbGZYmsZATetpbqkXBpeM6Cp1fvisHBrRzVhgGrfb9QlshKgPnPdIBXX+Lx+A6dsrAb5QKxnBgxl2iX4b2oXNu01ZesYLHUF/RBCNdN77GYHXd4oAH9XXUpUwiQWGD3WivT0+HE/KsN8n0eXqv7tEO17n3pxIbyKUDfQxbUdz4jVw98hsMEW4RylbBZAcPjy8KtDILlQGdXwTKgWI8eeuk4jkEAyPYAptNWv1A5MJmyUP/SXbKYETUZrAKUVZyeEmj6IlyndDiogKNr6F6T35gv17jPaam9e+Zm+z5Uq6T95LN2sIDm0R6IKafALmKmrO4nABL0rlD2FKCxIKxdEhqtBxLFZksYCghaRv55bnqkS+av049PXzIFYkne8vg+3tBnjm/15T6L7gVB2RGPfeOUYcoOIw7JFNz1v7tObQHBUA15yO529i1UR6BY5yFjUm7QPezs+bId9ZnKY382PRZA8B4DlR+Shpudx7JxOjXgZRMxSL7EJlio2diEyvDt+6Vy5jwPXUm6S6axybQO80k9JDC5VSzgCpl7Zr2vZdNlIcvctkLq7BqonEM68knjyot24rODVDJverFkiwo494mTGNwfsVn1KmVhfZZbTiqYVBIW9SJN9mkvRT1KoXKOyQ3u2d1k0HmCzm4cM+n4iQFuWzx2Cv9jxTR9sfaBD3wAW1tbGI1GePnLX45/8S/+Bba2SP34jjvuQNM0eOUrXxmOP3HiBJ75zGfiwx/+MG677Tb8yZ/8CVZWVkKABAAvfvGLsbKygg9/+MNHQdJfxDZ+fo3+udwJ+iHI0BM+wE0M7liSz19Oa2s3gQQfJcfIaXoMwk/uhmjQ2rGEJlcG+Gv33IbP3Hsa/btoRla9CJb2vl7p0yc49k3X4OP2Wougiu0FCqsVgc41hWTOwqKUzImmX634dLeAnClYbh9WsjIJD55y3atOYM8SnRYARONYep6Rw9yks1ABF8UsYW14bQL7BXAxjEvj9y4U4E2Oco0HcLZOQZR/3sLszAmf4sttorZU6mMx4GKGUXmOIZQgvC/VsuebR24/ZMJoB1ReXDJgm6j0VQ9FEI5UPYtkxqBzC+Ym6f7FBtl+BStcySoTpAatY5knlQxpV6Jak0gdWL17uYYoFbRbSFRXgDcGaW1CGWbhsGfjm9KIaWEUKPrxmY1J4FDnLAKBrcOjPeS7trF2HjOl8xg8llv05Uef58h3/b0R5GyvECwsqiFHsQWop1Ctht/fRWeXFspyFO9pOeJI5wbda7QwqZyhWmkZinYoYJBFxE4Rbd4DieO10kLsvopyQoiyFQwM6Dkr1yRmJ+lZ6l9oSNrBqUHbVKAeEq4snEsDMLRAh/KaovKptxKhDQT97DcOycw6uwxSswaobGYFMHmKO3ZBpaf5SUDnLjjQDMkU6OzpUPKrBwKyjL6FRiKMy3QWH9TFpkA9jM9IfbwBrxIMLviSOLDYjHhCwClaWwLS+12eFTGgDaXdh4JXjCEGoOQwbg5oVlJH3UcoA3JNYGZP3tAZiX823R76F6izREVyAExb1G5uanrL8ASexkDSCzlKx9xkxkZG3wojbN2aC/hc2S+ZR0yekcDKvSx47gHA4AF6r5w7TOWuAtcWpptA9/1DQZs+umYXJPWojxabES/mBXy/3M2APW7A7clksvR6lmXIsuyR3vLntttvvx1/42/8Ddxwww24//778dM//dP4pm/6Jtxxxx3IsgxXrlxBmqZYXV1det/29jauXCE9lStXroSgqt22trbCMV+JdhQkXae952W/ir/2sdejc09KNgxu8hAVPRRtjyYGepCDazd7hF1lDWSVU192hynnh8Yat3juSHxudhPSeXSxzvYjZsezv/i9K/jk0wf4mueTz9fTR5fxBXsTBudqdPbcDqtH9idezde/Jhc61O/LVYFUOPqvZ6TNFHihYRIW/Ip447IvyiLbjyrHRnJw6xcEAuSKSpPZKACxILsID3IEiN3mAaMAAGvR2algRR6CJHDaYcO2dtypC1bdfVhsMvQ0p8Cv8ho3DCyJEgSAy/y1EgXe/gCSLeGVHmpLwhiB0P1OOnjHtUCgqk+v984z5Acxc/T/Z+/PwzbL6vJQ+F7Dnp7xnd8au3oAmmYIggZoxAiOEIVjYg4KOUoSg1c8nosk6pcjBi6IR02+5EvUD485XJ4vUQJxyhWP49GIgkaZxAYEuumu7q7u6qp6q975Gfe41vr++K1hP29VF7R0Nwd813X11VVP7Wc/a6+991q/df/u331HEQcvLFrnODeGBS4YAFFppHuNJxo7rytXDcYr43VoHBLSVQbRXGK2GdADXgEmCorfsqAFXkVA56ol4x8UMJJDx8LLCeiIPPP8s9EnFWaRB7kLHVluRxIWgqZDXRQlfIDsZA/UHk2y6SGdN5q2qu4YaS1NbuPobNFH8dgskGr9YnvkM15TpZjbJMi5DbwcamZ5PnU/LKj1gGQWkoOAlLqS93hkg4+YE9dnTMrVAHFo6j5xsdz5u1ewoMYuagOlqajAcZKo2IDeW7+hYlT8sHRvIHI3GV1H2/IHLPCSACAZA3JP+QW66ZClR2dX+aKI+YnIK5OXVnKAH0riI5nQJxdguUfdBZiGh2dGRYykAcbwatNy3tAmoo0kCYZ6EGN20m7gUuLaMRPGQc6JFK4StzEjJLDuMQj7rKf7GrOTApVkXn07P0m/67S3mC2OIRTUjmdhq24bCswBeImAfJOOKdYNupcYOtsakzMO0QbqDDD9UG3cu0y8KVk4VwABaNLEqlMnHcDQdATqHg9cz5KCeGekDIanj7htngwxSeIYDYfDhc/f/va34x3veMdf6pzf8R3f4f/8vOc9D1/1VV+Fc+fO4bd/+7fxt//2337c7xljgkk1sPDnxzvm6W7HQdJN2khHwFTQy2/CZN/Z0YinGtOT0pvJwgCiae103aLMwoRpWrvc4OcG1D0DZZ3Zky0JURBE7iBm9/tgYVKDBrIrHH/G7wAA8BcaqI7G9HTkF+JyiXbR6Z5BchgWM51wzNeog1y5XTH3AZFbQIDgt+arx1QQhKuWIsQHVSinlgy80QuCcbwOlWzucyMZWGtHB8bAlEY8amAYDRZvDOabVFHkUbQpVUf5MU5okuYN82RTCqqsn1vLZJRSZSHYccHXAlFbhfsCXJ+mc+OQHCq/qxwpSsN0djVqW3VUDQRUkqKz7baxwPxkDF4ZpNa6gdWUfuOV9oGGGxt3/0izScOIll6PMohHDWAkpqddSsbt3u05OKUseN3qf0vwTxRuEaSFxVUOJSOy2sj2jH9+m5R5NE/YIDCeMI/Mub5mOzXqLEayT19sB6ailYk4antSDSiAYa1AqhraKieb3nYIqSgW03KsaaFgAmA2tcXsPY3G4Tfde1f1GLQUMBF9UUXMBmHGB+h1FsryHeI7O20FQl1lYEroj25JKvCG+f7uP5c+S3c42dbY88QT8qSLJiENXw0N5IxSae5Z717T4I1GY0URDaONjJwTIgnACphS4OXsipIDGrv5RggOREn3wL03TWrvHW+jsjRG2Z5Gulvb6yHvOtYWlBQck7ORF56l98ym9GzQXvUZZifjkAKvDeJD5gs+APoO07bM3+l9Pcop+rbPbLlCkiZyFpAypoHdF9D73L9An2U7GvFEo7hiteZWyRZGJcF2ReY0BtqE9yS7VtIGzm6o8tM9xKMaYl77isemI1AOOQmAzt1YGch5QN7rLtklfSm1breLra2thc/+sijSjdrJkydx7tw5nD9/HgBw4sQJVFWFg4ODBTRpe3sbL3vZy/wx165db4K3s7ODzc3NJ61vT7QdB0k3aX8tTvCWr/9N/J//7//Bp3AAV0mmke0qMCv8RzokLUVZ0/qPtz4SdtfttXdAUHjPrSTSp/Y8+pEC1bImcTk7EYuKFoloRAedP1iDHjaYn4gXlJcNA9L9xnup6ZiDNQapE4jjJEbIa1I0BoC6R4J7hjEvmMaUBjgDL5XXy1ExsxoqdicmOKBIWTugIsRFYo0GbwUD7WYkg+YcolQe+QBjqLspihWG+iRdEH8sJg8lh7Ad0jU2KUNkd63aluiriAUPKmtO2g5QmaGxb3NcjqZLAYvyuQCloViDNwaR5Q5lu6RlND7LfZqTVyTuqSx6ZjgtzvkGw/Bhmu27V0romMbT83JigWop8qiYqF0K0fhjlHTyDAodm1bIVxfTKc4smKq9hB2XDqJpTWrnLgizz6dbrJN94qOI2ngTzeSQ0kzVIHBFsl2D+TqDGgKG0wD2rlAqxPvb9ShQbTpWmwZUdaRiOp97RpmiCifXdxW7sWst6pmVL6gJFfL3RQPRi4lQNZsniO7rkExGS/bABScejbTcs+kpq+c0I22iYoWH981ubEQZqsSqZXrfHB8ue1iDNwbT08L70JkSnrfkq7FstZYr7XdcRpHjSBUjiY66VFrd5WAqbHgmZwQhP1mEzo6rwiOlbi2CcKnMA1IEEMJGHnNAPHfoMaNAmAXhRl5TaXs011BWVVwUDVitfLk/0xpMacgiBNYqss9ZBm8wWxmAaQ5mLF9NEX8qnmhUA4tAdUiINl8PEh/xaJHXqRLnYhA2Sipi6D7rAC89+Sg+uPUVAIB0r0E8qrzCt+ERbfxS5jeVwlYhxpVBdytE7SqTqKyI7vSkAD8hEc0SP+6NDURdoE7jQEGykxxoUsAUeFrak6m4PRgMnowu3bDt7e3hsccew8mTVMb9lV/5lYiiCL//+7+P173udQCAra0tfPrTn8a//tf/GgBw9913YzQa4aMf/She/OIXAwA+8pGPYDQa+UDqi9GOg6TP0d403MIvXarRdHggm8IiCgc1apu7btJQqh6OaSFKWJy8woeUQ9eHNAvIGQC+aE9hGMAqCmLcBOJK311KYnTvClhic/L79P9kbCz8H1Z+XpNFiNN7YY0hWJzBcxB0ylH1JZKD2u9ajeBWSDHoyiSHDSFHwskQKG/dsWCKaX/HcZBUJuk7xvEmSOeo7eCuU4FoTkJ2nSVa6YqDCPGIOf43mthNogxNEu6N4+csWJPYINbdB2dIHG5WWNjaZGcgBMeKGyvGGUipyaGGjgTmJwAd2fHfpe9IV1rMGXoG0FJ64c24J0kpu9EIjvQaIleeu1X0IjQZQ7bbID60UYUBAAE5q9G1JdAyj6n0fd2tNnQNTQrMTljEsBbI9jjS/dqnVRurS+OquctloLMNW3ptTzXRqBWHavFDmoyBa6COAnow3xDgDfwClK9HnvvSTtmqiH43sc9tMtFW4iAEotmOQdlSfq57Fq2JWkiUBPJTNb7l9MMAgJ859VF8161fiw995NnglvSc7QDygJ4tt+jxhu6hS53HY+O5Tu6Rdb/nrHEAYOmzWNDZcjY0Ogr6VLymxTwew4tsOkNhZh82Lez5k0D052O6LlEapHuV/V4EFfMFp/l6YI1/bWBqBDya5YI5t5gH013bFwOM7nDPGWAYW5AhqPqUMupcowDcPY+sagDhmPcAa8hMt+pG/n5Fc2MDUxtE75kFjlI00zSXNBrlSigLZ4aCb48CW7TM6UCJgvh1UW78M6QloH9rGe979hISew+dirpPSW8RWl71OJL9kPJLRhrpQe1FbXXEUS5FKFYst6ikZ6NYbsPLpBLPVNucmp5jFYW/ixZP7KlsTwZx2/wlvj+dTvHggw/6v1+4cAGf+MQnsLKygpWVFbzjHe/At3/7t+PkyZN45JFH8CM/8iNYW1vD3/pbfwsAMBwO8T3f8z34wR/8QayurmJlZQU/9EM/hOc///m+2u2uu+7Cq171KrzpTW/Cu971LgAkAfCt3/qtXzTSNnAcJB2343bcjttxO27H7SbtYx/7GF75ylf6v//AD/wAAOCNb3wj/v2///f41Kc+hXe/+904PDzEyZMn8cpXvhK//Mu/jH6/77/zkz/5k5BS4nWve50Xk/z5n/95r5EEAO9973vx5je/2VfBvfa1r8XP/MzPPE1XeeN2HCR9Hm3rZTFOfyAHc9suh/BIhngcSEh1L0ToWgTEwvNCmOXBoAXdWruTBcsT+59L3+iUiKpHxSt1RGRbgHZl6Q6HKOH7QX5UgIoidHapn6JQVAnkVImdXYE23jJAFgrVMIJKBeSsBX0ZqgbzBraMoHKuHWnawKTc5hVtiqUb2bSSRjQNHCRCK2zawPafy+BLpROByWmBckWDP0iwcDKy1gNu/O27JechbSZKA65ol+yRI3sPQprJfX4E+QOltfxGy6bl/LBzi1AJBljiuyg10n1SOa6W3LjD85MAIqqn8xrRNEK+TjvpYkVA5hzZjka5FPlzybnCfDlUAxhu75Fy90FY3hIHt8hbdq1EPBbgtfUx63GozBKZHZm7BGYnBSZnRSB818DwYe3lIEa3RX6H3L1i77Fg0JJZMVWLjgwpreP8CgFCnwwzRPYFMLjQQMUZqmFAP0UJRDmgNDxxX841ZK69uGDd5dARpWgqmw1QGYhQWwe0z3AgXS6wU4RJ+AX9S8BLgKl12f3Mn95BPCIDzxmRBUPVg/dc3H8uR+cKkasDF4yuj9do2Q6RzERbnNPYa2oXBZANUEhpVsvw9kAAoG0VHVMBpXLomKiCp6OoDIpN3uI7AdlV4jS5qk1eAzyie+yEDpsspBhpoOg+kbcdnTuaEYdIxYHszBQQHxKilx7aviaS0tS1y38BhpHIpSvb58pKBVQa3W2LAM0VdMxDFWxOKuhMGy/iqiK6D9l2EKc1toDEjUc5JImMMgqVm+1qV/dZscxheOpFbdPdEmxiEI04krFFvCoNkTcwkmN2iiAoHTOPBtEgEOdPpcFIWBaUh4/mBtHUhN9rGXSzFhf1qW7mSahu+8t8/xWveAXMUb/LVvu93/u9z3mONE3xzne+E+985zsf95iVlRW85z3vecL9eyrbcZD0ebR7/9G/xzOW/hFu+T1bBq8M6p7EbFMgmjsOgU0PtFI1PtXmAiKEBdunlTVNxG13bx1RIUm72qrp0mTsgikjASXDROGOK5ewEJTpiEih8w06cHYHYLjAyfdTB3qXSohCocmEt+NgiqxSilUJtkwXke3Uto8mEI2ZAURI5xlJlg864b6KRRQkZmQEQ7VMs5ucNRCl8kRXlQqq7BkmQb2aM0zPGZhhjeihxJ6LSL1ewdwS2o0I4yesAS3LiJfkxmrB1Ja5RT2MnecosZCmY9rA6Bb32XFIBIDEpW8YoinxgxrHUbD8jNpyHZLdkjhZhUJyaPV6uCTJgyzxFU0yF0jGAlWP7k33WoN0j/SQIuufpSOOciigRah0FKWCyBU6tko27gqMbpXQrVJ+FQG6B5TLgd/UfYz67qp7ulcpvSNq+Koq3mjygesLLydAiurkn+U8rsolhnRf+4o+CIb+pRpTHfmgve7R2HWvGfQuW55ZSXpSXcu/mp5JUHQoPeQCC14Tt8ZtMgAbZNzTx0dvowXvzsvfjU5WopdUYPbF0WdzFPsZ8pOhdF9Pwv1xrVglHlS7mq7pWjsRr/7sHhQ7njEt9KwBjOO7NvQu1z3mn5loYoMktwGZWbd6hpZyvDXEPqj8s5evCBzcFd73wUMG/Us1eK0xO+lMrmmzke6FuYIrOrcLCpm2/C0FDM/TZ/NTQLFsNyb2/HJOx1Z9oChsBeBeaQNHR+iymyEVgiSAeF7pdhBQY9qgQQSWOvI43U/W6AVZAa6IuJ3t0MtrIlKbd9zI+aZAuerI8Y4badPVoyCjoiMia7u0PzMJYAw6l2bgLV6fSgTG5xKMb7fv2wwYPErpQwCYnI2Jw9TijDJF7gbMwKfh6i5Vf7oCF7Z4WU9pM+bJSLc9SZ35K9KOg6SbtBf8X/8AF77rFwAAD37n/4FnT78PAHDm/SV4Qzuqg2fbF25Ok6p/fh1o0n4gLe+h/ZErKXcl+UwDNRiRru0xorQvYrbIo+ENwGbMHwNGAYG2ZouiYIC2aJJDD3o1Xv+8j+E/519N/X5/DCPIO8tVCjEbLOgIiGypdTQVEPMGTS/CfIMW/2ynhii198ViyoAzDQ0eduXK+FJilbjJ1l6rncAEACZ5IFSDbAs6WwzmauJ3bE2GBRNT52fXLrmmUnQDXjO4EWSGeWSv3RasGnQIAMJxDFoGwrcjOtNxrk8MEhzRVCM5sEKKQ5pAZ5vUqXIgkIwU5Ex5Qnu638AIqhJyDvVGAvOT3F9L05FIDo0NwNMwnrmGnAd39moYQc6aVomyhswXyaa8ASabFDC3bVS4Mh5VTPcBpiMkh3XL84ysbIgI67gbdA4tmN+91x1Bwpdw48MgJzW6W0Ddpx/MV4n7Fs2Cbx9rDHTMPToZjyWqvoQztQUs+pQA8xOAsrw7XjPEIyDas+gZA5prGcy5Eb73mX8CAHivfjFGUQadavAdR0a2KK8dA25NhutesA5xVY/OPxCAlQAJCEM0N4FT56xRIhaOb/mEFasAeyURWib3rCO7aoVoo3DubE9ZwVjqGDOEHlfLjsRvEVwWxlxHwiJe4RklZIsF3aQB9cEFQQBV/WmJBR9GR4TnKkgh8NJx6lpyGhEVDjiErVjmOLgTWEo7WPrsxD5XDOWyRLEcvM1EGSHerT3SGOUSKrbEeRtYawPMTkSoBm7zZMnbnfBc8ZICJTkLiFndo+vIdqyVSM8G9MpAzKijqhsj30iw/1zACIuoTcg3rmwht3KOxQna3ldRGb8hdpWC6aHT+uLQT1PkYfAkSAAcG9w+oXYcJN2k3f5fcjzzljfi/NdSoPT/+5/+dwDADz3wPyMeK1QDhrv+xkMAgE89dgrNw5nfscq8tYtzAEYTFtq2YzsMgmdTaWAYRxUFwrCvlLGBEGArd1ppJiBA7l6l1wRo2nkm4XyGX7rycvS26JjxrfQ906qmS/dIsE7mQUgNjKqviIhrfy8jtVqnoitKBV4psDoobuuYwzAGOW8gZ85ACwtu4izXYBEHU9yn21TKka8J0v+xQYQLhrxWTQ6v/NweK4eEeX0qQX1246JbQYJvjIHBuZwzfy4jgsaOU/J25sbuHM7wNtu1C1rFke2pFvFYWNJ+CEh4qdDRBjAR+FIof9QSqE7QyXPBUK4wW91Dx8QToLfVkLO9RR3mJ1P6sw30nN+VE2EEgPTAoHeRIT00C8+anNQeYWu6pDbMGu134DAGQmmIvEa1TFHE5BabEqsYqboD6G0ZgDPUS9YJflWSanUZnqHuVY34kNAQF+DpRNAzYlGAuicgc9IQcu+INrQw9i4C01vcAmpQ9xgGD9tjLkaQucFu3MdDZ0mQbinNsdMHOpeCV5wrdtD2fXAopI7Dc0bvIwWZbiGWuUWg3LvY0LNBxH17n3kYdx+c1kQkrpTwfwcDwNHqEz2v1XLstX+YBvqPGIytnkGxBMSrCZL9akEgtLOtIArdUjpnC8hqd8ug6pGWkUOpREnPMG+9N9JuNjo7CtlVp3TLANNKQ4KjXEkwPRVyVI2tnCtWGMo1gud4paES5tEzlTDkaxJyGsGx47WwROqd0lfQmohDFsZXUbpgleccOnNFHwasBooNoLPVCqZSoHfFoppDjs6OQTNMoJ0qd1eg6TAMLoT3ubQVm6Kk82Q7CvFYQScckzNWGTyx70xlAJf6VJQmdoU8dcaA6XHg8eXajoOkmzQjGLof7gBfS39/fkyzWtVj6FzTSPcNGutK/vyzV/DgJ+9Atht2FKJaLOUXtVvAQ3UU/VBY+L09xg2aSwkBIeByDuRawHKNWqiKXWSqJY22DkG6y7xukuEM6AGNWITssz1F1VkspMSajoCcNRheWOQpeY6PYFbTJ1yfhgY3drKz+jzt49tjbSIOY40i48Ma0VTg8M6gRyJnhPI4mD0eh1Jkz41x0L4y/pLBCHlwv8sMg4LxlgpuXNAOhuy5FpW6rVVKW7maMVIr16yljmxQ9Tm6V2gXm+5WxClq9clwDpErZLsM8dTqtKwKAAy1qzxs8RycWW62pyCnDdBGvIzB9IT0i7WOCUEQRViIo6km+QQdtK94Q7IO7mrKAacARXIoL3vgKvq0D9QMc4FFGD9Rkj6W25VXPQ5mDOKL9UI6tulIxOPaI0flWoYmCwatxTKlo6jikc6trCCqzA2ybRfo0nFtBIUZINqXKO0L8O0n7sG/bm6FaJnlOi7fQrNpp2Tf3Rub1mOBL6MFW0ihFylDPIZVLG+jplas0L3PKaGK5leWAQDduUbdpco4l/JTKYMsOeqMYXqWzpXuUZ+bAV3gLOKIZhwiF57rwzQFWKLQaGzZfjw2qHthAyBKAzNkKJdZeL5NawNlP1u62FhhTA2e2/TXEc5e04uRr0mUSy0+jgLEnMb04FkWYd4jlXU/5toF7hyVVfovlxm44tCRAOMOuVLEIbP3T0seNohNuOZ6SCngdjWx4cDhHUH7bQ6BqpeG7ysaE6Zamy5DaWjHDUv3aoABxZr01Y+8sanVOBhPi5rmK+21lFiwTnqKm35SxCSPA7on0o6DpON23I7bcTtux+1LoBl8cSQA/iq34yDpJq1cSTA9FxCff3bl6wBQKsowoHu1xqX/ehsAIF8DNj/bIJo4DQ5GKINkC1wYJ1jonlNHiHXeYypmaFLiFAQxRCwgGgBxjNqCe8w4gmPYNXeuAuyKITVtVylnlbzLVfu9xvJ6FvpEqEavUJ43BGNQDSTqnvAokZwpyEZ73RGfnlIm6CslEVitPKROY2O5CiYgDExZixM7DiKvsfQQYGTqkbG6R7YTbudHKsGLmiwwliMQM19BxRtCoxxtgNIVlrPkuGMaXolbH3krjvLMjPEbcDAGcDBoZrzQp8xJnJBvUF4y26nACwMm4NNKLg3pBUdBqAUhZa20iE3P9K2VSLJXghc1dBKhXqabOt8UIZVrWzSh9JYTvWS1gSwUtOSorcAkb0hNOXhTGVQDDpkLnxo1gioOtfU4o+uj1BMRnh2TmnhfDlWhiwQZm04dzEfo3eRcGnzEljipczt1dMuV0Yxhbo0JVUzIClIWRCgbGhcnhMlroLFIX8dKfP/3w2f5aijpOEKGOF7Joe8ioWtzoHfZ2bUwVP1FjhzTBpgGzhFvglq0QxQ6OwpVn9A4JzRY9y0yZPkrVY8j3VeoexyzExaZA1B1OYoVoB7Yd2vOFlLyOtMoVjlEHbU8+jRYrWEk82Kx5bKEFiHVVfWtfU0LQeM1IZMqhveYI+V80u3yCK8G6ZxZRCnfiFEuE9Itp+EeO9NpNzb56qIGkygJyQRnKIeWG2bFNJueIO4kSFcsmtSI9+lmpbsRRrcnlLpzz8sqUJ5REHtyAWlNd7HAtRvfDiT7DKk1+lUx3TtXWUg/SAiTQ5Ym5xKvr+bSrKIylM43oequydiCzpqOgM7oqADeU9O+WNVtf5XbcZB0k5avCvQvhAfqpQPiH/2F+msAiHS6ch/N2k3GEY8bv8jzGuSVpUKEZLhNbwmGYtVK6C8zRFPWsmCwlhoi8ImMwPUWGW5xb8HezHJ3vU9VRiXP0dR4giYzQJkzTM/aY5YVzFggmgT7iKYL5GsMyUgi2afro4VEo0kC7EyihDJwhAqqVNIxh0oifwxTAtG4hrBRSj2MwSsNaUmVLjUWjaoFwT4xrzG4wLF/VxCgy7bJc8m1ukOpGJcGEaVG1ec2bWMXqAOFYon7czuV5WgaLBF8ykQbvyi44HQhyLUCk95RxaphM8V8VZX7jarvUkMxsp0GomgCJ0NyEvZULT5Jj/nUEo0d3WeZIzxXZQNWNBDKgFn1YhUDLEKwnlEUfHWvlJ6A7bhV5JsXVpd6EKHoR7ZPRE6eb0j0LoUxZkxDRyH1YST9x1RImTqis0utufRGscR9pVU80SiWOcol5tOHzt+Qt9IVVY8q4OKRJab3bQXYFJ48qxJS725zw7SgiqXndx4DABRphP/eeS74DmulbSyHZo3+7gjA6Z5GNKFVsMkERLy4uWGKFnpHTue1Qb4uUXe4VxR3gQovNbR0RHEGOdfe2iOaaST7FeRMINl3wUeEfJWh6cF/RiKQQHbZBlICXnAyKJFzyLmGjpgnSedrTnLBpoKsWjnTQLVMfci2KNh0vpEAUC3H4KVGvJ/7ZxTcQMcSxQZFP6NbOZoebVJmp+iQ+NAaySqD2kpE1D0KotrPcbEsiCQ9tBuAiq6pWBLoXXHjVlOFaRQqOdNDep/dM5RvGLCpQO+xMBm6asF2NaQRxMdyG7pyaJ8XFoK5uk/33wU/dYfZoMj4e0p9NeClhnLpZk7WQcVqGGO/EThuX3btOEi6SZO5hjAG/3LvWXjL6gP4/f3nAKCJjipKDJjdhSeFop2yW0gEh0rEwkRLnlkGqiOCxscaMD9lwCs6sHM1kEbbNhqO19PWCwEQeDcGYI4Mar+nJU1YTLNFLRcFLD3gFjfrIh4FQrYogOEFhXgUqpyiSoEpkvsPgSC52juegUo4RKGRr8uFXTDTDGotRjJyixCHBGByt3KBAo22bYnVUjIilGanB2RtkDj/M2XAlyPoiLd82QyiGU2sbjKcnRCY3G6QXnPXTMhVZ0f7SdQIboOdgHA5orhKW0RuG6wGbz6LInF4SQPeGPDS+AogFTPkGxGSEacqNDt2TGnoJHBMmo41TLXXW3dpQo9mQLFGgYzMFVgaoe5Jr5ydHpBKceMCPUuMN5wB0nI5atrJ60R4xXSHEjqF47pLi6csjDeCjSaNX+ycKrYW8NIVyvOgrMO6k0+oSG9GR/DVSrNT9NwTl8z4326j//GUxk/ONZgOZPUmZYhnGvHIVhR1BOpO4IGJkgxV5+sc7/jdb6exmnEMLgWU1fWd10C+aQObnKF7VZEfngt8bWGFlq2CiprQY/e8iKKBLIjIl+6Fe8pLBhMxj+CpmBZed+541IApDd4wFGuJH3cjaJF3JfnVkPqR2Ao/VgKaA5PT3G+oOjtkHj1biUndHLZqjgO5NVNn2vodagT/P1e01oRihnxVIBkxRJMwUVTDGAfPigM3zHKo2s4B1ZINMkzQwyrXNJjivohFJTTPzWLuETwj6N7HkxYsa1szpIlofiJC1aeKQvcMxofke0foDn0WTYlc7ubEZKwxeLQhPz67AWky5hEpx9XsbNG1OCRJlAayMAu80GimvbG10wkTFfH33FysUvhClae6HUsAPP3tOEi6STNW3PBXfu7r8Z+zr8fSgzYgmtW+OmuhxJ+1NIMcCiEWRSBVwlFn3C+gvAKqJQMsOUhIonvJ7opchYxC8C9yEC9bTMEZSZ+1mydw64A4yQIoVgBhRQaTA4tUjFrVPczunirtBR+r5RjTU5Qa7F12QYQBg/G767rLIWcUHDiDzboDrH62gYoZ6p4l9Q44kVUtQdlIjqZrjW3LICegI0I/Vu7N7XUYH8zQ2CmIkgjL3taiFl7gzqWH6i7tnl1qoVwiIqssBTpbdCN0RGlOrlsLI0If6JiwqLjmrUtgrhOhdNVmMicid74q4FQQ5FxDlgpMGL/A1T2SkXB2FQZAsW4DNS8gmCCaa8w2xEJqqy2ySdVVzJN5ARIqNIwMiH1jDKLUiGY0TsqiJ8UKR2IXuGRXUcAlggeblswvxq4SqRoC6T7zfXfO820SsyiA1YcbROMmCInGMapB2N3Lgr5T97gfFzknrRoao4C66ZgFccKYbGyYBtbuoe/1L5Uoh5IENJ0xtB2k+ND+f2wIBeCAtpVkRCSH910DADlVNkVqz6LpGVUB5ARTlNpuOhyZNTfubBuIvPEVoCrhaDoJ6p4IVhuCni0jSXySrscgHofNDWuhbfHEblzGjUcI2wKShgcSuksLMkUBIY0h/Vu6b/y4koGrIuFIh/j2MlTD8Dz6dHTrmlVCc13d80MLOeY+JU4dsu9JWzuuhq24A6oltwxl4I1GbQP0JrGGwgaQFkFc/wuNusOgoxAUda41qAYC83X6YL7O0aQRornx74gLyOKxRmQRoWSkCIVz1aWM0qFcmYB02nsejxs/z6mEQw+DDALfh9+gPNXNHBO3n/Z2HCTdpM02GNiUoXdJUSlz4TyNDKBtgNQOy1l4cZkmnpFuBUlGMKiUOBjS7jR7lzh0wj1aoQUJQma7AVkplxg47ILTWoiP8pSAsDgBFvXgi8GTqGhCnZ+i3y82ge4jHOmB8byNYoVh626J7KpEZn3g8lXaXfWuaD8ObmfaTr+phFv/KZpJ8xXuq2Yqy0coBwyyYJCr1vcuIwd0puHLm6mSqUQ9jNBkdK7k0Np4u8qhWCAa0281VrF5elIGwUG7wDlfK7egUYWXHSufhjFghi3669kqucju5h3apqLFVIw71v9RMBhlIFpoWjwlnsrsBPUzGWt0aiq1d0q+5RKZafqdvqbFrl0OTwKC3FbXtK6TwQcC0diV/3NCSEALkRYMnauFhyGNNSxO9t0DKlGsCCjZ4m0wCkDy9RiVTZU0qS2Bb+3Cm57BtAMMbHrapY3bfffSCa3ncfBIhdEdMQobHMxOAekeCVO63yvWaafefyykK2UBlK2FqVhlmJ4R0BF9HwDKoSQ+XhoCAxgKRF1VpOvbfDP2op4qpYAu29P+PRVFA2aED/R1xKEjcod3iF401tAxW1gw5byhOUI4tE6g6lN1m7vv2b5GMmI4uJN5VWwwCk68ujSnPpFStr2+ZYlyiS947RlG707aUvNWMW16XMCgYwBzm3au2tV7DEbykDJtKE1frtC54kN6b4r18C5FU9q8VUuB1Og02zx6F9nUcYt/J3Pj57By6DZUMUQZ+FyOY1n3g7FxekABTIOQIjeSec4R4BAvCqTaQWw81hC1QbFC34vmDNnVAnJOB03ORvZzg3TXehBuRKRgn0Yh2C8NRGF8IGgEQ91/epCk4/b0t+Mg6bgdt+N23I7bcfsSaE9GddsxcfuJteMg6Sat7gGzPgNvOAwP6RRRaNohLnBoYCvZwgPIau3J0AC8GrRKuc9hU67bQK3SgRubI2xfWEE8ET61M7ldgSmG/gXu8+m8RVYGAqrUtkZZ8IyzG526Syk2545dnFCYnzZQHYauTaNluwYGDNOvnoP1aLtk/mAFSw82ROTWYbdbD2LMTtJjVKwwzDcidLeET1klY/J1ajrCy/o7pGZymi5A5oYQgAPTqmRjYDpGNQikTVEbROOW7g4nMULeaI9AFSuM3OyvBpIvb4in5P20Bhy8YpBzA2XTPlrYihXZ5m6YhbSZ0JbPkKG1cyf9pYW065Exd4hUPDFglshf9jmAGL2LBfoXCbeXZYRiiaOwlYfVkM6b7AXkqkkYdI/BCRIC8ARmpx8VWdJsscKhZWTH3CDKNYzgmJ+wnI91DlkA2W4gncrcIBkZZM5mwhBip2WoKiyXAVMt/qbhDCq1KWFYtGYeHN0BW41lOT1OrFKlAum+hrGqzqM7NcpVoHmM+3PJwuqNSeZTHonVRXL3VM6sKGoCr3FT9xyKCSTeH42eP4euqoj64vh7AL1X0ZR4Tu45pqpHjukpq4itZAspcyl2tlBZRn2QKJclql4g+cYTg3yNKrAAYPlehqXzOepuxxObTUS/6ThKACFARBK2z0JG74YRlDak8SSUyj2P2a5Gsk++adWA7jtrVeblK44kDehYQkdZQL45pajcPOSLPxogcny7xIk5GrA1ekDUbmKRspAuJeJ7OAdXtuDAhFQy0wblkLesiSxK1Q/Ipo4Y5EwBCPY947MSTSegbtWSQXwYqtlcn6sBFR+4+6wjAaZT5CshLR/NCCkqV4JPJ9MGKgqaWbLUYIb77xWrACZfOum2YwmAJ9aOg6SbtOQAUJuUaurWBsrOrLyiF1gUIZ9iIgHTWixZY7zgn0/faANuDIxkKOwLdnAnQ7NcI+nRrDOap+AFx/wEiaYBAF+qoGuBqh/7wCg+pGq0tkyA+7//M1pkY1dpktDE071suxQJqNig7sJXY2X7BumBgflYB7/6j0ll/EfX/iYe+nd3wawngUshKYfvUi71gCZEHTHPSSpWgeX7pU/PADQ5jp9poLo08yVXJeScJlA3qeUrDPmqoIomS/LlpabKLLcISQpedcy9hELT4X5iddczeNSQp5ud7LPa+PviSMxgNGETWdyODbNEbp9+I7K+LOCfBRUHUu7R1g6YAZpsneUDGEc55IhW48BzaSjF4wjL1RJ1QmUte4yCiMyGBfNaLe2i7uIaCUDR9TvftHTPQFQKOuI+nTG9xaBZaZBepgWh9xiQ7itKCyfUB15T9V2xwkP6ckTBRbUcSLDMAN0rrXuc0GeiDIusYYCotQ0a7bNdMURzhugRWyI/iHDLqx/BQ6fWwB6g1axzNaghRxPHIbPBre2nSijtxFs+iE4JnSkgcTwvHirq3H0vl63H3qG7ccDg0QqGA6PbaOVdetig7gm/wLqUnJwHArxKbeDEwr0vlyjAdHyuJmMololALOch/aUyCaaB9CDc02It8IHoQyJhe6PmyECUZGnk07HWyqNYp7+n+0CyX6BcSX2gke0YJGOzqEIfMcwHwPgW6QOZwWMKooQnitd9K9qpWhVhdjxcRSUA8JKBNyEd3GS4rkJXxXQdbS/G5ICCfxfkArRhW35Ae+pBPG7QZALzdeErFH0Kz6bF6y7xtJKxQWKlF3htvEK2E6Y0jOgA+YZ9Pt3caQTimeNGUp/jsYF0UhmFBq9M2PQZLBrlPoXNgH3BQc5xkPTE2nGQdJPGDOlv1AOaRBwy4XL37fw9YHfH3kSVlIyJ7BiOMYJDi1CVAw4sbU5QVvSWlVe6iKcMKm0RgfcSQBroJOiTRDloJ+/4RzzIBqjWAuD71qpQVSkAS8yNJgDLqDLFSw4wy1eogR+++G0AgFevfQr33Ppc1MOwCA0fpADB9ZMmGFdKbj+TwPhWjqYLVMvW9DbnEKfmWOrSzvMgX7YcDxbsUxgRS+OJgSwCL8QIDmYZ7azREJrQkenpsCPOrsGb2gKEQDETFJxpsmbXkbCZsveq9XE76ASISOqDpsdphi2ifI7I3V4k4rGCKInM7fpB+kqBW5Fuk15W+/eMpOBI5sD8TLip0YPC/2YT2UopRVwWwFaYMYF0zyDboxPODwTUhraK7EC9z5EcMuRrHJWVBehfJB2m8R2BC9a/wCAMjbPnfHCrfeMqfmJAK0Yl63aBm21y8DoiZWPPXyGUQ1iV5+FDAvfdfwbxjgxaRpI4fKJUUFmYskzE/MKnYgomRR4QRC0Zsj0Nw4VHpYDA0wFooXbq3n4DMiak2HAgPaDfazpUheiV3XlYQB2Z3JW4Gw6Imr5Xd63xr0ObOMfsJIOc0yaMOgHk68R5aauBizwUX/AmKPi74IDXjMrvp6Gq0AWnzuw4mmkwRfpULjCUBRU6UKm7DfAK4ro1nZbMQsSQrzFkVmuozpiXE/BK5wXNB9k2g7pIg6xj4jH518ii29cpnVtOm/ucaRqT9MAGlCnZviSHdTBOZoBajmjsW1V38UHoU7YDV0sRNnQRcceSkfLPgkoZBXmtwgKqZDNeYw0gBLEaAKq0quYsBOkAaXiZ+VEo+bh9ubTjIOkmTcVAXNFurkmZf3lNxNAIaT3JnB/Z4ktirO8REAjY7jiujNdkGTwc4+BUB9inWTuaUOl6vdpg4+QhAGBnZwi+HwEa0HYBnW/Qn12AEs0simMDFYAWSp2AJqlWpVx7EY8mNmVQ004RAIqaKsEMBz7+sTsAAKdePoIRRCRO7Dj0HiswP5WgtrA3U0QeLofMpxnNlD7TkQG6VhDxZIFIKhyeJ0Zousu8Lsz8lJ2gM4PuB4HO1cIHojriC6Xv8aSBHFWQ8xq9yzQQ8VRClBqHt8tQhWPVH00UiJ5asMUAzyJIRzdZ7cpAhzbRfWULxxxttGgZPy5EWG6RrQ0tUkwZX4XXpCRi53fSFvVolzs7cUlRAsmuJcKfqTE/zdG55NAtuvfRJKS7tKT7oqX06bX+RYP5GQmTODFJEuFs0lAt2JXk+9W9FPruqtZUDG8KK0qLdrnCBUO7d+UqlEDIUzHnYFr6oCGaOYFLOrksNc783wzJfo7GBmqjcxLzTYlsPwiEiryBnCtvIqwjIqzTn0O6lO6P8c7yWi6igzqiqkwqkQ99hyZ7FocewFCQ4DYIcmbTfRJeo6i73VjNIkZ+XiBEMzkkBAQAmixGug/0rjQ+9agTjqpPFXXlUhhPWYT0pRFUKcpaj54R1nZEMI9kyBndA0cKl7lCtZKi6ouWzQtdj+EMib0+h8J1rgaCt0rpWIeCrZxXmJySqPvh/Exbg2YDH0zNTi6W7cNQIBHNg8YTdc5pP9m/2qDQp8gUEE8UWdi4irtBgmKZNl0O8QID6iG85EBIRzIwE1Cjqs8wOSMRu42Y3bi430sODeIxBfVeykIbRDNC3ZysA0CaSm4zk4wM+MHTo5Nk8IVzip4e2csvn3YcJN2s2Zx4NIOvAnFNC4DFHLq2EG6hANHiJBkQ2sRot+xPWeuFlzDd1zCVgNggVKVKY/Ccg88EdnaG9CVmJxdOKAEAmIh4TP1lwpinjw7Qvcip/NtO5KKkSa5dnssrSpFwL25HKINK4StrDGdIDgkF6z9M13fPXWdQLRt0Lwf12+ktKbb/ukHnSvi9pkMaLb1Ltp8MaHpUci1ymlVmpzl4onxJMq+Dvkt1mjovOzVU3LHVTIFbJGrtOVDMSAirOxRN6Hui0ihWIirl9y7hYkFRXEfcL5auOS5SW21bVCbwklrtOmFP19qfLaCHCKKUtjqLKUJmeGN80FINBKVinKhnD9AJrYoulaZjK5bHgaUH6DdGOkJ+tobesaKQCVBJCgZ9abg3bA0K21xxRIfC95sWMYNsL5Q0V0uksL70oIKyPLpimVMAF4cFDsx6WrnUobLBig7okpxSIDU5K/wz1N2iYLHqu7SZfb5Y7NXr4wmZnqpEIhlb5GNTEmJpgwgqdQ/CkwBtcFRMAbtXnLdmzq7EG4ZSxtUgLOp1h0HHHOPbU5+yHV5oCNVrpa21pvcn26d+ptsFopFAchihXLLGqoohX+VIDun6ZK4BQ7wal+oV4wZ1N0aTAvO7aEDFtZgqBT0/CDCRDZ6cdpIL4E27gpBSb8koPNzG8uNcYOoCQpUE7pKWjHTfDCAsyp0eKPCG++dFCyCear9JAehZJDHJgNoYW4nnkWluZUYaeM6Ve/eiKXnbuX6RDEV4kZwyvbYIYjWUPoXsWnIAdHZ00PGygVa2G+gQxQohtrwK16clvVfu3YonxNuLxjW01Rejil1mde/c9XFC4eahD9XgcQw3n+R2zEl6+ttxkPQ52oJdhc1teZsLRoEQAGL1An4R1BFD05UL4ot0Ppu2sCNfDjggGgj7/dWzB/jm0/fh9688G1uPEdLCStrpqtR4FWnNGIximFwjUkA0436X6cieyYgIh8wwNHaRiGZAcqj97pArg9mmRNUBmp7l9Ww2KHcSJCOD1JaH7374BJpzFfI69kRqMICvF6hm1v27BqBpCJymS9RYJCkOk5EYS8g8Qu8xOyiauEvVEBD7tNCLQYnpGYbhQyHNAxiIWYP+Y+4+HBlXALxUYDpCsRpgfFp4pUfAHMfJtJ5+ZzPg7jUA8Ipc7L3EQWsC9+Kc7e+h9RlvLXAuqDLwpN6QtglBYDRVmK9LTP4aDVTv3hi8skaorcVSJcD8ZFBn7lwF0p3IX2+xZiBKa8Dq5kNJfdQy8I3qDkO6F3bgnWuNl3fwOkYpB2sMolnjF45s16BJBYo16dNWJERpfFrXceGYAQqrpZQcYgGxAUjokzcB2ax69IzONgVEZe1rNN2zaG68RtDkjMR8MwQH8YgQOK/1BeL8VD0GUZuW+TADjsg8OPkHt1jLwuCxb8hQDzV6F8Kz3kYdnKrzwnpjSFuM16G0Pj1wCEbINQkrclhZexgV05hEcwqOADp31Q9IHcmGLEp8yDkVOMCEFDiz11NadFKtCYxvo3S6S+/JgvodzUNaKZobL87pZCOYBsyAe6TYfdaWcXDvUrHCUK+Ge193AXOOIvRmLiFfMEfx0ADmBH0WP5Chd6l1T+ACvIB+kjyAAJB41M2lOdtWRKKmPjP7Us43BOZDAJBI9+laepdKZDsC+br0hG9Ruw1I2NDUHQ45CRtbI4kIX3fJvQCwtIaMB8sfHQLEp7oRkH0cJD2d7Vjc4bgdt+N23I7bcTtux+0G7RhJuklL94GIE5IhCxNQAGdNUOpAKDTGlzEDgIm4tacIsLpLsTCtPRThdrh3bOwCAA6LDL9z6TmYzFKIadhJQzOAtwTMMqCznINzywNQPYhCWBFBOiaa0XflPOx4eW1TLoXbBRnEU6DYMJ6bsrE+xryzjmQUdtz9Rw20jG0axZ5/AvQ+mAVjyxMGOjaIRhy5ra6RBVXiGRHIsukOGYw6NKXqWf+sfQSo/aE+WEPVd6IJsDnT2lc4GcGhE0GVUo7HoEmEcvn+DONzdK5ildJWBmGHGk8s98B5j9kdukM/6GQMhrUqFi16SCaYrQfFtP4DvIinkuHfeasiiPoOeHjJeWUZEuvkTezve9W3qV17iKhsdRYLKAO3O+tqif6uY0IctQCKk/Z7VkOyWGNILBk5npBYYGfHpizHZHIKxsAr98xKEoaMeSAzaErZydwQ985eSrkc1MmZJRrz2njhRoDSNbIIaEE5JA6eJyirlp9byz4j3df0vtl3b/hQBS1IrRsg5KLpUsrYKbk7Uq6oWqieVbd2HJ6mB5v+AiKLrmY7Nfafk8BIg6WHaWxErr2hMgCImqGRlP5yis28zhBNG8SjulVtKcB0UNeu+qDKLyGgW58V6wbdS0B2zSHRdP996lUGjpBKqQ/JAfMoYGn5hHJO3ymXnGAoXXPTMSG9rSjllO41fl6YbZIHnbPgAACVcURz45GXakDk72QUvM1UQty+tnhkuaLBDPCSWx8BAOyXHfx/b/8V/JvT34T33ftsO572WY5biK4Vy3XvSdUjdNAw4edJ3hBvKJ5qn6J1fa2tvQ8MpfHm6wyV9TccPgwk2zMwk0EWNl2vgcPbwssVj6nfJuIwxqakbeWik28BABMzNN1AMBeVQd3mnT6F7UlR3D7WSXpC7ThIukkTlUFk4V+Za8iWiSHTBrxSFPAA9KK1nj2SADjyuV+EDZg1Ge0/mkP+RoKLz7gVQNAdqTZqMBu0sH4NeSmFmIfJXuZAfX6Aeo1m0W/+qk/hv913F/he7EuLqwFDckjQv1uUqgFQ9QR4Y725ZgZ1BkAzMOsfdzjtINEhrQiQanVni6FYC4sz8Rfa/AJG1THTxbREW5cGoMm0WGt9b0a8hHiifd4/3Sd7hKYrwGtXxmtJpj6oMKgHEnWXI7sWBKkaq2rsCJwqtQuTDeaqIbPl6a20i1VLd4sKACjDwNSi7g0Mgks6LAnYBki+es6ex3EkRAkY7Xz9Qt/tGfw9ZZrSDf1LFupngMw5iop5fkfdozHtXgu/V/UAHoXxjA854oMw1kBIf5bLhsq1AXSukU2DT6/UGkxrqtq055bz2nvoufHXMXkSpjslUqcp1cRoMuZ92rR0nBkGaTWsmoyCqHhUA7DVnMNF8q6WtHB2r7XKt5U1T26MTwM2XeKZORKuSuwGYB4CtaZDgbzMg4ozyTwEvozIKbBok+OZMtj8s4ZsZA6s9k9CC7XjszQp82lWl2JsMjJzjisdZAEiSrn3tmiMzTVgekqS155NDZKcAEMyMphthqBdzEKKuq1ebqoQ7Du/Qdf3JiNagFNfZ8Zx/ljgMikbwCqD0vouysLAaXs5Dbcm5TYNaO+7JHNibqv6/H02uJ7jt1Jha0YRbCIbXFU9/K8n/hv6lgz22xdfAr0DSxgPqbRkFOaAYpVbukNIedM91+hs5Yh7kf0eQ90NPpnM8uDqPlDaFGA0k4gPBESugL4TYiJ9NheA9R4ewwiGZpD6zVrdk9h/LpBdDWlBKsoJit/RVKG32/KFeYrbF5wuO063PaF2HCTdpE3OMlQ1R3pIpbKuks24wMfA70Los1BWHgjcLQIvd5N0KGPnhUL34hxdW52lMol8PcbBsyKvQVJHBqqjoRSDkXYXuc/JKHRKE8Uf7r0AErQoobVIaGkrTVxAUlDJbGU54XWfWR0TE9CDB7p+h+qrvyLiLYzWNd769b8OAPixj3wL4kcTX7YfjYigbRi8xYmrJNIRvL0HGPXBCzKKYB3idoNGMHSu1mRS6XyvMgFeacjcoXewflPGVzRVQ4l8jWP08hxml6Ki7BqHbvFHnBcaVfnZRb4Oi4RrhHxxXyYN0OSronB/mTGBk9QOknUopfamuAvoE7OeaPD3y1XYuXvFG4PkUCM9gOeYTM+Qbxpvgv6QQ7gcSduVMjcd+MoyHVttrYL5+yALjWSv9As6M4b0vkSro9qAKQ0jZRDZzBswZaUL7A66/0iOehBjbMUr5yeB6apGNOLoPxIGJppTSbqv+DTUx2zfIi8pjUG23SyINLJaQ85qlGvEf6v6VBThnqF0z0BNGcqlEORGMxoLX3mHViGDbeRiT4iis8fgTQw5VxAVML6VdgTpoSIisyUxzzdjNB1Cr7h9dqse2e3km4mXJtCWtO/QmHhKCFy5xBbED+WMxE15becPa8TrKx1ZuM9uAyJKC0iLcO+NcJIJ9ms6BO0u4GoyehdVyj3ipCVtCkWpvbhjk1IhgwuSkjGJfopC+8CQ1yQAWQ3gn//eIxzFLMVOnwY6n8X4p7PXYe/K0AfoyQ36pQXda/cui4ICEdWSX1Epg5yT7IfjehZrMVmHOLuWJfs+zAFu56ZywDC5rQumWiibsAF4FZ4zMLLqccbevFIYPtglRNIWuzSdxeq9pisgZsfMlS/Xdhwk3aSpCCgHwOAxAzmp/YsDxmAibpEh5j8jUmhYPL3R6RF/N8CEhciW7DvneTGv0X1MIZrEKG1pcbkkCXYWtPgAAdJ2L2fTMWBLFTq9EvkjNgfBmdeucYtld8sg2TYQJX0vXyPRymjEkVyxKM6B8Q7hblfuqmb6Dwr80YvuBAC86jmfwZ+vncXODv1eeiFBk9pJzyIZ8QQeyWrv9LSAZ8QpDsw3aTc4eintNHUpMPh0jKULCvGBFRBMOJqMt9A0BVZrRKXyULjKBHgN8EspVObSo4CsQvChYwogklFIuxjGrNN7EJN0u1gP47MggueatrtKxgyYQ/uPbNSIxG2DqdaOWB0Rm3SNh3wfmKIKu8ylxOYCsxMc+TrzgaVTpPa/JwMZ2H8mKB0nSoB59GVxYteRrXTTxj+PRnAYxu2u2x4f03G80j5I4jU50svc6QoByS5HchA85UjfSQDaeFR28CiJNLq+Gk5BC2u9E9Cg9ysWpHQPQM45dl5d4WvueAgA8Kn/8DwiH/cYeBF+r+4A07PMp6nzExom0Ui2XYUkoGNKTbq0T90lFJJpg3zdpa0EBo+WyNcpsimXgGpVUTq4DOnS6Skis/tUof2/NwLucSKSz6lvAKVHcesUW0td9B4J97RpbSRckMeaVtrW0LvWFqyVuQ3u28itdCk39wHpdLX9BmVBZrfxYY1iLba/yRaefy0JgVUJg7QonxGMPPZYuNb0wCA5BPQjtBPrzQ1knuDWufJyF+WQNhEqCoiMjoA8DdWd8dhAxwxGhqo4w4FMkyG2e37LIc0JLl2qI+qPKE1Icw6wIIjqxjLdNz4ozM/0EI1qGME9cVtMS6x8hiPfTPz34rFe8JlUKUN+Nvz7U9n0k2Jw+yR15q9IOw6SbtKGFwxYBz4doRM7XPpo0NNKwbh/surbTJnFxYoDTLcMUt33W4EUUwbJQYl4RP/WjYV1DxeIRzZwWmbITxgfCKDbgEuNE4MxLtiJvIpjpJVAfAhvdZGvM8iLxrtqi4JBzjjSfaB71enl0OTSpCFVaGy5cbZr8IlffS4AOmfn+fuIu7Zsf5bQxF0EA1GmCdIWFU2MAC1GXIUUnMxpAm8yeL0o0TBUy8C4EVjfp+PiwxpNR/oKLCfeSRWGxvZBIepyrHwGnrPANC1I3izUpi50xIJgYQRo1yF3zcxaJrhAyqXiWAthsykQpkI5NW4wCYVqNxu42ZTFUUNdV5rt+sQbBsMNhD1IzhUGFzWmp+SCwW07SGI6pLBcmq3p0W8aFrhhxCcJ5dVNRkEIrzWqJZr055sS8UQj2as8+lP3JOqeQLZbQTh0VTDwSiOySsXpNgVSck42Jm5cp6cZ5FwgmtoUnwvG7HhGU7vItd6H2YkIVd8KQ9r37PAOhq++/WG8ceNPAABveuXtGP5hh1BN+27lG6SIrnoa6Ra9N3y5hD6MfQWoSws6BAaAT811rtbQkgar6TA0qfD2IsxQGlwXPChgGwrMmA7oCNOE+LlzmwQwJaWYEvt+i4pjEnXAVVj85pt0jnTP3qsCqAcGjDPEO/C/ZxihTe7Z4zXQu6I8+pmvSVR90vRxQVm6bxGgZekX+t6lCnJW2bSzRZIyekZcqt5V+ooKnmeZrwgUq5YnZW+iimhzNXjUPhsctHlsbQp4Q8GJSphXjgfoGXVjJUpm39OAqM2eW8LckwBGhuAtCgGLGxdt3yP3/Gc7BqIiCoLf+DU0z2kXrEqBOCVEKrVBYJpXkJMSGWOobGqySYUX43TPi8mfphSWwVFJvid+iuMg6Qm14yDpuB2343bcjttx+xJoBl848dp8gd//q9aOg6SbNK5IUwgM0EkQ3WsTbdthOSn1trV7QIiSbhEt4XLxIU1H5w1IlOHE2fA7nlKBVwqi0l7YLRkLiJI83gAAYwkzivDQ7mkMzx0CAA4LiSYThNIMrTDfWCCaG1KxBSALiXqP9FHcbkwUljDAWgJxglISzADdLepDdwuYHK6A212ezIHeZY18nePgeS41CQzOc0Qz46t5nOmocZ5vXUo5yBlDbEX3HBm17gWUTkxrcBlgOfqcLVjDyGmNXtGg7keouwQBRTO6B5XloogGnnDtdrFMAYit15hD95xdgqPP8FYqrf0sMHi/tHDfEXg3jCamtjWK12JqFlGptlUKY47QyrweF7eCjeme9uNZd6wAo+unbIk8unM5zpSGV0MXpQG0Qb1MaEmxLJCMOKJR7dGsfJWhWBYY6pjMjQHIQkHHDCoRfuxZS7MKIMJsc6YA+0Tqry8eUdojX+PItm1frbFwNK3teQxVFzGg6TqEi0T/orlG47zaUoYPPXorliIi4+jtFKImdMK9Z9k26QjpmUBGxaNI9zPilLVMVNtcNSD8XSWL6ZlqSaBxZssFwK8RcuqREMuxaafSeMQWUSrrfyYKFng9DdB70OV4YceYSOUOCYlHNFZ1z0DZgoBoav0bo/A9lQDpbkkehwCaTgcq5uhuhetIDxVEQabQsZ1P5KwCNKBT7s/FayBfB2Zn6Zj4gCOakvJ4k1B/qyFVBsaHbOGZk0WYY1QqUPUERBmMqKkKl0Fr49H0JqPrTUZ2KJSh9FLLu5ALjektBjLnPhVJBtkMtWMwlAZxRf30NkBTg2hOFjVOIFSwUIQC0PjONwjZmpyjG9ZfGqJ/sQBTlIoE6F0qloVH2KK5Ad9ffP6P25dPOw6SbtLkTENqyt23vXpcY8aEBbq9KAILk6trxkLOhrGgUKVBkytrLY7KANz4+IvXGkxpiLzxi5EoNKKZQDy2yr49yssbyVBvkQhlFlkvqymJTQJA51qNeFz760l3a8QjtqBIbTj9vc4YymX6kFfWBdu0AzwSy3OLhOGU91cRcOJOygm86dY/wR/89bvwp3/xLHQftpU0uQ0YW4t60TWolg2iMbdjT8ckhwRvA0RyZ0oTHwyA0QxGMoi88fILvKZxorQBzaIqJg+x2F5gsQLoLpHFnSiktOJ0Km6lGNulye4ewwW54T6HSq5wXLs5P6ijyt2u3Nr7vDEscEm8jUQUng8nP0Dl4XTCaGbQtBY31jguXKi8cgReHR8hMQ8iK9hHKtu8y6HiJKgXR/RfOeA+/SvyBtm8XkjNgLGFd6Tpajznli08dP42nzLqXVEwV0mBulylwCzZr8FEUKDnDUlmmFigXA6WI9R/5n38Tr74Kr5u8wH8p0+/BADQvchR9WyBgE31xpYI3aRYXJxjFooiei6t2XK511Splq+JBUVvR+wGYF3tma8wAxzRmP7sFL1daldl4e86psIJd3/TbXgDaEeqFwV81RgQFvN0l/nr80GJCedKRnZOss9LslchHnOoVEDMnXdiA14rMGO8QKiRAioRmJ4JJtq9LQUjBCb2WaiXNETBoTOAmfA8kgEsVaYBwOwUt5s/F1Aq8A6V2rsNTzQhBVHDub8Opul+OGoAa5g31E1tyj26J6MxFcEuJZob5CsM1Yo7D0Nn23ifQwBQMcfwgkJ3qwZvnK8eX3hXyyEwvV0BvQbRJbqR+TqDjlLE45BKljMF0Q1EcRUzVMtPD3H72OD26W/HQdJNWjxuIKS0VQ+L/0YKzdyPoFPSXiCbAnZ3GRCUo43ZYCFwmmiCM63ScCrzZWCN9jtEXmvwJiBLTt9HyzARaUnmndGk8eXbzsmeOX0gzrwSs1/EBXFC4kkona4HgMyZJ0fSl+3iY8KCyi2xdP8jmwCAHz94NX7uJe/GP/vm38X/8IHvBwDIrYRkApxKcANkVziK9cDf4hWgOqQhM76Vgp3ONkcyalD3aNAnpwVUCmQ7kbeGgCJ1aMMDWZcZgyZlPohxC1DdXUQUeEPGnM5SJZqQj5U5Ov8duY9Oz2bB9oGFhautPdMuZT5qeqvbelr2mDb62P4+BZn0F1mQq7svc7daVlqG452Te9X6rO5y1J3YL+xggKpIG6pYcUGLDRrS8FwZwcEaqgDygJrkQGyJ9QB6jzI8MLsNojUOYEB2rYAsYu93VncSyMKgXKEbEU0jyJz83ByvhveErfozGNsdfj3t4ucv343hPYm/Vy6gd6rcvCZ+kx4g8GoUBRwe2ekBzYkKbCTRvehI2kRiN7xVCceceS78uGTXsPCOOBSpbW6sIktcdwjRHN6mw1WYgnjxqJZafBwrneF4fMUpBZ5zdC+zQBi2pGYjQgEC3za+WhEA5KxGfrIDlXBfmUfIJwcvFaanE3sf6DkwAuhs2/FrDJKRgbjfIj0dFoLtFjIGQyhQYyv68nXyXhQVnXvwSIlyyBcCm2Y9wnyD/DDducohUK0YxGfpYV0bTLH1yROID5kPklzlXjQ1QaojpmDKV8j2ieidjMwCX0xHDMl+QNlmZ1J6pu27WVhtN8ykryJ2L0vT4WgsB0rONaoe916XTQpoZ3PzFLdjnaSnvx0HSTdpVKL9OAFSC00BAkr0RJtRZgHFYWAhmLIBl5acqqcYfHDDGqvTZJElY33jWLO4ovNaWWKqC9SIYOu+xwSD5gwmYmCV8ecWDX138Bh9Nre76qOkVKZD4KQra2/QZd4yQ3yyg3/04D8Cv2uCu5/1MADgM8snML4yQLJNs1y6S5Nfssd80MIVIPdthZJdqEZdgXgiwqS2RoTvcplhXLlFFgAi7yMHEPmVqbDLn91ioGON7KoI9iAR6Q1V6w3MFTpXMqYd6sL94iHlBcDr0PDGtIw5b8CMZFZXx5/IPUetQ7RZMF9tI1ZuQTCM+UDcpRsaUPm4s4jQEaXg6m4LsbQBV3oQrEOKJRpvF6wC8EFh2+uMl0S+ztdt+nIqwGsDOWtrU0UoVoTvZ7pvMHhEw3BGXm+gQKvpRpDTBqkdo9kp6aUPAKDsS/SugrRz7DDWHer36FaJ3mvI3n73wydw5uMK3AqN6oiBN9zactg+dYgwzVoCqyql6wl2HwZsJNF7hHuEJpqRttjkDA8Vb30DkYbnkylKgYnK+PsgKvJmm20K/+wVGwYiZ0GvipPsQDwmMjH1ica8Ahm1AoCZ0tjXPZsCn3LImS3Jd0URxqCWjFAru0jHUw2mFKo16sDBs2KoyAof2kHmlYIomoXgvVym5yCaw5fgz9cFqkGoNiObI5IMCebRNLaenA8btGUahfWvy/oSB8+mQGbpvD3GPpfzzXD+cl0DBuhmBC39v27/b/jBe94IpkL60hk+q6Q1pvb7TiaANywQzHlAEGWuwaoGwqP/ht5d+/3+I0A0FijWgv6WLKia1fCAdtYdol600eTu1tPHhj4mbj+97ThIuknTkoOzxTQUAEJagAWdHVfN5vlK7kFsV8JxtuDjBVCQYlr8owXz1BA5UYpFtDggwiJCLthpDLjR8MQY0I5fSwadcCibChG1XnxJXD9qs4h0OV6C3X12dgxmJyVUsuh1xJsWd6qiLjtzVMDyL0pA3dPHPZ+5CwDQZAbIwuKiJU260Sws/HWXIHamCFECKDXWdGlHD1gV8Ig4BG7CUon1gKvCpcicRDW9QaUwNJEeCUh4BXQflsh2HBKhwXRIY4HBSyn4j7QdAxWCo7aytju34TYFYhY/bwfg7WAlHGT/137m4FC/EBQZERAGmRvIuYYsQjWW03fiClA2IHF/9oiesscx5tNMTRfoXgFQhnSTE1KMp4JKyQF6NmLmDWgdiqMS5vVlVMJQLgvIeeDjxGPi2LhhSQ80WEMpI8+9yWmcVELBEQBs/lmD+LCCSl0K16BJIysKSWcrVjiaFOhsBx2t2S0anUuBz9K5Qi94lJOqNwBkuxV0xDE9HYcAYY95tW4AaAYUGOjL3D976YHxprGdbepD74pB2Q8Let1voUiWq5geEtJZDzWwTgFCdRCDNwzZFp08mtgy9rWW3hjsOyewwCfc+htLeOnf/QQA4FXLf4Ef/PDrIP809Qt/uRoj3YWXlwCAZMQWgnIgBNHu5lR95sVMB4/SWI3PctQDA3EQkFqdhGsDKNhyHDSvjcYp6CqWg2r46scZilWGg1XKkf3o/d8COaO5IUiR0HMsRehrk9Ec4jSmXPBHqelWil1G6Mquvx6VEPXB0SRIq4tDJwzZNTpG1AblgIE3wdhbpTQPOKQ6rojGcNy+PNtxkHST5gjWXgxQhKDlaPOqy+0A5GaCFC62AgARdtKLTvL2/8oAYDZQagVlGuC8/XdaXJiygZPSgBRQMUdleSei5BB5sHfwu6H24i0ZoBY5NLwyiKYG843gLC9LNy7hGEcmdhOKzGki1AJedBKGQW3U4CdpQRhvpGA1R3Qggor0eoP5OYZ4RwQblBlNfKXlHsiCVIS94SXCZCrz1oKW0cA69GDwIKeUVByG2FheUTQL90CUZHDrUhntybd9j1xptEeSbErC31OXWmt97UbcI+AIsmRgNYJauz+7kBnAFwS4PgTUiUrMRaERwwU2tCNWcRAxdEGiGxdR0LmMgJdGiMc2kIlCqTYtLNYKxMkHWLmIdsqwSWlhcQsxGIMWDOVSyEcxFVJFNBYGOubQUdi5R1N6trtXgfi8TTdXGiqVPvgxgqNJydBWt+4XkaaZ58ukVzkJax7QMekBaStVvZCuYY0GNzYgtl3nNdBEYWFMp4RIjp9BNjsABeMqISVtR7hOdxVEGTYSxTJH3WH0LrjnrCIU06xWMJZ9LNcKNLspsm2HJlPwxauWThejcTaS+ZTf9lfEeMN3/wHesvqAH9NfOHcZD9x/B+Kp3SiVBnVPQqXcf4/XJFVQd4IQJQVfQLZLnS+XKLhMd+EV7mcnEsSHhGa1TXajEUfHImXJWIGpQHoHbNm8oHfGPY+8IoI+u0oP5P5+gtTY4N3x2hWlIg0LvycLi3DZ59Px9posBG7mFYc4t7aDC//pmcSthEslG8+/iycGg4sVhheMLxqYrwk0dhPmuWBWrLW9oXPK5U91MwbHittPc3t62GaP097xjneAMbbw34kTJx73+P/6X/8rvvEbvxHr6+sYDAa4++678Xu/93vXHfdTP/VTuPPOO5FlGc6ePYt/+k//KYriaUoaH7fjdtyO23E7bk9Bc8TtL+y/L/ZVfGm1LzqS9NznPhfve9/7/N+FEI977B//8R/jG7/xG/ETP/ETWFpawn/8j/8Rr3nNa/CRj3wEL3zhCwEA733ve/HDP/zD+A//4T/gZS97GR544AH8vb/39wAAP/mTP/mE+qYj7neq16FHxn7m+CHCpkBapFuXXnMIFNPGI1NHK50WTu1+0iNRgVQdvkZpOiOEP5ZpA2gEJMl91uI88cYsepHBQtJi8QKZvp4rlYwVyqH0BFm2R3C0817iisAz3tjUEgh10DHtBOdnqF96qUZ/Kcdz1gnTvtBZwfb2AHqpxLNOEi7/0M4atGKoOhKmtFwK6xjrUmm1FUh0Sth0kBVRbFrICidOQ5tA7NCfdgmwsOlCl6KK5oxQEDsIXFki6BHhRqawMKbGpVOPpDXb4+meH9cP10//fSCQuxHOr1moqDoqIOkLBLgBiylF4NIwsa3eq7ssWF3YfrjKKxhC+4wMfZEFjYeWoVJOS1t6bgsDAMvTapkYNykn5WkVxBmTQ2PHKpDcVUw2FJ6TBI4oJ3+5bKe2v0dVjOVQeJXl3edL1D1g+JC7d0HV3SETMqfxqzuAk0OPZkGlGnC2F9aqx1qH5JspmCbulkOEmP2z99AbUAqujWLWfas0fgA/X8w3JbI95U2xoxnzvLZAWGaYPENjZWWKvUtLAIDsk4lX/QYozaa/coJilMKrm3JA5JQCq55N8O7rn/sx/NDKvfj9nG7y/+fRb8blwyFdh3H3hqEcRiQw2XXjF4olCuaQMboe91xR5RzD9BZAJZaU/ShxHssBRzwNkGi+KpDYVGyyW8LwFHMZ0r/lEj1n0SQgw+rOGdR25s+RnZmg0H3Eexz52ZDDHnxWktuAnXdEZZDtKeSrthigyyBzg4O7AG7V0G8bjvDu234bX3Hun2Dtk8bf06rHPYHeSInhwxrxtSlgaGD0CYl4RHON411l21gwd2YCPjV83L782hc9SJJS3hQ9aref+qmfWvj7T/zET+DXf/3X8Zu/+Zs+SPrQhz6Er/7qr8Yb3vAGAMCtt96K17/+9fjoRz/6xDvXJucdWfRcKq6tayNL7SdDl54jQrX9jtVEMgyh+s3/Dp1HRwFOdxNkSOeYBahdlJo0jRDSgozDB05gdC4dB3Jk3eFUSj1T/twqZai73B/TuVqAFwo6FaQPZa+X1UD3msbkDF3Q5BaGbJehe9WeSxF5WJTMT1jEaSFl26m1YlGbCl+xeRn3H2wAAMpGIO7U2Fia4LYe1YtfGQ8AANlwhq3LNDtVJ2ucPrOHK/fT95JdcpCn6wzDGbkUXNO+V/ALl7ONYE1YBI21h+FNMLw0bFHJ1z0PbfK0S8G1+U2uQnHhHrvfONqOBE7MSkK020KqzgXLbe4aFq9PW3Vv3eJt8IbSCfHEoFihD4sVWqjanKtyhYIYx/tywSVv4KUQRG1TqzKkM5y9jmtVjxZc1oRANJoxMFsZpa1WDa+wwHFzlZa8td0VhYbqCJu+o8/XX3EF37B5P37l3a+k7zXMXYJfdEsA0EC6E+6pKCkwc1Vj7dSjCxaLJbIOSffJN4/GgaFkDLVXbafrl7NQij8/QWPQvWJ8FdfsBIdKJPoX6eVKDhs0WQTn4wgQ10j1FCbzFIgsp0sB8cwg23baPBEOX8Rw+swedi/RfKklkG9qsLUSxqp1T1WCbZ3jRz77egDA3sVlpFcFhlco7U19p2stlwzUOp0/fSRGckDvhuPdOUmE6WkX5NK/pzvA8nl6cUiWxEDkEeqes1Gyvnp2bpzeklFw3gmBYTymZ8oRsgGg2UvBVXimTi+NcP4wgx7HfrCcN6VqpT6ZAqBC4YQoKRgXBSP+I4Crkz5+eXIWgxfson6QNAZ4Tal4d54mBeYbEeJd0bJmscGhCYbZTFEqzz3XKgZYi4f5VDZj8CTYkhwHdE+kfdGDpPPnz+PUqVNIkgQveclL8BM/8RO4/fbbP6/vaq0xmUywsrLiP3v5y1+O97znPfjoRz+KF7/4xXj44YfxO7/zO3jjG9/4hPsWEB37AQufa8nQdDjm644TAURTjo4l/YpSewHGUDoNVLZM1hFcmTJoMo7a7rDqjOYDmcObQboJ1XM77Ll4KtBxhM1SWd8x5vtZ9QXyVXJLTw/o9xprN+Le6XS3BG845hupF6YsVjtYeqBEdJgDsJNFKsEVGWByy5uolgzqASAK+nu2T7tKUQe0qu5QVBiPDbrWG27Uy3DvYNPn1kcHXWAqMUkrfHz3DABgPOoA+zHmJ3LIQ3pMzYkCtw/2sHOSyt30CaC8v2dFA6nvboeuW1YNnnjvnnYTAqX0IJCKays5IIswiXBl0IhQGt72daN/hzVsbSN/LTQJWCjl9+0GaKIPho4E4+54OsZ4W5sFYctWIMU4/EKj7Be1MOCCREOzXUfUEMg3Wr/fAKpvoBUQTdz3LPLTMvRkjXv+g5GxkfTscsuTKtaBwVfsYvqxtUDoFRT/Of4LQEgGbyjYcGPAFP379LRj+dLv5evA7KR99j51Ej+/vYx+S49IpTQWbQK8nIJ+1I5VNDMolhiKjbCZIZQl2FVEU+MlAzrXaOUtBxEFUe48E4ZyxUBpho4l+Zpd0tkplgQ6tg+GAbPTgFMmHT5CwYWraHMt2pOopAYfOw0fCuaYjSrKZQZzvoettAtpn8Glr9iB4AZbl5eRbNH5f+exF+Pst+1jPKWIL90SiKaEVjkOTbkKnHjJFXzzifvwyw+/CAAw243AS+bnHndPgSBVMH1Gg7WPSgweqcBtwUi1HKPuEM/K8b60oADC2QI1CbPzowmourLyGi0+WrIr0GRUVAEAD/3FGcRj4pAJOy68ITmLugfPceIK0JEMRO6UQS2RRIMr1jhc6eN/e+RvIRpxrNm5t+6xhfdQpcDodoZk1PNFGP1LDbRkKFYF0n2HfloE0qHXWHz3n9Jm8IVXtz05Pfkr076oQdJLXvISvPvd78aznvUsXLt2DT/2Yz+Gl73sZfjMZz6D1dXVz/n9f/tv/y1msxle97rX+c++8zu/Ezs7O3j5y18OYwyapsH3fd/34Yd/+Icf9zxlWaIsg1zseEwMTqZbKQZBgmSA06BhyFcZ5ifDIzc/YzC3k3j3MreidC1FXlgPrQYoJ3RcPMaC6FndtwhG1ZrsjRWn060qmR4t2HWPPlj+rKZ0nmQebcpXBeanaKF1xpLRjCYr93cgQbpbktJ0FAK10e0JOjsC6TaNC6+pnBtJKK3vXqI0TGX1QuIJVS05zSEAyHbgERinv8IUx/5qD8++hcq5i0qitlVKVy5RwCs7DZqOwq3re3jwKk34/GqKP9m7y6MGya0T5GcrwITZiilg/CwFExmkW7byaWbH0gkrNuRBJ8qgcQPAG9/GY5tuiAmFc5OhlmwRyWn9ZnvmaqfLgMXgKhzUOuaIAfLR9Fv7nMFQGY/LKFzYKLZ+R0tKRTjkI9vXkCULfmQakHPy+XKBjayB8TPJALl7xZ7SEfWblq5PYc2BbZ+iKbB7ZYgUYacezUNw5MjAsxMcVb8ljKjoz00aiOI+AOJAuWor0LY44gczcp8HMD+tkV3liCfwAZcR9GwaHlKoKmKoh4Ccuvwzvds6NijW6KPkkO755BzzwoO8NmA1VcG5cRU2mHaIQrkMiKUKdT9DVbTuEyi9RNcbocno2tx7RGXoDNHlJCiRcyDfhNfm0RJIn3NI57AG1ttbS7SRYiFo710E/tO7XoXUoddO4sGEdNE3v+pjAICf++DXgtkNjl5uEI8i72kHWJHShKFeoYchW8lRLvUxuj0O6ODUWMFF+JSmLAhJdc9heqDRZFT56O5/k9F3RKvAwo2De99YFarr3PXphIKraBrQwbrLKEB3c7XTCMtaIpyXI8gZzaNtXasFBHoCoA9UA4HuZRoIpjSajsTg0Vbqc0l4Tz6Anlen0v9UtydDTPIvQ9z+4z/+Y/ybf/Nv8Od//ufY2trCr/3ar+Hbvu3bAAB1XeOtb30rfud3fgcPP/wwhsMhvuEbvgH/6l/9K5w6dcqf4xWveAX+6I/+aOG83/Ed34Ff+qVf8n8/ODjAm9/8ZvzGb/wGAOC1r30t3vnOd2JpaemJX+eT1L6oQdKrX/1q/+fnP//5uPvuu3HHHXfgF37hF/ADP/ADN/3uL/7iL+Id73gHfv3Xfx0bG2E7/IEPfAA//uM/jp/92Z/FS17yEjz44IP4x//4H+PkyZN429vedsNz/ct/+S/xL/7Fv7ju82JVoLaicioJXA6VwlfOyDykDZouQ7lCb7OKOdJdoFpGCN0NGW4KBA5G07WKyk5rJafF2r3oAC1GpdsltyYxZoDcTuz89gTpoSbV6yWarMoVoBoY6K5CZT/LrnIkI4PKzqLzDQFmEvDGoLPluAfaT3T10BrO5o0tdQ9pOaaAuGihFgmDsgul67ssrNFvy8IlPdDofSLFfcq+QNwgzmqc6E8wmVBA1OmUmOwlOH9xE2zZ7r41h9iNfJA0P8ggRtJPjtQpmmh1r4GyhsTxiO6Zg8vTfUMGltogsvev6nMwzVE1YfHSM3bdfNJOX9EYtDhoR1pb+NE8nobWkW3hYtDUSs/6fzc0UbZTbUdSb07MEq2Ujra8NmPgwEEwYxBPtC8NB+DNWP2iFDGMDVDfWqAo6AXIduieyzpUMzKbmnQ7d1EA0b70/DAgBGgqYQvvRDQNzxBtLBjKZXixPpHTYu+sagDafESTsMCKnNCodnrPCTmSkKDrw2LwWa01kIcS8Zi1kAhasPuPBo5V3WMLz5mx+ktgQfBRVIC4P4NO4FFZSm0ZH1A1Haqkq4btqi66Lq4CIipK0lNqp0Ln8wTPOb2F3ZQmga0H12ASDRZr8Nqqk8d0732qGRQcNc+Z4e0v/C0AwP/+8CtR/OYGzj1cw9j0XjmUUBEJcrqxggG4BpJrVu/oL/p+Y+HSZFrQ8aQdZoPGBF4qAIDn1qkW9yyakbyGShmEUxmvbF8HDpECTAN0LwYh02KNOFhaBsSr6hFnKzkI45nu0T0y9twqcRvdICwqSgOuQ6DEmb2nplUh3BgrTKu9sTZtkEWodDzUqNWR3PqXWZvNZnjBC16Av//3/z6+/du/feHf5vM57rnnHrztbW/DC17wAhwcHOCf/JN/gte+9rX42Mc+tnDsm970Jvzoj/6o/3uWZQv//oY3vAGXLl3C7/7u7wIAvvd7vxff9V3fhd/8zd98iq7sc7cverqt3brdLp7//Ofj/PnzNz3ul3/5l/E93/M9+NVf/VV8wzd8w8K/ve1tb8N3fdd34R/+w38IgIKv2WyG7/3e78U//+f/HJxfv/1+y1veshCUjcdjnD179km4ouN23I7bcTtux+1JagZfFFuSV7/61QugRrsNh0P8/u///sJn73znO/HiF78YFy9exC233OI/73Q6j8tBvu+++/C7v/u7+PCHP4yXvITshn7u534Od999N+6//37ceeedT7jfT0b7f1SQVJYl7rvvPnzN13zN4x7zi7/4i/gH/+Af4Bd/8RfxLd/yLdf9+3w+vy4QEkLAGAPzOMncJEmQJMl1n49uZ5ARg5ySRxm3mwVld+4yD+iEKKz9gN2ORlOgd0VDbzOvw8EMGS0CpqXrY1B3uIdyqx6HsuJoPufNaOdsRLhhDj2oLHKlE455zklcrqUPxAzAcwGdOII38YQc8ZiX8LwpT1gWRHJhyqAa0DYrNgYyV2DKoH/J5vS7dG3eGJSHKjy3k3UVYSphYFHgEAweUUgOqaPzTQawBPc/KwIb0Wfigxk2xgZNJ0FpUyoqtcKJ9veyixHp8FQBbREVIKcc+krid5pkKBosEbLdhvzdOPMmnPEeQz2IYCLuxTCbjKwxPKeMUzrzqCfaQtUasJBqONraPCnP8XH/V0CbMaCloZvcTru1+DWPO9e1ECyv6Wcc3wfelqBhhAQ44rQTF2yLYcpcY/PDHDsvTDC/tbbnjRDNiEvkuisAsCogQtEcUIfMV4X567OE8mrNpoRPGehMId4OqJ97txyBWCd0/+o+AIsqstM18mcz4AGCXrJrDN1rGnWXeyFMlcF7z3kF8Yj64BANeSi9LpAvbhgQiiNHQd+o7lL/E6ulVPUodc1VSLuA03tfDYFiwyLKHY3lk2OsvoBygA9d2gAmErzkgKDBawYGyQ4pyTsUrO6RiGcysgTwkwxFLvHXhpdxLz9JY3cuwmSSwWylnmjfpFaR2p5n8uwaJ08f4OqVZbz1jwgBuO2/aPTmc+hEgM0tUbwyGJ+L4JTZAUJceBXeZRgyPC7XjE9XNl3Lj6zgU5/OXsWNixEM6b6C4cz7nfHGGtGOARG7943uvR7SD5qag0/lgiZRsm/VsA08jzMZURrNPXu8IX5kscT9u1v1SZy2s6M8cpSvC4gcSPOQtmsymiecdUk9SFAtCST7DUROD0h2VUElmb8WGHhft6e6GXzhnCL3fUcrce3x1sG/TBuNRmCMXZcme+9734v3vOc92NzcxKtf/Wq8/e1vR79PkPGHPvQhDIdDHyABwEtf+lIMh0N88IMf/KsZJP3QD/0QXvOa1+CWW27B9vY2fuzHfgzj8diTrN/ylrfg8uXLePe73w2AAqTv/u7vxk//9E/jpS99Ka5eJU5LlmUYDgnzfs1rXoN/9+/+HV74whf6dNvb3vY2vPa1r72pvMCNWroHSOsVlIy1FxNLDtyLbsKLwgC5B68eq2MrjNZS046n2itYF6sWHo9ooXGTYTJuyJOJWdNa0ATJG4C1Um0mokVSzgKpuOkC6CxymcScoekbIKOXvlzhSPcZhA2IZGmCf5vjMq0BohDI9rSX+jeCQUuadJJ9+oHOlkKxniBfpT4wTQusjlhQn64NVJctVPPwmmwfXNDS3SKCrPkLgWhKi2CTCRzcSWXKyT59j8qRA9maSLEkIuer1GqaMJuFoIXGxHERpqclls434PPaV6BxoxGNASMZ1Hrix7Tu8gVLEM+PaKWLHpdJ2UqV+GNc2bANrBhYEB01i2k7XgNGGuI7hEN8Ku2GwqPu39l1H1NAxz11xFZetsm0zD+vfsEBLRqrn2aYnKWVqh5QBaPnCgHoXKG0g1PlVglDukeBvreaySn9ylVIUzddhloLf4wTBKR3Z/H6jDAwM+pD2XDwWKFrn41sh569bFcFHo9gSHfpvXPWKNWAzptvGttv6kfdxwIXrO5ZMUIXBJZA/5L2CuOj26kfsggWFuUynV/OSLQSIGNp9dkVPPQsiiBe8vyH8COnfxv/5+7fwO984CtpXJS1G5ksVtsB4Zmdn9JAw/CfP/XXveAkhEHySEJm0G6jIoiYXW/SO7q6OcbWpWWc+w0G6QxuZxSECNX4e88rDVEZK0QaxkFUxpP4RWUgCwZ+LaTkjAicP5/GtEGo567V9ryN8RsXd10yX+T2iByIL9EgVEsaRpLyOmsF7q6YwM1hFESxVoBOivOiy6HsDYwnDOmBhsgV6rXIX1+Uh4oH1pC9iCg1mj71oViTNt2m/LsrpiXkPIHhTprEgJVPV5D05BjczmYzv2a69va3vx3veMc7vqBzA0BRFPjhH/5hvOENb8BgMPCf/92/+3dx22234cSJE/j0pz+Nt7zlLfjkJz/pUairV68uUGdc29jY8Gv9F6N9UYOkS5cu4fWvfz12d3exvr6Ol770pfjwhz+Mc+fOAQC2trZw8eJFf/y73vUuNE2D7//+78f3f//3+8/f+MY34ud//ucBAG9961vBGMNb3/pWXL58Gevr63jNa16DH//xH3/C/eMNEVejuQavjQ8YXJl/3eV+UXA7TWckqgUFUaIymJ6yFSpLArwSyPa1Ly2vO9zaHVhF7IoUYHUE9C7TWz9fF54D5XaMrqLJ8WeMMIimNFE4BEqU9B1WA9mAEujPeeaj+Dh7BtY+bk9jrSiKJearWFhDpM140kJR7ETGawNR2El2ViHVBjAUVGjJAEZqz97WRNHC6ErsF8bXKebmGum1OVij0QzoXJNbUpRLgUwNAE3HQNw6Q/0pQg/6Fw3kFCjXsMBrcKXsjjMgZ9QPhzAU6wyGZVi5bw7WOJ6BRN2PUA0ExucCQT/dg/dEM4yQHbZAtAZNniYolBNrqO3BZ2/XjXhE1q8NQNCmavGXjOUkmYXABtDtIMlgMWC60RxqcB3Rm1Cp0E8mKXJrFyyYhu5TPNEYXrDPeod4IYaFHb5KAnIAWJ4Hs4RsG9zzhqrGtAzcG6aBaNr6nrRcuw1ApTaQnzGv1m4s+tK7L8bSQwrxuArjwmjBcrIYyYiBlxpNV/hqrPSAEIxoSu+bLAxEBRQV82iTuzdOgwug50pLYL4h/THlOrD/Qo2Vjwvf92qVbE/c95qUEK3sEn3vnv078d5X7+Gfbf4htu+mHfSH770dch4tWOmojKrimoGV12gY5LBEU0jIbXrBo4kN/ntoBZkafKOAsDe1ft8qnvHxAiKvYVxZHGc+aHdzGRNkS3T4bI3BeVuBOyI18qB9ZTdrLe6U97LTwS4lnlBpv6t8FLbi1Clc0/GkEq4i4mgB9Myle8DccaKEgeoa1LlYUIBn2njVd3cvnD0LAFRdBsNk2NCAAqqqz9CkkQ/QmoyeT+kkD3JCUvM1iXIY+FUr9zeIDgsycQZgrPmyM2CuewLV4P9RSZnP2brdLra2thY+ezJQpLqu8Z3f+Z3QWuNnf/ZnF/7tTW96k//z8573PDzzmc/EV33VV+Gee+7Bi15EVZbsBgR4Y8wNP3+62hf1zrZZ7TdqLvBx7QMf+MDnPKeUEm9/+9vx9re//QvoGbXONY2YKfCKgqS6bwMZAKJQiKYGA/uiVAOB+Ubwcepe0+CVhpw1GNpjZicjFCsMc8m9thA4TXTO36pYttVFVhcJsNouGZW0Frboj1eEcAUtoKDjEdnFWs5pd2s4Q3GJZqJH0gryzAzTXfp7Z4sWgHhqkNkS1+RAWYG/MJkaBuiYQ6XBDTtOBHTE/aLOjJ2g+KLWkC+Xt9fIbVrHuMmxNGj6CVQqfEDpgk5ekbkoQGXX7NM9VMv097FgSHeA7qUQyCQjGtdoKlA5LR5rfeFLvCfUh+nZ1KdC6x5DseJMSuk4h5QkY3stiqGRdFHauoQzEwQn20RtYBGZ8OKa7UDFpcBczGUDlraprZN0WCCBO22jxwmMjhK5XV98bGfCV1xZPgAY3UK5uLt/DIYZ8JpB2tSMnJO9TZMGi5r0UEMWGqVdLNyY66gt/slQDWwa0C2EzOrM+MXMpqyWAHELQbf6fM+mWBhiKwcxeEwj2y6DvlgsAEG6ZR6BqjUFaoWGtBscLQgtGz5s08/O1oQJvzDmJ4ho7Qnw9l7MNrnfBHV2NFW7JhqjZ9II9h9hWHoQmG+E9J6J6R11BRfzW2v8yie/Eu9ffyaevUKlbMONKcory5B5eL9XX7SN561u4W+vEvH1zb/yPdCKozMoUF+mkzvxx2pVAV07nxQcqhIYfJyO2fjzHLxsCDH1AYr2CKpf+DnD9BaGZ7/gUTyycyvd54ItBDvuOWwjqU68M5oD8TigKZ2dgNCohGHveRxN1yDZC++BkbYS0R6XjIj4Xw/tvDfjEKUN2lv0BJkbCrhbNAYgzC9G0Bwaj42vmiyHHGXfanzZc4nCBvx2E8aMfd9MqARkCpAzBZ1K5CcyP1aGw78PTcYx7zxOqemT3Z7EfFsb5XkyWl3XeN3rXocLFy7gD//wDz/n+V/0ohchiiKcP38eL3rRi3DixAlcu3btuuN2dnawubn5pPb1ibQvrfD3aW4qZUBB3JxyWXoRRR0BTZeUWJ3fU3qg0X9MeX0gJwDJdHBL710xkHmEasBQ2hL8/qP0b9JWTqT7EtNTkna4hXsJgfz2CqKI/W7JVb+1VZBljoXdUzKiEu/ZSQZR2mq27TXUGxr1CctlijhEQZN4ekjfYzYYNDLIHsjcWAXloDSbr8ekaROFFbkcMuSbi0rIvLa8BzuJiUJjckuM3Re5vwv0LkpUA9JeAoB023IrYiAehfPzCl5Fl9kS9PTAYPAwbWN52UCnEUQhEVu0QMUMdYdBWANMURiIGphv8qA+bQD1vBnYp7oY3kuRZ9UjV3kH66s4BCxBid2iS7plEOwCIpem43jcia0dFDlEKCiFM48k+Wb/fSHYOrrJOvodgCrd3KLvss52sXO8Jb/4GQSpAZCQnmattFlNKSxZMF8p5MbLNS3oeIMWEtgNY+Gr7iwi1r1Mfx9cbCjd0RGYbFMUnYwM4rFCJttaRmrBu82r27fGhQRDzYKGDd2uIErp0iSxYDj4VoIez3/Nu/H6C1+Hj33wWVAWySkKjmSP+zEolzhYA6SPRAul58Uyt2aroQ+8CRIAt53bxiMXN7C718c3POMDAID/aeOD+IEPvgnJyGD2SgoMpVD46NYteN+9z6ZxuQbkOsNsQ8KCzqgHQLWsIFZKnFwdAQC2D/s4t7aPy58lsiyviEcIY8DqxYfQyIB4qY6AFiTi6tJtylbZljZwiw+tZIkIXEw5J05QZ7sJQUOHe2V0gIyGX/h19+NcZw+vGNwHAHhVVuGO9/191HsJBuepE8UqaW/FhzYVN6PfZirc17pDzwBVq9lNkFN9d9WHjfEVjQ79j6caXFEA5DXpelQR5/3dJFBHxIHzFIkxBU7z0x1MT1p+5pRSu8pWCJdD5oVWn/r2JKTbnqSetJsLkM6fP4/3v//9n5eEz2c+8xnUdY2TJ4ljd/fdd2M0GnmNQwD4yEc+gtFohJe97GVPQa8/v3YcJN2kybmmNBNjkIVGz6KT83Xu4eag5cKRHGpvOpruN15byCMtjUHnaoF4Evn0VTStF8xJ48MKfWUw35Q4fAbh3EYCYp9IlS6FVPdoEmBz9/vO/d1geppOJkoiVrcXad4A/Yf5glqs29lPzjqeABnHNp2wwEVTSnmIKqSV8lXiA/ljxvD2FHWH+T4YTsrYbkFlGpjcAiSnaaa9c2Mbn9w8C7EToRnSjCVnEiIncqybE7QVgHQcJd7YNBaHL9l1jXg1Ngiz/BaXNnASBdHELAh2yj/ton9RLSBjvKUG7Rf41k85rRWhsZBuu468fYN5je4586iNV6926IVDhFhIwWl5Y6To82mG+fht8Xd46//2+txx2uZ1KeAPY0V2HMabhdYdEkTVrSDeEZF9SpiBkNMyoHXZrkE5ZP48MidRVDlXWH7ArV7w6bJwLQwq4dBxWIzpXrRSlcJAKydBYT90f2+lTJk2SA5qpB+il/mXXriMs50DfKSnsXrmEABw8MAqhg8FkcnRHRQoRBNaVAGbkovoOZDTMMZt+5hJleAr73wEbz71PnxNyx6mc82gdzHH4adpYrl0aww+ihDNbWrI8vFYrNF0rVbUNcv/KiSubC/R+CUKj37wFpy6x6lZ0nU2/cS/IyJvrKZVKK2v+hzgwOzeFSxZY1qVWr6fPZW2wte8aI2nAXqXayR7BVht3915jOnZ1M8Bk3PA+YM1PHi4in+1+Ul/zV//7Pvxhx98/sImK9sJY1INgOpMCX4QU4ADSl/WfUak+dodx0hl386NycgGhpwhPrDyIYnA4TMSNB0s8MxkERApV5BRdYIZt6iA+YnI0x0AmsfkfPF7Dsn+cm3T6RQPPvig//uFCxfwiU98AisrKzh16hT+zt/5O7jnnnvwW7/1W1BKeQ7RysoK4jjGQw89hPe+9734m3/zb2JtbQ333nsvfvAHfxAvfOEL8dVf/dUAgLvuuguvetWr8KY3vQnvete7AJAEwLd+67d+0UjbwHGQdNyO23E7bsftuH1JNGPwhRvU/iW+/7GPfQyvfOUr/d+dZM4b3/hGvOMd7/Dij1/xFV+x8L33v//9eMUrXoE4jvEHf/AH+Omf/mlMp1OcPXsW3/It34K3v/3tCwVV733ve/HmN78Z3/RN3wSAxCR/5md+5ol3+Elsx0HSTdp8Q0BGAqLkiKahGqv/mEJ3i4iHs5MWfZkD3a0K8xO2KmJFonOtgok4yiENM9NAcliDNxqscMQQQhIccVulVEFWDZhXsjYcSHYZst1QKTe+nSHeI5kBgFCTdL+GSgSYtjvMmAxO012GZELH5Ssc42cYiNwhWY6jAxQWaamGtty6tQNXKWyeP+z6nWicQ0CKExrQDPEoyAmohGF6hqFY137np9ZqyLRGN7OieNMBsn6BXAPCSgCUqxrpDgfL4REPmS/uymGF4KanGbJd2pYnOwUpflcKQgTyJeT10AtvWqRsbrlfKfMSAEYAQoZKPRLpM5Zv41AVRlVXKhzHcCTd5TlHrY9caoghwOcs8GZcnzxx2yE00nKVPgdKdcOJ0P2mCYfcEJDSgLH3nXEAnAoC3PWBWYIlC7v5aK4hqqDerRUZx7arksCBZI/EPJ0tT7Jfo+5LTyDWzu9QLiJxXHGPAgGwyvJtzhDzSvhhOB2fxYT0mqJnXVsmFm+In8OUxuafE7z1s//r/4iDZwjIVWD0aUobrH0GGD448/3p7GQ4eIaAEUBuLU6KNSC7asnNnhdoqPLUoiq7W0NEXOOlSQM3/f72PEU0NWCNwfonaMB2EC8+67A8xYMgptp0aEzZWEJM6eXllUWbHTFb0dyiEu7nLwBgtQIzBiqzRSVDkg9RqUGx4lBgul8+JZfQeCczDW1T3tFMQ1SazmfRPj6rkO0KHN5NEFty5wj71wZgscKepjH8Xx59DT7+h3di7bzxaLyTqPBNA2wUwayVmMe2mnC1QHG5A8MZepctitklDpJT9E/2ylCxaUv5WUypRVLUp9On+wbJSKFJXOUjgywIvXaiqA5JrjMWRFEzEALtQMwvLPv1xJrBF0Un6RWveMXjSujQOW8eeZ09e/Y6te0btZWVFbznPe95wv17KttxkHST5lM0mkEvBUd1Z6jadJjnKKgMqIaRh2B5DZr8a+OJzpTjj5EcNmBuFXKTmXEQN8dsU6BJQmqNJP/Jv8kREeWMgrV01+rGGIDVeqHqoxoS2dmIMPkMH21Q96QvNY6sVL+OWiRwW86r0mDx4FSYmzSU7+oEMNJA2pRAfZpOUCviXQFE3s03NU4+cweVopV+b3sAVQkUFU18WjPk17pgNQNzWYITFcxeuqByzCun6UR/rwdAsaHAG4b5VWdBIsEbs0A6J84QPDfFES+P+qnVHUbcltbnhhHZ040xYL2oWlU92jAwacBN6zhDFW7t8xxtzjTZWyO0fPf8d1y6rc1v+nzakd8z7vfafCWGRSJoO/3myPaK/sjR4ikxQ+k4xvyXWUM8n/QwBDtNRro4vuJT0kKW7YQyLh0R7wvCpRODKXQ7xej4Up5fxBbvofdJbFX9aQZfLeiV0u2z7QoImWZgNj3qWrpbYX0uMT0dOE/ZXgOVypC+yugZUAmgO3awsga4ltC740r5GwZRm2Aa26tw18pV/MOLX4fv3vhTAMAP3vM/IjvJMTnbRbZnU8QlUPcMdGIX6SGguwowQHbJSjF0DZBo8JlcMHOm++YCewbWkFq0tiRtAXdDw3XLuUGyz8CrMFbJmCovfYAuqAKsTY5Pd0qIeW3vB7ePh0F0WEJOKUiazWOIPQnV5/h7D5JW031/fitWHjaW/G/nPs28STBAm6LsKkc9TaGsUa2qBLjlG7m5Njmg3/TVlYzyytVShGhi+YUD6XmbjnMlc4Nkr0LsU7QJ2accav/sJYcNRKkwuiP1+mKRJZsrW3yg5Q33YE9RYzeeTJ5IOza4fULtOEi6SVMxIBWRsqOpgrAkz7ovMDkjoZKQu1YRcHib8ItEkzKoWGDwSOWdvJuuWFyUbKMdethZNxkHb9gCetCkQL4OL3wn57AGkXbim9Y06fHw8jYdi34oeOf3dLfG4CL3E4yWwW7FTR7aVp0wFSqTXGl23YefsJq1BidOHeDaefJGiS7HaG4pIM7NMANtD3nNwIYVGsXxmrOfBgB8IH4mLjy6gXxGK8m5E3t45HIP8SH3lWydfonZaQneSD/GTUaBmVuAVGYgVkvomkMldBFMG6ri8Yu4W2ANvECQbeZoQOSQnVYQ4TgmAAWRLkhrVz2x1m/R/QzfAVrna0E33swW4bPrjrd90SKUYS+gSEeDnSO/0xas9OdtP3tH/t4mkLe55DD0dLrh04zZyzW+M4xRYOr1lZRBNDOQOfOBDQULBk0m/E7f8a0WyOvu/60g178L3FVjIQRFgJcAWBgX44Ik49E5wemZdqfWgoFJRpV9ripRGYi8QfcKMLmFXqb9OyXS/ZAWqHrME7Q7j9lCByZIBLaFrhoBqIb5TUlTSGjD8b+d/m2sC2serTiqIT3X5UpAEVVf45l3EqP9RDbBf3/gGcBE+meB1wzJQxEhTu6aNaHDLiCi61EQBbwEgE4k8ZMU3QuAximaGSSjUGlrGHF2vBZcwkIxQUtAlqzp4cfPaAYdC6ycpwBFZRkhzocC5//4NgDA0mWan+Yn4I2vRUkIt3IcNk5zUudq0NUyPLUehIEMH80BXjHMTrgNagJRaJRD4cnqLvDmLf0tpg1V/lkErD+voTJp+Uf2XHkDXtYYXGDYfw7NMcoKdta2eIs1YSN13L782nGQdJPW2daQUlOKpWnpA5UKojQoVoTfwemI0gsO+ah6NgDpCUSj2n6vVYfdqh6CCbtmGANeGQgZUktaMFTLQL2kIEqXlgPydQ5ZWARlSsKIrNa+ZLe2Ja/xyASFaGPIN0nRjJavWofuGaDttYjSQvtNCMrKJTLn5VXoe1MzXHtgzRN6mQbYdoIGiXfyNhwwmmPnsWX8wkN/gz4b1OAzAUyo7xfGJ5Hsc0LMNgg++6Zzn8Wvjb8CTEm/cOrITpy2nw0HlgZzPGNpD598iIh9y/cRMsAaDWYXCsYtYuBKoDm7cTbKHNEfAi1y7VLjdprMH+ODKzvZV1Q95+41b5WS+8o3hoXFnP7t+j4ZzrxqtP+to52/AULUVvamTsAHDQ4lcufzAQoLC8gCYiVB5sBH9PJ064dIiBJgPCxK4VwtRIiH54I+Yz4Ad9fiUSMXnNaEeOj2OyFb7wxawePRMdR0HGtdY1vck0kGbYLivesvlZordC05+rArkK8xpBbpSQ8MkjH1w6WnmDXnLVvVbSq2qWpH0p5LfOixW/E9s9djKaFnvZlG4JnVhbI6UHLKgYZhIyMY7mreB6YSciL8psG9o2oQgjKmKdg5SnR3KcX2/QAAbVNNblybrIXsdBjiqzW4JWQX6wk5AiQMsU3fG8FhIgFWKsAGsDqTKNYTP8adq6S3xFR4vkRh0CwzJHtUqu/uYTwFxhsujUbBUXwl9E+UBtE80AzoXBrVMOibzdcFkglDNNO+argaCh/ILKDAsQBzAbumND3Pa+gksvdUAVojOsgxuEBzb74hUXdb4pXTljvCU9yeDE7SF8xp+ivWjoOkm7TO1QI8E5ieialiZU6rM8HNBp3WBAIARvKwgzMRyiFDk3Ka8ACrOquv+x1wBp24fArQuVYSgmA/m52IoHcZkl3hlblVwhBPA8Q8P5Uh2yrAK43lz1KUZJ6TQCVAvs7Qu+wmNTJrdDusYomT0m0ZSvl5Y3e1y1hAK6oljfiQ+0Wa5wIiZ9BOmIkByb5193YCghWQC5pwnHKvmiZoutqX+vKKuB3luvIB19VigP4wR9lN/O+pzO4E7ZCLkmEySzHvRcjP0gxYDyMkexWYMoEjxMPuGACQ2DQEN60U0vW3ZSHAQAvkuEHKS7NW3Gt5Zi5Q5I25PmhBCLoWfpO3UCpOCITjgvhOfB5o+Q3RJmav4Shqw1vfMYv9MsLHVz7QYMp+5eiEzcIC7FKJ3twYgDGkudSu/DMCAGNoWpWH7YDJndj1yT2j1/GybnTt9tra1aNaMBISax1ECJgJwor2nxljiMb0zq9+RtH767S+rKyFkQzxmJ5vldrzaB50oED3z4ozQ8w41LyLi+d7eNhWqUU5p7Qah+cbVUON+MQcHzx/OwAgvpAi5lbWoFVZRlyxFrID0lrrP2ZHTllOjtYwliDLlPb8IW+70iGHAFmYIBAaUQVhdEhcrSiTVC3r9LsAVEMJZgzqjQy8snIKowpyrlAuUadEDUQ7QUAXIKkEpoDujkayb21IOEOScDQp9XPy7Ab6ZINonKJ7jb7be6y0/Tc+Z8qURjTlULFVi2VAuttAzmoUG6n/vWhmoCWQ7dGYJPvOL8c+C4LSv3Rf7VgVGkZK1GsZVGaDQEH/pbv0tWSkUbTu91PejoOcp7UdB0nH7bgdt+N23I7bl0Iz+KIQt/8qt+Mg6SatXEkgYonOdgNeaZSrtnJtSaBcZogmBt0tm4KrNWYn4oXURXpAqbr5Sfu9ZYbhIw3krPGIE9dmQbXaSAaVciT7FXhD505GHCqWqPphJ60SYJ6yBXIwkBKRu7XjjyZOY4WOm56KwTTQvUK7qO62wuS0QLEaeC8wlFozAhg/w+66dgV4xaBSg6Zn9VZmHJ0rQf9IpaSlYjg8UZUpEodrukSyBoBkT0DOuIfQ5QxQAohGAtp60f3ZlbtQr9VY+8pdHN5LFUZMA3LKvIEva4D6sS42b52gs0y73dnGAMleRVwDdyuMgALAhE2/aUbpR8ergUUYHLrR5gS1U2sOgGgPuaU/tY9TnNBG3kpfti0Z0Dr+aJWaFoGfo2Pilxlxg/TZkbbQrzbXyKNSN/7edc0ChW0tKC3pfG0SfYvzG36/ze9yP6/htajcABoekAjDCbFwnB1HYnfGwTe8ztZ1XfeDNzqetQ639zSIgTJoRagfsyrqvDFewNCnYoyhKq5WhR8MwCuNzjWL3AqGJhWkcL9K36u7Vo/H6kIZR3rWQLVuUZ7VEroSYFMJXtnfkwbVLEZ0OeRxdGzANPO6UzJ3KSP6N4CQ3O5V7VEdMAYoA9NWxtf0uZHcV2k6+5/ONfgbW/eAnRdGWP8L0m7ipYaOSHvNPcvRjBwJRKkp5Q+AVQ14LYPyvCKh1arHvbijzMnkO7tW+zQgrzVggBNWdHl4IcXu81Pv5wcAYlYTKia5T/8azsEag2yXXrhyWaLpcMh5i4Q+0kgOFMAAOWuVXLa4b0YKur+tZ78epjCCYXo6InFhEMpeDeB5cukhvKL7cfvya8dB0k3a4R0CrC/QvcIRj8lhHAiLhkqYr8oxiiQBXDmrEXYxbjlyw1CAJTrcl5h2rtFL6yYKHQtrJhtUgUWhMbhYoeoLTE/Rmzl6fg3ZrcEuEsQscoZJIyBy6YUqDbd+ZQaYnlpMg0RzVw2moCNBqTX7nseHQLWsLZwfgjI5pzy8yK2xY71Yks90a3F0KTLLx+A1IOY0fvVQH1FH5iGN4Ca1HSDZj7DHB14g2skWuGBOzmhiv2f7DPIRDbLsA8VajHQnOHkbwYCW6KCvdDPMd5jZ1FF7cXaE2JsGGAwwVnxxYQFnFCzReSgoY4rhaPAChPNT8BDG1LSJyUcCs6N9uC515/pzNPC70SW00ltekftoSg5h7eDG9tPAL6hM4YZcB8bNQhqSKUoRu+dfxTe4Thae03b/2hY31zWz+J2F62pfx5E+OoHNBWkEDjDDAB3Mfw1jRF5fUNQnNW8fOFkeUzJmKJYd8YU2Kt7stSFH+qYDyAM6RikGVnGwOjxEZ+7Yxmo6x2euUbqNFSSt0XTC88GU3ZhEQGMNbZPdGJ3t2qfTwNji/QTgiC3lWorJLY50bgtEOgzpPn1XxQzsxSNcXiKG8uqnBJltV/Bm37zSYI1CvB02Z2AMYlqjV9L7l2+mfiPlrYIaq8bfaPC8vaEJY55dLXD6UODgmamvqK1WU0ST2m7EbHATkahobLmfMlcoVmOUKyHA7G5V1FdLWAcAEwvoVHjLGJeGZLWCsmm3+cnYpiIN+hfp/PMNSXO7450lN+Y4PmXtC/2x43juCbXjIOkmLTkkIm85ZFCRCBU2TqXawFd+qFgQwda+l/GYAgtvxQAgHtGfiyWOylZGRFPaebqdX3RYwkhud350DFeG8vUHjZfCFyfGmBUxUNhJp6bcPzOAkgHZqftWSkCHvss5IKyfnMwV+pc0mOF+IV16qEG2JzC6I2zB0z1SR6bfov+PbuOYPFMhGlm5/gMsohigCVzOaSydhk5+AuCncsQxTY78jMF0twN5IJFt0THxxOoyXYl9v0TlZAfo7yojYuf+xSUYiwKoDJivS2jJkIzo/Kwm7z2nbA1oGMbBRGvRZS1+kmum9R9aaNGRSYZx3ICbs3gefYOJyRPBjwRX1wVGNwhwFlS/b/QZx/V6TTdCX1q/v/D9GwVlbtwFgJLWRG9pYrk/OBqYtDztADKqbBOwjTxSpebQh7aC+ZGxdeduB4bX/TYWj/MVTS0vwvbpsHCM/axVdXcd8d5+yKwPIUCkXxgDOVfobdkNwYghnmoUS3aDFRGS9Iqv/yTed+9dAIDBx2MkhwazkwzFJp2rG1WotEDvUfq9/mUF3hiUQyKQAzQ3zU8Z6JMlhCV8V8tAOZToTlpuuYwRcVvrMAaM+JLOK67pWA02ziDsnCJLoPjkENmIjikHhC7Fh63hlSQxcNQ8mRnjNYpEoVEuEbLkSOHJgSJStTF+o8lqBVe+78afVxr9SzXGtxJ8PD4XhQ2etUGJZhoib/xLJqY1ZCbQZBzpfm3vjab+CIZ6KfH3Yr4hvVZa/8IUOpHwvi+gquKqb9cCu9HiCoimwfCcNwbqBu/oU9GMeRJsSY7TbU+oHQdJN2mGU8BgBNkkuDbfiDA9xdAskQM0QFpDTYoFMTSmaRfpSMyiMuhcq6FSDh1ZI9cep4nTwg68VjTZthBh1hiojEM0ZF0AAKP/sgJxMjiX8xroXlWIRhVEbsv9hykASqM50i2vgM62QTwONavZtQLpHkfdoz5FkwbJXonOduyvLx5TmhDaoOnRhMXPxIhGwus5gcPaVYQKtLpPZqG8hl+RsqsMs6UISY++WJQRRKcBDqRfqPbvrtAZ5uCfHJIeDAAYsktxxNK6R9ou6ZZYQOt0DMxOiKB1MmpsObNNb8wMtORousKjg4RsGKrYcmNlCCVyQZk2dI1HU2421rguqGlrRBp3IMJn+khQ9LjtBgFLu7yf0ietQ2xse8Pz3ihousE5j15LO4Byv/e4lXZonaf9MzZtyJuArDj7kqN9MqE+YFHGoHXNC2PaQr4WfvtIoHtdGlJQlR5j5FEHAEYb39ejwZHmi9+ndJwbnJCuiW2ALucMTcZ9xakRtGn4019/ATYfoc4MLszBKoX0sIMdu0Bf+O/nsPoZjc3PHtLllTVQN+g0DbrnSHJj73kZkgOG2YpA97NWbHEOjG8RSPfp72Ja04ZLMPDC6RIY6DRC1Wcoz9ggIm0QPZBB5EQLAIAoB3qPhcCy7hCKxBWCOGetQ1Djbo7LxdqgLDkoYSIGXhlEE+dxYnzQ5htjtFHxoqwaRnCIKpC+tQhWS+XAzr05R/+x4JFpGKPUqA7In444uDaoVhNMT7ZY7gyQFvHSscDsNEkMpPu2Kq4fXphimW5+k1Gg74KkZL8G18caAF+u7ThIuklTKU3W6XZBBqZ20ZV9AUBAzsNkX/cIseldoRcn26nBS+WrYAAQQsRIMNLpD6V7FHxUy1YzJZNkSllr79DNNAU1hofFqv9oBV7HnvswfLhBup1TGav9vWhukK9bBWA7P8qCUhxNN0gH8NrANAZJ0YrwAMSHJeIDl0ogWLtcS3Dtr9sAr09q3g6uNpw0S0QRApl0h5G2UdraqSsgvRhhcrBMp44okNIxUN5NJc9fffYi/uzSLWgGBnd+5aMAgM8+dgLZfWlQeVYUfNUDoFqnD1VHeoXomR2/ahBBxQydbbq+aKoogJ0G1WPDibPBYajcHaG/XifGpoYgjiyWBteDGO1Ul/vsBov15+PDtlARB1hUYDFNdvSnjcBihMIfp49HUlQ37I9LQ7aqqvxxYvG7C5VX2v8x/BwDRAPweRgAHbdK+6PASzrah+v6/jmuJ5hM3+B0rXGhg0OVprE7CicK6v598QTGf9/7x3ETvOEcqlFRNOfmjlgZpHsNVu5rFs5tJEe2NcfpUSiTkqPCV6fpJAJjDKxpEF8haGejaFCuZxg8ItBY7bJyyaalBjSgiTI2fR84SbwhFfDOtQbd+53CvUTnymIVaNVjMClVkdK1kJktNPzmolqKIEqrtu1QukgQv6exm5JGIxo3mJxLsP1a+t6JP2HoP5LbYMrNMa7izsHedlgrEnh0fWcaKFYEGcvCBi1R8KEDADFvIAxC2lEw1IMYs03h56YmAbI941W1Z6czumbBvFkzr4FsTyOaax+UsYi67eYFw5nnYz0t7Tjd9rS24yDpuB2343bcjttx+5Jpx+myp7MdB0k3aTIHVMJR9yPUvZC+ydc4mCaOjqv4iaZU4eHgZF4TCZCUXu3O1FaZRDOF/mPGfza5NcVsk87du8IxmNRg0MGpvHEaRwJueyVKhcGFHNmO1SIpgn+E24mluzVcKZgjTu4/l6EZaPA/pc8HriLFtDg5VkVXS95CwgyM5MjXBIpTVhwza9DMM/8QyRnQ3dILlgGiZChWbVrDVTBllIaUM8edIuRHR0CREhH9T/efhc6jEiwFLh0uUQ8q4QUlAUIDyhWgHiiImSWTF4xSbjIo8qqYqutc6hBGQOY2PTmzvIa5Caq8XguH0ASnSA4wIsK3Mgw3JEa7VNQR/gyNczjs8VCko1pKR0nZ7jO30/ZdcbdKA1BWz6XFJ3Lk8gV16yPnPUpX8EjMjTg/bRSKXf/vznOOt1LHPk1l0yCyAHTLC08LXDcr+W7eaAfcyvTccOmwffRCoo9H/G5/xf2+gh8QbzXTTvcdufdaMs9PC+KODFwZz48DaG4ghXA3GPS+8RrErQEo5cQYjOPHMEZcmTQJ3JtRjlQZsDqFssrZk3MC9cCgs01/FwV5qxke0ONo2oBXCsl+ie6WRVIl6R/xOXwKv+lS4Uf3iu1SbCs1a+NTclVPgqsUyU4OWB0mQmXhxSWhyeYjX2X4Oy/7MwDAb+6+FHvP66I+XeHsr9H3sq25vWWtPLEiYVj3nvKGKveiSYOuRfAMo/lPpXQt3KJ3Oub+TLxqCCmuW0hlQu+C43mWQ06q8JXB9LRL1dN9TXZL8IomMH2SimNcAUm5LCGrp2kpPYL8Hrenvh0HSTdpK5+aIOIJquUUdZf7CgumgJX7G6RXcw+H+wnN5dMNGUvqRCwo3dYDiXxF+KAFjMqE3UJSDRhUJiFnwTvBCA4jOXZekPq0RPeqQWe79gKXJK4GH5ABgJzX6M0q6FigWKOZr3dRIBlxdKxVijeC1MaLs5F8v4ZOIihbNs9LEtOLpgaDeynAyjciqNR4AbzkgOB63gSCZt3lAGMQeZh8tbSLiB0CObVCkQ3Qf8BOtIJI53IO1H++BADIDAVSzj6FGSJVMsUQHwSht2LdgJ0osDKk1N3OIyvYuG0fO7vkGMylgaoEzJ/H6G45/oWGzA2alpElQfsGcRNyM85I1We6bNCxUEnFPvdifEPF7fD1cIz99+sUqXUrKDgaNFm+DmPhudJ2TBkPxx0NbG5Egr5RGf6NO/s419cKygwjNXQtQQa5oOdOVFZkEvCmtAt8KnZ98GbsNTO9eAxji991fLD2ddxIXsDZv/hrNmYhlUgEdXZdxVzbgsZYEjIzCGZerl/+3hjwmPvKLHcxvDa0x7GbEgZAx1F4PxVZaBjGwubJGLCiQrKjUa/Q5qJ7RWB/U2FyhiaKeCLAa0pRCcu9EfMaxqb4Otv0gDRZRBYkCbD7tfSCpQ8m6G6RlAlA/BwVM6+eDVAKrupzxPth0J04pxPa1ZEA0wYrDzT4w599KQBgUBu88H/+JP6PMx/C8+7/PgDAmS0slN+3K91ccxQGI0K1BK80CfK2xlwnAnVfQhR2EzTSiEYVutqgWLGpyAltoMqhDbY4bFq+Qe8y3fy6xzA9zdCkXSQjW4EsiZvlNs11l0MtP51qksft6WzHQdLn0eKDAvlGhHyT/i4KQFQCYBniQ4sclY3lI9jARnJUq7Tri+3LVS1FyFcFZqcWCdeGt+w/hsDsVIzhg7XfaZJfEsPsjIG4lWpv1Sd6qHsxuraKJt2trl9JVJiIHddg9dO538kCsC7hnHg8ToW3MTawaRbOxQsg3avRdJw8MkO1HCZMlQCT0wLMANmORbP2GhSrkdWmoeOc3ou3KKjsoiCCFk8TWwJ43dqxy0WOCVkwANGME8IDW1qcM6SdEs9a2QEAnOhN8JXLF/EfL72cDso5mA1OteUVFIlA1ScJh8QqgWd7ivgKdrF0asQNWCjDZnaubi/YR5orcV9AcRzp+Qb8Hx+gAMQxU+EzoBUc6CN/b6E6LjDwtJcb8KY8GnIETXrcneqNAqLPwRVaCEhsIMK4CTYhhnkT2vZ37D9df87Wn03r747vhFbA6uJGKu6yfByLBt3wEln4HgOD5q0A7wa2J/5Z9GPMfMAaVNNxwyBvIcAzVMHKq4BM88ZYNCl8R845RNEsOK7znMr9XXWsKAyG90r/bFR9DqYEonHtjzGSg9UKBpwQaAAyl1S8YACxTe93tksWJ66f8dhAx9YI2rYmBaYnObJrciGgkbMa2qJgOqZSy+lJ6edQLRl+/NT78MZHX43Bo8YeJ6hoxd1/yWkOtNIRdB84jDKW+B3GxohQIcwMee8x1QqyGAOvGsT7CnLi9BgYxrdlmFsUP92nc/LaIJ4oex84VQGuwCvok58d/PxcDRhUeiMi3VPQngwk6RiJekLtOEi6Sdt9UR9pnaBzrYEoNHgdfNMmtzBUQ4loSp8NLlQQeeNfFpUIIv+xAOfO1wWyPY1qzKDW6TeMIGTEBU0qoWDCiLAz45VGPZRQA4WIuQkFyNcAFdMtrPoC3asVxLxppRaI/M00ICcurWZ3ei2NERNxVH2JdLdcuP42GZN+lNCpvp3U0gOJg2cGw00ipROJXVkofO0vavQua4xu5Z5wnRwwOxmF77EGSKYhtZVvGERTFlAnWBSpRnjJ7SJVDQ14GVA+oYD5A0P86aW+7/p9a5tgEU2i/c0JXn76Yfzf9z4XgM+loVoiHyYztv3ilIZwEzRvDFUqGvhdK9lq0O/6ih+9GBCpxJoIt+fRdoDUTln9/9n783DdkrI8HL6rao3vsMezz9R9Ts80UwsEZXIgKoM4Bz9QiQrKpflifoJAX4maENEgJvgZ4Eo0F5EOYDBgTJyI+RRIAoggmBZQkKHp4fTpM+1573daU1V9fzw1rbXfc3qgaYZv13V177PWu1atWrXWqnrqfu7nftCePC1S0SJp64PGIjTa6UVs/4T/vpxxYC8/ZwB2EW8saOZlotq62kY2L5z285RpA2ufzzA/ge+VSteAsv3ZqTeUFHD72EHb1FbhjjFtseiSc58GLrZuHjx73606Wfu8lrHbJbnnHqmyedksqZgpDZVwRNMAheIAyyJE44qizAD0NiXiKcf4pHEhLTBoLsAajVgb1LlsSIBRKScFku5FmByLwDSwcKe/5+3HMix/li4XTTXiMalsWjHOJmNI90xyYyevwBC+3zrmKFZiZC+4iAVBbbi0P8Qq7+NlJ96Hnzj+OADA4F4YQUfl6wEIMTPfoExJE6kVEGPSiUR2n024q32qGS24W8SGZO50T6LuW9SNBEOb3OvYac4QjwHWR5ASR5toXaq7HjA0D+SdfVjKHIv7wZZDCYAHVQ6NpCuUakAfqmYRBudqN1jsXytQrlAGa5fcVSbobQoXFVEucdQG3chNUsx8UyLbrFANM1RL/kWNJ94Q0IL4GbOjKeIJ7az7ApoD6UUBfZEmfuIYeENjeowhmkUQuUBkzov3lYM6bHgz8YykG8BkJiBT8sXbXHFCSaBRNEgFyR8B0x9m1R+PGvTWBaZH/b2Ikr7BZGQMi1oi3QWSceqUgqslandkBOmimdEemWpMjOilXGigIwGZMZfjLd0xaFRgTDQCSHb8rGdRm3jEEE0tnwNopj1g1eTh6k/wrMVP4+9OHMc9ZmnbOxMhmgHTa2vIzCS31ALpHnHNADKCKHko95O6mTx5rX1uKqMFFE7kMmPO6LHnuYk2dG11kCTnWguMJAptbk/I3XHPIhIu2z2MrlFgTLUm77AExsZlI8MYDgR8ddE0pgz/I+gHHZzvTuNwUaItF6P/54F2uR/mWX33t1IO6+kYVbYNVpU7bFervy33qCOzoO/n+hYdbPVTQKhSNgJN6xb6yhRrLbj8BQV0zF10VTRpIOPYJY0tF5nhO0bgF8w3WUkwJclQMsZHslujWBbgkpBSgDh9ovT8Qs2AJhctnllU0DEq5U7JmjeS6AY2OrfRiAqF8ztD4G5S2xU3UZbeJycJRtdRZxz/iEF+LA/MLPZkFkH2rGaEdnkntXPZKkST2rshGYPKuMmjFxwrGKph6pL6WrFQy0GsBgxMC4hKu5e7yYiHqDgt/gAynJJ9uIi7XgPMHiFv28OR4PYQSXpw5dBIukJZuEchFhLJSEIUDbBI3dX0aJKSqefj7F/PMD0eId2lc5vMCB8KoDKpBgbnG4hSItuWKG1dOa2i7aATjwglGp0WiAwZWdQk62+zfwPeGLDXUxEwWyVic2Lg5P5FcplpHqh3SxJws/IC8X6FiDPILEI9tAkwNbjVMbGDuSGaWigbAMAZ8o0GjTEqqgUjADkCBvdZETfiECzcw8Gk4TKtGaFNm15hCvQuSaMfZS43jhBNyPdvdZhECRRrgDApHpIRgB7Aaupr2y9Oq8q0vcmBeMwQmZHsTLqKV575YbCJAFI6SKb039WntnCOkcLetIihBUe+YSYX5xJRDnqXCfPSDIHVYIno9GyYnzwDg4iZfe4sjQNIhMucHhgtXeFEp1gdGhghymT+KuOudGlPIv9vf8FOO7qGCS7zmzEsurIE4fUcstQxiuapawdVtI2lyyyCW4hXh5PUQjV454bMvYZGpubmmYmA5MvgFNnD4zQO3vO8NrZQI3evuvW7TZwNkJEbuiotOiljjzbZxMlNTzjEJNovjXFgZ22BapFhtsIgDLk4GhmDQnlDQ6VGLLcB0n3LoeRIxvS9AkA8YUh3NaJCu/5KRhrproQopG97IclVZtFwRobS0nt6rq7JxR6+/q9fiLX+GPkls7BcTRGPGif6ypSGFhxN7rme0czoyGm4zAI2SKbl3oMhwTupBuJ1VouRM/qajEFmcK56FdNCDPCSFDZjgGZ+jNaMxi5hxvV4ohGHHLPD8jVVDo2kw3JYDsthOSyH5aulHCJJj2g5NJKuUNL9BmnDIFOBYi3FdC0IOS0B1jDkm3RskxOSYgnYlqcCHYTU9gSggNkRgWqR9kVTU5d1Z3E6vlqAywMnCiJ6y4xSm9A+gn8rT7uBMCtf67LauzHB4hdiSg0ysy6/BsVK5CL1BucaRNMGMuVO5p/SDFg3ovcPMcBFKAEk3BhNG0e8rAcC5SJvETuhAAgSplw2irjJOMN0jTsl8nxLIduqsXdDGiS9ZYhHoES55qOOCg1eMSfESXnk0BLZbEyUHHSQM8/0l3VNNhczsGMl/vk3vhu/c99TAAAX770aALB++3FkBqniJVDngDphkLkLElyy1v1ZoU6mfHi45uQa8UlU4RPqznPzuMqCPoNBm2SAJsGgDw4xsRwJgwpZ76IM0DRbLEIUhK47xKnLs3mApSt7EOavsyhZi8cTkNdDNfJu9F7YZLfrfmgUIW8qROZsfaF4pGbMgX6Uw+9gXVxqKO6Ps9wz28dhOUDx6Lgru+iT+10GbiVNivxtnlnQNttHjEG7mwKJzqacxhYAohCIdmetNnEpUA2YG7+AHIN70FK8rgcC1QJD75IidxMIWamWPCIaTYHBuRrQQLlEH2q2UyPeLQmdDtJ56Mi75UXRQA8i5JsSmpnIsl2B6u5VnP3GCPIJFIV6vj/A8Y8xpDsm8pYzE1HbeGK/1pRjTWooZiNhOZpBjN0bUnf9pTtLJ05pSzMklXGLJNV9tFJJaUHjh6g8F403JlCnpHfC9rvmzOXDzDcY4ov3E876cJVWHqOHWr7Y879yy6VLl3Drrbfif/2v/4X19fVWkAMASNkdGO+/HBpJVyj71yYQSQZlUhS4iLSGBoymB8dDSUw4qc2FJCqaZHnjJ4DZiqAwdsGcsZPtEPfA5mPSPTPJM6BcsRwCmnR5RYrWAE2C9YJ3WYmS3Ha0zxgxCxLbaYTFL8DxhibHEnIDmvGyHsRI9iMk+/4jTxiF2grJ4JJzmRLqKfGKtJxsItlozMDrFM2piJSGAXLrcQbWyNZkEU2BwXmvUiszgajwmiyiArJtRW7EI14BOZr4iUpF/jg7iE6PmSTDgXsy2Ucrncni5xiqixle23w3+C7tzAyRXBTenVctA70Lvo/HJwWyXeIeucS+zsXCHLRPWc+ZnygbkowJlaRdxFpoT7LgN8Bzj7qRPGz+X3s9ZVTBW6XjTnL1V4F7SuDg+DnHaNIMpAcUcosatMLvHWcqMMIAcwwA3Z1TbF+F22FbLkecnlPmer3CNui2q8vWdVAt3L/ruuuaDNyDB6LwAheq428F53XTxlgD74Gs8Olc0yZuxweNJjcuq5UUqdSI9mgFogWHTCh4wxr3xRJHdDRFtlE6wrcS/n3lhoOnhYBMzUIFZpFSSpcqhNrDvIFk+1QwqFg4HhGTxOtregL9S1RZNeTQnGPn/MDdWza117fSAZwkFRoFnXq/pxYcKmHOnVctxbj09QK4mYwt/YUBknGCQSmdG7JeiDE6naDpBd+NMEaS+ZaTfeKHEp/SPwwV08LILnqiqUY0U+76xSqASddvfVi+HOUlL3kJ7r33Xrz61a/GiRMnnNTIF1MOjaQrFBUBiM0AiWAVyUwUWgLM1ughDO9VSPYZxgYh4g2dJ2M/6UYljZ6kv2MGrGUSJasNIiRKk6i2IR4NQP+2iWIjk87B6ixZQ4A3ftUXTem3dCeCTOl6VkJ/erWCmHLENJ6g7gPlMkM0Fhh/HQ2syb0pjv41kcydno2JCmklgWWkgWKNJIAIzDzIrWmFKcGYE7Ore8yE01Nd45MRcRv2JYZ3la5uP1ESaSCaaWQRRxGQ3vsXFXrnZ0GbehifJK2jbFu76zV9OIRGZdSnix9P3FhYD8gI1ZFHoKQ1Js0x1RIgamYkG/ys3uQM0dSHG1tUyfLAeKOhKgaZMmeodVEc6juPHgFkbPGGjKZWAtjAGKHr+efhjgm27fXcvg6SFCaSfaDRZa5fQiPP5CcFPFGcKbTSvGij08S6bbXNa89PVLipI2h7iFoduEdXWbt9reNCpCf4t/udtdE4h34F/eg2WXDaZdrQMtw6VpxDZ8N713p+SpWgLhUxcKNJZPlA9UCAyRTpBlkc0ahCBqBa8lZetcAwOhWhWBEuuevujRzxhCIxpTG40j2NasFzoOo+Q7mSEKJiF0pONsTLisgsgkq4M8CYJsMn0v55JyNKTNu/6InTWlASXxvdxhmDYpQr0i6CZMbR5AmalPnk4pSiEtFfk8EVT2nBOD2eOemTYomjGsJwNoNnKOGQ6f5FZaIKtUu7snMzIdq8pgAT6nf6m29RO8tl7lKkfKkLw+UXBw+4fA272z70oQ/hz//8z/HEJz7xYavz0Ei6QtGMiML1gkY0Ys5dA9BEi0BHpRpyJGPtxAkBCtHXAhjea1xdl2qMrkkwvpo5t5xK0SZlM/rAdQSXE40pQlDSfb9ErRaAclmDLdCOwVma5FkaIEkJRWFMjxojAUA0oVB8CzHLlM5r+gBmZAWm23AhsK3+iCiM1rqFVCLMTOF9GvGoxuJdXrfFugxkKjA5YQjeQ4bFLQlpokxkCmDcvhaTCiohMc5qaIiYNmllcJwoaca1eaJEocEkQzzxRMu6TwOcMM+PN3T/vPaTUBmTPcdLuAvwxksaAEB5Y4FyJUXvPHMGrIqp/Yt3+dmMacoLVZrM76Igsms00xTlBqDOzSwbRjmBIH3bJt6Q0clkiJ4x1/8W/leMtd1AXRQG7e0Drjgd7NdoIUpzDaYAOXHjrTGuHAqmcSAKx4oqhijKPHZ21+hzruiuAXc5g66DrIWGLuvU6ZBe7Sd+gKKZVOT1sA6oo1tPWHvN0LpXuy90B7tHExpq1iCyLjV0kK2gzrZRySgcvgncdFKjHkaIx/SBi1GBWCkwmUAURl1bRNi9iSG9ZQ+bZxZo31KJ/qdTJHud6zKPJFlifrrpEShWS+hYuJyJACFAPNBuYo2ixRZXLvxeSQHpjrPiUCbHXGRkVHKBJidSetMzqM2KoGiz2CPDUECy6wnYvCIytRbAzIwd1SJ9b+muT0xbMgahzLgKIB5JNH1CxCyy/8znfQL/+66bMHx/H8ufK0w/kPxLbSKZoykwW8AjU+YtIh5KHV+j5dSpUwdcbF9sOTSSrlBsklBeGd+0WXFY1w2T8LyaTVpG24+LSWBwDhje10BMaRAYn0owOW7RBjqv7gOy50fHaJ9QnlCRmklaHeWbErMjXpGa18xdP5pq8JrRhH4Vfczi8zmWP6/IXWXfG94eYCzfRQsgO0evw9KdxFGqF2LEe+amFaPwW67dIAatwSsPO5Ogm0TUSJ+KRRkjRvmIGLo/7foznhDiViwJqCgz7dKoFgTq3Atv2pQk1lhN9jWS3Qoq4Q6O18IYlVPtQ5djQCqPEImCniFTHs0SRZubAHhNpt5F2i7WEjBNRpM1OjUHmlwjmgokY+2ebTXwaQtUTCiSKLXLBh+VGk3KECZDteeGE14XbSKUikEHgYeI0KYq2Mk02GfdQq360RkvWTCZW1dQwB+aW0IUJbie4oHSQMiz6Rgwrmrt/3YNBGugtPSNTAh+y5hj7VW2NWKsZhNgEK6Qh2U/DcZaApAqYi1XzFyjs2sE2j7oHhq45EL0iQV9Qa5V7VO4zBvnWfuvjgAJ1nI9ckmRtfXApOiY0MJGFA10TJ2Qb5E2UPV4BqyYj7DmkBndt5UxkbFPXk3nKSR7NQnnBq41KxNikSTibEqH/qgs8kaTTanSNOBOa4yupyJOi0TzLc/WYpSLHMm+cqrYTY8Q4mjmkdtygaFaMMl3QUaPigjhtUKR0DbNlB8/huckpe4xiNRsLUI9YOA1d/Ik7/vzJyAeU3Js+50WqzFmRwTKZVM1B6LdOc/rS1E0HhjUe8U6HhnU68tR3vjGN+Lnfu7n8OY3vxnXXnvtw1LngzaSXvKSl+AnfuIn8C3f8i0PSwMOy2E5LIflsByWw3JYvtjygz/4g5hOp7jhhhvQ6/UQx20Bq+3t7Qdd54M2kkajEZ7znOfg1KlT+PEf/3G8+MUvxlVXXfWgL/zVUEQFJBcBJgmhsavKJqdVXDT16ETd5+hfqNG/6B9Kvl6DSY3JSYIUJicohUcyQ2vVyiuASYu8ELoTTTxh2B6nIuai0qAJkbErHutLzzcYmrHJ43RRo+4xlIuUGw2g60azwN1Qex0iW8pFSi0SBVQfzQEGBm0zeIIIoxYpojZoR0axxFhm8jdxLZBv0b+ZJPXgyVGT2HJLIt1poBLuCKhMEQdpejRY4TOTwsS4mfJtaXJENUTyBCDKCGAEqTeGqMqMS8UiUkwC+X0S2VblV61HY8yOMCI9BzpLKuAoJXscojCojU1L0pBwZbEKzMyqNRpT38Xm2VRDYHY1Rexlm/556Yiu1UUPPKeIcIeum8nm2WMd9GVuGg97SIiy2P7sohWWi6PhxUMxB026HJoduIPmpkGZQ1afq8GkO78FLrIuedrxqYLvqYVSBUiaa5cAJPffkW13F4nTHbfjXL5R5x6cWy7cF+hHuRQ2AVomasNTDNLPdO/L3a/ySKdmIJdjRzeLN3DRbkkWgc9qQOqWeni+qVDcvgh2hBoR73PnOnbfs9TIdhhmR6neOjckas69yKzWXg/J3k9DgRQqDXzAjTnWfKdMUsCHjgSUce3LnKNYFp5rlBodudQ/9HhMrrRoplxetmyLYXR17INRevQQmNSOxmBR4noIxwdV+8wR0KlNJL5ZLvoAmdW/obFstipQmPx4syNoucCZJFHNR6wcutsuW974xjc+7HU+aCPpv//3/46trS284x3vwNve9jb84i/+Ip71rGfhpS99Kb7v+77vgOX21VyiqQYW/ORqi410aXI4knK6J8EbhcG93rJgUmN8OsfolIWVgd4lCvWdGsK3qABRshZHQ/O2IVMtGQFLJZwLJ91FazBucs9LsW0qlhmano3Cg7semJcXSHapLkolQvtmqwz5ljZ8GEuqNO42pbwrzcr7m0a4tATKN0wL4YTrbCoPDiDdrhxHIjLSALyWjnTOGoXZ8QxQQGNI7dOj3IjXKXNeA9mLKMrOtKUakhr2bIW75yZKGsSKI+a5TkCD7KQCn3FzbYVoGmN61PexDasvl0y/lmRUhhE/lmzd5EC9SG1obiox/IvcGdWVOV+mvg2iMBNl4wdtm4g0dAXJhHnODLwRQRFv3nV7OZLvAXupY8gEj8/9buu0xzi3liWPdwyPbv2275ghcofJ7rvcmwMl/K3TFgjfVpvgVofHdl1y1ngIDC+b3011iPCdJrTqm1vsK34/E46tK3S3MUWGjU8fhIM8MXOTLpLNtqcrlNk9hdF5llMorZHUKHe8TDg0Jzey/ZabnN7latG7o+IxuRwrw7eJ9xmKIynS3Rpiag5iZlzQzHWFyw0ZGP0q4hC1cuMJAhezdcuViwLlInPXy7aMKz71CzZRapQLHJNjPl9jMmq70cDou822gciQq+seufKSkXZyKNAak6ORe/7JSCHd04hmnsM5WyMuaiixEU9MUIh9plKDbwd5Lr+U5eHgJH0Nlxe/+MUPe50PiZO0urqKl7/85Xj5y1+Oj3/84/hP/+k/4Ud/9EcxGAzwIz/yI/jpn/5p3HTTTQ93Wx/xwmsNyT2SYD8SMQXiGVD3PE8JoMEnNuGrWjCUqymKFd7iuPQvNqiGHNxopESGYGwNGy7hiIluwokpr1E80W7G0ZyMHx1MXHaQsOTZfIsSMdY95snHK6S1ZKPbuCQDiTVAYgwpm9JgeizCosn5JmaNU+m22b0BQPZiP/BJE/3GmUN2eK2gTd4pS8BOxorI6obvxDQl2dUBOqIjToq4QR9rRiR0XhtuxTrlntKCuwlneKYAkGH/euZC7rMtMgrrBWp37yKRM1Ua+XsqJfJLEvEkRmWUxycnBKqhj0607VCxn/RkTogirwFmFHjlVop8S7kVcZMDOgayS8zpY2GBCPKcA5UxykTBkIw8L4U4RPqA8WP5atyMllzA5JgzBwTRY+0TzfsSGAgthMQaKOEgbBEPDyC2I7S65UpcGnM9a+C0rhlWYQyCkMvUiiybV3doQMxBfMLI1JCE3UWQLnc/8/rliuUyxlVocPqkvrr1xx4YGsfgzHHKutGLmrMD59rfZMoRMQbWND7BrSGSx1ONdNu8syeNsdZ0ojsjoFmmF3I/57DaEqk18GwbdZDcm9MiJzILFx1xqIhDZcItZmyEnMx9QEfdJ46lRcdVQouJbFu7b5kp2m/RfNvOeOwDGZqcjDtRahdhKhONqNRgDRx6NFsVaPp+ATk+waEjMoJ6l5S7HgXOyEDhW0MU0o1x8ahCGT1CRhJwiCTdT7nzzjvx1re+FXfeeSfe9KY34ejRo/jTP/1TnDp1Co973OMedH1fFHH7woULeM973oP3vOc9EELgO7/zO/HpT38aj33sY/H6178er3jFK76Y6r/spTGrD0pB4j8mLolozIPV32xVYHDeh6/WwxSjqyMnFQDArU5VxBwMzPomms2uUvY0+hcUqiFHuUQfZbpNBpJM4BJe1kOGcgkoT5AR078zBmzUmjGWBvfOUK6mzqUEwKW0CDOtp3sa+Zb0ucdAob31IHJRY7ASABF3g4U22bldstc8dhIBe9dbFjSQbyvMVrlbIcpEQLMEvQsGo1barD65T24pOPLNBnU/QWWlVDghQs4wjDjYrIFmDE2PBtp4v0QyTlAcBxav2gMATGcpmos5skvUD7yiAXV0be4MmXxbIrs0Q7I5RbxjJoK9BPvXJO4YMIMqaW9kVstkrPIa6J+n4/oXiBBuieP5Oh3j3gNTl47pPbKDloro/uKJnUg6k7h5ftz0uXUZ2fxu1rgiMUk6xk4kNjLqQL63gNPv7JDAcGppCAXIgCuXMUxCNKhlXAXuK3eewgH0SAeGDWM4iJoYYyd0xbp3u4OMhWhRiBLZe5kXuXYllCw87sDuEHm7nxK2XfPLoEnzrtch14eIsn1eDi1hjNzflUmtBIDJGDJlZsFBh2Xr9DeeepTZLcJs3ZlCNOOIxxLVEn1vZDAoMtItOqTasgSQGhyKiNlmUaIlpUaqFrywbTUw0WfGSBqfou+s93mFYtnCn/StpfsIUHWNdF85wrlaoL4M39O6z9D0GLJthWyzMv2Xou6H4mWESjETVQoA+XpJ6U1C4c2lHJOTiRv3cDLG/koJfGj+Yzssj1z5wAc+gOc973n4xm/8Rnzwgx/Er/zKr+Do0aP4m7/5G7zlLW/Bf/tv/+1B1/mgjaS6rvHHf/zHeOtb34r3vOc9+Lqv+zq84hWvwD/8h/8QwyH5Rd71rnfhH//jf/xVbyRZ6J5po8Jqw+Yzgn8t4gIA2Y5CvFc5//xsLUaxSqsiO6j1LgCiIOPDDkTRFNA5oM0AGRUa+aUCmmcoVugDHp5VSHcb7F2fuI8XmsTP8s3AvakocsfC0NMTGUV5ZEEbzhtXiDmFBCpp1SoKwxlSNNKnlYI0xgeTmqJapM/7xrRGk0fYfjT5tYpVIN+gFdz0mOmrHKgWaSa2aJZKgGTMKGwYQLRXwc5MzoUnNSKtMbiPYXLC6CsNGQbnFLItGuSs6JyOuc87pxPUPY70EkPvOpoBnn3q8/if8WOASxbGAcqlGOWSd8nVgwjRNEG8W7oVcbRXYfFOjWLVShdwVAMGRIQk2ntx0UkWARKkxuuEPk3+ORWTuxWgY63h5ML7TaRNk9OOwTlJ6sdSz520nSqw9Pn97PV4o6G5DvgdzOVqC6Pbwn9Yo6kL6Wug7SIzLsEDed+6dcEYDJZDY9xKB8LbO0AIMMfQ6fzedYNdzqhxRpE9b4577bLlSitu00fhdV0EYWAYzi3WKAsRPdsuHRx0Pyt+y08KDRlbeMC1sUrYrPJohxaUuDU2u2wSbhUB2Q7tq0uG/Rs0oj06Px4xxFMFUUr3jTS5wOxojHiqEI/a9XtuGfGthJRuwVaupmhyjtkq98YhJ3d0tmU2G/pPC+Yi0FihfdSncTcnY4V4vwGThoc1Clz7ZryMZhr1kCL3bBvySwVEmaBcMv3TcCycqdwCEQCKIyl4kyDdrmj8A0W37V/HMTtGfZxuccggCvBLWh4Oxe2v4ei2n/u5n8NrX/tavPKVr3T2CAB867d+K970pjc9pDoftJF04sQJKKXwwz/8w/jYxz42V7Tpuc99LpaWlh5Sg76SSjzVgNCYDZiDngEj+FjSJBWbFVs8lqQTYkXdemRhqRhYuIv2LX5hitnxDNXAT4yipEnNancAQNOnlV5vw9ddLkWohl6/iIwav/rUgvgEKgESM8gVSxxc0uTpFb4VJsc5sh3Tzj5J/2vOnDEQlRpiakJezf2ohEMlCcTML3cVZ2h6AqNr6JjkhhH27xpC9hR4YVxr+8SVkhlahNNsyw+o9VIKUTQmcaWB8c3KLZo2LmFvVGhkW1XAhTJ9OC7RMxyJZphAJjTQ3rhILOlSRmg+tegmBBUDk+NEVLWGW7IHjE8m6AmGdHNm+lgjGlfIHBSSohpSUkw7AcQTWtHGY0+On63SBCHCgZPRsfmmvT+glAzVECiOGJfAUEGMudNSkjEpfCcjjcjISFj3AWsALtoQjTVauAklJ1QhnMUZVIjsGBRnnsaS49AEiGN4LwfQF7TPs/UfQJ2YuSbzxyDkU+mDRlQoZ9A6r4OgzGtD14VoDZkDgpsdY6dbuv3h2tkxKG2TQgO0Zahps829ArsWAKR1p2m/L0DvukibPUZzI68RPENyjxp01xKrtYYWdFDTI05Ssuff0XhKyM9sRWBs4nCqRY34mjHKdbL2e+cFkhHx/4TRSRIzCZmkHlUJ2urcb1aVO+KQGXV8NeRQMSO9MuPGTvaI/+QRbYbaCEBa1KjJGbJtjWRfuWuSdIJCbLIG1IsxpXgJ9JWSkUa2Q1yp4ogZ5woBUSiX0FemDE2PI9lTiPdpPClXEmw8IcLKZznydaNibiQ34pEd44DFT1b4Ar70xS7av6g6vobdbX/7t3+L//Jf/suB/Wtra9ja2npIdT5oI+kNb3gDXvCCFyDLssses7y8jLvvvvshNeiwHJbDclgOy2E5LIflwZalpSVcuHAB1113XWv/xz/+8Ycchf+gjaQf/dEffUgX+mos5SJD1hBPRJRBwlRleES5d7vwWjk+DQAsnKmwcC+toKwbC5rcbemucK47pkiN2XIDZMpQDwT65yu35B+fzjA9ygg9MAu2pm+iPgw3JprBCEwGN8CBqkerMWlcP02PkyCi4QE0PQCMoc6FV6yVQDQTSPaV4w2NrqabT/ekg/Lj/QrpRonFL5DvcLvfBxdANBIur5QSgNDUrlCYrliNkG/6xjb9GEnh81roiDtCdm44BMVqQlC7RVOUIq5AqJQcczQ9Ikj/+SdvpvuZCORjjx6US0ZQsgiQEpskNuY+ek+TYJ3lcuTrlsfAWmH71ZCub9OgiFJTtKFpVrlMSGTIOakGxAfhFdC7SAfWY4FiTbl2NqeAZsCQ7DHwhnamu7TyZY0npVo0o53yRBvQxrgdIhLUnJf4tsUZQhv5mOfKcSWETEKEB/O9AjZkPTwuDMEH4EjaB9CeB7D6PRCmHyJSHfTMjnzzXIaXywvXcq3B9/tl+Vi2/su03yLTdY9BcxJXtZw+QuECN6sJOScEz3eslQZx1zbP18ZSMEnfErRGtUrf8OgUQ/+CRr6l3HscFYTMyNRLnQzvZlBnh8jMMYMLCtFE0jjnIg4ZehcKImcnhhMUc+IYBglhVcJRLSeeNxRTu1njuXSiJEHcqDDI+110v3XOTD41GAkTjXKJO3fz8D4JVivUq+Q7H10dEVI2IsFWwCQfnwGLd9XOdTc+ESEqtOuzJmMYXR0hXhFe6HYsEU0oWnZ2hAZIy53Se7bTgWL5EdJlnvO+PaQ6vkbLi170Ivyzf/bP8Hu/93tgjEEphb/4i7/Arbfeih/7sR97SHUeKm5focgYKIYUcSEaTyhsMkPkruEiu5hUCFMbxPs1hctHArNj9PHWfQ4l6NzakJirRQZe+sikbFcj2WvASwmVGQ2fIzThpuO2dlJxlFJwAOSHj6ZtNWiA3G8smEBlRgaCrUfaSC0WqHA3BGs3uQA3luH0KPVDnUdYOGOi0moJBmDhDBk7Ko4p8gtAtWyhcKNurdvEYpkwR5pMtytyMQ5iF1psuV0yixzfqFxgkGmG3iW6vpg0QAzIPHb8JJkSz4gpMo4AINllLWJsXNH9N4AbMFQEND2GclFARdQ5yV4NXkgfkTNrEE9j8Nq7tmZHBEk7bHhCqIo5RMkxWzUq4Daqi5t0JABma97NYA2MfB0QBXeSAckuDexK+Nx+5TJHNOXoX1SO4E2RRT7rujU6mNQ+c3lJz1pxdmCMdIYTCyb1jnvtcqXrXjvw767R0hnku8YUC8L8D0gAdOqypG936OV4SgF3yLnpQjdix+V2WQMpbPtlDKS2r+2gm8wdHty3joGGMaMmHxq+gRaW8K7CFu+L+fcLgCG5B67XRtN7wRjye8gPf1QvoViNnMo3QBwmzTi4BIZn6NzB+RrJXu2MMJkZOQ/GwJThA4KDaQpYYcaQbwYx8X7sq7cSo1wg95oddxSHy0to2yBqImDbRLzauCSTsSZuHkDK26bfo9KOvSRNEk1ogInHgmgJwbNp+jQWjk7Hzghj0hhhJrWTTOByu9k2zVYFkjEZqPsGnJC5wuLnuaNIVEOG6dF5BL3D8kiXX/mVX8FLXvISXHXVVdBa47GPfSyklHjRi16Ef/Ev/sVDqvPQSLpCkRkQmdUokxQBBfiBR8wkoqmJGGk0VCLQGDJyuRyRjztjjsRcLWnk62YAMIMar82qynBjepuAKBqoTGB2hOAmS7ZVUZvHwOsQQTHk8rITRWWPNSiOHVDH19MsMbxmD/JDywC8Fgkzuc1kAhe2rzlJHvQm2ou2LWdgjUY9MLmWUq8bZKP3oimcYKY1wrimwXFy3BCyBwKi1BR6zxJzP5T4ssl9/qy6Rxoq1ZCMzsU7GZKdAjpiUMxmHNfOILL51XQEsohMu6Mx9YHMPI+ISdpucgbeGML8vUD/3trxozTnSLZtdnWqu1zMwRRDfqkAt/mslIDMuTMKLR+s6XudJG4QymxHu+SYKjJimQFKxRtj/JoBX6YkTqkZR7prDWTik4RyFC7oIAij57IlT+P2OwIxcMBA6nKR/A+derpG0RyDKKynJYjY5RCp9rkkZIqW2GIoTQDARb0fMCLMcaFx0jKuzJw7r63zhC1bdpi91jyDMLwfHSBlzNtWrdvmBp22EYuSkMxWShd18HmQIcHayB/zyDSrTZQZY8CIVnn550rg0cdRLQhvAKWc/ouBxBgfWjCTasg0IuBc2uANKDiBSW0QGlFQ2pNymT74vWsFRo+SiPaE+xZUZPIp7vt3lFcUaDBbpbpFRdpFyb50CWdlQiju8N7KIbxgDFpwikID0LvEUa5ErX5Jd4PFoBWCVTS+WB20ZA9GdJa1jGor+9Y/R/vKJZJ1sUnGdbDA/JKXwOB/qOUKa56v+hLHMX7nd34Hv/zLv4yPf/zjUErhSU960hclSXRoJF2pMEBGADSQ7imk2949VK7EqIcRqkXqwulRDl4BE+P2LE9XYOMIyU6QJDOiCAvWeMKk5jR5ukS5A4bRtTnKJe4i4DSj0FyVtIUtoyncwFwt0HY88ihRPaSBxrrkAEKwmj6QnqQBs6wjCGFJxTRa5RsNmh5HsSKw8yj/ScUjYHSaYXKCDBlreM2eRnU94erz+OSHbkJ+ybv9sl2SLpCJF4kTBZCM4VaHFvXIN0uUK1T37EiEaoGhXCaNKACOAG7VbcvlCGBkqDm0p6FZryW6J9FCkmRqUK3So1syo4Eu7FOZMuiYg1UhRMLMM7OwG9zKtF4w/VKRO8xmIFcxQfnQvl+iKbkNmoxhtuarFxWcG6ZY05hcp5CfFw51YpKIojIBZkftCp+ht64oeAAgTZ2Om4xrDVFxyJj5r/7ATG3vsTMZzyssQHjC80M0Zh5yxOf8Ze3znJZSKGip5gzuoYFlEZUweq3bHvh6uKlbNfAJfUPk6krGkb0eOr/Z+9Dt41rXD9rdlUbQPJhsI4Ap1jKSKOo0qFoHz6nbGIdAseAYs7ORiPcqNHnmRScTZvKdwfn0mRK06DCGVLxnUF7OgmevqV7BfNRro8AbjWJg3dZAtCvQLEpEJok2r+FyI1rXfzxhXm4D1nBkmB2JnJwGQGhstRQhv2AWqLUkY9m0KdkuIUqF4kjiCN9WlJLXftyRKYOSJNlh21T3SGOptNIgWwrZtmwFE+yfilAPfKYCHXlx0q/V8sEPfhC/9mu/httvvx0XLlzAH/zBH+D7v//73e9aa/zSL/0S/uN//I/Y2dnBU5/6VPzGb/xGS5eoLEvceuuteOc734nZbIZv//Zvx2/+5m/i6quvdsfs7OzgZS97Gf74j/8YAPC93/u9+Hf/7t896ECwU6dOoWka3HDDDYiiL87MOTSSrlB6lzRiTkvUZBSE1HKG8QmB8WmgMQKFw5O7KP9qyRsxHBATjjhYKTVTTu4v6dGedJuOtVFWs2MA06Qm6yLgCkJi7EQO0G+i8lEf0dToNwXcl3hi3DUR3OAZXyRZ/eoMXTCaMVTLNBBakcZ0lyGeSGjBkG17baEmbw/k0Yxcjz/y2L8CAExlgk/PbqIkswEKQJFeGiufo46gpJWe2zQ9TuH4mqduJc1rivq66okXcKk8Sf0wo75w3KJFDpnGyDYbxHs0YjX9mNCYYKJTMSFG1kCp+3ApJ+I9v483dJ5FZPav5Zge7WPt4wSxReMKMo5RrsRuchElPYPJVZlD3Yb3VhCVgkwsUsbQpGj1i8xoEnTikqY/ZeJFL5/59Z/Bxy9dhWJ72RtJykdEOvmCPrB/DUe+QW3KdqRL2WCLEgw8Rkup+4AGE0BoRTdMvjP5t1CjzqTtijWQVFBXgPBciW9k9Zxc25RHY7qJb3Vwztx2z7lGiDa5exbt3+dFrs0zKO93Vd+xrlp8pU57GALDyfab7TOBdlh9WAF81KOjAATIEtMARHsWj3YmyCLmjH2ZMhTLApPTCqKkk8engcGZGL11swAphZEd4KgXjHzHVCKaNGCNdC6qcjVF3WOkAQaANSR9URXCuwVtWg8W6M0xiszMt32/1AMGqf1ChNB0hmKJIzVjU1TWgGKOSwgQGs+bGMxcMN2XYIqQccfHNIZXb9NG6pEOVLEsvPjuEkc0U0h2KlgqRW+DY8J569keWDB8KcsXLQHw4E+ZTCZ4whOegB//8R/HD/zADxz4/fWvfz3+7b/9t3jb296GRz3qUXjta1+LZz/72fjc5z7nQvF/9md/Fu9+97vxrne9C6urq3jVq16F7/7u78btt98OYSIvX/SiF+G+++7Dn/7pnwIAfuqnfgo/+qM/ine/+90PqJ3T6RQ/8zM/g7e//e0AgM9//vO4/vrr8bKXvQwnT57Ez/3czz3oe/8at38Py2E5LIflsByWr5GiH6b/HmR53vOeh9e+9rV4/vOff7BJWuONb3wj/vk//+d4/vOfj8c//vF4+9vfjul06sLx9/b2cNttt+HXf/3X8axnPQtPetKT8I53vAN/+7d/i/e9730AgM985jP40z/9U7zlLW/B05/+dDz96U/Hb/3Wb+F//I//gc997nMPqJ0///M/j09+8pN4//vf34rAf9aznoXf/d3fffA3jkMk6Ypl4e4CMTfiZdJHUalEOKRCjI3uxyeXUFzTgCVmSTEWECWtlpz7a0FDlIxk981iYHxaQ8caw7sMNC1tUkdPwJ4d1cgvMqgUKK41cEjBMbhbOMg32TOE8JUg6a0kZCJcEUczDd4E0PVQgdcGYl4xbWpiJGOFdLvB4t3muB5DqTlEDQdriwooF4GBacRv/81TsXjJRN2ZBJFMgRJYMs9vIh0hgXhqtldIe2h6VLRWwNkmw72fPY7c3LIoYSLz/HYy1kj2fCSgmDUQJRHILceK75vcd41vE6/JbVcbTpJKCWGp+3ArYIvWqYRWOdVKjnooUA25W4VS+gOKhLTRgfvXJlg4UzvV43KRkYtQ+2dqSaJQvq+0MGkYNuh6H/j4Y5CuC0RF4KabEQqpIr+gZIr6fP962jEbRRjeSwKk3GpKaQ7eaHDJIO3jt0uk0FWjAxQDaClZO/TkCqvmLlnauZfQQa1C1egQMdEH67cCja0oON05z/7eRW7sb9xv2+u0LxL8aA4MsnscQKPcN9VFl7r75pRuv1jXXVcRPaxrLj9Ke6TWnmcRpbAOVisKbbRISyQAqRBvTsALE/F2Yx/FKnDL192DUUUQ5W6RYy9ecp3OZIRsq0a5EjsF7GjGoTmRoS26Wiyx1jgBmDFJ+zyGvKTIYJkEmQwajSrnlH4JRrcosxwii5IqxFNJATAmsEVmfRexBgDRWCLeKygSb4HaOT4RobcukexJRCZfY7nIiZto2s0bhmRfmn41QRcRMDkWQcXcIehNxpDuazcGyDgIwngkykMwcuadv7+/39qdpinSNJ1zwpXL3XffjYsXL+I5z3lOq65nPvOZ+PCHP4x/9I/+EW6//XbUdd065uTJk3j84x+PD3/4w3juc5+Lj3zkI1hcXMRTn/pUd8zTnvY0LC4u4sMf/jBuvvnm+23LH/7hH+J3f/d38bSnPQ0siAJ97GMfizvvvPNB3xtwaCRdsYhZAy4iiujQXmlaM/Jrx2PmPrB6oMFnHNoaID2J8rREIRlYSefxktOEGAyi6TZDPGLobRjX0zHuFLKrJcKh167bxv5ojQaaygx0ggQDLZG7WjRE5NRLFYgCSHeAdCfMYUQpUSzxuFjTkLkCFEdkiM7pnkJ+qUS1nGD/tCVEU7ubDJgdo7osMfotn/lG2r4zN/5+H8VSLVDknkoJOgdIlVsLz0XgEqhjMgDjSzQSJXvGqIk0ijXqmwycDAIrnyBNmhbGoBOfLDfdS1EPuCeE1kTctNywqNCo+wzRTDvV9Hzbz4qzI9SuaA9YuLt2BNFiLcV0TTi5AICMr2qRBklrZGoOJOPIzcQ2Nx9vtCN71kuel+FytXEAMZAbVe58XdDArP1zbnJgdtRwzYwxrM3/bI65pkfvkaiFO4baQVFClvdh87g5fpA5LnRVMmt4MD8Rtybs0JiaM5G3ygPxElymjjB6q3uYbVPLqOlc1x3bNQyBg8mBQ+OF+eNZ1zU472IdY+0Aob3r3kS7bVr5f7euZQzXkBtj8/pReiCzz/3V7q8TFI2s/oLpBKmIzwNaPCUj4FP3nnQipbLmSK+aYGx8T8Uax+BsCt74xNcyJQJzscqc+5dJm1SajtERo0gwzqBtOiZtIvkU8xGYOnCbAqj6hsfI/TeCHSAahTonQL0Qo1j20XS90kiUjCrECyYydllguiYQFdy595KRQv/eiVMil8MM5UoCUWn0L3mx23gkEU1rt1iqF2LIhKExyt2iBkTA+/xqKJPJBIuLi619v/iLv4jXvOY1D7quixcvAgCOHTvW2n/s2DGcOXPGHZMkCZaXlw8cY8+/ePEijh49eqD+o0ePumPur2xsbMytYzKZtIymB1MOjaQrlJZiLPM+b6Y0ehsS248TwDFCUdRuguySR3aKYxwq1khG3EeyNSZpJPfoQb5BA+rsiNEPifzKKt2ij3B3fw1RQ4ZGum7DgshgsVwKmcKF2btQfhsRFwFNzt1xovSkw2yDiM75JrB4Jy2Hkq0ppqeH2LlROBIxLwl5iabwSTFNXdGHyeesBIXDhshH0yP0oziiwU6Zm17vY7bq84rFEzP4jwXqZenuPZ4COlbQ5n7qGYfMFZId7yWeHhOQWY7eBWp7NKkQTxSybUYkZZiUHRxutVklDFDE3Uk3qbN5JaFiAZlH2LueRnuZAaKK0Vs3oc05R7atSCpgyRh4NVD1bD8bg2udYXKUEYIGE+HHrP6UfT806gWN3gW/QuUmO4tFpCw5usn85Gf7QhQelaIEnP4YXhGRf/d6gQUaoxDNDO+i0IiEf6/DRMq0013aHANA+InrcuVAiHyAxNiJ36E687gcocVwOQMrMCJY18IwxkMrTxvzp7rbs4ZOYMQcQHDQPsbtupwRZn9/IOSFeUhTuC8wAq2mlC22j726OBkYDPoAgtfWamLQiQCzlr3Vc9MavKQPNZpIMCUQ3ZuhGZjfl2oUuxmysW/w9BgQTRkSA0KoGChWaBywBGibd8+hrYIWSCqCQ5fiiYlgrXzbi2XeSuDLG41si3TerATG5DhH3c+gOZBv0guvIoYmZUjG5h0vDeovtQ9m0AJ1HyhWWbAoEZgcX8DaX5OFE13aA9QCdEwJeakNCtEOjVvcIFBiUkH2E4iKPsbJUYH6Si/Gw1i6z/ah1tHv93HhwoXW/oeCIrXq7RghWuv7NUy6x8w7/oHUY8s3fMM34E/+5E/wMz/zM636fuu3fgtPf/rTH1Ad3XJoJF2h1MMUspe5VZP9eEWpEO/V6J+NUO/TjNY7T5OxnZjBGaIZkRftZNb0NZoFhXhHOONmtgYj+EbbPv+Xn/SEPLhSlzm5zSxSEJtJkslgpWlWa6L2JF8lAJ17Q6x/URsStXaISXWkhwvPMAbJtoHVNSABTK7STnrAIiF2ABMzT3p2q7V9INtWSPYYygvUEU0GTE8pMIO6xWMSVuzdx9HY/G4pUDMgvRCjXqAbak6UEBuJm0hkbtCzhKHJCF7KNwXGV0VklAQIjeYsSG0AlGsKk1MCax+n50cwvITsCdRLJpS/ZhifYiRNAMqh1z9fYHYsxfhqj7Dxmp5P74IxyioSrCxXDeI2Yuifo2NsZF66zVANyUjpQvWW1G8T52oGJOa8KjMROoEwp5hRfzfWBc+oX6oFIDZaUfGEIxkrMhiNwaQiDnDmvCLhOOQmfItodAjTB4asea4mi+CEaEoXcUJguMBPAPO4qa3rdg0WSW2dl3Kk1axOG53bq+PmahmJOHi/zhacg6odbPjB7fCS7lodY0kDfoSW9B/rXqfrXTPpTJzxyRlUKsCzxMseVDVdTCqgsbImCkxSIAmT1IE1YuTrHPkGnTc7Su8db3ygiWY09mRbGrExUpoex2yJtY1Gbt7T2rczmmnEE439U9ztS3dJagSgRY2ogOSCRrHiF2Ylp+9mdoQ6J56YRZ95gavFCKJoUBzNUJi8bKIEWGTHRNMGg1rNjtMYkFvNqLIBr5jpq4Zy3wUfRzNMwSuJdJM+yjrvoWwDJF/a8sW620xZWFh4WOo5fvw4AEKCTpw44favr687dOn48eOoqgo7OzstNGl9fR3PeMYz3DGXLl06UP/GxsYBlOpy5Vd/9VfxHd/xHfi7v/s7NE2DN73pTfj0pz+Nj3zkI/jABz7wkO7v0Ei6Qrn0lBT9cUzKrZl3F/UvKCR7DdJ94ngANGGrmPlcSCOaqHjjEzbqXYZyKugjtSHIMcCER3Ysj0kjCDE14esqUMOVuUayy1quNZXQIGOh4iYnlMi6vAATCpt5JCvbbJDsVuBVA+seKtZSxCOGbMMPdJoD+49uMDw+xnRGs3g1iZGsx+6emQKatLuK9T5/e02ZAfkF7w6TKZyxECJQsdHFsUaZ2ovBy2CGMNwiMP9s4gmHTMgQsv0HUI4oa3zwBhjcw1EPgP1TxuA7yRGPYzAJDO7xz6LueWQuniiwSqLJmMu3xmugfx8ZYE4oLzLojlmBxyO6j7pvuBrmnuOxf3b2XbB1AmQwa0bbFl2ana6RrMdI9uDyBtY9yo9nJRa0ka2Ipv48K95JnA6r+0SreKtKbKONVIAcdZEXYA5qdLlymXMPnMbwgFbHLDBeLmdMuYg6BKhS4G5rGUtBG7tRfpdrzrzjWm0yO5xQpLn+XB2pVsWd86yopjV2bBJkBNFgwQ0p4XfwRruE2SpiLllrZPhprJFA3QB17b55mQqUS/ReWpQoX++4t410SbrnlaybPsBqkqHQE7MvYy7kntpg3uXIv9u81uC1poWCqV9UpAFmDRJuF4fKLyQiG8qPtiAuk4Cyx5j3u+5x4j+CpEiybY3ZGneLCYvAWg0mvpYhmkqSNrELscUMvFLgswoqp4bKTICX0qmjx2OJOHmYLJevwnLdddfh+PHjeO9734snPelJAICqqvCBD3wA/+bf/BsAwJOf/GTEcYz3vve9eOELXwgAuHDhAj71qU/h9a9/PQDg6U9/Ovb29vCxj30MT3nKUwAAH/3oR7G3t+cMqfsrz3jGM/DhD38Yv/Zrv4YbbrgB73nPe/D3/t7fw0c+8hHccsstD+n+Do2kw3JYDsthOSyH5auhPJDFyQOp40GW8XiML3zBp/C9++678YlPfAIrKys4ffo0fvZnfxave93rcNNNN+Gmm27C6173OvR6PbzoRS8CACwuLuKlL30pXvWqV2F1dRUrKyu49dZbccstt+BZz3oWAOAxj3kMvuM7vgM/+ZM/iTe/+c0ASALgu7/7ux8Qabuua/zUT/0UXv3qVzsJgIejHBpJVyhWLbpyuhq0f/80R7YTY3IySCeySau/bJdWMTIB9q/j6J/TSMaGRKk0lBAol40+EoDeOkHOljNSDwSqISE4NjpKpuS208LD6MkO6XS4iIseAE31yZRWRnvXE98lHgHpLtVVDSlixK4O62GMpTsZ8vMSpclNVC5yxGOjjWRWa6IChp+PoO5cQmK1mYzKNw+QJNmj1ZlFl2RGuiYq8W21H7pFUmRCdYjac3bEhokAi4B6kdquMoXsHu7QkXJVo3eBUf4zQ3zPL5WIigTFEqVBAHwKELsatekO8g1CvgDj3utRxI09TkXUptXPEjKXbJVoFhPKYJ5I89yF45o5dEOYyLs92uYSTg/JutuyTa87ZVEbmQPxvndZQhnUgMGpAqdLJcSZGLzRHlVMAyFMAMUaUC9KZBeFVxcWtJKfCQ5xjvoq2W/AGwG9aDlXbK7WUCuizD4//3O76OAP6xxgkJGuWy/URJqX2qSFytjSITY79CeYRFpRZBYZM7/Pdb8F12Nme256lbCw9k+XQ7o8j+jgfbpzNXzUXQf6chwl5sElroP7Ca/XQXI1Z6iHEaBpAIuVMnkPOSFKIHcbNL0jMugPUQeI7Njksaw6d8xJ9d2iud2IWlKEB6bHGAb30c50T6JYEZitMgzO0T5RasyOcEKTQGMWU4T82rF3dC19O4BX9bcBJRZxZlpTNFqQ006mFKjRuyRRD2x0GzPfqkGFG40mF2Apd4Et06MRBV40mauvXGKIJ5GLwqsGDBLdpIhfuvJwcJIebPm///f/4lu/9Vvd9itf+UoAwItf/GK87W1vwz/9p/8Us9kMP/3TP+3EJN/znvc4jSQAeMMb3oAoivDCF77QiUm+7W1vcxpJAPA7v/M7eNnLXuai4L73e78X//7f//sH1MY4jvEHf/AHePWrX/3gb/AKhWl9IBj2/+/L/v4+FhcXcfMrXgfWz0gxNogSovQVQHlEon+WHvDgnMb0CMPggomsmCgUy4JSQZiJqhqSeGC1qCEK+uDWPiGhIobKDDC8MSTg2H/gxZGAZ2GeVjTzUD5g3FU1CWBa/33TpwFueFY5DtT0KEfdA8oVY7hJ4sssnmnQpF4h1wk2mhBaS/BVkf8t2dcYnG8gMx9Sa5WlW7wPyzW3xkBsJvXCH5OMKOrKJZs0Rp+oSNQOAJqBQrrBW5FI0RQYnCOuEACwSkL2IqjUf3jTYzFURG5Hume4VAIuYkuRkSSqwChQdOzy58lHkG4VGJ/uY3KCO/eGqOn4YtWfF4/pXOu2AIBymfrOGU6Nz7PnBnvQPbu8cMeIuJrsB9FtfaqXBzyzcpnux05mzYDkJqIJc4aTNfzSXS+MmuxJc76ROBhyer5dd9s8QvI8YyMsbM5uNud3Nmd7jr+ra6xQ2o52fY6PZOsy/z5g+HVW404dfJ60gW4fc2D//R0buNDcCbrT9jnHWvJzyK+yfEP3zjb0/vHap6ShyLPgclapG14eINmtEW1PwMoGKGg1U958HBe/IYUKuLvpDl3Duffs2FPoljI2tBFsNYaMEtR2ywnMNzWybQmZ+PyCTc7IqNJwxkbdpwWcTRpuOUtNzrD+DXTM33/ap/DBD9yCeMyQ7pjLC9MXtg9qMq6aHnMGFyW8NQtPcx/WqLOJqfsXK7BGo1qKnVxJNaRFGNP+nmdrIMNwz3dBUxf4zL//Bezt7T1sXJ9uef7zn48PNhpL3/QtX1Q95X1nUb7rHVhfX3+YWvaVU378x38ct9xyizPiHo5yiCRdocgUiE20hYr8R88aGqiydeGWdVYLx4Z4i4oh32ygEr+iybckNCfdjdIgC5oTgdeGndsBPQ6y1gOUWiSewEebVTT42QGz7hE/abbK2rnbtP3NGwjxGE6J1qoNV0OBwVljaDTK5G3iiMc0gY6ujlEcoYHTImP9CxWivQrS5KsTRWTysPlM9sWycOH2zs+fMpPYMmgmJ/0jO9BOr2kQ7UTonyd0BQDyS7wVMk9GlIaKgWqRYKp6QET7aOYTXi59poBKBGbHqPLJcYGo1KgWfNiy5maQ7UxeooJbVao0QtMjQ47ZqELLpygPGoPhNi/R0qJKd8xzaOBSZFgjx/ZLukvPkilvp0RTo8rdJ8MMoN9FQSiebcvix+n9sHnhtDBRRn24KE0tGLLN2oVqNxnlJAxTuDBGaMUBY8lyeeCPm4e2tHYHSFtonLbQmJDoEyIiXd5SB8LSczZspBSFyXfOCxYdGldYXQfGXtcgOoCiBW098GMXnQvuzwE34XnWuAs4gWAgArflXEXWIGPOEGLKpqQxhlENF1WozHtcL8SA7kFMa3BmxwVNofZ10FeKDHkXbburoWKKGrUIc7EKJDvUDht0ksxIP82+z9FUI5pI8IqhXPFTDr23HhFVCS0CrFHGa4p40xzonadG/e9PPhaDTUZItyVgR+3+rBYpFUsUBJLYRVHNAl5gYscd0y+DCExpzFY46qEZxwuSREn2G4yvojGmt85QDfzCJZ4AeCR1kg7LZcuNN96If/Wv/hU+/OEP48lPfjL6/X7r95e97GUPus5DI+kKJZoCGBoIOgeUJUMmcK4QG5LdZCa32ow+rmTEoGKGeL8hIUoAMo+gUobh2RoLZ/yoLCqOum9SWAzNhF160UReAr0Nmvgnx43uxxIgataKFuGNIXCbD9aiMUwD/YsmiqVSNNAZ1EiUCqKktCRWnG1yPENlBonU6AjFUw21Z9EuM6nmApqnmJyITZsYkn0NngnnWhOlphDdHsPoWjqvd57uz2bRnpxk2Dum0buPeXfbvkAzUChWuRukmxwQAuAGeRG1Rr7RoFyJsHedSa+QETKz/LkKwiQf1oyBFzX6d9fmmAVMjnGHCNpnynbJcLHXUwkN4jbFR7VE+ihMe8FJgAyfZM+7AZRBymyUDgAXBWdLNKMVb1R4YrtMzDvloig1epcU6j7H3o10TDQ2gpI5IHuWiEsr9DDiTXMg2VdorOBeQlGSdc8bBLVkYCuRi0pK9xSaHqGBLv2NQTC0Xw+4KDIWuLxMcvgrFjePhQaCRssFpy2SpnHQsOkaSQFiYxPHtor2BtKBpl2p7nnbOGioXdG4eiAlNMDmIHFawBtJYTvtPXOPUtsDnLFkfWZMO/TGXkTGDFhMoFKByIS6xxtjLN2RYP8099G4PbTc5EwzlIv0/k1O09tw8uZ1nL+0hMW/zNwYozkDFEe+ZVzSWzXluVwQrn1W/6vJfR64uk8udP71tCoa37mAxTvoOGtwLXwmQhwI1QLkNdQcUCF6qc03Z8fCHM6wtQsbMG+kA0A1oBQkbhEGk7Nzr0E0aRDPqLOLjMapwuRc5BLgUzwypYtoPtQ6vkbLW97yFiwtLeH222/H7bff3vqNMXZoJD3cRaaA7gNg9LGJQLBNm312Qmt6ZmVnPrDJcQ4mOdJ94T7KakAISjzWbvUuU9ZWyTUDu0zhMtQne0C23YBJ7TLUV4tkFLFg1ZVtA+muQrnoXWTD+xT6ZyZOCFPmEcAFnH6bYKj7JIppYefRaYZyhaLnbHLLfFNDlBQGbDWXeM2RjLR3DWoagGervu3pttEl0UBmcovZyBe7gkv2KBw9nnj0JdtkaJ4yRhnn6N1Lr6nVfbL6VUwBxWqEMHpIxTR4Fdspsg3bN4aDAb+6tslyfbQNGTs6goPxVUQ0EOtOnK4JNJlZnYazLtdg2g/2lmcUukqsflW1QG0QJaPQagZUJpw6GWuMr/IZyKMJg0oEqgEgbyZ/r7ynD7ZBk1eya2dQ0x6H1AGT4yRd4AwzZt7nCNA2CqjUUII510K21SCaKVSLApWJ+NHWvRqgWc7QeYjFvg+Ad02FLk63HRoyc4wYDbTEJQ9EyYVITRe1sceYfjug33QZI8kfMKdNc8oBZW773fH2MV1Bz1ayW3iD4ACCZlFu+5w1Wm5DkhLRbeOOU9QbMgElCEqNOcfgzASi6mH70cawTttjjIrovZscUfjH30apJG7fuwaJaHD29NXobXjUMt8m44KuR3nhmow5ZKcaEtozO6FRr9BxT3nM3XjhsY+hNnD525eejrNb19D9mHuKCvpenvD/+jt8+AvXAwD6n8jAG89ntMasDma3VrJksy/ZM5zJ4Lgm5yhWfFRvsUz5IZmKPb9P0rdj0fhQ/f5LXRi+SMP8a7zcfffdD3udh0bSFYpMABGoD9uBSIyNkCP3vBDegKBw87FYZGl6wn893EyuTcacWjVvNMoF7rNVjymUe3qiPYHvXRe3VGz7FxihAtZw01SXioF0n0aE/rpCPGrQDBMUqzTiNj2GJvO++ulJjWyTCJWioMryzZg0Sgq4wWl8gkiOk+saRLs0iGYbDCpizrBh2hs5dgVX9+FWfnaFxhtSvLacnf56g8W7NOkUpZYfI7CdD9GfAokheJdLQLmsURkXkuWJidI/oyY3LqmUoVqie063K6gkIpkD+FVsPPKTUz2k85iE0zxhDSUALo3WSrnIUByh4+yArAQgCkpn4iQbGKVfsG5CbSZhXsIll5WpX9FmO9boIzjGklRtCL+OAHYXLe8HZ3173cBv3L92ELf9rOK28aEiuEzoAFAuMCQjjWLFELezGL0NiXiinGuGSOGsTQjW3oVzOUOhixgh/GdnIgf8wO/sCd0+3iJLztawhO/7QYAOcIlM4XOMKecetW1vVdSpy7rbHoixFKI/9j5CpMweExhqLh3MHDedQ+yUPy+U6rDX8eexzrW0M5SYcb1WSwmS/Rq9+yYoF2hwGJ32EiO2TVoAfK3EzRkJEb53/dEYxBWa5QazVXoBeUNucF5Th/Igma1L2l1SuH8zUPjFb/5jAMCTszOQYPiz8eMBAJvTPiWCbnyQh4rp2/rUxnEog+yopO0ql5lJIC39ojUq4NyvTmrDuPztGF6sUOaDbMfLYtQDGjNDHlk0heEcmkWX1NBWtPJLXQ6RpCuWy3GRGGPIsgw33ngjvu/7vg8rKysPuM5DI+mwHJbDclgOy2E5LF/15eMf/zj++q//GlJK3HzzzdBa44477oAQAo9+9KPxm7/5m3jVq16FD33oQ3jsYx/7gOo8NJKuUFRPg1mSaePD4XvrtOKv+0EiV2PhtwipILeYz7VEyEM0hZPQZwqYrTHUJiCid8GvBkM0xqYAyYwrKB6Tq8SiBlYksFwUKAyfaHBBAUqjOJpg52bDQSoImbCrqfwiEc6zHYnGiM6xBkh24ZOIgkLkNQeSjajFg0LtV4e2P1jThv9VRH3nxBZjUgq3bsbpmkD/QgMxrhHtGfK47GH5cxGmRz3KRqRt5omeMRxfLCRQx2MbMmz5WzmYAgZnibRTDTgR77c0pmuWy0FutjAvm+WzCIPepbsANAnlJTYiJ2eY5IC2q1lznpx48UobicVrvyJmkrhYyT4AW9eAY+EeUkAHaKVfLnFMj3tXnuVWhLnoZAawwB2sYnpPopmPyHEpZKZBkt0UGC/6iELNGVQcIdtWiCfm/dQctUUvLBphEVN+GTQE/m8LkJmDIHVJ2u6f3WMvxx0KSxedQlCH7vwwzy2n/M9A2yXWRazY5TZCN6JpUysiTXk+W/f+Wk00kZXzcs2FKuA8dE+CREN5E6Yp8Tcb5ndTggFBBBpjRFxOGonhGfoGm16O2REfTNEMNFSigErgX/7d9wAA9rYHEIkEUuVkKiiKDJRXEUA9FK1gANuG/iWNbFvgl/H9tDPSSFdnUEbxu7mYQ69KiCl3gSfxmBDk8vYV5OYe4xEhqxYBtpG+TFFbAH+fWgDT474dg7Nwz6/uA2qbId1T4D3aKSpKQaSFlzqhRNFwXEWVMIe8PhLlyyEB8NVSLEr01re+1UUZ7u/v46UvfSm+6Zu+CT/5kz+JF73oRXjFK16BP/uzP3tAdR4aSfdTLCQbTeA+pskxMk6SPb9PM8PfMOdZToqKNeByZXGIyieFBCj8Nd31BsTkKiIAR9PAdSUBMTIRbWagkQmRay3xOBkzFEuksWRTBsg0QrorUC4zn+fLhhE71xf9na4Jl9usyem+mhRukA7VoN3Aqg20bcnPkdfziW26lLGPIgkhc5kBheE7iZr0oXiTIt7x7ONqQJpIzp1nyM/WeExGRNpUiXe9NX1qP1O+HxqjMN5kmXtWK5+pMbo6dvfSuwjUOVqRgbyhwTffoufX21BI9xiyrdqRYWdHY0R3MSjhQ+urIUc8Ym7Qln0bUeiT0EYzo6W0r90EF03JIMvP00E65hid6mNybYP+XfSpTk9qiBlDsuv72GrJ2GibYk2DV0Tmtsaq5ZOk220jSWY+lDnZJ62byQmOwTmT+HSq0KSc+BeBEWHnXpsrda7yNdBaOEAFbqYwUi40roJ9blJlc37vFuv+6hpXuuPamuces8benDa4+7ncdef80EoQbK4fGmvOTajbdTDA97F1o4X526yxFfStvV/nrowAxYOEt0pDMWNkW0sv8Fu2uEoxh8xjRGP6wAbnY1TDyH37yS6j/IYzjuIeclfkZpEUBRGLWgDFMkM1pBOZNEEtiXFdgYz8Kqao1xMfpH3lokCTD92YFkVAtaQhc43G8JaYjGkxooiDCRgdJu0pBLwBxJTc/C6xd0pWKK99BoR6QO+/DZARJbUbjLtFghYUSWfHEgDoX5KA1JQjDqQxtfvAsmZ88eXQ3XbF8mu/9mt473vf25JhWFhYwGte8xo85znPwctf/nL8y3/5L50O0wMph0bSFQqfMQjmOSyOpL0A9M/T4GRD+VsrfZjVTEN5u+wqiNfeJ14s075qaAY0y2XqK6hdTkRwy5EwiSGTETAzZGdpkRnLGWI+D1hqJj0ZA/vXUhi5S3UhjbaPuUebFHe25sPh4xEdF088OiITj7K0dIS0N5KYpuuryBst9hgmDRIDmpyL1eAYTaR2FUWOF1UtRiiWjTG1Rvt650RLqJJJIDG8HcvjESW1rx74iZHXNEibfJQUypyQEWPvrzZ54EQBFyJsExFbg0gUEtl6CVZKyucEoCcVRCmhEuEiFGdHBA2qwQRaLgH9CwFfKIJ77oNztbkeg0o5dGyI8ZXE8KyEiiLH38q2GdJ9RWlGehbmo3u1gpXHb7mEnUmO6d8tuvdD5hrxmAxHa2QWRwCV+ugnm02+6QNjg8LlW9rIICBQMTT/qeA9shFvlrx+GatirjECtLk+BrkK62jlNQuPt9e3585BiA5cJ+T+cHgOUhet6lzvQOLYeQZSZxKzPB7LI3JyBCGPyGwfMKbQrickHx+4p8uUVmRfB+UL74GBQQlNaLLZJwqFbFs7CQzi2mlEIx9JyRQcmmyN7QkcLYEAAQAASURBVGxXoVzgKI7QdmQSWNttgFBbLoGoRAsNlxMvZ6BihnqTockZqiUrW0ERcGLGgrRDQL6pkIzMIi9lyLcVmsx/I8oI8VLEqrm/0iLFtM3NArLOvRXfpNSBvPHPq1gSGJ6ZgdfGkGIM6e48ka3D8kiXvb09rK+vH3ClbWxsYH+fBtGlpSVU1QPXbDg0kq5Q4gnAUwORZ4H7RFGEBW/8xCQzjXSboRn4EYtLGkzsefUiJXWNx54QqRuatJzbTnJ3PZ9fzeQQiwLDyYRJR8EHLyqzWgvIlirxKyvASBkkvp54HJB5LTGxJFeNEn6EFSUZI+mOh9+rISEiiXEB6sgjGhatmF5Tg08Fsg2vlA1QKHtI+FYJQ5kxpPuxuZ5CNONUZ0WjE2voHHteE3k3m+vPCIiNoeQHX41q4F2aTUmRX8P7FNIdMsAmJyKMTjHkm15RuO4xDM81aEx0m0o5GQqCORI4twkwY+4Hd2MAuVx10qBnKZwMguZAfomBNwzx2KySKwlwgDV+wO3fN4XMBk4ZvH+Rohx3b0ycTpIo6D3omdyQ5+5YAziwchYYX20q0kBxVIJJgd5FuHbWwyAEewiXWNkigkqQezGe+lm7SRl0ggPGhrkM/Q1n+C5q0UVz5rm/ugbKPAQI7X2XI2lDey0qa7RcqZ7LGnGh4aY7f8PrBwaQK3Zb+nq66JZG8E3Y3V33HUMLbeoapS7K04zsmjPoWpNqtK2D+/NsZCqTGkwxKAY0fevH1ehfaNBbp2OKFYGmRxFqoZE0Owrk64ToABQlGU84VGwWDUfp2GqtgegbGY7NHMmmhig1xiepc9I96gQbPcuURjTTSEYUDQoY0dahpiCSk8xcj2HpYo1oRt/y/nUZRldzJPsaoqY2JRMal3ij3YIxNlkOpkeMW36ZIRlrZDvSuc8045AJI62nICJ5ejyDqOj8JueQZWCxfanLIZJ02fJ93/d9+Imf+An8+q//Or7hG74BjDF87GMfw6233orv//7vBwB87GMfw6Me9agHXOehkXSFwoyrTZuUDi2DYUCTYL1o3BJHClS65xWjhwrq6grYSJGtWz4QJ992MMhY+LR3id7casCwd7NCssNbis28E5khEzovDBMWZUcMjvlIDBeNZSc3O/hKv0IWgXFdDZhL0GvbWS3S8XafzKhuey9i6sPrrfFoGyNTj2CUqxq8ZE40rjEhvlEBZ5DMVjmKNaBaVsgu0b6oJN6BSwxb+8nc9pVM/HOLnAiegqh8mpLBBQlRKBLQs12lPAqVb9AN5pxBFBLJjs1TYjvNz6JaUJh+04vQ9Gmw1VFbJkAL6vfZmp8Eo6kxlBmlhgGAZFcBUruZUQuByak+ygXmDLemJ0zkHlAeMSvZWCPZFBieoboXPyvcM7bPvV7UgGKtd0pUgJ55I57XPqWEQxBzevd44ydBpoBKsLbMge0P2zUhX0n6fa3JHjhoENl3s4OkPKCBvYu0WOSnc40WN8YaK0F7L4t2hcUiO/Pa1TGSuiH5l60yaKcDvILQ/1BSAkBb5DbwpLWQIhvFpjxiyLR27jenoWSvAe+2hSA5Csu9Gd7bQCYZpsf84iSaAf1ztBCxx4lCQiXcfe/xyLwzSxwLJ+iFrLIcotLoXSjQO2/e7X6M3ZsSxwHUgt77MGo2ngBccocMATQOTY/HSPaF6wOXvslE+mZbNXhJD14aI5BXynAgh6avBBllu7WLjOUSYEbw0iJqxTIzcga0XQ8YMOpaxV+a0kUzH1IdD09TviLLm9/8ZrziFa/AD/3QD6FpaByPoggvfvGL8YY3vAEA8OhHPxpvectbHnCdh0bSYTksh+WwHJbDcli+6stgMMBv/dZv4Q1veAPuuusuaK1xww03YDDwpLInPvGJD6rOQyPpCoVJr7cBkEsNIAIjMz5qtkDYbRQp1AAio3+kUgYRKdSxBrPwbgGnim3VmBmoHsdRWgQwbCBnsYtMctFkIJcIzDlcepqIJdLmm8qteGYrxFMqV5UTHoymdGxm+AP9CxJgRAS3S4zR1ZzI0FOgf1G5izNJub3sKo3XxH+yJPB8W0KmDLNV7vgJ6XYEZRKwWsRCFEQodlwAE4nCa+1W88UKsPb0C3jJ6Y/gjW95PgBg6fM1psciH6nCySUmAkJ7qIZtV6TRlJCjfEO7vpoeM0liY9vHGktfUIRumdX1dE0gHnNgmSqMSoX8/BSaMcCIc2rOKZlmyjFdMyJ8JhlxyN3iNTA7phyq2Fs34o6MUr4AQC9lxHkySFK9nDpXhONFVYYnoQBuVrJaUSCBdWfad1ZFHr1LN+mZxEEi0nLZRFqaZyVjIDUcL8tPAwNkDagl5ki3yUibBLttAU3qSLg+ts9j7sr3ctwhiyLNOeeKCE+AGs1NSmv+zQ1q6fk9xkUqvN6XiucgPpe7j45bsHus5Tu5nwz6FmoguW0FXI6H1drfAS3Cb7L7u02QrIV33WmDSrfy3zEGDRMVFyClMgKUSUCajDQW7y4hqsSNTclIOaHUaGoiIhtFKvsBIboeANkGR7lOhO94SkKN2XbkgjXiUYXeeuRQKqZMYtw17sjVNjelXPB9qmoSTy0XqJ3RzHMJayOK2uQp6X/FzAXNNClDvpWgNHyneKqRbdZgNenLAYRsV0MS27Wir83ApIgy32Q1DNC3w/IVUQaDAb7u677uYanr0Ei6QimXATakRKHlinJRavVQkKBgASR3ERlGiQxJCeRPM6ETRYLmC0OkN44wXaBJcPBxOlZm8IkkFQDuJzMmAbFBx7cI0gpgAZdARxpaMYggtFUlFFllB5kmp3qTXY5kl/aRS8u7TvKLBalRM+aEHEWZoFgiNW2rmjtbi9HbVJge4ci3qRG9DSDfqFAt2NxtCtlGA9ZkbiCKZhoyZShWfei+i8gJpBGYIkPNTuDJPnDxr0/gNvWNbsKy6tpWqVtFxGOIZtrlxytSbzRZwrxKOPJzY2dR1kdy17eDs5Vpg3Zw/PQqsjaK5YAcDSAqGFTUpwSaJmqGNxrlUoTJce4MWKDN8RIV/ccr7lyF8URDFJTKxfGpthlNMIYUrgRFzYFRPiz7TqiY3hmrKJxuUhLO3SeSNS32IuTrDPXQhy0XVzWQjytR3NF3SuTJvn/vACAyEyqTnr/CjEu3OKkRG2JstUBpGUSBA64dxz8K3LghEToMX6eTgt/RMXACA+GA4WMWBS3JjSsZZN1ruLpZOxFueA7QMj7aDZ9jtIWGWugmM+48wHCFeKs5c1127ju37QiOa/GPtNm2k3Sn3UoYjn0gBaA5GYShCCtdg0Fz7d2WinhKdtFVLUQQFXF26h7dUN1jmB1l6F/wsgPTExnqAXeGtkxovItH3pAvVyjKbHYkct+dKCWY0i71UrLvXfeW36diartNz+O6MOBr2qAZzeHSK+mIOFUqDsadBhhfLVqivTLlUEmCxtyfTbodj/2CKgxmAUzkbJBy6EtaOu/yQ67jsDzgcmgkXaGoTKO3xZDuAlrwFmfBZak3aIhKaVsaReU0qcH3AfbRIZo181Z+yy5mn1qCZkB93CBQm0abyORa0kIjmjJEYz/g791Ehpo1dGwbbAZ6d33Wzilmo9xmRz2RszGpOHoX6fpMKag0gko4qiEdJBOGYsWstnKTOLbPkO1QVm5Ldo5HNWZHUyTGkIpGNHpkOzWU8dEnOwVkL0Yyjn3Ot5RSYThDqtA0mMW+P/vrEvk2w/TcMUSmH6ohM2lJ7P1p5Os1kj0/05dHMmiDjM3WrE6SQLoVQxkjcHYkJl2kfeWQhNHpFDJhyLckimVD1I5MnjTTp/WAISo48lHtUpWUA4GoVMh2Gbh5hnWPjFGXe88QzsE8QlObRLnCkOQBWonriLs2JXsVFs4KZxTavmt6JCWw9Bk7aJMhevUN5wEAn/v8VRBno1Zy0mhX4NqbtnCGaUwSali2yRBNTVZz8w6lWwGyAZ+/ix0vUPaoI+LlEtONDL1zArHRoYlmdI8OgTKGwoF0H5bHE3KSOiWM9gI6hpCrCAeNpCAU3rVBa28ImfN0yD+yvKqAX6U7dbt2hjyrgDvUapO1RUJjKkxbEdTRNeg0g+Pp2cjHeUraBwzJYOI8oORtzxWADljnNB6wYKFiEG9mDCWYfweGrY6YGXe048hNj1JARrorMT1K48foGobiqAI/Qh+z1gDWM0RjL0kRzajN02McxbKP6Ej3tTNCpieAYk0hGnP3ntloYjAftMIbjSb30bmVUaMPcxmG0a42qjYd0+LKGjuUnilGk3lJlmQE9C5JRKWGNohatUAIvUWvRUkSLY9EYbjMQuDB1HFoJD2o8mU1kl7zmtfgl37pl1r7jh07hosXL849/vd///fxH/7Df8AnPvEJlGWJxz3ucXjNa16D5z73ue6Yv//3/z4+8IEPHDj3O7/zO/Enf/InD6p9/fsY9EJABjb7tbCaH9oZN/klIrk+9giFGH3y4klkm2aiMCuQ00s7+Oz1KWQpEPdotEhWpuglFTYuLlHlMw6VMkQT7iBcLTSaoUa1qiGmJjy8ZC0it4qBiPlwW4ASpIrafPxmHIr3gf6FBrExaDQ3xp+CR2NWuJHwZ25QimZAuWQJk55YKVOf3w0AdMTBSwll3FHVUopoJpFulQ590WtpW6jPTAbJyBNJZyvcoEb+fpJ9tFaC6a5EujE1k6NBxs6U0EkEaI1sk0bNYi1FtZKiGhjoPSNC6mxFgC/QvulRhmoRkEFyXm10qFzYPgegNXitXPhv3U8QTSUZyIkZRAc08LqIRUXGAwuS16qIVviD8w2ydRrNedWQq8NEtzHB0D87gRYc1QrdC63gOWTuU9QMzlYQswZb1WkAwFV7CtG0xu6jYjdRrH2ywfRjV0M8hmPx6QQlbdyzgvQTwqGaya7XhrKTbvLEXUzGKfRmChjX8rBfYF8D5azn3n9RknuTNdbYh0vQ6sbkrhsK8IbFZaK0WufNQ1xCF5d5D0KE0uqXheHvIeFcc9PWwCBpp/QIzgsMPndIl7zeMQJVkKQ2vL95pSUBEN63rde2Z547cZ7RFtRjSd4AwGuyQJWA07mi56hNP7JWXW33HoOEN+Z6Gwr5Jo0FpUkXJDNg8bpdLGUE9ZzfWYSURrfI5g2cETLT9IDpSX+PvQ3tBHOrBYbhXRzDs9ItsIoVgWLZBH0E/cBrvyiR5psVAbqT7MEZNqHxRP1h7kzTIrJaItc4AOA+jnSXIdusYPNVKxFRAI6JnGMa4LuHaUm+VsuXHUl63OMeh/e9731uW4jLO3c/+MEP4tnPfjZe97rXYWlpCW9961vxPd/zPfjoRz+KJz3pSQDIkAo1ELa2tvCEJzwBL3jBCx502xbONBCppOzQhRcrqwfA8t0a8VhjdsQMKGYg+fwOLcurKkK/ogE7NyG09/7hdZA3S/BhhWGflkHjWYqNcyvO+ImmDPWihsx8nYO7KRR+erWCmBl31C597Ps3mYgSc76NmAK8cBuvvYhhtqXQOzcFlJmIGYNKEpQrMWZHDITeJ02jfFuiXLD7SGNHxXDhsUJrDM4WziegE6us679CmXE0PYF0u4IoDdK0rqGiHIXTPvGJL+09T04yFMclkiMz8E8TGUCWAToBUFZx2UexEiEZGS2ls1bS2uutRFOJyYnIIVei9AOmhdCbvgnTD7hTKqZr9c/7+7GuGmW0jLKtBtG4RrESo1ww70IMl5kcAFAC2Tatdq3RqSsAtR30afhdvKuAGJeADT+OOIVmS4n0Ej3AJOZId2JMTsbILKK3XwKNwsqnrVonoPII2VaEdI+OSTemSDeAaNLDxt8zN5hLyFy4HHP1ABhfJzG4S7iV9HSWIP/b3OSRI2tqmqVQKwpqKDGL7PfKIEovYhjN4HhLodtsXkRcWOZGo3WNBwTHWNBDAlxqr1RtD7P8MLvgsG0IpDSskdSqW1M/tgwndnB+uVyEXyhvEd6P5SLNu55NnE0Hzkey7G9h32i062Od/uuiWzoC0HijyBbFmDGSOu97eF8cYNw/02imwZSGTDh6m2ZMURxFuYK7ryHrg08FehsM2ZYGN2ODKGlBxLY0cpMYt8kYokKBmcXFymeNIKpRuAaAfFMi2WeoFjgJRAKQBg1y4rsNwMyYZxEvpijEP3QJy4TUtfN1ame1FKFY5mj6DLmJqFWxUeg/kbrxQ0fG4DTcrWSknbr4YfnaK192IymKIhw/fvz+DwTwxje+sbX9ute9Dn/0R3+Ed7/73c5I6iaue9e73oVer/eQjCQlGBDTR0ojBO3PN4HBfTWY1lCxSRybMaiYYfYhUkzLakIhsm2F/WsMOnJNjfR8DDnKkK+RL2xnu490S/jB2ISvl0cUxFGaGKf39pCvM8R73KETNsRcFOaL5ySQmG3BaepYF6CKABaSUiMOGASAaY1iLcX+NV7HiGmaMEXF0b9IMMrkRIR6QPB8YRJZ6oiBV8rp/ACAFhwy46gMidLyjOKx8FyHokGy20BFJgHtbo1oXKNaSrB3fezaiX4D2QhElgNg0pBYonidMxRLMWZHgWzThvX2kV2YoFrrYXyVqStiLc6C1ZGKC+0G2nhEgyqDX2nyhh67RdiSiXaq2qGQnMwjFCvcq3UbQ85q83CjFN4sA9OraGe6zZFtAtUi82H6g4iMJDP4qkSANRq8rAN0RCFWGotfaMCkJZ4oCq8PjFM+a7D4ubHbZo0CtEa6XUDfTpZa3/A2HFKWAP/g6f8X7159PNQFspLUVuq5S6b6qCB+VT3kkD3jQu3DrejtdrJvEQO45+A0gUJ3W9eF1ClOEHEOcuKCCBrSAmLKc2OcSGbXlRZuBwR7dz0cNDRavKjw+qFhExpcAbLTAo90cHrHndgydACooJ7Q9RYKTs6dmgNkrkXODu/F1K2sLADTjq9t3cZMwZO5Xd3ECVJmp2aE6pD+EO0bzjT6FwH5BcOtixi41EhGMhB4NcaL1Eh2TV0RJcy2gRO8obQ69P1pd16yr4iU3fOopU1FAgCo6N/JRLeRSQU0w3Z/iA2NZJP8b9EoBpM5mixy33KTUTviqUKTWa4gWtkO6pyBJ4/gVHqIJD2i5ZERd7hCueOOO3Dy5Elcd911+KEf+iHcddddD/hcpRRGo9EVM/redttt+KEf+iH0+/2Ho7mH5bAclsNyWA7Ll6eYBcUX9d+X+x6+ysqXFUl66lOfit/+7d/Gox71KFy6dAmvfe1r8YxnPAOf/vSnsbq6er/n//qv/zomkwle+MIXzv39Yx/7GD71qU/htttuu2I9ZVmiLL2j2sqXN30OnjFDqNQYXDArHsEwPRZT5I9DaChKyRGk++T6qobcRSHxqaDVmQA29siF1P+bDOPrJYYnifk32ukhPp+AVwy1iYrjJwsUMqf0GkFyV5kSNwkA5E1T1JcyZBvcIQNW8JJJj76oiKEZxBBGnVYZIqkofN3R1CaKVYj3TR4nqcFrumfrwlGCIseqRYPYJBzlggmZtQKFRoQwmgQEAQ5Ekwq5QUJ4pcDLGskOIBOqq7qqQnI2BWt8jrJykRA0uy0z6kumvDRCPRDIBIMoGmS79HAmxwTqnk+fIgrqE6seDMAjHPAIEDNcDhvKrCMGpjiAyIVAE4mfI8wlpYWJKrJuH00cNt6QO9V1Q0P93Fs3B3JG6t0uh0XgTzIuOJUaxKyooblZ45i/mnl0kGkNVnmEjwi5HGAMJ/+CXL31MMLuDT43VzwCduscP/t1/xu/NvoOd+psTaBYU4gNGT/dMhF7m6TYDRiXbhW8/zHA+pQ6wrpveEN5xFqoTNf1FLjm3C7d/g2AI5db9Iw3VH872ow4g0wFCtQd/tE8118L2QmveZnj3O/h8R2UqEWsxkH3oSOgBxcLU7PMTYESXjvso0675qly22fAgog3QBsuYtAGMOLiddqqg1dUcxOFCYtAmQSwDnW11zCuOoDedUbflCWKM6VbKKP7K1hLvoBVCvFYQizZCDSK/nSpiWogmvprAgAUkcKjIpB6iIzsiOVPrqTQnCHfUpiuGfrDjO6PVxoLZ2iOmJxMMTkWpEVhQcDCYfmaK19WI+l5z3ue+/ctt9yCpz/96bjhhhvw9re/Ha985SuveO473/lOvOY1r8Ef/dEf4ejRo3OPue222/D4xz8eT3nKU65Y16/+6q8eIJADwOgUQyKZg8btxBgVGuOrmCGr0rG8IUMkMx6Ouu9Vpi3NSicK5RENXnI0Z2nmVUcA1jDsb9J2fm+MeB9IxoA+YyyN79nC3vkMTeo5NNWShjxSI75Ax8Sf6UEUZmIOYHmZ0ERs591ymUEmKZKJdfs0SEYSotaOf5SMFNIdr1ALANGkBpcR6h6DcjwUCpMPB3xlItRW7vATdLxXQUwqKANJ60SANb7uZhgjAk3uNpnsaCaQbRhdIJsGZUlDRxrDuw0BOycXWTwKUqUsMMheDDGpEGXBzAi0QpLJNeOTXbIG0D24/Hqu/jKIOGoIWi8XfH6raEqRQvFUO2OgGtrwaN8vMHnRFu6iE8sFiipLd9DiSExP9pBtGVmCRrVSlNC9kMsNgMvxps0+527TZoZlzHHPVB6T+05qcJNOId0o0e9xTE74WfD9f/l4/J/ocejfJ1zXaQ4g0mj6Rr14nYOZ/nQTjnHrumz0PWB2nFL1ZFvekHIuudBICTg8buYPItXcpNw1ALQ3AEiV2c6qAadGebeNqz8wkuwx3T5utQXeTReShbs52MJIs9DQ6vKkrBHevZcDlzYuIsC7x1p6ScGxl4tY0mS/tyQ3qBHUX/adYTAcIa3bpPTglHnkeZseqftsQtkDe4yOmOMztuQdzD4uteFz+QWISxQeeRegEgyiVBCl/d5IJ8y9L0YjzBLJASN5wsiotu50mTIUKwLMrILKZYHZEY7+BeXTrpg6q6FAbvJKJiOJcsHrtakYqILE2F/ycuhue0TLl52TFJZ+v49bbrkFd9xxxxWP+93f/V289KUvxe/93u/hWc961txjptMp3vWud+GXf/mX7/e6P//zP98yyvb393Hq1CnzoRFXI9n1kWTlEsP0xhrZmdiFpkqb481OxDlcZE1hIiX+0Tf9H1wsF/GHH38Ssvt8hmymOJRBhLJNYOnOGtG0cQPiem8VKyb/WDKmN3x8FcNshSF5zB4AYDZJ0L89B3Q7Y7Wo0OJkKE3hrVYHpOonjvCa7lE743EDXkqaoM2AVQ8S1D2GauhXYqIg4yTdMfdsyObxVCMamaStMaeVZhK5FRurJFQeYXoiNf3Jke7SoGN5Btm6QNM3wobGAKK+YpictIgekG2wVgqQcoGhXE2R1wrlUuTaxWs4CQXLN9CcEtsCtPokNCII0x9SChRrJMkcUJLRgG/7wBhR46uZQ1WiiUGVzEBr5SJ0BCdIJ3PPpbC8KM0ZRKEcl2Z6VY5qyLBwb4V4m4hLfFZDDhPIxdQZO9G4ahkG1kjSsSfcMKnBzSBvjSxIjYU7GvSMoV2uJmBKHOTwAOjfLVzuOzDivTUDL7CablMEZRhtKYcSJQSSvY6R1ATpMAiYazXfRarNMV7CfUwG20GKDRZYNxTuz9xkp8y+uVFjYekiXPZ65l1ocZnmGFXd0o1aax2v5wBArLMdIlO6c1wAoB3oM+6NjZa8gA2AcIYl1aUEc+TqbrSfDv7dun5kkChtUT3mjFEAkJw5VDbkiVlj0n5Lspvqhvl6D6BhyudSUzFDteDD9nkDpDskF2K/9WRE41vd4w5VlCnVPzlBO1REEgU6Anob0rWzHnAk+w2EQcPFrIYoUtQmDZHMGJpgzP1SlocjLcmhkfTgyleUkVSWJT7zmc/gm7/5my97zDvf+U78xE/8BN75znfiu77ruy573H/9r/8VZVniR37kR+73ummaIk0P4qWyp8F6BAHXQ9bShIk2YhIZM7AulzTZWdRDcxI8A/yHffd0DRdmC4i2I1fX+JYS33zzF/DXFykT6X48RP+SQLxfoVqlNllxxMUzPtnqwhkALMbEBqYKDZVQ0IodZEQBN7HYAVIZ5V0X5i4ADTI0ehdNHrpxTTnEBEO1SKPM7GiMJif0zBpqyciQts0Atnd9StFhgsjMAPWJimNAJ26ALJc4psc80VKUgDzKXTRZ2GcqJqkFgAykaApUy37GUwn9Z1d+lCRTo15I/OBrwu954PFjmowZ50pjhFg1R7yOiihN/wQTj0zakywhFYQY1kMT3dNwyLjjamFoKSNnmyQoWfcZpiaqUJTA4oZEkxs34UmO2Rog6gR5ZKMfG+zdkKHuMwzO2+fVMZKCYl1yViKBhccJBkjvCpV5hLonSF5gYs/3xG6vPE99Ho+AZIcelKjJdesiAxMgOhMhnsBFPSnBMDrNwCuGxHLKrUHkXCtoR4YF/RcWUdLEKRwBGNDCEHWZn4htUugWchQYGvNcZN1wf1t/K+ebnbQ7If7dUPwDulDKH9YVimThPWq0katuG9Hebrkn5xlk8NQAZtEtHfys7AQckJ0754dq4S2ZANNfdp9ztQdGWRhV1mp+x/DtHmddcO4+DQKmuUfBdESorUxpO95naFK6fnbJjh0muCbyqNT+DRrLN29h/FEKtlm4m5Tkm8xHafYuFMg3GFTCoY3EBy9qRKMa0YQ+DC0Y6mOPkOUxB817SHUclgdcvqxG0q233orv+Z7vwenTp7G+vo7Xvva12N/fx4tf/GIAhPCcO3cOv/3bvw2ADKQf+7Efw5ve9CY87WlPc3pKeZ5jcXGxVfdtt92G7//+739A3KbLlWSLgZcM6Y7XSgKAfEvhyKdqKMFaVr2KGMolYY5hkDGJjg3uoVH0/ftPRHmsAT9RAutkTT39UXfhX5z8n/h/Zj8IALgjHaBYEtDcq0IPzzYoVgR2bozcRJ9va6x9ooH+G4M6HI3BpCYI3Qw6k6s14j0y7pz/PG1HgjBNE1odA2OTHmNBakTjGirmqBat2KU2Id7aCVbWPYZkx2sGcZlAJbSqm63ReXVOyJONFAOA4ghtW/XpZKyN9D/cAOlQn6FZmQIGhQDyC2aAFsD0tES6LryS9UxTtvI8IoMJpJEi00A3xS7buW+T7NNgGyYfTndpkrVIlhJAvUhGW7ZpJoSYoe5RPw7OGI5ESnU31hCN4AYmi7r1NqRPkWAnBQ5MTsStCSGaAMUSgzSuyv4lWm3LlLhWAMCbHrJLs7a7TWtAaqeD49xv9je7j1NiUYDcDU0fqFYlSpo3EO0LpDttdWZem/8arwVl+WFOOV4RCsgU9REAJ/hXD4D824xW09llLH4qchOq+5xYMKkbVIlJbxSJmvgk7p5daBacdaU5TaatNCNdyGaOIXIlLaMW+hNwnVzjQ8QH8KKQ1vA46BF09XYAIocw2brnzW1XamqrXeF924VU4NJ012fM85Q6p1kkiOmDjWmrmJu/wrfuAMJl70/Di1fa44K6CS3UbUMqovc2Kq1RZMQiA+SLG6kFaxCphFzxFtEFgGu+7hx+8vSH8Asb/wAAsHBPgmhGRpIVcJ0dyxCP6eVsVoVpI6VlaXK6IK80VNwRXzosXzPlyxrddt999+GHf/iHcfPNN+P5z38+kiTBX/7lX+Kaa64BAFy4cAH33nuvO/7Nb34zmqbBP/kn/wQnTpxw/7385S9v1fv5z38eH/rQh/DSl770Eb2fw3JYDsthOSyH5Uta9MPw32F5wOXLiiS9613vuuLvb3vb21rb73//+x9QvY961KOgL+N+eDCl6QExo6Sx8Vg6/Q4Y8ivjzIkKilKCG2IgAIBRcsV6wEnhFgA0UF2lsLIyxnhA8MRHPnMjXrj5Uow/uwwAWP0cQ7qvEBUKYmY4J9MG07UeVEKKtAARrkWpUKxaLSAgLgExUygXve1rE1w6P7wRXUv2/T1acqklOu7ekGJwQYCXyq2ioplE04+gOTC6ynJ9GOJJjGTfwM6MECvN4UQ2LQJQp0H03IyQCAvLz1YYVGpcO9YFZ7hUovKrUKYBXsKRu6OZBq8E5Xcy0W3RlBF5OwOyHTpucFFitioOkHeV8BFvLOAsWVQjGREp2+Z/UhHdS36JUtUAcKiOEgFSpQgBErbPDZJhIxsB4jkcWI1LoFhh3t1n3KX03KgNk2OC0CdNuQWpXyKkWwJM0nNgjXKRbiF0wWrpOSgAuVQ5XLqW6RHD10iVRzo4d0hebFAjXhPpvFrybrl4TLwPq4xcDRmEdWGeYKY/6bjp1Qr/6IY/BwC8Gd+MyT1rSIK0DhY56qYAaYlKakKRvEvOQxktochQu8jW1eE12f0hoNRKRIvgmJCkjY7Lal6xUAzrbAd1uKYHv4WexnlVtoo+6Jrs5qFrcZnC4zuVWvf73PMAx/FqEa8NN4zx8MA2KtjlkwHwqWSCjpgX9Wcj7wD6NjUj161V8GcKUDEFdQA0LtUNva9Wy0gZfbV6wY9D9/zdSfzCmR8AZtRZxTJDNGXIdpQTjiyWOMpF1nofbQTgbM13gth6QJjeF1/mPbsHWR6hln7NlK8oTtJXXGFAvaCxewPH8CxD75JRkG0UVMIhY+5IjsxweFxm6EWBeKrARsoRc9N9gDcJxmtH3OSy+P4IMl2ByRELUWmIUiHeq93HIFMBUWvwmmF6xE7YEeKJdqGqTQ6AMcxyhpnxj2frFPURhqjqmAyj2uaci+EHaOb3TY5FSPcUYpPZe3w1VdA/VyEqaBYqVhj2r42QbxqRtcQbRvZ6TBLvpx5oB3Mnuwzxno/yUQI0WMoglYemQS3b9JNeuWgmLmvE7EkMOHENKqN2XS6ScCSv4VVwO4OK5lS3zILfON13cQSol+gCy58SSPe0zzfFqN6o8CHGRFwlDoMjie4D+ZZ0xqqKyIEhSt/H0zXKhReSj7MdBSaZI9BXC0R6l4k/D8znr7NSCNUQ2L8uR75hUt3sVeRqk5IMoW5xrilN/W6j8hZNIt5zsTMUowk9k3rgAwKsW7I8XqOe0MPJ/o4j35QuIIDXhrwd8MWgDX9syvHa//N9dFzBkXE/mdlJIJq1XU02ws0nSWYHyN1eyJG556yM+jmCusJ3PZz4XF28vR84ODFdTjm8G/Lv5A7CbXdw528wAepwf9j2y21fZuKcZ2xaV1vbl4YDxhXTZMAd4NZ1kiLYZ2ONq3muNbsQc+Ol5XQF37Mld3fb7hXCAW3FQgNFfV4DomQuGwGXFFTApOfTWX4h26HIS9rHgS3uvm+ZAPvXcMQTfx4F5BDHqTKLkmQPSHe0yx1XDQE8khIAX+z6/xBJelDl0Ei6Qkn2aIKTGa0a0j0z6TUMk5MRZmsMyR4du/JZCsEul01S1UUOmTAkYwVhojBkyrDy2QrqCwwzkwxyesxHiwFAucKwf32EbDPy8gJSY/9aBvWYCeptQqD4pwVNGmZiZorIi83Aq3Dbyckm8ARocosb75fnhlTZ9P1gGs1o0tKcu2Sv9YAm5eJI7Iw+XpHBYNEzFVHqElF4boqVBOAVcyRpXhHqYu+vHhii7yTI7G0mxHrgB2l7fpPR9euhwGyNkypucD1lQvyLFd+xKgKyXRP6vg9MjhFqYgnsvKLz8nWAmVlAxbS6dHUIAJoGThuFl+wbHaipcgl1mSICqJVrkAG53IYtI6dcV/1LCpExRLONEul2BG7C/lVMqRBUAjD7LO3kbVbF9t7qAUOT0csgjsfoX6gR71fOaFepgJgZpMnqJ3EOlQoUy9QJ0RTg+4azZvpzcIFQzWJZYO865tuggWgnCiLXSPHdDsCi9M/Bp5Gha0yur3Hq9CYA4MInj1OUkbXbaurnchloDBF+cDend1mAUmdQK8A0ayG3LnGtBXzj9rdFx7X7sYX0hEaEMYgvSyoOjZ85CEmoSdTSHmK+/9zxl5u0OsfN/d0eYvk4JqfZPLSpe3+kqB1cKuiD8JTQyHTnhqlfOueFUghulzX+gohA9yS7fWr3O0TQ86Qcmqi1kyjpbUrMjvmci7yGkyCwRnU8AaQJQlD29ddGHsUGJJjvdDZoo96sQWsxUw8MT1IF1/uyElcOy5eyHBpJVyj1kHSPWi4SUFQXUzTJ29X1/rUpsh2F0Wk6RguazGXS/njzTY1oKjE4ayKhZELZ7c0xTQYUpxvwOkJxAw0Myekx1OeGkOd6yC2syyilhS1M0Qed7PvVWpP5kFab/NENbmYw4g0ZRC1Zf/O7zdVmt5UAZqvcR89VZCBZY0DFxhCo/WozmlrSJFAvGImBfU7yAWb11Zgktumeh9u5JDdV3WcoV0z/n6jAd2JHqpQ5d2G+vUse2akWGOq+DwkWJQnJJSYJZT0whOfKP1tR0cA6vE8hMvD79JgRTQyMTaYBrTyZWxqDD4whnlhjkQw0e39cAnxG+lqhTkw0M8/DpiFJBeVhs8l61zmaPAFTPou9TLw+ka0/3qfnY++3ihimawmiIkG6S3UVywyrny4RjSoUJ+mlLZYEmNIYnzSEcxPVJtMACTynkW4WSC9p9M+REVYPveUhCu8S1py5e2kGEaoxp3s25NlygaHpMcSbMbaW6YVkkkE/dozyHDU+2SE3ZGjcyIyeTVTOQSlYxxzooiHScLPD3G1dC4J19ncnfMBFyrndVvsoMGS0CK6vD9Zhrw90jIHAOGgJPqrg3NDw6RhOLKyvc/+2rS1SeWAoBtUfcB12kR3X7q4LUnTaZA2ZsPJ57j1LrjZjDJPMudPsMVYw1BL2eeX1wJSJNotHEqufhktgXfdtFBtagq7xlGQiLAJEqVS8O10alz9rAG3e/2JVI9lnpMdmFsSiCow1ALoEmkcKnZnXlw+ljsPygMuhkXSFUi9pJLvmowGc+0QUmlwimjsJAG0MCGuMRFMg2yVtI5uENtvgEHVCYo1GMHBwrkS1EKEyE0/dY+h/WFAEmQmpnWY9JBJY+IJfGVcGRbAuF14TopGMFaZrXjxRJvRRW4SGNyZiyyBQ5TJQrijwkvk8cGYC6GbaFpUPp6drE2fHITsmSWy9QNwhgPhDMmWIJ8DCPXRctt0g26xQLVMjxlXkV+9mlacFsHcjg7x2BlwiiyS5L0G+AcRGgmB6giIPeUVIir2/chEojkukm94YIvSLXnfLMeAKLgJOc8pIzpR29ycKGgxtNF9k7imawIWwy5TcjtUCa/VLyK9SmtrVu9RgdsRossTMuY9slEw1SDE4qzE9SSP0+DjJIqS7aAlaioqevzXerE6UM3KNW6tcgsnuToZyMxAQpcD6E6kNTY/0ZEJdrXpIbWplSpek4B3v0ksU79mZnMGKVYJz0iMy22JSI9niYEqhXjB6WAsxVAwMzwDVFr3AbAmUN6wyrhrppRIio/CtuYkqbIDeRfMMbLSm9s9PGfkAa5QdyHUGYwwE7jRgjnEUoB5OXyzyRjL1yRyDzRgVrLuvi7Sgs60DgyVwtx1AmLrGkb78/nnGWHi8E8IMmnNA3ftybQ+vaesJ0SWFlsvSGj7h/QHw6udm4cCYdtw9ew0Gi+IYJMnw0Lqik9HU8ydlylEsCzQ9/02muwpMG7Q99+9aGF1q3bPRFMg2XK8hmhkD2R4X0dDspDECiZUvdWkZxA+1jkMj6UGVQyPpCiXdYOB2NcXgkAJRaxRL9BFaQq1K6GNNt+nceEock3jMMDtuVzjGfaM54pFxyy1HjmNC20BhU1CYp5NdEuCGwOyUYKXd9jC05sD0iHAkZpowyJfeP0/7Fu+qUC5G2H6MdaNppFdNUIwTyJImM7uy1doPBPSDV6sG6NqiBCbH/SgajwmZyjf8OYt3VYCGcyMxqVEPY8eXybYlVEKSCY7wLeleq2mEfJf29c8bI8Z+5MaNwWWgzWJWef0zwnMNjGFnNayalAYbSqNhR3K6XrkQJPo1K92uq6Dpw62mtaB7rvvwX5Mm3SmbgVwLSpJcLkfOmItmCIxCOq7uc9QLsQuZpzBnkjVw104JHeQlnMtURcDkKi+pEE3ofvPNYLJsiGemuZeIsGiI5cfxhu5ndHON9CJZhrMVgWxDQExruPQngVKz3acSQZNaZZ+xEa7Uvu0qZi2tKIBcarwWzphzWlnBSl2m9GyaAB3sbSrqJ3N/TUbkdod8Am7yRsco6pYWD8huGzSrqwTu6hE0UYYpf2wJCd/23wd4QcF15zeq3SbgIGjm+ulK92Tf3xApCw2ccFe3nzrtu6zBZA51LnyFlsSBMogQCVia77uLtgEu/Qi3rjVjHPGAV6e5cbwJdoAb5RYNhUJvXaMeCGdciZkCUxqi5JiZFAj1gGF2xFMWRGmCSiq/L9ui76/ueYuy6Rsj3WzLxI8/h+VrrxwaSYflsByWw3JYDstXQzl0tz3i5dBIukLRnFaoln/kw+jJvVIu+hUbKf76lZjMQMKS1vcOIgparpJLCzIkQcFwxWpzrrn0GUakL1yFE39HI9umFTuvFJjWqBYiSJOzzK5ip6ckZGpdcAkhJjeRv6I3LCElh665SzEBMEiNVsLeaOJ9/LYN02O8FcmW7JHLrxp6mLtaYNiPEkRFO7dZNWzzBaKCUnbYe47HdM3ePbFzFRIczpy7bXBOo1wit1XoHopHhpBp2hlPDB/IkjinJvKqIzIoSh9qD9DKmAU5oZTwaUh6l0y/S3LDqpi5fqiWKKecfRm4gkud0EplwWi/DPhoANBbb0zdEXqXKtR9gelRu0wHtDIutirol6tnmO1QA9ItjnhkVuLhij4Cdq8Xrj/FjLhaIerRZMQZsgNpsQKfSkK0YQRK9xH6c9BW+DbIgMwtoZ2uUax69CXe55BV7lAwmZNYJy+Zdx9yEg1lNUO6ZaLnGkKo7POyKNLcsP37c3fZXUE/cEVtdFWZvMNdRCrk44REaH/AwetcbpJqRb51+EFg8FIB2u1y510uEm5eIuF5Odjm8rDYQRRMd48NkSzrrRedazC00pAAcDyprsuSN4Hit0XqAoFJbZFfhgPQmk8yS2hpNJXuPC6pQ+Nx49DjYkkg3/RuQt6Qq316zLugI+NyZ1JjeB+9kOOTMaohQ2b4fumehlrAI1YOJQAe2XJoJF2hLN6twHONYoXSjZRLBipWHMOzDfJN7nhKdR9AALuXy0DT15CpRrptuExGt6NaBOoBdT2XVnPG85aSfQAq4OcYfosWQV42TQTEum/I5FJDjBvkswYAzbYy4dAREI8Fdh5nIqa+bR+zOsIP3vRJuh6v8dt/81TEm7EPe80N2ZZrNANl2iAcJ8n54k2SWGtoxBNSwa2GzGn4lKsayR4jd4q2ky1FkdnzilWj3xQHA52GC+G1hkXdI87M+BTVMzhrjAUdwN2xJ4DbMH2bH81G5dlwbxstZ9tk+9m6bGzON2GMCqsDFJVAvkEWSpMLJIweUtOzE7Yxhm1eOMMJ08zzhpoewArioLnwdxi5AGdIkR6XXoicgWf7wxpw9n742RyJ4emIAs7dZ1130uhUTU9q9IxiebatiVNn3QgZGXrxxE+MKgHAGeXds66SoiG9JcEAq7ZeamcguWJ0mOIxNTSNSaW46Xnie70oEe8LN3KzBojGROCtFu0MpxHtCpJesGHXAyL/NKl/p+ZJAhxwW3UNnTmuJzspp3ua3i9QsusDhGxbhc2jrODJzx0jpUXM7rruzPEhcfqyE9mVjJsrlcCYaRljD6CelusOnfu73OXYwX8fkEIwRp97Zuqg4e14YUE/elmFjhvOcpSMUc+lV2TXipmMBMJpeQ3vUyStYvWWamVc7DGk5d0pCjiICjhdpqjQGJ9iiAyHs78nkapHCJ45RJIe8XJoJF2hsAbIdiTSXWDv2sihHIBZkcSx4//wxojsHaNtzUmnSMXMoRwq0LtpAoJ3vA8kRl5AlHC5wMIVQzzRaHLmErLK3BhYR8jSaHoc+Tq1K9ukkV2mArNjMcangGOPopDrSgokcYNJQ9bIJ8dXgQlNKSYm9sYZoUoaSLapflHZ8HrtBiM7KbsotQyo+hwyI0MQAJoFhXRbtNJaTE9qVEuAPEkdc9WxHZz77DGkm7y1Ko9mADgwusYMYicKyFnkxN9kQjpGyb5y/QBGBGkdMWdAVgMyFkO+Sv+CQjJiKBf8JEuEawZp9ZtMShJHfTDIz2QR4BXddLZVO8KoFlYjiJHQokVsStAEkADMTLrZFnG6bKoOgCITVczcxFwNOXZvyrwwJDyy1wrBFsDiF4LkyhkZak3KXGi4JaTm68znVxMM5dBzlJrcCKiOfGAAhXtrglRsRwhKnitTgcjlz+KUEDkoTClozt2k1eTM8ftCBE/2lNOuSba4m0yt7k11jJI95+sc2bY12s1kGPBsQtI/4OIe2ivvDnrhOEvd4xgZZMmIOnp8VQQVB+e2JmuzjwOoAREslhyvSfvzQrkBty/8izl1t5vWqqpbNDzI4o4J6mqd0zVmWOeSHcPQ8aBCAFGjzWfqtvsyRqlFpRxQpQwCyYJtEzxixxxl+8XIAADeQLLfhjOWFNCy1hiN6TZSzn63VmDVIkYU0m8WWILBRuBNrqIPx/IC7cLM5tM8LF+b5dBIukJhmoirduVuP8JkpAClUfc4ZsfpY+I1Q34JfiAyCTF15hEFmcFkqPYaQU3PiJ/1gm2jLWOFFUVF10z2gckJYxRlhELY6LpihaHuJ+hdEki3aZYVswbJSEBzjouXyGqJz6VoBgp/NqWl/LBX4Lk3fQYfyG5EcaeZGTVpLbl8bzDaRjO6TzfBaYOIKN9O6h9yOQFAdlFgelJBzLiLTBIFw9XfdBbnduigcxeXobkmzSnmrxdPCXof3Gsmy/0cPAWydd/HAA2SliQtKmB2hAjSdhBTselzQ3KPZhrDO0dohinkDWRZKMFcZJUjgddkjFoJAqaBbIuQMkvA7p9rIHsRZkeEe87cIGw2wi/d1Q4Bsm2wOktuIoAxUmYMkVFat8gIV9oJdarYPIdAf0hrMrotSmVRxzCM3rqDeeUN+XhkchIGq/tkRPXbdz3f0hCTCjrizljTqUFBGw0V00V0zMkesyRbwSBTgfGpzEUTqYjeiyb3wqLZRUOiNaiRDRiw/wYAFiksXj/CqF7G4IKpvtSQmZ/RRU3vijXIWnWFaIXpV9d3MfUpa3BA7yieKCdI6hANO4F3Ij/dX+NacgKJqn3ePA2heeWyLjHWAlDaSXLnNImFG0GZS2TvGkhoG1ytdgQHON2nrl5Up4062GnbdUDRmzEf7t/ogzIEjEFxtIjfoYFk701zABELkjP7jnI5AaMgAAGE4nIA8VQ5VLRcoOvwGqhs0EWh0bsEh0xP1zga+UAhvS+yfBmQpGuvvRZnzpw5sP+nf/qn8Ru/8Rt4yUtegre//e2t35761KfiL//yL912WZa49dZb8c53vhOz2Qzf/u3fjt/8zd/E1Vdf/ZBu4ZEsh0bSFUq6V0MjQblISIjlTaRbFWROLpB00yMRACEEACk364gmHBeFpIxbTRNnBDCuoIxcXLSDkAReeeQjKuiDtPpMAE3e9RBojGZQPCZdHxkLaEETf7LfgJcKK3+n8a3f9X8BAP9z+bEoP7GMxmgV7Ko+3jvO8ejjl7DdI+jj3D1HEN8XUYSZQUOi0ofB1gY9Gz+qxvKxEXY3yQfIm4SiQSYa/fuoXaLUmF2lEI+4M/qUADbGAzR3UEXLd1F/VQtw6Sm49BID1viKJwzlku8XLQC+CzPwmUEq1qj7xP+xLp1kz7rJtNmW0BFHvRC5SVVU2iSc9fcnCkpVYo0mmcCF5Ftjp1pMUC6L1kAuKpDBbJs01uifm6E4mmFqEC9mUjCQ8KQfYJseQzPzlcnUcJY6i1VRkhvYtgscSAIJAFEQ38immrHCjiHiEo816mOUEgbw6KXqo8UDm5weIL9YgJd0AR1760tHVm2dkzvRRLfVCzF2b0pc8l+6N7p+tumFReMJvQ+NcSPXQ41oQrO1k2LYTLAfK8QT5urnElBSgzkXC4wxpKGVN0AvF9XWCp83hkwYNQkAk+MCyajT8XZittynjouPKod7Xs6AmIemdNvSLZdx7x2AkvR8e8rV7Xxk919ah16m7tBQY8EJVkG/xbvrHNO6Vw6gEzrPlHYLHmv0h0m7rQI3IUq+UymZcdA+Y1A65JUbg0jYG4NL72Oj53gDSEELrnTPyAkkkXOdW6RWJcSLlAFnVD1C+W27IORDrePBlL/6q7+ClP5BfepTn8Kzn/1svOAFL3D7vuM7vgNvfetb3XaStMP9fvZnfxbvfve78a53vQurq6t41atehe/+7u/G7bffDiG6iq9fWeXQSDosh+WwHJbDcli+WsojjCStra21tv/1v/7XuOGGG/DMZz7T7UvTFMePH597/t7eHm677Tb85//8n/GsZz0LAPCOd7wDp06dwvve9z4897nPfXANeoTLoZF0hSJmElFdIZ4INLnw0T2MQWacuEM2MmJGq2+nslwShK8FReYAgI4VWMNQnm6gjXoxk5QfLh5R3dmWd/mkO74t0yMcdY+hcsRtcseFgnnxiFZZlmejYkKD0n2J//b//Ua6J6MyrRLtzmtKgXt2VtBIw6mZCqiUfrMow+gYoIVGNGO48Zl3AwCuH2zhfLGIe00nbFTLmF7NAMnQO28QmoJBjAWiQLlcVID84DLyQK9HlERKb6ybQlLqEOImeZcHrz3So2KgMQRLi9ZVPeL46EB7SpRAMtbILxXueioRaFLuIsoiTdFRTkXZ1B/N4JLZNn2K1mMNIScAMDtCBGJReBhfCULerNq1qBSqpQTTI8LxgXrrFO2nIo5kZFCpITO8II+OyBRE4rf542bkVlKxR9hkQkiiQzSMCnk1ZK10H5OrNZIdhv59pq8WyB1hkx0nY43JcYZ6wYvpzQYM9UBQImODJLFSgmkN2Uu8EGCtAMYwOUU+x9EpjnKF3mXnNrZuQu7ROZ8CxNyzcZU4ZNW8C7IW6J8Hkn2/otVMtLgqDhGyqINx57TmhI5fiivirqjYcFjgAZxywaelKVc05FqN2Hy3Lvkw69Rp3Xihe435babmuNKutLS/HM8H7W12mf1zt3EQvZqLeM1ri7mXIG9yi/8U/g3ddHPv2aJLARLHA90ppkykmw7caQ4l8u82CUt2UMOOi5XazKBh0SRCn8IbCM+343q+LVENeCuggklyl9t3mEn45NZfRWV/f7+1naYp0vTKSeiqqsI73vEOvPKVrwQLHvD73/9+HD16FEtLS3jmM5+JX/mVX8HRo0cBALfffjvqusZznvMcd/zJkyfx+Mc/Hh/+8IcPjaSv5sIaCcYUeMXA4tAFQnnZoplCPPbhzYPztVNU1hFzRFgL32fnYmSbxHFxedlKgDceTo7HGprRRL9wr0+oOzsSo1zijgReL1Dd1r1n1ZvBfXZqvU1wcrnIsfppM2EXGqOrufOnL36BQZQpZJpBrlJdkYnqsuKCgBFZq4mE/oX3XwcAuO/vLeK/PfE2vG37aQCAP3z/N6NY02Cnp6jG5E+JOdA/y8A0sHcjtSG/xIxxaDrUcJvyDbRSo9R9AIyyelM/oJ0+QhseTyChoLmXEbBEdFGZlBh5bu5Fk0o4AxbO0mg4W7XP1J/HjEilSwkSUxui2g/k2bYiY7nRmBwzricj1WD5DuWScJwWO7COr+JI9jWSkZdG0JxjtsowOuVdZND0jF1CXRh1cRYY5LVJ4WJ4bfWQDOxmKTBQCqBZktBcuDBqzUn8dHifyemmgWI5At/0Bli5ApPWRiDeC9xsAKYnMy8PEJEQ5+yoPyDeNwreFnlnRFliCi5jez2gd98amDrWqBcVeMXBS+9Kzi8lSPeUi3xitQJveKDETImN55GiuyRjyz1r/R6c5yLQeBDpuFrjmY/+PP5897GmP/kBkVEbFXcgeixsA/OGXEgCPxDW1jEq3O55xlW4T/t2HDgOc44PDtEMLd6QjYhrVR8af0GbWLhvTru6EgDh716ZW7cDEhi52lqcLgCtPH3meiqIZLR80NAwY0q7PvfPyy++aJs5g8u9s5q4kUwFqv6VRrXAEJtvK5ppqOKLhXceWGHdPnwoRQOTyQSLi4ut3b/4i7+I17zmNVc89Q//8A+xu7uLl7zkJW7f8573PLzgBS/ANddcg7vvvhuvfvWr8W3f9m24/fbbkaYpLl68iCRJsLy83Krr2LFjuHjx4hd5M1/6cmgkXakwhmYQo1yKUQ2YI6D21inDu6g00n3DwcgZVMIdB6Rc9CHklpwaTWmSFTP/YbrweauWXACaaaT7GqKgESMalVCCoRomVuCYkpouKCcv0PQouzWhHHTMLDJJemOPTgzOafTXNSIzAeUbEuleY4jLdOLkKhN9xz0nSTTA5OYa0XqM/jnal/3XZTx752X4oScQ36l40gzxZ3LwT/ZdglRtNFNkAiw/miy6XbUKHQG3PPkuAMDfnj1JffBXuUNVygXSKuE1vI5JTYu+xJB3m8xwhPZ0EFrPCHlRAZ9qprHx9UC8T/ec7DDkWxq9SzVUalSkOSFJ2TmFvskDNzkqXJoZ+2ygyTBKdxrzTBtowTBbS8AN4VTFzK02bZusho/jO02BmFnSMHPtjCcMpY1SS+AyktvkvNRWMnLdRG8mjPHN1Ak//pS/wNv/z7c4TSHbd/0vUISmy71X0Ap4cswPA7wh5K21KldEarXpRbRgKJcEpse4M26KNaBeadA7E7m6VWKMNFPX9IYaT3rUGZwbLWL9DLHhr3/yOdx16YjnETEgiSWqcQLskIWSnGdYuqtBslVC9k1aF5O3yxGkw4k05L/ozr0AzlABAhKv7PxujrHf59qxPXzDwt34c/k4/xw6RkGYr+zAPGZRU9OmA7nb7EnhcUHd3fb7k4Nzw98vR1IKzpsX2dZtfKuay03OBsXrXqOr8M2Yr6IlKxBwicDgJRXAHNfQqW4HhqkKESF4o4cpWrRYDt6B5naedeu3DvKoBYOCRlToQEGf9OB08O7oySOUlwS4/HN4EKXf7+PChQutffeHIgHAbbfdhuc973k4efKk2/eDP/iD7t+Pf/zj8fVf//W45ppr8Cd/8id4/vOff9m6tNYtNOortRwaSVcoxVqGvNCIJhJNHjmybrEsUA+AWjNHumUKrTQJvAFQtrNMc7OCVYnf5wQbzYtfDShiLd+o3LJxdrKPJufQwhOG1z5Of6OCJuvxyYiik8aUswugCVmmQHm6wsoaMaL3G4Hyb5aQmwgxMIZqGEFmHo2JxsYAEUBqDI3ZGvANN9+NTy6cxN7QhHF9XmDpoyneWT4VAPDDT/ko3jl+KgZ3RI4crEB1iQrY+xRBVfGUof+UTfzBje+hum8o8fj//f8mw88Q0bUwQm7So27pnm6lIlAmy7uo4AwUZib0MAEsOAtyMZExWYChXE6ccRVPiEAdlT6vHq9ijE7FLcSGN/SMmPRpZcoFjnoAJPvUBhmbezYuJDEz0YgLQbJck5uON4wS1ILceir2SJYWhDRq5vNNZVsN4oy1pALqHk0s/TvJqHjb7FvAJaGRTpxzRBNLd+IoVvy/Y5POxL5j1AjKabfxJI5oSpXpCC4tSrVgEMrrJogVA7+DhpR6SClvwkkovhjjk+lVSPMaYok64o57j6H3uRTFUWNgcg1UDHHlI5OUAKJJg2hrDG40MOqlFEzDadxoAaPFBUCYfYodQJasqxLBxOxCxwOjpxsduH5uGX+xfCNUaq0Y0UaQgAPbtLN9/RbSE14vNJSuUBjaaJI7J7xHBIZH6APruKOAzj3Pu1h3V8enZo2KrsZTC5HCwXuz9xvmmVMRITncGh/G8CL0xxtCmtFYGxK1ea2dy4vXHjUKO4si5To3pby7zbrebCSo32fqtXpKpUY0U043SQsGWT+CRtLDVBYWHpwC5pkzZ/C+970Pv//7v3/F406cOIFrrrkGd9xxBwDg+PHjqKoKOzs7LTRpfX0dz3jGMx58wx/hcmgkXaGoiKJpkkqhXIlQBZo6NqFjmLMndIf1NhSqPncGC51IA76KAGH5D2blbmF9UdBHXi3GLqu1FbNMdwnFAoBkr4aY1pA9OlFfHSGaAfmmgjYjJK+o/iaPUSzSo/5/HvMBvHf1sfj4J6+juo8IJHvEn7H3Ek9oAtTCr9aiGXD7R2+CXGzADJmoHtCPJ/83Xe+9H/1GZKeAyfUSyRb9lm4ZFeWBR0yaHBBM46fuow/ks7tHIc6nZmKl66U75GaSqZ90KNJNBytU0ivJNhs3gKmIoRqSYWMjCLkkzpeVWdCMjBYtPO+Lcp1pFMsCxRJNxBaa9/orQBWT8TMzSYSnVyu87bv/A/4/Z5+Ls797PR1nkqE2pu9kbO5f+TYR8qfBJj7fGNMa+RYwW6UbrPs0syUjYLZiNZ/I7dTbkBgbOQg74dt7Wf6cxug0JVu2q/J4BICZPFOGh8Vr+s9yknhDuk1NwLeoFzXifYbBGQp9BoDZEYOUDYBm2SBqTKO+1EPPRiQV5EYWhZe3SLeBei9H8UTgW2/8PADgg3/2BIocNShfPaR2RlNgdoMxVuuEJjOtwSwvqo7BBAPvCFgqwSA7AWnzEsVa/lELgXInBP+0rvLzEf5q+zFIquCYjrstPP5AfR3XU1eSwLaLhee0m3zAm+XO0YEm1P1cGziIgM1t7+VKiNLZ65truPtgaKmTu4S0zJ/Xuv/QFRkBMhCDhAa4ho8WtImMQzkGYxBZ93aYELdFjNLa85yCdllDigw9DQZ2IOoubLM12nTc3v+IlMCF+EXV8RDKW9/6Vhw9ehTf9V3fdcXjtra2cPbsWZw4cQIA8OQnPxlxHOO9730vXvjCFwIALly4gE996lN4/etf/9Aa8wiWQyPpsByWw3JYDsth+SopX3RakodwvlIKb33rW/HiF78YUeTNhvF4jNe85jX4gR/4AZw4cQL33HMPfuEXfgFHjhzBP/gH/wAAsLi4iJe+9KV41atehdXVVaysrODWW2/FLbfc4qLdvpLLoZF0hdL0ONSY1FyrQaDoGuEAd0CmANvTGBqyteYMya6Ejrwqt1XstTwhgJAeFXn3Wz0AZM7BGrjUHjK3fCaN6Zpw1+Vlg+IILflVQm6aJmeuTVFByEHvAgM7TyS9N3z2e8EU0N+jY8ojGrVmSEbeVcg0MLiPNIoswTsqAFEylLPYoUsyIYTJohzDcxKDcwoXnxajWqYOEgUnd1MP6D1xGwCwnM9wz13H8MG/NqGlGkCiXRoS2y+kk8R8CgtNGibWHWQFBcuVyCkjF0vCqGf751guMsjc93k8Ig5YueSRlmrIXH1O/NPcn9VukgkwPaHRHGmQ3keVXfvY88hYjb/53GkYyo4TZbS8rCYnEcVo4led0QxY/DxrIZFNTvpODvESQLWswGuOYo3uOVtnkAlHMg5GOmXQP7Panh4ThpcBRBYlqgEwSgFj7y8e03nJhBo1OUZuQ155nSQVUVqZpTvK/x97fx7s23KVB4JfDnv4TWe+871v0tOEkAgGWwJM2W4bCNvQTTm6FWXcYaIhOghbYYMNZcpFtC06CDlMBQaHynaZatsCuxG0XThkImwGldvQLpVsEAgQEtKT3nTfu8O5557pN+0pM/uPtXLYv3Pu1Xs86QqJX0a8d8/ZZ+/cuXPnzlz5rW99C4b5W+0wC3yjfJ/6we5nGB0i5KHzAQXdEBgy9UE4B6cFyt8a4H//6FcAAIoOWF6K/I5sKkJal/J56pzyAHCZBLQKqt6ysaxwnvi6HIAidak4WCGCO4sagTOuJ2HQ4y0FpCHJU0guv5UFhusJUVYe3fAcnaScSe2R6jIl6MAD1y+BM8KRoRkJkuWAs9yfc1xrD73Xyn3PEMPdSttF4kL0bRU4E93Wo58k/ZHOO04gRJ9Z4QVeXYg2I3epCGloAEKQKHiCfvcIFLkQI6IUggwe8PzBbZgiXt6Nn7o4HQDnIk+K9bIeSfkCIUkf/OAH8eKLL+I7v/M7e8eVUvid3/kd/NRP/RSOj49x5coV/Mk/+Sfxsz/7s5hMJuG8H/uxH4PWGu985zuDmOT73ve+P/AaScDaSHpooQ9SohtINBvJR7ikBbPd7C+8sgOyKRlJzWYeJ0YV65MdkaI9zyYskt7YGgLW0AKWsyGzLMgIM2VMYVFvagy3xr1cZydPA8WxwOQmVdYOBNoRetE8xSErMvPEqheCiM6dI04HmBQ+FKQsy+492Tk0GwqLP7LEZEyNb//TDpyMKUjcbYK5h3cA1chQlyuAdtOisHTs8ckRbk524A680htgCwdxGrlDxPFCcFH6/qs3ZOhP2UbeQbVFB2fXmadUIUSgAezG9HwgSYZCL9LFeYHCGGEXXKBM2GxHAn/6//Cb+K82P4Ufev9/AwC49Z+u47v+/V/DdhWNWoDE5bwx4gQn7F0QrwqgZyNRumikVFsSQkVO0rAh19f8iQ5XbpCBeUfvAUJicOigmuj+BZK8frywmSIuCJ4ELkyMXBOO2lxvM/l/5EL/eJXzzU9KMoQvJTldBEJOu7B4STKuPNGduGKOSd50rNmi1XvyIhlrvs2yFWgmHKQwJ+FWVcdvxObA7FoO2YyR3SGrTx8vINsiuJttS2Kg0kgE34yPgvJRb0gW5cCDiX2xGkKeLibS9BfPUNw5xxCb4JPE9vhHAC2qK/yeXlk1PHybgD4nSay0m/9+xpBKnvncWwrvblq9sH+ycP3nSRPV9vLTSfSMt1BX0g+pe85f1+tj4Q1PAcsHnRAhGCQmpuX5a9VQSfvBUcqhXu48eU4fIxpBAEsSGMepUFx4ZmEROEkPdU9+iZRv+qZvgnNnB+pgMMAv/uIvftbry7LEe9/7Xrz3ve/9fDTv81rWRtJDyuhWA5GXqDdJ38YvRvNrNJuoWoQQa9HRItqNOSLnpEE7ySjtg/dvCzpP1nHBBnhxzmI9etmPMBrdooXC82gA+n36mOjlB1MNMLjnMLzLyVeHCu1E02TACz8lfo33a3hNaYciLHC6IvTK5gKWpQ9Ia8hi8OtDLDYJ6ti67TC7JgIXprxHD7X5nEC7z0ZSLlBtSZRfc4yjO7Sz+Mh/+nLI7fgswgCio5xifqGnRYsSnaqEB5ImgzV5NFybiQjPl82A0R2LihGuepMRu4TErJkr4xfJxSUgm4nAz/Htmj3mcPoGy+9G4PeOL+GXPvlm+GTk+ZTqNS6JBGxj5J1/FrWg5/VGcT4lNWxTAKaNbddzUh8HyOAY3Qa6ocYdRwzr8QsSxRGFIXvjDSBDxEfO6SUAS8/nxh51E2gn6O3ATQ7Uj9f42jdQlOFv3LoO93sT6Hkk/5eHVE8zkVH5fOEwuG8gG4fTx+khTUHvKZvHnXc7JE5S5nVvHD1TtQ0s30qdZU9z7P2axJLlBdoNSn48umNRbdH7MwN6v6dPlhhwYujiXgVZNQAbScJRfwiHmNQ3p2SpgcDNY6iHXnQI0YEBf1pFNoDzjaHkb+GQNxRk/5xeCrGAcvSrScuqMReuSw2NVcPuAeWzGkhJuxOKTvzb6nkujiHJ6Vx6BgqjZIFqtdIXabukPcfI87/L2B4n+g8gbNy86MqdMZJCHrrw3kVMLfOw4iJqBdCmT5h+3cI6GlerJPBHUD4XEgCvWULgD1lZG0kPKdPHc2BSUKRSlSzQDshPBPQ8LvS6Aka3TSAM2kzCDBSqvbgj1ouIJvmiGmBwaGOERyaglw6mECHDubB0H1vG6/yOy6MewtDikk8d9IwOVttZSMQYssg7WnS8W0QtAQjSwwmJTgtCtLJpjKqiqDGJ8W0H3IoL4fanHBSniuhGvKvPRUgBgtpBNg71v9nGJX7u4sTg8I0Ks6fpQH5PQzbkzpxfp+vyY4GM84j5dtXbUUYBIMNGdkCzIQOipthV5GTUFpINMH1CULQVyGCwKyk6vIZRmgRXdKBIqRGd5BYad371GgYVAmnTRyf6BLKAR4miIawYuVPLaPBVOxINRx/6iD7Nf/dGNSXVJPK74BfYDahd3UiFMaMrGh81d3D+KR0J7y6iTWbAKT+41I83eOdXfARvGZKmw4c+9npMjnyi3dgvHhUZ3KcXMbzbQNYG7SQL30Q3oLQyY5aHULWj7OnL2J/+59kTFpub9Mvpfol2LFCwceoUE8LLSLRvx9Sf7VBguUcN27VAeasNop4mk5CGFq6UHCwMIJ2DDVYRGwl8jjQI+d3SpLur4eseWeqhJUj+hjhOewbRClrSqy91+9izi9eDXHKr910tq4cDQpQYiuf6mvhvZ6r1/emondKkKI47g8b4KLWe4cT3TA0goI8cBWMuqWvVwAtGmombhIDseN0jX6cQQRPM6j5q3Hvmc0oggaf93ktCl7bpEVsdayPnkZa1kfSQstwRMJcROCb+I8tPafKXHUIUhLDkC282eDErBRYXJaprLUbPsvGgaafsF1MAMIKz1Hv319Kh3pSEQK3sjFTCFVnu0aIbjIMKGJyQfs/8GqsePyaJF+MQPRCOjCCfO0svgeEdB5OLgGCYIQkB2hyo9uIXWe8QMhAWWnYjlYwkadgzE6RqHfKpQXnYBnXkbqjoOTjTthlSBJXoEDhCZkDP1I7jMwuOBPSRbNlcREkFbqZaUmJSAEHDCgCWFxRURfeniD8ycPxkn5+wXINNwvQHQHlXop1TJ3uOihlE15SqROAetdEFT/3B2ld+UU4T3DYTaruq0XOXtYj3H+wTl6n9ozNMBnTw+HiI5SLD8AWNERurphSQNZAdUUWmQFAP7zaZw1OLIM7oSz5ukAmDv/Ofvg0A8MS/cSjvHMNlKkZNKoHZ1RzzqyLw0+rtDF2ZI59ZlIfUhmpHoNoF7n8FP7OkhWb0YpQqUDVQ1A7mlsSx3KRjhuQlRrfonGwBnLzRoNlQKA/oWDDYMiA/5POmJJolWsvvSsUIL17gaBFlaQseIFaKfnLUsKDGseAsYFOxQiAYNQ9bD8+NGvPIT2pouJVz+JqeIXOeCyhtS3K/c5PhntOm3ine6Dv3opVzkug7r4jt+Tgx1D5eLo2DcwgaRz0+lm+7N3ZWDUNx/rP6YiW1QXZ92Q8AEdnhUH4SsIwPc65xdMbwXOmY1XGSntqTF3hElssaSXrkZW0kPaS0m0BWxRBpb5A4VuKVbVzAZecwvaZ74eLtGChfyoIOjik5LL9G+AjNAGgQkR5TUpZrlyjIqop+zqbA4D7N5O1IkbghF10R+lTtaUyvsZtph8LC04zx3jjIj7numnZ+lrV9AKC52GHjWYV2LIIIZTtxkDUlbvVtqLYVuwFFqGtxUSGbOWw8l0AIjrgpfhXLjy22P+UgO1r9po9H/lVI9uqi28pPVJoFCi27U7zQZL0DdMxpqXcFZk8AbrNF/iK9sMmL5DbyKV18WL/NgeoSXVfeVchfIq5MmkCX9Hfi/X1byn1uA2eDdxJog7YKGbSpu00YehbvFsxmFG4fOGXcLqeS5LKKXaPSYqOgl32MIUQtUV0kQre/XzsB1JJRIw3UFwz0qeot9E4BXR7RJP0bY/zCL3wDrp7QKqPnZIjJqoNc+lh+h0G5iZPXaRx8JR3aeEZBOHK/lsd+PGoMDgC2JyGMRHmPjB7fZ90AgBScOJnaPnyZFi8//vUcKA4U6scatLXP+0Dv2ipCqwCg2cyQSUCykSRbB5sLiI6Qy9DxYKKvNygEIBIhQuHigt0rltfLFO05r6QGj3zAiSsL26rLp2dkJOedZ/ycufa8cwCseKfOtuecP6Z91Gu3704bN4MegQ1JhBP07Qyyk4Tvn6clFa5D/NZ6z5r0sbCsidSsuNeSvhcQrMAtem3qPVNyX1+8KndqCTsBSqLseUm+PMqw/3X5gpa1kbQu67Iu67Iu6/LFUl4rErRGkl5VWRtJDyvMU/B5gQIHZEnjTFWJ4q8gUqrf/ciWdiwqQXucIiSJhBX5FuzCSdWFnSR3mEcZsjkrHCugnbBLpSRCbSBbL0h0UC8jrJyfkgClqh2mj4twPyCGmcs2umc8YVEfabQsfuhdHuWBQHnISrNcvxcfPH2S3TB7Em7UYvzxDBCDcI5II0QAFIct1LKDqmn4De8Q/8hLJADUv9kMGN61yBaxj+stGXhSqiHkp91tMdimjh4NahzeH0PeK5JUJXStd+VZ/lk2QH5A/ZmfEPJHucpc6CvZisgRUkBxAmRTQi0AoJlQHxcnNrSr3iDZAo+OCcckcYeQtgagfu8GsV0+8tD3k5eO6D49wfMXCeZ74xO38Sl7CerlEs0W+sWPIQ3KfWaAbBrRJkouK8KYFI7G0aKU/HuBgQPUrIZo0kSyJCr5FV/zGQDA706fxvhlYHpdB1ejcBw8sIwuTS9L4N2JpqTf5zdiOh1d0xju+FnkPjB+EagvKtS7NBiyqSR3cx3dxItLGkMpkJ1QA1RtYDMNUyT53CzJRQgroytGEJHbK1L7PHu9wq4Vl3zzAc1I0QfPI/LIxANIwWdSkKyiRu7seWeOpfdfdd2dUx62DorzTkhRM5ecl57i4ny1KoB5xlWYuNw92iSs66NKiaszbUM4J0GYwjMbH6SSQm79fy1HxKXokkeQgsgkn74qcOnnqX5anhUU6QtZHvLOX1Ud6/KKy9pIekjRU0rQqpkb4w0ZPQcggfLIhgWu3pTBLQDQgu9J0d7Y6Yb0c3kQFw6nousHIOPJMfHXu128vo7nqvi62nE0erI5yO0yivo8qZaTN8qyGXFdvJFkCnL5uaSdg31g4/mWouPY3WZycg1V2ypOppIXPn6W4r6EvCUhO2Cx5xdLgeGBhWri5Le8mGN+SWL6FH2t+REpfudzF8jqzURgdgOoLzoUd9mQOQXqd8zw1ddvAgB+6+ffDNkAg5cy2DvsusMEA8535tvVTIB2w4UFvDwE5JSNlyRM3wypr31GerBbxrtbbebflcCcUxcVR8DWpyuIzmJ5iQwZqwDhYtJiUgkXUIgGUVcm79i7E5dENg/Efic4TYnAbJM67/bpBopPlygOo4uq2aa+9VGGTlG/yyZGC7YTGgPFNJL2zZCe39/PZBLLvQGyeYnyvme0A/PLGsV94NM/9zQA4OJzBlYLVDsyWfTombx8QXkIQMRxBiAEIKjdGmKfOt5qVmBnw63epu+m2FeoL5hwXbtp0QrA5d6K1hBWQVX0ktW8g1YC7UgFg0i2DgKss5P0ad9H5hfweCicsrqAf5YF6oHKy6m76Lw6EpdeT49nRXvozLHz2nIuyQg94yqts1eFe8DlK/3SM5L8ccRjniieEqXDc4c2OMCnGlmpJ9x2VWoA7GqrXb/hK30U3Gypm9O5M30v/PMmituhHef1d1okzlXlXpcvvbI2kh5SVAWIIsm2HoQOadIV1kG0cUKGi/nPilNCcJqxCDt+Pac6s1lcnIO0gEdnDNk2oouGjRepUw0RrH1dIkEdZEcGU5sDwssEFEDLCILnLznN6SUS0oLNAFcQkgEQj8RmAqq2WFygIUJ8KhbU5EsNP4NHm0wRRQulX8sU6TWZXGC5E7eG7QTQN2hFXW4UgMwwellg/DJZmcsLGZqnKjx55QB3LhIR5fT+EF9z9Tb+26u/AAD48298HdRBBlWJkO4DAKpdB70UISTfSTI0PLIz3LdQlUM7loFPJUA8LU9sB8iIcDoS7VVDs+r0hkB1hSPJjjXmV3KYggRHqf9cz9jJp5ZRPhvIz+1IAneI1+M5NE4Tl6Ib0Ooi27gbHj7PiV2f2cb4iMOP+d1nS0ZjGGXJZgiE6oaRKyfJ2EuNVdcyWuINdk154NqhQLUdw/yEAUYvO2w+R1a6mrU4eeMY7RDJCtcPLChOLdqRgJ6LQPK1mp5nNssgmf/mBTtDFJxLpBqYiE4GlOTFixqfn1JghdV08vglAb3okBuHrqTrbC6SlBVcP39rgbdkSXoizQkIi0DuDvSUdMFFPNb7F0l/rJbVSK/VslL3w1CiM/Wkxs051zh/ToJAee3NM9Ul5z1IXsCJpL/EOedy288zGsNpFhxGjxjenyA6AHocr8ALrCkYxJOz0zb17uP5RavtWnmOMyH1YgXte0jx7X6U0W0Cr6xtD61jjSS9qrI2kh5S/IA0fhHxEH0uUB4ZlPcbTB+j2b6ZCNbuoHOGdw3mVxRlqWfjw2YIEgBhoFoiKBfHdEC2tGgtLojohjl2aMdU//g2rbyLPUWuG25TV1By29FdF9w+PqS92omq1V1JLrnxy1SP1QKzawpdR24jgCDn5a5CcWJ7oeCqARaX46KqF4RueSMydZf5Uh6SwVBvyTBhdUOqo7nLLrnNFlvvOMLy310Mqs6jOw6mKLH/setQPEFuVcDz//n1+Bv/DeX/+Uff8C/ws/fejo/uX0XHL2n57EZUSU5yKw3vxsgykwvSGapdeGa/gE9udTi9wRGKjNSFCEIWrywPAdnFRK4nQ4luCHRjuvH4OQnVAuURI2XTDnrWQi4amDFVVhxJqEULOBciyYRxsIXCnBGpdgyOEgOGd9nQyAWaDcHK7XSsy0Rvx2syMnSES8ROZxwZ2CIkEPV1r+rYqCYa8e2YjJR6R0A4artsC1Q7NPa8kKiXQAi56UpyUw6PDZqJjOdUDuNP64C45qdknGUzb0gBpiP9sZrr9q5R2fXd1xA0tukdZyiPFPLjFnpBY7sTGt2QyOsedfOuNKwiIaJfL/0BPSLxeeVMdNY5dWB10X3QIuX680JvfX/YNQ+rU/A8toJKOd/Gz4aMrRgR3n2aJv9dJXif6z7s/ym4usiY8hdQY88jd3tDWzYuEsKRXJe22ythP8BAWnV/YlXv6LzOXo1ks9E4ckL0dZw+n+WVGM/r8jktayPpIaUbALpJXDcetWkd8mkH0dleBFO6GxUuRovpJV24HAmolhYLv/AWx8DodofygKwrm0vMr+QQVoQknJr5SCYTmF7nxXlIE7hflPzu0OrYjmxOi3g3QlgUml2DeqYw5OgsVVnkUwmTiZARPpuTq0bYuMOvhiQwaAsHU/pJSEJVcefvFBlAwgADVs4e32xgCol6SwbXj9O0WGenvONfSBw+dxFXP9VGlEMJ7P2OYReN32k66IXFvV+4BgD45//1N2C3mOHPPPYJfOTwBgDgmXIMPSPUwQS+Fi32HulxCsgHCqpyIXrPi36aTKDgaC+bS3SDaDB0I85S3sa0J15OweZx5mo3gUbElC5AjnFtgGEOYahuYR1sxklyL9NAqTclTBkjuIQBIMhYszMRxlK9warDHAloWS7Cu9sgotyEd3cN7nJdA0RjqIrK43686KULgqN0P4HlRaDesSEird2gaMfiUAQXsOed+N/LY4OukPBK5v55VMMCnIkeFWyMkDQlp+Yp4mI5epnFKtvI05pfdz10ECBNL6sy5KdRJd7kQDuUZzV1/EUNuX1MgqgF/goQvsHASVwxfs4gQEA/0mrFzXSG7+LPAc5dAB+6HiYI0pmFPz3nnHofiCYkLrhzzxHooZh03lmtpNV2pCgWEOcomRjpPnH4GaQtQaVSLaT4d05KmyJgYuX+vp4Vg69X13loYahABEPJCQGRPKxw7hFKAHwO7vWodZ2+yMtn0x9dl3VZl3VZl3VZl3X5Q1nWSNJDitVApxnmT0T/yiMLNe8wfXKA5S4jMpwWJD8lK72ZUHRROwIWlzwKAAzuO1TbMWJqeNdg+MIs3FMYDeHyHvdnuSdCEtyw+9G88+KdezZ1yOYWi4squDKKqYUpSI0641uI2wqbz1nkx+SSqPYytEMBWyRE4AndqxuqkEbD59ISVqDeZl7NBmn1hEg9B9TbDtlUhIg0VXWQncTGCw71JlVW76KHVuSnAsWxQzeQUDVr9lQWXSkhjEM288iAhTAOF36LULdP4A1443/9DD55chHP/y4xqctDiWbTAQ6wGbWhOBZMfqffdU3uzGYc3ZB+B1lvyqADtbwge26ZIFxpAeE5Xhm9p+JIYHGF3V8sjtkjLRsHm0k47V1PEjaXMLkgVyQIqTMlQpv0jN1jiQq3ahyyBaF+Pv9ZNmceRRAkBQYHFvVcoLwnwnixihEcTw1jpMq7iPWCEIHy2CA/bvlZNPKpRn1PBpSo2QK23nCI2aKA+i2CB8t77E72qXsuakI/E8FOYYBqS/SQVH/cj7N2g5L62sKhvE2VZUuH4tSxS9SjZxRI4FE32Qlkc4d7X6mgF3Td8K5DtnBoJzH1S37qepFRwgo45YLwIZAAGYnr54ybTpwBTpI6zztI/zisIEcPKcEltlqPW/n5HBTpld5j1TV2Hh/nTJ1pcxIU6YFo1nl1mf49Y4UrCJDXZkrmmDN1r7i6KP2I6PeVAyEo5/VHD/XrE7yDAGZyXwFGt79QxO3XCgStgaRXVdZG0kNKNgcUKxfrRXRdWCVgS4V2JGMWeY7s8XygaocMizQtQzYng8cJYPd3aREq7leAErA5VVTvFVjsSXTjeJ3NyS2SHycRdpwLzAskdoUALiiYMrq6ZOMwvm2xaGNSWD2nNBhOZeE6U5LbLihNczqEajuSgTtEUcqwYC8kuoGDYiXnwT4rO3cIUXHz6wOKtmojebzpyBXpM8XrBTC61ULPWjS7tHpWW8TnSrPbdwMF1djAOdn7mMD+p59CftLiCU4QduuP5bClhZ4pDNilmM0c9NIFY8BmgGISeph8WUkYggQx6T3z37zbcwFY5iX5RV20QL6k5yuKGG6vExFFk1PC4+KoRjug97zc02g2JBYXY/2qosgum3yVwiXRiuAEv4LesXcDmpLa7V1Rekmkb9UAk5fo4mpX4/SxPlvXFWx483NW25QrL1tIqJkfDA5dkcFpoLhHDZ0qicODCcSphtlkF4QUZNAlMgReJFJ6N3VDaXOcilwiKzlVjk+5YwE4gfK2DOO42qZEwLKL/Z6dAnYHaHbYGG8EZlqi2THoxiwvsCSuiEOS/BcCxUnkpwnroAQR5kXSzzbjKEW//ibSFPHl4Gxx/b+dpyB9xp31ALfWqyln1L5fwUJ4LrF6xW22KiYZSdku/D1NbgsgJI59uHF1/s9YMZDC/TzPyOGMUUTHYyVOSticOIZeogXd2XasShAEA/Yct9tq+pQzf1ev8oX9PssZovnvs451eeVlbSQ9rLio6jy4b1AbmnyrHYlqt0QzjpN7OyHtHG9USF5wkYTum4J2/9u/51DeIVKL6CxcptBsM8qyqVAekeERuCk2LpxBh6mjST3kWxsA9Q6FuXvCq9UKqnE04TMHxGZAhXRHjsA9mtykVUIvOiwul6g3ZUClvIK4GdigvaMa8s+HlCeGovraIaVNAYDlRQmrKFGrL3pJRpdHoGTn0GxqdCMVDBNPPIZzIf2As4DJJWRNVlp+XFNmWUlBSwAweilHN5C9ZLLVtoAax2g3XdPuN58Cw3uMUrUO9aZCN4j9t/zqJdydEqObIvR55lPK+AlTss5UHsPfvRTE+BZntl9Y6KWBXLbIeDLX0xbtZo52FPOf+bQovqg2GqSBFzUkY8PpyIvySWt1QlRvNnybNfc56VX5dC8AjR9p4pqYLcG6Qg6Q9FKbrZzQrRyYX6Fj3dhCHWTQM4FmhxpcjSzyIxUMuoCAJXySdgR0QzKmAl+vYs6Y/24MkB8RcduPDzMEMCZ9pzQpcopSgSPYRi+qyBNkA7A4dSE/HhxvEnjMqsZBV663oApH5P6uFGEMhSS4qRHi0YXzDBNvWOCc4hGOlAuzcopL/55ec849zuPwrN5vlR90bvTcCqoUuFmeD8R6Z15lPq0vfR7PyTqTuy2t298iRetwzjmC0aqHoFRixXDq6R75Oca40EY/960Sx4XluWa172UcO/TvOS/sUZY1kvRIy9pIekjJFkDh8wO5GIFjdsjdQYKOPOIsAtkZYGNk1yGbx8UZjhCbbG5hS5rJZdWi2yhCos7hXbJKFpczmDK66cojh64UAS04fZImDp+PrB2TYaWSNCqyc5TmxE/08IKCcXGRLbXLsQCbL8IBgwMX0mg4RcTgalf20II0TNfmQFvQgufde7IG7AYhawEx4QS0zTb385zkAdpxdP34Sbbalmgm9ADFqYOqLbohDdvstIV0FtaHa4GSBS+mErZAQPnAudp838mO0Lw0rUu1rVBvcwg+ow47mzMc3CsgOJ2KMF4DKV5Xb5PxWBzG+kQHjG8bjF4kK8ZpCdF0ZPBxug+XazQbijSCEv0r4ShKEQDaAb0SvYjPohfcjoykBQBg89kO3VBifoXRyI1IRF9c9CGZCDIA3pirt9Bze+SnFjYjuYbqIlll3UBi87kO80saywt03vbvSsjGwebk3gTofqZMUrpYinwTLkpEdGMaM/lp1BODY6FNdr/lx2kUKDVsdJcQz3YSo0CFBbojAcOIpa6B8W0Lq4HZlcSIV0A9jPnjSr6+3kjGekeGUgjMMI6MvNaRVAPIqBNyZYF26Hl00neYnMIH8eAiXsG6+0oXthWkITVAzkWuVupNUSFh+vWlyWxTTaTVulSCyAHRGFl9QG8gpVpb5z1Pzzg8p5PsytwFJMb+Kop3jjXqUSq/GTsTFefJ4pwsF7yxCJ5Y69ZpSr6Ey9pIelhx5D7rSqDZUHFHPASqi/TheIFC0ZEB4g2UZoMF/eqINgnOnn30eoWDt9LWuTihD9dzmcSWZHdYdF10JdXVDYHlNWrEW7/qOXzs5Suwz5IPUM8JeVFNvJ9r2Y1g4uQQIo38MzIvJT91UIzQmIFGftJhsB+d7vOrBbnQRNyFe3HMXpSII6XpIHK5oOeY3XCwBT1jeU9CWGDyAv2eTx3ml2UQawRYZbwl4yCoW4+Bwgo46RdBBWEtZGsC12d2TaK6ZNgVyHXNRD8KTxKKVBw5NOyaqTcpwa4Qcfd5/5ldOO0i0uP70KOEANQCEC211SNQ0gDmUAAdR7JxyLDLVGhns11iuSf7hnUOiDoaEDLlevjkq6xFtfVsF/WVFLnJ/O/ZghAQ1UQDrN7kvjR9jS5hCLECgOOnJVRD/CrLC0JxYmByAWEd8ikdMzlpX4U2gjWSFjGS02qB6kKfT5VN2SDK4ncCsDbTyFvaxCUpjmO/VFuE0HojD+BoOBnflV4AqrGE/PLy5UP/202gChsFgeE9GwRDvVSBXtqw0FIEo+U8ghzpqGl89iKogB4K5I/1VJxd/C749ucX0V/Pg2HwMJfeOcceGD3nPss5PurPISz4susjOOdxj+L9XVQ1f8A9zhgsksZJDNFbOS9pb0AMPRp0br/EDW16LuANMg9n9S+LukyC1LVtv/6QBSCRIndKhITKREN4RNDSg/r/VZQvJAj2xVjWRtJDiimipkzHvA+A0ARZ06zmFyGPjnhCqimAdtPAJSiHqmnRaLYQDAbHyWVbDk/3WjYeiQGAZpfqri7Er+P3/r+vg5Lx/t69MXkpChZWWyJudX3ILbssUpXcbA6MbjXQU9oCGubNqEVLCAiAsXEwhUKzpXHyOFVW7TnomeihZ04Rjymkoliw9s1eCz1kFOX+iJXGqRGDezXaUYFuGAnt5AIRHFYen7veTHg1QqAwDnLZhQmwKwCx2ULMytA37cRBNiIYH3oBjO5YOCnQsBtGWGB012K5I8NOeHAgUW9EwzKgJBpx1+ypOyryY8p9QW32bsiWKmj2RuiG1DFESgeG92Kqhm4ggio1QAtDPiVRShNcaQKyc2iHEtV1NhYrdvUmxrg0bLAEdETAeUOLv3pVEYKTIo3FIbDxYhsM5m6g0EwUrBYBxfGuvGwW07OYHMhnLqZwYS6famL/qTq6FL0xPHvcEW+JjZZ2k8YUBQn490zfg15Gt1m93XfndEPg3ts0xi+5MLYXV6K73JP4mw0ysof34sX1hgSEDAZeqtKsmuhq9TwrXxy3LSjbp6635KRV3stDydjpsQeVxJhYtaPOJW4/YGFNlbNDupGE3LzKNQr1rNZ3Dtl51Q0ZjKbEJedFcnupXxzOIF4p2tnjLK0arImR6xW9U65W4EqtJKIWiZtVGPoe2wkN0GpHYrhveumYnKJNhAjSJDZ8W4+kvEYjae1ue3VlLQGwLuuyLuuyLuuyLutyTlkjSQ8pnoCoGtqpBtTGAk462Ms1xE3avpcHkQcCkLvAFLRlCRFUHVgpmVxAdJDQD+/y8C45U0aCbXlI/nJhI+HaQ9+B2yGJr1EetEG1uiuywHnxorZ+N1Wwe88UAtncEorE7iG1aMml5Vwg8MplA1lLCOcwYBeVGVIYdn4U2+Ast9u773N2hdzP0HacUqKLAo8AcPDlJSEmSVoLmwkSOuwA4beDlpPCcpRhN6Bwb9FZNCwvUBwD3bMlnTeh59l7w33s729g9PEi9N1yVwbhRADI5qRUni0dshkdbMeSUqr4PHc5IRppElqVENuLA2pnfkpcKmHoAVVlKf/YRBFqkTyfzSh03defuhZ8yg7i8fBu11ra1RqH5R49czsSvdQbxWlMiRI4ZYi7d+9uowS+EU3Ip4Tgza5pZHMau7qikHnVRkTPI2qD+wYNJ1xe7giYTECKyO/Q7Ir26GpzgRqSzWNby3uiJ3DpFPGHmgmwvBDHejYjl6h/LlVR233Kn3rT95VAzaKoAKEKqiIXoi/1NqAaeg/j2x0gGE3id6orJC4fdqtzSqBe0lV/ypkfPktJkRX0kaZXtcv3br2HuWAe8Ddp4lwDIKTxeCXK4OehSGky4PNcbc7zuRI03keEnSFlByQruvtWVbjP9J9zfReZAxxcRJeUd7khHPNcJJHcj34QuP9WjpB82wm6X9nE1mdcrz+EjXVDiZ6r8fNZ1tFtj76sjaTPUoRjhewBMH+MZpTBlTnecfUlzNoCH9t/is6zIsDHaWk3LIr79MEVR6y9NO4vVCZZJGQHQNMk5pWfh3drVLs52rEKhlpKhAbI4Bjco4XYZnS/wX2DxQXF7aDzBnf7GbS9C0FYC2GpQgcJIWgSsDm7hzZzwC/0nA/Mh7kPDv1Mxv9IWvwBInELCwxfFlhy6Jow5Br0bp5uRO4gU8YPWC+oumYnLrLZtE8KPXmjg+xylEcay1023Aoi/zqJQIY/vjqAayXqXcd1C6gF8Qi8gaIax25OA8O505ox6UdFDg+5X5xKItlAzycNAHaR2gxYbAjUG/R5bT1nUNyvYUoZFL69a6wbIMz23tBJNYS6UqA4cdCsH5VNO3KD1gbb3FfNhoawDrMrnAh4amG1QDsSwa1lmVgtFomLUJCR5xcgU8T0LF43qN7kKEEJSHY9eW6OKTTlbwON2+LUomLNJ2/k+78DMUm0zaNhLVtAJ5O2KalP3DiOb72ge6ZGdLPJ+f/4d1mTBhltQqKLzCcSTiNMbQHMHuP+HWoyQF2UrQC869AFl3BwTSXuomDcROWHWNJfznERrf68eq5LDKBQ30MMqVekjxRcr95Acj2jI4TcP4xLBKCn2CwEE6fPtqUnSyASIwV4YMg8GQHu7DE/17nkWHK/1Phxgh/jjAsz5kIEaLNLfRBPlJ2FhQxZBZ7ePsQnL21i4/nInbIq5iMESKXf2vOf5/NS1u62R1rWRtJDiq6BboMm4+pKh9e/4RYAYFqX+N8+9TpMfqvA9pFHZBCT1QKE8nT9iXX2mMPmlx2irXLY36RQK1f2Q5k92TafOqiGw6t3cxIcdOiJO0LExXp82yKbW8wv6fDRDw8IwTBFkmaipii5VFKgHQjUewMU9+gk0VmgdTCjHPPrZCFUOxLZzKHaFaj+K8qFYp6ZYO93HJFeQXyhsKjwJFIc0z0BIJszisKLlkeEZMsLo+pPrE4xYsCGUYiO878b4PRJ4PiNApazwxcHMizQ/pmzXxtDZ7HvnOT0F5tAxak9Np4j9GN6I4tETcXaPif0OyX9jdcDZHyagjhpIbQedJ0P0ZeNRTvOoGqL8oi1hq4TeV22ieYS39OjU7IlHo7s4i5WTxvAOVTXxyR/ACIsw0XSNBwwuNegmRQJl4kTLCdGZtidez6FiFpEaaoZ6wPkEsL3YJ8iJ30/5PscjeYXEk3/UWg9HRveiRGGIfLQEHLjjdVmItDyxsEldSGP0ge+zU4lSIghTldIsgyWbGCj08tpNHsG+YGK8gkCWFwWGN5xKE6SNgwFIXI+OW/CEwv9x6hHWB4lzmR88PSYz1r8OavGTq+i3ikPNoRStCH5OUS0di5ykM6JUjtjEyXRZ8L1DZw0aONBIpS+0eehLZ9Nf8gfT8dqQI58KD8bSME4Y0kMx/eNFRH81JMIMBGBUo2FbCxEZ7H72/ThfPzqFbgrNerNPAjkdqWAcCLR2opJqT/fZY0kPfqyNpIeUrqSiNumAORS4ZkXLwMAinENcUKLqSdcO0mLsl/wTEkfz+hFGULd7ZUa1gnsbcxw8JV87PcmKO5H46ob8n8DgdlVej2ypUSx+dRFvaNWEALF32Z5v0N2XEM2BU6foEbML2p0A9rB+wmy3hC9nbXNSBFcdhkAslrykwais9j/6lHQfVI1LR6v/z99Gv/Pxz4AAPi/5v83yN/YgmIjKdMC0xtkgE1uRuhaVS4sYL5vnIoTHwCIjhZx/wFbRtNE3RdXBOLEXu5TWHg2o7B1gK6xjtrrk9dGhWk2aEvKR9YNo/GxuCSw9zsdFpdk6GPZ9NuYTSPyFsjWJS28xb4KC3h+DKgkAm65R+rTunKhnd54XV34hUWImtGcS63ajsZncahhtYCsbdhxN6VGcdxiY04vWXBYc5pw2VmQlpBNXLRA0P8BYqSiahI0i419aaKhkJ+SIZdKPTjFIpzekK2jgeRRM2lj3X68W01okK6igZIt2OD0fW/ZBSaAxUU6VF3rkN3XqLf4lNwF8rdPKO3L8qLDX/uWfwcA+Oj0Bv7pjf8N//3+2wAAv/A/fz1kSy7LIUdzDhcO1Y7GYk+FdqkQScj3yyhytJenLdkQhfIwN1pKbPY/JobQeXbSA11gCdLSc5vxouqRI4C/K4+iJAaKNzRSlfEUVXxgo/pAMoSLQRfnPeuDjsW2xPs5ABIubj557Kbq6xArc4nf5OjEMHOEjAsXQ/29pEEkrzuIxkAA2Pw0WdGL3xhj+fY5qt0ibPYAQh19hKewgGnW9N4v1bI2kh5Smg0gd8ypqQRajiNuhhqqEjh92kDt0Co0+I0BhYJ6hMa7ObK4CBXPlFjKEhtffwsXNwmNubecQJjoDhveoV21E0DDbi3RAcO7HUwpMb3BatCa6vbujPyChikllnsqcH28mFq2SBb1AU8e/IxWk1F28rQIHJfyMIOTwOJqdIvkpw6LiwL/y+t+Gd6Y2h4sgZMx8kOyuIojAWGHmF6XQV9GWIfi0GJwt4GqabWcXVeoduPklB/3DQ/qQDY4k8grP6mWd3nBEgJOA8M70W2QRoK5xGUkDbnZ/DshmQKB5TVaGOdPAtufkpjcJM0hgCbCejumFwkTqiN1cQCYvGxRbyjMrwHNWwi6Uh8Z9nhZ9TZLEOhEnBDU7xoRtZGc2iagMRlQb1FamQVPyKYYYHTXAMah2qUHzWcWsA6S3aWiczAj3TNMfYRZev9sQePM63EF47BLFg5GltJkx06SKzJFckxJ53pJAB9FNHm5w2LPwzC8aCWLuNPcDl7MTE7jVdn0PXKD0wXWCuSnwOz1tGrqUYvyI0N0w8hTChGiN5YoBZ13WJPV/56Lvw0A+FePfx0u/RcfHchu8ZMO5bHBYk8FiYh8bpFPbRhT7QhwpegbCQJnxKCDC37FIHLJ8wTjJ3p0kNBpzj57WlYMl3TRB8CRjq7HWeu5r8KJbPStorlSnPkufR3hmVeqcuzWSo2t8EzpfQV6+kLCuphOpFdfNNSE5udJXJEO8bv09dpstd3RQPIGr6osVG0IOQfLPjQdYC2cokG3/UyH6dMl2nFsg2TZFO+GBwCbZFb4vBZG0dbl0ZW1kbQu67Iu67Iu6/JFUl6zu2xtY72qsjaSHlKGdwHN7rZuEN0UbsqRMFbBNoSOhHO8zs+cdt4+QskX2QAv/+YVZCzMN7xH13oXgU9lQclIGTXKCBrWxy2aCW2PFhcTCBzA7KoEIM9Glnj/fOqvT1xBcLSbN1l0VzRGYLkHWB11Yob7BrJVeMdH/8/45mufAADc/M2ruDK0KO7TV7e8MMByV8JqBDeIqgQRoY+BfErPppZMQPc7QXZVWo0eh6ZmN6VHbUj0Epi8QOhduaFRb0qMb0aijbCUSHb6WBYSwNoMcG3kQJmC+iObAZNPMjF9DNRbDhvPVoE71d0g2MWTOO1Oi8Gnc2QzBJ0dvSSEoR1q1AtqfL3Nyup3/Y418rI8sbkdEWLTbMR3kXV9/ph/Vz3EJhdYXFAoj2xMo2FdL9rG5gL1pibNqAQR0ktye0bdItJbCsllx+SCJC0mOkaJgYmf5917TtBun/gp3Mca0AlfRxhAKKAdxu28qoH8hLhM3gXnCkKlPJ9qcEBpL0i4lM8RUXfJ5/tTlWINJH7m/TKkMmknnifI/pqbA/y9u//HUNd/N/kK/PcXPkx9/tQMR6cTFIfRNWPKDHrpUJxYtD6SMycOiqosPy8lJ+4hJmlJXXDq/M1/j1t0njsqPX5e8ZyoFCWyOONC8mlEAtqapOjwblyTJ2hN4qp7UKPTOcattN2PC++elcm46D3nSjSdE6Kvku2RRxFd7vQMon+diucD9ExeGT9FzzyKpJY8D1WG0SN2U9ctcZ1yDZdHVf/iYIDqkkFxSDcqjx1U7WA4OCVNRv55LwmC9prqWJdXXNZG0kOK7BwkHNSMPtaK0zKILgo+itPoEqMIIDpHL+ME4N0Y3dhH6MSFqh0S38IbYCYTJJgoEn5TAZw+nmN8p8PkBWIHZ4sCsysxwW5KUkyJx1byJJW4o2DjxNVx9NngbuQQdAXQblJU3vjlGA6vWof631zA/7JLHTH0Yo0b5NNZ7qmY2DRx7y0uKOhFHsLFPSG719eNw/Cujcllsxie7gm1TtJCtbjMbqapxeT5iiBzFmw0oxxWC+SnxOGiNggsLkSSsSlowW7HCLwXvQCWuwKqLkL/mYKI2pPn+B1/JsforoVqbHAnUn8JbD3boTyilzG/Rou5j07shhJHb6TzRy/TNYN7JHbYbXfQJxxuPxfBXeX7QDCXKBi5zBnKT1rImvszE2jHGbIZDSIBknXYfJ6kCAAynExBfDTH92g2JKrtOE71kowO2cZ+8eOwKyKPTRgyOJtN9HK1WZ0szpbG2jJJY+Nz5lEuRLppbQSmT1D6HgAYvQQ46XoRft4YcDK6sf33Ur4UpzBP2i7vxQ2EbIHiWAQphGbH4ed+8evwb8zX0SkOaK4ZNFsSGX/L7VhAVQKqRUjFIjtALxTGt5IXdN5i8wDDpmcQ+d/Fyh8fVB9WjYmkXu7PtN/pv8g/gvMRbZFo7BTNNZ4jZ7OkHXbl3zMP8YCS0H88l83f74xKNnOlVjlKTohz+yFGYIpgMJ+5b9Kfvh888V50RMxWlYWq2EjiecPPHRACdlTA5irytxqL0ctA9/oGs8dpAslnHGDj+X56bXd8KZe1kfSQstwV0Fqg3QArP9PxnOhESWZx0IeZKMMKx+hSEflGXnHYakAk+kamiHW1jBQ5IOSzMgVF+3QjjcEBL4yCyLOd6tcDRKNMNSCDqO0rQ3dlNMAkp5MQtr8jLu9JjF+KisrtMKpI+xxl+alDcdjAZStb6WTX2k7o/rOruhdZlXKHpKVFXLUO41sdt1NguE8TZnHScT9ItGMZ1bUHEvkJUO+VYbJXSwtdGRQHVcgj0o0zWJVjwUrk3ZUObrOB+swwzG6ypmi0elOGCbiZcHSdV6oWXndIBVVzvXQoD1uYXGLzWXqp5XEOYRzml+jzWlwBqgsGeqZ6u+R2Quq+xX2qK1vQcd8vflzkp3EB8H04u1YEJMmjLsWxl0EQpDdUxxD2jqMovW4QQNFqwiGk9sgWCDtwb4S0Q0BJyrPXcZBCNqM21jsW2QnLTczB0g/czhqEFIi4oFnNhnsGOH4OkwG7X36A0wUtQNViAl0JLC4D45txrPh8g8KPY01I2ORlRui2ZQ9x8/ejkxMJgHGH/DhDdsLnFIAwKmx8fN3LSzSG2z3qnHynwnKeQTb0kY3uWqjWBamLtKS8m2Dc+r+lHKJkLKy2WRj0DObzSppKpGckefQI3oDiTYaKyIeThI6FKMNzkCEh+78DONfVcybFyXnnMRerZ/Q5QifTlB6pllJK5BZ8oQMjqys8qR5ZnY3EdBzLlvTFVBU5SBTt5kKqIDvIYQpF17ae32cxudni6F4B/RhBvLOTEQZ341h3Im4KP9/lXD7Zq63jNV7/h62sjaTPUswgarT4XXN+4qMt0tmN3Cz+oxSWDCRIirwBEFJj+MgfgCas+ZNR8j47lSgOWH4gmaQ8AuQjKspji+LE4PRxeoWUB4lD07lZsqXryiMXE+dezCCsiFnXWUrAT5wAGUH5KcHv3WZsp1MC9UYC23eEtjnLO7OGc4SpONmrhvpveVEEIm2YpBnZgXe9WQfVehdVB9kYDtHlhf7aELJxUUfFAe1GhsUFheUeL+BzhcE9hWFnUe/k3DckUJgf02XVnoIpNZBERAlHfbrYkygZgSqP6T0v9mRot80Fk+Gjq0vNWkgtQ7+bTKDblMG9p+fA5idVQEMAMlJVJVAe6DBmuiF6An/e8E0XTL10yKck4lhvSu5jB71wWFxiFE7RtaoWAQ0I6RhS0rQCRrddcHX1ntVHUWa0Kah3gJYNaz3z/8reYhyivQBkTXSb+YXYcPSUFxildwGYZYHqlIyPIY+N/CSOj5BUOEEnZAeUhzaMl/klIrirBr1tvQ9a8ONRLhSyk1iPU0B22jeuzIDHrQYki2q21RDZTGL6FI/PlsRIpQFMzDxEdWJlIYqe0PNLYjik7qH0b+eRn4OhYdNv0vWRJYdAvk5D+QNBOrjg+gZKWh7KgREJIpS6286r6wGuoijiCED2DSavm3Tuwu43ON6dGPqMExR36XxiKLy/jVGhsIBTCpbTMNlc0t8T3SThHLJpi8mzGdrrjFa/bYajO0PkR9ShegG47iF99Lkua9jqkZa1kfSwwt9rcQiQDg39brVAtrCwKrpwhGNExn9/GhReLcmNAvAEyFE7fnGRDTC8JYMBMX7ZYHi7gs0VlhdpVRIW0JVFftTAkxtMqWFzCc2LdbPBvvscvQlVMS/Ku7psBpT3bVBDrjcFmg26ziMMTjKaYePi4pOhev0igMKmF5cLDO/QNl0vLaxWdK5fzCqOwpsgGGbZnPlBfpLjiK56W2N0q+a+4oVbiDCJCesAJbDcVfEcQe40v6hTTi8JXeUxum2DUAZvDIxuCph7OUwZ0Tcyggkp866Z0R2LeiT72kKKFxSezBcXFJrxCCR1YMNYUE3kc2Uz4gI1G9QPAL0XmxESUd6jY6EtiUJ7fsKuLH6+ZiKQzwTKgxbVtudMCdK+8kFklgyLdhCNK8Uq0sEw5Wemdvq+JgO5GyaaUhm9+/HNGMoPQUZENu9rT6Wh2DYXcB25W6dvow6c/HYedMA8QlkeADjYwMjzV1jNXC/jM1OFEYUKz6hFcCcuLxF3bHA3umucBlwN6EU0hrO5ZF0brpb5abKJ49oU1Fdpv8uWeFqdYuRqV2D8EvFcPFrnEmMpoLLeqFkxGM5NQosV9OizLYbeQDIxrH01uo1C+M/e3yddlituvHOVo1fcWSHHW3pKGl3G10QXsej9vvoM/efpG0Sr9QaDLEHkvG5SMKBbMo5Ea4PWnOgsROfo36Tt3SgLSLisDfGTVixa0TkM7jssP0kuAfmGKbIrC7Ssj6Jqcf6zrcuXRFkbSeuyLuuyLuuyLl8MJUXMfp9lLSb56sraSHpIURWAjNMptOSCAigiSRqBbOFQHjMvZES7+bB7YtdXs20ha+avLMj14DQARpKyOWWCL07oQLm/pN2OsRhwdJRwINdT00UypaP2eVrUaZ6R0nSduAWnwOZzNaFXrAFTbStUOyLsPDsWvVR1hPq94rKYR9XozvUVsv159aZEGXKWdcjmCssR4LwyuKR2ZtN4vY9mk4mgZTMhV2J5n/PdNRZOSggb3Wbzy0QMz2Yu1AN2UShG+cyAeDQmFxjco44oDgVMqbC4qEN/io76yUdCOS0gO4f8JCIDRGzuIxPh+flYtUP8n/I+MHqJ7ic7x5wgarfVgt0GUSeJoiUFvxtue0KOBgitkSswvuwAzaKRqSK1VbFNktEEpyN6B0suQuFEQFEk6P1VW/G9eNK9P8eUjDhVCceHd/Gqiu/QqegS9s/iBnyNi8/jf041ZvQyPnc3YDdumyA7jI52OQKXTzaEIHq0UNXkDvbEdgCQS3J5m1yEcTy6a9COZEhBElxQWV9A06M/liMbRSugAOh5JJ+EPg/cGf+SEjRnxRXlNZPO5B5DdC/1rkvLOeiTMBxgsjJOYvqPs/fxaXS8ay48T+Jy9/c7TxDSay/5dnouZhpB208T4qK7L0G5V8uZPHT+Z5f4KxMUKDxPRwiU5+ip2kF25FoLEaCGlLRhI0zlFPGP0CbHNIUiep6SEAKy6TC+WWN4l47d359g+lUV1FXyGS+yAYpnzz7P56Uwj+o117Eur7isjaSHlK1Pt9Ba4fTJDM04LpKju47cQtah2SI8vhsoIlLzgjd/Qwt1qAEjglggZjFBarb0vnIiFOYnNPs7KQEmg/oP3CmBbphBSQFZ02worIXTCvkR+chKTp7ajoAxywlkc4tuqNBMVJDQ9xOVJx6XR0B5aJBPDdoxrUonjyt0Izb0+Jk75mYh4RJR6g0RJjC1NCiPLEn0+8nQAmbIfvvEHSEbBDVvb4Da5O+io6gTl2ssd2hyqjepTd6ISZPkeleoz6fVTCQEW2p6QaJxJs9CH6iGXC3ewJMNEcOzuYPm6Jf5JR2U0wEmPktg9rjF5qeYYM8cm24I2MI/gIGwDsNb3nKjSdeWCvU2tWF6TcXFJQmjb8eU44/ef3SfBIV0DSwuZTB5fA9OsGuJ6/HJX1NSttNM1F1ROnei/15MRgEB8ph+r7cQOGYZ84h80uTeAirjtb7eZoPO0/c8wxVh4U3T4qSEb1NQSh4niZcHAMKSZMDgXhSdlB25CX3bh7cFcQK7yHeyGRn5No8GUDdgA4n7ID8mgyENZnDMgeoGgMuiMSBbEeUZJGAzMqx7O/MHuF3ShT8YLf7cFSOmV0967ByStjSOFdHjiU6KcFkI67d9wVXZOFacjhux4G5L3WuJ60lYF46F6K/OQTYW0iSCoKWEyUQiGcFtWnH7PdRI5GePxmbfOCLDkI9x/rXgcjRsHNnIXwwGkpRBWsVpnxiabuKUYENbwvn0SuyKzY6WgRt5sTJQ1QDTJ7kPrlVY3ng0bGiBNZL0qMvaSHpIKe8toaWB7IboRipwTvKjCt0ox/yxIkrT8y7IL9bFSxlNiOmixAZGtnAoDmn10hxt0Y7pVTSbnMJiYZNQfkKp9FKhZM0g2XQ9UnN51KId5ZCtiKRiB9SbCos9ESKTAGDyosPwDq1m0jhkRxUgBGRNK1W5UaLZovD3lnf8egmIWYy0A2iyVbULk0y1l2N2VcLmEc2yeVwURbLbTbkT7YQnoiXipG0cXKYwfXIYlceZkOoRKc+PCqRk+AhC4vrML9PBwT0BVcqIoLC0gDcmAGBw38IUAvWGgFMy1F8cI+gt4a1TfM21m+iswsefeyO95xNA7TssLgks9+gdDvcdTJLfyhakQG6VCM/nDZyQOgWJQcPP4gwjXh1xmnx/Li+IoCsF0PuQKW+KDZF0vfbvQCfq6x6pEsxJslpwaHi8Vi+BjRcsnEIwVgN6kNRvM+IzhbZLIkTLLhqipIRM/0YdH7qmSZAl6gMBJFwpMtIjItQMBNpxTFDskVvD7fN168ohP7CoNxhJ3aKF0Rs7QNzYZBy12myRXpQZODjmIKlGYuN5h/I+vaR6S4XUJGn+uPMUsIEVQ4D/XeXw9Dtg5e+ef3TeMRvb4GTfeA3fU3KObCgv5Gp9AuihKsQHiid4fo/VMhofxsVoMZ6LROcgs5g2xPG4cl2/H5zqc3mCAeDi/BUeNeFDyc5B1cQxCs/oEqPJuXBMpMajUnB5TABOCt8OPlTOKREMeYS5V8LmGqqt4ROAy2WH3d9dYvIyzZeHbxzg9PoM6/KlWdZG0kOK/8jzowp6oQO075REvZNhcVH0yKXLiw6qYoLoDKDoq7Q+muyXewIVIwrCZLB5FE4UhpAEXck4STpO+2BkD7VRtYXkSS07rrG1NLCZhM1ZsHBMaMXwAGiZPF7tAcsdgeFdrmfRQnQWTgjoGa20m88C+ayAyYFql+rKT3iCN3ExoWSoAu0mE4hzGdwkITxWnYMidbRAh12ioGODeyZomDTbJdqxwuyyDFIIwRBIyLuqJgQgyAkIkNaPcRgcUAfmpx0Wl7OeS8IbWsN7dE5xbHD6eIZmKy4mekkLqDcqpLT4tZceg/j4GAPvZpKMbDng9Ak6sRkXhMTxvVRLfZfNHUZ3+Pk2ZDByfPLV5cV+/rroghKod7hNCwTj27uVbMHGTrJYBwMoQZsGhw56YdEwYlhv+rxpCVpgAGVif1IKlw5OihD55tE6L/IIkPFc3o/GX7NLv+fTiLSYXAQUKqRHkYBaENoI0NgyTEIPIfmKEUQgIKLloUM2N2HjUu9kJPBYRF0tes8CgIx6QHmM+gTIHdmO2ZhjoUoSMhXoRhZ7144BAPfdFiVrXnCEUyFhLCB0TH/Rk9FIF/9Vo0kk/wHnup7C8dV6EneUj4RMo7FWUak03U/QTuoIdUlztwEryCC3VbZxjhEdh8+L6OYEk7idioiy6CxkJ2F9aL0VcJns51dzgOM0JKvHV4nnvs0AuwkbblMwABklC6iYi8iRr0vLGABiolFHORSjkeQJ7SKpGwJwWsb0JU0H4Rw0P9+F37LQ+xo38QjKqvH8+61jXV5xWRtJDymyMRBKw1kLoQxkkuAwm3bQyxxt4vsXDujG/gMkI8cMLWTF+jU5RzVpoOVFzxQOZmJQ3maF1yn9vRkjGAdxBwicPq7CDYUBcubnTF6s0ZUKekmhrgAgrMLiArngvMFQHgpUOwiuNbWUkErAFtHaE61FedBgeTGPoo9s8KUchm4IqBw4ZXXa/NQhnzksyzjbeleNUwnKwEZUEAQ8ADZeaJEfVagvEKQwv6RgFQn6efRAVw66cqgS95ts+wlSrfYGkgmTX72j0UziZFweOuQzatfwbhOe2SlyY3nBx2JqsbgYIw/V/7oJMQa5EL3h1jLKkUfOmmMeThD6NIRalUcWzUYM0yetGsQw7IYRO48oeVeUjtyfdnyeECcjMn4xZIkCkUThDQ4syqMW0xs5UqFIYeLi2JUI2jIhUshvkJVAcRpRiXYgSGOI255P6R0ENwVzvryBCNCY9qjhcJ/qqrdIQDM/oXOyKYlwLi/IgJ6pxqE4Fiy8SteNXiLuXjcha6sdCYoiLAWqLbquOCVphHpTYHmJn6ejvkyjUlVF79AjhoO77AoeWHzZzl0AwIdnA1hdBoOhOGzQDSlfopNsDOQiLGKr+c7OIEBIjJJzXHRnoqXc2Z+FdSwSefa6VHzU6wyFCDrDSJLpaxQJf26KEjEiQwe8YWQBwRGmzgGdhWxjXUJLWAfIgAjR7zaXMQIT6Ik9pu1P+y64nBPUSLb2oQu96CwJRCZcJlNookQ0NvaXc71NJT0bIODg0peiBCBlfInWEjcuuWzjhYRM+Hkur9nd9irPf/e7340f+qEf6h27dOkS7ty5AwBwzuGHfuiH8BM/8RM4OjrC29/+dvzDf/gP8Za3vCWcX9c1vv/7vx/vf//7sVwu8af+1J/CP/pH/wjXr19/bQ/zCMraSHpI8T5qALSQ8UffbRSo9mhBTTPGl/cEap6gzdDSBDXuoGfMjZnTAtRuAN3AoxykleN5KM0GTRKyiYulbIFVsqeTNKktd+hANyihFw7jlyxm1+l+ywuEdDmF0C6vgZSzQKOe1oAQMAONao/RLQ79T4UVU/eU51jVG+gtCFYJlEeuR7rN5qTTZLIYWp9mlAc4DH3Zob4wwOwKDcmuJIOhOHZhIs2ntHucXo/uML9T9qhLNqdFbLlHBHWAJtp6KyJg5WEH2To0mzRxAoAoFbI5Lcb+fnrpMLptgx6RE/ROnAaWe3TON/2p38AHn30j8v88DoZFNnMYLhxmV6LmkLYkeHjytEc0HDaepXQcQauJ75H5RLKCFhGxFAF1akc8PrpIVi+OI8ICkGK0J7V7d5SuLJYXMjQTEfoqddkBRPj2yFkwTJe+n8jlSw2LiEgqfkiEfEbmjujZrEJAcQSfZzNEA4glB3w92Szy5Xxqm2wmoCu61reh2crRjRQadiO3I7pfqsptlUBxQm7UbsxGdGbhchdUzlVFhGzZRZJ7+xQ11hmB05asqbddu4VPPv36kCJHT2uoqkM3yuEU8xJLAWgeJwlv6Ex4PHfGuUBSYjidIU1b9JW6gTAnRTdndG0C0ehZ1RlyUvQMpB4PyUY3mVAO8CRm64BCMQLDyE5jaNxaG5SrKQBAAsHYJ96SsA5ukFpJID6RV8V23kgS4feQcucVlDS0nwxDwA7o3bRjDQhBrkIRESeTJwgXI3c2k5RKCTSGspkhFyIjScI5iKZDfo98tu3OAPUgmdA+nyVxR/7+63j117/lLW/BBz/4wfC7UvF5f+RHfgR//+//fbzvfe/DG97wBvzwD/8wvvEbvxGf/OQnMZmQ5sn3fu/34ud//ufxMz/zM9jd3cX3fd/34Vu+5VvwkY98pFfXH8SyNpLWZV3WZV3WZV3W5YFFa43Lly+fOe6cw4//+I/jB3/wB/Hn//yfBwD85E/+JC5duoSf/umfxnd/93fj5OQE//Sf/lP8i3/xL/Cn//SfBgD8y3/5L3Hjxg188IMfxDd/8zc/0md5tWVtJD2kTJ+coAClf7BaIJvR1qgbKlRborcLdxKY3zB44o0EQT5/aw/yIIc4ygJJdHjXwRSU5sQLTOoZoyC8k2vHHI7vYvqP8pBUkbtSBDePMECzGdtQb5DSs1M5FpdEaFNxROhPeUy7IF1RaKyPEKsujWALCT03YZcu2J3UDSLvxfOGUmFFT7r1m1SbUU4uJyJXa3jXYePTM9S7JdpRjHLKFi5E3swvC3TlIHC2gOhq1BURTQFgsUcRhD7KquZcYd7VCZDCsyk5FNyLYzJh2fdns6mglw7tSAR+jnfTjV+2EeVYGuTHFqagxpgMkJ2AFUD+evKt/Y9X/wv+xeRZ/N3f+b+gYJeR56YEzhALMtZbArbwrgxGhFqEqBwzEAExAADpPMk8klNSvlJQd2ekwEsjFCfkklhc0DGpb64pLF9FlCO48pJktibv88dsBrRSoBsKUpAHIYF0r+jCNDnJIYT8bpaJ4ElYuXcL0jugf7MZqX43G37suZBexL8/1RCipZrYV9WuRjuKcAyNA/rd368dE/erGwJqwQjehoCaShKd5OLD5H0/+Jx5wgjkrPp62hZoNoHlReqE8j4Rk9sNHeQEZOdgpein3/D8Fr95FwgJgntRbv73hIfYA4z8OVyHrwtCwEl3xj13xl2X3I94QALwXCIAkCJEl7kE2bEr0W3+mXxSWAAwAw3Z2ogIGUeAR0KQFtbBOhEWHKslE7pF7CvDqHF7DtLRi8j0aUoiauakCwgYlIAAcaIa5ku6jNS0vcwFAFgh4XlJdBJxpMxAhtyMVpMrWS5biK6Lz6cEJcQFkN0zMJPUv/r5KwKv3d3m+/v09LR3uCgKFEVxzgXAM888g6tXr6IoCrz97W/He97zHjz11FN47rnncOfOHXzTN31Tr54//sf/OD70oQ/hu7/7u/GRj3wEbdv2zrl69Sq+/Mu/HB/60IfWRtIXc5nekKiFCovJ5CX6CLO5JcVskYQWv3WJd775N/Grd56mA0tFUTTzaNjMrwrkx5TV3pN1Pfk4DWXPZpRyoTzkdB/LjichCvsHgG6ocTTKYVjB2XJIeL0tglspnwHjl1oKE2YuRTvWWFzNQgZrp2lRGN6NSXfrTVoQBaLLz08kPoEtQO6rdEEVHXFGilMX3Hs2A7pRjnasYp6ojlwqftF1mvOktTGEXDHXaLjfBs7A/IpAmxA7VRM5DN7tYgpyGXUjoH6S1buPM4yfi0TpdiAoyeq9LnCzTEGE+m4gegl1hbEojqMkgDDk5lzcphf24brD//zCHyOSNxt4zQa5XQ1r7JQHAg2nttn6Pe8eRQi/z2b0QKNb5MbwmlY+J5twMVmucALTxyi6zUdS5lNy9fjFOp85DO6Tqy5keWcDSbbR+BBMnPWuOmGB/CSOV9/H7YgI194dNbxL7rJUW6vZYvefX4g10JTocZ6cBupdh3I/qoxPXmpR3K8wv07W3OKSCi6/lMMmHKCWLrji2iFpTvmABwiKpktT4nTbQPtkBSEdBr9NL6e8LzE4cFEHiqPtiuNoXNWbQL3ngE7gI8/fAAAMRg26p5a4k3OS0+MBZOsDKug6vyEhzSh2R3F6jKjEL2AVWUB+k+ANO5Gs+7DJzzj/Z6vIQEpzRgaJgdRtBwEnIj/HSeKOUVJuEdoFR8aN8Ofx9Z4PBICiyoyN+RPH0UVvhnk4r94rwnXFUU3ny5h3Ec5BNjw+U1dfkqctzeuWKvBDIujM+etWDQenJbpxFo1Az30SKdHeATa698xAkWEvojFenFjkxzWEMRCJYQihIlHcOKjqEeUlYc7ba61jPp9jc3Ozd/jv/J2/g3e/+91nTn/729+On/qpn8Ib3vAG3L17Fz/8wz+Mr/u6r8Pv/u7vBl7SpUuXetdcunQJL7zwAgDgzp07yPMc29vbZ87x1/9BLmsj6SHF5oARFGKtF5FH4SSRobthXLCfvrKPX73zNGb/60UAwGZDOd5k5wJC043YKGoiydcTRoMRkhEBenyzimRF3uE02yW6oeTrJFxisJgccCNHCFUyoVZ7TFr28zFzeMpDT+4m7tHiokR+6ieiuLvWtZ/sHRolQbnouHKHwNHxdZlCYPxyh+G9iEoJ5wjB6iKBHZJ0bwCgmwtK1+GAzWe70E6TC2QndQjJH5USs2sSJpFU8GhMWKhOCCEyJcJkkp1KCCA8n80EmgkgrAoJStsBRVS5khAEALBKw2UiIFlOkwHSDgW2f4dW8O9+5q9i9+MNLrgGh2+kjpk9bvH/+OZ/g/ff+iMAgE9/7DrUUqC8J8LiOX7ZBoQyFAFUOwoVzyXtJlAcCORTwI2jcWUKMlADasOoVOSGAcsd1TtmSgR+GsvEYHBAyKLfNTsFSufg4njx3DFVRWPHo03lkUXOBt6J1uiKaCh67aZ8EXlRs2sC2RMzzHcKLI9ocR3f9sY0/duMGYETcZwZ4xMsx28wRCB6TSQ27NQSMNx/3YaFm+vAJfPtgovjv96QZGDNI2mn2ZAwOy3yWzkaUMWLwwJqESNO2zF/I8oFVNgpQE8dTB6/S/+9B5QlA4SXDhCJMZ6ge0A0mHpaQiJuCgAy1kMUYLifI8MnMZp8ie+UuEYymSiCBlLSBidZBJUrIdIzGUjd0CdnFJC1JQPLG+QDjWpbYbhPnU4cH91Pe6IE5MJAWZcYi2T0RauJG5fwqYRzcFYENA4AE8yT55R0P1PGHZyfS+nZRDwm0duUeKmEkiVairtzwHBUX8uDSEmI1gGd111QEPWj0Un6XJXRaITbt2/3jj0IRfozf+bPhJ/f+ta34mu/9mvxute9Dj/5kz+Jd7zjHQCiYe2Lc+7MsdXySs75g1DWRtJDijDwQRyQXUQrMBbBjeF35S/8h8fJxcDITv4SRTyoWRvrcw7dKOtNYu1EQziEfGSOJ+yX//gQmsmzW88S4lFtxV2QY4KoDbttB9HS7tAjAbYCUMXoMoDQjHzqMLhHH7xsLbqyIGSAjYHhfodqh9wIIUx9Itk1tKJT1IPxgcUFiWxuUd5dhueud0ssd1VY9GxOi4dXGR/cc1hc0nR/j/7XpLhthlnQNdELA1gZMoC3Q+r/VCcpP7TITxWJGL5AHSE6Fjb0SuElsNghTaTyiI3AzkEvCUXyC/HygkBxGsVAi2MLpwRHC9KkOHl2CtFZVJdHqPa4W/Zq/Oztr8Ezv3eN+1gEN5cfQ+UhQfbLPdXPJq4REL3sBGi2KSIsZLEvCTlKDaCuZCPTI2wttbmZRMQyP6F+yKYp6uCQzftGUlOKEFEEkIvQJ9kNiuwFvXubiYBIig7AgIwcgEQa82MERWgAyBYC809PUC6A6gLd4M5fqpD92gS7v8cLai7RblA4vn9mB3qWQeKuc5LeZ0CNBnRiKrzpTiXUgYSqyA1M55G+UshJmAHzq4BsZXC3dV81w1AZVEcZygNeQEfepRnfVTvmjQFHyrVD1s1qo8SB1Yy+ChF+B7AS6u4tnPC/swliRXLsHANIxPW6V4Tru/rSsioq6nWTguHu7+XRpkxSKH0he3kCKVm1RrXLJOmRgM1EMJraSYZ2pFCcdMFY8Z4x0bmQry6ITQZkieZJIZNoMwtCknjz5fsSNtFrUoIi6XQkmHtENrj0wDIOpQzPS5IHlEA6v0/zl3epoe2A1meiFkCeAY7v1wECj8ZIIuX+114HAGxsbDz8xAeU0WiEt771rXjmmWfwbd/2bQAILbpy5Uo4Z39/P6BLly9fRtM0ODo66qFJ+/v7+Lqv+7rf30M8wrI2kh5SRnccxIh83dnMoeUFruFQeFkj+MqzKSFFzVbcEuazHOObNvrvDUeTJd+TbHNy56kIVR++SaN6UwV5hxd5p/sidAB8VJkf8HpJ7gef0sGX8qBDcSxChFGzIVHtCFhNE1p+yklWjxwmz/PEYB2qXQ2bMU8GvGNtSCk8isTxIuAj/HiRmV/S6Aryzeja4vQG8WF6meVPSDCT+sDC6gw2E8FAkZ2CbB1OnizDAhBSdnhezjJZuHmhml5XgWfjDZlsanD8+gw1814C8iSihIKuLKptheLUwGrvgqN+G79I/VJIgeM3DtGVCDIEg4MCy70c998qUX7lIQCgPR3g5i8+joLb1A3JAJNNNDDnlzRU63ooYjalNvnnnNy1zOmJk321JYNAaQhj73iB4YWrG1AXtZOItOQz4nZJQ25FgHbc2dyiPPKoChk93jUH0HhTdd94Ew1zkLYFTE4nDvctKiPD2MsW5KqoJxLNWPLzOZT3aCDvfpSOTZ8Yo/ZinQBGtxymT/gxwONFkmTA+OUayz36TqodikoKBuUpay25GPWXtWRQej4dQKhWvSkAj+6WwO4fvYu7BxuQmvphZ7TE/gs7KE9FiGYrGH1MozLL+6xhtUfXNdsSxyNB0YZVvJ/JE3TXRTQ3TasRUSM+KAVFsMfLelwnuhBBQT8snIbcaYKtkFRbKRW2dCnaBPrmRefgMpEYDSzQyEaF1V6ROrmONZecFlhcoj8sLwL5EYI7SrYOuqboNuE1l4ykVCEOiOKRXL9HFxJjKaBngowjkQhFEpJkg9ijKTPiO6WoEUsgyNaGTdfRmzISHOVvpDi1yOYW+cGCUkD5UrdkICUpTdAAKHjOlhIwj0gCwLnQr6+pjtdQ6rrGJz7xCXzDN3wDnnzySVy+fBm//Mu/jK/8yq8EADRNg1/5lV/B3/t7fw8A8NVf/dXIsgy//Mu/jHe+850AgNu3b+NjH/sYfuRHfuS1PcsjKGsjaV3WZV3WZV3W5YulfI6I26+0fP/3fz++9Vu/FY899hj29/fxwz/8wzg9PcV3fMd3QAiB7/3e78V73vMevP71r8frX/96vOc978FwOMS3f/u3AwA2NzfxXd/1Xfi+7/s+7O7uYmdnB9///d+Pt771rSHa7Q9yWRtJDymjOw2aayW7LSJEr+e0Q00JriYH2o04+toti5PXSVg9CG6l4qiFmtWkO8K7EtEZ1iCi36c3ctgcKD9ZBgK2YIhZdv0dvkOyOzSe+xCjv7xYmz5tsbhCsEMzJoaB54AsLlFFqibIHADm13JMr1OEWnAvKEJBxrcNRi/TLuvoDSXMKHJHHN+/KwEZ9GtUSEXhuUv1rsPoNqDZFbm8UlJetMTdkM0d2qFEtUtuG4B335aQBYDQETh6D2mOMnA13n2oFx2ymWb1ZdpJbz4fo/0AQFUGXakhrMLwwPB5lA9PXBtwm2LkW8uI0MHbSkyfsrC5QX2b4etGorroKDwNQHFPYuMFh3ojuu4WVwFhBJava1C8SAfL++hFg2Vzg+I+QRLtRsHtVEQE7lzgp0EIzK1Es0W/el6QniVpTwShHtIkwQZD6jCP8FHiUgAm8rdU7WBKgWpbxMSxrYPsiPzun6cbCMBGfSUAaMaEevlzWi2QLZg8zsTY8U1yRU6v0YuzHLHYsVsRIP2mZkPgVBeBuO15bSYCsFAL4ib5nIB6Se+63ohaTaQSHxG9atfhsXKB+STH6W3ylZ/83gjDiur37spsCpSHUXBStoQ0NxMRkqHWuwZis0VVKRSbhCzI3xlj89kkZYaIKI53+0h2z6UkZo8dBU6SjK7t1Rxv5yplBw5O8kevqcTfEc0fjMZ0hAaZLN5TWhDy44ncHD3o1br9vYRzcE4EjSzZEifQk6bl0sC2AlaJwHEU1kQ9JG5DQMs8WsfP6ZIINI8g9dKhsBvNP5/NFCGgTUSupHGsdYcQCGIKikge3qIb6prchD3PWWeApiFiXFqkfM2IzBdLeemll/AX/sJfwMHBAS5cuIB3vOMd+PCHP4zHH38cAPA3/+bfxHK5xF/5K38liEn+0i/9UtBIAoAf+7Efg9Ya73znO4OY5Pve974/8BpJwBfYSPpsSp6r5ed+7ufwj//xP8ZHP/pR1HWNt7zlLXj3u999JoTw+PgYP/iDP4if+7mfw9HREZ588kn86I/+KP7sn/2zr6p988s52gtkHHXjuHipJeBmbLR4jpACussNUPFLryTqPQthJU6foG7e+YTE5LQmSHnAoakSUPMmfLjNBvNskropwgQY3TbBvVBtkS+95XHoQ5iFIc4RQGTk5Z6GHqlAHpeGFqKGr6P8VnTs9AnvyhBotkjw0BbRRTV+kSDrdkLP0445vN3zLFpAcX6mkGVdcb63IjFgQIKHJ2+glWq5Q+rNqo4RW92QFs/8JOaKc/z3kPONF7peVBDoXvokppDw0UWBwzMAnJJoB8DiKl2499sqtsVHEJbE02o4vLcbkosn49QgAOfiO5HQC2BxlQ2vueQUKmzwzZgL0UVpBKcA3QCbH8mD8dFs0LneEK63NWRbQM/bIGRXHFHS4mpXh6i0/LTDUOqgMA6+teySZMQcddXliduSpR68u6s4JCQ/zek1uNdQ0MC4CDnOpCYDSdWRv1XtChTHkUDbTATaTWrD8gJfZwC5T2PZG7D1lkC1i8C/Uw31rdNpUlov9hgXS70gWQtVxYVqcUGh2ovR4oN7jpPlysCVasc8xtiozk8EPvGRJ4BLFfJ7NED9GLSJYd+Oyd3sXXmUMJjcbz6diWwUmkpCAnAb9FLrPYtZLZHNqJ9UTUaBrpEYAX5DE7k6QQ4gddP5iLfU7pGC3cuJ68kl3EUV3eNpBByAoNhNzyPQFZLHDRvIlYVqbBBcheF0JhYx2kyLIFjpFdlVQ++1ZTerqiRLO0SDUhgimDsg8JTovkkwAwteigT68HICorMhvYjoiDhueU61uYRsLKVtqnnDYy1MqeFUdP8WR8BgX2J8yycXZxK3EhCV5xslbrdVo8i7vRTQXNkAPoPPf/kccpJeafmZn/mZh9cnBN797nefGxnnS1mWeO9734v3vve9r+7mfwDKFxxJepiS52r51V/9VXzjN34j3vOe92Brawv//J//c3zrt34r/vN//s89f+g3fuM34uLFi/jX//pf4/r167h582bPqn2lZbkjoHiidAIhd1i9Sx98Po2ohs0BlVmYGafoOGa1aouQa2x5QUI1Y5QHTaLlklG46iDubgBAtxEVrbeB8h5NoosLXgIAISWHLz4/WHnAek7XMlQ7FLXWI3QmO2RV085btiKkvrA5YAYWahHVaEVHObWOn9RhoZIGkFP0Fn6AFzQ2YGyGgFQJzxWZ0k56ucuowID5RTZyPkxGWeBVFY0baUhiIGMSbjOWkTTL6EHgd2ZAvam53zNUWyJmlWcOCAQpQwOENkiO1PPkauHo55COwzjKKp9EpJW3LfITgWZTwBXUMbYVyI9E4MEIByz3iOwaDEpLz6ySyLzxLbreL2rNREAvFLLTGjajTp5dzdANydjyhmg2yzF5yaA4dqGfVO2w3JOhX8oFML7VweSS0meArncq6kflUxcSNvs+n1/NA4Lp+9o1dI+6iP1gMuIoeRTTozWyBbptjlisJYoDiaJywcBTFRlIAbHUwPgmZbZfXIzvQdbAaN+FKLRyv4KaNwGtaHaHwCVFnCiv5eWAwd0ag7tAu+GVlyXqiQyGNwAM9gXM8SAYDFZTO1SVaG0JGqdpUmFhBPHbAi8QkA0Zct0JdXzWkTxC2MxI4t7oBWktAUB+GqGLgJgIfvDAcGZjatVIEjTWje0bYTFVCVtbK5wkIjDH760bSDglGCXkcdQQf8ejzqom0nOaziQkbM5kTG8zYMkIHkslELIXdBxxJjsiXPkUSuF55AqyJICQsDbpH4LD+Wfj4AoFw8acqg3UskM3zmC05n4SrNslQnj/+HaH7KSN95MCpszRbRTI536yOEf/SAhAyUho3xiEzcLnv7izxtqrruIPBwL2uSpfcCPpQUqe55Uf//Ef7/3+nve8Bx/4wAfw8z//88FI+mf/7J/h8PAQH/rQh5DxwuJhwVdbbA6IjD7c/DQuZjan0FibfD+qArLfG3h7KIgZmjIaV6YATp7SWFzUlGIDFH5vsyzC6oaNMhVhdB+dNr+swo7YRyClofx6YWBziWqXXuvwXodsbiCMw/HrytAGVUe3iGxpAq9HcbG0GVDcp5xlfmHMZsD4lsH8ogquEl051BsyaNVIQy6SfOrCM3vBy9SgcyJGuPm6hWGExwsWdgiRep4MLDv6zxspo/0O1ZZCO04MEutTWyCkL2k36X1kL8XnK4/JjWQTQ8ZyzjVvqA7uO0rPwppPoiN3QzOJROCuJEPN5iC9CADZCbXHu32CKKeLfSA6YHjgoCoX0ArZONSbkojFoHGglxL5NEe1Qyd50UW4iLDZHHBaYfwSdej45gKm1MhPFBSHJjdbuieQB1AUo15GYVGbCegFrcQhqpENUZNFBMoT3wmRAz8QtcfrfdkcnKYEGH1Gh/EhHCF0wvmwa+onL4mRnTiUx9QmVUcXWX7qMHl2AeUXL+coq7vymwaJ2Q02utjgmj4uILsSo9stivt0XX4sYJ4cBOOKRDlJNsImRr7V9AzeLeeNRD8H5Ef0DQkbUdluSGNPtrGv2gnQTmxIfC0s9+Wbp5h/jHYq5X3qvzRZdjAGZHTTeeQlnsRtkwJWezSGjLBAhmYXk7BRq8mnSiGSfnSlBUSr8URqB6NlT2xxtTgh0DHSGnIVsmvQB7rUW5oi2yob+t1qwfIBJmi48QOHcwKp27meHAoASmfiidRawpQ6oFv6qIJwDnarCJtPj5AJG+UenO/TNHoPQLWbQy3o41L3DZBRSpNA3C4KuHEZWry4NkQXLPPPb/mcRLd9bpryh6Z8wY2kByl5vpJircV0OsXOzk449m//7b/F137t1+Jd73oXPvCBD+DChQv49m//dvzAD/zAA1Gquq5R1zE6wSuR+jxQwpCh4yeB4pAmwTTKw7sEgsJxScJ3sqXIGwAo7zuo1mG5K1FzUsx6kwa9XzxVCxhJE6Y3NMoDmnhThW9vPPhFsNyvYUuF42tZ0K4ZPXtCk4xSGGzRDFxvK1gVd+7locXCSFRvMhCHitvpORc2iTYD8uMW88sqLOLdgCJ+Alelpgm2OOpgLiYcE3YDev6Ij8jxz+ckcVB8aDlVFnWlwmKgyBXoOTftfUK1ugJhV+lDs8v7LlwnIMidI+P9SEhORJ2pxHj0C38z5kgvjxYOo4ZPagzIGqguuIAk6QVV4MPc3UaHtlLID1R45nwBDPZbmFJivk19lS3JQPDuDct9PLuWB7RhdIdUg9tRzBMISeMnP2XEprMwpUK1p4OhPb8cDUlfhKEIQr8o1ZtkAMsWYQyZXKCdkIp00PJhOYNsEUP+uxEbHD4SkaPKfOQQQEZG4BB5tGybov/SqXuxp3oq6sonMJYC3daA61JQVeS11FvUZ+0obi4896ne1pjeoPE/OLRQDSnY+2JzEZBVwCNx5JELcho5jWP/PJLHczZb4QTZKA1AnUU6XWbIiEtuoZYS9fMTipwFML8iMb7lFavZQECwD6gayZXJyBkK7rjElUyRdI4MekTjKF1YrQJcJkPkpK9fdoQiiRDNRqrYUcNJQNSWZENGNGZPH89Rb9J3EZIit30ZhOkNCUBjcNBAsQvcaoF2I0M3ycJYULMWsm2DS85p0vry7aGDxC0SSaSZUyK45gCg2yzQjTSWezpw0bKFRXHU1yYTlhLcprwsvbBoxyok2i47C3nsiXYs0zIZoNkbBXcfsNLGdfmSKl9QI+lhSp67u7uf9fof/dEfxXw+D2GFAPDss8/iP/yH/4C/+Bf/Iv7dv/t3eOaZZ/Cud70LXdfhb//tv31uPX/37/7dM9wogHdcjNiknBpZ8+8iTgykoJvC8/Qh6kVEbbpSQLU0QXtXib9PiPzlTPApGdnmcdceJu2Cd7y8+5k+NQxuosE9gl5EZ+G0hBnlkeTrqJ7QTkX8ko1PqrBLzZbeaElInI0jDZth5LD48Nk+uVtgdi3D0msGOb5XslP2KTtSocN2QuRYX5fNySWoZwnCpUFkXd7EHb/JYfgyuXgCobbhNAKdC2jd6JZFO1E4eZImufwEGNxtsdgr4nUdUNwHMhuTtFa7pIflw8C9XpGqopHkw9CzUwFZcaLTIdBsOew8TZIA95/fJgNKKAyZbrf5fIP8YIHFjXGsSwk4IXuGRkhOyu8hW1icPqYxvxYREzKyHbqh4vFUQlUG7SDD8iLXrQF9FPkvAKtWFzoYuZRY2bFSPA2+PBfohqqXzNlqQDASGBZG5uukKtnFERk37Yh36taP2ciDLY6A4jBqdLVsmPpvz9fVTCSmj5dhgWvHJKngicDNRKA4ZGPXu6AFML9Mbu/o5pTQCXrnyeWiQ3DHAjTGHBJ1ck9S9x8JvxObJYbhKfVDN07Ok37MeLRCASIixgCwuEJJfLNldCsRJ0nEelzy30pxAoBHaFhZ23/NoiVUiVKRxPcHIfrClI76QHYufpcDRbwl3oip2kC0FvPrgx5K244d9EL0xmPaV80GuTlHt0tsfZo6VC07YCrQbGWodsmaynKJbNpGsvWyIaQwpdolxkhqTME6GM+VHJBLOv1u6i0JVbsgOwIQgggZ52p4Ad25CfPl9KkJJs8CYtmi2x1yv2h0AwnhIoq5GD5CAvJrtcfW9tyrKl9QI+lhSp5/42/8jYde+/73vx/vfve78YEPfAAXL14Mx621uHjxIn7iJ34CSil89Vd/NW7duoX/4X/4Hx5oJP2tv/W3evc7PT3FjRs3XuPTrcu6rMu6rMu6fA6LS1yOv8/ymnO//SErX3B3W1pSJc+HlZ/92Z/Fd33Xd+Ff/at/dUZn4cqVK8iyrOdae/Ob34w7d+6gaRrkeb5a3QMT+wkDuIwN78Qj4BTvMtvoauoGAGTkWaqGQpJlG7kjzQSodyRkHa+zBWKaDTCK5F1tnpOkPBwe+TluSTwOL5DYbJDraOMFg+xgye0UcJlCu5EHF1l+Srtvz7+YXxYRWfIogATKIzrHcFScrmg3mk9jctKUb+VLN6Cdo0d79Ix22k4gENgpPBiBKJ7NKcmpU0nU06bs9YEvso2SAKIjFElVLkQPDe9a6KXD6eMK9aYXfCS3oUc9VEMuJtWQK5Mq40i1ZXQ16aXA7FokP+cntNNux6KHvjhQm+Y32KViBaAcTj9KcNpgSUmRNz7jML7lXWIO1aURCTy2sQ0pATRbOqjK9kK6AUq94oRAtoi8k+WuQLVNL3D8ssDo5SWypUPdsmuhAnzUWEoeN0Kg5vdJUY6K+Bw6D23y/4bIw4L7KonEUxW53/y4sszlk61DeUjtXFziQAAX0UFy98UQfTA3rBfpOADqPQp88H3l+T9d4zku5GpL3T4++lI1kSPUbBKvzCOk1S6jYIkwrH+vNotoTzZDFO0EQu7GZgIsbvgoKwE9p/yMaT6ybNmv16NIgbc0IldWm5DJs6U7yz/y7uPEteyPR5ef5ydxv6jE9RdytyXXep6SISqAaG10QWeCOUrscm8MYBxufUuLv/I1vwIA+Icf/pPEjxtL1I9zNOlRhuxEBBROGhov08eAfEbjanjLQdYGei4DatNsaLRjheKIXmB2UtE76aWucLC5IhI4p0bx4phe/d3mAu1IkCI/93F5TM/nleKpXYkiNxdVW5rHO49YSiyuj1DeqylbAoBmQghbaJdDjwqxLl9a5Q/Uq02VPB9U3v/+9+M7v/M78f73vx9/7s/9uTN///qv/3r89E//NKy1kMz7+dSnPoUrV66cayA9rDTbgBg75CeCFoRkEpUtMLxnkTF8K4xGbSMniTKWc0RMkkrBKc4F5zOcL9HTxmmH0dKXibvNlMRt8td5XoyHis0AqLcdimMJnzXEaY12a4DTx7Ow0MuO2uYJm4rVk5uNyLfwBpmTgGDuRlcK1I9l6IaRZGuXdI53AXpCr1CJiu0JGSX1lggcBZ/h3S+eeukwulWh2ciC2wCgiJl2EvvD5MmCxs/iBLn4vGumHZJL0OQIi8pyj6B2b9DZDFhcylAeWUxepJezuKxhMnJ5+BBhvaRoNv9OiVBM9YYJmQnNzSZQcLSSbAFxKGOy3pqiDmXrML/CE7ulPtWVw/CeDzsCljvREMinhhagQqJL3EzdQCCbu6AWvtyRPQidZBqy8H7p/SGE1adka/8+ADJ+pAZULVDtsOv2wEA2pBod3DBDln5oYlShqvgeicaNzdiw4TaIDoCm+/hFXdeCU5fwZYr6L5vHpL5OkcRCs5W4/HKOGJvG53YKxAlifpEpgG7ksNzqUL5EFxKZPLqx9DLy5ZyPCGMjTtj4fD5lhnettRO6Lj8FlmyI2pGBsQo4Fj23o3NkYAMc8dcBrosLazYH6i0AQoRvwpOtE2Ld2VQl/rwVZMAJTqILUHCWi3X44l383vjWS3KryXMi13x6EScFmp0C7/yKX8P371C8+29+2Q3cnm/AQuBkSTujxaBAKwbACbvkJhbZqcTgACFPW7uRQc8NVNUFY6rZzrHcUWgmNFHkUw1TSnYRx3aSARsTOtssBocAtAn0Suepi98JQMh+B/bUvB2CoewNy/J+h2aisLg6gJ7Tyyftrxj520wEZtcfETzjgNecAeW1Xv+HrHxBjaSHKXkC5AZ7+eWX8VM/9VMAyED6S3/pL+Ef/IN/gHe84x1BT2kwGISMxn/5L/9lvPe978X3fM/34K/+1b+KZ555Bu95z3vw1/7aX3vV7Ws3DFTuYBciiNz5Upw6DO/UwX++Ne9w/HSZiOsBihEgv4DnU5p0dZVEbBlAJFwIVfPOUiOQNrshGR3pdTZDj1OglgC2aOF1BafVGGZYXiIDKaSLSDg/AEXoed6V3zWbgtGCIuaoA2vnuARRwJKMupgQkyZ8dehC+LZToInckMHkzxvfMhjeooq8iGU2a0My29mVEu0YvUzz6ULq6/HvJWSjnwDSxkUOoGcf3I9icNnUoN3QxLXwaJ0WKKYW1ZYMxodTtMPWC29QWuKX1CLufiekVVXvRFRqeJfSG+glcys4zPn46TIYlLHtApufIouSMqrrYIDVmwqLPQEzIO6OfzdW02LjidtWx+g5gMaAbCXK+x30gto5v6xw8kYLYQUmn4nvxr8zOkAGbT5zmF1hvocSFK3YUYJagMZjcUD97K/1C13HY8iWgD4kQ94jVT4fWzNJEFEBQFFKCIClC3YV8pkN79kUTKiXcdy2W5YECrmvslMyWGwWDSlKheMw3l1gecqRZAeEHoREuYm8gUnGseioreWR71NG4TypfwJMXiQUc/IC1VXtaNgcWF5xkYOkgO5Kg+IFarhaUooV1QAnr6NzvuXbPoyXl5v43z/xNIa/Eo1oCEQtHghIBzgZOUMC0UDqkceTnymCCz1jyn8vsnNEiAcgOW3IavE6TABgcwWbS/x/fuOP4D9eeT0A4OBoTAbxSY7s2If3A8pSvjsAuPqmfUyrArNntmClR6Ylvzcbvg9V2dCHAKGapugj6KYgA7cbiJ6Yb7VHBjEATJ6jNFIp+tZMBLpSQ1cO5ZGJfZNw9JwEwCmAYo5MEaRA0nRFHqkC0Asy+HwXn5LltZW1v+3VlC+okfTZlDxv376NF198MZz/T/7JP0HXdXjXu96Fd73rXeH4d3zHd+B973sfAODGjRv4pV/6Jfz1v/7X8ba3vQ3Xrl3D93zP9+AHfuAHXnX7smMFbLMmzTguBMUp7TBsLtHu0BdtcppMUvG+jDWEvKEhOPJFdghqu6PbDvmpCREeVgtMrykynnhd98Rhj0QB0W3idWmyuQuoTXWJcPvZFYr+2vpMF6BhqyjTvEmMufI+tckTiLsBkM0FyiMLdZcJtUOJapeUdD0yZhWTlnk3b0pa5Ab3DWTHE6bhaKmp7em05EcNLOf9araygKos9jgybAeo9yhprzcQZOehde4DEX8PUUcsE0B5p+iYXhI0Xm1FKLAdSZhtGRZLQnpoMRwceDeWoySYPBk3YxkW8+HdhtuQo94QyE7RC8H3UTgAIFoDO8hC5nhfTA4sS+D4TTRAihOLwb0W1Q6t8ouLEu0muSz9u/HPmE7MXg7CLyTLCwKHX6YxvEs51ahujnIyCLvl6NZiN2Eu0GwIQsD4nMVFCgEvjyyKEzYWazL4hYsZ3D1R1yNX+YzdnDOB+aVoXOkla195N13jjX0+p6Rvpt6IO/U0mar/Bss7klTivSt7kzYANo/fTX4M6DsSeG4TXkaLotISVJP7rTyM40q29O5Vi6Cpk2UiENABuodVgBv06wIAMzYwe9yImcZwY4nFE/y9f6yAbIG7X2/xlV/+HADgRy79Jv7f0x3cvLGF6dYV7mOH1MUvGEEQDiGvalrEqmEU/sDX2YiCkbApIFsbwv2lofuJzoVVwSlG3IKStcTxUwoia1G1bMxJB3dnAJVGTjIJPJtTQ+79l8voRpSitmWDed6SwKZsbNgYBQOJn89TGESTGIPJ2PXGsKqB4e0oj0GBNkTs9+9ENRRtvNwRsMobcwrZ3AZJk3YooZe2J4vhxSuLUwfFmx5bSJhMYfE0j40Z6aI9srK2kR5p+YIaSZ9NydMbPr78x//4H19RvV/7tV+LD3/4w7/PVsViSwc3Mdj9siMc/vaFXjgzAFQ7GWbX6Kv16TJ8ZI1PKNmOgepxWr2KlzLkJ8DwrsPoNkcPHdcQrQmzXL1bQi8l7Va8/qWjyBthkklCAi4D7ZhAiVrrTQGTAbPLcTe68QIpJp8+7okoJJxoeeEv79MiaTMB4+HqU1ocBvst9IIOmkJhcODDaum8ZkNAV8DkJTqnG0iYUkAYh9EdWi1NLrG4qJHNowK2sA7tRoYZu57aSZQ48Hwn1QCyFjBDh44Xp5AZ3k+YyrutojimY1efNCKgR3phUO1o4qMA6AY0GdvEzamXdN7GTaC8T22vdvOwk6XnlTA5pfZwUvM5Au0GMHkeGN7xIYMOaZbyFMJPjSTZAbPXGXQjOjjYVxjfijt373oSLoZTZ1Mea1k0GPy5fmdbXaDzTAYs9th1WAF7H4lCf74Ii5CuxWrmlI0ENl4w/P4E5pckukL0Jtd2DBJT9K5k3pWnhmk3EOgKEZDHdkL3qC66sJIXx/R8p5zUdnnZYXCLEU9ed7zLmpC9+L6EA4QXr+R0JN3YQrBe1fbHBHHIRtHt4tXEg/BmR8jb+HYXUgNR6g0KyTelN9o1C4nSKbkVZLgmvJ5sClbL1+Gd5icC3eEmCm/YSOCd/90v4m9sP4cfPnhT6M9fnz2JRZMHzbHyUPTUxJ10EBA9w6nnektQIvo9XuuNaB8ZJg3pDpGgJG8IOkcJZy3QjOPGjzYK9CyHb9JYXLMoPlPi5Dode/yJe7gtHb7q+kv4yM3rAICmURh8vAwq6k4Tr8kjRPQ85DpeXMrCxkMYYOMFS2MNnApKkXEdVM0NGebp5rMb0mZzdIfqaSY0f2YLFziOPrpPZnG8CwtK0Mz92I4AYSQ2XuyClhIdJ+PJy63YQkI1DtsfZ6pDCSxfHZNjXb6Iyh8oTtK6rMu6rMu6rMu6PKAkyN7vv441lPRqytpIekgRFshva9xVWxjMI+pgCmB2LSOOhI928aQ/jxo05DKqLhmooUdaNLJTgdHtDtnUO9kdXKYSsiS53ExCghVMUBaIuycAQIJMLHdkEET0RXaOIzHiDtzmtHv3u7fRrRo2l2gmGsN7dI5qHEcmmfBB6VkDfVIjO8lw9GWxEbJ14VmKgw7oLKorI0xvcB4lJdCNSPxQsbvLow4pouHTJni0zua84z8QIdGpqgk18ueUhxS1ZjIBw6hbNkNQKPfRbXpuoJcWsqHffb65doKwA2+HAqqy0EsDxYl35YaGsDJoDbVjh0v/hSLjFpdY8FFFzpPfqZtSk7vPKwk7gXYz76Ul8XpLxV2F8U06NrhvoFoXtK9Mxnw2F/lOwlG/pRFvgczPSFk2JTdsEOgEKGKHE6gGdXLD7qeEDO0UoXI240i52wY7n2zQjhRyjiBsR0QcL45bNBMvGipQHEefS7Wr0Q4JtQppO+bME8pJsRpgsceNyC9xyqHedcjmose3EyByc5qGJ43cKg45vU4jQ6JpQmIJjfB8O7EE2gwYMspR3u8gO4fsqA66O+1mjnZCQpzVjgrjAwI4fquPEJDILy9gPz3G1qdc6IN2xPzBhefeIKbBAXHXZqbEn/vUn8Gz//EJAMBPDv8E1JNzCOEC2b8rBfTCkno2vGvZPZB7FHTWOE9hWAiFYJcRAI8kMYoknAucJ+LhCLRjjeOnIhLtZES0qwuO0MMaKG/RoNl/6SqkAH79xTdEHtbQEUKdIGGmEJg+DtS7nn8kw3MadruP7nbsPvcTH/0zu+Fw8ia6buejCtncBc4Y1c3CrzxmfbJsm4kQmQfJSudV/Aa7AZC30T3pKQfzKzpopQlHrk+nBEyihaSXLsyh9aYM+QkfRXnUudv+sJe1kfSQImoB5YDt38h6JEFSPO6HFgexyQT2rq4a5HtLNIdEVBIlpcIgobgEShcCLvNhsCpymLxQn1f3XThg7hcqhESSvk2qoknYX58fCwgjMdzvMLlJVtLyYkZRW1OqvN7OyBicdsiOaLW1uSY13KGGYr6AqjrYkUK1m2N5IRJ/s3kMxdXOQQiBalfH/GdsoDnFETzcVr1E5FvwYu1sX1JBWFrw/aJuclqANl70LkABUyiURw5D5hE1YxnTRLB8wex6BlMkBFTmc8k28hqkAardDMVxB83vph0RL8ZLDuiFgGwtiv0WQDQCiyNy1RHxmsL99dwEvsz8SoZqmwjYweUgSDIgmwoMDund5NMOVgk0rGasOA+ZVbGdAPVRNqcM9NyjcFKEBLQ5u+RkqtDOyVh1FTlIxQm5aKttXtwKIDvpS1tYRbwRpSlZMtVFhmk2FSiO2KBsDJyWgVuUnxoiyi4RIkDrTQkIYOOZGP1VbQvUO1FKYvMT9AdVx77qOC2KbKOBrBqgHcSNC8DfQA24WfxGAunf70ly4pwN7tGgUpWBrA2EMagvUAdOb2TBiPfXFcdkfOhjHdrXVSNkFfG16H7k5kHCuTI5UE7j77IGfvoDf4LccnwsmwrgY2PYLH7zNiMBRL95Ko8MCz2K3jOTQYSoGu1/99OLtdHVFvhNDk4L1BMdolxNLsJ4On3ahv6UjQjXZaciBHl4WsHggDllOhq63YKkAzzXxwkymE4yiW/7Y78GAPi53/gqZAcZzNCi42jSbEEpSSqe00iI16IbSjScQ83k1Fa9TCQivNst4afpBYAksMVLOozu2jAvzMfkMs05N6NckqHrZJTikC3lwWuHMvQDZTqIia+D8v2jKKkM+2upY11ecVkbSZ+leNK2MHHiscyNaEf9dCLWJToxCkBpMB7UONngD3ypIW7nPR6DsBaidb2oTF0h5DdLi8miArYwCJndATLS/CQVCKQD0s+pt7Ko6yNowak3adU1uYCqHYRTyDkhrF5ayNah2tNhQc1PgOVejvkVGbgw2RwY3YnowfJSSZEgG31ejWoZceliG2SHEHLtjRWTx82x7IiHIru40EFTGL6f+KodEqwSIKIvECUUTB77wwkBWxC5EyBj06McmddyacjoGNyzMANq/OKSJNmBJDJQOFIL1gs6x13wKteRg9SOJNqRTJKHcvqTBD1zkiQDBveAapvavtwp4DRxxgAy3GRLz1exgrleAJOb9H68grg0cVHx/etz4/k2qJqI1DYTyFgHani7gmoKtGyUVUyU9WliAEBMqI3ZtEXLPJt6U6DeBoTNwvtvruQ9Bfp2SDICnicCIChpO5WkdWFjdXDgx4JDeWShFxY16z5Nr0nKJ3jUzy8oTX9MeSPJj8+QxFYiIDSwQHlokN+nk8xQw4w0IBByHjYbPEbbaFBmcwtTCIxuMa+tIBVtUwAnTzD/aAps3DSUumeL26UYofO8lwnQXmsgTjQEi6rpGaXN0YnMSDMB7K4IY7/eVpRAt0MgWwMUZStMTLVhcwqN97/7dEl64TB9InLPhnf7wQCio3bqOZAfeUTNsVxHRChNSf8Vx/H9hYAALsQJdMFgFpbC8ct7wAd+5Y8CAMa3CKESTy5hLtF1+9sDbHwqpgoi/SGB8UsOijjuWO6wPptJCP08F4c0NhWNk8WluInNpoAyPkqNDaCOpmGv0ZVPHQrjelplPs+drl3gStmMI3/LOBbWZseXblkbSQ8p0oAjXPqhxTajnZRs+pEk/qMF2Dg4yHC6vxOigrz7Y3FRIz/i6CgtKX1IFreHqiayaXA9ZYBPgeANJ8PaSV6QUVUIIfp+AjMZOFVIXJwp51wUQ6R0CwKwwPKCdyEpWnwEMOfcw/UWuYWKExeg6G4gcPKUDu1sOHrLFoDwi1LSLyFXG2vlePJ4NovP4yc1wQJ0bZeEqANY7sqY5kITgdOqxEDkKKAuQRHyU0AdI2Sep8S8ZDhp7itdOc69p+CUDu31iwDdD1hcVKg3hlH8kNuvqi4k4SyOJEwpQ0JZvaD3We05SE7aqpb0/PUmsPGC4z6mRTeQoX20VRMlAIpjB720WO6qmKTYa1olulpOUD05Sz4M9w3qDUnkda54fm1ASUj5+Yoj+tEbSFS5wNEbSmQLF8ispgRFHTnA5uzCVESSDhnjGxqMqU6Szeid+CSwABkkqolJYmUroCuJelMGlMNfo6voYhQGkCpmdD95PbmB9BLhefSc7iNNPJbNHfLjFobRz3o3x+KCQrbIwyLo9bc8qRsATp8gt7XfzeRT6qduFOeF0X6H7LTD6WMqzAuqIrd7cHE6ADMFtYh6WILHq14kopcbQH25AzLvDxYo7mo2NEWouzkhVC5IE2wD3Y0KbsqNKg3ETCM7lejGjBAJYK5kT15DtbQRKk4c8lNGWizlngyG2xa7qoSDZG0o+HyGrr9Jq3bSDR3l/nMKGNz2CA31RVsruI7R6k6g2qVIQ/D5PjgjbDi8nElilciWDL40sKYZ8/tPkDmASNu9ZOKJzpvz84yI40w1ZDT5SFrff10pQiQzueTwSIoPCHqtdazLKy9rI+mzlDQPkY/W8KHG3iigExHyHwEUfVYe0CQTjKcBJfNcXhAY3iVrRy86LC8VgT/jP856KwrQGTauVBUXUG84+fuBo+s8dAwg5odLPqog4ld5HgUCH8gbH/V2Mjnx5FLvGrQbEuWBCGHf3djBaofyIC78qmZ3TcgYTwtnV0Z5BFPSzq+9TieNPl6E5LbpjtTnGUtRKSuAJo88DVOQEeivCwZXnnKXHMYvN2FnC3AEj6CdOEAGlM0FTp/IYiJesdpn0XUYdq0NghuEPQLIjxrMnhgEN5Zs2aBtAac9b4ncGOWhC1ye2bUc7aSPCEH0o7pOnxLIphqDA4fRnfgO682I3vkILm80A8D0miJDWRA6BHh3ZT+UWjigyeO4bgrA7AH5qcBwX4b3J2saX44FW4f7hhTeZeS9LC5K4jvx+CPk0SCbxoi+uVGoEj6H7IBqS4S2puMAyfOQLIEJivDFfTL2nELgsG19pkK1SxINfmEZ7rdQiw41y2Qsd0mLpxtEt5LJo0vT198NorEG0HNYLTC+AxSsu1PcW6DdLtGV0QhMXcUALcrZKaOPLh4L8iHJ8+kjDfcEDQZTaebaxU4Qlr7VFKmSHSBvFyH83hSEtuoZoGfe6o68Pf9N2pyPZ/1xpKo4PuI4jH1lNYIYbnAJO3BEG9fDGx7ZRTkTq2k+bY5yKN44ZHOBvW+4jTuHBKVu//shIUYawX3fTuhZUj6msISM+W+knQANy02E/i/onfosCr54rTCAo3WXlFsz1DWUIcm5SXTkUs4oACTT1ue3rInbj7ysjaSHFFtQJvDiEBwuSseFTxiZEEeB/mKml2TAOB2vM4UDJO0SfVbyyS2B6Q0ZQtjLQ9JdaTdc2DF6UbSUZAvB6JHfCfqkuBYhBYk/z6nopvCcgqD2O3OoNyVmN+KzOEntFomI2+C2ohDrQZz88mPByVDjM8vO9fgI1TYRn9Pkp10JtLsd8gFV3mwU1MYEMXIiST8S+BV8/zQRaQHUFwzU3DPmBaorBqIRGNyL8gyZl1oA0G6V0LWDPELYkZpSohnF5Kq+77zKNwCM7tICDwEsL3CKgrFAPrUktMc7Uj1tgyEJsKyBpV10xSTwZtti+LKEzQSO3kgDpBuyqyxRCe64XwPfY8DoxHZEA71rxyOIQSCxiO/ZZgBYTyq8BzZEgto7u1+8iwEARE3voThxoS6vP9MNortZzw1kZ2O6j72c9IeOYjLn8pjcaCdP5cjm1PHj2x1sFgUEVR1ds/752hHCN+DDt7sBoCuRuEroGbsy4aiMNJHLx3lIdixr4k5596zfJEAki7rlRT+PPK9sTt+KDUKfJAcBEdEmtcxRb2pamL073TBCx31cS6CdOJgiESmtBKfuSNxmTy0wGtY42aeXouYK7bUGqCSKu54sT99GeS+6Fm1GaJw35rITRm5HCMaqcMzLywHLSJVeiLD4BwOvRtAdAxDU+V2iUSRbQo2FiwaXsIzE+ZRFkt6JKV2Q4chOCTHqRhL1LstN7Fr8t0/9Ev6n/I8DAO4Mn6BnUoRiAWQghdQzPPcVJw6TF2qcPEUfiQO5iJ2Mc4ef82yeuJKZLxn0o2x0TfpvxGqgy2mu98mwmz2D4U0V6vGbt3X50ixrI2ld1mVd1mVd1uWLpbxWIGgNJL2qsjaSHlJECwje1fqdOEA7x3bIUQ6eZ6MA1wUUmqJoTknlNZB1WVTNaWBxlY61Ix1QJPqdd4NN9HmrmuqrH3eB65PNBfJTYON5aoAtJNqhxOjlKqAAs8dKzC9JzoPFO8YlCaxlM9oetmON2Q3aYRXMBYAjFM0JoLrIPIbSIjvQHHFCp3kBSI8KWC0CfO1RMOH5MgnJthsA+T0N8C4ZDGeDESyAyZ+8i/W8kyCPwEiBz+M2vKnCM7djQDQCrrBoOfrE3QPqvQHyY4Kgmq2MOF9LFxCvelMGQn5MzElont8xys4hO63RjfLAzwnomYuie4urJbpS9Hbb2RSAAGrv9rklMbzrCOHgCKbiRGCxR5IJAI8DRWiSH3vlQexLv+NXS9oNZ0k6lU6JHrFfGsAIYPqkQ3kvvhuZCJTKNo5pv2sWHSAZjfQJdes2PltI+7BFIfPFCb3k8rCFah30zOD0SUbKSoHuika1E6OHsoXoRbI5SXXS+/fRURxxlCQsLY4okjGgYsOI2HoV+mpLweQKphBQLZORNzJk0y7wxZoJCbU6GfvBCUIv2kF0r+Vz1+OvVDsiptxh+KyZDDC/7pCfRBV6JzkSj8dQs0FoMoYGrtbhfs2NGlJZOP5uXK1wuhxBzRg1yiw2d2aYPrsV6g5pdwxgPb9JUF/6kHTPLeyGDvCu0IogFlmL4OoKrl0ZXXdZBWw+30HwN9INJWTncPq4xuxxdlMvBLI2cYkicjVDUEnp0aXoyvPRraoCyrss2Pm6Du+7/fX41C1ichdbNNZT3lc2pT6XhugO/NTYrE0yFoHsFqu3+6hkS242q2J6pDS61fenz1GYIsGKx0dx35+nIGsgYwTK5Hhk6dA+F2lJ1pykV1fWRtJDSn4MYBuYX6MPfvICHR+/3KHaVmhHMfpEGgqntl7bpQGGdzosL2pMH2cIPbfIpqrn568uEAwdk9mKAAN3Q16U9iwwMECtMLjvlXyBrc/UyHjhd0Jg4BylwOBcaLIFRncsiqNEl8kBZqBR7VEDqh0J1TAPhj8en/ai2Qayq7TivPHiPj7x4SfJeEuhdsvEb7A7QpOS7pKjsfIpLeymiIuYj9pKE58qTgkSOBKaVMSHdzropcfDAZsVwZ2YukBDWLQA9FzCJPi3yQWqHYV2RNZqvSWhKofR7RbtmLVVkuS7XoVYVzSpjm7TFDi45XOfgPK+AXCayOvZVAX+iskj+RegZytOLRlgjT+HDUolyIUHwGYK0oowFnyxeRKxVTLXKCGrV3t+geJxxu5VrwcF8DXsQk3JuqIDLBudumJuVtbP4E4Li40pOmYS7QhYXgJaTgVRHSmoBkE/yklg/LKBWnYYHETyhs1YtbyL7tg0wbMpYvh2yNbeRpeb8Pw0NuID9827ghJjh5IB+4AJNkQPSVnau99mNyiJss3JbeXHgOCgjZD2RBJJ3JP/i2OH5R5dW13gTcJOA0w1bC6hluxKYwkHnyjX7raQxxryREOxu00aADMNO+5w5dIxAOD+r1+CWgLNJhs2TmL2qW1k87h5kjXV7wnkAPP0Sgcz4OsakmrIjwWlGUHchDlJ51IfCwhOyh2TUztkp13Q+xJWQ1UG2a4KEW+e+waHkIMyWyAo4ftxxR7NmLNPct+6uGhntwp89OQpuC06qd51GNwWlJMtUAEEfBCL129aXnXYeLEM301+0t/UUkMRpD/8plTPAMlct9h3ZPT6OU5xhKMTAqWP9J0Kij70c49C3B1/3ov7HHCK1lbSqylrI+khZXnNITfEJTITi+Z+nOwnN2ss9/Iw2efTDsdPFwE1yqYOqrYsIMiDcmggbyvIJok2awCIGHHhyalm6PCOP/YJAMBH71zD4sUJBndk4IBMbhrokwboeALzDbMWckmTzPi5KZyUcFqiukQzVr0he5EZTqEXiQawH34E2NKhPaITP6N20U0ssK+CDz+IQSbRX3436ieQZkLH2nHclasFL9De9jFMJHeiR4guDw2KgyT3hhLYesbFkPU9jZYRiXqH2zR2GN0UgecBUN16SYlTQ1UcNu35JD5CiO7vjTDHvA3mwUxy2EyiHavIackEYOMiDNACarOY0ws2agSFY46u7QYC2TBZLG00iJTgyMQyLiROA2pOC/Ob30Fx0XdmExx+fC+0wWa8m05CymHJ6B+/BBTH1DGzqwrtKBpbpiGOVU98VALlsUNx1GJ5gRoveCevK6C67NOXSIxejqibzckAyk900FLqhhqqNshPutCnp09kPWNO1XEBDIYaG9M+iin2MyL3h/kkqWCnMBEF8AENetERR42RwPxYot4BbOHQeKJ9QxGRTiIY+8URILsYcaqXFrID5k+2GO7QR5lnHY7rDegjETmATFr2CGnxQk5Rk3kSkOCAcl/BHiocvEQoSn4CQFJeMP98TkVkFiCUJA2u8P3udCRp64p4SauaQS4Dqj0HO6L3Jxe00mdTYPyy5Wfu4KTA/leRFX38tg4XP5TBahHkNGRDBqAp45jxBmEIwmCjnr4TPmccOVWB/2Zog9NyKhgzsgAUstOY0qQb0vNmM6CbsHGaU5Lc4R0m0B9LLC5oLC7Re6X3HDmkIUl4Achlos1W8gYnQVetQ+Cf+px5TpOBF7TzBJPJ1+VLsqyNpIeUb/i6j+FXn/lKqKmCmspACD15SkPPNertGJqtKkoW6ThCrB0JTB/LsbgsYMY8W8w1QbwF0Fzk3G0HGrKJBFiT00JfHAl86Ncot5PbaCE73gkf0UdfHtC2TVj/5TpASjilwjHRGoLYlQhaPPWWQHEclX2bsYAb0qTgCdGO5Q70TGD7DccAgD9y8UX8wsmXA1BRQDOn84Kra0SuRFJspmN6Dprs53Ey0gtyofmFvyvJWICLrrzBgUU2NSSNwO4oWbXQyxaa825l8wGWF3LK1ZRHF5KqgeE9G/I20XPKJLs4UG8L1Jsxks27cpyIO1SbkcGwYPHM5W4ZFiu/gA/uW+jKYn5JBRdSPrXI5hbzK1F8MRCoE/cXLHqIiWpsJOuD/ja87dCORdRJmjP5WgB359TQ/VtbyGyysxWAGZBQ5fCu7yuHxSWJaktgyNpFGy86TK/rkLNPGKDdobZ516tgN970RoHai06yS1VVQHlH8Tskgnog/TY0HhZXcgzu0cHj15GQ6cbzTdjkZzNyY0VhTACO6vZ97BQo8ioxLLxyckiUW1O/dIOYlw1gaYfWJXnZBKoLOU4fj6KV2SmCSrO/n1WATYxTvewbI/PLCss9QBYG9U2y3MRdie1TakO9SedNbjrU2wIdIzvD24SEGAMIT9Bn8rhsk2isAXqRV8IQ2V/PZfj+HEcxlvcRcr5RWxOD/T6N0fll2YtkoxyHArZhd17h0G5YiE6GHGUQQDtRYezJipIdD+4blIf0TtuxxskTOoie+vfXThLEsgHgDe90M8iGU1BWrzkooGO9qosdJS5mFxsQI0WtBnCRB8hRjvyoCZvDbrNAvUVGXzAMFV+bRG7ajAw6v3kjlB1odXwPXgYkm8fvVHQ0788eo3O6UUze/XkviQzFa6pjXV5xWRtJDyn/6f/35cglGTGyQwi3Fx1NAotrDrO30so//3SB/DROFHYEzJ4A8idPIeYc7n+zJCXdXQcwR0IYmty9n9xJYMERLoPbdE5lckAC9Q4ZT3Sdg2y6AL26XKMb55CthZqyUN7GAM1mhm4gQ9QMQLye1LWmFjTxeANlcRXY+IoDSDicLmlm/fe/+TYMbmrehUd8Op+6wFVZ7El0JXFkQqQcQ9F6Hg1BJ4Fs6UICz1xSUspmQ2DBE3K9KTEa5tj6VJvsgCUEIofHauJItGOBEe9s80856MpCzw26IV3YDUhbKXC/gnEWFz6f0oV0o/jvw75bK5vSe1JzcgNS/zl0IwWrRWIsUrt8gt1qWwUjztctO0I3mo2IOHWlJHHFxN0mHInceVeJk8x32xe4/zHqLKUA+ISrYEOxElAtsPE8raiytVBtAWEcsrlXYHTYqizqLRbPvChJ1NECk5ep7apxqLYUjt8gQvShqhHCor1hnbcIRp9vt2oJjWs2mHvDrtijNxU97aRs7jC8G3/vCtYn88hECSwvAmZgMbjLMgQDqi/wZ2aO3cQC8yu8oTgGRndaiM6h3qY2LC9odMPY51KAo/7iMdHFd5+d8jFDYoQpvxASKJ4pw9gb3e3QjCVOH5dhLJw8zfIXE3bvlQL5kYRexG/EchsG+7F+UwDNVhROLQ4l8iPJaWNoAA/uRBQ0RWOK44i4mQJY7skorMl9bIZUd+llHRoBpylicnFBh3dvNTB6ma4b7EvAOVJTH9BEt9xVUafJG3RtRPAAQj2zE8HJlLkRgrl6s+SZWUE9cO2mCmbooKuo0A5Dz9psO+hn6cKdTziYgUa9x9FtkjZepAzuLV+EaLcgl9AAyGLknFOcdqWJhpTTgKyAxRV6HwAwfl4imwHtNg1Ql1lg+egsj9fOSVq7215NWRtJ67Iu67Iu67IuXwxlnZbkkZe1kfSQkp0KZIIjNXTiu+YcXHoq0HDenmbXQhoZxSQ1sPPmAzyxeYSP/PrTVN+Mo68uV8Ax5/4KJGiOICkpP5Ip4o4qPxSB3Op3Yve/fITB/TLstqstUiguTi1k64XyJKo99HNC5STX7zV1vKKuMIlibgeczgZoZznUIQ2Rnc8IqIY0b8IOOAMAEXIXCcuQeUeQNcDQeIagAA0QElJMI0G6GUi0nEcp6DcFpWRP+QRcruCcA9idKDuLbA5sPg+INrpT2rHC4lIW8lllc4tqW0Y+EMhd4xV9fdsFKzN7N4/VK2J6OhKLPVl9eVEHIT1/XTcQMLnGaL8LdcuW3ErtOCJCqmYEIERNxl0uPV8U0QsuFoEgAGl2+aUuFPLjSPj2aRmKYxtIt3LZYMiRkN02oYMnT5ZEhvZdZ4HRSwb51CA75ajJXOH+mzWabRPGerkfXT6+6Dnv0hP3kE/OGxTSld/lU0QZQGKaTgI5p0ppRhJKgtWu6Zx6Gxi++QiF7nDEvp9sShF23oV08JiFmLQQ9wrkrHAvDxxMKSE6h/lljqDaY5eO51x5nSTXT2EhHHOcknE8fcoFsczBbYnsFNh83qC43/D7ctAzheJU4fQGXXh8zXL+M44Gu1ihGuYY3MyCWKyPKDR532UKICRo9Twm0rlit+6M0bzOBRR4uUv8xqDWzxwt0QIl3y973gU+nH9f86vxfXnXnRfYDNpvc3JTd2WO8pjel3dBeXcWvXz0CNnq8TmqewMMX4p5KSEdigMR+5/bKhIkVXOS4zRXnT9fL0TIHwcA7YbG8etU6LtsTgEjXqjUB6OYRONJ1VSXYd7S//2bP4if+I1vQPnJMiBvRgBiQMmtxS4NkNOhxtZvZRg9S/erdxSa7UTue12+pMraSHpIMTmgLbnWrIqGhmOC8vAusPu71IUnT0rUu1F9WnbAznCBX/v0EyiOWU2buUfukwOMOZy02QKWl10QyfO8BKP60WCOJ54Q0XHJYX5dBcFFH2k2vS7DJNNskHBdt93h4hU68fDje1BLEcmVNrk+IU3jEyNs3keYwIb7lGDTXtJh0XMKqMcJPJ5EVPm2qyqSav1k5yPpfBqPdiixuBwnK3pmgWYk0G4UUFX0Pzkl2L9ELkdTKsC4kB6j3lLoSoHixCKbsXjkmFwCObtOrKZ+rC93UKx+nM3JBamrCP975WpPUh3cp9xn7UDAFN6VRxFUXjUZoGuKY4eu5HNKcrn46DJqOylW+8gg/w789QC52XRFYp9p3cKyO4UX7GzKMg/Mxdl8tqZUN1omnAwFAQNYQFZM1u2IcD5kY85mAsNbS8AQj436SlJ/aAeU7IJ7KQ+GhV9AfVoU/wbN0IdYi+CCdorFMJv4zCYjYmzNUXLdkP6m2vgeuolF1WSYPbuFnJWkVYWeGKLaajAeL3G81FB3qLOKE0rk3I515Iyw8ZO6p8ALqDc0OiZEp6rKsgZkJWA44rTZdrj46w75aReTyxqL7KSF04PEYBUY3BYQPNU2mxra0jfg3ZebzxnI2mJxSaMS0UqWXbQOTIEQ1eg5NMN9C2EcuXd5wyENjyl+L/kSmLzUQdYWsom60N1QQy8Nmk3PHtf0vDLOMU4RNy01iLO5Q34aXd5ekFRVCOR0q9FLJWKeH0FqCuLwivOOhR1VEyUbvCK2d9mqin6ut/ubBOF43AR+k8PpDYXpU/R8eqrQjYELv9nBampUO4n1eiPJf3s+xcr/9OE/QSrnLO3gC/WDAI44Onbb9UQ3u4nBlYvHuIlHVNbRbY+0rI2kh5Sg0ppRrqJeRJjlj57TMtQ7DmZk0bQ0MwxvA8//yuPQCe+l2Yq7WD8RNVsOZmzgFNWjFhLFMUW3BA2YEe+CBi5MMrISKO/HSBRhHOotIjCnC0A2FXjmL/6/wjN9Y/ktuPvvb/TyLMkGQYXXX6dq+t3rK5lCwJQSunaBEC1bgtkC6ZzTKqSh2RkbRLJF4OgM9muasLmd9ZaifGdFP8N5vQ0cZTm2n+G6jpis7nlYivgy9ZaI6r78fqbXJHKO8GnHhEr4ybjdANpNC32kQxSSbIBujD6ywpNoSOGSCUommgss92JDBfMkvGHoidX5KT1vMxGEPEVQjKUT2BD34f05HffGHMkiiKg4DRDRlXfb2S264fAu8P9v78+DL8uq81Dw28OZ7vib8jdl1kQVcxVIRhiELDGIBqmFaCO3ELICt/qpu+1uIYWfkNqWHA6wX9gansPIoQ6kZ54eErItKdpCLVlSCKriGTRQgCimKqCghsyqnH/znc+09+4/1h7OLdJZFDVgqPNFZFTlzXPP3WefPay91re+1bmq0blKnSCmFZhSMHFjenPQxilYqLcWUQX77JLdrY0BGIOR4dnkvMLWpwX05wAt7cl5SFyqJr+jzmi8+Kwu61Fslktxfdqsg6hSBjk3PjHBSNpc6164Jj7mwHEPiWrMpaHNOu3Qy0rjGqMLQ6R7InDfGMALBR4HY5HXlhTuxgsjPSSY8P50w4vri6hy8r4ubDbk4GGG7oU58lMJ0LNG2WEBRBz5uvTtHD7AMLkxZFlFExqr2YFpzFODckVgfAvziSArD2o7l43tc+4TIpyhXXUY8e40QvkUa2Q6T7FhgJzVENPKv9fR8zqY7jL0LkoMH6RJwUuDoxdG5IWxOkzxmMZ+PLbeSEWGP1fwRq0oiPxcd+H3XjkL5HCAyjNVfWs81YG0VvVo3nnJBrsmNmvaQcMnS1D/WcNME2EdIC/k9HQYs0aGIrWdfWrU8VrIIG1m3XnPFoDOuYi8tHHjQCysYYbwfKJgwdgHrdlHn9jCMwKDlrj9DKM1kq4DHcF7CZphmGREE21xilzwAKD6NRgDSiu+yKsI3Uv0/fm2u59BuWJsZhPzn4mZ8ANXJwbFGhkgzTpVTNNpxnmqVh4qER0XPpNNxxJGZKgapFSXWfaWB9+AP7ztwwCAh85vYmURjBh3cuPlMqmymVoN0Gm/7DObnm3DaxV5xjzxMrblWHjze/Qb6bFGekCrSrlCddNU5PqA+eyh3kX6YbkwmG2TF2O2TTvOcFqBFzXqPll40xsSLNbtydnp7NgQzGLTALZiO7PP4d5D3dPoPcwRLUJJj+Y7d6EKJ9joFtWyS8bOY0uH+JpuzRRrAZQDl/kV9LRciECLkBno+mr2ohKdwQLFxyk1Skvm7+V1tGz4JB6FDTw9MuifDTVddCbBag5WaahuZH+3AlOh3AJA3sF4VMFE7sjfEKqz/2WFQnppCiOE7+P0KEKdCtQd7snxVOdLe0kMMPIgTU+LYJCrcEBwxkA0M7Zqvf37hLwm813jSbeOFMx4wwugiXyMzI5/w8BzjvSo0VcRtcN5BF3/U0jYGdpEcDci3Ds5gs84cwZXnREB2ZeouVyjWE+QrwrEVsRTJwJlX2K6zb2x2LtcI1+ViCzJuntFoc5IDiJfcROcvJ8qCb/XvZijWI+Dl7GmBATXjwAdXFwWnjMw6ozCTN3L1quyoPR4cISsV2ugcwVvOHFlAEa/7xIxoplBNNe+5uHoFok6AzpXjZebyNcAp0HkQ5hRmAMArQsiB5JpeA+LTYZquwK4RGQL6lZ9Ax0biLk9MBbkNUv3Q9is6lH7ZR7CseMbpJcPAABekJBqsSKQ7de2HyIqMF2FsLQrH+Pmn0qBcqghFgxSWaOzZ6h9OhwIHP2idrIOJwyRTTxo8a2H1ki6DnRigMhAFKQLkh0G/ZBoXEHHHONbaKbOt0nvZbFlMyC+4wDmjzes0JtdnFZKkgGYSX+yBKfFybmqxZxDJwb1RgVYN3B0JJEcAetfqJAckXWjo6ZSGi2AcqbIS+AmM6dT8Zc+eiue/1f/TwBAd2S5NtZI0rHlJcmwgEVj49WtnfJssQLMdg3JA6S2H/YEeWfsIpMdGOs+D0eVyRnSVRKlxuRmq9U0ZIjHJvBXNPwJyXGzeG0QTwzJCvgsmQTMJJhtUme5wraicRp1XpbuxbAYKvuMmV3I2CUOUZEHwZ9Qx9Yo7gSPmrHic9EitFNHDS4RADDKUqt6jbBSRJuvO21H86D/03w3kTVi4wk98+xqhPlMIHVigSVd42qJARQ+oYKsYVMqBgwqi3xYcrGZ2HYalFZ1vHOVlKfjkwrRCe3gclTARAI6dZ1AIVBWax9ug2DQqbMIbWhrWkFMKySC+Q2UFzWgDIxTHT/dwXyTDKTgNdWQc+ZrlgEUrmyqtsvc2M+CYakTElUUeeChGAmItRL1lNqmH+4CEXkfnXwB1dQjQ87z0RynyxVJ5vDFT52n0en1LRV4tp7i4Reda4lh2igaTONDYrEmljLJDAM6e0HuIl8VyNdprrh7LzZIvNPVnwOA+XaCqsuRWC+Oimnjd5lYgDU6rWfO6fRk+ySfkZxU/l2ZiMNwjuJUYttAfVsMGMyNNNiimUbvokbVZX48Jic1VMK9XIKWQLZP83I6cAeccKjwB72Y6rJVdv7l69bj1G9opZWAGknSNHM1+noMnDU8NAXQP09iuNVlmpSTM9zWgQNKu9aymkEumBfGrLcqyMsxZlsMck5t7z9KXlKn5ebes8yBxQ3UV6ziMFJDawFTBC6Yk28wuzRvzFECUYR1ts4AluIZwVOhuN1G254YWiOpRYsWLVq0+GZBm932jKI1kh4PnJSKh4/UDbE5hnwrhVgor0OTHUoqf2HF9SYnGxCngOF37eGOAcUWPvnwTeBzYcnZdDNVcVKWdaTmI8CUDKqSPiTXuULeCh1zLKxytoo5ERjtCVklDCpiyA6Mz4gqh4FMnVgvSnpiUKcM4+fQ35MjINsnl3o5cIRsirfFM43kiI6D45ti8JohOgaRpUH6KclIk5o0AtG5zviSgrhKgGIoQrX0nE6NzptVdamfs8NQ9qHqkrZTehS8UuMbJWWYuewUW7vNiVoCwYVOmjzhNYp8mTj+WIXv9IQ8W3PBfOaVU0YP/AvjxR69aGJtPDfHK4hbL4R0ZVeU5ZSloe3RjLSi6oR5scrsCiCKkAHEAJ9R57xbychAFOSNc9yROgOOXpSid8mpX5M2TpNMroWElsx66tLQdrUshGckA8AD70uSNg5TeonLYCQHL+twqmUMkByLHWr88W0200iGMHXn9ASzow7S/chnUMk5tdeFb1YfUFAJle7hjrPWIeVppojDB1jNpKSCPrDaODZE7MK7ACAXpOdTDEXgnlUGWgTNLKfjpTkgdOhPxxUrh7b/zuQQl1MMHnHMe0Zj1TQ8nSvcE9jd8J+elij7IZTtQ3qjhvaPAbIjDSMCd2q2LbDYAiKbWGAEhcLEItxLWfXpziVg5UGnh6XAamPfIyjMVhGJf37KcspWaPyWfYDZsJKKOeoOs5mR1IHzTfKOO89D9zKtE7zS4GXgtdUdCu07bziryZvlvN2qo2EEo/XDjtnOFUDMSUnbzVNe0/xw5ZjSA4b0sLb176yH/iJx/OoO85pZdQZ0r4ZEiekNMequQd1jOLmNPosnFKJuZk06OoUYUcPlnAFMkEfKOVIXDOWqhhnWePh1vwUA+DdHt+K9H/1eRCPnOsZS/c2nFU+FBEDrSnpCaI2k64AvGKRmSE4AOal8Rsf4ZlIf5pXwHAVZ0Abfu0i7Z/88cOE1EZ6/uoeP/s2LAJAgXJ1RXSXHtyB1XO5dubmOIccCvAglEKKJQdlnKIYiFCe02TduvKfHBslEIz5RiCe0MBx3Kdwh5oFnIHIDXhusfcEaYFcr8FpjsRH7oHvVpTCIXDBoSau6yoDY8kjcQh7NSM3YLSj5uuVIxA1eiM1icQYSQK54RwgHLCG6ovu5MEjZJ3Jkvhr4HSIHUDXItNYYEUVDEI4tq5cD1mAxgYwpSoPsSEFFDLMdulmVMXQOFHjFUNqSI64elltUqVQKGaGuDEo8Ip5WOQx8I5f5xRuK33XGfHjQQS4MYICTF4bvDR4KYUKmQzaP21CLVQpVrn9Re5XqxQZt+jMl/H1F0SgbAwov8cpgciaCsPoMvYsVonEJVjcaxTnAGYwrzaJ0kFeAa5e2xgEnVXfb+Hy3g72/FUhP8QnZVYkz5v5iiJWahCrLniPVE4fO9WfVE2AKyI4MZts21LtGStPRBGA2BM0lQ3m2j8TyWVQKUqDWgWhvOEPdESTQad+FKEG16txwbIyXwm50OqGxoqIg3KinEU7dB8S2gG/dERicqzA9nSBfCyTmOiPSubH3jCY0bh0vS9bE5eOl8VIFxSoZLcOzNSZnbBacVax2hoczwtNjIG9kaaUHwPBcCTlv1BZSgRRupECxHqMYCORWMV3OyWhQcZBQQEEHEiMY8lWbPappHrr5vlinuRhP4KUKqqGGmPMlAcbuZTpwuPHHKwammZemAEIIHBwkYAqgLIFShRIkWgLT0xGFsm0/dK9ShqmOgME5y7uaK3BlkK9Z/l1JsiTNhId8zR5MZsYb7fNN4qs5gd70kPq0GLIlzqYoOapZjNd84f8EANjKppDrOfSMrNVoytBIRGzxLYbWSLoO5AIQhjwIdVdisUHdla9RppJhYSMtVmkhkAuaLdGMOAyvW/0S/mJIOkk4SqG6GibWcF1vdnJknRKdmKyt/XIIfiwQTYH+BetVySg7hJdhUeMVLUbZnttRyQNUd8VSZocoDZ3ArXdJJcxqmtjFXzLoSPhsJSAsZDqihYSus+RWGVSIiyGDEdx7f2DIEBBV4B7wmjKhmtXFyz58tW26CGjq6bjfYwqoh4Er1dnXEIVBZguR5quCUnHrhifJ8nhUEnSZxIIMTWcoZgcVxEJhemMaCuRmQFlwxGPtpQiKFWYr27vHY1CRQdVdNnbKPm2Q6XHIgkuPFCprCLiMQzHHMmkU9GzpnvPg0X19EVBGxmQ81V4DpuoCmjNEMxOKK1cAa2xSiw3meVO+nlaH3rVKge5luzkPJaanI3SuUmOS/dy+C+ON77oTgRmDOhNeEyc9KCGmBXkcvS6SgZwrDM7SuFYRw+w0ZUi5MctAHjsVh5p5MFQexWVxyYWVPBBhbomcIZoCw4e1N64WW8skZiOcRyoY5IYziFKDq6B4XvWwRNZlVl7DcPgNlVdBfV1ayYHOwxLDhxY+M9A98xLPTASyf5N7JsqQ7u/qrdUd5snVVVdgvsmx8mCNnv1sHAkykmW4d9UjA8WVmtGCITtU4IVC1aeBzGsNMa2gE6vT9NwUi1Oklh5PwjPrmIwYr1d1TFpiMEBs+WGiMFBbbElXKN7XmG9KTG6ia3o3jjF9ZIC65n5diGYGo1uCyrfTelIpKVcD1AfpAZacGp2rhkql2N/TETDbCTXXANLRArMHo4EztGkNcqWXDCc5DBU1igHX9LzjW8IaQxc35s0m/be5drji0p1LwPx3qfH3rzDw1fCORQ7oZ8o504pJPuNojaTrwJUiydcZFhuRPw2KgiaRjrGkAQM0ywEwdB9h+J8++MMwO2QAFacU5EhQur+dYPe+5n/BR/M+fv3CawEASVRj//wOkmN4nZ/ZpiQXfjPDwi0c7u+WZF11ghufKnHTBu1CafQP8IRblTBbsZ75BYtItAy6Ds9VDunUyBe8IZpIuixeV0jRZiBKA1kEIywek/dFWMMgmlOYyW2Crk+ZCjW8pjfSSbvYVNA2+yo9tM/owok96+qfB8PNlargVcig6p+vkRyVfiXjZW1/M/EkUcPJSBMl81IFvBKYbzJUNrwhIgCG+sudNDv7GnPGyTPmMsIMIOcaifU6TE/HlMHXIHxXPcAIbsUAbX9GWCL9woYv06s5VgwNvrIvwGvyBiZj5x1h3oABYPsoSDvQs9h/LENowJWrKFZoV+oOBeITBblQflzNtyOvczWzhokRDN1ZSV4nt+Bqheh4gbUJdUy+2UGxEpHRao3TeEKk7NkOx9xmTKeH5FlyCQKHLyLDVxR2EwXANEM0NehcLdG5Qp9FiwSLteB1i6ZkCIKFAqt1xpEclSgGws9LbeeNy2p0Qo7NwrFO44cBGJyzxsCFCnUm/LwROZHCXQYiQOPRhXubhVx5HTwvrkgtACgbHupepXT/qicQj+kfexcpM7B2c1lRX6XHCsmxFSlVGuAM4+ekWGw4z7BBPI18rbqyzyiVfx7S4ussrB9eOoOmPJKR9gcoI5h/94AjmzMsToXPpuPUhxhdSn4xIM04Z+DxEp507z6rMyvsWTTkFqyHqH/ehvtOCdSbVpDW9kO2T7/FzXLZn3wtCPkyDWT7NUQpMD9lM+VymltlP6zROjbegAMAnWnoYY1skCNNqFHDdIGzF09BxApqTvOk/4UI3Yvwh0PDGVQjtP+0wia4PCm0EgBPCK2RdB1kBwYipnBS2QunkrobFmKHaAaQ8JkLazGvx6KPbQZOomkirlTgR7RqvmvvlZjWMV61/hAA4EOXX0QTOkUokGozmQxHOLnntBDIOa0MxWqEYoWjzkJWkOG2Jts0VEEnboBZUnZ2zxaUlxlURBus21CLG0p81wsfxLnxGi5/we5wDFCHLChUZ3QaS46Yr14OBsRTg+zIoE7Cb4rKQNosJBUxzHcAPmB+AqtTFUoWIdkTPnNHS1L99oYpf4zuCShDidW0CXbtiTs5LFD3Ip9Cne4BvKggZwrM7kKq63hLDNIap9GkRp2lyC1/prb6OaIKG6qrMJ8e1raoJulBiUJDFM7YMrYgK/OhIKYY8lUg3zTIrtrQlrGhF1cPzZ78860Usc1Wyq7mmJ3pYH5KBMHOAkjGuhEGtVljUfAKOnVxVof+cpwut7kAQL4hYbhE1QkbqlfSdt6ljMOkEqyswRpCn808ablQWH2AU3adHQrJSQ3DKb3IGbDxBCgGwhvHLoTslLld25MTQCxq8Dnt6sNpiX4il07Fi60U09OC0tJBHoZ4RJltfsxYHSZvPFplcJcWD9A44Ioyq1x4zRU7jqZOxIdR7cDKWhe2P5Utiuu4NzqiZ/SekCF517p7yredVwb5msRsS3hPWedqhWTE/YFAFMRhS0a1H1dGMGjGKAPU9vHiFBlbzgvd2dOYb3EsNpmfR9EcXl2+Z2sQyqlCPCbPW9WndWe8LciT4jXPgqHFbfaXOiauYrYHDB6hd5OvS8Qn3I+zaEpeeV4H6YDpGeIeSRMOdU7Q0xnMnX1Foa4u80atlnQwyjcY6oza2b+gkB5oz6UygqHOiPs5eNQZUhxVn/l6cYAN8XITMo0BYCGw0BnWzhDXwRiG4eoML98+j76dTH9y9RWIJsDgEfrK4KEpcHNDW+NpxjNdu+0Xf/EX8cEPfhD3338/sizDq171KvzyL/8ynv/85/trfvzHfxy//du/vfS9V7ziFfj4xz/u/14UBX72Z38Wv/u7v4vFYoHv/d7vxXvf+16cOXPmST3P043WSLoOBmcXEDHAtMF8J8VJL5xKdERS9aJonEQieK6RKMOiKyc2ZFUIK37HoNdosf3fLz4Xp/sjnJ/RTnz+YBVYp8XIKy/7cA+FbABXlZxhtkuWx/wUR7lCi7PjMqmUFhSn9A3YRSYNMXdREfFY5A1vAyh0ZDh85ff4UoyPTV4A06/BhDO4DKqu8Bueye0prxFuKPvkiWMqeElcyQpvXEXAYleBD0qYQ3qe+EIMLWzqvOM/9AHwhvdgDl8Q1n0WT6jiezzj6FwOCpPzzQjzLWrUIOJIDwW4MogW9CzlgMF0AGYYupfs5pUrxBPtBUNdORDHEwJAJ1gFLDak195Jri5gIg6VWpHBsUZ2oDHbibwQJwDkf2uBV958Dp/5oxf5fuGGeCcAkV/TwxJVT/qNy3CGfIVTBXIXhrxMYoPO45WMDZJxI4wE57myvIypC49Sv2VHtJEk+wuMb+shXw3FcuUCSyR8wIVeORHLMzoAsKICjPEClrPdGKIwKFZYQ8+JI55qzAz3Xof0WFmPhd18y5DS7hSwdQSkhzWpiNv7G8bAFyWYVZquhylmO+QxcuNfFOT56F2soGMaRMUKzRHnISqGDKUVrnRFdqHJ0M32ClS9yL8vUShoK3EAbT0fMhgoxm7g8XFoO1cGcmHQf9R5FSOIEqRPJQJpWkcUavRimQOJzlXyhtDz0kZfDBOsPEjjWs5qiLpGNIt9OFEUQOeqQmX1q2bbHFU/HCjcuJILMqqjieVYdclIk4sgG1EOGpwlALzSYIlAOQwyIPGJ8y7CK3r3ztfgVeLDYYYB5ZAOTukxfa/qEPeNl/BrhUro/YiCvpecaC/D4MuubDrel8bJi+27zwQ6VzmEFfKNphqsNkinYf6X/YQOrSyMZx1TCajKaaz1FVjBER1FOFqhyRRHNUaPDvEJzTFf2IUoJu7k6DmOP9mDKnJ8q+KjH/0ofvInfxIvf/nLUdc1/tk/+2d4wxvegC9+8YvodoO34Pu+7/vw/ve/3/89juOl+/zjf/yP8V/+y3/B7/3e72F9fR3vfOc78aY3vQn33HMPhHjmjMwnitZIatGiRYsWLb4Z8JRktz0x/Pmf//nS39///vdjc3MT99xzD77ne77Hf54kCba3t695j9FohN/8zd/E7/zO7+D1r389AOA//If/gBtuuAF33XUX3vjGNz59D/Ak0RpJ1wEvFTg0hYxGNeIJWcZV157SpQGfhBOwzNEISZBHpe403PgFwDSHmcUw1htzlA8x24hRHJKbRYwEVFdDb1UQF1P/e+UKea26F+le8cTACObd14ttoDxdYjEX6J6z2SkmuKedm1xHlhDtarcpOk3xGv5EV3UoywQIKeQwQHaFI3owDhyMAQC+zOVIRstudRfG0JLKqgAhdJPu276rAD4s8Q/u+AT+05e/g773xR44bMjFcWUZAE2FPd19eEVkYMfBmJ4R6F0A+mfnPitL9SLiTdiQx/Q0x/Q09a2299YxueE7e4rEFAGwWiMeK08oZ5pBKQof+rRvy4vRkiG1tdNMxFEN4gZfx4DVRCR1bagzBpVLvHb1fvz1GXJbDx4Q6Owb7wErVyRGtyRYbDLPz0lGnIilDQFNh5DmTv9mGrPbkbh5GcIZ0mY6ijl5AOpuTOrIJcCs97Kzp2EkUPY4opn1AnQ5Tp6XITuMfWhSLCT4ogpp3wnDfJODaaB32apiR8R7S0YhbFMOOOQiZGPxmrwXMgeGZ63HZF6DL0o0S6aUqylEoSAnrhAvqbNXXaB3ge4Vj2rPG/J1xGKSvXDPUgwF4gl9z8lNZPsl5LhAtZp6QjlXBjrimO1Yz5kiz8diMyQNqAQ0RjmFilwbmDF+TGWHHMVQUP08J8RZavQqA14q76F0qfbxCbl8jeCIZjHmmxLzrciPBcNtuM0dxBlQ9rnPZHOSCKIA0kPHYTNQCffFjwEav+WAoRgy73FyRbVdiZPkuML8lCTie+m4WeQVrnrwwrqDcwU6l3OIKvHPomNBtSS7IXN0/QsK3Uenfp4sTvew97dCSZfZDnnB0gObCQpgdpq4RNlV7q+ru8Bsl9lMYaBbA73zcxI3jW0IfL9GNONgyvjsvXKFVM6Nq/83JHFSVTLoB4hnUCQGHADnGrdtU8bIl4sdGCkhbdmjyY2AmeKZgcGTZ4nb74/H46WPkyRBkiTX+sYSRiNaHNbW1pY+/8hHPoLNzU2srKzg1a9+Nf7Vv/pX2NwkNvw999yDqqrwhje8wV+/u7uL22+/HR/72MdaI+mbFoyBGUPhFhZCVMpmtDQl7qMZsPqVEtrqk0xuiBDNDPJV5iX1Xbo61f2xG9VViejBPpGCYXVgMgVxKfH3BgA1rKE0w8pXrEt5RhlBRy+k+9z1Y7+CG2Uf/9/pED//ybcAALLPZYhH1DYX5llsENHUhWbqjMJt2VUG1kh71RFtWD5MFwVuk2vX6oMK4xvkkku+6lG/eNVqWyyS1/BPMBlrAABlIklEQVS5y+W6gphyvyFUAyBKanxpsoNyj4zFVNuQXKMmm44oO6gy4d7c0PtwC1+dAosNAcMzpMdOSVeDN7LGyj58fa5QAJZ+KzksA6m9F4Ep4xWIDSeZh3LAveFUZ/ReIwPPJ8k3UxQDjnhiN8a9AnVXQsdA3gnGY3w+xl+/6DavVaOlC+WFRbAcEB/DEWzrLnFs5DyEQlRC/etUq+OpRsloA3UEYsNIOTyaBQNFS5Ci8i2xfT67abBwL2YA1CEb0r2HqsdQdSS6V11IJULZD4WwTp7HUPcV1u4VyPao4yc3xqg7jMIodiOebTPEU4ZiGN5DcmSNa1tEmE0cUc3AxC6MJMGV9Jl6vFBITgzydeY3VADI1wSmu2HuRhPK5JpvhDAW1xSKcokS8ZUJKZE36twpwbBYb7z3FJg9R8FIDel0dqYMrkSHKKjN3EkkMMfV0iHhwhH9awNhDIUObdN9hNOORVYrpHsFqp7wJO18RfpyKi4cxWpKQHAaUwYAm5MGmTN+8lVOCR6co3NgswXXOerUGtIus8sq8zvjUaWkMB6PQq04HQOwBW1daL4YRUiOw5x0chh6wGBcpqMhfh3uPwtmwzJJdjN6F4QPyc22SMKktpmZgF2DToiK0CTj151AHFcJg0ooQaaZ0BBNKCGB23JB2VWaQ5UtgzIXKVikobcLsEOnQ8AgpwyTcYbXnaEiklfW+hgthogu0SXVgMqZfDNhNpthOBwuffaud70L7373u6/7PWMMfuZnfgZ/5+/8Hdx+++3+8+///u/HD//wD+Omm27C2bNn8c//+T/H6173Otxzzz1IkgRXrlxBHMdYXV1dut/W1hauXLnylD3X04HWSLoODGNgFYnbzbal31CTMcWkmWY+tVcuDKJx6Rc+uVAQ8wqdYYJRRRNuciNQrSjIKfcbIy/ppFb7DY9Bn0RIDlnwBBhAHkmozQrjW+jDwVnaKPQNZMWMrFXyJ4cvxfoqHWtmMkN0HHRjAHgdI1+mgQOsYJ7I6VBbjRHWWDBnp0lwrf+oLTSrDZiWoRikpBO5K5UAwJctIOIyfVZsaZiIBOGon4H6fA+fmN4cuEzPWyB6MEN8ENoqcvg0eYA8EiphmG83Un2tAORsh4PZH+xeXEDONXiPFkeXSQcAspFuLwoDkdeYPIfcbMWQI9tXvsxKNCrBNJCvZij7oa8c8ddl+c02Sc9K7tmNMq/AY446aUgVWO/df/3Mi5DajarqA1PBUHVT33eGW00bl/U2t+TWKBiQ8dhA5kGIk4rramT7GpMz9IOGU1mdzpUS45vJcqo6lrCvwntwZV3cmJE5GRRVj4FNQh/riMbR+CbunydfCzwz1dVItueYX+3DMPq9Yo1I6ckoeCt0AvAjYHhW+3bGU20lEywRlzEwxgBuUPfpefJ1Zo1v+nu2b5+nE7yYdVegTpZTyFceqiEKjXLguEZk0KsY3rNTr3WhrVir4+yAAdOdNGSt9QEx4zCCI7OGWjyxY6jCUg07mJAoQXpHBsVG4g2gxRoVd5aLkBVqOINcaJ/txpRBOSBCfeW9wDQGsn36TXf/qseg7Fyres4YbmRqAN4D7D1zQzsH8nCAMgIwKnihozkPxZVt0kVtEz7iSeD6VF2OaBqEMVVEhr7KgpbY2lcU5PEcuq6BlDpVS0bkb/uu5hvClyxycyua0OGm6oXyMyqBFds0tp0aOhbINyRJBthr+hcVRK4h7XzmNYPKmS+CO3iEoU4E5tsyCFxWtJ6JRzN8uPsCACC5FoOl7Fw3h552PEUSAN1uF5cvX176+GvxIr3jHe/A5z//efzVX/3V0uc/8iM/4v//9ttvx3d8x3fgpptuwp/+6Z/ih37oh67TFENz+79jtEbSdaBjjlrGmN6YYr4VjJbkiHkPi1t8dcSsUrDNFhnlgKbK9dkBfZFUkAXiUcgYcZk8DqIEMGGYvbiAuErWQXrA0LnEUE1JSRYADu8AureeYDOhnfIL5S5KXMVffvoFMIlVp7X6QYvushHBFRoimAzRhDYWd422YQMtyVPj2qVj+lOs0vNUPY5iGDZrFdkQT0OfRyhrIDWUdcVYIpoGwqsoAcwYylQiWqdd9nuf82X8ZedWmL1hIJQzgC+A/nn6ID4poVKJ8c2xP8U6IjcpXIeOVSnzSsWipJN1nQSCssqA5JyGkUEZvOoAZlMEde0NgXiiEc2pUCYA8JwWbmaM9zKolAxDbkMsRnLoiArqOufgfJskCvr3hymoMmtoNjYpXxeskcHnhAWdzk7nSuFP2gBQ96Plunig8SbnGuVA+g2zXCGjxWnl1DYTsn+h9oRoIxniKXkbnXcpMho5o+w6l/3ojDbXV/2HOcrDPmnd7IZrYq9PZO81ZYhnGr1HaULxeYXyVIf0fqxAIlPkzVW9CCPr9aozJ4po+5gxLLaA+CiQg0VJAqv5Ke4zmqJpjcMXpUHVXIWiqU6xORIMs50YydgWh7X3J6kM+z1DGkqiAPoX6Zo6YXZsmFCMtjbglcL0BppckxudZyiQ/6mtgK4Z5i7LLwGYEuhZA4ySAyh8OTjnDEqGzl4FXmnk69Qvs21OOmTWqEiO4YtX+wSILoWsoknw+LoQfN0xvhqAU4r3KuBbwo+9aBKyNLWd88qN2znNB5cgUO4yXxvP9V/n3ARm7wB8ZYhLb30u9c0rF9AzgfQSNapzyRrkMox/BsoAlXNgcJ5mk4oYqi5H72Koazk9E1HhZPvMdQYcrAqIQniDPD2soUVIkGGKvHndPYETexgtV+hdaQnkORnWs4s98CIYgSIHuHwGN/qnSCdpMBg8oa/91E/9FP74j/8Yf/EXf/G4GWk7Ozu46aab8MAD5H3b3t5GWZY4Pj5e8ibt7e3hVa961RN8gGcWrZH0OMg3YuSrFIYo12w444TUsKtu0ENhCpjvJIislkY0qiBnVFzSbUrR1HoQyuXfqDrWMAF5eopTCv+vl30U7/3L7wUAmCPyTGR7QShv8sIa44MuuqfpZm/rHePRukB3dwp8jNyorkArU+GE7yCd+nRB/KZK29O5/Swa00LrNhMw0kCqU2D8mHRXv9Aaq1orwmdG0uLbFJOEIEFBvzjltsCsFFAFre4frl4IHCRgaRBpzA4Uoony6sKGMYhFje6eAFeu5AIZPm5hA4BqEEPkIZShLdcCrGHkSuLqOPE/gDaVUgYjgtUMzK6MTf6P40r50J0NmTn9IWigWKFK5a4ch7htgtnVDtKrwocJABoHXrvJpu270iuuTa6vffHaK8Rr4YU1KrRBfipBNRA+dMcMw/yURN0JxqphwaB0YJo8Ac57QIWObbjDGcwV/Qavg9xEsRbGt4OYA1yGfnHvOzmug9fmmIQl57v03nsP0zPUXel5PLzSmO9mmO4Kb5TJGXkS5cRe35H2s/B7xVBQSRkTPGPHz01J0NC2JZrZ8JIJGl1Mx5juUqgmctw6581oHGgMA1hFukAAeY9ITiMU1E1OBHnZ1oPhwUsaU6FkjDWalPHeVW1DqrMde++Kru9e1eidW9hrOOqOxGw7QbFiDd8hloruUiFlyvJrvgc5Y94zC9C95RyohmRAATTeZbUswBhN3BgJh5JoTtmAU5v5p2KSR+hcoUlSZxmp3B9UKIY2OzES4FLi5LW34jO/8Oto4m/sOP4ffuOnAUYK5o6Tlx6R6GU8NZAzGmxc0ho7PWM9lgPytjVL4vCS+qZYN/4lpocROgcKcmTXE8GJ5xkxdA6sIXVCfDYVM5QnHd/HOmroO0lAR88QmfobUJbEGIOf+qmfwh/+4R/iIx/5CG655ZbH/c7h4SHOnz+PnR0S4HzZy16GKIpw55134q1vfSsA4PLly7jvvvvwK7/yK0/8EZ5BtEZSixYtWrRo0eKa+Mmf/En8p//0n/BHf/RH6Pf7nkM0HA6RZRmm0yne/e534+/9vb+HnZ0dnDt3Dr/wC7+AjY0NvOUtb/HX/sRP/ATe+c53Yn19HWtra/jZn/1Z3HHHHT7b7b9XtEbSdTDfSaBXiawoF0D2ZUe+pJNZ97JZcpnXKfOlS7IDjt4jGuVa4kuCuPh6U3U3WtBpr7SeJK4AMOB/u/87fWy+2DDAIQmhOa9G9qjEYkchtaSaH3749fh/7HwE/9fn3Y3/zx4NuuSKhFjQobLJG1Jxw5sVUbYXM+GkL0pqZ90D6q4NXeQM8TFlZ7k6W7wkL4bjUcw3SRmZ+DmWxDmk+lfpPgsnr5Kj7hjUPecpEEhmQPcSoIXl5xxnlHWngO5lanx6lTJWYHkUJpFgtUKdcqgohDFcKNOVVDE8WiIL1x3yODWJ4ckx8W90JPwJn1fUD84rIB0ptgpaQ06pW8chdJEck0KzI/3OdmMSZ+SBk5Ff6AIrFeqOgLJJJsJ6pRxvCQhij86jV9maXvExPIGXqwy9C0XQ8DEGyVGFaMKxOOVCo6Hcg3v3ogxZbwAgFXlDqi7zfJm6Q56OpqeF18wXCfYehdSgNiyEB2srWjgL3iUtbc0+Ax/G6lw14MqETCvGEJ0UMLFA3bFh6k2BxbqgemYuC05ZUjNzIU4OV97GeW7zVRqv8Sj01XwHljRn+UA7Gr2zHN0rQWl6fJPA/G8tMD2JMfySrYeXWw0f65nI9oDOVY30sIKyBPO6Q+KZhgcPTdVllhRO906PsFT7D7BEeE2hR+cpjEek5u44NcUqfSZyg9wWuc5XOeanSLne90tF88hxHF0ihUoCh0bOyctnZFgXpAbAgVKGvmGwHmAe2jQ4R9m+ro+rLkMy0hANYn/dISHc7JCuWfnS1HKgODpW/LZcT3Hu/3Irfv71f4zH4kc+8o8AAMM5qXIDIQQtco7Vr5TgdSigO9+k+m7umfNTNvlg2qjjmNgEliLQCiY3M1Q9ic6eDbPONUSufdYs9ZVC2beZeZaHaORyJrOOADF/pjhJeAqy257Y5b/+6+Tpe81rXrP0+fvf/378+I//OIQQuPfee/GBD3wAJycn2NnZwWtf+1r8/u//Pvr9QN58z3veAykl3vrWt3oxyd/6rd/671ojCWiNpOtidAuHSMi1H83DwpevM6SHZCg5QmE8IfKsE3UrBhxyM8FiQ/hJaRgtktmBQnISlBvztQiG2dpcfQDMoLzQ9YVqo7HdFERwlPYuGnSuCFw+OA0AmLz8AP/T/E04GPdw6swxAGC/XEc04cQXsO1UqQ3h2IUvvUxkUcMAZqs0qtRyl2w5AYD+y2sKfTn+S7EGUIkTutf4hRU6j0QUPnIN5YDuaDAjvJeYV4CJjFftdRytZGxQDO1nY0q7zldCyQ0jOZhSgAvD6BJGCKiE+WrthtMmYDj86C77xBcL6dW08YtGSK6zT4vj8QsSr+4r50HGgX6fOFHxVPvNpezbtH4dNirKyBGQlnhc9SRmu/S7zoDjOYcZUcmPyiWZjLBUeoZbo1rHzTRzA7VWI9uLUNnP5hsMWiaB22aAzoGiMJMls8ZjZ/zAk3xJQdqgf4GsJl5SqY3FqcgbDFXJUPYZ0mOD9KCy78+AKapaXwzIuk/3bUh6aAnfMwrFsRjoX6CO6V4swCqN+W7qQ4UqZkgmGv2zVgHSDpKqH2G2TS+nWCEDVC6CIb/YtOnnNk5d9RiKFWuM14Ev48KFng+X00a52HYGOkO+DoxfYHDr8yhd6crVDciHMvQukDAjQAaJyIMRGE0N0sMK0biA3qDJNd8itW+mQjg2GRvEY+XnTNnnKFaBfCPU3pMzBhOTEeMSQbpXNKKJojAxACMEdARMzogwHm0ShijDmBG5rRvpiNqMxrqRQQXcieHKBdC7RI0oexwnz2Mw66XP7KLxyhp8LoNooSnLUCwnQdQJD4VpI9j6fPbAeMkAtQEkYKSwfSXxtu/+GP7vw2Xy8HPu/B/Q/SLdKN8gwV65aNaVJKkElQpMbghihc0QvytR1CwHo2IAZpnbxw3N1SDxwVF1OaqM+fdFZZeoFIu7F8xyuK0pL/L0QwPmSf7YE/y+eZzwXpZl+NCHPvS490nTFL/2a7+GX/u1X3tCv/+NRmskXQeuECTmAAx8uQOAFGSZosUMALKrBXQsYJgl/A04pruSJm7zxFEadC4vyCMCQGcS6WHl9UNUyhCv5bjl1CG+cp7Kf8gvppBTqr/UTM2uMyCzWkPjz23gcK2mGP4J7QhxwTynxZ8erG6KWyw6BwrJPh234k1a7Ee3SKugS/wrAOheolpJ6ZUFuiu0MR3eHmO+HYqAphcj1B1gPDDoPRoIqCbSmN3QyABZKyEuJUgOAlcrnlJpj/QgZPfomEFFEqPnUJ/maz10L1VeO4bVGibiXgsKgO9vppb5RioJJ8H4OHBCnOFbDDilQWfhdM00vBq563Md2ZpNzuASwXPg+DL1roGoBLiV8jWcDMGmh4jXgCkoO9LzJiyvyV3HLA+l7jSMpI4mTlcj7TsZGVvPjf5edRmmOxKiMEhGzhiwHBpreDloyYKeVCIsSZohX3GbhEE8NuheLBAd2VINnLw29SD18hbijhHkx4dBliAzMJIhmlD2FgDEIwmRkxyDJ7nHzvilF5jtl6i7EqObpPe2ONXleATAlboY0zM5Lo7KAJ0YzE4zqKPQ77yyc5iFe8EA8bH1WA4NqltyvPZ5X8H3rd4HAPh/n/1hDB+lBIFiNWiOdfaN1x+qOwwq4WC92JejcRmPzDQkG1KGOpGPIU0D8XEo5+Pqo1X9hgeqw5CvRp5LVWdkRDkDB6D/l3Oa386wj6ZA50oNLYOeUzwxmHeYVyJnmgzl7EAjOaSGLtYz1H0NHAUdNJMYgAUukxEMZZ9D5MbrTBkuoGOGeKzQueq8x8QJcll4i50OZZVNK28Ep8cKv//R78TVV/bxg2ufBQC8+4s/CLEX+wPd4CxdqxJ4Y1EUNNbrKBCnZWFsPcXA56pT+p4vrmwzUKNZWBfiCZHsvebZCqf1M2vw9DR5/VTH+HXOCGtwujWAAXgMz/Rpg8FTRtxu8bWhNZKuA5kD4pgmWLEKVANr2CQGcsaxfn+JeESzg9UaJq8DYVJEfkF1m5fzYOiIwzqOwIsaxVoaiM4MYF/s4f7dFH/7RQ8DAD51fBuG54DOpQLCuqsnt3VRZUGcMN0DmJZQNmsFsERam67tCKeuzIR3J08D09alO4tC0sJUAINH6Af6Z6dgpavzZAnDVyUWNfendDBApQYiD6GdamBw+20XMIhzfOYSZUTkkwRMhQK32YFBclTbxSqET2bbgk7B9vZlj0HfEENYMb3O1QpcGdRpyJTTEREr5SJ4bbSk0GHVs16OaSje6/qhzuxpkzXelwCUaHh/bJYQrw1qG2IpLImTqSC6V91cYDpPfU22eGLANLWxSZLWq+SdS0a2DSl556qVcNKLRmQEupN0NBJIHxAQZYNUDwo7uXYzRd4eFTPk1kDRgt4J08vJBiqFz7xKRgpypkjM0mXuzGsSOcxtQVsLE0vk6xFWvkIN278thhzQ5g8Esj4QjMfZtkRnX1Fo1EUnuPW8ebFTDhVTNpQvW2M9aVoEyYZ48hgpCwOIBUe5Rh4uAIhPLOk9aRSezrQdA/ZQ0quRZRW+dLSNk5I6NL0YoXeFpAKMCN6saALf7joFFpsSdRIFb4XLIJ2F91UMLVHahn3iMc1LMr5tQsKh8okM3gumiXheNQj06b4zWu38nlFhXBWHUHY8M4imFVa+QoPBCI7FZoTOZXiD2QiSFwCAxZYtnNwDxJxDpQZ6aL+rgWIteFUMJ09YPAntNJzWyapLJWcAIlfPtqWvOzc5I9HZp39zWY0qYlj9AsenH3oJ/nrzJfbdk2CuM47WPnWI4vQAJ8+NvTFcdxjSQ1vw+tjpNzHUXYbKlT0CjZe6G+q0yQV53KKp8e2Uc4XFqQizLXcgCFIlPr2fkYEkFiFxgdqBYCQ1PMQtvvXQGknXQb4KSGssVF3K9gCAzj6zGT7wHiEjGKX8j2i2iBVJpxYFpCd2gdCk9pqfSvwioyULeiCgDXpwzqB3QeJT+W10TVdBRRIir6FtUUdeGojKQFvrQHCrIRIzXyDViRM6RWHAih82XM7FWgSxoDpmLmU+mhtEc9o00z3aqZyBBMBr1ciFwfBcjZNbbcaKBOIRhV0cl0CvVPiBrXvx5fk28qu2HtKxIM6Kdf/3LtUQucJiM/b9IGcKIufQfeb303hiqMCtDb9NzxAXwfAG78XQRiQKBO+BAtWVs1yLukcCcBR6CX3hPBbe62ZszTO72XcmBiqldOPChot0ZMOXObzeCj6TYnqzplAnbNjH2Ps3ODs61ah63PeD8/q5wuRmWKMuIxIedXwxu9lW3cBPM5KMeBdm6J8nroyOmFeI7u3VUAlHMWA+08qFXJy3IjksSPuqDBIADkZK6A7dq9hIUPY4iiH32kzx/Rni7ziG+qtVuteRDR8tglclGWmIQqNYEUisOnm+zqB58Czl6xKGUThtfsbWlNsTiBqGJACvzO2MccPs77Agz1F3KVuwHDSznDiqFY1qxSqFzwTKaQ/7dQ+jEakDd/dpAxV5DWHTvlXC7AHHvofCctEaMg3JkfV4TYPx5jLnvHQArCenMF7gNV8TyFcZ4olZUkN3Bj9AHqNoYdC7UKBOyUhYnJKkyaQCN2yxwVBlHQzP0eCITgrUPWH5ODSw51sRFhvSh+Fcw8SCDM/vehGlbd/90C3ge8H9qRIA3Im5On4mUDGg6jNfRDs7IK9zbA3Rssdw8hyOxU6ErU9aT9JBjWREKfjZPjVivkneocE5u+aMp5i/bJ3GtjV2mOVOmQYvStk5G1lPGYwLJZNiPAAIe2ioU+aFRFVKYpXOiHcHGKbpsAeEA9BShqkgr6+386uQnfy0w5gnz0lqPUlPCK2RdB30LgFmBZZoGty0Kw8pSrmutU+xNmCACOS9eFQjmjDIXHmSatWTiMeMPCTNCaeAxKa5R3uOQ6Kx+gWatbkNA81Pd0IaPbPp1w0PFGA3pUZqqpv4buMwHEueiclpSp9ndfAwZEcGvUcXEItgGOksgrYS/84QlBMOVmtkK3RMz08x2sQTQN9CP3jr1gH+50+9AVFSI9kPbhSRh4Vl9JwIa1+i9H5nqBnJIAuAGeOJ7yolfoDXTDEhnd+70I8tf6xBngVos3FehPiEQ8cGxU4NYdWS0wMqtsmrwCeJ5sS/ChsVI7XuKKR4e56MaYjuTYHsKg/keGZ5G1kwuIwkXpJKjT9xM0UbVeeSNa7OU+ijasiZcKuGrmNAO/L/FEgMVUMHSFmYKY1oodG33CI5KcG0RryeQUfU+HKAJdVxV4JDpxG4k1lIBFQnQt2VXtxxui1J04nDa+oMzxok37PA+TPUWZ1L3I/JaB4WZZVy5CsM2RF91j+vML5RYHEq9LEoDY3RKlgWzNB7cWO0zuDb4K5RqYGODYz1pObbCnLCPZ8OIA8Aqxh47t4VhaSrXgi7kKq6BM+EV+EePEJ9JexcZpWxBGbyeAI21V6SweDnoy3CrBtGkyhowy6HjqfkNIpCeZHsKkP/fAXDIv+80x0GUcToXKH5Vw4F6oyhs6+QWWOgHEoUQ47Fhgu3GaiYjGNmQoo+rHfLhelUSp4Wscfw1/c8336XDE5nmOuIQsDpYTDw0qMadUdgkgZ9MZVwpAcV0gOaAFnEUfVTy1+0BnrCUXXIa+jeKayHe75NH0xefjP+7H/8FfzChR/A537/xXRJDWjJwWqDZEzvphgKWgMcn63DvOaXe38q5chXqZSJM7QBGkPVMIxPOaW+ce/BSAORc+KDNgzKppgrM0DdCKU/rXhKwm1PSUueNeCPf0mLFi1atGjRosWzD60n6TpYeTAHT6lApsqCPRmNa/IiKROses0AEU5KckEnGBKIsyEyZZAclTA89oKMTJEb153CVh8oYATzJ0GATqN1J9QGA4DsoIbMmReyA7gXCvT8lQl9t7NX+89mWxL5qnVRg062tQSQwp8wFusMQAZRmEZae+35Vy4cxZUNh5zQf+VU+rIh5iod1x4qtyhsddRB2uD6iEaBVhUDs50Ydcp82KBzoMFLA7kwnpBZ9qksgvM6iNwW1K2MryVVrACLjk3/dZwWK6jpwqXxCOAHDFMhvQgcr4KHqrNna3hNFBbrIQRIKsj033zdhqhOGLI9691xhOuaxO+WVK9Zgz9j33s0pd9tijQaFjKcohkVMXaioK7voxkQ7YcMJh2TV6BZwoIZYLGTNpSzE8hJRR5QX/yOQcWUuQUA2b6EYQxGMvDEeTFjlH0GLRiSceNMZZ/JtT09Vnjk4gZgCzc7/hYlO9h2Cqonxwx8G5LDEitVGOvRtMbkhoRqhJ1w359eYNM2wdXeW+zYd3VqAV1I6IVE3XHuOqDuachZCGczTV7DpuJ9uYKlOox1xmCYFSy0hZNZTRIR8bEtqCsZ6o5ENFVYeVD5zxabkngvVpFaLmidMC5bynpgJjdEIXRYEaews2+8ByOeaMh5jWhhZRC2yDtVDDg6l+kegwenVFbEGBSniJCTrxL52BOrRZhTjpNEYWTyhsZTG64/oKQQIwG5aGTauveMEFaqOyGDsBhKzLapv4ePWqmOvRxGcOQboSagnAPd8wylDftPdyVECXQvazDjuJsG2X7tFf3PvPkcTos+chUhtZ7HeKKhEoZiRfhM4qpLnjD3d66sxAGHHwu8NkiPNBan+JL3kbhsgfdFHkl4iDl5hFnQP/Xlgry3SYU19+mHeQo8Sa0r6YmgNZKug8V6DN5xctKB7Kgju6AaAxO58uJmKc3cYb6dYLFO18RTDcMpZOQLbk6pKOeC6BDoX5JIL00hpxU6ln9UDiOc3Cp8OAcAqq6wVbrtIldSqQxjtWIAmuzJsYFcaPCCFvJMMBRDEQjm7oYNUnEdUeFRmEAILYYx4omEXJCh5/pkvh37zZopUrRlNfOhJn459irjzkhw+izu3swA+RpHsRLCEnLB0dmvAcaQjC2voKCaebx0ujRkSCQj5YtpUuFOMmSaKbvJcQiHGWuwpHtE+HT3YookGroXF/Y6htUHOOanrJr3kAjHcg7wYSCzavs8LnsOJmhNAdSvtZNeWISxwbRLYw/vQEfwpP4KDOBUiNPd2xly8VgjtuGGusNRrAhUPRov8XGJYjXCfFM0DL8a0Zgq2TtOVzx1KdB0zfR04kOXLvSW7dfQUsLwYJS50IOWgXtWrEjwY+N3C5dNx2tA202p7tB4cvpCACBnlU9GCEhQb1bQNhSaXWZ+7Exvp4nz7beex+fueQ6MrXMipUJxkiK7IH3pHjkRfhx6TpALVzZCJ3IWwmL0LEB6xFAMGBYb1PhkZCAPatRduojVBjJXFPqxmw6R8IlM3b1IFrqWDNUgovAciEeTr5JUgA+t7dP3oonC+GbL+1oRYCryRhW3RDVmKOOMnomB5TUguZdUKFaAzp5BfBLSrdIjBblQ/uBVDKn4cveqRu0M7YiRlpZkS2FqXtPcAYDeldobIvmKnYOrtJZ19xQia6TzaQ7VzzC5gfqqTm1o3QQjt85oTPOK+4xdpz/nDOiHP3oz8Dzg/7z5KfyTlxM/89Sn6HoVsaUEBF7a7EfQvI8mBqKkYtdAyJ6d7XAYJ4QvrHGVuYlK4V6mATkKYx2W7+bI3LwijpLnwzW4ak87nqrsNvb4l7UgtEbSdaBjhnKVo+rSxIhmNOH6ubYDLXAPmHDcFLdJOIOI+00oP8WJQGoCYdhtlO6asseRarqPHFkCY61hnttB1SPxPYB+ixdUPRygxRhrDMkxnYwBWgSYpgXdCQ1WXQ6ZN7w4tuhqNA18D27TZY0Ip6q6C8y3OVUFP3DVVq1Xh4VrdGwQz5nfUGUOyOlyVpUrGqldSQQBKEuQdpu6SojbQAaGJYD2GbJD4zkudZc2T8PhC6bKBXlkRB4ycIoBZXa5a+oOPVtyHIix9YqmU2MNKKvvImYVskszMJtGptIIpfUYuTIJ0KGIqLsXr2nB9rXiCksSr8JG7Ayrpr6KbnjWgGAYiQIYvZiMivRihO4lV0TUeis1naKnO9Y7uZnZLDwgs96DdK8Asxw6905FTsTW6a4V5TvFsPJwjfik8hliTGkwLRGPFCpbIDjitKmahn4TBwnqiecTw3Za9iAXlKXmPRE1bYTxREFOnPViiMtn+6FaSZCvMRjFAvHeEurnNyl81/MfAgCMyxSsZEhH1IDFvI9o7jxPjsMWSqfElhyvBZbEF9NDID3UGN3ClwQZRWFQZw2+DACeK0xuSOw7oppzWoYU+eREgdfEN1KZ9cStRlSzsVH+Q0vqE6+11QNEyZCvRN5LyjRlA7qxLhckqDi9gaHq0PzrHFCJEpWFQq7JCUkAuGeZbyeoehyi4L7ESTw2Xj9r9BxrRIytR7sT5mA0BfExXZKJII4PrzRE7tJz6fmLoQAv7L32jiAuK/S3ngcAOH6uAKvo/bs5mB1Q2n401eAqrGHFivDrQjXU+I3RafyvD30X0KfOmu3EGDxq+8gdAA419bsz+ATDfJN5DysAVJqhGEZL3ly6CSVQ0BeBmjPoVENPhP93KpDMoBK79mq77jSMyfqGx9R9erpgNKCfAp2k1kj6mtEaSY8DZyjwKmwITkkZ2vhTJB6TDQRjoBOJZKxQDqibdQTvZXCnJ1EZxFNKtQWA+SmOaN7zWWUAFctdeSjB5IzwFel5RaGYYmizrJyaMYByxe1KlD2kksQvmi4c5p+lsWn7SuUyLC7+hGRCaNAZA1pQppgLWTHFwGoib8cndE1ka9xxBQibfcU0nfbc5sIVgAXtlT5tfIUI0t2rOlS3HzIs1oOyNaXBM+QNlU1RkvubBN+Yb4OOGoVjrQBfMjKY3mw/G9TgoxizMxqlrbnWGecUrmq42V3hX08e52TM1JlB74L1SiX0HOVqOKEOHqQwk+/jlPpILgwWp2yYoMJS6jtm9JsqAqIja7gV8CrfVd+qaXeYT5EHAGW1eKJZYyNejRFNKshRidX7bSbUqQiG0Ybl3qfIqYJ63XWbPEfZZ5AD7see91r2GoT5ETB8WOPSqu3krgavv1pJV+YGchaEVFUnwnwn9QZfOSRvbO9BuZR56Eizf/0l8iiwSAPbBdhDZPHFxxz56Qrol2AP0YlDxwa6ZMhG8OGveGrAdBB9dVpYyahhDESM5Cbi0Kd1SlmdXtqiD6vvRFmwAFB1Jfrna/DaYLZjyfF9MrRChh95OPKVEN5TjbngFaJTOoDMeQghyTnJRDnju+hzsBsy8pDacdXZJ2PWhZl0TB4xI1jjAMKQr3L0L1RBKHJuSBWeIWyghsafUyufnab6eN0rGvHICqVm3EocGCQH1ns2nkIXOdKr9HdxY5fW0YZeVb5GgrxgFHoDYOvHhdB55zzDb/zGmxFPDLZs/803gNkWQzQHhmdpHEWjEtUgwvxUbPuY4masXn5/zFC/OsHTeGxrcFolebFgUF0NMSyBaWb7isL9rrCvayfjj0mQGT9TrqQWzzRaI+k6mG8wKrB6RNof7lQXn1RgtfEnc4Bi/7xUvigntIaOEvDSYHCOZlO+LsFrg+RY+QrxKuYY3xQFnR8JLNYF4hMBOaeVoR6mEAuN7DC41eWChNCccVD1KB25s28g7cJXDULBS+cy5/VyuEHklM7qwiMAeXGyQ6ujsu6INUBpT09uka4zMpCMLYgZnZCrnjadEHZx2TRe3DEir0lTpJHDbtKNkhm8AjpXSt+ndZah6jF/GtUSXmbBl8yoQHopDVkAV83cg4XvJLa4rDmKEY+BeMQx27ZtWHSQ7M996CI7kFishU0LaHqDmPcEZYcGxYBBniZyUbmfgdcCojReUFOUwOBsBYhg7DgD2p12RU6bZp0BnUvhe8zQ7wZOV/BmAOQVSCYa0Uz7UO/khgjpsURyopcKnc62uO+naG5w9MKYwmluLKRkmMk5hW0AoOoJn1XoRS5T4uxsfcymp68zFKv0XccnSUYa8YjmTrlGHpnj58dQCby6fGWLs6ZHoQ2LLbp/dlGgth4ac+sM9SgJ/DgNiKmEmgtk1msk5xxr92uk+4UPH1bDGNMd6dtd2wNQZz+cruengqZUYudSMjKYb0a+r7J9g3LAyPBuhPJYbZCelIgmNvQ55r6/ABqz81OULh9Zb07R5yiGbKkchkrovk2tqNoq4Q8fdsYqySmIIniGtGRYrAevmEptxuS+8qVDiqEArwx0HFSyRWHAFUPZaczLGoAO2lSV5eHMdsI6lB0oqIQ8x9UqvdNYcDAhlvSVWE10Nb/OxXSAqzvCH17iMZUgKof0jnlF2kvxRHtjB0YgmmmkhyV4Udt3mmC2G3lvvEo4VArMdzXQoTGbnosADpSrGtpmuS42NfiJRHrQmDyMQ3UlulfdS2W2CgKF4gCg7hnoRqaeEQZy9tUHgqcNbXbbM4rWSGrRokWLFi2+GWBa4vYzjdZIug5UB+iOgJWHqOaU0wmqM4FIG5iIo7JEzmhqvUtuADKGaFwCxvgMo+TIZvdo43kvs5ukD18BQPeqBtPA6NYUnX3Lt1gTKPtWr8cdcCormufCN4y8GypqZFowoLwlBztIfAZJNAkCaQAJEhpOJ+ViELKl5luc9HesR6jOmK/n5jOapqRdVDW+pzKDfIM8awDQvWi8Fo876bmSJ67tIqf21yuhH1zNrWIl8kTxaEYlM8KpmUoSZFcrzG14g5cGyQSoshDO8F4m+8y8BLh9bud1c6EwXsPX4zt+XoxVxpDsk0pd90KOfDWDamay2TIX0SSEhIohqYkXJxQKyq4Iqtc3qjHfonbWCRCPSuhEID2y+jVRIykAluD+GJJoncKTq2ubcck0feY8AL3LNaJJjZPnppjt2D4ugHLAUGciaG3Zd+EELuuMWe8gvDiIim3fcCA+pB8oB93lIsD2/rNN6Tk8vUsa/fMkeCoWTuG4BlMai+0Ms83A+VApsLA8KTmj/iz7od6gjohfFI0YYutpVF/oItLBm2YkEB8x1B3msxp7FzXicQ0T8aDa3qcsUF9CxL6/OmWIJ66dgfTvMx1HNfJbE/+97MBACwoLu2zEqgfsf3uE5CTCwGZ6JcdUcsiVHXJFeo1gmNswFtX6C6rQAPGBirUQGuqdd9lxISxY9oVNQggh6DpjmN5gy9cASA450prI/bZKDvJVhs6eQdnjfqwv1rkNtxKR3L97HcaeywaLJsD0RvpodKsgpXkDOEZ02u+h/I7nY/JPaXK9eOURfP6PXoj+oxqlzcatXIKJWVa4L9Zk4HXWVlxTGapjCCAZK0STGkYwTG6hBWV+iqPqB86j432ylRJra9SG2bkNVH0DbBSADQPH3RL1yfIWqHoaaa8Ar2kAMAOokojnnn9aU+jchYPLVQNTPUOGhwFaMclnFq2RdB30H9Hon5SQk5JqdNmq01VfYr4tUXUZpA3BpSccZY979eLsagFRKOhYQNksNTKayE07eg7N6Lf8j/87AODff/x7AABbn3K/LjA5Td9TqeVEdOENFKlpgY4tz0fOqbzB4lTYBKMRUE4iyEWDGNsP3CgAPnMjX2GhzIWhBTKpDXq2+KnqCPBKYL4VrkuPLOGzsZnxklLW56etiFsikByRUebF1zQtOG5hVwl9Fk/JeAACF+zkNoH0yBobhxpMay970DlQkHOF6GSBvhW+rHsRIvu+XCZS9wqQr0nMt2w6sOWXRfPQD8pKILAa3kAwAhjfHKHT6fl+qW34qSnwGI9sGMwu9sUaPV+yQrt1LhKY+yn7zBXwrVNgemNG2U+dkGrYDJu536O6VPRRnVFblwpq2rBM9yo1ILs0Q77TxXwbtDGA6lqxmkjCHStYmh0qKrhqM5XIWAby9TA8oil9vtgEVI82wapDWXe8Chya9EhTuQsvxEmk5mhUQU6o8aobIz+V4eQW4Yv6RmMaGy4cLEqDYoV5FWTXDyKnceUN6xLITxlE45CxaOIQigRI6Xm+GZMBbsdoNaD+9CFHK+DJNJCM6IvRTGNyA4VvHOevziJUPaBYt/fpU+01kTeSDVIKGVddOti4di3W+dLcEuUyhzCak2K2ioL1qoaWq2T5fsUK8Wd4GZSm6X0RadknTyQc8x3uStyBKXp3VZd58rpKyLDIDjUyOxZkQcKg2SPKS0ksTkUQufEZYoZbMjcL41GlBnWHeIjeULt1F2d/KMGD3/Zb/nn+0d8r8Olff+lSId70xEDk2otx6ojWodgdXCx3cnxz5N+Xy2yrM47prjUyGwWoAQA241Q+mmL8KK0dApRYUE5imMhmKT/cRTIOXK26A0ADxaUuhFubUluKqApULbkIyR8AvaNnrsBti2carZF0HZAWkSEDg8N7hOqMYbHBbOonTZ2qLyBn4ZRnBKfU7LXIe2gG5wxl9QiGg5fTrPr59a8AAP7khtsBAMXKKVJwTUP9s8Upmxk0DwsyL+i062uwFVR0sljhEFXwGoEL5OsGqpH+Lhrq4Vra0yIPRozbIXsXKkirvKxSju6ego4lRht0gZrSidT1QTkMqcqGC3+vaGazelwmlM3ycidId6KMZsbrRRketHbmtrZSckKEdcetqDoMWghEYwleUDvjsoaJBOqOBLPvIhoXkDOJOstsH9DCGM2M9zIZRpl4zJD3xvWNlmHDqzPq83IVKHeps7pfilF1bZ82vCvlEIgjy+GJFSY3EO/Mly/QwPQ0X64gbqgNj60u3tlT3jNX9rltOzxU5Lxz9r10IuQrRGaPpqE/DacFvnuZfiDZnyNjDIvT7kQuaLNOGh4aFlS5i1V6gaRVI1BnYbMsVjhUQgra1HdUAoJ3JcpVOpWrmAzCciV4apLK1dlzmUPkkXHJEgCNeTm3nzmprhSkhmw1bpycQbN0SOEMfxZ4XnK+/HduOV4qZphtx/b5FOKJpnRx2++8Jo6VkxdABkSCPDh+Ds6A/iWDzr7yRYOZBvoXSVWc+gBITkiTKLZGGTMG0ZSj7AtMzljCtS1gbBYhMcMImjPpUe37uOoK6x1jfixEU+LW+TGkgmYYYHW2ZrReOMOpGBJvSjTkQpJjjvmWDAarDpxEN0d4xVDcXAATiZ5Vih/dmkEPAjkfAH7jzN14yfq3+UNd71JtM165tz6qLh1cnLHIDGWsuT4DaJ0TlYHhBrFVVi9twofnc8Whr5whU65r8AWHPBGQeXgXKg3lfEQJUsEf1Kh74UDlNd0aCRxNCZNkX6AunqF0MWNgzJOzyJ7s959taI2k68AIhnIlhig0dBxc5nXC/KajGhu/M1QAKg8w36SNJD127mMNcKDYSNHdoePSX+caKa9x+RE6onZv5N4o8+5dTeJoTuwMAKIFkHeogCZAxolKGLJD7Q2UqkcEUx0xH+pyBpKwdY50HDZZZ4AZYU9LXQEtaVefb0nKBFIkCgdY8nOX+Y1xzASKNXt/u3m68AmvQ0ZfndHCx5eMAxJAXBINNCGbC7DyBQvjN9RiyIEeUGdd9M7n9vcqlMMYs90IsdVXkrMS0EHgj1cUcnDtAChLz+k3Cbe+25CfC2MZQW0qN2rEfWro7GYBVjOk+3xJAwkG6Cb2mks9KsMSh+xdUVqPVAfeq8Iru/A2vHxEgq99aRuZR6G4rvUARdoQgdZKJUxPd1Bn1rDIQtuNsMRhWyBYzmOAMa/hA4RK9G4DAKN2F2saIyuA6t5temS85/TwdvrtriWYc0PZUvOtIJrYv2BCsVc7/qqeMyqDsVoODSAMmGPCIxCsfUo+J6E/Nx+6VxSKAcd8M2R6MUWezuapP5rRePeEaBm8q9zKOngpANXwmCQk+8DtAcSlygsd+qp3mcQQYYyXAIjGNXhlMDxrScZdjuREIV+TKG0NRBKmtG2x81TbUJfT/iGDl7x/jjDMK4NoqqBS6aUDYChc5uZa1SHdn8GjZSAacyCakMzD3BqGxQqR0KenI/Qv2L4alegA3kXjMgLrNEiDqMSgO8gxQ4rFOr0cHcGLijZRZ4HEX/YFqh4VpfX6YhzQJbwkgErIUz98qAqHuoTK3chCI7JEapVSGRJnuEWFFWblwYNYrlFCS3IYwu6ur92Bo0gAvVaht7JAfZEmpbQHvLrX0HlLyHPkSvIADYrD042nonZbayM9IbRG0nVw+MIIGEZIbbjIu5M7FPoqVzTS/XDiKIbMc5SceKDhtCEDQHoUgVcS+arA/FFaGd4+/r8BmiG9GGQCqIp7cB+7027dg99AJ8+rsbI9xuShVQAUjlj9SgVWaci55TKdIl0Qw4LbvuwYKrxqJ4oLOWkRjBEXjij7HNx6oFTMkB5TZpTXnJEMKqUCnQAZbsq53d1GBXjhwZANQu59r5MU2T4y4XuisBsUDyG4usMsDym8Ix1RvxthQ3J7EkwTD8rxH5JuDF7rpQrrdcb8aROgPq+sfg47sf1gE56aHrDIAIP7JbSgEFyMEBZoeoSSI+DqpRVq01Xhw0DeO6LtZl0BsIt03V2uXK4i+v26I5AdkAWWLmrU/Rh1V8DY61z2z8mtNm1/A+heBFYeqr0wn5EUzpntCF8rrlyhVX+6I/3vqwSoBgrJYQgXcUXp0Yttulf3IsPwLOkpOe+SYZQK7u5dpxzzHdK68aQn0DtucuJUCuRbBmadXqoZxUC/htiLghQCLG+Nh82IV6Rx5Cq6xyc14jFgeIz5TuDvaMt58oZ2z3pkXfi5S+N58IhCdjUMLCMSRJMgHbCIlzmA2dVQk861iWmDyZkIogrZZtmcCg3XnSAo6+C8plrSGFdpuFdyCPSuaGT7yx4ZaCC3opCiNOC1QTxWvkisKAx6FyssNujvXNE8bYZyVcJRd1IUA+bT7aOZQWfPQBQa0YR+k9Ua8WEOZi3TfF3CCAY1Yai994yhyofgHRNqHC6AyMnrW/zYudege7kx34zzZgFsz76LzElZhENYNKM6eqX1xhcDhmRs0NlTwateAlEd3ocorHcwCp5UUQiUAydgad8xB6qBgRPnFAtA7kUoL8fIbPh3sWXDmkcNDtKQwozuFKtTDfPMSW63eIbRGkktWrRo0aLFNwXMkydet8TtJ4TWSHocuBIXvAw8Ch3Bq0Y3pfFFFbwjxmaCuNpQAHD0fInVB0mVt3fOZrsMk6UTs0oBYTkY7tQ1/T9MSavy/p4PG4hOhUQqmOecAADmxSqqQQSoUJJgMC4xuYVIGU6FmHG6d9Jw4xsBQBLhmC4CuhcAOdfe+9PZtyUwMr6kdaJF+J6Y2/IfqfV6gSrNq4i8FNKGWOSCyrFI7a4hIrAR8OVZDDfoXLGKx3ZO1xkQl/DlYaqO8GVBHCGaKdKiqjrMu/G1TDA8WyA5pKOgFglkbt389t7xifU4iEDQ5ApA3fAU1IDRsIrkISSgI+KIOYgcKFaB6DAIQNaZDd25cjQzy8GYAdzxY6z4nQsR6MjyygDUK+Qp44UCM/bEP6UBMnpOgvkmC16II2DwaI3sag5WWY5QLDE/nYHpEIqJpjVmu4l/f3WHTshyIpb4HXIGROOQdbfyQIW6wzG5KYWw4bZ4RP3kQpz5ivWcHAbNnmLdgJfMhx6p/wAda2BuPR8lg9aATg34Cb0JOQu6QZn1OjBFISQ5q+14AcSixkADOqJJWVnPXHoQdL+imcZ8QwShUcvJK4YcKkn9e3beLlfKJp7Qd51IYzLRVG0+a6jua/JO1oaFkKmyoq82NMoUPUcxZEuaYMnY+Ew1gEqApHs5Cqs9lG9IlL0Q5qebucy/UO6G1QxVTwR9Mzs/pqeDxlPVZShtWNVxhERJWaA8FYhtv5MmlwEvaQyJgjhTxWqYy8mIMv2K1ZBhZzigLnZwf0Uu0p9/5C04+8Fb0Z1q7z1zIpswgccmFxSOdGuoLIjQXg7DvVUMqIKI2y7Uazj1W92o3whNnmAXsa26bqyFWpCGAdEkJKJwAQxvP8T/8cwX8ft/TIk0dU/DcINsT5BILQA5Zag7BsaGFKMRR9lMuHg6oQ2eEsXtFl8zWiPpOhg8qrGQtMmrhsS9WyB4xb2R5IjJviaUaQgD3kE73StvfBh/84GXIj0JhWO9wm0j60PmQO+SQnJCO9X09UCWlphLoNqyrvDDBKOHMpz5O+cBAA/vZshXE8vvsYvavEI01YimHJVNJdapRvXSHPklWhkGDzIvJLkkXJcB+arwddOSQ8uv2c78ouJSz50go45sgV0d+FRGWF4MCxsjcuIReO5HTf3lOAIA8B3f9RV84nO3YfAVsaSa29kD5Mwuql1ueWNB4FILBmbYcvYJB6a7sScsR1MFUQjiMdiFT1TURlfEFgjGsf97HO4nXC0tTYZNvgHUfXqglS9RONMZpk11cxdCdYT5OguGtiMmB1E824ZaYuBSoGcVxMJATrQ/EdZZSirVF+nvnSsV4uOcMisHdPPp6cST1L36OiMitRuLVd+AKeK0eH6aDVdl+0BmDeXFKcrsbL7n2EpLCEserwYUjhY5Q2QJtiozkHOGeBSeueobGrPH1Ag5B+o6ojhZI7nAhVC86nFEGYvClkrRgoquVr3AU6LipUDvooKwhWpVRgWr02NrNM0ZZlsMs50wCSnzDIhHxocPo9wgnmif7Ta6hUj3coYwd23BVlHqwFGx/J+eDXtqm5klCuYNIlEZZPskIeLT30sNI7jnmU3OMNQ9m6LvwlM9OlTwMhjTOgJGt4ggSFpQP6iENQx7G/LO4flihjPMTgOdK4CoEjuOGOS4gMjJuspXUyoG3CCCuxD28KzyfaMSht5F4O1f+FkAQDLW6BlKKnGHIFbRekA8OXqHMrcHBPveGZiXNHEZi71LjtPEkJzQhdmRhooYprsufm/D/DysAS4jVHe0LzacXeE+U43GBvBDN34OP7/+FRx+P03Ci/MVfO7hG6BS4Q9CzADpAffzhkpLPUPEbaD1JD3DaI2k66BzJUeiY4xulpALOt0BlO1UrNCJ2HsQmPU0NIwmR/TND8k1cNfhi3HmgrKE3JD9xXSYqNHEnWSZV5o+/b4Yo1t6iNbJIwAE3tJDX94FQCTW5ETRImx5UeUwgoppM0v33YYtMJcJuD0F1SmDtFlVnnszocK4nb0aYuFIBAy8UIimxmewZUfKkkvJ+Ki7EtMdSVowjjuSW/I1C0amKG2WjPWYKMvnUVkwQP/m7I2ITihDy22M5YbdLFLLuVpnNjMJvkhluUqp7lwF/RpuOUNNpensUNNG1eCFFCv2dG03HGi61p1YWcpQ9YCy21Al1oEUbYbUD6PbYuj1EuLAZnUp8jYZBi8vAEv8VWnoT9cO3+46kE8db0jFHZv5GE7Oi03aOJMTW8vNGKhORJIDtqJ6vkqGIy+DFzHfoLT2pjfURKRF5Z7PFeql+lxhE5QLg+MXME9kXnnAIDrQXom5GhgwzVCt1xA5vS9WMVR9A14wbywyZfuEhXfFS7aU9eeMcZhQZNQwYLER1MJdaY9mXUKA/l51ueen1QlDNA+ZZSe3xVhsk/G2er9NuhhwLDbIcHBjL19nWJySKFbte4oN0v3lUjMqpkLNvUvaa3uVqynKgYDMnWUKgDEkx7U3gEiHikEnDKKg6ya3ZCh7HInVbkoPgdLWP3MK1fm6TYiYksEE0PyRi9AmsJA96AwaOaeitKQ6T5dVfTLyxaPcJ0ZAk9E2u4E6fXqGITtA8D6DjFOZG4jSeC/iYkNAxQzS3qfqMi/54Woepsfa6nKFPnYZhf7dzQAhw/wHyFPJa4PFuvDeOYzo2bxmlz2cqqQhkZLT30Upwvgw5PF1a4fqKnA76A4L6tCzx2vATCx5iw2jtrmxWA4MMG0Nj29VtEbS40BHlDHWuVL5E0jvkkK2z6GTUCG+zmgxapbMcH96D1A3J8dANC19dhFA35GL4KVgihbN2TZHvkaroShMo0xByFBRNg0acJuGoOrY1vjgJaWPJyfGZ2xNbmCIDqU/CeqIjCJeGR9ai04M+ucLCu3YVOZqJcF8O/Z6LQClC8ej0l8jphVEHmN0c6gVBxbqJrmNsZYhow6w/1W0CLl2Jh/PICrraXE6KBG59X1tOliNGwNoGU7qumuo2KptJ6vJGCzWqAHxRINXGp2rBvMta3z0GaIJ/X7/UWqEnCvAGH+Snp3pQEs6pTdDr0YQkXcBsjai544xP868B8URRMUibAiGA/VQA4p5o8h5mly/uH7WMTA9Q/8/vUFQO4tg3MQnVLTU1cCSuYBcGBhBZFj3Hgwncr3zzE13OMoVoFxzlUKtZMEkXvLG8JLCo06fh8KSDIOH4TfxeEIlUKannddIQY4FolEUQjoLEkSt+mRk+PsX3BvQABn//UeM92JOdwXyDQA6eEzc2HDGAZ3u6f9L+5nLTmPaIF/ltp0GvQtFSHMfRaj2GDr79DngDhfSym5Y4vYpK11gEyC8dywOc955BXktUKfkoilWOERpQgo+o3Iicl5DuBiNJvLw4lTs25mv24NEN0gAaEk6Tc6bnJ2NrOxIMAzlnLLDvPaWDtQAR2zmFXm65EwhX7dG9BrDyhc5sgON5Nh6q7VBPUhRde2h6IBK6XBFBHWADGauSD7CEa5lbrDYYCh4CFWqiAjP4XDBwSuDOlsmlDvpD4C8VGJOkgdOOLVY4dZQAma25qEWjEjsjUK5oqT30ixarCK6t8/MS4G6Y3xolNUMv3X/K/HG77gPpZ0k48t93PqCS3hI7qD/ZenHGmtkNUZTBvUMeZKMMTBPNtz2ZLPjnmVojaTrQMecwjeasoOccZPtVcguTlAPUzC7WxZDgXw1aBsxy10RReAW9S5WKFYk5lvMT1ReUWaLV4NWZPAUa40FRTHLFwkTs+prmESD2ywSsS+Rr5JAXc1DdojTeXEuVsMZ0gPSHALIg5WvA3LB0L9o3ddXKzBloGMBafkIvFQoVmLK7LAGQnoiEI/gRTYp9V569WDA8gNAG5hTIy8GdAKPbQaJzEmQruqG5+N12BicsRO5WlDehW5Q9clwdDyDuqvBauKGzHfthjZj6FwOWjwiV8h3YsRj5TdirjiiqYbINdL9sGOzSsFENvW9ND402KxQXw7JwOxepM/KyQD9HJiftot2osEr4mDImV3YE+KgRLPAX3EeNpclRF4VG8YcUjuNNIhPBDr72nsL0iPa7HIrdBhNiD9EqdIhu41pSpV3YZ5yhaFcVejuUppcWUpUCxLu84KkMzIsDAcOv5uMCJnUEPd3ceqzyvfD+dcL6NUK8qqdAN0aumLoXuJk4IDuaaRBctsYi0fIhSHHHAomePlq4NR9NXhpMLNGnxGkZWN4mEtOHsLXEeyQh4XXwRhXwr2rENql4qcBg4fnGDwE8Cq4K2TM0bvEvUYPYPksUdhc6i5Ja8iF/R3QmNfS8mOc4OqxhpwHeYaqy9G5WkEnAqNbnHIqja2qy7ynynnSmsrgRpAHI73XKlsfG2jpsjSDZ1iUWBJkBIhb1b1sPY0Jt/XbGFLrfeSKI1+hYsaGuUKxNXm4nHzC1EClHGJS+wxTpqlgsJbch/NEYRDNAleLVwYitx5MH57VNpQbardVHWvYNuQn4qlG1RXeQOcVUPWIt9QURZWTyov2FquCvMm18R5tw0ESETELdRyN9eTW7nAoUJVdvOf0G/Clq1vUhILjdZtfAWcGV758o3//TW8lq7HEK31a8VSUJWmLtz0htEbSdTDbTdA7qREfFaiHsT+VR10B1U186ihALmUnNgjAL+JyEU4z0bQCrw0mZ2JvJDkxOuc1MNzqujQOJry2YTgFlCvWZT7l0AWD2LPeEUuc1RHzwmtln3lxOrcQZHtA71Ll03x1JFD3BHhpIOeWBMuAxSYt4F3rRWGa0m5VIrzhM76Ro+p2llKZ46lBemy8fpQR5NofPFpC2t8sV2LkGzKo6M5JX6rKRFjArHFQZ0D9YnK11IsInQdiwKfRMxRrGnLBPNm0HAAmAlQ3GBG6ZFhsAifPpx9cuT9BMtY4er70m67MAcNI8oBbokY0sWrjTjF9WkPmgiqqN1KZ00NSHG6G83gdvH66Y2BqBkTGe/lYzWB6ipTcjRtDBji9wL9+2f8PAPALn3oLOv0Fji8PIMfUMXxCnBvD4Mto6IhhsR5CZMmYlIzLPvcE5e4VhXS/gJgWmN5K6oDluoKJNTq21sd0v4v0skR80iiPMSMPUtUDevfSmGCKuG8nt4mwqa/n4Eex74P1UxMc1EPMtznKTeuZyAXkjCN/cID0OIwZPmPeOCZxUo6qG8jW3asGk9MUxnXGt5FA0Q9GU7pvvasNjhezJWZqV90ewPhmAXaDwOAR+mJ6UELHVNQ0OrZaW1OS0hjfkmJ6JnjPWM0g7O/pyBL46zDnywxeQiI9tp6qgwL5Zorj5wUBQ5gIySgYZWUfAGeIZqRiD4TwooMzVOuU+edbnApEbndIiKb2gNEo9WEE9clsx+ky2TBkTyA9sHOyK1F1aSwV3lMrke2X4HXQSZpvCSw2wjxlCkBhfGIHQIYgq+F3F5kDvDRID4OcgSgU6o4kIrx23kcigcdjy6nMSXJkuhN7KQZRGBRDjt6VGvGxUxalYuPO4xuNufdu+/I6qQRMjKorGhwkMqK8arsAeMHw1x9/EYTVYEpK4H1/893IhjmYs2md8efEd2OgCsGBpxcGT94T1HqSnhCeKU5+ixYtWrRo0aLFNxVaT9J1oCWpTkcTDi259/7UKUO+RX8prXx9ZbOpvAfIeRU0vLDcYiulYoyDcB2DzTZxRFbLL4jHDaVnGcQK5dyFlQxUqqFT54bmPlTX9ErpmE570vJCehcrJMeFTw3nXEHOGbnVbdgs34gx3eW2dIIrHKuQXcmRXWWY7dKzz7aJQ+CKgEYz5VOyK6smXHci6seOoBIEIBmBOmGe+MgU0LtQos64F43jigjNvALqA/q9+EiAVXB1NKmcgKEyHS6skF1lKNYtbyaznhYBmA6ge1YZ/DkSKw8Qd8udAM2JJbHnDDNbLLezRyd+VyZBpRxVRp4xFx41DGDOW+jeqSJCqC9XcSwpuy8ynugsCsq0UjcvUNuTNOMGg16O7+9cBQB89HlfRMQU9tb7+PjfPJ++l5P3YLHBMd0NaeW6kZWnJTDfJB6PI5tmhwCrqbyJU45P9hhUKnAQU0wn3peQs+Uxy2ubAaWBwXlLbO4xjG5jqE4X6A5pkA7iCof1wPfBaJKBLQQJAh5EdoxSmJcyreg6UdmsI5ft1mWIJwbdy6VPGmC1hoq74DX3vJo6YWCK+dN8Z59UkMc3Mh/GjWY2g4oHZXwwkthw9ciYJkV9XmkYGc6MYl6js1djYYUbmaL56NYARyav+o1sVxA3bfBI6b2yTFEIV9ni2LCenyZHqM4YDABhCdD0DhmSsfZjT8cMVUYhaRdGTg+Yr0PovEt1F6inoX+dYCmvgMVGGHuiMFRfL7acp5gUw1UEFFb8tuoxcBV5GQSnMq4FcdsA8mZGc2qnC+3XKd3PhdG0ZFb2gyMeu5o/BqJQ6J3XXvVb7hlkVxYoV6xyd8Ih5gq9y8pntPJKo3NFg+fKr7FGCqhuhHJoJTdyDV4ZqI7wUhR1RgRzlQLlKnUOWy/AL6UNThIgFINhxnso81MKbCKx4CkS56HUQS4EeIaJ20bjSafwt9ltTwitkXQdMA1MzgjUaYaqs1wxu+piSVma1wCbh4wHV0YALChu1xlDvmWgNipgZsMnOYdOtQ+nRFMiOsajUEZjtsO95o/nSHQ0IIyfLzqypF8OGOuyT0YGdcl8dhIAzLclIFioLWUAOauhJYNObCruXGP1K5Qqy+11vKS8WlYqZHtWbyhOEY8V0n3aKF02no6FL6NhOPGrxkYEo89uFOkhPV//0Rys0ug/skB+yqas7wha+DkwvN9ygioqw9LM3HFE9yYnQMcGImdgtTVgNyrEexHkiQ15dDWO7mDQawXiDi3a+UM9dK44hV/6gekuQ74mfTtde5qZgEzTZ07ZHAjZfD50CEe0DqES930ZK6hzNGjEgmEaZ3jJoz/tn4/nHOL0HNqSqstUo1xjADc+bT7bt4RUF4ZZD5w0h3yFo856EIXB3BFeYxpHMqELXTam4yEBVF9Oy2DcABS+YgrgJ5ETC0cex0DFIfq21l8p0LnIkR0EvgzVwTI4egH3RqYjwbrxqSUZgHWWwNj4RjKiTTKWzOv/ME3jJ7Vp4LwymO5I6DhoUfHaIDvUqLMg1eGkBJzWliioVhkVRLab9byCiUjfyXH34hlphs02raGxoPBQsW58WDU+IX5MsSYx37Z17sYa8aT247PqUxhcR2FwJCMyjnhpfHag6x+X7VVn8CVr6qHVvhpJKjCtGjISNovSGUnxlPpGS+YNRbduMWU8IZqXdF2dsKAsPaB0TK/bJQFZAd1D4+tYRnP6odl2CGOJnNYixy2ScwrH6Zhhdtoq4++TdVusRKGtowqsUlQIHJbvVGt6H45nqQ2M4Ci2Ml/o2ulc5VbvK5rSWF7K3LRhNSPgeUp6GiGqAj/UZ+HOG9mdcw7csMCwv8D0giX9Wa0rH17XCKLyTzcMYJ5kuMy0RtITQmskXQfjmxikBHRkBc7sRHDcIxU3+UZYqi3lyh80s1/iKVD3GOoVBtaYVWLGoRObZj6hjbTuMM8nKVYtaVUB9cAxvA1YwcFz53kh3lKzDlYzO8p5NfIeA1MCvQthB53ekFLmkz3ZpldzMK2hOpHPAirXE9Qphyg1lD19zjYZ6lQgObQLGAAYEs/zp7oS6J8zVKvMnpKTEyKECntvMStRrmdEPl51aeYADHw9JsD2ZRmexS140SQIJBZDBjmjDDzH05gbSV4cX2vJQA9qnN4+RieiBfmhC10ADFe/p8btt10AAHzhSzei/2WxpC+k7CZsGppJ2mq5+Gw94zhOdixYTkixXUNMrK6PXWTV2S6SI3t/A5g5kB4If2+mgGrcRdwwjvWwxnNvuoKebftnHrwR8fnY16CibD9LAl9xBgpDaYisnW87o8hA9ipIafWPBgrpvkAy0ksFbn1JD5sxtTgF1D0DVjHEj9KAr/sGyBSUzdjqfz7Bqc8UEIWishgA5rsJ5qc4vT/HF7MZR87jVWd2E9MhCaLsSy8ZkVpNrsGjJW2idrNmtYa6WXpNJyAIoKaHBVRKfVr1BJKjyvNXVCoxO51AJQz9R+zmXNaAEIhGJdZPbLkUxrDYTn1xVB0DfEa8OK/RFZGHqs6CgSoXDNDBYKgATG42towQtT0eK4xvooQHlwkYTQ2OnytCXTObvRfNDFY/69zO1vuUhwSN2gq1urmcHVRYnIow3WE+w04uDGl7rQbl1HydnkXmwfAEXOYY/T8vDaKZQjyqwvjgDDoWyI409Mh5qijTzGlTOW0oAN6wieYSUAbT08LrDcmFBKuDR48pA8YCnwwAitUE0zMRFqfCuDSS+sd5FYs1IN9UEAsO1Q/rHEUFGBk+oOQJdfMCurKG4oWEPIZFw8gcMxRXUxxOYsSNAzFvrEsAAzt+pqykFs80WiPpOhAFueZh6ETp9HJ0RAUhmxNF5jbttjGR3GLpT7YVnbLEWHriL6/IFe2VtOc06ct+CMG5WlNUpTuEBHjBfMaN4UDvIpF159v07073h9XN0zWl+8o8MA3zNQpZ+WK5Rezd4s67VCeMwjuGL4kjurRfaoSBicRS2KJ7hUTmihXmjRSVREhOFOITW808llicijDbCv0Xj4D0hBR6Q0o86aHMduiDxSY9G8kMhPilnJJx5t5PPGJLFevTA454xHF4ZhsnNmwwmNJ7fc7NV/HqjQcAAA+sn4KOe2Beb8mAKRLlY3bFNDKEdVTTc9T0bEkqYivGwofbVGbQP0uaOs1MGVLcDmRhw0k6wpNFNQcQ4+GDG4Az1HhTM9z6PedwvKDd+viTm5QRt6r8sXkuLNl2M8fpjREA4PLBEKoUyO174Tn348ydttMjg3LIUA5CJpmRpGXUVKFX/RqsZoguWXK39bAxpcFtXERFzKbIh7AVDB0eHAl9vsUxfLhGNKn9WFAxR9UnxenIp+RHKLshrDR4tIQoKdy1ej+9aNUROL5NYniWCqoCwOhWhtWvRBC5zYSy2k/R1CxpgjFjUGfS10krBhxVD35DF1ZklJeAcGGXoRVONUDvgk2wWGiMb4kx37LPy2jOp0eB3J2vCFQ9Olh5QdcVZgvRhrEhymVvJEBGVXqsvJGkIoaqy3wotlhJkO0r9C/opQw4MArfOeK9UwKnIst0HRWU1uhcdacSQ55lY6BTZ6gx8FKh90i1bNyoYGipRKAaRNARQ7QI61U5lEhOjPdGReMKszOpn8tJw1hznr98XaJYsYWRG+XhjAjeWZUY8NUSdSq9i4fFGsowRBPhD48q45DrCorb5+1rJAd2rPoCvjQvgJDdKHLypLokAt14H08/zFMQbmsVt58IWiPperChgOTEYOWBBcTcGg5phNnplFKTpyFsNd2WfmFPjinTqhnymNwElKdqz/0Agk6SU1nmiua1zsJG5U5yOgKyK/QD6ZFBMQAKG1qJj+FFKd0iMN8hDkw8D0VTZW5Q9sMi6sJ30irzArQQiUqj6knPX6kT2OKTWCq8y5Xxi6NRHPmpFIv1kP0iCmC+yWxf0WdVh8JY+So9dDzVXj4h26dGDM8WENMKqiO9OKacUwV055mAITHBptK505zSERp6TnYzcyfpHOherdHZp9MxAJJ42BS48pEz+N84iRLJ2mbm1G7TYFb1mS1lWdUZ9cuSACOwZDCLHIhGzBsVrGZIjwySo9pLKFAxZBGMUA5wO/6aKuAwQP8cA3uo4z/7UnUDXvuyLwAATl43xkNH63je8ASS0/j88t4mbt04wOGii4sXKS7R/VIMWcBLB7jis3XGljhJKiZFcadt5MQIgYbH5EQS/8hu6kwDVU9CzGowKyORnigwI1B1GapGVmF6pHxGk4ojv8m6EIuOObK9AtkeaRgBwHRNoupQGjxA6ttVl/4ejalxzMTQkcTkjAwcMgGUXY6O5QxlR4oMMgC15dFJO06BYLAaTuPX9ZWWlhLDsJSF5kQHXSHb49skprdony0lF6Rq3dlTqLz2D4XESRDUhnatIn05CO1WRfAQAhSWF5VBORC+LA+sF9HpR/GKxq8GvPSDlgzZfo1EMKhE+uuyQ434pPLjturGEIXxoXkdc6hMgpfap/vTIkntb3JdirUEcDpJhwXkTGG+HXk5gdmWRDzR6F4pfdmTYj3BYi0cwqIZg8w1tGCYnrZG7YCkBXgNVD3riTuhz5woJADoXILlHOmepRDMEAor20NJcshgjntBALin7TsnaQAAUH0NWL4hV+EgJspw6IJeHgNPJ0gnqQ23PZNojaQWLVq0aNHimwHGAHiynqTWSHoiaI2ka8BZ2vGlBSJukBzUwPEMpSV25isScn9KOjrWqjeRgBQpasupYblBNLOZPFbQj8WkpyMmtR+nugQwNVCOUyBoDJsZwJ23orQenCp4Y2oAJQeYzeSJLxssbPFH5mrMGaDYNChXDTbOWR2VsULJpPcAMMuZUjLE+Gc9YH4LYHjVKD8AyAnQOdTILXm27AOIgNKSAaoNifGZGiqtg4R/h7qIz4DYilVGU4XJjRFGVmRQ9AHoCvwEEFYbRxUFqoxjtgss1i13qaA6UOkhueH6VzV4paATgdpzTjiYFYzT9iRbJwDLA7ky7wOLxHp/rNdH5oDmNcw48BF4Ccix8YdEwxlKqhcMSwcCy4EyBfg8LF3aKv36ulgKwBhABSh7ws93KphZhLo20MKS3AVgRA3mCO41UNkMRecJSY40ookCWxGobLHVWgA8nWFQ0mC4ME4w2QeuVmF6d8wIB0ccJ3NA25qA0RXrEYiCF0JpYDIE+pOwkJopA6KgF2I0hTl5CTCnl2MzMI31JKkSON4C4g4nXg6A5GSO7qUSVT8oevOSMsuKNetiG9dQpQJTNbSgXzzZYkiFoQzBmNre/8IUVU9CLiyJWTCIOIbJDeraduDBAiufLlH2pffK9k4Y0mOFwnpxTARUmYYoNGobWs6Oc4AzmLxEdJFedHdSYXxLhnoYSO9RQV5K7bI0ZzYklgInp+izxaACJgawIqJKAWZqwI9KzHfpmecdRs4IDQjntQSDmAZvRdUBdIPnBgD8xKCQxEtijX8QALqWl9W9RFmCOhFYnKLfqyRDNi3AS42EkxtFzjVYqWEaJGlxUMEsatQ2dGZKBiM4eKm8MCxAfC2qe0jjreoLlMMahU2AGCgDw2tMhsaHCkk/zmDW156rWGQVClF7r3B5Gp5r5LzXsJpxFeALzFaKUS24hjtHPsAQjxREReOld0lBFBr5qkBlyfELmwXLLQ9MjoGa0XxVVsGf7cOG2EGEbfsO2QkgrTAplxwmpzH3dHppsizDOdyPi+bsk7qPhsKpbP3xL2wBAGCm9b19FR5++GHceuut3+hmtGjRokWLbyKcP38eZ86ceVrufXh4iE996lNPyb1uvfVW3HbbbU/Jvb7V0RpJ18DJyQlWV1fx6KOPYjgcfqOb802H8XiMG264AefPn8dgMPhGN+ebDm3/ff1o++7Joe2/rw/GGEwmE+zu7oLzVqP5WwltuO0acIN8OBy2C8WTwGAwaPvvSaDtv68fbd89ObT998TRHqi/NdGavC1atGjRokWLFtdAayS1aNGiRYsWLVpcA62RdA0kSYJ3vetdSJJnVCXsWwZt/z05tP339aPtuyeHtv9atFhGS9xu0aJFixYtWrS4BlpPUosWLVq0aNGixTXQGkktWrRo0aJFixbXQGsktWjRokWLFi1aXAOtkdSiRYsWLVq0aHENfFMaSe9+97vBGFv6s7297f/dGIN3v/vd2N3dRZZleM1rXoMvfOELS/coigI/9VM/hY2NDXS7Xbz5zW/GhQsXlq45Pj7G29/+dgyHQwyHQ7z97W/HycnJ0jWPPvoofvAHfxDdbhcbGxv46Z/+aZRluXTNvffei1e/+tXIsgynT5/Gv/yX//IbVon58frugx/8IN74xjdiY2MDjDF89rOf/ap7PFv7Drh+/1VVhX/yT/4J7rjjDnS7Xezu7uIf/IN/gEuXLi3do+2///b4e/e7340XvOAF6Ha7WF1dxetf/3p84hOfWLrHs7X/Hq/vmviH//AfgjGGX/3VX136/Nnady1afN0w34R417veZV784heby5cv+z97e3v+33/pl37J9Pt98wd/8Afm3nvvNT/yIz9idnZ2zHg89tf8o3/0j8zp06fNnXfeaT796U+b1772tealL32pqevaX/N93/d95vbbbzcf+9jHzMc+9jFz++23mze96U3+3+u6Nrfffrt57Wtfaz796U+bO++80+zu7pp3vOMd/prRaGS2trbM2972NnPvvfeaP/iDPzD9ft/8m3/zb57mXro2Hq/vPvCBD5h/8S/+hXnf+95nAJjPfOYzX3WPZ2vfGXP9/js5OTGvf/3rze///u+b+++/39x9993mFa94hXnZy162dI+2//7b4+8//sf/aO68807z0EMPmfvuu8/8xE/8hBkMBkvXPFv77/H6zuEP//APzUtf+lKzu7tr3vOe9yz927O171q0+HrxTWskvfSlL73mv2mtzfb2tvmlX/ol/1me52Y4HJrf+I3fMMbQZhZFkfm93/s9f83FixcN59z8+Z//uTHGmC9+8YsGgPn4xz/ur7n77rsNAHP//fcbY4z5sz/7M8M5NxcvXvTX/O7v/q5JksSMRiNjjDHvfe97zXA4NHme+2t+8Rd/0ezu7hqt9ZPsiSeO6/VdE2fPnr2mkfRs7jtjvvb+c/jkJz9pAJhHHnnEGNP23xPtv9FoZACYu+66yxjz7O6/r6XvLly4YE6fPm3uu+8+c9NNNy0ZSc/mvmvR4uvFN2W4DQAeeOAB7O7u4pZbbsHb3vY2PPzwwwCAs2fP4sqVK3jDG97gr02SBK9+9avxsY99DABwzz33oKqqpWt2d3dx++23+2vuvvtuDIdDvOIVr/DXvPKVr8RwOFy65vbbb8fu7q6/5o1vfCOKosA999zjr3n1q1+9JM72xje+EZcuXcK5c+ee4l752vDf6ruvBc/2vgOeWP+NRiMwxrCysgKg7T/ga++/sizx7//9v8dwOMRLX/pSAG3/Xa/vtNZ4+9vfjp/7uZ/Di1/84q/67rO971q0+HrwTWkkveIVr8AHPvABfOhDH8L73vc+XLlyBa961atweHiIK1euAAC2traWvrO1teX/7cqVK4jjGKurq9e9ZnNz86t+e3Nzc+max/7O6uoq4ji+7jXu7+6aZxLX67uvBc/mvgOeWP/leY5/+k//Kf7+3//7vlho23+P339/8id/gl6vhzRN8Z73vAd33nknNjY2fLufrf33eH33y7/8y5BS4qd/+qev+f1nc9+1aPH1Qn6jG/D14Pu///v9/99xxx34zu/8Ttx666347d/+bbzyla8EADDGlr5jjPmqzx6Lx15zreufimuMJS8+XnueDlyv737mZ37m677vs6HvgK+9/6qqwtve9jZorfHe9773ce/b9l/ov9e+9rX47Gc/i4ODA7zvfe/DW9/6VnziE5+45ubt8Gzov+v13atf/Wr8u3/37/DpT3/6Cbft2dB3LVp8vfim9CQ9Ft1uF3fccQceeOABn+3x2NPK3t6eP8lsb2+jLEscHx9f95qrV69+1W/t7+8vXfPY3zk+PkZVVde9Zm9vD8BXe7u+EWj23deCtu+Wca3+q6oKb33rW3H27Fnceeed3osEtP33WFyr/7rdLm677Ta88pWvxG/+5m9CSonf/M3fBND2XxPNvvvLv/xL7O3t4cYbb4SUElJKPPLII3jnO9+Jm2++GUDbdy1afD34ljCSiqLAl770Jezs7OCWW27B9vY27rzzTv/vZVniox/9KF71qlcBAF72spchiqKlay5fvoz77rvPX/Od3/mdGI1G+OQnP+mv+cQnPoHRaLR0zX333YfLly/7az784Q8jSRK87GUv89f8xV/8xVJ67Ic//GHs7u76xesbiWbffS1o+24Zj+0/ZyA98MADuOuuu7C+vr50fdt/y/haxp8xBkVRAGj7r4lm37397W/H5z//eXz2s5/1f3Z3d/FzP/dz+NCHPgSg7bsWLb4uPJMs8acK73znO81HPvIR8/DDD5uPf/zj5k1vepPp9/vm3LlzxhiSABgOh+aDH/yguffee82P/uiPXlMC4MyZM+auu+4yn/70p83rXve6a6bCvuQlLzF33323ufvuu80dd9xxzVTY7/3e7zWf/vSnzV133WXOnDmzlAp7cnJitra2zI/+6I+ae++913zwgx80g8HgG5YK+3h9d3h4aD7zmc+YP/3TPzUAzO/93u+Zz3zmM+by5cv+Hs/WvjPm+v1XVZV585vfbM6cOWM++9nPLqVqF0Xh79H237X7bzqdmp//+Z83d999tzl37py55557zE/8xE+YJEnMfffd5+/xbO2/x5u7j8Vjs9uMefb2XYsWXy++KY0kp3sURZHZ3d01P/RDP2S+8IUv+H/XWpt3vetdZnt72yRJYr7ne77H3HvvvUv3WCwW5h3veIdZW1szWZaZN73pTebRRx9duubw8ND82I/9mOn3+6bf75sf+7EfM8fHx0vXPPLII+YHfuAHTJZlZm1tzbzjHe9YSns1xpjPf/7z5ru/+7tNkiRme3vbvPvd7/6GpcE+Xt+9//3vNwC+6s+73vUuf82zte+MuX7/OdmEa/35r//1v/p7tP137f5bLBbmLW95i9nd3TVxHJudnR3z5je/2Xzyk59cuseztf8eb+4+Ftcykp6tfdeixdcLZkwrgdqiRYsWLVq0aPFYfEtwklq0aNGiRYsWLZ5qtEZSixYtWrRo0aLFNdAaSS1atGjRokWLFtdAayS1aNGiRYsWLVpcA62R1KJFixYtWrRocQ20RlKLFi1atGjRosU10BpJLVq0aNGiRYsW10BrJLVo0aJFixYtWlwDrZHUokWLFi1atGhxDbRGUosWLVq0aNGixTXQGkktWrS4Lvb397G9vY1//a//tf/sE5/4BOI4xoc//OFvYMtatGjR4ulFW7utRYsWj4s/+7M/w9/9u38XH/vYx/CCF7wA3/7t344f+IEfwK/+6q9+o5vWokWLFk8bWiOpRYsWXxN+8id/EnfddRde/vKX43Of+xz+5m/+BmmafqOb1aJFixZPG1ojqUWLFl8TFosFbr/9dpw/fx6f+tSn8JKXvOQb3aQWLVq0eFrRcpJatGjxNeHhhx/GpUuXoLXGI4888o1uTosWLVo87Wg9SS1atHhclGWJv/23/za+7du+DS94wQvwb//tv8W9996Lra2tb3TTWrRo0eJpQ2sktWjR4nHxcz/3c/jP//k/43Of+xx6vR5e+9rXot/v40/+5E++0U1r0aJFi6cNbbitRYsW18VHPvIR/Oqv/ip+53d+B4PBAJxz/M7v/A7+6q/+Cr/+67/+jW5eixYtWjxtaD1JLVq0aNGiRYsW10DrSWrRokWLFi1atLgGWiOpRYsWLVq0aNHiGmiNpBYtWrRo0aJFi2ugNZJatGjRokWLFi2ugdZIatGiRYsWLVq0uAZaI6lFixYtWrRo0eIaaI2kFi1atGjRokWLa6A1klq0aNGiRYsWLa6B1khq0aJFixYtWrS4BlojqUWLFi1atGjR4hpojaQWLVq0aNGiRYtroDWSWrRo0aJFixYtroH/PxT6AaB97//aAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.green[:312, :312].plot(vmax=2000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/netcdf_searchable_catalog.ipynb b/examples/netcdf_searchable_catalog.ipynb
new file mode 100644
index 0000000..96de743
--- /dev/null
+++ b/examples/netcdf_searchable_catalog.ipynb
@@ -0,0 +1,1474 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load NetCDF file from a seachable STAC catalog\n",
+    "\n",
+    "This notebook shows an example how to load a NetCDF file from a searchable STAC catalog.\n",
+    "\n",
+    "A searchable catalog implements the [STAC API - Item Search](https://github.com/radiantearth/stac-api-spec/tree/release/v1.0.0/item-search) conformance class, which provides the ability to search for STAC Item objects across collections.\n",
+    "\n",
+    "### Setup\n",
+    "In order to run this notebook you need to install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac). You may install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) directly from the git repository by cloning the repository, directing into `xcube-stac`, and following the steps below:\n",
+    "\n",
+    "```bash\n",
+    "conda env create -f environment.yml\n",
+    "conda activate xcube-stac\n",
+    "pip install .\n",
+    "```\n",
+    "\n",
+    "Note that [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) is a plugin of [`xcube`](https://xcube.readthedocs.io/en/latest/), where `xcube` is included in the `environment.yml`.  \n",
+    "\n",
+    "Now, we first import everything we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3.64 s, sys: 407 ms, total: 4.05 s\n",
+      "Wall time: 2.25 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "from xcube.core.store import new_data_store, get_data_store_params_schema\n",
+    "import itertools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we get the store parameters needed to initialize a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 291 ms, sys: 35.9 ms, total: 327 ms\n",
+      "Wall time: 325 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "description": "Describes the parameters of the xcube data store 'stac'.",
+       "properties": {
+        "storage_options_s3": {
+         "additionalProperties": true,
+         "properties": {
+          "anon": {
+           "title": "Whether to anonymously connect to AWS S3.",
+           "type": "boolean"
+          },
+          "asynchronous": {
+           "type": "boolean"
+          },
+          "client_kwargs": {
+           "additionalProperties": true,
+           "description": "Parameters for the botocore client.",
+           "properties": {
+            "endpoint_url": {
+             "format": "uri",
+             "minLength": 1,
+             "title": "Alternative endpoint URL.",
+             "type": "string"
+            },
+            "profile_name": {
+             "description": "Section name with within ~/.aws/config file, which provides AWS configurations and credentials.",
+             "minLength": 1,
+             "title": "Name of the AWS configuration profile",
+             "type": "string"
+            },
+            "region_name": {
+             "minLength": 1,
+             "title": "AWS storage region name",
+             "type": "string"
+            }
+           },
+           "type": "object"
+          },
+          "key": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_ACCESS_KEY_ID.",
+           "minLength": 1,
+           "title": "AWS access key identifier.",
+           "type": "string"
+          },
+          "listings_expiry_time": {
+           "type": "number"
+          },
+          "max_paths": {
+           "type": "integer"
+          },
+          "requester_pays": {
+           "default": false,
+           "description": "If \"RequesterPays\" buckets are supported.",
+           "type": "boolean"
+          },
+          "s3_additional_kwargs": {
+           "additionalProperties": true,
+           "description": "parameters that are used when calling S3 API methods. Typically used for things like \"ServerSideEncryption\".",
+           "properties": {},
+           "type": "object"
+          },
+          "secret": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_SECRET_ACCESS_KEY.",
+           "minLength": 1,
+           "title": "AWS secret access key.",
+           "type": "string"
+          },
+          "skip_instance_cache": {
+           "type": "boolean"
+          },
+          "token": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_SESSION_TOKEN.",
+           "minLength": 1,
+           "title": "Session token.",
+           "type": "string"
+          },
+          "use_listings_cache": {
+           "type": "boolean"
+          },
+          "use_ssl": {
+           "default": true,
+           "description": "Whether to use SSL in connections to S3; may be faster without, but insecure.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "url": {
+         "title": "URL to STAC catalog",
+         "type": "string"
+        }
+       },
+       "required": [
+        "url"
+       ],
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7fb31973f8c0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "store_params = get_data_store_params_schema(\"stac\")\n",
+    "store_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We determine the url of the STAC catalog of the [DLR's Earth Observation Center (EOC)](https://geoservice.dlr.de/eoc/ogc/stac/v1/)  and initiate a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework) where the `xcube-stac` plugin is recognized by setting the first argument to `\"stac\"` in the `new_data_store` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://geoservice.dlr.de/eoc/ogc/stac/v1\"\n",
+    "store = new_data_store(\"stac\", url=url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data IDs point to a [STAC item's JSON](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md) and are specified by the segment of the URL that follows the catalog's URL. The data IDs can be streamed using the following code where we show the first 10 data IDs as an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 18.9 ms, sys: 360 µs, total: 19.2 ms\n",
+      "Wall time: 1.37 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240711?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_COT/items/S5P_DLR_NRTI_01_040201_L3_COT_20240711?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_CTH/items/S5P_DLR_NRTI_01_040201_L3_CTH_20240711?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_HCHO/items/S5P_DLR_NRTI_01_040201_L3_HCHO_20240711?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_O3/items/S5P_DLR_NRTI_01_040201_L3_O3_20240711?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_SO2/items/S5P_DLR_NRTI_01_040201_L3_SO2_20240711?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/ENMAP_HSI_L2A/items/ENMAP01-____L2A-DT0000081510_20240710T140913Z_001_V010402_20240711T071813Z?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/ENMAP_HSI_L0_QL/items/ENMAP01-_____L0-DT0000081510_20240710T140913Z_001_V010402_20240711T004247Z?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240710?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_COT/items/S5P_DLR_NRTI_01_040201_L3_COT_20240710?f=application%2Fgeo%2Bjson']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "data_ids = store.get_data_ids()\n",
+    "list(itertools.islice(data_ids, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next step, we can search for items using search parameters. The following code shows which search parameters are available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 23 µs, sys: 3 µs, total: 26 µs\n",
+      "Wall time: 27.2 µs\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "properties": {
+        "bbox": {
+         "items": [
+          {
+           "type": "number"
+          },
+          {
+           "type": "number"
+          },
+          {
+           "type": "number"
+          },
+          {
+           "type": "number"
+          }
+         ],
+         "title": "Bounding box [x1,y1,x2,y2] in geographical coordinates",
+         "type": "array"
+        },
+        "collections": {
+         "description": "Collection IDs to be included in the search request.",
+         "items": {
+          "minLength": 0,
+          "type": "string"
+         },
+         "title": "Collection IDs",
+         "type": "array",
+         "uniqueItems": true
+        },
+        "time_range": {
+         "description": "Time range given as pair of start and stop dates. Dates must be given using format 'YYYY-MM-DD'. Start and stop are inclusive.",
+         "items": [
+          {
+           "format": "date",
+           "type": [
+            "string",
+            "null"
+           ]
+          },
+          {
+           "format": "date",
+           "type": [
+            "string",
+            "null"
+           ]
+          }
+         ],
+         "title": "Time Range",
+         "type": "array"
+        }
+       },
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7fb319713920>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "search_params = store.get_search_params_schema()\n",
+    "search_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's search for Sentinel-5P TROPOMI L3 Data of radiometric cloud fraction variable data (`S5P_TROPOMI_L3_P1D_CF`) for 18. June and 19. June 2024. Note that [item S5P_DLR_NRTI_01_040201_L3_CF_20240619](https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240619) covers the entire globe. We thus do not assign any `bbox` here. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 141 ms, sys: 11.6 ms, total: 152 ms\n",
+      "Wall time: 6.42 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'data_id': 'collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240620?f=application%2Fgeo%2Bjson',\n",
+       "  'data_type': 'dataset',\n",
+       "  'bbox': [-180.0, -90.0, 180.0, 90.0],\n",
+       "  'time_range': ('2024-06-20T00:00:00Z', None)},\n",
+       " {'data_id': 'collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240619?f=application%2Fgeo%2Bjson',\n",
+       "  'data_type': 'dataset',\n",
+       "  'bbox': [-180.0, -90.0, 180.0, 90.0],\n",
+       "  'time_range': ('2024-06-19T00:00:00Z', None)},\n",
+       " {'data_id': 'collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240618?f=application%2Fgeo%2Bjson',\n",
+       "  'data_type': 'dataset',\n",
+       "  'bbox': [-180.0, -90.0, 180.0, 90.0],\n",
+       "  'time_range': ('2024-06-18T00:00:00Z', None)}]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "descriptors = list(\n",
+    "    store.search_data(\n",
+    "        collections=[\"S5P_TROPOMI_L3_P1D_CF\"],\n",
+    "        time_range=[\"2024-06-18\", \"2024-06-20\"],\n",
+    "    )\n",
+    ")\n",
+    "[d.to_dict() for d in descriptors]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next step, we can open the data for each data ID. The following code shows which parameters are available for opening the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 725 µs, sys: 87 µs, total: 812 µs\n",
+      "Wall time: 816 µs\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "properties": {
+        "asset_names": {
+         "description": "Names of assets which will be included in the data cube.",
+         "items": {
+          "minLength": 0,
+          "type": "string"
+         },
+         "title": "Names of assets",
+         "type": "array",
+         "uniqueItems": true
+        },
+        "open_params_dataset_geotiff": {
+         "additionalProperties": false,
+         "properties": {
+          "overview_level": {
+           "default": null,
+           "description": "GeoTIFF overview level. 0 is the first overview.",
+           "type": [
+            "integer",
+            "null"
+           ]
+          },
+          "tile_size": {
+           "default": [
+            512,
+            512
+           ],
+           "items": [
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            },
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            }
+           ],
+           "type": "array"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_dataset_levels": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_dataset_netcdf": {
+         "additionalProperties": true,
+         "properties": {},
+         "type": "object"
+        },
+        "open_params_dataset_zarr": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_mldataset_geotiff": {
+         "additionalProperties": false,
+         "properties": {
+          "tile_size": {
+           "default": [
+            512,
+            512
+           ],
+           "items": [
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            },
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            }
+           ],
+           "type": "array"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_mldataset_levels": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        }
+       },
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7fb319636a80>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "open_params = store.get_open_data_params_schema()\n",
+    "open_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We select the asset data, as this is the only asset, which links to a netcdf file, when investigating the [item S5P_DLR_NRTI_01_040201_L3_CF_20240619](https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240619)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 156 ms, sys: 31 ms, total: 187 ms\n",
+      "Wall time: 2.77 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
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+       "  --xr-border-color: #1F1F1F;\n",
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+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 130MB\n",
+       "Dimensions:                                    (lat: 1800, lon: 3600)\n",
+       "Coordinates:\n",
+       "  * lat                                        (lat) float32 7kB -89.95 ... 8...\n",
+       "  * lon                                        (lon) float32 14kB -179.9 ... ...\n",
+       "Data variables:\n",
+       "    data_radiometric_cloud_fraction            (lat, lon) float32 26MB dask.array&lt;chunksize=(900, 1800), meta=np.ndarray&gt;\n",
+       "    data_radiometric_cloud_fraction_precision  (lat, lon) float32 26MB dask.array&lt;chunksize=(900, 1800), meta=np.ndarray&gt;\n",
+       "    data_number_of_observations                (lat, lon) float64 52MB dask.array&lt;chunksize=(900, 1800), meta=np.ndarray&gt;\n",
+       "    data_quality_flag                          (lat, lon) float32 26MB dask.array&lt;chunksize=(1800, 3600), meta=np.ndarray&gt;\n",
+       "Attributes: (12/32)\n",
+       "    id:                                S5P_DLR_NRTI_01_040201_L3_CF_20240619\n",
+       "    title:                             TROPOMI/S5P L3 data of radiometric clo...\n",
+       "    source:                            Sentinel-5P, TROPOMI, space-borne remo...\n",
+       "    history:                           2024-06-20T03:20:22.883245, f_inpuls, ...\n",
+       "    platform:                          Sentinel-5P\n",
+       "    sensor:                            TROPOMI\n",
+       "    ...                                ...\n",
+       "    identifier_product_doi_authority:  https://dx.doi.org/\n",
+       "    naming_authority:                  German Aerospace Center (DLR)\n",
+       "    l2_algorithm_version:              UPAS-CLOUD-ROCINN-4.1.0\n",
+       "    algorithm_version:                 UMAS-4.2.1\n",
+       "    processing_mode:                   NRTI\n",
+       "    collection_identifier:             01</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1bc7441f-61fd-4d4e-ad78-b5ea97524b2a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1bc7441f-61fd-4d4e-ad78-b5ea97524b2a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1800</li><li><span class='xr-has-index'>lon</span>: 3600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-df2faa81-ce44-4238-bc0f-6ddfcade6c19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df2faa81-ce44-4238-bc0f-6ddfcade6c19' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.95 -89.85 ... 89.85 89.95</div><input id='attrs-31a66eef-1545-438e-96d1-d12ec0fa9ea4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31a66eef-1545-438e-96d1-d12ec0fa9ea4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68b71ea6-1c6a-47c5-a276-5a279e5e4a22' class='xr-var-data-in' type='checkbox'><label for='data-68b71ea6-1c6a-47c5-a276-5a279e5e4a22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[-90.  90.]</dd><dt><span>actual_range :</span></dt><dd>[-89.95  89.95]</dd></dl></div><div class='xr-var-data'><pre>array([-89.95, -89.85, -89.75, ...,  89.75,  89.85,  89.95], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.9 ... 179.9 179.9</div><input id='attrs-c0edbda7-dad9-49ec-b96d-bfd06077f3b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0edbda7-dad9-49ec-b96d-bfd06077f3b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02287db0-0b94-42c4-9a43-bfefbd43ca40' class='xr-var-data-in' type='checkbox'><label for='data-02287db0-0b94-42c4-9a43-bfefbd43ca40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[-180.  180.]</dd><dt><span>actual_range :</span></dt><dd>[-179.95  179.95]</dd></dl></div><div class='xr-var-data'><pre>array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-09376992-f7da-4837-b6fa-d08dd3b33b59' class='xr-section-summary-in' type='checkbox'  checked><label for='section-09376992-f7da-4837-b6fa-d08dd3b33b59' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data_radiometric_cloud_fraction</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(900, 1800), meta=np.ndarray&gt;</div><input id='attrs-4fc05329-16c4-4d61-83e3-58984d656225' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fc05329-16c4-4d61-83e3-58984d656225' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad42a6f4-0e24-4ddb-8037-3193d8e21881' class='xr-var-data-in' type='checkbox'><label for='data-ad42a6f4-0e24-4ddb-8037-3193d8e21881' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>effective radiometric cloud fraction: mean</dd><dt><span>proposed_standard_name :</span></dt><dd>effective_radiometric_cloud_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>actual_range :</span></dt><dd>[0. 1.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 1 day)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 24.72 MiB </td>\n",
+       "                        <td> 6.18 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1800, 3600) </td>\n",
+       "                        <td> (900, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"60\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">1800</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_radiometric_cloud_fraction_precision</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(900, 1800), meta=np.ndarray&gt;</div><input id='attrs-8278b1f1-9a04-4679-88c6-67b1cb2001e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8278b1f1-9a04-4679-88c6-67b1cb2001e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0955ab83-ade1-45f4-8e5f-1497134d6fa6' class='xr-var-data-in' type='checkbox'><label for='data-0955ab83-ade1-45f4-8e5f-1497134d6fa6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>effective radiometric cloud fraction: standard deviation</dd><dt><span>proposed_standard_name :</span></dt><dd>effective_radiometric_cloud_fraction standard_error</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>actual_range :</span></dt><dd>[0.  0.5]</dd><dt><span>cell_methods :</span></dt><dd>time: standard deviation (interval: 1 day)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 24.72 MiB </td>\n",
+       "                        <td> 6.18 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1800, 3600) </td>\n",
+       "                        <td> (900, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"60\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">1800</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_number_of_observations</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(900, 1800), meta=np.ndarray&gt;</div><input id='attrs-85d58c46-9c1d-4509-ae0e-af7ba84d22d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85d58c46-9c1d-4509-ae0e-af7ba84d22d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02b74532-aeda-4723-8a85-4cade853d2fb' class='xr-var-data-in' type='checkbox'><label for='data-02b74532-aeda-4723-8a85-4cade853d2fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of observations</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>actual_range :</span></dt><dd>[ 0 33]</dd><dt><span>cell_methods :</span></dt><dd>time: sum (interval: 1 day)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 49.44 MiB </td>\n",
+       "                        <td> 12.36 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1800, 3600) </td>\n",
+       "                        <td> (900, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"60\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">1800</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_quality_flag</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1800, 3600), meta=np.ndarray&gt;</div><input id='attrs-84f05681-5a3a-45ff-99b5-17d284a6c6a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84f05681-5a3a-45ff-99b5-17d284a6c6a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-049623f2-2baa-4096-acec-33a4e2c40b68' class='xr-var-data-in' type='checkbox'><label for='data-049623f2-2baa-4096-acec-33a4e2c40b68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>quality flag</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[  0 100]</dd><dt><span>actual_range :</span></dt><dd>[0. 1.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 1 day)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 24.72 MiB </td>\n",
+       "                        <td> 24.72 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1800, 3600) </td>\n",
+       "                        <td> (1800, 3600) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.0 0.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
+       "  <text x=\"140.000000\" y=\"30.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.000000)\">1800</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-773534f9-1044-4d11-80a7-c0a7888df311' class='xr-section-summary-in' type='checkbox'  ><label for='section-773534f9-1044-4d11-80a7-c0a7888df311' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-649d4e51-030a-4d33-a295-fd1b6e5c85e0' class='xr-index-data-in' type='checkbox'/><label for='index-649d4e51-030a-4d33-a295-fd1b6e5c85e0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.94999694824219,  -89.8499984741211,             -89.75,\n",
+       "        -89.6500015258789, -89.55000305175781, -89.44999694824219,\n",
+       "        -89.3499984741211,             -89.25,  -89.1500015258789,\n",
+       "       -89.05000305175781,\n",
+       "       ...\n",
+       "        89.05000305175781,   89.1500015258789,              89.25,\n",
+       "         89.3499984741211,  89.44999694824219,  89.55000305175781,\n",
+       "         89.6500015258789,              89.75,   89.8499984741211,\n",
+       "        89.94999694824219],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=1800))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a2576fda-632e-4eb8-814c-5d1fbe226b37' class='xr-index-data-in' type='checkbox'/><label for='index-a2576fda-632e-4eb8-814c-5d1fbe226b37' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -179.9499969482422, -179.85000610351562,             -179.75,\n",
+       "       -179.64999389648438,  -179.5500030517578,  -179.4499969482422,\n",
+       "       -179.35000610351562,             -179.25, -179.14999389648438,\n",
+       "        -179.0500030517578,\n",
+       "       ...\n",
+       "         179.0500030517578,  179.14999389648438,              179.25,\n",
+       "        179.35000610351562,   179.4499969482422,   179.5500030517578,\n",
+       "        179.64999389648438,              179.75,  179.85000610351562,\n",
+       "         179.9499969482422],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=3600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6fe688f-39d2-4ba5-aa03-0a6f920ce96a' class='xr-section-summary-in' type='checkbox'  ><label for='section-e6fe688f-39d2-4ba5-aa03-0a6f920ce96a' class='xr-section-summary' >Attributes: <span>(32)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>S5P_DLR_NRTI_01_040201_L3_CF_20240619</dd><dt><span>title :</span></dt><dd>TROPOMI/S5P L3 data of radiometric cloud fraction</dd><dt><span>source :</span></dt><dd>Sentinel-5P, TROPOMI, space-borne remote sensing</dd><dt><span>history :</span></dt><dd>2024-06-20T03:20:22.883245, f_inpuls, umas ingest -p CF -o /mnt/data/dbs/cache/nrt-l2c-processing/l2c/L2C-698616765 -i /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613638/S5P_NRTI_L2__CLOUD__20240618T230524_20240618T231024_34625_03_020601_20240619T005925.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613643/S5P_NRTI_L2__CLOUD__20240618T231024_20240618T231524_34625_03_020601_20240619T010016.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613646/S5P_NRTI_L2__CLOUD__20240618T231524_20240618T232024_34625_03_020601_20240619T010210.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613649/S5P_NRTI_L2__CLOUD__20240618T232024_20240618T232524_34625_03_020601_20240619T010137.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613652/S5P_NRTI_L2__CLOUD__20240619T000024_20240619T000524_34626_03_020601_20240619T010331.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613655/S5P_NRTI_L2__CLOUD__20240619T000524_20240619T001024_34626_03_020601_20240619T010549.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613658/S5P_NRTI_L2__CLOUD__20240619T001024_20240619T001524_34626_03_020601_20240619T011037.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613704/S5P_NRTI_L2__CLOUD__20240619T001524_20240619T002024_34626_03_020601_20240619T011123.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613707/S5P_NRTI_L2__CLOUD__20240619T002024_20240619T002524_34626_03_020601_20240619T011306.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613710/S5P_NRTI_L2__CLOUD__20240619T002524_20240619T003024_34626_03_020601_20240619T011438.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613713/S5P_NRTI_L2__CLOUD__20240619T003024_20240619T003524_34626_03_020601_20240619T011756.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613716/S5P_NRTI_L2__CLOUD__20240619T003524_20240619T004024_34626_03_020601_20240619T012055.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613719/S5P_NRTI_L2__CLOUD__20240619T004024_20240619T004524_34626_03_020601_20240619T012329.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613840/S5P_NRTI_L2__CLOUD__20240619T004524_20240619T005024_34626_03_020601_20240619T023851.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698614008/S5P_NRTI_L2__CLOUD__20240619T005024_20240619T005524_34626_03_020601_20240619T023921.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613891/S5P_NRTI_L2__CLOUD__20240619T005524_20240619T010024_34626_03_020601_20240619T023940.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613894/S5P_NRTI_L2__CLOUD__20240619T010024_20240619T010524_34626_03_020601_20240619T023714.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698613924/S5P_NRTI_L2__CLOUD__20240619T014524_20240619T015024_34627_03_020601_20240619T024201.nc 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/mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616578/S5P_NRTI_L2__CLOUD__20240619T235024_20240619T235524_34640_03_020601_20240620T004804.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616581/S5P_NRTI_L2__CLOUD__20240619T235524_20240620T000024_34640_03_020601_20240620T005125.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616634/S5P_NRTI_L2__CLOUD__20240620T000024_20240620T000524_34640_03_020601_20240620T005227.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616637/S5P_NRTI_L2__CLOUD__20240620T000524_20240620T001024_34640_03_020601_20240620T005334.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616640/S5P_NRTI_L2__CLOUD__20240620T001024_20240620T001524_34640_03_020601_20240620T005534.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616643/S5P_NRTI_L2__CLOUD__20240620T001524_20240620T002024_34640_03_020601_20240620T005723.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616646/S5P_NRTI_L2__CLOUD__20240620T002024_20240620T002524_34640_03_020601_20240620T005857.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616745/S5P_NRTI_L2__CLOUD__20240620T002524_20240620T003024_34640_03_020601_20240620T022053.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616754/S5P_NRTI_L2__CLOUD__20240620T003024_20240620T003524_34640_03_020601_20240620T022117.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616757/S5P_NRTI_L2__CLOUD__20240620T003524_20240620T004024_34640_03_020601_20240620T022146.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616760/S5P_NRTI_L2__CLOUD__20240620T004024_20240620T004524_34640_03_020601_20240620T022020.nc -j 4</dd><dt><span>platform :</span></dt><dd>Sentinel-5P</dd><dt><span>sensor :</span></dt><dd>TROPOMI</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.1</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.1</dd><dt><span>spatial_resolution :</span></dt><dd>5.5 x 3.5 km2</dd><dt><span>time_coverage_start :</span></dt><dd>2024-06-18T23:05:24.649000</dd><dt><span>time_coverage_end :</span></dt><dd>2024-06-20T00:44:35.462000</dd><dt><span>project :</span></dt><dd>INPULS</dd><dt><span>institution :</span></dt><dd>German Aerospace Center (DLR)</dd><dt><span>creator_name :</span></dt><dd>Remote Sensing Technology Institute (IMF)</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://atmos.eoc.dlr.de/inpuls</dd><dt><span>publisher_name :</span></dt><dd>German Remote Sensing Data Center (DFD)</dd><dt><span>publisher_type :</span></dt><dd>group</dd><dt><span>publisher_url :</span></dt><dd>https://geoservice.dlr.de/</dd><dt><span>identifier_product_doi :</span></dt><dd>N/A</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>https://dx.doi.org/</dd><dt><span>naming_authority :</span></dt><dd>German Aerospace Center (DLR)</dd><dt><span>l2_algorithm_version :</span></dt><dd>UPAS-CLOUD-ROCINN-4.1.0</dd><dt><span>algorithm_version :</span></dt><dd>UMAS-4.2.1</dd><dt><span>processing_mode :</span></dt><dd>NRTI</dd><dt><span>collection_identifier :</span></dt><dd>01</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 130MB\n",
+       "Dimensions:                                    (lat: 1800, lon: 3600)\n",
+       "Coordinates:\n",
+       "  * lat                                        (lat) float32 7kB -89.95 ... 8...\n",
+       "  * lon                                        (lon) float32 14kB -179.9 ... ...\n",
+       "Data variables:\n",
+       "    data_radiometric_cloud_fraction            (lat, lon) float32 26MB dask.array<chunksize=(900, 1800), meta=np.ndarray>\n",
+       "    data_radiometric_cloud_fraction_precision  (lat, lon) float32 26MB dask.array<chunksize=(900, 1800), meta=np.ndarray>\n",
+       "    data_number_of_observations                (lat, lon) float64 52MB dask.array<chunksize=(900, 1800), meta=np.ndarray>\n",
+       "    data_quality_flag                          (lat, lon) float32 26MB dask.array<chunksize=(1800, 3600), meta=np.ndarray>\n",
+       "Attributes: (12/32)\n",
+       "    id:                                S5P_DLR_NRTI_01_040201_L3_CF_20240619\n",
+       "    title:                             TROPOMI/S5P L3 data of radiometric clo...\n",
+       "    source:                            Sentinel-5P, TROPOMI, space-borne remo...\n",
+       "    history:                           2024-06-20T03:20:22.883245, f_inpuls, ...\n",
+       "    platform:                          Sentinel-5P\n",
+       "    sensor:                            TROPOMI\n",
+       "    ...                                ...\n",
+       "    identifier_product_doi_authority:  https://dx.doi.org/\n",
+       "    naming_authority:                  German Aerospace Center (DLR)\n",
+       "    l2_algorithm_version:              UPAS-CLOUD-ROCINN-4.1.0\n",
+       "    algorithm_version:                 UMAS-4.2.1\n",
+       "    processing_mode:                   NRTI\n",
+       "    collection_identifier:             01"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = store.open_data(descriptors[1].data_id, asset_names=[\"data\"])\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We plot the loaded data as an example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.03 s, sys: 397 ms, total: 2.42 s\n",
+      "Wall time: 5.07 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fb3131f8e60>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.data_radiometric_cloud_fraction.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/nonsearchable_stac_catalog.ipynb b/examples/nonsearchable_stac_catalog.ipynb
deleted file mode 100644
index 02ec9f2..0000000
--- a/examples/nonsearchable_stac_catalog.ipynb
+++ /dev/null
@@ -1,339 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Non-seachable STAC catalog\n",
-    "\n",
-    "This notebook shows an example how to access items in a non-searchable STAC catalog, which does not implement the [STAC API - Item Search](https://github.com/radiantearth/stac-api-spec/tree/release/v1.0.0/item-search) conformance class. When searching in such type of catalog, the catalog needs to be crawled through and the items properties needs to be matched to the search parameters. This process will there be slow, especially for large catalogs.\n",
-    "\n",
-    "### Setup\n",
-    "In order to run this notebook you need to install [`xcube`](https://xcube.readthedocs.io/en/latest/) and the [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) plugin. You may install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) directly from the git repository by cloning the repository, directing into `xcube-stac`, and following the steps below:\n",
-    "\n",
-    "```bash\n",
-    "conda env create -f environment.yml\n",
-    "conda activate xcube-stac\n",
-    "pip install .\n",
-    "```\n",
-    "\n",
-    "Note that [`xcube`](https://xcube.readthedocs.io/en/latest/) is included in the `environment.yml`.  \n",
-    "\n",
-    "Now, we first import everything we need:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from xcube.core.store import new_data_store, get_data_store_params_schema\n",
-    "import itertools"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we get the store parameters needed to initialize a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We determine the url of the [EcoDataCube.eu](https://stac.ecodatacube.eu/) STAC catalog and initiate a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework) where the `xcube-stac` plugin is recognized by setting the first argument to `\"stac\"` in the `new_data_store` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/konstantin/micromamba/envs/xcube-stac/lib/python3.12/site-packages/pystac_client/client.py:190: NoConformsTo: Server does not advertise any conformance classes.\n",
-      "  warnings.warn(NoConformsTo())\n"
-     ]
-    }
-   ],
-   "source": [
-    "url = \"https://s3.eu-central-1.wasabisys.com/stac/odse/catalog.json\"\n",
-    "store = new_data_store(\"stac\", url=url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data IDs point to a [STAC item's JSON](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md) and are specified by the segment of the URL that follows the catalog's URL. The data IDs can be streamed using the following code where we show the first 10 data IDs as an example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lcv_land.mask_eumap/lcv_land.mask_eumap_2014.01.01..2016.12.31/lcv_land.mask_eumap_2014.01.01..2016.12.31.json',\n",
-       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20.json',\n",
-       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.03.21..2000.06.24/lcv_blue_landsat.glad.ard_2000.03.21..2000.06.24.json',\n",
-       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.06.25..2000.09.12/lcv_blue_landsat.glad.ard_2000.06.25..2000.09.12.json',\n",
-       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.09.13..2000.12.01/lcv_blue_landsat.glad.ard_2000.09.13..2000.12.01.json',\n",
-       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.12.02..2001.03.20/lcv_blue_landsat.glad.ard_2000.12.02..2001.03.20.json',\n",
-       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2001.03.21..2001.06.24/lcv_blue_landsat.glad.ard_2001.03.21..2001.06.24.json',\n",
-       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2001.06.25..2001.09.12/lcv_blue_landsat.glad.ard_2001.06.25..2001.09.12.json',\n",
-       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2001.09.13..2001.12.01/lcv_blue_landsat.glad.ard_2001.09.13..2001.12.01.json',\n",
-       " 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2001.12.02..2002.03.20/lcv_blue_landsat.glad.ard_2001.12.02..2002.03.20.json']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_ids = store.get_data_ids()\n",
-    "list(itertools.islice(data_ids, 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next step, we can search for items using search parameters. The following code shows which search parameters are available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "additionalProperties": false,
-       "properties": {
-        "bbox": {
-         "items": [
-          {
-           "type": "number"
-          },
-          {
-           "type": "number"
-          },
-          {
-           "type": "number"
-          },
-          {
-           "type": "number"
-          }
-         ],
-         "title": "Bounding box [x1,y1,x2,y2] in geographical coordinates",
-         "type": "array"
-        },
-        "collections": {
-         "description": "Collection IDs to be included in the search request.",
-         "items": {
-          "minLength": 0,
-          "type": "string"
-         },
-         "title": "Collection IDs",
-         "type": "array",
-         "uniqueItems": true
-        },
-        "time_range": {
-         "description": "Time range given as pair of start and stop dates. Dates must be given using format 'YYYY-MM-DD'. Start and stop are inclusive.",
-         "items": [
-          {
-           "format": "date",
-           "type": [
-            "string",
-            "null"
-           ]
-          },
-          {
-           "format": "date",
-           "type": [
-            "string",
-            "null"
-           ]
-          }
-         ],
-         "title": "Time Range",
-         "type": "array"
-        }
-       },
-       "type": "object"
-      },
-      "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x71d3deb4b2f0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search_params = store.get_search_params_schema()\n",
-    "search_params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's search for Landsat Thematic Mapper data for the European region during the first quarter of 2000."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'data_id': 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20.json',\n",
-       "  'data_type': 'dataset',\n",
-       "  'bbox': [-23.550818268711048,\n",
-       "   24.399543432891665,\n",
-       "   63.352379098951936,\n",
-       "   77.69295185585888],\n",
-       "  'time_range': ['1999-12-02', '2000-03-20']},\n",
-       " {'data_id': 'lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_2000.03.21..2000.06.24/lcv_blue_landsat.glad.ard_2000.03.21..2000.06.24.json',\n",
-       "  'data_type': 'dataset',\n",
-       "  'bbox': [-23.550818268711048,\n",
-       "   24.399543432891665,\n",
-       "   63.352379098951936,\n",
-       "   77.69295185585888],\n",
-       "  'time_range': ['2000-03-21', '2000-06-24']}]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "descriptors = list(store.search_data(\n",
-    "    collections=[\"lcv_blue_landsat.glad.ard\"],\n",
-    "    bbox=[-10, 40, 40, 70],\n",
-    "    time_range=[\"2000-01-01\", \"2000-04-01\"]\n",
-    "))\n",
-    "[d.to_dict() for d in descriptors]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next step, we can open the data for each data ID. (Note that this is not fully implemented yet. So far we can access assets which will give the href to the data resource). The following code shows which parameters are available for opening the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "additionalProperties": false,
-       "properties": {
-        "asset_names": {
-         "description": "Names of assets which will be included in the data cube.",
-         "items": {
-          "minLength": 0,
-          "type": "string"
-         },
-         "title": "Names of assets",
-         "type": "array",
-         "uniqueItems": true
-        }
-       },
-       "type": "object"
-      },
-      "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x71d31f6732f0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "open_params = store.get_open_data_params_schema()\n",
-    "open_params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We select the Band 1 (blue) and get the corresponding assets and the corresponding hrefs pointing to the data resources by running the following code."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsat.glad.ard_p50_30m_0..0cm_1999.12.02..2000.03.20_eumap_epsg3035_v1.1.tif',\n",
-       " 'https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsat.glad.ard_p50_30m_0..0cm_2000.03.21..2000.06.24_eumap_epsg3035_v1.1.tif']"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asset_collection = []\n",
-    "for descriptor in descriptors:\n",
-    "    assets = store.open_data(descriptor.data_id, asset_names=[\"blue_p50\"])\n",
-    "    assert len(assets) == 1\n",
-    "    asset_collection.append(assets[0])\n",
-    "[asset.href for asset in asset_collection]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook will be continued once the data access is implemented."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/searchable_stac_catalog.ipynb b/examples/searchable_stac_catalog.ipynb
deleted file mode 100644
index 97ab96f..0000000
--- a/examples/searchable_stac_catalog.ipynb
+++ /dev/null
@@ -1,381 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Seachable STAC catalog\n",
-    "\n",
-    "This notebook shows an example how to access items and the corresponding hrefs stored in the assets for given search and open data parameters. A searchable STAC catalog implements the [STAC API - Item Search](https://github.com/radiantearth/stac-api-spec/tree/release/v1.0.0/item-search) conformance class, which provides the ability to search for STAC Item objects across collections.\n",
-    "\n",
-    "### Setup\n",
-    "In order to run this notebook you need to install [`xcube`](https://xcube.readthedocs.io/en/latest/) and the [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) plugin. You may install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) directly from the git repository by cloning the repository, directing into `xcube-stac`, and following the steps below:\n",
-    "\n",
-    "```bash\n",
-    "conda env create -f environment.yml\n",
-    "conda activate xcube-stac\n",
-    "pip install .\n",
-    "```\n",
-    "\n",
-    "Note that [`xcube`](https://xcube.readthedocs.io/en/latest/) is included in the `environment.yml`.  \n",
-    "\n",
-    "Now, we first import everything we need:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from xcube.core.store import new_data_store, get_data_store_params_schema\n",
-    "import itertools"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we get the store parameters needed to initialize a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "additionalProperties": false,
-       "description": "Describes the parameters of the xcube data store 'stac'.",
-       "properties": {
-        "url": {
-         "title": "URL to STAC catalog",
-         "type": "string"
-        }
-       },
-       "required": [
-        "url"
-       ],
-       "type": "object"
-      },
-      "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x756ddd5cad20>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "store_params = get_data_store_params_schema(\"stac\")\n",
-    "store_params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We determine the url of the [Earth Search](https://element84.com/earth-search/) STAC catalog and initiate a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework) where the `xcube-stac` plugin is recognized by setting the first argument to `\"stac\"` in the `new_data_store` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://earth-search.aws.element84.com/v1\"\n",
-    "store = new_data_store(\"stac\", url=url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data IDs point to a [STAC item's JSON](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md) and are specified by the segment of the URL that follows the catalog's URL. The data IDs can be streamed using the following code where we show the first 10 data IDs as an example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064657_20240530T064719_054097_0693F7',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064632_20240530T064657_054097_0693F7',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064607_20240530T064632_054097_0693F7',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064542_20240530T064607_054097_0693F7',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064517_20240530T064542_054097_0693F7',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064452_20240530T064517_054097_0693F7',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064427_20240530T064452_054097_0693F7',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064402_20240530T064427_054097_0693F7',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064337_20240530T064402_054097_0693F7',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240530T064312_20240530T064337_054097_0693F7']"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_ids = store.get_data_ids()\n",
-    "list(itertools.islice(data_ids, 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next step, we can search for items using search parameters. The following code shows which search parameters are available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "additionalProperties": false,
-       "properties": {
-        "bbox": {
-         "items": [
-          {
-           "type": "number"
-          },
-          {
-           "type": "number"
-          },
-          {
-           "type": "number"
-          },
-          {
-           "type": "number"
-          }
-         ],
-         "title": "Bounding box [x1,y1,x2,y2] in geographical coordinates",
-         "type": "array"
-        },
-        "collections": {
-         "description": "Collection IDs to be included in the search request.",
-         "items": {
-          "minLength": 0,
-          "type": "string"
-         },
-         "title": "Collection IDs",
-         "type": "array",
-         "uniqueItems": true
-        },
-        "time_range": {
-         "description": "Time range given as pair of start and stop dates. Dates must be given using format 'YYYY-MM-DD'. Start and stop are inclusive.",
-         "items": [
-          {
-           "format": "date",
-           "type": [
-            "string",
-            "null"
-           ]
-          },
-          {
-           "format": "date",
-           "type": [
-            "string",
-            "null"
-           ]
-          }
-         ],
-         "title": "Time Range",
-         "type": "array"
-        }
-       },
-       "type": "object"
-      },
-      "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x756ddd673ad0>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "search_params = store.get_search_params_schema()\n",
-    "search_params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's search for Sentinel-2 L2a data in the region of Lake Constance for the period from March 4th to March 6th, 2020."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'data_id': 'collections/sentinel-2-l2a/items/S2A_32TMT_20200305_0_L2A',\n",
-       "  'data_type': 'dataset',\n",
-       "  'bbox': [7.662878883910047,\n",
-       "   46.85818510451771,\n",
-       "   9.130456971519783,\n",
-       "   47.85361872923358],\n",
-       "  'time_range': ['2020-03-05', None]},\n",
-       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32TNT_20200305_0_L2A',\n",
-       "  'data_type': 'dataset',\n",
-       "  'bbox': [8.999746010269408,\n",
-       "   46.86543294418706,\n",
-       "   9.69101980741396,\n",
-       "   47.85369284462768],\n",
-       "  'time_range': ['2020-03-05', None]},\n",
-       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32UMU_20200305_0_L2A',\n",
-       "  'data_type': 'dataset',\n",
-       "  'bbox': [7.639190643894996,\n",
-       "   47.75741268155721,\n",
-       "   9.132768981529203,\n",
-       "   48.752927528985374],\n",
-       "  'time_range': ['2020-03-05', None]},\n",
-       " {'data_id': 'collections/sentinel-2-l2a/items/S2A_32UNU_20200305_0_L2A',\n",
-       "  'data_type': 'dataset',\n",
-       "  'bbox': [8.999741508947045,\n",
-       "   47.76332693643577,\n",
-       "   10.103302080212728,\n",
-       "   48.75300400802843],\n",
-       "  'time_range': ['2020-03-05', None]}]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "descriptors = list(store.search_data(\n",
-    "    collections=[\"sentinel-2-l2a\"],\n",
-    "    bbox=[9, 47, 10, 48],\n",
-    "    time_range=[\"2020-03-04\", \"2020-03-06\"]\n",
-    "))\n",
-    "[d.to_dict() for d in descriptors]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the next step, we can open the data for each data id. (Note that this is not fully implemented yet. So far we can access assets which will give the href to the data resource). The following code shows which parameters are available for opening the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/json": {
-       "additionalProperties": false,
-       "properties": {
-        "asset_names": {
-         "description": "Names of assets which will be included in the data cube.",
-         "items": {
-          "minLength": 0,
-          "type": "string"
-         },
-         "title": "Names of assets",
-         "type": "array",
-         "uniqueItems": true
-        }
-       },
-       "type": "object"
-      },
-      "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x756ddd4bab10>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "open_params = store.get_open_data_params_schema()\n",
-    "open_params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We select the band B04 (red) and get the corresponding assets and the corresponding hrefs pointing to the data resources by running the following code."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/MT/2020/3/S2A_32TMT_20200305_0_L2A/B04.tif',\n",
-       " 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/T/NT/2020/3/S2A_32TNT_20200305_0_L2A/B04.tif',\n",
-       " 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/U/MU/2020/3/S2A_32UMU_20200305_0_L2A/B04.tif',\n",
-       " 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/32/U/NU/2020/3/S2A_32UNU_20200305_0_L2A/B04.tif']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "asset_collection = []\n",
-    "for descriptor in descriptors:\n",
-    "    assets = store.open_data(descriptor.data_id, asset_names=[\"red\"])\n",
-    "    assert len(assets) == 1\n",
-    "    asset_collection.append(assets[0])\n",
-    "[asset.href for asset in asset_collection]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook will be continued once the data access is implemented."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/xcube_server_stac_s3.ipynb b/examples/xcube_server_stac_s3.ipynb
new file mode 100644
index 0000000..7ee5b7c
--- /dev/null
+++ b/examples/xcube_server_stac_s3.ipynb
@@ -0,0 +1,5875 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6cb23d36-5948-40f5-9018-7a3a01a68803",
+   "metadata": {},
+   "source": [
+    "# Load data of different format published by xcube server's STAC\n",
+    "\n",
+    "This notebook shows an example how to load data of different format published by xcube server's STAC. \n",
+    "\n",
+    "### Setup\n",
+    "In order to run this notebook you need to install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac). You may install [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) directly from the git repository by cloning the repository, directing into `xcube-stac`, and following the steps below:\n",
+    "\n",
+    "```bash\n",
+    "conda env create -f environment.yml\n",
+    "conda activate xcube-stac\n",
+    "pip install .\n",
+    "```\n",
+    "\n",
+    "Note that [`xcube_stac`](https://github.com/xcube-dev/xcube-stac) is a plugin of [`xcube`](https://xcube.readthedocs.io/en/latest/), where `xcube` is included in the `environment.yml`.  \n",
+    "\n",
+    "Now, we first import everything we need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "65c10f13-6855-4bdd-af8c-eb71203cf7bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from xcube.core.store import new_data_store, get_data_store_params_schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "677fc0af-1499-42f4-8c42-f2d8a6e4b488",
+   "metadata": {},
+   "source": [
+    "First, we start the [xcube server](https://xcube.readthedocs.io/en/latest/examples/xcube_serve.html#running-the-server). Clone the [xcube repository](https://github.com/xcube-dev/xcube) and direct into the `xcube` directory. Run the server by tying the following command line in the terminal: \n",
+    "\n",
+    "```bash\n",
+    "\"xcube serve -v -c examples/serve/demo/config.yml\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "909a10f5-0e49-496e-b598-63fcd73ceb4e",
+   "metadata": {},
+   "source": [
+    "We can view the store parameters needed to initialize a STAC data store."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5ffc84bd-501e-45f8-8355-501c1740142f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "description": "Describes the parameters of the xcube data store 'stac'.",
+       "properties": {
+        "storage_options_s3": {
+         "additionalProperties": true,
+         "properties": {
+          "anon": {
+           "title": "Whether to anonymously connect to AWS S3.",
+           "type": "boolean"
+          },
+          "asynchronous": {
+           "type": "boolean"
+          },
+          "client_kwargs": {
+           "additionalProperties": true,
+           "description": "Parameters for the botocore client.",
+           "properties": {
+            "endpoint_url": {
+             "format": "uri",
+             "minLength": 1,
+             "title": "Alternative endpoint URL.",
+             "type": "string"
+            },
+            "profile_name": {
+             "description": "Section name with within ~/.aws/config file, which provides AWS configurations and credentials.",
+             "minLength": 1,
+             "title": "Name of the AWS configuration profile",
+             "type": "string"
+            },
+            "region_name": {
+             "minLength": 1,
+             "title": "AWS storage region name",
+             "type": "string"
+            }
+           },
+           "type": "object"
+          },
+          "key": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_ACCESS_KEY_ID.",
+           "minLength": 1,
+           "title": "AWS access key identifier.",
+           "type": "string"
+          },
+          "listings_expiry_time": {
+           "type": "number"
+          },
+          "max_paths": {
+           "type": "integer"
+          },
+          "requester_pays": {
+           "default": false,
+           "description": "If \"RequesterPays\" buckets are supported.",
+           "type": "boolean"
+          },
+          "s3_additional_kwargs": {
+           "additionalProperties": true,
+           "description": "parameters that are used when calling S3 API methods. Typically used for things like \"ServerSideEncryption\".",
+           "properties": {},
+           "type": "object"
+          },
+          "secret": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_SECRET_ACCESS_KEY.",
+           "minLength": 1,
+           "title": "AWS secret access key.",
+           "type": "string"
+          },
+          "skip_instance_cache": {
+           "type": "boolean"
+          },
+          "token": {
+           "description": "Can also be set in profile section of ~/.aws/config, or by environment variable AWS_SESSION_TOKEN.",
+           "minLength": 1,
+           "title": "Session token.",
+           "type": "string"
+          },
+          "use_listings_cache": {
+           "type": "boolean"
+          },
+          "use_ssl": {
+           "default": true,
+           "description": "Whether to use SSL in connections to S3; may be faster without, but insecure.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "url": {
+         "title": "URL to STAC catalog",
+         "type": "string"
+        }
+       },
+       "required": [
+        "url"
+       ],
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x711fb1c23a40>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_data_store_params_schema(\"stac\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7f2c69e-135d-407d-a774-212d3a83e885",
+   "metadata": {},
+   "source": [
+    "[Xcube server](https://xcube.readthedocs.io/en/latest/webapi.html#web-api-and-server) publishes a STAC catalog at `<base-url>/ogc`, with `<base-url>` defaulting to `127.0.0.1`. We therefore initiate a STAC [data store](https://xcube.readthedocs.io/en/latest/dataaccess.html#data-store-framework), where the `xcube-stac` plugin is recognized by setting the first argument to `\"stac\"` in the `new_data_store` function, and the url is set to `<base-url>/ogc`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1559a572-bd4d-477b-809e-f143681e7af0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 22.9 ms, sys: 10.6 ms, total: 33.5 ms\n",
+      "Wall time: 31 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "url = \"http://127.0.0.1:8080/ogc\"\n",
+    "store = new_data_store(\"stac\", url=url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a34a4f31-7ab3-4c45-be5b-77c28e20109e",
+   "metadata": {},
+   "source": [
+    "The data IDs point to a [STAC item's JSON](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md) and are specified by the segment of the URL that follows the catalog's URL. The data IDs can be obtained using the following code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bbe0f659-88b7-4774-ba3a-a5c6f31d97db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 13.5 ms, sys: 5.41 ms, total: 18.9 ms\n",
+      "Wall time: 141 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['collections/datacubes/items/local',\n",
+       " 'collections/datacubes/items/local_ts',\n",
+       " 'collections/datacubes/items/local_1w',\n",
+       " 'collections/datacubes/items/cog_local',\n",
+       " 'collections/datacubes/items/geotiff_local']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "data_ids = store.list_data_ids()\n",
+    "data_ids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8944302c-b1e5-48c4-8d0e-d5b64a6aa8f8",
+   "metadata": {},
+   "source": [
+    "Now, we open a `.zarr` file. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cac9aacb-f999-4acf-b9a8-112ceae58a83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 193 ms, sys: 28.7 ms, total: 222 ms\n",
+      "Wall time: 229 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
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+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
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+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
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+       "  list-style: none;\n",
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+       "\n",
+       ".xr-dim-list li {\n",
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+       "\n",
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+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
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+       "  margin-bottom: 0;\n",
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+       "\n",
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+       "\n",
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+       "  color: var(--xr-font-color2);\n",
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+       "\n",
+       ".xr-var-name,\n",
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+       ".xr-attrs dt:hover {\n",
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+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
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+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
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+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
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+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 400MB\n",
+       "Dimensions:                 (time: 5, lat: 1000, lon: 2000, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * lat                     (lat) float64 8kB 52.5 52.5 52.49 ... 50.0 50.0\n",
+       "  * lon                     (lon) float64 16kB 0.00125 0.00375 ... 4.996 4.999\n",
+       "  * time                    (time) datetime64[ns] 40B 2017-01-16T10:09:21.834...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    analytic_c2rcc_flags    (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;\n",
+       "    analytic_conc_chl       (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;\n",
+       "    analytic_conc_tsm       (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;\n",
+       "    analytic_kd489          (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;\n",
+       "    analytic_lat_bnds       (lat, bnds) float64 16kB dask.array&lt;chunksize=(1000, 2), meta=np.ndarray&gt;\n",
+       "    analytic_lon_bnds       (lon, bnds) float64 32kB dask.array&lt;chunksize=(2000, 2), meta=np.ndarray&gt;\n",
+       "    analytic_quality_flags  (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;\n",
+       "    analytic_time_bnds      (time, bnds) datetime64[ns] 80B dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a3b77c9f-b7cb-4051-b571-18fdf2c9cd39' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3b77c9f-b7cb-4051-b571-18fdf2c9cd39' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>lat</span>: 1000</li><li><span class='xr-has-index'>lon</span>: 2000</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ae0c8be3-e2b1-49b2-bf9b-167c1afe2d35' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae0c8be3-e2b1-49b2-bf9b-167c1afe2d35' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.5 52.5 52.49 ... 50.01 50.0 50.0</div><input id='attrs-93abb959-faad-43cb-afad-8090bbe7ad7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93abb959-faad-43cb-afad-8090bbe7ad7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4c929cd-fec2-4bf1-a69e-37239a02f742' class='xr-var-data-in' type='checkbox'><label for='data-c4c929cd-fec2-4bf1-a69e-37239a02f742' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([52.49875, 52.49625, 52.49375, ..., 50.00625, 50.00375, 50.00125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.00125 0.00375 ... 4.996 4.999</div><input id='attrs-7c566ef3-522f-49df-ace3-5a8910f9f5c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c566ef3-522f-49df-ace3-5a8910f9f5c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0108247-de82-4874-ab41-9537e502738f' class='xr-var-data-in' type='checkbox'><label for='data-d0108247-de82-4874-ab41-9537e502738f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-03, 3.75000e-03, 6.25000e-03, ..., 4.99375e+00, 4.99625e+00,\n",
+       "       4.99875e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-16T10:09:21.834255872 .....</div><input id='attrs-d5e1866a-3c56-41a7-8ccd-d035a01e0a1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5e1866a-3c56-41a7-8ccd-d035a01e0a1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8728b640-6ddc-4667-8071-be741a247368' class='xr-var-data-in' type='checkbox'><label for='data-8728b640-6ddc-4667-8071-be741a247368' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-16T10:09:21.834255872&#x27;, &#x27;2017-01-25T09:35:51.060063488&#x27;,\n",
+       "       &#x27;2017-01-26T10:50:16.686192896&#x27;, &#x27;2017-01-28T09:58:11.350386176&#x27;,\n",
+       "       &#x27;2017-01-30T10:46:33.836892416&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a25f9527-5120-4570-9861-a1cd19cf15e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a25f9527-5120-4570-9861-a1cd19cf15e8' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analytic_c2rcc_flags</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;</div><input id='attrs-0b61849c-aa4b-4b01-acde-41032ec141d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b61849c-aa4b-4b01-acde-41032ec141d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6f595ad-ce45-4858-8d98-30d0660f5f4b' class='xr-var-data-in' type='checkbox'><label for='data-e6f595ad-ce45-4858-8d98-30d0660f5f4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_coding_name :</span></dt><dd>c2rcc_flags</dd><dt><span>flag_descriptions :</span></dt><dd>The input spectrum to the atmospheric correction neural net was out of the scope of the training range and the inversion is likely to be wrong\tThe input spectrum to the atmospheric correction neural net out of training range\tOne of the inputs to the IOP retrieval neural net is out of training range\tHigh downwelling transmission is indicating cloudy conditions\tOne of the IOPs is out of range\tApig output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tAdet output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tAgelb output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tBpart output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tBwit output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tApig output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tAdet output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tAgelb output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tBpart output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tBwit output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tThe Rhow input spectrum to IOP neural net is probably not within the training range of the neural net, and the inversion is likely to be wrong.\tKd489 is out of range\tKdmin is out of range\tKdmin is at max\tKdmin is at max\tThe operators valid pixel expression has resolved to true</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, -2147483648]</dd><dt><span>flag_meanings :</span></dt><dd>Rtosa_OOS Rtosa_OOR Rhow_OOR Cloud_risk Iop_OOR Apig_at_max Adet_at_max Agelb_at_max Bpart_at_max Bwit_at_max Apig_at_min Adet_at_min Agelb_at_min Bpart_at_min Bwit_at_min Rhow_OOS Kd489_OOR Kdmin_OOR Kd489_at_max Kdmin_at_max Valid_PE</dd><dt><span>long_name :</span></dt><dd>C2RCC quality flags</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 781.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 100, 200) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 100 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_conc_chl</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;</div><input id='attrs-f3a9add8-48a0-40d2-a787-7d33676b602e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3a9add8-48a0-40d2-a787-7d33676b602e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fd69259-1540-421e-9d7b-82827ce384c3' class='xr-var-data-in' type='checkbox'><label for='data-3fd69259-1540-421e-9d7b-82827ce384c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>color_table_blue_values :</span></dt><dd>[128, 255, 255, 255, 255, 0, 0, 0, 0, 0]</dd><dt><span>color_table_green_values :</span></dt><dd>[0, 0, 102, 204, 255, 128, 255, 95, 0, 0]</dd><dt><span>color_table_red_values :</span></dt><dd>[0, 0, 51, 0, 0, 0, 255, 255, 215, 150]</dd><dt><span>color_table_sample_values :</span></dt><dd>[0.0, 0.5, 1.0, 2.0, 3.0, 4.5, 13.0, 25.0, 30.0, 40.0]</dd><dt><span>long_name :</span></dt><dd>Chlorophyll concentration</dd><dt><span>units :</span></dt><dd>mg m^-3</dd><dt><span>valid_pixel_expression :</span></dt><dd>c2rcc_flags.Valid_PE</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 781.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 100, 200) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 100 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_conc_tsm</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;</div><input id='attrs-10b20a9d-0919-4d46-8bf1-40479665d8a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10b20a9d-0919-4d46-8bf1-40479665d8a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93e9fcfa-b89e-4a23-a0c2-4aa50332b6b6' class='xr-var-data-in' type='checkbox'><label for='data-93e9fcfa-b89e-4a23-a0c2-4aa50332b6b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total suspended matter dry weight concentration</dd><dt><span>units :</span></dt><dd>g m^-3</dd><dt><span>valid_pixel_expression :</span></dt><dd>c2rcc_flags.Valid_PE</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 781.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 100, 200) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 100 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_kd489</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;</div><input id='attrs-8b297195-680e-4eec-8d52-57e7d09c709d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b297195-680e-4eec-8d52-57e7d09c709d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b7a549b-f83a-437c-9d94-570e2cd79d73' class='xr-var-data-in' type='checkbox'><label for='data-4b7a549b-f83a-437c-9d94-570e2cd79d73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Irradiance attenuation coefficient at 489 nm</dd><dt><span>units :</span></dt><dd>m^-1</dd><dt><span>valid_pixel_expression :</span></dt><dd>c2rcc_flags.Valid_PE</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 781.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 100, 200) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 100 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1000, 2), meta=np.ndarray&gt;</div><input id='attrs-7dc19df4-eb0c-418f-9354-608ed6b804bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dc19df4-eb0c-418f-9354-608ed6b804bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff7ab4be-48c0-4319-8bbb-9800e85f2a06' class='xr-var-data-in' type='checkbox'><label for='data-ff7ab4be-48c0-4319-8bbb-9800e85f2a06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 15.62 kiB </td>\n",
+       "                        <td> 15.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1000, 2) </td>\n",
+       "                        <td> (1000, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2000, 2), meta=np.ndarray&gt;</div><input id='attrs-a74f0907-25c6-4d9b-a3eb-d873bd613072' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a74f0907-25c6-4d9b-a3eb-d873bd613072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c170ae6b-4daa-43f7-aea0-e7b424d5540c' class='xr-var-data-in' type='checkbox'><label for='data-c170ae6b-4daa-43f7-aea0-e7b424d5540c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 31.25 kiB </td>\n",
+       "                        <td> 31.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2000, 2) </td>\n",
+       "                        <td> (2000, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_quality_flags</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 100, 200), meta=np.ndarray&gt;</div><input id='attrs-e2dd5fb3-9994-4e06-9282-7b8a5592f10e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2dd5fb3-9994-4e06-9282-7b8a5592f10e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-789e8613-6e36-404d-9af4-bbfbbf5d506e' class='xr-var-data-in' type='checkbox'><label for='data-789e8613-6e36-404d-9af4-bbfbbf5d506e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_coding_name :</span></dt><dd>quality_flags</dd><dt><span>flag_descriptions :</span></dt><dd></dd><dt><span>flag_masks :</span></dt><dd>[-2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288, 262144, 131072, 65536, 32768, 16384, 8192, 4096, 2048, 1024, 512, 256, 128, 64, 32, 16, 8, 4, 2, 1]</dd><dt><span>flag_meanings :</span></dt><dd>land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun_glint_risk dubious saturated_Oa01 saturated_Oa02 saturated_Oa03 saturated_Oa04 saturated_Oa05 saturated_Oa06 saturated_Oa07 saturated_Oa08 saturated_Oa09 saturated_Oa10 saturated_Oa11 saturated_Oa12 saturated_Oa13 saturated_Oa14 saturated_Oa15 saturated_Oa16 saturated_Oa17 saturated_Oa18 saturated_Oa19 saturated_Oa20 saturated_Oa21</dd><dt><span>long_name :</span></dt><dd>Classification and quality flags</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 781.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 100, 200) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 100 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"74\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"94.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2000</text>\n",
+       "  <text x=\"164.948598\" y=\"44.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,44.948598)\">1000</text>\n",
+       "  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;</div><input id='attrs-26a46ecd-1e3e-4ee5-ba89-5ec65ff04884' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26a46ecd-1e3e-4ee5-ba89-5ec65ff04884' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8427a506-9b1a-43a8-bc21-091b26115ed3' class='xr-var-data-in' type='checkbox'><label for='data-8427a506-9b1a-43a8-bc21-091b26115ed3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80 B </td>\n",
+       "                        <td> 80 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 2) </td>\n",
+       "                        <td> (5, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"98\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"48\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"48\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 48.0,0.0 48.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"24.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"68.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,68.000000,60.000000)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d11d2b25-9409-48b5-ae89-f4f8fd9ffbd6' class='xr-section-summary-in' type='checkbox'  ><label for='section-d11d2b25-9409-48b5-ae89-f4f8fd9ffbd6' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f44e8e9b-5de4-45d0-8246-7f930604165c' class='xr-index-data-in' type='checkbox'/><label for='index-f44e8e9b-5de4-45d0-8246-7f930604165c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([52.49875, 52.49625, 52.49375, 52.49125, 52.48875, 52.48625, 52.48375,\n",
+       "       52.48125, 52.47875, 52.47625,\n",
+       "       ...\n",
+       "       50.02375, 50.02125, 50.01875, 50.01625, 50.01375, 50.01125, 50.00875,\n",
+       "       50.00625, 50.00375, 50.00125],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed2651b8-2a9b-4094-ba03-ce5cd7e13d97' class='xr-index-data-in' type='checkbox'/><label for='index-ed2651b8-2a9b-4094-ba03-ce5cd7e13d97' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             0.00125,              0.00375,              0.00625,\n",
+       "       0.008749999999999999,              0.01125,              0.01375,\n",
+       "                    0.01625, 0.018750000000000003,              0.02125,\n",
+       "                    0.02375,\n",
+       "       ...\n",
+       "                    4.97625,              4.97875,              4.98125,\n",
+       "                    4.98375,              4.98625,              4.98875,\n",
+       "                    4.99125,   4.9937499999999995,              4.99625,\n",
+       "                    4.99875],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ab83071-e06d-4a88-aa1f-dd174b58162e' class='xr-index-data-in' type='checkbox'/><label for='index-6ab83071-e06d-4a88-aa1f-dd174b58162e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-16 10:09:21.834255872&#x27;,\n",
+       "               &#x27;2017-01-25 09:35:51.060063488&#x27;,\n",
+       "               &#x27;2017-01-26 10:50:16.686192896&#x27;,\n",
+       "               &#x27;2017-01-28 09:58:11.350386176&#x27;,\n",
+       "               &#x27;2017-01-30 10:46:33.836892416&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00ea562d-0f39-4f3f-8432-071a9e3266bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00ea562d-0f39-4f3f-8432-071a9e3266bf' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 400MB\n",
+       "Dimensions:                 (time: 5, lat: 1000, lon: 2000, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * lat                     (lat) float64 8kB 52.5 52.5 52.49 ... 50.0 50.0\n",
+       "  * lon                     (lon) float64 16kB 0.00125 0.00375 ... 4.996 4.999\n",
+       "  * time                    (time) datetime64[ns] 40B 2017-01-16T10:09:21.834...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    analytic_c2rcc_flags    (time, lat, lon) float64 80MB dask.array<chunksize=(5, 100, 200), meta=np.ndarray>\n",
+       "    analytic_conc_chl       (time, lat, lon) float64 80MB dask.array<chunksize=(5, 100, 200), meta=np.ndarray>\n",
+       "    analytic_conc_tsm       (time, lat, lon) float64 80MB dask.array<chunksize=(5, 100, 200), meta=np.ndarray>\n",
+       "    analytic_kd489          (time, lat, lon) float64 80MB dask.array<chunksize=(5, 100, 200), meta=np.ndarray>\n",
+       "    analytic_lat_bnds       (lat, bnds) float64 16kB dask.array<chunksize=(1000, 2), meta=np.ndarray>\n",
+       "    analytic_lon_bnds       (lon, bnds) float64 32kB dask.array<chunksize=(2000, 2), meta=np.ndarray>\n",
+       "    analytic_quality_flags  (time, lat, lon) float64 80MB dask.array<chunksize=(5, 100, 200), meta=np.ndarray>\n",
+       "    analytic_time_bnds      (time, bnds) datetime64[ns] 80B dask.array<chunksize=(5, 2), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.7"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = store.open_data(\"collections/datacubes/items/local_ts\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f37e534f-ac9a-4a76-89bf-3d32659e2492",
+   "metadata": {},
+   "source": [
+    "We plot the loaded data as an example below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4a77d253-232d-4db4-9f26-819d5dcb8671",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 947 ms, sys: 160 ms, total: 1.11 s\n",
+      "Wall time: 1.34 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x711f84320d70>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.analytic_conc_chl.isel(time=0).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af8c698c-2776-4213-9b73-793ed786b116",
+   "metadata": {},
+   "source": [
+    "We can also add all open data parameters which are provided by xcube for the data type `dataset` and format ID `zarr`. The name of the keyword argument is given by `open_params_<data_type>_<format>`, which is analgous to the [xcube data store namimng convetion](https://xcube.readthedocs.io/en/latest/storeconv.html#naming-identifiers). To view all possible open parameters for different data types and format IDs, one can run the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "526b6230-0fa5-424f-a762-8a796b9c2185",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.38 ms, sys: 0 ns, total: 2.38 ms\n",
+      "Wall time: 1.93 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "application/json": {
+       "additionalProperties": false,
+       "properties": {
+        "asset_names": {
+         "description": "Names of assets which will be included in the data cube.",
+         "items": {
+          "minLength": 0,
+          "type": "string"
+         },
+         "title": "Names of assets",
+         "type": "array",
+         "uniqueItems": true
+        },
+        "open_params_dataset_geotiff": {
+         "additionalProperties": false,
+         "properties": {
+          "overview_level": {
+           "default": null,
+           "description": "GeoTIFF overview level. 0 is the first overview.",
+           "type": [
+            "integer",
+            "null"
+           ]
+          },
+          "tile_size": {
+           "default": [
+            512,
+            512
+           ],
+           "items": [
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            },
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            }
+           ],
+           "type": "array"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_dataset_levels": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_dataset_netcdf": {
+         "additionalProperties": true,
+         "properties": {},
+         "type": "object"
+        },
+        "open_params_dataset_zarr": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_mldataset_geotiff": {
+         "additionalProperties": false,
+         "properties": {
+          "tile_size": {
+           "default": [
+            512,
+            512
+           ],
+           "items": [
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            },
+            {
+             "default": 512,
+             "minimum": 256,
+             "type": "number"
+            }
+           ],
+           "type": "array"
+          }
+         },
+         "type": "object"
+        },
+        "open_params_mldataset_levels": {
+         "additionalProperties": false,
+         "properties": {
+          "cache_size": {
+           "minimum": 0,
+           "type": "integer"
+          },
+          "chunks": {
+           "additionalProperties": true,
+           "description": "Optional chunk sizes along each dimension. Chunk size values may be None, \"auto\" or an integer value.",
+           "examples": [
+            {
+             "lat": "auto",
+             "lon": 90,
+             "time": null
+            },
+            {
+             "time": 1,
+             "x": 512,
+             "y": 512
+            }
+           ],
+           "properties": {},
+           "type": "object"
+          },
+          "consolidated": {
+           "default": false,
+           "description": "Whether to open the store using Zarr's consolidated metadata capability. Only works for stores that have already been consolidated.",
+           "type": "boolean"
+          },
+          "decode_cf": {
+           "default": true,
+           "description": "Whether to decode these variables, assuming they were saved according to CF conventions.",
+           "type": "boolean"
+          },
+          "decode_coords": {
+           "default": true,
+           "description": "If True, decode the \"coordinates\" attribute to identify coordinates in the resulting dataset.",
+           "type": "boolean"
+          },
+          "decode_times": {
+           "default": true,
+           "description": "If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers.",
+           "type": "boolean"
+          },
+          "drop_variables": {
+           "items": {
+            "minLength": 1,
+            "type": "string"
+           },
+           "type": "array"
+          },
+          "group": {
+           "description": "Group path. (a.k.a. path in zarr terminology.).",
+           "minLength": 1,
+           "type": "string"
+          },
+          "log_access": {
+           "default": false,
+           "type": "boolean"
+          },
+          "mask_and_scale": {
+           "default": true,
+           "description": "If True, replace array values equal to attribute \"_FillValue\" with NaN.  Use \"scale_factor\" and \"add_offset\" attributes to compute actual values.",
+           "type": "boolean"
+          }
+         },
+         "type": "object"
+        }
+       },
+       "type": "object"
+      },
+      "text/plain": [
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x711f8424fb00>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "store.get_open_data_params_schema()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86c4e9cf-2349-4be7-a779-5530b8603938",
+   "metadata": {},
+   "source": [
+    " The open parameters needs to be placed in a dictionary and given to the respective keyword argument. In the example below, we change the chunking of the represented data source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6372609c-cb37-4acb-b2f3-06a5e9e429c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 64.7 ms, sys: 4.09 ms, total: 68.8 ms\n",
+      "Wall time: 78.1 ms\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/konstantin/micromamba/envs/xcube-stac/lib/python3.12/site-packages/xarray/core/dataset.py:273: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 200. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n",
+      "/home/konstantin/micromamba/envs/xcube-stac/lib/python3.12/site-packages/xarray/core/dataset.py:273: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 400. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 400MB\n",
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+       "Coordinates:\n",
+       "  * lat                     (lat) float64 8kB 52.5 52.5 52.49 ... 50.0 50.0\n",
+       "  * lon                     (lon) float64 16kB 0.00125 0.00375 ... 4.996 4.999\n",
+       "  * time                    (time) datetime64[ns] 40B 2017-01-16T10:09:21.834...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    analytic_c2rcc_flags    (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;\n",
+       "    analytic_conc_chl       (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;\n",
+       "    analytic_conc_tsm       (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;\n",
+       "    analytic_kd489          (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;\n",
+       "    analytic_lat_bnds       (lat, bnds) float64 16kB dask.array&lt;chunksize=(200, 2), meta=np.ndarray&gt;\n",
+       "    analytic_lon_bnds       (lon, bnds) float64 32kB dask.array&lt;chunksize=(400, 2), meta=np.ndarray&gt;\n",
+       "    analytic_quality_flags  (time, lat, lon) float64 80MB dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;\n",
+       "    analytic_time_bnds      (time, bnds) datetime64[ns] 80B dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a94c8890-7b28-49f4-8623-23f6c4d6cc91' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a94c8890-7b28-49f4-8623-23f6c4d6cc91' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>lat</span>: 1000</li><li><span class='xr-has-index'>lon</span>: 2000</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-89891c2f-5db7-4991-a9e2-e8146a8badab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-89891c2f-5db7-4991-a9e2-e8146a8badab' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.5 52.5 52.49 ... 50.01 50.0 50.0</div><input id='attrs-30b14cac-446b-48e3-9219-9182020c5bfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30b14cac-446b-48e3-9219-9182020c5bfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f08378f4-3bd9-4e13-a934-35a6e84231a8' class='xr-var-data-in' type='checkbox'><label for='data-f08378f4-3bd9-4e13-a934-35a6e84231a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([52.49875, 52.49625, 52.49375, ..., 50.00625, 50.00375, 50.00125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.00125 0.00375 ... 4.996 4.999</div><input id='attrs-47da2cc0-6e2f-4813-8700-5e23de0fcf89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47da2cc0-6e2f-4813-8700-5e23de0fcf89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e7fcae1-2c2f-42d9-9d64-516443e1a9a4' class='xr-var-data-in' type='checkbox'><label for='data-9e7fcae1-2c2f-42d9-9d64-516443e1a9a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-03, 3.75000e-03, 6.25000e-03, ..., 4.99375e+00, 4.99625e+00,\n",
+       "       4.99875e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-16T10:09:21.834255872 .....</div><input id='attrs-bc41129e-8c44-4f5a-a8e7-25cec4e466bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc41129e-8c44-4f5a-a8e7-25cec4e466bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b09d7921-593b-4e3c-8807-acef26e66ed3' class='xr-var-data-in' type='checkbox'><label for='data-b09d7921-593b-4e3c-8807-acef26e66ed3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-16T10:09:21.834255872&#x27;, &#x27;2017-01-25T09:35:51.060063488&#x27;,\n",
+       "       &#x27;2017-01-26T10:50:16.686192896&#x27;, &#x27;2017-01-28T09:58:11.350386176&#x27;,\n",
+       "       &#x27;2017-01-30T10:46:33.836892416&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f74ba6e-cbf4-44f4-886f-58805454d1ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f74ba6e-cbf4-44f4-886f-58805454d1ee' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analytic_c2rcc_flags</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;</div><input id='attrs-12850341-2ca3-4617-8ceb-3b85bb8e5836' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12850341-2ca3-4617-8ceb-3b85bb8e5836' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d08fdbf8-ce5d-41e7-bc7c-8bca79571a1d' class='xr-var-data-in' type='checkbox'><label for='data-d08fdbf8-ce5d-41e7-bc7c-8bca79571a1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_coding_name :</span></dt><dd>c2rcc_flags</dd><dt><span>flag_descriptions :</span></dt><dd>The input spectrum to the atmospheric correction neural net was out of the scope of the training range and the inversion is likely to be wrong\tThe input spectrum to the atmospheric correction neural net out of training range\tOne of the inputs to the IOP retrieval neural net is out of training range\tHigh downwelling transmission is indicating cloudy conditions\tOne of the IOPs is out of range\tApig output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tAdet output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tAgelb output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tBpart output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tBwit output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tApig output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tAdet output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tAgelb output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tBpart output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tBwit output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tThe Rhow input spectrum to IOP neural net is probably not within the training range of the neural net, and the inversion is likely to be wrong.\tKd489 is out of range\tKdmin is out of range\tKdmin is at max\tKdmin is at max\tThe operators valid pixel expression has resolved to true</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, -2147483648]</dd><dt><span>flag_meanings :</span></dt><dd>Rtosa_OOS Rtosa_OOR Rhow_OOR Cloud_risk Iop_OOR Apig_at_max Adet_at_max Agelb_at_max Bpart_at_max Bwit_at_max Apig_at_min Adet_at_min Agelb_at_min Bpart_at_min Bwit_at_min Rhow_OOS Kd489_OOR Kdmin_OOR Kd489_at_max Kdmin_at_max Valid_PE</dd><dt><span>long_name :</span></dt><dd>C2RCC quality flags</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 3.05 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 200, 400) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_conc_chl</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;</div><input id='attrs-99bf94b4-aa05-4453-880e-66b728324878' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99bf94b4-aa05-4453-880e-66b728324878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2bf5f38-1e5b-4d3c-8eae-64e6d6eb2c39' class='xr-var-data-in' type='checkbox'><label for='data-d2bf5f38-1e5b-4d3c-8eae-64e6d6eb2c39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>color_table_blue_values :</span></dt><dd>[128, 255, 255, 255, 255, 0, 0, 0, 0, 0]</dd><dt><span>color_table_green_values :</span></dt><dd>[0, 0, 102, 204, 255, 128, 255, 95, 0, 0]</dd><dt><span>color_table_red_values :</span></dt><dd>[0, 0, 51, 0, 0, 0, 255, 255, 215, 150]</dd><dt><span>color_table_sample_values :</span></dt><dd>[0.0, 0.5, 1.0, 2.0, 3.0, 4.5, 13.0, 25.0, 30.0, 40.0]</dd><dt><span>long_name :</span></dt><dd>Chlorophyll concentration</dd><dt><span>units :</span></dt><dd>mg m^-3</dd><dt><span>valid_pixel_expression :</span></dt><dd>c2rcc_flags.Valid_PE</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 3.05 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 200, 400) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_conc_tsm</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;</div><input id='attrs-b5b92a9b-1814-4d08-8617-30e8df1398aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5b92a9b-1814-4d08-8617-30e8df1398aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-383c7521-f723-4e8c-ae2e-d704065c98a4' class='xr-var-data-in' type='checkbox'><label for='data-383c7521-f723-4e8c-ae2e-d704065c98a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total suspended matter dry weight concentration</dd><dt><span>units :</span></dt><dd>g m^-3</dd><dt><span>valid_pixel_expression :</span></dt><dd>c2rcc_flags.Valid_PE</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 3.05 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 200, 400) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_kd489</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;</div><input id='attrs-2236c79d-6fac-474c-8607-9dac96db874a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2236c79d-6fac-474c-8607-9dac96db874a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c3637a6-8bb0-4191-b057-e6d9b66df133' class='xr-var-data-in' type='checkbox'><label for='data-6c3637a6-8bb0-4191-b057-e6d9b66df133' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Irradiance attenuation coefficient at 489 nm</dd><dt><span>units :</span></dt><dd>m^-1</dd><dt><span>valid_pixel_expression :</span></dt><dd>c2rcc_flags.Valid_PE</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 3.05 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 200, 400) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 2), meta=np.ndarray&gt;</div><input id='attrs-6bc37cf5-9d73-40c3-8684-af217a1c9dad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bc37cf5-9d73-40c3-8684-af217a1c9dad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1957f31a-0a24-4083-ab88-196dba9e5316' class='xr-var-data-in' type='checkbox'><label for='data-1957f31a-0a24-4083-ab88-196dba9e5316' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 15.62 kiB </td>\n",
+       "                        <td> 3.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1000, 2) </td>\n",
+       "                        <td> (200, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(400, 2), meta=np.ndarray&gt;</div><input id='attrs-46cdf31f-a962-484b-bf80-2c601810d5c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46cdf31f-a962-484b-bf80-2c601810d5c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2eb2a64-849a-4189-851f-9859b0761463' class='xr-var-data-in' type='checkbox'><label for='data-c2eb2a64-849a-4189-851f-9859b0761463' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 31.25 kiB </td>\n",
+       "                        <td> 6.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2000, 2) </td>\n",
+       "                        <td> (400, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 5 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"25\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n",
+       "  <line x1=\"0\" y1=\"72\" x2=\"25\" y2=\"72\" />\n",
+       "  <line x1=\"0\" y1=\"96\" x2=\"25\" y2=\"96\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">2000</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_quality_flags</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 200, 400), meta=np.ndarray&gt;</div><input id='attrs-90eb149d-acfc-4cfb-afa0-834b2e81f02f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90eb149d-acfc-4cfb-afa0-834b2e81f02f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da8a6a29-75f3-4e80-9241-eb9ded2fa557' class='xr-var-data-in' type='checkbox'><label for='data-da8a6a29-75f3-4e80-9241-eb9ded2fa557' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_coding_name :</span></dt><dd>quality_flags</dd><dt><span>flag_descriptions :</span></dt><dd></dd><dt><span>flag_masks :</span></dt><dd>[-2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288, 262144, 131072, 65536, 32768, 16384, 8192, 4096, 2048, 1024, 512, 256, 128, 64, 32, 16, 8, 4, 2, 1]</dd><dt><span>flag_meanings :</span></dt><dd>land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun_glint_risk dubious saturated_Oa01 saturated_Oa02 saturated_Oa03 saturated_Oa04 saturated_Oa05 saturated_Oa06 saturated_Oa07 saturated_Oa08 saturated_Oa09 saturated_Oa10 saturated_Oa11 saturated_Oa12 saturated_Oa13 saturated_Oa14 saturated_Oa15 saturated_Oa16 saturated_Oa17 saturated_Oa18 saturated_Oa19 saturated_Oa20 saturated_Oa21</dd><dt><span>long_name :</span></dt><dd>Classification and quality flags</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 3.05 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (5, 200, 400) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 25 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"74\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"74\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,74.9485979497544 24.9485979497544,74.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"94.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2000</text>\n",
+       "  <text x=\"164.948598\" y=\"44.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,44.948598)\">1000</text>\n",
+       "  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;</div><input id='attrs-eedd828e-5006-44b9-9d7f-8c3afd655e33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eedd828e-5006-44b9-9d7f-8c3afd655e33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cb7e75d-c4c6-4460-aebc-70c9256cf91c' class='xr-var-data-in' type='checkbox'><label for='data-3cb7e75d-c4c6-4460-aebc-70c9256cf91c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80 B </td>\n",
+       "                        <td> 80 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 2) </td>\n",
+       "                        <td> (5, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"98\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"48\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"48\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 48.0,0.0 48.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"24.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"68.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,68.000000,60.000000)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a201d1f7-dea6-454e-bab2-f55284ca1795' class='xr-section-summary-in' type='checkbox'  ><label for='section-a201d1f7-dea6-454e-bab2-f55284ca1795' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e6a5b64-e06c-45ea-b63d-7ce22ddc96c8' class='xr-index-data-in' type='checkbox'/><label for='index-7e6a5b64-e06c-45ea-b63d-7ce22ddc96c8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([52.49875, 52.49625, 52.49375, 52.49125, 52.48875, 52.48625, 52.48375,\n",
+       "       52.48125, 52.47875, 52.47625,\n",
+       "       ...\n",
+       "       50.02375, 50.02125, 50.01875, 50.01625, 50.01375, 50.01125, 50.00875,\n",
+       "       50.00625, 50.00375, 50.00125],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2306cc20-fa27-496e-a31e-8e0164e2b7f6' class='xr-index-data-in' type='checkbox'/><label for='index-2306cc20-fa27-496e-a31e-8e0164e2b7f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             0.00125,              0.00375,              0.00625,\n",
+       "       0.008749999999999999,              0.01125,              0.01375,\n",
+       "                    0.01625, 0.018750000000000003,              0.02125,\n",
+       "                    0.02375,\n",
+       "       ...\n",
+       "                    4.97625,              4.97875,              4.98125,\n",
+       "                    4.98375,              4.98625,              4.98875,\n",
+       "                    4.99125,   4.9937499999999995,              4.99625,\n",
+       "                    4.99875],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0eb20ca3-a13c-4d46-9df7-a0d5aa528b08' class='xr-index-data-in' type='checkbox'/><label for='index-0eb20ca3-a13c-4d46-9df7-a0d5aa528b08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-16 10:09:21.834255872&#x27;,\n",
+       "               &#x27;2017-01-25 09:35:51.060063488&#x27;,\n",
+       "               &#x27;2017-01-26 10:50:16.686192896&#x27;,\n",
+       "               &#x27;2017-01-28 09:58:11.350386176&#x27;,\n",
+       "               &#x27;2017-01-30 10:46:33.836892416&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc83b9ee-b2ce-4207-be26-61f8596c3b53' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc83b9ee-b2ce-4207-be26-61f8596c3b53' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 400MB\n",
+       "Dimensions:                 (time: 5, lat: 1000, lon: 2000, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * lat                     (lat) float64 8kB 52.5 52.5 52.49 ... 50.0 50.0\n",
+       "  * lon                     (lon) float64 16kB 0.00125 0.00375 ... 4.996 4.999\n",
+       "  * time                    (time) datetime64[ns] 40B 2017-01-16T10:09:21.834...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    analytic_c2rcc_flags    (time, lat, lon) float64 80MB dask.array<chunksize=(5, 200, 400), meta=np.ndarray>\n",
+       "    analytic_conc_chl       (time, lat, lon) float64 80MB dask.array<chunksize=(5, 200, 400), meta=np.ndarray>\n",
+       "    analytic_conc_tsm       (time, lat, lon) float64 80MB dask.array<chunksize=(5, 200, 400), meta=np.ndarray>\n",
+       "    analytic_kd489          (time, lat, lon) float64 80MB dask.array<chunksize=(5, 200, 400), meta=np.ndarray>\n",
+       "    analytic_lat_bnds       (lat, bnds) float64 16kB dask.array<chunksize=(200, 2), meta=np.ndarray>\n",
+       "    analytic_lon_bnds       (lon, bnds) float64 32kB dask.array<chunksize=(400, 2), meta=np.ndarray>\n",
+       "    analytic_quality_flags  (time, lat, lon) float64 80MB dask.array<chunksize=(5, 200, 400), meta=np.ndarray>\n",
+       "    analytic_time_bnds      (time, bnds) datetime64[ns] 80B dask.array<chunksize=(5, 2), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.7"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = store.open_data(\"collections/datacubes/items/local_ts\", open_params_dataset_zarr=dict(chunks={\"time\": 5, \"lat\": 200, \"lon\": 400}))\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15de1fe8-70d3-450f-8698-71b5dabf19ab",
+   "metadata": {},
+   "source": [
+    "Next we will open a file stored in the xcube's [multi-resolution dataset](https://xcube.readthedocs.io/en/latest/mldatasets.html) `.levels` format. \n",
+    "\n",
+    "Xcube server punlishes each data resource as a `xarray.DataSet` and as [multi-resolution dataset](https://xcube.readthedocs.io/en/latest/mldatasets.html) with the asset names `\"analytic\"` and `\"analytic_multires\"`, respectively. If the parameter `asset_names` is not specified on the `open_data` method, `\"analytic\"` is selected as default and a `xarray.DataSet` is returned. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9b94ca56-1af5-4782-97c4-21b7d294d625",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 63.9 ms, sys: 3.98 ms, total: 67.9 ms\n",
+      "Wall time: 95.7 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
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+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 560MB\n",
+       "Dimensions:                 (time: 5, lat: 1000, lon: 2000, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * lat                     (lat) float64 8kB 52.5 52.5 52.49 ... 50.0 50.0\n",
+       "  * lon                     (lon) float64 16kB 0.00125 0.00375 ... 4.996 4.999\n",
+       "  * time                    (time) datetime64[ns] 40B 2017-01-16T10:09:21.834...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    analytic_c2rcc_flags    (time, lat, lon) float64 80MB dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;\n",
+       "    analytic_chl_category   (time, lat, lon) float64 80MB dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;\n",
+       "    analytic_chl_tsm_sum    (time, lat, lon) float64 80MB dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;\n",
+       "    analytic_conc_chl       (time, lat, lon) float64 80MB dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;\n",
+       "    analytic_conc_tsm       (time, lat, lon) float64 80MB dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;\n",
+       "    analytic_kd489          (time, lat, lon) float64 80MB dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;\n",
+       "    analytic_lat_bnds       (lat, bnds) float64 16kB dask.array&lt;chunksize=(1000, 2), meta=np.ndarray&gt;\n",
+       "    analytic_lon_bnds       (lon, bnds) float64 32kB dask.array&lt;chunksize=(2000, 2), meta=np.ndarray&gt;\n",
+       "    analytic_quality_flags  (time, lat, lon) float64 80MB dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;\n",
+       "    analytic_time_bnds      (time, bnds) datetime64[ns] 80B dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7c1ca84a-596c-42cb-b932-ff7dc375d3a1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c1ca84a-596c-42cb-b932-ff7dc375d3a1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>lat</span>: 1000</li><li><span class='xr-has-index'>lon</span>: 2000</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a4a37f4c-faab-47d1-91c8-6727f6eb22af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4a37f4c-faab-47d1-91c8-6727f6eb22af' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.5 52.5 52.49 ... 50.01 50.0 50.0</div><input id='attrs-a4ecd745-76b4-4d6d-879e-0c492f0e3728' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4ecd745-76b4-4d6d-879e-0c492f0e3728' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96937040-de76-487b-92be-45a0ec149704' class='xr-var-data-in' type='checkbox'><label for='data-96937040-de76-487b-92be-45a0ec149704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([52.49875, 52.49625, 52.49375, ..., 50.00625, 50.00375, 50.00125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.00125 0.00375 ... 4.996 4.999</div><input id='attrs-a92da019-eff4-45ba-ab02-c0494861867c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a92da019-eff4-45ba-ab02-c0494861867c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6379c648-98c8-4c42-85c9-3bdf8886b3ac' class='xr-var-data-in' type='checkbox'><label for='data-6379c648-98c8-4c42-85c9-3bdf8886b3ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-03, 3.75000e-03, 6.25000e-03, ..., 4.99375e+00, 4.99625e+00,\n",
+       "       4.99875e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-16T10:09:21.834255872 .....</div><input id='attrs-fdf11a01-1657-4a16-899f-8d0f07f449ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdf11a01-1657-4a16-899f-8d0f07f449ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2607d27-613e-4869-987f-72e9dc618208' class='xr-var-data-in' type='checkbox'><label for='data-e2607d27-613e-4869-987f-72e9dc618208' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-16T10:09:21.834255872&#x27;, &#x27;2017-01-25T09:35:51.060063488&#x27;,\n",
+       "       &#x27;2017-01-26T10:50:16.686192896&#x27;, &#x27;2017-01-28T09:58:11.350386176&#x27;,\n",
+       "       &#x27;2017-01-30T10:46:33.836892416&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81cebef9-6e50-477a-bb70-c0e07046246d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81cebef9-6e50-477a-bb70-c0e07046246d' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analytic_c2rcc_flags</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-6314a5c1-5ff8-4a00-839b-8613ffcae878' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6314a5c1-5ff8-4a00-839b-8613ffcae878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07d9117f-b804-4074-bb0d-f70f3d479d06' class='xr-var-data-in' type='checkbox'><label for='data-07d9117f-b804-4074-bb0d-f70f3d479d06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_coding_name :</span></dt><dd>c2rcc_flags</dd><dt><span>flag_descriptions :</span></dt><dd>The input spectrum to the atmospheric correction neural net was out of the scope of the training range and the inversion is likely to be wrong\tThe input spectrum to the atmospheric correction neural net out of training range\tOne of the inputs to the IOP retrieval neural net is out of training range\tHigh downwelling transmission is indicating cloudy conditions\tOne of the IOPs is out of range\tApig output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tAdet output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tAgelb output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tBpart output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tBwit output of the IOP retrieval neural net is at its maximum. This means that the true value is this value or higher.\tApig output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tAdet output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tAgelb output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tBpart output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tBwit output of the IOP retrieval neural net is at its minimum. This means that the true value is this value or lower.\tThe Rhow input spectrum to IOP neural net is probably not within the training range of the neural net, and the inversion is likely to be wrong.\tKd489 is out of range\tKdmin is out of range\tKdmin is at max\tKdmin is at max\tThe operators valid pixel expression has resolved to true</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, -2147483648]</dd><dt><span>flag_meanings :</span></dt><dd>Rtosa_OOS Rtosa_OOR Rhow_OOR Cloud_risk Iop_OOR Apig_at_max Adet_at_max Agelb_at_max Bpart_at_max Bwit_at_max Apig_at_min Adet_at_min Agelb_at_min Bpart_at_min Bwit_at_min Rhow_OOS Kd489_OOR Kdmin_OOR Kd489_at_max Kdmin_at_max Valid_PE</dd><dt><span>long_name :</span></dt><dd>C2RCC quality flags</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 488.28 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (1, 250, 250) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 160 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_chl_category</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-0959e9d2-4764-4aa6-aa9a-7cdbc8c03fef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0959e9d2-4764-4aa6-aa9a-7cdbc8c03fef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe531ca1-7663-4332-bc62-b6d363af617f' class='xr-var-data-in' type='checkbox'><label for='data-fe531ca1-7663-4332-bc62-b6d363af617f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>color_value_min :</span></dt><dd>0.0</dd><dt><span>color_value_max :</span></dt><dd>2.0</dd><dt><span>color_bar_name :</span></dt><dd>spring</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>long_name :</span></dt><dd>Chlorophyll bloom risk</dd><dt><span>description :</span></dt><dd>Chlorophyll bloom risk in three categories: 0: CHL &lt; 3, 1: 3 &lt;= CHL &lt; 4, 2: CHL &gt;= 4 mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 488.28 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (1, 250, 250) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 160 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_chl_tsm_sum</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-74a21d13-4766-4f00-a13e-c761a90d5e48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74a21d13-4766-4f00-a13e-c761a90d5e48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a423ae3-d097-4d41-86bc-76e324515abd' class='xr-var-data-in' type='checkbox'><label for='data-4a423ae3-d097-4d41-86bc-76e324515abd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>-</dd><dt><span>long_name :</span></dt><dd>Weighted sum of CHL nd TSM concentrations</dd><dt><span>description :</span></dt><dd>Nonsense variable, for demo purpose only</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 488.28 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (1, 250, 250) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 160 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_conc_chl</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-9c8425ac-b0eb-4a98-93c8-6dafe7b0c265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c8425ac-b0eb-4a98-93c8-6dafe7b0c265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85923f31-e074-457a-adf5-070d4fc49840' class='xr-var-data-in' type='checkbox'><label for='data-85923f31-e074-457a-adf5-070d4fc49840' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>color_table_blue_values :</span></dt><dd>[128, 255, 255, 255, 255, 0, 0, 0, 0, 0]</dd><dt><span>color_table_green_values :</span></dt><dd>[0, 0, 102, 204, 255, 128, 255, 95, 0, 0]</dd><dt><span>color_table_red_values :</span></dt><dd>[0, 0, 51, 0, 0, 0, 255, 255, 215, 150]</dd><dt><span>color_table_sample_values :</span></dt><dd>[0.0, 0.5, 1.0, 2.0, 3.0, 4.5, 13.0, 25.0, 30.0, 40.0]</dd><dt><span>long_name :</span></dt><dd>Chlorophyll concentration</dd><dt><span>units :</span></dt><dd>mg m^-3</dd><dt><span>valid_pixel_expression :</span></dt><dd>c2rcc_flags.Valid_PE</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 488.28 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (1, 250, 250) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 160 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_conc_tsm</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-544bfe94-200a-4bc2-be13-e32eaec65e64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-544bfe94-200a-4bc2-be13-e32eaec65e64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef6e1a00-92f2-4e40-992c-e532407defd9' class='xr-var-data-in' type='checkbox'><label for='data-ef6e1a00-92f2-4e40-992c-e532407defd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total suspended matter dry weight concentration</dd><dt><span>units :</span></dt><dd>g m^-3</dd><dt><span>valid_pixel_expression :</span></dt><dd>c2rcc_flags.Valid_PE</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 488.28 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (1, 250, 250) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 160 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_kd489</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-dd75c4ca-604c-4555-b0eb-2f6ce0907d8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd75c4ca-604c-4555-b0eb-2f6ce0907d8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a920e08-12a1-4a9f-a46d-6ffe8b22e856' class='xr-var-data-in' type='checkbox'><label for='data-7a920e08-12a1-4a9f-a46d-6ffe8b22e856' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Irradiance attenuation coefficient at 489 nm</dd><dt><span>units :</span></dt><dd>m^-1</dd><dt><span>valid_pixel_expression :</span></dt><dd>c2rcc_flags.Valid_PE</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 488.28 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (1, 250, 250) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 160 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1000, 2), meta=np.ndarray&gt;</div><input id='attrs-944662ca-1048-4f2e-a7bb-941d4f42fc0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-944662ca-1048-4f2e-a7bb-941d4f42fc0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bc5bbe0-8408-42d0-888c-1f5ac923cb5b' class='xr-var-data-in' type='checkbox'><label for='data-2bc5bbe0-8408-42d0-888c-1f5ac923cb5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 15.62 kiB </td>\n",
+       "                        <td> 15.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1000, 2) </td>\n",
+       "                        <td> (1000, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2000, 2), meta=np.ndarray&gt;</div><input id='attrs-ae3abe9e-9f82-4b74-a19c-e8d911140397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae3abe9e-9f82-4b74-a19c-e8d911140397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c1046c3-e2f2-4f6c-aef3-9ce41d6008ec' class='xr-var-data-in' type='checkbox'><label for='data-1c1046c3-e2f2-4f6c-aef3-9ce41d6008ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 31.25 kiB </td>\n",
+       "                        <td> 31.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2000, 2) </td>\n",
+       "                        <td> (2000, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_quality_flags</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-883671c9-48d9-4bb9-b619-8bcb9a0fa088' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-883671c9-48d9-4bb9-b619-8bcb9a0fa088' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e60badd6-9155-4790-87c8-78791013c1f5' class='xr-var-data-in' type='checkbox'><label for='data-e60badd6-9155-4790-87c8-78791013c1f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_coding_name :</span></dt><dd>quality_flags</dd><dt><span>flag_descriptions :</span></dt><dd></dd><dt><span>flag_masks :</span></dt><dd>[-2147483648, 1073741824, 536870912, 268435456, 134217728, 67108864, 33554432, 16777216, 8388608, 4194304, 2097152, 1048576, 524288, 262144, 131072, 65536, 32768, 16384, 8192, 4096, 2048, 1024, 512, 256, 128, 64, 32, 16, 8, 4, 2, 1]</dd><dt><span>flag_meanings :</span></dt><dd>land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun_glint_risk dubious saturated_Oa01 saturated_Oa02 saturated_Oa03 saturated_Oa04 saturated_Oa05 saturated_Oa06 saturated_Oa07 saturated_Oa08 saturated_Oa09 saturated_Oa10 saturated_Oa11 saturated_Oa12 saturated_Oa13 saturated_Oa14 saturated_Oa15 saturated_Oa16 saturated_Oa17 saturated_Oa18 saturated_Oa19 saturated_Oa20 saturated_Oa21</dd><dt><span>long_name :</span></dt><dd>Classification and quality flags</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 76.29 MiB </td>\n",
+       "                        <td> 488.28 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 1000, 2000) </td>\n",
+       "                        <td> (1, 250, 250) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 160 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"94.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2000</text>\n",
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+       "  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;</div><input id='attrs-ae5cd02b-94d1-44d2-a36c-39fe6a270117' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae5cd02b-94d1-44d2-a36c-39fe6a270117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ce8adfb-7217-4b4d-b4b2-90e7a63a3c34' class='xr-var-data-in' type='checkbox'><label for='data-5ce8adfb-7217-4b4d-b4b2-90e7a63a3c34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80 B </td>\n",
+       "                        <td> 80 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (5, 2) </td>\n",
+       "                        <td> (5, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"98\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"24.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"68.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,68.000000,60.000000)\">5</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-427aecd9-eae9-4629-a352-634cff87953b' class='xr-section-summary-in' type='checkbox'  ><label for='section-427aecd9-eae9-4629-a352-634cff87953b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b4f748f-9337-46a7-9cc1-36e4b88aed71' class='xr-index-data-in' type='checkbox'/><label for='index-4b4f748f-9337-46a7-9cc1-36e4b88aed71' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([52.49875, 52.49625, 52.49375, 52.49125, 52.48875, 52.48625, 52.48375,\n",
+       "       52.48125, 52.47875, 52.47625,\n",
+       "       ...\n",
+       "       50.02375, 50.02125, 50.01875, 50.01625, 50.01375, 50.01125, 50.00875,\n",
+       "       50.00625, 50.00375, 50.00125],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cabd3704-bcf3-4828-a4a5-b2ee02056d71' class='xr-index-data-in' type='checkbox'/><label for='index-cabd3704-bcf3-4828-a4a5-b2ee02056d71' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             0.00125,              0.00375,              0.00625,\n",
+       "       0.008749999999999999,              0.01125,              0.01375,\n",
+       "                    0.01625, 0.018750000000000003,              0.02125,\n",
+       "                    0.02375,\n",
+       "       ...\n",
+       "                    4.97625,              4.97875,              4.98125,\n",
+       "                    4.98375,              4.98625,              4.98875,\n",
+       "                    4.99125,   4.9937499999999995,              4.99625,\n",
+       "                    4.99875],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=2000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f6a0b287-e2c8-455f-a46a-5867bb9a77a7' class='xr-index-data-in' type='checkbox'/><label for='index-f6a0b287-e2c8-455f-a46a-5867bb9a77a7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-16 10:09:21.834255872&#x27;,\n",
+       "               &#x27;2017-01-25 09:35:51.060063488&#x27;,\n",
+       "               &#x27;2017-01-26 10:50:16.686192896&#x27;,\n",
+       "               &#x27;2017-01-28 09:58:11.350386176&#x27;,\n",
+       "               &#x27;2017-01-30 10:46:33.836892416&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7f727b4-7a45-42e8-afcc-3cf99cff60a4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7f727b4-7a45-42e8-afcc-3cf99cff60a4' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 560MB\n",
+       "Dimensions:                 (time: 5, lat: 1000, lon: 2000, bnds: 2)\n",
+       "Coordinates:\n",
+       "  * lat                     (lat) float64 8kB 52.5 52.5 52.49 ... 50.0 50.0\n",
+       "  * lon                     (lon) float64 16kB 0.00125 0.00375 ... 4.996 4.999\n",
+       "  * time                    (time) datetime64[ns] 40B 2017-01-16T10:09:21.834...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    analytic_c2rcc_flags    (time, lat, lon) float64 80MB dask.array<chunksize=(1, 250, 250), meta=np.ndarray>\n",
+       "    analytic_chl_category   (time, lat, lon) float64 80MB dask.array<chunksize=(1, 250, 250), meta=np.ndarray>\n",
+       "    analytic_chl_tsm_sum    (time, lat, lon) float64 80MB dask.array<chunksize=(1, 250, 250), meta=np.ndarray>\n",
+       "    analytic_conc_chl       (time, lat, lon) float64 80MB dask.array<chunksize=(1, 250, 250), meta=np.ndarray>\n",
+       "    analytic_conc_tsm       (time, lat, lon) float64 80MB dask.array<chunksize=(1, 250, 250), meta=np.ndarray>\n",
+       "    analytic_kd489          (time, lat, lon) float64 80MB dask.array<chunksize=(1, 250, 250), meta=np.ndarray>\n",
+       "    analytic_lat_bnds       (lat, bnds) float64 16kB dask.array<chunksize=(1000, 2), meta=np.ndarray>\n",
+       "    analytic_lon_bnds       (lon, bnds) float64 32kB dask.array<chunksize=(2000, 2), meta=np.ndarray>\n",
+       "    analytic_quality_flags  (time, lat, lon) float64 80MB dask.array<chunksize=(1, 250, 250), meta=np.ndarray>\n",
+       "    analytic_time_bnds      (time, bnds) datetime64[ns] 80B dask.array<chunksize=(5, 2), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.7"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = store.open_data(\"collections/datacubes/items/local\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1253d7c0-8eb6-4384-87a2-d5ce40435233",
+   "metadata": {},
+   "source": [
+    "Plotting the same `DataArray` as before will give the same plot as before, since both, the `.levels` and `.zarr` file was derived from the same dataset. (see [server example README.md](https://github.com/xcube-dev/xcube/blob/main/examples/serve/demo/README.md))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "70dc824d-f6e5-44f2-81b4-313bc6a24d61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 487 ms, sys: 64.4 ms, total: 552 ms\n",
+      "Wall time: 587 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x711f8424e8d0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.analytic_conc_chl.isel(time=0).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b374c0d-4105-4d07-9673-12437c245af9",
+   "metadata": {},
+   "source": [
+    "To access the multi-resolution dataset, assign the parameter `data_type` to `mldataset`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3c768d4c-3eff-4439-82a6-7d34d005bb8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 90.5 ms, sys: 728 µs, total: 91.3 ms\n",
+      "Wall time: 132 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "mlds = store.open_data(\"collections/datacubes/items/local\", data_type=\"mldataset\")\n",
+    "mlds.num_levels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd09c58a-10d5-48b8-932e-24002b61ccf5",
+   "metadata": {},
+   "source": [
+    "Now, we can access a dataset with lower resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "71fa6b37-684b-4177-a906-113564494b58",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot rename 'lat_bnds' because it is not a variable or coordinate in this dataset",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_36919/108624633.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/micromamba/envs/xcube-stac/lib/python3.12/site-packages/xcube/core/mldataset/lazy.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     88\u001b[0m         \"\"\"\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_level_datasets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m                 \u001b[0;31m# noinspection PyTypeChecker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m                 \u001b[0mlevel_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_dataset_lazily\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;31m# noinspection PyTypeChecker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_level_datasets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/micromamba/envs/xcube-stac/lib/python3.12/site-packages/xcube/core/mldataset/mapped.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, index, mapper_params)\u001b[0m\n\u001b[1;32m     29\u001b[0m     def _get_dataset_lazily(\n\u001b[1;32m     30\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper_params\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m         return self._mapper_function(\n\u001b[0m\u001b[1;32m     33\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ml_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmapper_params\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/micromamba/envs/xcube-stac/lib/python3.12/site-packages/xcube_stac/utils.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(ds, name_dict)\u001b[0m\n\u001b[1;32m    341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m     \u001b[0mReturns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m         \u001b[0mDataset\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mrenamed\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \"\"\"\n\u001b[0;32m--> 345\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/micromamba/envs/xcube-stac/lib/python3.12/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name_dict, **names)\u001b[0m\n\u001b[1;32m   4392\u001b[0m         \"\"\"\n\u001b[1;32m   4393\u001b[0m         \u001b[0mname_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meither_dict_or_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rename_vars\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4394\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mname_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4395\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4396\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m   4397\u001b[0m                     \u001b[0;34mf\"\u001b[0m\u001b[0;34mcannot rename \u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m!\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m because it is not a \u001b[0m\u001b[0;34m\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4398\u001b[0m                     \u001b[0;34m\"variable or coordinate in this dataset\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4399\u001b[0m                 \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: cannot rename 'lat_bnds' because it is not a variable or coordinate in this dataset"
+     ]
+    }
+   ],
+   "source": [
+    "ds = mlds.get_dataset(2)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01a39978-07a9-4545-9ad8-e0271c4c2ef8",
+   "metadata": {},
+   "source": [
+    "Next, we can open a cloud-optimized GeoTIFF file, which alos consists of multiple layers corresponding to multiple spatial resolutions. Also here, if no specfic asset name is given, the data resource will be presented as `xr.Dataset`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2642bcb5-499f-49d4-a8b7-8103d2ee714e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 61.8 ms, sys: 202 µs, total: 62 ms\n",
+      "Wall time: 82.1 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
+       "Dimensions:               (y: 343, x: 343)\n",
+       "Coordinates:\n",
+       "  * x                     (x) float64 3kB 4.001e+05 4.004e+05 ... 5.096e+05\n",
+       "  * y                     (y) float64 3kB 4e+06 4e+06 ... 3.891e+06 3.89e+06\n",
+       "Data variables:\n",
+       "    analytic_band_1       (y, x) float64 941kB dask.array&lt;chunksize=(343, 343), meta=np.ndarray&gt;\n",
+       "    analytic_band_2       (y, x) float64 941kB dask.array&lt;chunksize=(343, 343), meta=np.ndarray&gt;\n",
+       "    analytic_band_3       (y, x) float64 941kB dask.array&lt;chunksize=(343, 343), meta=np.ndarray&gt;\n",
+       "    analytic_spatial_ref  int64 8B ...\n",
+       "Attributes:\n",
+       "    source:   file:///home/konstantin/bc_kon/01_coding/01_github/xcube/exampl...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4fa25a4a-aee2-4bfb-bf79-2ae2db0493c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4fa25a4a-aee2-4bfb-bf79-2ae2db0493c5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 343</li><li><span class='xr-has-index'>x</span>: 343</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2c79b909-330a-454f-84b6-14187d44720e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c79b909-330a-454f-84b6-14187d44720e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.001e+05 4.004e+05 ... 5.096e+05</div><input id='attrs-b5497374-97b5-4b1c-9096-f2fbcfcf0f30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5497374-97b5-4b1c-9096-f2fbcfcf0f30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6410a3d-cb57-4fe8-80b6-3ea340327a36' class='xr-var-data-in' type='checkbox'><label for='data-b6410a3d-cb57-4fe8-80b6-3ea340327a36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([400120., 400440., 400760., ..., 508920., 509240., 509560.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4e+06 4e+06 ... 3.891e+06 3.89e+06</div><input id='attrs-5404e264-cead-4d8c-a298-4466042a9d78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5404e264-cead-4d8c-a298-4466042a9d78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88edc3c0-d10f-4890-ac1b-7f27a44f66df' class='xr-var-data-in' type='checkbox'><label for='data-88edc3c0-d10f-4890-ac1b-7f27a44f66df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3999860., 3999540., 3999220., ..., 3891060., 3890740., 3890420.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f383ea0a-c2bc-4856-b427-130ac24a3f80' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f383ea0a-c2bc-4856-b427-130ac24a3f80' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analytic_band_1</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(343, 343), meta=np.ndarray&gt;</div><input id='attrs-fe986b38-08cd-4e79-bb96-534bfcc8c86a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe986b38-08cd-4e79-bb96-534bfcc8c86a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-114e3e48-327f-41b3-aec4-f119b62c14dc' class='xr-var-data-in' type='checkbox'><label for='data-114e3e48-327f-41b3-aec4-f119b62c14dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 919.13 kiB </td>\n",
+       "                        <td> 919.13 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (343, 343) </td>\n",
+       "                        <td> (343, 343) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >343</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">343</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_band_2</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(343, 343), meta=np.ndarray&gt;</div><input id='attrs-39a362ea-6f9a-435a-8bc2-bf729aac9eca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39a362ea-6f9a-435a-8bc2-bf729aac9eca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d712580e-ea28-4588-9654-66f6043d864c' class='xr-var-data-in' type='checkbox'><label for='data-d712580e-ea28-4588-9654-66f6043d864c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 919.13 kiB </td>\n",
+       "                        <td> 919.13 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (343, 343) </td>\n",
+       "                        <td> (343, 343) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >343</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">343</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_band_3</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(343, 343), meta=np.ndarray&gt;</div><input id='attrs-e31f22ff-c8bf-4229-998c-34ff7f14a1a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e31f22ff-c8bf-4229-998c-34ff7f14a1a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c43bc7a-0b2c-4a86-9b29-a6f72c6559a9' class='xr-var-data-in' type='checkbox'><label for='data-3c43bc7a-0b2c-4a86-9b29-a6f72c6559a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 919.13 kiB </td>\n",
+       "                        <td> 919.13 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (343, 343) </td>\n",
+       "                        <td> (343, 343) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >343</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">343</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c8ecd21-b770-4610-8c91-8334b96ac7ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c8ecd21-b770-4610-8c91-8334b96ac7ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-659bcfa1-322b-4a06-93db-c7a67e21a5ed' class='xr-var-data-in' type='checkbox'><label for='data-659bcfa1-322b-4a06-93db-c7a67e21a5ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 13N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32613&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 13N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-105.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 13N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32613&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>399960.0 320.0 0.0 4000020.0 0.0 -320.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-39305e65-3c59-47c8-9ce7-2244e93457fd' class='xr-section-summary-in' type='checkbox'  ><label for='section-39305e65-3c59-47c8-9ce7-2244e93457fd' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1ff11a90-8c1f-47fc-ac5a-aa959ab846d6' class='xr-index-data-in' type='checkbox'/><label for='index-1ff11a90-8c1f-47fc-ac5a-aa959ab846d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([400120.0, 400440.0, 400760.0, 401080.0, 401400.0, 401720.0, 402040.0,\n",
+       "       402360.0, 402680.0, 403000.0,\n",
+       "       ...\n",
+       "       506680.0, 507000.0, 507320.0, 507640.0, 507960.0, 508280.0, 508600.0,\n",
+       "       508920.0, 509240.0, 509560.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=343))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-01c54ba4-cce5-443e-9194-5c86b7f3c0eb' class='xr-index-data-in' type='checkbox'/><label for='index-01c54ba4-cce5-443e-9194-5c86b7f3c0eb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3999860.0, 3999540.0, 3999220.0, 3998900.0, 3998580.0, 3998260.0,\n",
+       "       3997940.0, 3997620.0, 3997300.0, 3996980.0,\n",
+       "       ...\n",
+       "       3893300.0, 3892980.0, 3892660.0, 3892340.0, 3892020.0, 3891700.0,\n",
+       "       3891380.0, 3891060.0, 3890740.0, 3890420.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=343))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67174af8-10bf-4da3-9207-3d8d7b36797f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67174af8-10bf-4da3-9207-3d8d7b36797f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>file:///home/konstantin/bc_kon/01_coding/01_github/xcube/examples/serve/demo/sample-cog.tif</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 3MB\n",
+       "Dimensions:               (y: 343, x: 343)\n",
+       "Coordinates:\n",
+       "  * x                     (x) float64 3kB 4.001e+05 4.004e+05 ... 5.096e+05\n",
+       "  * y                     (y) float64 3kB 4e+06 4e+06 ... 3.891e+06 3.89e+06\n",
+       "Data variables:\n",
+       "    analytic_band_1       (y, x) float64 941kB dask.array<chunksize=(343, 343), meta=np.ndarray>\n",
+       "    analytic_band_2       (y, x) float64 941kB dask.array<chunksize=(343, 343), meta=np.ndarray>\n",
+       "    analytic_band_3       (y, x) float64 941kB dask.array<chunksize=(343, 343), meta=np.ndarray>\n",
+       "    analytic_spatial_ref  int64 8B ...\n",
+       "Attributes:\n",
+       "    source:   file:///home/konstantin/bc_kon/01_coding/01_github/xcube/exampl..."
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = store.open_data(\"collections/datacubes/items/cog_local\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4663093d-8190-4d3c-abd8-ab79b4a7fbb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 64.2 ms, sys: 8.02 ms, total: 72.2 ms\n",
+      "Wall time: 88.7 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x711f657b7530>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.analytic_band_1.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60a2bef7-30f6-483d-a275-2eab2f5c89ff",
+   "metadata": {},
+   "source": [
+    "To access the multi-resolution dataset, assign the parameter `data_type` to `\"mldataset\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c5bac383-796e-4d12-8457-eee49d16eda2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 67.3 ms, sys: 3.44 ms, total: 70.7 ms\n",
+      "Wall time: 123 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "mlds = store.open_data(\"collections/datacubes/items/cog_local\", data_type=\"mldataset\")\n",
+    "mlds.num_levels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6740171c-8e75-4e73-a946-9ae8939cd2de",
+   "metadata": {},
+   "source": [
+    "Now, we can access a dataset with lower resolution. Note that the variable names in the dataset consists of the asset name and the variable name given in the loaded data resource. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d1989bcf-a5c7-426b-a41c-8939bc6f93ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 27.1 ms, sys: 840 µs, total: 27.9 ms\n",
+      "Wall time: 41.1 ms\n"
+     ]
+    },
+    {
+     "data": {
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+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 179kB\n",
+       "Dimensions:                        (y: 86, x: 86)\n",
+       "Coordinates:\n",
+       "  * x                              (x) float64 688B 4.006e+05 ... 5.091e+05\n",
+       "  * y                              (y) float64 688B 3.999e+06 ... 3.891e+06\n",
+       "Data variables:\n",
+       "    analytic_multires_band_1       (y, x) float64 59kB dask.array&lt;chunksize=(86, 86), meta=np.ndarray&gt;\n",
+       "    analytic_multires_band_2       (y, x) float64 59kB dask.array&lt;chunksize=(86, 86), meta=np.ndarray&gt;\n",
+       "    analytic_multires_band_3       (y, x) float64 59kB dask.array&lt;chunksize=(86, 86), meta=np.ndarray&gt;\n",
+       "    analytic_multires_spatial_ref  int64 8B ...\n",
+       "Attributes:\n",
+       "    source:   file:///home/konstantin/bc_kon/01_coding/01_github/xcube/exampl...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4d31b861-dd26-453b-af76-60c39c2d87e1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d31b861-dd26-453b-af76-60c39c2d87e1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 86</li><li><span class='xr-has-index'>x</span>: 86</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-723d5bbb-faed-44b9-a1dd-eb608b1c1254' class='xr-section-summary-in' type='checkbox'  checked><label for='section-723d5bbb-faed-44b9-a1dd-eb608b1c1254' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.006e+05 4.019e+05 ... 5.091e+05</div><input id='attrs-2b9961f3-a224-4d32-ac24-9cfe3d520644' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b9961f3-a224-4d32-ac24-9cfe3d520644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b54be8ef-5cbb-48dd-9840-82b00749a491' class='xr-var-data-in' type='checkbox'><label for='data-b54be8ef-5cbb-48dd-9840-82b00749a491' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([400598.139535, 401874.418605, 403150.697674, 404426.976744,\n",
+       "       405703.255814, 406979.534884, 408255.813953, 409532.093023,\n",
+       "       410808.372093, 412084.651163, 413360.930233, 414637.209302,\n",
+       "       415913.488372, 417189.767442, 418466.046512, 419742.325581,\n",
+       "       421018.604651, 422294.883721, 423571.162791, 424847.44186 ,\n",
+       "       426123.72093 , 427400.      , 428676.27907 , 429952.55814 ,\n",
+       "       431228.837209, 432505.116279, 433781.395349, 435057.674419,\n",
+       "       436333.953488, 437610.232558, 438886.511628, 440162.790698,\n",
+       "       441439.069767, 442715.348837, 443991.627907, 445267.906977,\n",
+       "       446544.186047, 447820.465116, 449096.744186, 450373.023256,\n",
+       "       451649.302326, 452925.581395, 454201.860465, 455478.139535,\n",
+       "       456754.418605, 458030.697674, 459306.976744, 460583.255814,\n",
+       "       461859.534884, 463135.813953, 464412.093023, 465688.372093,\n",
+       "       466964.651163, 468240.930233, 469517.209302, 470793.488372,\n",
+       "       472069.767442, 473346.046512, 474622.325581, 475898.604651,\n",
+       "       477174.883721, 478451.162791, 479727.44186 , 481003.72093 ,\n",
+       "       482280.      , 483556.27907 , 484832.55814 , 486108.837209,\n",
+       "       487385.116279, 488661.395349, 489937.674419, 491213.953488,\n",
+       "       492490.232558, 493766.511628, 495042.790698, 496319.069767,\n",
+       "       497595.348837, 498871.627907, 500147.906977, 501424.186047,\n",
+       "       502700.465116, 503976.744186, 505253.023256, 506529.302326,\n",
+       "       507805.581395, 509081.860465])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.999e+06 3.998e+06 ... 3.891e+06</div><input id='attrs-47ffa9e8-b624-4ee8-90bc-f449e64181ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47ffa9e8-b624-4ee8-90bc-f449e64181ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af4df17e-8f51-4fda-b801-fa4ee795f961' class='xr-var-data-in' type='checkbox'><label for='data-af4df17e-8f51-4fda-b801-fa4ee795f961' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3999381.860465, 3998105.581395, 3996829.302326, 3995553.023256,\n",
+       "       3994276.744186, 3993000.465116, 3991724.186047, 3990447.906977,\n",
+       "       3989171.627907, 3987895.348837, 3986619.069767, 3985342.790698,\n",
+       "       3984066.511628, 3982790.232558, 3981513.953488, 3980237.674419,\n",
+       "       3978961.395349, 3977685.116279, 3976408.837209, 3975132.55814 ,\n",
+       "       3973856.27907 , 3972580.      , 3971303.72093 , 3970027.44186 ,\n",
+       "       3968751.162791, 3967474.883721, 3966198.604651, 3964922.325581,\n",
+       "       3963646.046512, 3962369.767442, 3961093.488372, 3959817.209302,\n",
+       "       3958540.930233, 3957264.651163, 3955988.372093, 3954712.093023,\n",
+       "       3953435.813953, 3952159.534884, 3950883.255814, 3949606.976744,\n",
+       "       3948330.697674, 3947054.418605, 3945778.139535, 3944501.860465,\n",
+       "       3943225.581395, 3941949.302326, 3940673.023256, 3939396.744186,\n",
+       "       3938120.465116, 3936844.186047, 3935567.906977, 3934291.627907,\n",
+       "       3933015.348837, 3931739.069767, 3930462.790698, 3929186.511628,\n",
+       "       3927910.232558, 3926633.953488, 3925357.674419, 3924081.395349,\n",
+       "       3922805.116279, 3921528.837209, 3920252.55814 , 3918976.27907 ,\n",
+       "       3917700.      , 3916423.72093 , 3915147.44186 , 3913871.162791,\n",
+       "       3912594.883721, 3911318.604651, 3910042.325581, 3908766.046512,\n",
+       "       3907489.767442, 3906213.488372, 3904937.209302, 3903660.930233,\n",
+       "       3902384.651163, 3901108.372093, 3899832.093023, 3898555.813953,\n",
+       "       3897279.534884, 3896003.255814, 3894726.976744, 3893450.697674,\n",
+       "       3892174.418605, 3890898.139535])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-260d15f0-e844-413e-9aee-c15b7571535d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-260d15f0-e844-413e-9aee-c15b7571535d' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analytic_multires_band_1</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86, 86), meta=np.ndarray&gt;</div><input id='attrs-2839a3d1-4d1f-4ac5-95c8-442264499e3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2839a3d1-4d1f-4ac5-95c8-442264499e3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07ae970a-5e08-44b8-b8a9-6cb43fa869b7' class='xr-var-data-in' type='checkbox'><label for='data-07ae970a-5e08-44b8-b8a9-6cb43fa869b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 57.78 kiB </td>\n",
+       "                        <td> 57.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86, 86) </td>\n",
+       "                        <td> (86, 86) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">86</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_multires_band_2</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86, 86), meta=np.ndarray&gt;</div><input id='attrs-7f3c4032-ce4c-4a86-997e-6a3f1ad1883f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f3c4032-ce4c-4a86-997e-6a3f1ad1883f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-179693b4-cf08-4855-97a4-11b9f7897940' class='xr-var-data-in' type='checkbox'><label for='data-179693b4-cf08-4855-97a4-11b9f7897940' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 57.78 kiB </td>\n",
+       "                        <td> 57.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86, 86) </td>\n",
+       "                        <td> (86, 86) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">86</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_multires_band_3</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86, 86), meta=np.ndarray&gt;</div><input id='attrs-65bc3d64-422e-42a6-b26d-da8e31c2758c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65bc3d64-422e-42a6-b26d-da8e31c2758c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74c5f061-052a-4256-a56e-725368abde2e' class='xr-var-data-in' type='checkbox'><label for='data-74c5f061-052a-4256-a56e-725368abde2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>AVERAGE</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 57.78 kiB </td>\n",
+       "                        <td> 57.78 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86, 86) </td>\n",
+       "                        <td> (86, 86) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">86</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analytic_multires_spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-802cca96-f37e-46d8-ad9a-9106a3589164' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-802cca96-f37e-46d8-ad9a-9106a3589164' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dcce31a-ba74-43ff-a726-3b764c4c9958' class='xr-var-data-in' type='checkbox'><label for='data-8dcce31a-ba74-43ff-a726-3b764c4c9958' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 13N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32613&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 13N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-105.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 13N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32613&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>399960.0 1276.2790697674418 0.0 4000020.0 0.0 -1276.2790697674418</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3771274e-323b-45d9-9ccf-94d1885075a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-3771274e-323b-45d9-9ccf-94d1885075a1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e2b47138-2c7b-43f5-ab2e-32d58f6e3baf' class='xr-index-data-in' type='checkbox'/><label for='index-e2b47138-2c7b-43f5-ab2e-32d58f6e3baf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 400598.1395348837,  401874.4186046512,  403150.6976744186,\n",
+       "        404426.9767441861, 405703.25581395347,  406979.5348837209,\n",
+       "       408255.81395348837,  409532.0930232558, 410808.37209302327,\n",
+       "        412084.6511627907, 413360.93023255817, 414637.20930232556,\n",
+       "         415913.488372093, 417189.76744186046,  418466.0465116279,\n",
+       "       419742.32558139536,  421018.6046511628, 422294.88372093026,\n",
+       "       423571.16279069765,  424847.4418604651, 426123.72093023255,\n",
+       "                 427400.0, 428676.27906976745,  429952.5581395349,\n",
+       "       431228.83720930235,  432505.1162790698,  433781.3953488372,\n",
+       "       435057.67441860464,  436333.9534883721, 437610.23255813954,\n",
+       "         438886.511627907, 440162.79069767444, 441439.06976744183,\n",
+       "        442715.3488372093, 443991.62790697673,  445267.9069767442,\n",
+       "       446544.18604651163,  447820.4651162791, 449096.74418604653,\n",
+       "         450373.023255814,  451649.3023255814,  452925.5813953488,\n",
+       "        454201.8604651163,  455478.1395348837,  456754.4186046512,\n",
+       "        458030.6976744186,   459306.976744186, 460583.25581395347,\n",
+       "        461859.5348837209, 463135.81395348837,  464412.0930232558,\n",
+       "       465688.37209302327,  466964.6511627907, 468240.93023255817,\n",
+       "        469517.2093023256,   470793.488372093, 472069.76744186046,\n",
+       "        473346.0465116279, 474622.32558139536,  475898.6046511628,\n",
+       "        477174.8837209302, 478451.16279069765,  479727.4418604651,\n",
+       "       481003.72093023255,           482280.0, 483556.27906976745,\n",
+       "        484832.5581395349, 486108.83720930235,  487385.1162790698,\n",
+       "        488661.3953488372, 489937.67441860464,  491213.9534883721,\n",
+       "       492490.23255813954,   493766.511627907,  495042.7906976744,\n",
+       "       496319.06976744183,  497595.3488372093, 498871.62790697673,\n",
+       "        500147.9069767442, 501424.18604651163,  502700.4651162791,\n",
+       "       503976.74418604653,   505253.023255814,  506529.3023255814,\n",
+       "        507805.5813953488,  509081.8604651163],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-82664f86-32a5-4f90-bdf2-742a55bf6d6f' class='xr-index-data-in' type='checkbox'/><label for='index-82664f86-32a5-4f90-bdf2-742a55bf6d6f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([3999381.8604651163,  3998105.581395349, 3996829.3023255817,\n",
+       "        3995553.023255814, 3994276.7441860465,  3993000.465116279,\n",
+       "        3991724.186046512,  3990447.906976744, 3989171.6279069767,\n",
+       "       3987895.3488372094,  3986619.069767442, 3985342.7906976743,\n",
+       "        3984066.511627907, 3982790.2325581396, 3981513.9534883723,\n",
+       "        3980237.674418605,  3978961.395348837,   3977685.11627907,\n",
+       "       3976408.8372093025,  3975132.558139535, 3973856.2790697673,\n",
+       "                3972580.0, 3971303.7209302327, 3970027.4418604653,\n",
+       "       3968751.1627906975,   3967474.88372093,  3966198.604651163,\n",
+       "       3964922.3255813955, 3963646.0465116277, 3962369.7674418604,\n",
+       "        3961093.488372093, 3959817.2093023257, 3958540.9302325584,\n",
+       "       3957264.6511627906, 3955988.3720930233,  3954712.093023256,\n",
+       "       3953435.8139534886,  3952159.534883721, 3950883.2558139535,\n",
+       "        3949606.976744186,  3948330.697674419,  3947054.418604651,\n",
+       "       3945778.1395348837, 3944501.8604651163,  3943225.581395349,\n",
+       "       3941949.3023255817,  3940673.023255814, 3939396.7441860465,\n",
+       "        3938120.465116279,  3936844.186046512,  3935567.906976744,\n",
+       "       3934291.6279069767, 3933015.3488372094,  3931739.069767442,\n",
+       "       3930462.7906976743,  3929186.511627907, 3927910.2325581396,\n",
+       "       3926633.9534883723,  3925357.674418605,  3924081.395348837,\n",
+       "         3922805.11627907, 3921528.8372093025,  3920252.558139535,\n",
+       "       3918976.2790697673,          3917700.0, 3916423.7209302327,\n",
+       "       3915147.4418604653, 3913871.1627906975,   3912594.88372093,\n",
+       "        3911318.604651163, 3910042.3255813955, 3908766.0465116277,\n",
+       "       3907489.7674418604,  3906213.488372093, 3904937.2093023257,\n",
+       "       3903660.9302325584, 3902384.6511627906, 3901108.3720930233,\n",
+       "        3899832.093023256, 3898555.8139534886,  3897279.534883721,\n",
+       "       3896003.2558139535,  3894726.976744186,  3893450.697674419,\n",
+       "        3892174.418604651, 3890898.1395348837],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ebc9f7f8-4f32-4ce4-a1a6-6a66e6718360' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebc9f7f8-4f32-4ce4-a1a6-6a66e6718360' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>file:///home/konstantin/bc_kon/01_coding/01_github/xcube/examples/serve/demo/sample-cog.tif</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 179kB\n",
+       "Dimensions:                        (y: 86, x: 86)\n",
+       "Coordinates:\n",
+       "  * x                              (x) float64 688B 4.006e+05 ... 5.091e+05\n",
+       "  * y                              (y) float64 688B 3.999e+06 ... 3.891e+06\n",
+       "Data variables:\n",
+       "    analytic_multires_band_1       (y, x) float64 59kB dask.array<chunksize=(86, 86), meta=np.ndarray>\n",
+       "    analytic_multires_band_2       (y, x) float64 59kB dask.array<chunksize=(86, 86), meta=np.ndarray>\n",
+       "    analytic_multires_band_3       (y, x) float64 59kB dask.array<chunksize=(86, 86), meta=np.ndarray>\n",
+       "    analytic_multires_spatial_ref  int64 8B ...\n",
+       "Attributes:\n",
+       "    source:   file:///home/konstantin/bc_kon/01_coding/01_github/xcube/exampl..."
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds = mlds.get_dataset(2)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f40ef9e5-ada6-4bb3-8f3b-ea31b7e57613",
+   "metadata": {},
+   "source": [
+    "And we can plot the same image, but with the given lower resolution. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e7ea4684-7a96-460f-9f65-9827b8b69ad9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 42.5 ms, sys: 15.9 ms, total: 58.4 ms\n",
+      "Wall time: 59.3 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x711f6555e8d0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds.analytic_multires_band_1.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b234148-dd7e-4403-a725-a50bb7401056",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
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+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test/cassettes/test_store/StacDataStoreTest.test_do_bboxes_intersect.yaml b/test/cassettes/test_store/StacDataStoreTest.test_access_item_failed.yaml
similarity index 64%
rename from test/cassettes/test_store/StacDataStoreTest.test_do_bboxes_intersect.yaml
rename to test/cassettes/test_store/StacDataStoreTest.test_access_item_failed.yaml
index 833c3c6..65fe10d 100644
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       message: OK
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+      Accept-Encoding:
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+      Connection:
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+    method: GET
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+  response:
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+      - Authorization,Accept-Encoding,Origin
+      Via:
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+      X-Cache:
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+      X-Cache-Hits:
+      - '0'
+      X-Content-Type-Options:
+      - nosniff
+      X-Fastly-Request-ID:
+      - 225c0fa3e4593131cfd4d65461ba22876edc0c93
+      X-Frame-Options:
+      - deny
+      X-GitHub-Request-Id:
+      - 9352:3739A6:25B85B:27B591:668F9D3B
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+      X-XSS-Protection:
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+    status:
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+      message: Not Found
 version: 1
diff --git a/test/cassettes/test_store/StacDataStoreTest.test_describe_data.yaml b/test/cassettes/test_store/StacDataStoreTest.test_describe_data.yaml
new file mode 100644
index 0000000..a5d2afd
--- /dev/null
+++ b/test/cassettes/test_store/StacDataStoreTest.test_describe_data.yaml
@@ -0,0 +1,347 @@
+interactions:
+- request:
+    body: null
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, br
+      Connection:
+      - keep-alive
+      User-Agent:
+      - python-requests/2.32.3
+    method: GET
+    uri: https://geoservice.dlr.de/eoc/ogc/stac/v1
+  response:
+    body:
+      string: '{"title":"EOC EO Products Service","description":"*BETA* Provides interoperable
+        access to metadata from EO collections and products of DLR''s Earth Observation
+        Center (EOC). *BETA*","links":[{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fjson","rel":"self","type":"application/json","title":"This
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+        definition for this endpoint as application/vnd.oai.openapi+json;version=3.0"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/openapi?f=application%2Fx-yaml","rel":"service-desc","type":"application/x-yaml","title":"API
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+        definition for this endpoint as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fjson","rel":"conformance","type":"application/json","title":"Conformance
+        declaration as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fx-yaml","rel":"conformance","type":"application/x-yaml","title":"Conformance
+        declaration as application/x-yaml"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=text%2Fhtml","rel":"conformance","type":"text/html","title":"Conformance
+        declaration as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=application%2Fjson","rel":"data","type":"application/json","title":"Collections
+        Metadata as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=text%2Fhtml","rel":"data","type":"text/html","title":"Collections
+        Metadata as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RESA_SkySat_Collect","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L4_P1D_NO2surf","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/TDM_DEM_90","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/GWP_P1D","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RE_L3A","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/ENMAP_HSI_L2A","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_SO2","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_CF","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/TDM_FNF_50","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/GWP_P1M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/FOREST_STRUCTURE_DE_RH95_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/FOREST_STRUCTURE_DE_AGBD_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S2_L2A_MAJA","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_COT","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_BUILDINGS_COMP_GER_1M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/ENMAP_HSI_L0_QL","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RE_L1B","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/D4H","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/GWP_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_SURFACECOV_NONRES_GER_10M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S2_L3A_WASP","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/SUPERSITES","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/WSF_2015","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_BUILDINGS_NONRES_GER_1M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_O3","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/WSF_Evolution","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_HCHO","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/WSF_2019","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_CTH","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_SURFACECOV_COMP_GER_10M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RESA_SkySat_Scene","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/FOREST_STRUCTURE_DE_COVER_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_STRUCTURE_VOLUME_GER_100M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_STRUCTURE_HEIGHT_GER_100M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/search?f=application%2Fgeo%2Bjson","rel":"search","type":"application/geo+json","title":"Items
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+        as text/html","method":"POST"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/queryables?f=application%2Fschema%2Bjson","rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables
+        as application/schema+json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/queryables?f=text%2Fhtml","rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"text/html","title":"Queryables
+        as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/sortables?f=application%2Fschema%2Bjson","rel":"http://www.opengis.net/def/rel/ogc/1.0/sortables","type":"application/schema+json","title":"Sortables
+        as application/schema+json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/sortables?f=text%2Fhtml","rel":"http://www.opengis.net/def/rel/ogc/1.0/sortables","type":"text/html","title":"Sortables
+        as text/html"}],"id":"GeoserverSTACLandingPage","conformsTo":["http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core","http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30","http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/html","http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson","https://api.stacspec.org/v1.0.0/collections","https://api.stacspec.org/v1.0.0/core","https://api.stacspec.org/v1.0.0/ogcapi-features","https://api.stacspec.org/v1.0.0/item-search","https://api.stacspec.org/v1.0.0/item-search#filter","https://api.stacspec.org/v1.0.0/item-search#sort","https://api.stacspec.org/v1.0.0/item-search#fields","http://www.opengis.net/spec/ogcapi-features-3/1.0/req/features-filter","http://www.opengis.net/spec/ogcapi-features-3/1.0/req/filter","http://geoserver.org/spec/ecql/1.0/req/gs-ecql","http://geoserver.org/spec/ecql/1.0/req/ecql-text","http://www.opengis.net/spec/cql2/1.0/req/basic-cql2","http://www.opengis.net/spec/cql2/1.0/req/advanced-comparison-operators","http://www.opengis.net/spec/cql2/1.0/req/arithmetic","http://www.opengis.net/spec/cql2/1.0/req/property-property","http://www.opengis.net/spec/cql2/1.0/req/basic-spatial-operators","http://www.opengis.net/spec/cql2/1.0/req/spatial-operators","http://www.opengis.net/spec/cql2/1.0/req/functions","http://www.opengis.net/spec/cql2/1.0/req/cql2-text"],"type":"Catalog","stac_version":"1.0.0"}'
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+      Access-Control-Allow-Credentials:
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+      Access-Control-Allow-Headers:
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+      Content-Encoding:
+      - gzip
+      Content-Type:
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+      Date:
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+        rel="self"; type="application/json"; title="This document"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fx-yaml>;
+        rel="alternate"; type="application/x-yaml"; title="This document as application/x-yaml"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=text%2Fhtml>; rel="alternate";
+        type="text/html"; title="This document as text/html"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fjson>; rel="self";
+        type="application/json"; title="This document"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fx-yaml>; rel="alternate";
+        type="application/x-yaml"; title="This document as application/x-yaml"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=text%2Fhtml>; rel="alternate";
+        type="text/html"; title="This document as text/html"
+      Referrer-Policy:
+      - same-origin
+      Server:
+      - nginx
+      Set-Cookie:
+      - GS_FLOW_CONTROL=GS_CFLOW_63faf165:1909fd548f3:40e
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      X-Control-flow-delay-ms:
+      - '0'
+      X-Frame-Options:
+      - SAMEORIGIN
+    status:
+      code: 200
+      message: ''
+- request:
+    body: null
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, br
+      Connection:
+      - keep-alive
+      User-Agent:
+      - python-requests/2.32.3
+    method: GET
+    uri: https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/D4H/items/S1A_IW_GRDH_1SDV_20231031T030757_20231031T030822_051003_062646_8C53?f=application%2Fgeo%2Bjson
+  response:
+    body:
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+        as HTML","rel":"items","href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/D4H/items?f=text%2Fhtml","type":"text/html"},{"title":"Collection","rel":"collection","href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/D4H?f=application%2Fjson","type":"application/json"},{"title":"Collection
+        as HTML","rel":"collection","href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/D4H?f=text%2Fhtml","type":"text/html"},{"title":"Collections","rel":"collection","href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=application%2Fjson","type":"application/json"},{"title":"Collections
+        as HTML","rel":"collection","href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=text%2Fhtml","type":"text/html"},{"title":"OpenSearch
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+        O&M XML","rel":"item","href":"https://geoservice.dlr.de/eoc/oseo/metadata?parentId=D4H&uid=S1A_IW_GRDH_1SDV_20231031T030757_20231031T030822_051003_062646_8C53&httpAccept=application%2Fgml%2Bxml","type":"application/gml+xml"},{"title":"Queryables","rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","href":"https://geoservice.dlr.de/eoc/ogc/stac/collections/D4H/queryables?f=json","type":"application/schema+json"}],"ogc":[{"offering":"http://www.opengis.net/spec/owc/1.0/req/atom/wms","method":"GET","type":"application/xml","href":"https://geoservice.dlr.de/eoc/demo/ows?service=wms&version=1.3.0&request=GetCapabilities"},{"offering":"http://www.opengis.net/spec/owc/1.0/req/atom/wms","method":"GET","type":"image/jpeg","href":"https://geoservice.dlr.de/eoc/demo/wms?SERVICE=WMS&VERSION=1.1.1&REQUEST=GetMap&FORMAT=image%2Fjpeg&STYLES&LAYERS=demo%3AD4H_WATER_MASK_S1&SRS=EPSG%3A4326&WIDTH=800&HEIGHT=600&BBOX=36.211544470664904,-15.48028,37.86349678056911,-14.169830229695073&TIME=2023-10-31T03:07:57Z"},{"offering":"http://www.opengis.net/spec/owc-geojson/1.0/req/wcs","method":"GET","type":"application/xml","href":"https://geoservice.dlr.de/eoc/demo/wcs?service=WCS&request=GetCapabilities&version=2.0.1"},{"offering":"http://www.opengis.net/spec/owc-geojson/1.0/req/wcs","method":"GET","type":"application/xml","href":"https://geoservice.dlr.de/eoc/demo/wcs?service=WCS&version=2.0.1&request=DescribeCoverage&coverageId=D4H_WATER_MASK_S1"},{"offering":"http://www.opengis.net/spec/owc-geojson/1.0/req/wcs","method":"GET","type":"image/tiff;application=geotiff","href":"https://geoservice.dlr.de/eoc/demo/wcs?service=WCS&version=2.0.1&request=GetCoverage&coverageId=D4H_WATER_MASK_S1&subset=Long(36.211544470664904,37.86349678056911)&subset=Lat(-15.48028,-14.169830229695073)&format=image/tiff;application=geotiff&CQL_FILTER=eoIdentifier=''S1A_IW_GRDH_1SDV_20231031T030757_20231031T030822_051003_062646_8C53''&scalesize=i(1024),j(1024)"}]}'
+    headers:
+      API-Version:
+      - 1.0.0
+      Access-Control-Allow-Credentials:
+      - 'true'
+      Access-Control-Allow-Headers:
+      - DNT,X-CustomHeader,Keep-Alive,User-Agent,X-Requested-With,If-Modified-Since,Cache-Control,Content-Type
+      Access-Control-Allow-Methods:
+      - GET, POST, OPTIONS
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '7320'
+      Content-Type:
+      - application/geo+json
+      Date:
+      - Thu, 11 Jul 2024 08:52:14 GMT
+      Referrer-Policy:
+      - same-origin
+      Server:
+      - nginx
+      Set-Cookie:
+      - GS_FLOW_CONTROL=GS_CFLOW_4ab6db45:1909f9d9216:397d
+      X-Content-Type-Options:
+      - nosniff
+      X-Control-flow-delay-ms:
+      - '0'
+      X-Frame-Options:
+      - SAMEORIGIN
+    status:
+      code: 200
+      message: ''
+- request:
+    body: null
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, br
+      Connection:
+      - keep-alive
+      User-Agent:
+      - python-requests/2.32.3
+    method: GET
+    uri: https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/D4H/items/S1A_IW_GRDH_1SDV_20231031T030757_20231031T030822_051003_062646_8C53?f=application%2Fgeo%2Bjson
+  response:
+    body:
+      string: '{"type":"Feature","stac_version":"1.0.0","stac_extensions":["https://stac-extensions.github.io/eo/v1.1.0/schema.json","https://stac-extensions.github.io/view/v1.0.0/schema.json","https://stac-extensions.github.io/projection/v1.0.0/schema.json","https://stac-extensions.github.io/processing/v1.0.0/schema.json","https://stac-extensions.github.io/sat/v1.0.0/schema.json"],"id":"S1A_IW_GRDH_1SDV_20231031T030757_20231031T030822_051003_062646_8C53","collection":"D4H","geometry":{"type":"Polygon","coordinates":[[[36.211544,-15.169252],[36.859985,-14.70841],[37.831688,-14.180731],[37.863497,-14.16983],[37.553288,-15.48028],[36.211544,-15.169252]]]},"bbox":[36.211544470664904,-15.48028,37.86349678056911,-14.169830229695073],"properties":{"created":"2023-10-31T23:41:33.743+00:00","updated":"2023-10-31T23:41:33.743+00:00","datetime":"2023-10-31T00:00:00.000+00:00","platform":"SENTINEL-1A","constellation":"SENTINEL-1","proj:epsg":4326,"sat:relative_orbit":"51003","sat:orbit_state":"DESCENDING","processing:software":{"proc_hr_semseg":"1.6.3"},"cnt_flood":54323,"cnt_refwater":42063,"cnt_valid":98016249,"cnt_water":96386,"providers":[{"name":"DLR
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+        all water pixels in the satellite image. Water segmentation is done with a
+        pre-trained convolutional neural network.","href":"https://download.geoservice.dlr.de/D4H/files/2023/10/31/S1A_IW_GRDH_1SDV_20231031T030757_20231031T030822_051003_062646_8C53_WATER.tif","roles":["water-mask"],"title":"Water
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+    headers:
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+      Access-Control-Allow-Credentials:
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+      Access-Control-Allow-Headers:
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+      Access-Control-Allow-Methods:
+      - GET, POST, OPTIONS
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '7320'
+      Content-Type:
+      - application/geo+json
+      Date:
+      - Thu, 11 Jul 2024 08:52:15 GMT
+      Referrer-Policy:
+      - same-origin
+      Server:
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+      Set-Cookie:
+      - GS_FLOW_CONTROL=GS_CFLOW_4ab6db45:1909f9d9216:39a3
+      X-Content-Type-Options:
+      - nosniff
+      X-Control-flow-delay-ms:
+      - '0'
+      X-Frame-Options:
+      - SAMEORIGIN
+    status:
+      code: 200
+      message: ''
+version: 1
diff --git a/test/cassettes/test_store/StacDataStoreTest.test_describe_data_assert_log.yaml b/test/cassettes/test_store/StacDataStoreTest.test_describe_data_assert_log.yaml
new file mode 100644
index 0000000..e0bdf8b
--- /dev/null
+++ b/test/cassettes/test_store/StacDataStoreTest.test_describe_data_assert_log.yaml
@@ -0,0 +1,300 @@
+interactions:
+- request:
+    body: null
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, br
+      Connection:
+      - keep-alive
+      User-Agent:
+      - python-requests/2.32.3
+    method: GET
+    uri: https://geoservice.dlr.de/eoc/ogc/stac/v1
+  response:
+    body:
+      string: '{"title":"EOC EO Products Service","description":"*BETA* Provides interoperable
+        access to metadata from EO collections and products of DLR''s Earth Observation
+        Center (EOC). *BETA*","links":[{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fjson","rel":"self","type":"application/json","title":"This
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+        definition for this endpoint as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fjson","rel":"conformance","type":"application/json","title":"Conformance
+        declaration as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fx-yaml","rel":"conformance","type":"application/x-yaml","title":"Conformance
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+        declaration as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=application%2Fjson","rel":"data","type":"application/json","title":"Collections
+        Metadata as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=text%2Fhtml","rel":"data","type":"text/html","title":"Collections
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diff --git a/test/cassettes/test_store/StacDataStoreTest.test_get_data_ids_data_type.yaml b/test/cassettes/test_store/StacDataStoreTest.test_get_data_ids_data_type.yaml
new file mode 100644
index 0000000..f2fae39
--- /dev/null
+++ b/test/cassettes/test_store/StacDataStoreTest.test_get_data_ids_data_type.yaml
@@ -0,0 +1,768 @@
+interactions:
+- request:
+    body: null
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, br
+      Connection:
+      - keep-alive
+      User-Agent:
+      - python-requests/2.32.3
+    method: GET
+    uri: https://geoservice.dlr.de/eoc/ogc/stac/v1
+  response:
+    body:
+      string: '{"title":"EOC EO Products Service","description":"*BETA* Provides interoperable
+        access to metadata from EO collections and products of DLR''s Earth Observation
+        Center (EOC). *BETA*","links":[{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fjson","rel":"self","type":"application/json","title":"This
+        document"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fx-yaml","rel":"alternate","type":"application/x-yaml","title":"This
+        document as application/x-yaml"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=text%2Fhtml","rel":"alternate","type":"text/html","title":"This
+        document as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/openapi?f=application%2Fvnd.oai.openapi%2Bjson%3Bversion%3D3.0","rel":"service-desc","type":"application/vnd.oai.openapi+json;version=3.0","title":"API
+        definition for this endpoint as application/vnd.oai.openapi+json;version=3.0"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/openapi?f=application%2Fx-yaml","rel":"service-desc","type":"application/x-yaml","title":"API
+        definition for this endpoint as application/x-yaml"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/openapi?f=text%2Fhtml","rel":"service-doc","type":"text/html","title":"API
+        definition for this endpoint as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fjson","rel":"conformance","type":"application/json","title":"Conformance
+        declaration as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fx-yaml","rel":"conformance","type":"application/x-yaml","title":"Conformance
+        declaration as application/x-yaml"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=text%2Fhtml","rel":"conformance","type":"text/html","title":"Conformance
+        declaration as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=application%2Fjson","rel":"data","type":"application/json","title":"Collections
+        Metadata as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=text%2Fhtml","rel":"data","type":"text/html","title":"Collections
+        Metadata as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RESA_SkySat_Collect","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L4_P1D_NO2surf","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/TDM_DEM_90","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/GWP_P1D","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RE_L3A","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/ENMAP_HSI_L2A","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_SO2","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_CF","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/TDM_FNF_50","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/GWP_P1M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/FOREST_STRUCTURE_DE_RH95_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/FOREST_STRUCTURE_DE_AGBD_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S2_L2A_MAJA","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_COT","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_BUILDINGS_COMP_GER_1M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/ENMAP_HSI_L0_QL","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RE_L1B","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/D4H","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/GWP_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_SURFACECOV_NONRES_GER_10M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S2_L3A_WASP","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/SUPERSITES","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/WSF_2015","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_BUILDINGS_NONRES_GER_1M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_O3","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/WSF_Evolution","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_HCHO","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/WSF_2019","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_CTH","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_SURFACECOV_COMP_GER_10M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RESA_SkySat_Scene","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/FOREST_STRUCTURE_DE_COVER_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_STRUCTURE_VOLUME_GER_100M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_STRUCTURE_HEIGHT_GER_100M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/search?f=application%2Fgeo%2Bjson","rel":"search","type":"application/geo+json","title":"Items
+        as application/geo+json","method":"GET"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/search?f=text%2Fhtml","rel":"search","type":"text/html","title":"Items
+        as text/html","method":"GET"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1?f=application%2Fjson","rel":"root","type":"application/json","title":"Root
+        Catalog as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/search?f=application%2Fgeo%2Bjson","rel":"search","type":"application/geo+json","title":"Items
+        as application/geo+json","method":"POST"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/search?f=text%2Fhtml","rel":"search","type":"text/html","title":"Items
+        as text/html","method":"POST"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/queryables?f=application%2Fschema%2Bjson","rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","title":"Queryables
+        as application/schema+json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/queryables?f=text%2Fhtml","rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"text/html","title":"Queryables
+        as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/sortables?f=application%2Fschema%2Bjson","rel":"http://www.opengis.net/def/rel/ogc/1.0/sortables","type":"application/schema+json","title":"Sortables
+        as application/schema+json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/sortables?f=text%2Fhtml","rel":"http://www.opengis.net/def/rel/ogc/1.0/sortables","type":"text/html","title":"Sortables
+        as text/html"}],"id":"GeoserverSTACLandingPage","conformsTo":["http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core","http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30","http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/html","http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson","https://api.stacspec.org/v1.0.0/collections","https://api.stacspec.org/v1.0.0/core","https://api.stacspec.org/v1.0.0/ogcapi-features","https://api.stacspec.org/v1.0.0/item-search","https://api.stacspec.org/v1.0.0/item-search#filter","https://api.stacspec.org/v1.0.0/item-search#sort","https://api.stacspec.org/v1.0.0/item-search#fields","http://www.opengis.net/spec/ogcapi-features-3/1.0/req/features-filter","http://www.opengis.net/spec/ogcapi-features-3/1.0/req/filter","http://geoserver.org/spec/ecql/1.0/req/gs-ecql","http://geoserver.org/spec/ecql/1.0/req/ecql-text","http://www.opengis.net/spec/cql2/1.0/req/basic-cql2","http://www.opengis.net/spec/cql2/1.0/req/advanced-comparison-operators","http://www.opengis.net/spec/cql2/1.0/req/arithmetic","http://www.opengis.net/spec/cql2/1.0/req/property-property","http://www.opengis.net/spec/cql2/1.0/req/basic-spatial-operators","http://www.opengis.net/spec/cql2/1.0/req/spatial-operators","http://www.opengis.net/spec/cql2/1.0/req/functions","http://www.opengis.net/spec/cql2/1.0/req/cql2-text"],"type":"Catalog","stac_version":"1.0.0"}'
+    headers:
+      API-Version:
+      - 1.0.0
+      Access-Control-Allow-Credentials:
+      - 'true'
+      Access-Control-Allow-Headers:
+      - DNT,X-CustomHeader,Keep-Alive,User-Agent,X-Requested-With,If-Modified-Since,Cache-Control,Content-Type
+      Access-Control-Allow-Methods:
+      - GET, POST, OPTIONS
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Thu, 11 Jul 2024 08:52:20 GMT
+      Link:
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fjson>;
+        rel="self"; type="application/json"; title="This document"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fx-yaml>;
+        rel="alternate"; type="application/x-yaml"; title="This document as application/x-yaml"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=text%2Fhtml>; rel="alternate";
+        type="text/html"; title="This document as text/html"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fjson>; rel="self";
+        type="application/json"; title="This document"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fx-yaml>; rel="alternate";
+        type="application/x-yaml"; title="This document as application/x-yaml"
+      - <https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=text%2Fhtml>; rel="alternate";
+        type="text/html"; title="This document as text/html"
+      Referrer-Policy:
+      - same-origin
+      Server:
+      - nginx
+      Set-Cookie:
+      - GS_FLOW_CONTROL=GS_CFLOW_4ab6db45:1909f9d9216:39e5
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      X-Control-flow-delay-ms:
+      - '1'
+      X-Frame-Options:
+      - SAMEORIGIN
+    status:
+      code: 200
+      message: ''
+- request:
+    body: null
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, br
+      Connection:
+      - keep-alive
+      Cookie:
+      - GS_FLOW_CONTROL=GS_CFLOW_4ab6db45:1909f9d9216:39e5
+      User-Agent:
+      - python-requests/2.32.3
+    method: GET
+    uri: https://geoservice.dlr.de/eoc/ogc/stac/v1?f=application%2Fjson
+  response:
+    body:
+      string: '{"title":"EOC EO Products Service","description":"*BETA* Provides interoperable
+        access to metadata from EO collections and products of DLR''s Earth Observation
+        Center (EOC). *BETA*","links":[{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fjson","rel":"self","type":"application/json","title":"This
+        document"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=application%2Fx-yaml","rel":"alternate","type":"application/x-yaml","title":"This
+        document as application/x-yaml"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/?f=text%2Fhtml","rel":"alternate","type":"text/html","title":"This
+        document as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/openapi?f=application%2Fvnd.oai.openapi%2Bjson%3Bversion%3D3.0","rel":"service-desc","type":"application/vnd.oai.openapi+json;version=3.0","title":"API
+        definition for this endpoint as application/vnd.oai.openapi+json;version=3.0"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/openapi?f=application%2Fx-yaml","rel":"service-desc","type":"application/x-yaml","title":"API
+        definition for this endpoint as application/x-yaml"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/openapi?f=text%2Fhtml","rel":"service-doc","type":"text/html","title":"API
+        definition for this endpoint as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fjson","rel":"conformance","type":"application/json","title":"Conformance
+        declaration as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=application%2Fx-yaml","rel":"conformance","type":"application/x-yaml","title":"Conformance
+        declaration as application/x-yaml"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/conformance?f=text%2Fhtml","rel":"conformance","type":"text/html","title":"Conformance
+        declaration as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=application%2Fjson","rel":"data","type":"application/json","title":"Collections
+        Metadata as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections?f=text%2Fhtml","rel":"data","type":"text/html","title":"Collections
+        Metadata as text/html"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RESA_SkySat_Collect","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L4_P1D_NO2surf","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/TDM_DEM_90","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/GWP_P1D","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RE_L3A","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/ENMAP_HSI_L2A","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_SO2","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_CF","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/TDM_FNF_50","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/GWP_P1M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/FOREST_STRUCTURE_DE_RH95_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/FOREST_STRUCTURE_DE_AGBD_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S2_L2A_MAJA","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_COT","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_BUILDINGS_COMP_GER_1M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/ENMAP_HSI_L0_QL","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RE_L1B","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/D4H","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/GWP_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_SURFACECOV_NONRES_GER_10M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S2_L3A_WASP","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/SUPERSITES","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/WSF_2015","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_BUILDINGS_NONRES_GER_1M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_O3","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/WSF_Evolution","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_HCHO","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/WSF_2019","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/S5P_TROPOMI_L3_P1D_CTH","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_SURFACECOV_COMP_GER_10M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/RESA_SkySat_Scene","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/FOREST_STRUCTURE_DE_COVER_P1Y","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_STRUCTURE_VOLUME_GER_100M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/collections/IOER_STRUCTURE_HEIGHT_GER_100M","rel":"child","type":"application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/search?f=application%2Fgeo%2Bjson","rel":"search","type":"application/geo+json","title":"Items
+        as application/geo+json","method":"GET"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/search?f=text%2Fhtml","rel":"search","type":"text/html","title":"Items
+        as text/html","method":"GET"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1?f=application%2Fjson","rel":"root","type":"application/json","title":"Root
+        Catalog as application/json"},{"href":"https://geoservice.dlr.de/eoc/ogc/stac/v1/search?f=application%2Fgeo%2Bjson","rel":"search","type":"application/geo+json","title":"Items
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diff --git a/test/cassettes/test_store/StacDataStoreTest.test_get_data_ids_include_attrs.yaml b/test/cassettes/test_store/StacDataStoreTest.test_get_data_ids_include_attrs.yaml
index cb38203..b8c1f0e 100644
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+      X-Content-Type-Options:
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new file mode 100644
index 0000000..98adeac
--- /dev/null
+++ b/test/cassettes/test_store/StacDataStoreTest.test_get_https_accessor.yaml
@@ -0,0 +1,53 @@
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+      Accept-Encoding:
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+      Connection:
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+      User-Agent:
+      - python-requests/2.32.3
+    method: GET
+    uri: https://earth-search.aws.element84.com/v1
+  response:
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+      Date:
+      - Thu, 11 Jul 2024 08:52:31 GMT
+      Via:
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diff --git a/test/cassettes/test_store/StacDataStoreTest.test_get_open_data_params_schema.yaml b/test/cassettes/test_store/StacDataStoreTest.test_get_open_data_params_schema.yaml
index a7ee1db..a3c16af 100644
--- a/test/cassettes/test_store/StacDataStoreTest.test_get_open_data_params_schema.yaml
+++ b/test/cassettes/test_store/StacDataStoreTest.test_get_open_data_params_schema.yaml
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diff --git a/test/cassettes/test_store/StacDataStoreTest.test_open_data_netcdf.yaml b/test/cassettes/test_store/StacDataStoreTest.test_open_data_netcdf.yaml
new file mode 100644
index 0000000..ea290fa
--- /dev/null
+++ b/test/cassettes/test_store/StacDataStoreTest.test_open_data_netcdf.yaml
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new file mode 100644
index 0000000..e193eea
--- /dev/null
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+        \"./lcv_landcover.211_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual non-irrigated arable land
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.212_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual permanently irrigated arable
+        land at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n
+        \     \"href\": \"./lcv_landcover.213_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual rice fields
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.221_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual vineyards at 30 m (2000\\u20132020)\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_landcover.222_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual fruit trees
+        and berry plantations at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./lcv_landcover.223_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual olive groves
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.231_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual pastures at 30 m (2000\\u20132020)\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_landcover.241_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual annual crops
+        associated with permanent crops at 30 m (2000\\u20132020)\"\n    },\n    {\n
+        \     \"rel\": \"child\",\n      \"href\": \"./lcv_landcover.242_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual complex cultivation
+        patterns at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n
+        \     \"href\": \"./lcv_landcover.243_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual land principally
+        occupied by agriculture (with significant areas of natural vegetation) at
+        30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.244_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual agro-forestry areas at 30
+        m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.311_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual broad-leaved forest at 30
+        m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.312_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual coniferous forest at 30 m
+        (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.313_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual mixed forest at 30 m (2000\\u20132020)\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_landcover.321_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual natural grasslands
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.322_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual moors and heathland at 30
+        m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.323_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual sclerophyllous vegetation
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.324_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual transitional woodland-shrub
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.331_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual beaches, dunes, sands at
+        30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.332_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual bare rocks at 30 m (2000\\u20132020)\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_landcover.333_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual sparsely
+        vegetated areas at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./lcv_landcover.334_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual burnt areas
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.335_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual glaciers and perpetual snow
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.411_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual inland marshes at 30 m (2000\\u20132020)\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_landcover.412_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual peat bogs
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.421_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual salt marshes at 30 m (2000\\u20132020)\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_landcover.422_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual salines at
+        30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.423_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual intertidal flats at 30 m
+        (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.511_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual water courses at 30 m (2000\\u20132020)\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_landcover.512_lucas.corine.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Annual water bodies
+        at 30 m (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./lcv_landcover.521_lucas.corine.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Annual coastal lagoons at 30 m (2000\\u20132020)\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./veg_abies.alba_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Abies alba (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_castanea.sativa_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Castanea sativa (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_corylus.avellana_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Corylus avellana (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_fagus.sylvatica_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Fagus Sylvatica (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_olea.europaea_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Olea europaea (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_picea.abies_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Picea abies (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_pinus.halepensis_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Pinus halepensis (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_pinus.nigra_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Pinus nigra (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_pinus.pinea_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Pinus pinea (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_pinus.sylvestris_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Pinus sylvestris (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_prunus.avium_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Prunus avium (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_quercus.cerris_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Quercus cerris (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_quercus.ilex_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Quercus ilex (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_quercus.robur_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Quercus robur (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_quercus.suber_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Quercus suber (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_salix.caprea_anv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ANV - Probability
+        distribution for Salix caprea (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_abies.alba_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Abies alba (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_castanea.sativa_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Castanea sativa (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_corylus.avellana_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Corylus avellana (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_fagus.sylvatica_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Fagus sylvatica (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_olea.europaea_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Olea europaea (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_picea.abies_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Picea abies (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_pinus.halepensis_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Pinus halepensis (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_pinus.nigra_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Pinus nigra (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_pinus.pinea_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Pinus pinea (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_pinus.sylvestris_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Pinus sylvestris (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_prunus.avium_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Prunus avium (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_quercus.cerris_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Quercus cerris (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_quercus.ilex_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Quercus ilex (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_quercus.robur_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Quercus robur (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_quercus.suber_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Quercus suber (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./veg_salix.caprea_pnv.eml/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"PNV - Probability
+        distribution for Salix caprea (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./enq_pm25_et.eml/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Air quality PM2.5\"\n    },\n    {\n
+        \     \"rel\": \"child\",\n      \"href\": \"./clm_prec_era5.copernicus_sum/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ERA5 Land precipitation
+        daily sum (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n
+        \     \"href\": \"./clm_air.temp_era5.copernicus/collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"ERA5 Daily land air temperature
+        (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\":
+        \"./clm_skin.temp_era5.copernicus/collection.json\",\n      \"type\": \"application/json\",\n
+        \     \"title\": \"ERA5 Daily land surface temperature (2000\\u20132020)\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./clm_prec_era5.copernicus_sum.trend.ols/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ERA5 precipitation
+        trend analysis (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n
+        \     \"href\": \"./clm_prec_era5.copernicus_sum.trend/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ERA5 precipitation
+        trend component (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"child\",\n
+        \     \"href\": \"./clm_skin.temp_era5.copernicus_trend/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ERA5 Land surface
+        temperature trend component (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./clm_skin.temp_era5.copernicus_mn.trend.ols/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ERA5 Land surface
+        min. temperature trend analysis (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./clm_skin.temp_era5.copernicus_av.trend.ols/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ERA5 Land surface
+        ave. temperature trend analysis (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./clm_skin.temp_era5.copernicus_mx.trend.ols/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"ERA5 Land surface
+        max. temperature trend analysis (2000\\u20132020)\"\n    },\n    {\n      \"rel\":
+        \"child\",\n      \"href\": \"./lcv_building.residential_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Residental buildings\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_building.commercial_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Commercial buildings\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_building.industrial_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Industrial buildings\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_building_copernicus.osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"Copernicus-OSM Buildings\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_cemetery_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Cemeteries\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_construction.site_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Construction
+        sites\"\n    },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_dump.site_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Dump sites\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_farmland_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Farmland\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_farmyard_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Farmyard\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_forest_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Forest\"\n    },\n
+        \   {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_grass_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Grass\"\n    },\n
+        \   {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_harbour_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Harbours\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_meadow_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Meadows\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_military_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Military\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_orchard_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Orchards\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_quarry_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Quarries\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_railway_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Railways\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_reservoir_osm/collection.json\",\n
+        \     \"type\": \"application/json\",\n      \"title\": \"OSM Reservoirs\"\n
+        \   },\n    {\n      \"rel\": \"child\",\n      \"href\": \"./lcv_road_osm/collection.json\",\n
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similarity index 98%
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rename to test/cassettes/test_store/StacDataStoreTest.test_search_data_multi_level.yaml
index a5ec8dc..cabf0bf 100644
--- a/test/cassettes/test_store/StacDataStoreTest.test_search_data_time_range.yaml
+++ b/test/cassettes/test_store/StacDataStoreTest.test_search_data_multi_level.yaml
@@ -9,7 +9,7 @@ interactions:
       Connection:
       - keep-alive
       User-Agent:
-      - python-requests/2.31.0
+      - python-requests/2.32.3
     method: GET
     uri: https://s3.eu-central-1.wasabisys.com/stac/odse/catalog.json
   response:
@@ -416,17 +416,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:54 GMT
+      - Thu, 11 Jul 2024 12:06:06 GMT
       ETag:
       - '"326fbfb3eeb064cf4c2092261381fb89"'
       Last-Modified:
       - Fri, 15 Jul 2022 14:06:49 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
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     status:
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       message: OK
@@ -847,17 +847,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:55 GMT
+      - Thu, 11 Jul 2024 12:06:06 GMT
       ETag:
       - '"326fbfb3eeb064cf4c2092261381fb89"'
       Last-Modified:
       - Fri, 15 Jul 2022 14:06:49 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head06)
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       message: OK
@@ -925,19 +925,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:55 GMT
+      - Thu, 11 Jul 2024 12:06:06 GMT
       ETag:
       - '"a361cba8c583c9649b61c18c2e7d7eb8"'
       Last-Modified:
       - Tue, 05 Jul 2022 13:52:04 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R207-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
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       message: OK
@@ -1181,17 +1179,19 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:55 GMT
+      - Thu, 11 Jul 2024 12:06:06 GMT
       ETag:
       - '"a993e5d942f36c4207b394eceece6a7a"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:42 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head04)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (F8-R407-U17)
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       message: OK
@@ -1435,17 +1435,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:55 GMT
+      - Thu, 11 Jul 2024 12:06:06 GMT
       ETag:
       - '"fddbe5a9c03b5246a0e9a69ffd5596fd"'
       Last-Modified:
       - Thu, 19 May 2022 19:08:00 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head17)
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@@ -1689,17 +1689,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:32:55 GMT
+      - Thu, 11 Jul 2024 12:06:06 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:09:05 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
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       message: OK
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       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:32:55 GMT
+      - Thu, 11 Jul 2024 12:06:06 GMT
       ETag:
       - '"fc46871f9da7a16c36dd032d48bae72a"'
       Last-Modified:
       - Thu, 19 May 2022 19:09:23 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head04)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head23)
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@@ -2197,17 +2197,17 @@ interactions:
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       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:55 GMT
+      - Thu, 11 Jul 2024 12:06:07 GMT
       ETag:
       - '"21bfdbaeaffeca0a1d32f5beca1fa9cd"'
       Last-Modified:
       - Thu, 19 May 2022 19:09:39 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
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@@ -2451,17 +2451,19 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:32:56 GMT
+      - Thu, 11 Jul 2024 12:06:07 GMT
       ETag:
       - '"ad0212ec15adff8c522ae234a5e80860"'
       Last-Modified:
       - Thu, 19 May 2022 19:09:58 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (F8-R407-U17)
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       message: OK
@@ -2705,17 +2707,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:56 GMT
+      - Thu, 11 Jul 2024 12:06:07 GMT
       ETag:
       - '"8ccd5383f6138ce12635ddc2505dcf9c"'
       Last-Modified:
       - Thu, 19 May 2022 19:10:16 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head06)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head04)
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       message: OK
@@ -2961,17 +2963,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:56 GMT
+      - Thu, 11 Jul 2024 12:06:07 GMT
       ETag:
       - '"762c2bcb816331f727e31da667b8c1e7"'
       Last-Modified:
       - Wed, 13 Jul 2022 14:05:45 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
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@@ -3201,17 +3203,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:56 GMT
+      - Thu, 11 Jul 2024 12:06:07 GMT
       ETag:
       - '"b8b0cc0367278a2e2ba8554c2d0045aa"'
       Last-Modified:
       - Thu, 14 Jul 2022 16:39:57 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head14)
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@@ -3319,17 +3321,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:32:56 GMT
+      - Thu, 11 Jul 2024 12:06:07 GMT
       ETag:
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       Last-Modified:
       - Fri, 01 Jul 2022 09:11:55 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head23)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head04)
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@@ -3437,17 +3439,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:32:56 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
       - '"d08079eba3cde9ad8047950037e1c214"'
       Last-Modified:
       - Fri, 01 Jul 2022 09:11:58 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head13)
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@@ -3555,17 +3557,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:32:56 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
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       Last-Modified:
       - Wed, 06 Jul 2022 10:13:44 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head21)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head16)
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@@ -3673,17 +3675,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:32:57 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
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       Last-Modified:
       - Wed, 06 Jul 2022 10:13:30 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head02)
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@@ -3791,17 +3793,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:32:57 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
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       Last-Modified:
       - Fri, 01 Jul 2022 09:11:49 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
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@@ -3909,17 +3911,19 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:32:57 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
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       Last-Modified:
       - Fri, 01 Jul 2022 09:11:43 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (F8-R107-U16)
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       message: OK
@@ -4004,17 +4008,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:32:57 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
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       Last-Modified:
       - Thu, 14 Jul 2022 16:41:01 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head10)
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@@ -4099,17 +4103,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:32:57 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
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       Last-Modified:
       - Thu, 14 Jul 2022 16:41:06 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head12)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head08)
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       x-amz-request-id:
-      - D7D1154946D7AA13:B
+      - 002B9725A4A02F0B:A
     status:
       code: 200
       message: OK
@@ -4194,17 +4198,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:57 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
       - '"c3e2192a4c45ffc4e5fa25eb7de81f11"'
       Last-Modified:
       - Thu, 14 Jul 2022 16:41:17 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head22)
       x-amz-id-2:
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     status:
       code: 200
       message: OK
@@ -4289,17 +4293,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:57 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
       - '"85a2c51465742d22a19c7de850bf12f5"'
       Last-Modified:
       - Thu, 14 Jul 2022 16:41:13 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head13)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head08)
       x-amz-id-2:
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       x-amz-request-id:
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     status:
       code: 200
       message: OK
@@ -4438,17 +4442,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:57 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
       - '"08efe1030bbc57fc7fa37a4e1c7acf07"'
       Last-Modified:
       - Tue, 14 Jun 2022 01:39:27 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head14)
       x-amz-id-2:
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       x-amz-request-id:
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     status:
       code: 200
       message: OK
@@ -4587,17 +4591,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:57 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
       - '"fc0628e2ea060e61d359b4c5e7c8d998"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:46 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head01)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head04)
       x-amz-id-2:
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       x-amz-request-id:
-      - C27FDCE6C3B08C18:B
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     status:
       code: 200
       message: OK
@@ -4737,17 +4741,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:08 GMT
       ETag:
       - '"08867bd337935524a94c636ce05b9eda"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:47 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head01)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head15)
       x-amz-id-2:
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       x-amz-request-id:
-      - 3EE8F05DC032A818:B
+      - D148B1350819BF68:B
     status:
       code: 200
       message: OK
@@ -4888,17 +4892,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"31f96f2e4c7a7d8550d9251c1b650ef9"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:49 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head02)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head03)
       x-amz-id-2:
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       x-amz-request-id:
-      - ED9B7717A74A60BC:A
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     status:
       code: 200
       message: OK
@@ -5039,17 +5043,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"c5c09e5f0168c6e50588d2b6650636bd"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:50 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head10)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head08)
       x-amz-id-2:
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       x-amz-request-id:
-      - 683DF571EB2678AD:A
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     status:
       code: 200
       message: OK
@@ -5188,17 +5192,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"372b263503ffb3270a09b027e7f491a2"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:52 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head07)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head04)
       x-amz-id-2:
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       x-amz-request-id:
-      - C10217FC956D57B5:B
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     status:
       code: 200
       message: OK
@@ -5337,17 +5341,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"59fcb232752b28a7c592adeb9d0eb074"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
       x-amz-id-2:
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       x-amz-request-id:
-      - CB082D2ABF7C05FE:B
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     status:
       code: 200
       message: OK
@@ -5486,17 +5490,19 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"f935d021e84d97b7fdcb5e4b0019a90b"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:55 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head13)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (F8-R407-U17)
       x-amz-id-2:
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       x-amz-request-id:
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+      x-wasabi-cm-reference-id:
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     status:
       code: 200
       message: OK
@@ -5635,17 +5641,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"7b8cca387fc9bc96f77d30a9b15f6a05"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:56 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head06)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
       x-amz-id-2:
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       x-amz-request-id:
-      - 9D4D8C009BDFA2BD:A
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     status:
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       message: OK
@@ -5784,17 +5790,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"8c0afed1f782e98cdee104d8d16039d2"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:57 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
       x-amz-id-2:
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       x-amz-request-id:
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     status:
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       message: OK
@@ -5933,17 +5939,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"67a3268fe5d78b55b75656250d45ebaa"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:57:59 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head06)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head23)
       x-amz-id-2:
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-      - D383A97A33E502C5:B
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     status:
       code: 200
       message: OK
@@ -6084,17 +6090,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:58 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"a7bcb7a27d0f24419b331c0ecfba6a92"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:00 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head21)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head06)
       x-amz-id-2:
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       x-amz-request-id:
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     status:
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       message: OK
@@ -6234,17 +6240,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:59 GMT
+      - Thu, 11 Jul 2024 12:06:09 GMT
       ETag:
       - '"cddc8f3050dac0ef479185475049572a"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:01 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head07)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
       x-amz-id-2:
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       x-amz-request-id:
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     status:
       code: 200
       message: OK
@@ -6385,17 +6391,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:59 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"dc25b1b9132ab4efdf165e961fc49ad8"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:02 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head01)
       x-amz-id-2:
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       x-amz-request-id:
-      - ACD35572AF29F43D:B
+      - B09B331CFFA95237:B
     status:
       code: 200
       message: OK
@@ -6534,17 +6540,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:59 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"0aa374e27af2fcd44bf1273c8d84929a"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:03 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
       x-amz-id-2:
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       x-amz-request-id:
-      - 588CC61190F1664E:A
+      - 7AD29C5C60A718C0:A
     status:
       code: 200
       message: OK
@@ -6683,17 +6689,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:59 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"f78f6a9c486e8d76b370662c227e1a2f"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:05 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head22)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
       x-amz-id-2:
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       x-amz-request-id:
-      - FFA806414BDDB621:B
+      - C7D7A66FF3C30087:A
     status:
       code: 200
       message: OK
@@ -6834,17 +6840,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:59 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"53faed960f563c49b89bb49e5e0eb18e"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:06 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head23)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head13)
       x-amz-id-2:
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       x-amz-request-id:
-      - CD48B8AB4B5CAC0A:A
+      - 5197E495DA080A21:A
     status:
       code: 200
       message: OK
@@ -6983,17 +6989,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:59 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"2828a2b52161b97db10b07a04cb83cbc"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:07 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head22)
       x-amz-id-2:
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       x-amz-request-id:
-      - F4432067B369C988:A
+      - 1CEB1CEB4008B489:B
     status:
       code: 200
       message: OK
@@ -7132,19 +7138,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:59 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"9e21d59c60a2de97f48fe48aa5599935"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:08 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R107-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head04)
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-      x-wasabi-cm-reference-id:
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     status:
       code: 200
       message: OK
@@ -7285,17 +7289,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:59 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"90389886cf787910ce17cbb8b23e1342"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:10 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head22)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
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     status:
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       message: OK
@@ -7435,17 +7439,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:32:59 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"7bf4f45ef277eca5c3cee7fa5c2d3922"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:10 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head03)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head13)
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     status:
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       message: OK
@@ -7586,17 +7590,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"568e5d4fdc5832f7f86cb5d23dd6ddf3"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:12 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head22)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head12)
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     status:
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       message: OK
@@ -7735,17 +7739,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"b44059038a02a9eefaf873cec3b1cd22"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:13 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head14)
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     status:
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       message: OK
@@ -7884,17 +7888,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:10 GMT
       ETag:
       - '"75b69f981702b0bdabb0dbca0499deef"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:14 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head10)
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     status:
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       message: OK
@@ -8033,17 +8037,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"1fe1ff4529ad70c79f7d9282dc20d1d8"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:43:23 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
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     status:
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       message: OK
@@ -8182,17 +8186,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"e4a045fa2388215682a8a9c40f395ed6"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:16 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
       x-amz-id-2:
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     status:
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       message: OK
@@ -8331,17 +8335,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"e5e9537c7c8dd7adf46f054e2ace0819"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:18 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head08)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
       x-amz-id-2:
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     status:
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       message: OK
@@ -8480,17 +8484,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"f7a3a1297dc35f4c5446bbfb4d90c9e5"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:19 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head03)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
       x-amz-id-2:
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     status:
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       message: OK
@@ -8630,17 +8634,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"2c5439e79e58599274d647ccb08c1194"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:21 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head04)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
       x-amz-id-2:
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     status:
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       message: OK
@@ -8780,17 +8784,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"489a9a04c32840391618b4423f256daa"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:22 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head08)
       x-amz-id-2:
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     status:
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       message: OK
@@ -8929,17 +8933,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:00 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"9898c14300f55beeaeb8dceb75fef27f"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:23 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head22)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head20)
       x-amz-id-2:
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     status:
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       message: OK
@@ -9078,17 +9082,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:01 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"310a85bcd09afdf22c0373593abb420d"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:25 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
       x-amz-id-2:
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     status:
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       message: OK
@@ -9228,17 +9232,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:01 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"aa7b632aa6d25ef7fe690cae2b9fc278"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:26 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head10)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head13)
       x-amz-id-2:
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     status:
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       message: OK
@@ -9377,17 +9381,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:01 GMT
+      - Thu, 11 Jul 2024 12:06:11 GMT
       ETag:
       - '"cd9829e9642c86f90a20e401617396bf"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:28 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
       x-amz-id-2:
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       x-amz-request-id:
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     status:
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       message: OK
@@ -9528,17 +9532,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:01 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"e18c0914a99086b67fc300197d71cb0e"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:30 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head13)
       x-amz-id-2:
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+      - D0AE3DF890C77786:A
     status:
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       message: OK
@@ -9677,17 +9681,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:01 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"830528e41072a16d8bc0420c1ce85cc9"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:30 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
       x-amz-id-2:
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       x-amz-request-id:
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     status:
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       message: OK
@@ -9826,17 +9830,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:01 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"daec4e3fd9b6263479e710eedc611584"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:31 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head01)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head16)
       x-amz-id-2:
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       x-amz-request-id:
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+      - C88229B795E3D5EB:A
     status:
       code: 200
       message: OK
@@ -9975,17 +9979,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:01 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"fd3ac809f02cd9e5c48b28e01cf2d44e"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:33 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
       x-amz-id-2:
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       x-amz-request-id:
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+      - 7DDCF79CF3EAA835:A
     status:
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       message: OK
@@ -10124,17 +10128,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:01 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"67af74891981f66468b496e167603527"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:34 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
       x-amz-id-2:
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     status:
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       message: OK
@@ -10273,17 +10277,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:01 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"1acf1749a2d38d1a2feb08860fe3cfff"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:35 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head03)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head23)
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     status:
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       message: OK
@@ -10422,17 +10426,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:02 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"81fedd05652865f1eabf45350bac0f65"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:37 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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     status:
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       message: OK
@@ -10571,17 +10575,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:02 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"24cd585ce222116a93f49da926497904"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:38 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head13)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
       x-amz-id-2:
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     status:
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       message: OK
@@ -10720,19 +10724,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:02 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"7f020a7d3515127fdecce6d8b8a68e35"'
       Last-Modified:
       - Mon, 13 Jun 2022 23:58:40 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R407-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
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-      x-wasabi-cm-reference-id:
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     status:
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       message: OK
@@ -10836,17 +10838,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:02 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"18bcc6bfff4df8fc7d88a140ae436fee"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:47 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head04)
       x-amz-id-2:
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     status:
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       message: OK
@@ -10951,17 +10953,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:02 GMT
+      - Thu, 11 Jul 2024 12:06:12 GMT
       ETag:
       - '"b5c69adb2801d80be6524352e2f73d63"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:10 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head12)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head15)
       x-amz-id-2:
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     status:
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       message: OK
@@ -11066,17 +11068,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:02 GMT
+      - Thu, 11 Jul 2024 12:06:13 GMT
       ETag:
       - '"2a427adbe4d8c10ea34a09a6ba919b2d"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:51 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head12)
       x-amz-id-2:
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     status:
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       message: OK
@@ -11181,17 +11183,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:02 GMT
+      - Thu, 11 Jul 2024 12:06:13 GMT
       ETag:
       - '"4a8e6e5691069b2c778b096acb2a0cfa"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:08 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head17)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
       x-amz-id-2:
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       x-amz-request-id:
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     status:
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       message: OK
@@ -11295,17 +11297,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:02 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"9bb2a1ba9f3b73b5681814d0e1c9065e"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:06 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
       x-amz-id-2:
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     status:
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       message: OK
@@ -11409,17 +11411,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:02 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"df4d3451528ba8570017a6de09f5acfd"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:49 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head13)
       x-amz-id-2:
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     status:
       code: 200
       message: OK
@@ -11524,17 +11526,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"7b7b1bb25db9ec6dffad0114720133b7"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:45 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
       x-amz-id-2:
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     status:
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       message: OK
@@ -11638,17 +11640,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"4ec19ab023ca1ee7ae5fd9e9d0e8527a"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:55 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head23)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
       x-amz-id-2:
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     status:
       code: 200
       message: OK
@@ -11752,17 +11754,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"62f941a707cb8eb0ab62e9a1740de2ba"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:57 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head13)
       x-amz-id-2:
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     status:
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       message: OK
@@ -11867,17 +11869,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"d878390620f886f519fdb9ae5daf4f16"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:07 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head06)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head22)
       x-amz-id-2:
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-      - 03D05F6717968CB4:B
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     status:
       code: 200
       message: OK
@@ -11981,17 +11983,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"64a002f2c4c1fd273a6f00f47529e376"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:44 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
       x-amz-id-2:
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       x-amz-request-id:
-      - 30861D237CF003E0:B
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     status:
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       message: OK
@@ -12096,19 +12098,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"d4942a244260372bcf7a9d74382c8818"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:05 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R407-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head08)
       x-amz-id-2:
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-      x-wasabi-cm-reference-id:
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+      - F4FB54A2EF8B000B:B
     status:
       code: 200
       message: OK
@@ -12212,17 +12212,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"cbd79b2c986991cfe5e31bb157fff202"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:43 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
       x-amz-id-2:
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     status:
       code: 200
       message: OK
@@ -12326,17 +12326,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"21d702610dffa65961fe8c53386d3ce1"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:59 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head17)
       x-amz-id-2:
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       x-amz-request-id:
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+      - AFEBF9AD7AEB47F3:B
     status:
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       message: OK
@@ -12440,17 +12440,19 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:14 GMT
       ETag:
       - '"d10e4bb09b0e89a3fe7c85dc11869287"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:56 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (F8-R407-U16)
       x-amz-id-2:
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       x-amz-request-id:
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+      - F2D24B61285A0382:A
+      x-wasabi-cm-reference-id:
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     status:
       code: 200
       message: OK
@@ -12554,17 +12556,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"a64d183796f3ff772447546097953237"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:02 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head22)
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     status:
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       message: OK
@@ -12654,17 +12656,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:03 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"2065b0dc45aac59cae98e4124f18f5d8"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:41 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head08)
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       message: OK
@@ -12755,17 +12757,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:04 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"86050e581b4d87537e7661db7f452327"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:48 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head06)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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     status:
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       message: OK
@@ -12856,17 +12858,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:04 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"c01f0143503921acf98256b8453f8199"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:54 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head06)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head23)
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     status:
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       message: OK
@@ -12957,17 +12959,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:04 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"359d9fc9a09a456f88d7f2fcf57ca8bb"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:03 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head01)
       x-amz-id-2:
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     status:
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       message: OK
@@ -13057,17 +13059,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:04 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"4d1a6025b1245f5c543599de61ecc954"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:58 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head04)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head18)
       x-amz-id-2:
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     status:
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       message: OK
@@ -13157,17 +13159,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:04 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"18ca67cd6ff2a9c323ef84f39d3acb32"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:52 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head06)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head16)
       x-amz-id-2:
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     status:
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       message: OK
@@ -13258,17 +13260,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:04 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"fe232b3624954297182f9dade2775dbb"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:12 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head17)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head02)
       x-amz-id-2:
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     status:
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       message: OK
@@ -13358,17 +13360,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:04 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"02948c63311093ec8ad04d31ce66920b"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:42 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
       x-amz-id-2:
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     status:
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       message: OK
@@ -13458,19 +13460,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:04 GMT
+      - Thu, 11 Jul 2024 12:06:15 GMT
       ETag:
       - '"7390bd66b698719561ca284bdc50e26a"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:42 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R407-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
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     status:
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       message: OK
@@ -13561,17 +13561,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:04 GMT
+      - Thu, 11 Jul 2024 12:06:16 GMT
       ETag:
       - '"69552af45be8cf9a6ef0b77d38dbb47f"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:58 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head13)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head03)
       x-amz-id-2:
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@@ -13661,17 +13661,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:05 GMT
+      - Thu, 11 Jul 2024 12:06:16 GMT
       ETag:
       - '"864ed4bda77833c16b392af5c09d57d3"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
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     status:
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       message: OK
@@ -13762,17 +13762,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:05 GMT
+      - Thu, 11 Jul 2024 12:06:16 GMT
       ETag:
       - '"8894b2d9764132a6c58431d029be9f99"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head01)
       x-amz-id-2:
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     status:
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       message: OK
@@ -13862,17 +13862,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:05 GMT
+      - Thu, 11 Jul 2024 12:06:16 GMT
       ETag:
       - '"9bdbbb6b71a5a8488f03d47d730f3ebf"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:11 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
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       message: OK
@@ -13962,17 +13962,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:05 GMT
+      - Thu, 11 Jul 2024 12:06:16 GMT
       ETag:
       - '"580533833efd1fdb7f8bd5de9b3d6821"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:10:52 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head18)
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     status:
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       message: OK
@@ -14062,17 +14062,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:05 GMT
+      - Thu, 11 Jul 2024 12:06:16 GMT
       ETag:
       - '"a160fb1438a32ec3b7d65fe2f2cfaa8b"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:01 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
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     status:
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       message: OK
@@ -14162,17 +14162,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:05 GMT
+      - Thu, 11 Jul 2024 12:06:16 GMT
       ETag:
       - '"781c5919b91b0136996cc51681aeec45"'
       Last-Modified:
       - Mon, 05 Sep 2022 13:11:04 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head12)
       x-amz-id-2:
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     status:
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       message: OK
@@ -14311,17 +14311,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:05 GMT
+      - Thu, 11 Jul 2024 12:06:16 GMT
       ETag:
       - '"f8632c89d7e03434a33fde1c950049fa"'
       Last-Modified:
       - Fri, 08 Jul 2022 10:16:25 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
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     status:
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       message: OK
@@ -29747,17 +29747,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:06 GMT
+      - Thu, 11 Jul 2024 12:06:16 GMT
       ETag:
       - '"e39f2806959f26375c2d328f89d4dc42"'
       Last-Modified:
       - Thu, 19 May 2022 18:47:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head01)
       x-amz-id-2:
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     status:
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       message: OK
@@ -45183,17 +45183,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:06 GMT
+      - Thu, 11 Jul 2024 12:06:17 GMT
       ETag:
       - '"92e825c84d0ca8675d09905cbc90b6d3"'
       Last-Modified:
       - Thu, 19 May 2022 18:41:37 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head01)
       x-amz-id-2:
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     status:
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       message: OK
@@ -60619,17 +60619,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:06 GMT
+      - Thu, 11 Jul 2024 12:06:17 GMT
       ETag:
       - '"c5851bb5bc587088fea640be890b5ecd"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:36 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head14)
       x-amz-id-2:
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+      - 216ADEF0663B0CE9:B
     status:
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       message: OK
@@ -60717,17 +60717,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:07 GMT
+      - Thu, 11 Jul 2024 12:06:17 GMT
       ETag:
       - '"9c54b9bf0c87f98d14cc8bfaba9a99b7"'
       Last-Modified:
       - Thu, 14 Jul 2022 17:04:40 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head18)
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@@ -61315,17 +61315,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:07 GMT
+      - Thu, 11 Jul 2024 12:06:17 GMT
       ETag:
       - '"c5ad9285cde54e12cf880aed79818577"'
       Last-Modified:
       - Thu, 14 Jul 2022 17:04:30 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head10)
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       message: OK
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       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:07 GMT
+      - Thu, 11 Jul 2024 12:06:17 GMT
       ETag:
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       Last-Modified:
       - Fri, 15 Jul 2022 13:57:46 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
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       Date:
-      - Tue, 28 May 2024 11:33:07 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
       - '"aaf9e1c8aa78bb52aac8723172ab2efd"'
       Last-Modified:
       - Fri, 15 Jul 2022 13:57:51 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head17)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:07 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
       - '"be175975eacaba47143a63e39d238048"'
       Last-Modified:
       - Fri, 15 Jul 2022 13:57:50 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R407-U17)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head12)
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@@ -62213,17 +62211,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:07 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
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       Last-Modified:
       - Fri, 15 Jul 2022 13:57:52 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head23)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head04)
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       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:59 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head16)
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       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
       - '"3b3384832a5b855a6e923e26b73f0645"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:59 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head23)
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       Date:
-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:59 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
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-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:59 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
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-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:59 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head10)
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       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:59 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head22)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head02)
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@@ -62788,19 +62786,17 @@ interactions:
       Content-Type:
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-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R107-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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@@ -62872,17 +62868,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:08:00 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head22)
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@@ -62954,17 +62950,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:18 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:08:00 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
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@@ -63036,17 +63032,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:08 GMT
+      - Thu, 11 Jul 2024 12:06:19 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head01)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
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@@ -63118,17 +63114,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:09 GMT
+      - Thu, 11 Jul 2024 12:06:19 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head04)
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@@ -63200,17 +63196,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:09 GMT
+      - Thu, 11 Jul 2024 12:06:19 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head16)
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@@ -63282,17 +63278,19 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:09 GMT
+      - Thu, 11 Jul 2024 12:06:19 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:09:00 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head01)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (F8-R407-U16)
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     status:
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       message: OK
@@ -63364,17 +63362,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:09 GMT
+      - Thu, 11 Jul 2024 12:06:19 GMT
       ETag:
       - '"019c81cefe5dd2459faf6d4411da274a"'
       Last-Modified:
       - Thu, 19 May 2022 19:09:00 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head17)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head23)
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@@ -63446,19 +63444,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:09 GMT
+      - Thu, 11 Jul 2024 12:06:19 GMT
       ETag:
       - '"5aaa3425f8e9b2aaaebf11a2acac3d02"'
       Last-Modified:
       - Thu, 19 May 2022 19:09:22 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R407-U17)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head10)
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+      - Thu, 11 Jul 2024 12:06:19 GMT
       ETag:
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       Server:
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+      - Thu, 11 Jul 2024 12:06:19 GMT
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       Server:
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       Server:
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+      - Thu, 11 Jul 2024 12:06:19 GMT
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       Server:
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+      - Thu, 11 Jul 2024 12:06:19 GMT
       ETag:
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       Server:
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+      - Thu, 11 Jul 2024 12:06:20 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head12)
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+      - Thu, 11 Jul 2024 12:06:20 GMT
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       Server:
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       Server:
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+      - Thu, 11 Jul 2024 12:06:20 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
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+      - Thu, 11 Jul 2024 12:06:20 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
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+      - Thu, 11 Jul 2024 12:06:20 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head01)
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+      - Thu, 11 Jul 2024 12:06:20 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
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+      - Thu, 11 Jul 2024 12:06:20 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head12)
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-      - Tue, 28 May 2024 11:33:11 GMT
+      - Thu, 11 Jul 2024 12:06:20 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
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-      - Tue, 28 May 2024 11:33:11 GMT
+      - Thu, 11 Jul 2024 12:06:20 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head12)
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-      - Tue, 28 May 2024 11:33:11 GMT
+      - Thu, 11 Jul 2024 12:06:21 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head06)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
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+      - Thu, 11 Jul 2024 12:06:21 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
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@@ -65351,17 +65351,17 @@ interactions:
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-      - Tue, 28 May 2024 11:33:11 GMT
+      - Thu, 11 Jul 2024 12:06:21 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head12)
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@@ -65455,17 +65455,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:11 GMT
+      - Thu, 11 Jul 2024 12:06:21 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head10)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
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@@ -65557,19 +65557,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:11 GMT
+      - Thu, 11 Jul 2024 12:06:21 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R407-U17)
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       - '"16b47f8a640491b205e063b0d031379d"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:45 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R107-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head12)
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       message: OK
@@ -67336,17 +67786,17 @@ interactions:
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       Date:
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+      - Thu, 11 Jul 2024 12:06:24 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:45 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
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-      - Tue, 28 May 2024 11:33:14 GMT
+      - Thu, 11 Jul 2024 12:06:24 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:46 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
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       Date:
-      - Tue, 28 May 2024 11:33:14 GMT
+      - Thu, 11 Jul 2024 12:06:24 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:45 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head02)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
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       Date:
-      - Tue, 28 May 2024 11:33:14 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:46 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R207-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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       Date:
-      - Tue, 28 May 2024 11:33:14 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:46 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head23)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
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       Date:
-      - Tue, 28 May 2024 11:33:14 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:46 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head12)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head02)
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-      - Tue, 28 May 2024 11:33:14 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:46 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
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-      - Tue, 28 May 2024 11:33:14 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:46 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head07)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
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-      - Tue, 28 May 2024 11:33:14 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:46 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head21)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head10)
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-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head03)
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-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head03)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head06)
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+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head08)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head04)
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-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head23)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head02)
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-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:25 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head06)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head17)
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-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R207-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head15)
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@@ -68480,17 +68926,17 @@ interactions:
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-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
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-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head04)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head23)
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@@ -68632,17 +69078,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head04)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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@@ -68708,17 +69154,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:48 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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@@ -68784,17 +69230,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:15 GMT
+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head04)
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+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R407-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head15)
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+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head08)
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+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
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+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head12)
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+      - Thu, 11 Jul 2024 12:06:26 GMT
       ETag:
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       Last-Modified:
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       Server:
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+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head24)
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+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
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-      - Tue, 28 May 2024 11:33:16 GMT
+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R207-U16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head15)
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+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head03)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head18)
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-      - Tue, 28 May 2024 11:33:16 GMT
+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
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-      - Tue, 28 May 2024 11:33:17 GMT
+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
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-      - Tue, 28 May 2024 11:33:17 GMT
+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
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-      - Tue, 28 May 2024 11:33:17 GMT
+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head02)
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-      - Tue, 28 May 2024 11:33:17 GMT
+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head20)
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-      - Tue, 28 May 2024 11:33:17 GMT
+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head01)
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-      - Tue, 28 May 2024 11:33:17 GMT
+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head08)
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-      - Tue, 28 May 2024 11:33:17 GMT
+      - Thu, 11 Jul 2024 12:06:27 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head22)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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@@ -70156,17 +70600,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:17 GMT
+      - Thu, 11 Jul 2024 12:06:28 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head22)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head03)
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@@ -70232,17 +70676,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:17 GMT
+      - Thu, 11 Jul 2024 12:06:28 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:51 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head21)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
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@@ -70308,17 +70752,17 @@ interactions:
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-      - Tue, 28 May 2024 11:33:18 GMT
+      - Thu, 11 Jul 2024 12:06:28 GMT
       ETag:
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       Last-Modified:
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       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head12)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
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       message: OK
@@ -70384,17 +70828,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:18 GMT
+      - Thu, 11 Jul 2024 12:06:28 GMT
       ETag:
       - '"75fc57703495cfda8c639d7d5cc8780d"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:52 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head21)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head01)
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       message: OK
@@ -70460,17 +70904,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:18 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
       - '"54ab403bfbb69cba4c519247d3dbe387"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:52 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head10)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
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       message: OK
@@ -70536,17 +70980,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:18 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:52 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head01)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head10)
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       message: OK
@@ -70612,17 +71056,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:18 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
       - '"121153dc6d7809deec5c995d3babc2cd"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:52 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head01)
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     status:
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       message: OK
@@ -70688,17 +71132,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:18 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
       - '"13f6f586733d577b632a2eb6f765d11a"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head01)
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       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:18 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
       - '"be038958555d3a9219ba724d3b65508e"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
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@@ -70840,17 +71284,17 @@ interactions:
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       Date:
-      - Tue, 28 May 2024 11:33:18 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
       - '"a79aee6926590a9d236b657c19fdc880"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head15)
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@@ -70916,17 +71360,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:19 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head16)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head03)
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       message: OK
@@ -70992,17 +71436,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:19 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
       - '"04c3d5edf8e9d55b91f5801cb2b89c1c"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head13)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head15)
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@@ -71068,17 +71512,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:19 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
       - '"3312f26684ee03eb402dbe01c97dee4e"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head03)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
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@@ -71144,17 +71588,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:19 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
       - '"ad2082b31ce1519190253d6131016973"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:53 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head04)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
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@@ -71220,17 +71664,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:19 GMT
+      - Thu, 11 Jul 2024 12:06:29 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:54 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head12)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head05)
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@@ -71296,17 +71740,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:19 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"042eb413b7392ece4ba5e78247dc798f"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:54 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head23)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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@@ -71372,17 +71816,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:54 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
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@@ -71448,17 +71892,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"76a99c16b2fe909c021092bb190d6745"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:54 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head08)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head10)
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@@ -71524,17 +71968,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"934086bb8d20248ebead374480cffe48"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:54 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head13)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head06)
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@@ -71600,17 +72044,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"7e2e43a379154606597cc0d2002535aa"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:55 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head23)
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       message: OK
@@ -71676,17 +72120,17 @@ interactions:
       Content-Type:
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       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"254953f859e562cd17739a26e233b497"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:55 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
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     status:
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       message: OK
@@ -71752,17 +72196,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"094e7fee6c163a378c7fc8fc921f8e2b"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:55 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head18)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head13)
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       message: OK
@@ -71828,17 +72272,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"2e04b53ba35ccd39e03c8aa5d38dcae4"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:55 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head11)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head24)
       x-amz-id-2:
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       message: OK
@@ -71904,17 +72348,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"585e532fedcf1c09a2ddbe1a8fe24201"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:55 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head10)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head01)
       x-amz-id-2:
-      - 00pAuIbwWzbDn6rJHnETWYVNxrYq6jExu26FSCLAV9G2W+rSPfeEsYjAv7Xg74MPynD7xQWJD7Ah
+      - /TGq55bp1dLIczdkRcuMVfwMbVNl7bSYeXYnqcbNy7u5LTP1SIGRexz0uVkuQ6NksVPzU57iFu7e
       x-amz-request-id:
-      - 5FC3C634355E40F5:A
+      - D80CE8D253025820:A
     status:
       code: 200
       message: OK
@@ -71980,17 +72424,19 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"1b6afdb08e7be5242a6bc15ea7dd1892"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:55 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (F8-R107-U16)
       x-amz-id-2:
-      - b7xJV5qEdw7gFi4/OxLn6saxEb+u4W+g/K56y1idEJfEk/uZgL57I4QEhAjJaX6A1yhAMy6YofcU
+      - +rfz6cC5cBWIu4cyNIqq8thbU5x3gTAV9iLEj+38bserKH0ALU7XqbuhFz5kaIkyqRJtbIgZFFev
       x-amz-request-id:
-      - 5C7CB83FDB450CEA:A
+      - BA7975F2A5DB669E:A
+      x-wasabi-cm-reference-id:
+      - 1720699590770 130.117.252.102 ConID:775860017/EngineConID:7481257/Core:94
     status:
       code: 200
       message: OK
@@ -72056,17 +72502,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:20 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"91644d29975b1f4d9e5674d8fddbaffe"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:56 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head09)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head06)
       x-amz-id-2:
-      - w493U2W4nJYVtGxlXArKcNDADJtcdH6iQOBXhM9MmHyw3sEiQyO6AgDM6wyxrbRxzZC8HzxKfr/y
+      - 4dD+52XkamYyuFwrV9ITRcgV4AJ6a1qKZGFX+EEYmrPS8Pk1iOTPd66fkhc+Ce+9mYjrXZASl0br
       x-amz-request-id:
-      - A39DFF40C4D63DDC:A
+      - 631F1CC512B7DC70:A
     status:
       code: 200
       message: OK
@@ -72132,17 +72578,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:21 GMT
+      - Thu, 11 Jul 2024 12:06:30 GMT
       ETag:
       - '"faa10ea34fd840097e8af5adc7e8033a"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:56 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head05)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head22)
       x-amz-id-2:
-      - 8yDCna2s477wwguPcZ0KylKfsc9qVNK0ETl95FcJ6/jLytDd0dqn/fLCbpga/jRVQ+wVT1phbs69
+      - fAVeDIWcJBFqycq+Pzx0fprZkamXR4nYGO2fvCqeBYI+6qj1LKLShDtgTckldCCVQj1XhB51xK0Y
       x-amz-request-id:
-      - 2C167CFFD10730F7:B
+      - EF5CC74BC6D19837:B
     status:
       code: 200
       message: OK
@@ -72208,17 +72654,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:21 GMT
+      - Thu, 11 Jul 2024 12:06:31 GMT
       ETag:
       - '"13637da4e6be14c1fcf0d8d0b3598aa0"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:56 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head10)
       x-amz-id-2:
-      - TvvDWrWWPtS7htq7fCz3HC1I+3D6Vy+lVBGOwpJSbRQS6Molq0mWN6EongaX0Q6vgDWLz8KWC7S0
+      - 3mbZw+QQgx+m/Q+Pyc7WzTCaUspPdq8uz74g/8VnnNbFrL2/gz2wjC9yzwgeVx1do6Su/co+t94K
       x-amz-request-id:
-      - 857007591CD6DD7F:A
+      - A6E563DE5B39E526:A
     status:
       code: 200
       message: OK
@@ -72284,17 +72730,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:21 GMT
+      - Thu, 11 Jul 2024 12:06:31 GMT
       ETag:
       - '"7f98802d34814800c291e99ee508e253"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:56 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head04)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head09)
       x-amz-id-2:
-      - Mpf6QMaZ3BVhAOhnWxti+so0HErpXFnUE7gCEZt1Uof6v7IadNwhJPIq3RT9YygAkPr1fcU8sXyX
+      - JQOIP+SA/IEjZa+AlSUip4fR0n3QLBk7GJhxnwuxnG8xbZixMmqytQNMOa/Ou4Jol4taqGrkUE30
       x-amz-request-id:
-      - B6A23AEE1FF17F3A:A
+      - 9B8C78B31C412B84:A
     status:
       code: 200
       message: OK
@@ -72360,17 +72806,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:21 GMT
+      - Thu, 11 Jul 2024 12:06:31 GMT
       ETag:
       - '"26e3b07815d72caeee4acf9365bd9122"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:57 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head15)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head15)
       x-amz-id-2:
-      - iWRFGrhW0QiijLI8ohpXlfvvlpbysQuNFRQpLzGmkkL0aMPcG75zHYC6EuAC35Q8nAu4KtpaA68Z
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       x-amz-request-id:
-      - A920F88DF9816B95:A
+      - 0A978D786E3A033A:B
     status:
       code: 200
       message: OK
@@ -72436,17 +72882,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:21 GMT
+      - Thu, 11 Jul 2024 12:06:31 GMT
       ETag:
       - '"e20452caddecf2c66cd1d703fc91bbc4"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:57 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head14)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head11)
       x-amz-id-2:
-      - JAXb6BWcVtuvlAxfuxSbS0sS7ilT9bjxUQQONhhTbgW4AZr0U0r58cuXywIrxbYxPGGVzl9ZqtTZ
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       x-amz-request-id:
-      - DD37BEE83FDDFD7C:B
+      - 894AAC227A344FF7:A
     status:
       code: 200
       message: OK
@@ -72512,17 +72958,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:21 GMT
+      - Thu, 11 Jul 2024 12:06:31 GMT
       ETag:
       - '"3d03b2db9e731b8a39751bf2747773df"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:57 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (head02)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head07)
       x-amz-id-2:
-      - wjVPXL3o0F894Pn5BqK6TXrxQQhcqNF+djDAj/DfHSPaki6mgNyZxIp1US3isVZroJf++tjg9iCW
+      - w8g7I1Dlv2pd2kUflCCj5uNU7e5+wgswsphHbBXANQKbb/BKOeGRRhdEkJstaiyHLuevClptkmSg
       x-amz-request-id:
-      - 1D2A940701A4ADC6:A
+      - 177318D9E21BE988:B
     status:
       code: 200
       message: OK
@@ -72588,19 +73034,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 28 May 2024 11:33:21 GMT
+      - Thu, 11 Jul 2024 12:06:31 GMT
       ETag:
       - '"02691d4778928a9f2d8c9dbc73a79da6"'
       Last-Modified:
       - Thu, 19 May 2022 19:07:57 GMT
       Server:
-      - WasabiS3/7.18.4828-2024-02-12-543e1ba234 (F8-R407-U17)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head22)
       x-amz-id-2:
-      - Kt1TtOi8CYtFIBm1xyHNSGJt79AlLj260OdTw6xBPW3kxLQNP6WDAydLpyus7EdgLxDM87+avhgc
+      - tjYLMPrwYShAH3/6uGEDYKb0eS7+IIuufG0ZQQeEBb9M/EEkqMBkcGR5U8bU7aOMYr31EDgyIjpN
       x-amz-request-id:
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-      x-wasabi-cm-reference-id:
-      - 1716896001771 130.117.252.102 ConID:2251161792/EngineConID:21662567/Core:46
+      - 2472DFB89A06C899:A
     status:
       code: 200
       message: OK
diff --git a/test/cassettes/test_store/StacDataStoreTest.test_search_data_searchable_catalog.yaml b/test/cassettes/test_store/StacDataStoreTest.test_search_data_searchable_catalog.yaml
index 93cb5af..7931394 100644
--- a/test/cassettes/test_store/StacDataStoreTest.test_search_data_searchable_catalog.yaml
+++ b/test/cassettes/test_store/StacDataStoreTest.test_search_data_searchable_catalog.yaml
@@ -9,7 +9,7 @@ interactions:
       Connection:
       - keep-alive
       User-Agent:
-      - python-requests/2.31.0
+      - python-requests/2.32.3
     method: GET
     uri: https://earth-search.aws.element84.com/v1
   response:
@@ -24,15 +24,15 @@ interactions:
       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Tue, 28 May 2024 11:32:43 GMT
+      - Thu, 11 Jul 2024 10:41:27 GMT
       Via:
-      - 1.1 ebe5c7b82bb332f597b412a5fb9a8910.cloudfront.net (CloudFront)
+      - 1.1 8f20db43ba7579b7216cf908572d5054.cloudfront.net (CloudFront)
       X-Amz-Cf-Id:
-      - 4-G2yygnfOByk7KK--Ge6EIZEkmE9Y90xsUghxCkUMHqQgIEPOuCQQ==
+      - hufp8PVvR2QNYcg8RSSEfIOUqcYRkJlKhS3j-LskQA57p_hfLNrQpQ==
       X-Amz-Cf-Pop:
-      - TXL50-P5
+      - FRA2-C1
       X-Amzn-Trace-Id:
-      - Root=1-6655c0db-7db160f67f2442c011ff5a6f;Parent=660e4051e8241d6b;Sampled=0;lineage=9e2884e9:0
+      - Root=1-668fb6d7-6a976efc4bfda056588fd06f;Parent=62bc121bfa702374;Sampled=0;lineage=9e2884e9:0
       X-Cache:
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       access-control-allow-origin:
@@ -40,11 +40,11 @@ interactions:
       etag:
       - W/"d29-QPs+H0wJLUX32EZKNgzG3k6wfQ8"
       x-amz-apigw-id:
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+      - avmBwGWFPHcEsTw=
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+      - 5e286176-5fc0-460d-868b-6e6af96b0883
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     status:
@@ -65,12 +65,83 @@ interactions:
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       - application/json
       User-Agent:
-      - python-requests/2.31.0
+      - python-requests/2.32.3
     method: POST
     uri: https://earth-search.aws.element84.com/v1/search
   response:
     body:
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+        1 (band 11)","center_wavelength":1.61,"full_width_half_max":0.143}],"gsd":20,"proj:shape":[5490,5490],"proj:transform":[20,0,499980,0,-20,5400000],"raster:bands":[{"nodata":0,"data_type":"uint16","bits_per_sample":15,"spatial_resolution":20,"scale":0.0001,"offset":0}],"roles":["data","reflectance"]},"swir22-jp2":{"href":"s3://sentinel-s2-l2a/tiles/32/U/NU/2020/3/2/0/B12.jp2","type":"image/jp2","title":"SWIR
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+        2 (band 12)","center_wavelength":2.19,"full_width_half_max":0.242}],"gsd":20,"proj:shape":[5490,5490],"proj:transform":[20,0,499980,0,-20,5400000],"raster:bands":[{"nodata":0,"data_type":"uint16","bits_per_sample":15,"spatial_resolution":20,"scale":0.0001,"offset":0}],"roles":["data","reflectance"]},"visual-jp2":{"href":"s3://sentinel-s2-l2a/tiles/32/U/NU/2020/3/2/0/TCI.jp2","type":"image/jp2","title":"True
         color image","eo:bands":[{"name":"red","common_name":"red","description":"Red
         (band 4)","center_wavelength":0.665,"full_width_half_max":0.038},{"name":"green","common_name":"green","description":"Green
         (band 3)","center_wavelength":0.56,"full_width_half_max":0.045},{"name":"blue","common_name":"blue","description":"Blue
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+        (band 2)","center_wavelength":0.49,"full_width_half_max":0.098}],"proj:shape":[10980,10980],"proj:transform":[10,0,499980,0,-10,5400000],"roles":["visual"]},"wvp-jp2":{"href":"s3://sentinel-s2-l2a/tiles/32/U/NU/2020/3/2/0/WVP.jp2","type":"image/jp2","title":"Water
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+      - python-requests/2.32.3
     method: GET
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       Content-Type:
       - application/geo+json; charset=utf-8
       Date:
-      - Tue, 28 May 2024 11:32:53 GMT
+      - Thu, 11 Jul 2024 10:41:54 GMT
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-      - python-requests/2.31.0
+      - python-requests/2.32.3
     method: POST
     uri: https://earth-search.aws.element84.com/v1/search
   response:
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+      string: '{"type":"FeatureCollection","stac_version":"1.0.0","stac_extensions":[],"context":{"limit":10,"matched":16,"returned":0},"numberMatched":16,"numberReturned":0,"features":[],"links":[{"rel":"root","type":"application/json","href":"https://earth-search.aws.element84.com/v1"}]}'
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+      - Thu, 11 Jul 2024 10:41:55 GMT
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diff --git a/test/test_href_parse.py b/test/test_href_parse.py
new file mode 100644
index 0000000..ba1ed74
--- /dev/null
+++ b/test/test_href_parse.py
@@ -0,0 +1,134 @@
+# The MIT License (MIT)
+# Copyright (c) 2024 by the xcube development team and contributors
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import unittest
+
+from xcube.core.store import DataStoreError
+
+from xcube_stac.href_parse import (
+    _assert_aws_s3_bucket,
+    _assert_aws_s3_region_name,
+    _decode_href,
+)
+
+
+class HrefParseTest(unittest.TestCase):
+
+    def test_decode_href(self):
+        hrefs = [
+            "https://s3.amazonaws.com/bucket-name/filename",
+            "s3://bucket-name/filename",
+            "https://bucket-name.s3.amazonaws.com/filename",
+            "https://s3-us-east-1.amazonaws.com/bucket-name/filename",
+            "https://bucket-name.s3-us-east-1.amazonaws.com/filename",
+            "https://bucket-name.s3.us-east-1.amazonaws.com/filename",
+            (
+                "https://s3.eu-central-1.wasabisys.com/eumap/lcv/"
+                "lcv_blue_landsat.glad.ard_p50_30m_0..0cm_1999.12."
+                "02..2000.03.20_eumap_epsg3035_v1.1.tif"
+            ),
+            (
+                "https://download.geoservice.dlr.de/ENMAP/files/L0/2024/05/21/"
+                "ENMAP01-_____L0-DT0000074151_20240521T055623Z_002_V010402_"
+                "20240521T143405Z-QL_VNIR_COG.TIF"
+            ),
+            (
+                "https://sentinel2l2a01.blob.core.windows.net/sentinel2-l2/"
+                "55/X/EJ/2024/05/22/S2B_MSIL2A_20240522T032519_N0510_R018_"
+                "T55XEJ_20240522T060936.SAFE/GRANULE/L2A_T55XEJ_A037653_"
+                "20240522T032513/IMG_DATA/R60m/T55XEJ_20240522T032519_B01_60m.tif"
+            ),
+        ]
+
+        expected_fs_paths = [
+            (
+                "eumap/lcv/lcv_blue_landsat.glad.ard_p50_30m_0..0cm_1999"
+                ".12.02..2000.03.20_eumap_epsg3035_v1.1.tif"
+            ),
+            (
+                "ENMAP/files/L0/2024/05/21/ENMAP01-_____L0-DT0000074151_"
+                "20240521T055623Z_002_V010402_20240521T143405Z-QL_"
+                "VNIR_COG.TIF"
+            ),
+            (
+                "sentinel2-l2/55/X/EJ/2024/05/22/S2B_MSIL2A_20240522T032519"
+                "_N0510_R018_T55XEJ_20240522T060936.SAFE/"
+                "GRANULE/L2A_T55XEJ_A037653_20240522T032513/IMG_DATA/R60m/"
+                "T55XEJ_20240522T032519_B01_60m.tif"
+            ),
+        ]
+        expected_roots = [
+            "s3.eu-central-1.wasabisys.com",
+            "download.geoservice.dlr.de",
+            "sentinel2l2a01.blob.core.windows.net",
+        ]
+        storage_options_region = dict(client_kwargs=dict(region_name="us-east-1"))
+        expected_returns = [
+            ("s3", "bucket-name", "filename", {}),
+            ("s3", "bucket-name", "filename", {}),
+            ("s3", "bucket-name", "filename", {}),
+            ("s3", "bucket-name", "filename", storage_options_region),
+            ("s3", "bucket-name", "filename", storage_options_region),
+            ("s3", "bucket-name", "filename", storage_options_region),
+            ("https", expected_roots[0], expected_fs_paths[0], {}),
+            ("https", expected_roots[1], expected_fs_paths[1], {}),
+            ("https", expected_roots[2], expected_fs_paths[2], {}),
+        ]
+
+        for expected, href in zip(expected_returns, hrefs):
+            self.assertEqual(expected, _decode_href(href), msg=href)
+
+    def test_is_aws_s3_bucket(self):
+        with self.assertRaises(DataStoreError) as cm:
+            bucket = "test_123-s3alias"
+            href = "https://s3-us-east-1.amazonaws.com/bucket-name/filename"
+            _assert_aws_s3_bucket(bucket, href)
+        self.assertEqual(
+            (
+                f"Bucket name {bucket!r} extracted from the href {href!r} "
+                f"does not follow the AWS S3 bucket naming rules."
+            ),
+            f"{cm.exception}",
+        )
+        with self.assertRaises(DataStoreError) as cm:
+            bucket = "m" * 64
+            href = "https://s3-us-east-1.amazonaws.com/bucket-name/filename"
+            _assert_aws_s3_bucket(bucket, href)
+        self.assertEqual(
+            (
+                f"Bucket name {bucket!r} extracted from the href {href!r} "
+                f"does not follow the AWS S3 bucket naming rules."
+            ),
+            f"{cm.exception}",
+        )
+
+    def test_is_aws_s3_region_name(self):
+        with self.assertRaises(DataStoreError) as cm:
+            region_name = "us-east-5"
+            href = "https://s3-us-east-5.amazonaws.com/bucket-name/filename"
+            _assert_aws_s3_region_name(region_name, href)
+        self.assertEqual(
+            (
+                f"Region name {region_name!r} extracted from the href {href!r} "
+                f"is not supported by AWS S3"
+            ),
+            f"{cm.exception}",
+        )
diff --git a/test/test_plugin.py b/test/test_plugin.py
index ec3dd68..b1b21c4 100644
--- a/test/test_plugin.py
+++ b/test/test_plugin.py
@@ -40,15 +40,7 @@ def test_plugin(self):
                         "name": "stac",
                         "point": "xcube.core.store",
                     }
-                },
-                "xcube.core.store.opener": {
-                    "dataset:zarr:stac": {
-                        "component": "<not loaded yet>",
-                        "description": "xarray.Dataset from STAC API",
-                        "name": "dataset:zarr:stac",
-                        "point": "xcube.core.store.opener",
-                    }
-                },
+                }
             },
             registry.to_dict(),
         )
diff --git a/test/test_store.py b/test/test_store.py
index 63bcd81..26d5997 100644
--- a/test/test_store.py
+++ b/test/test_store.py
@@ -19,18 +19,46 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
+import datetime
+import itertools
 import unittest
+import urllib.request
 
 import pytest
 import requests
+import xarray as xr
+from xcube.core.mldataset import MultiLevelDataset
 from xcube.core.store import (
     DatasetDescriptor,
-    DataStoreError
+    DataStoreError,
+    MultiLevelDatasetDescriptor,
+    new_data_store,
 )
-from xcube.core.store.store import new_data_store
 from xcube.util.jsonschema import JsonObjectSchema
 
 from xcube_stac.constants import DATA_STORE_ID
+from xcube_stac.utils import _get_formats_from_item
+from xcube_stac.accessor import HttpsDataAccessor, S3DataAccessor
+
+SKIP_HELP = (
+    "Skipped, because server is not running:"
+    " $ xcube serve2 -vvv -c examples/serve/demo/config.yml"
+)
+SERVER_URL = "http://localhost:8080"
+SERVER_ENDPOINT_URL = f"{SERVER_URL}/s3"
+
+
+def is_server_running() -> bool:
+    # noinspection PyBroadException
+    try:
+        with urllib.request.urlopen(SERVER_URL, timeout=2.0) as response:
+            response.read()
+    except Exception:
+        return False
+    return 200 <= response.code < 400
+
+
+XCUBE_SERVER_IS_RUNNING = is_server_running()
 
 
 class StacDataStoreTest(unittest.TestCase):
@@ -40,21 +68,24 @@ def setUp(self):
             "https://raw.githubusercontent.com/stac-extensions/"
             "label/main/examples/multidataset/catalog.json"
         )
-        self.url_searchable = (
-            "https://earth-search.aws.element84.com/v1"
-        )
+        self.url_searchable = "https://earth-search.aws.element84.com/v1"
         self.url_time_range = (
             "https://s3.eu-central-1.wasabisys.com/stac/odse/catalog.json"
         )
+        self.url_netcdf = "https://geoservice.dlr.de/eoc/ogc/stac/v1"
         self.data_id_nonsearchable = "zanzibar/znz001.json"
         self.data_id_searchable = (
             "collections/sentinel-1-grd/items/"
-            "S1A_EW_GRDM_1SDH_20240528T100847_20240528T100950_054070_06930C"
+            "S1A_EW_GRDM_1SDV_20240711T073732_20240711T073811_054710_06A929"
         )
         self.data_id_time_range = (
             "lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_1999.12.02"
             "..2000.03.20/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20.json"
         )
+        self.data_id_netcdf = (
+            "collections/S5P_TROPOMI_L3_P1D_CF/items/"
+            "S5P_DLR_NRTI_01_040201_L3_CF_20240619?f=application%2Fgeo%2Bjson"
+        )
 
     @pytest.mark.vcr()
     def test_get_data_store_params_schema(self):
@@ -62,19 +93,25 @@ def test_get_data_store_params_schema(self):
         schema = store.get_data_store_params_schema()
         self.assertIsInstance(schema, JsonObjectSchema)
         self.assertIn("url", schema.properties)
+        self.assertIn("storage_options_s3", schema.properties)
         self.assertIn("url", schema.required)
 
     @pytest.mark.vcr()
     def test_get_data_types(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_searchable)
-        self.assertEqual(("dataset",), store.get_data_types())
+        self.assertEqual(("dataset", "mldataset"), store.get_data_types())
 
     @pytest.mark.vcr()
     def test_get_data_types_for_data(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
+        self.assertEqual(
+            ("mldataset", "dataset"),
+            store.get_data_types_for_data(self.data_id_nonsearchable),
+        )
+        store = new_data_store(DATA_STORE_ID, url=self.url_netcdf)
         self.assertEqual(
             ("dataset",),
-            store.get_data_types_for_data(self.data_id_nonsearchable)
+            store.get_data_types_for_data(self.data_id_netcdf),
         )
 
     @pytest.mark.vcr()
@@ -86,16 +123,24 @@ def test_get_data_ids(self):
             "zanzibar/znz029.json",
             "spacenet-buildings/AOI_2_Vegas_img2636.json",
             "spacenet-buildings/AOI_3_Paris_img1648.json",
-            "spacenet-buildings/AOI_4_Shanghai_img3344.json"
+            "spacenet-buildings/AOI_4_Shanghai_img3344.json",
         ]
         self.assertCountEqual(data_ids_expected, data_ids)
 
+    @pytest.mark.vcr()
+    def test_get_data_ids_data_type(self):
+        store = new_data_store(DATA_STORE_ID, url=self.url_netcdf)
+        data_ids = store.get_data_ids(data_type="mldataset")
+        data_ids = list(itertools.islice(data_ids, 1))
+        self.assertEqual(1, len(data_ids))
+        item = store._access_item(data_ids[0])
+        formats = _get_formats_from_item(item)
+        self.assertEqual(["geotiff"], formats)
+
     @pytest.mark.vcr()
     def test_get_data_ids_include_attrs(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_searchable)
-        include_attrs = [
-            "id", "bbox", "geometry", "properties", "links", "assets"
-        ]
+        include_attrs = ["id", "bbox", "geometry", "properties", "links", "assets"]
         data_id, attrs = next(store.get_data_ids(include_attrs=include_attrs))
         self.assertEqual(self.data_id_searchable, data_id)
         self.assertCountEqual(include_attrs, list(attrs.keys()))
@@ -109,16 +154,55 @@ def test_get_data_ids_optional_args_empty_args(self):
 
     @pytest.mark.vcr()
     def test_has_data(self):
-        store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
-        self.assertTrue(store.has_data(self.data_id_nonsearchable))
-        self.assertFalse(store.has_data(self.data_id_nonsearchable, data_type=str))
+        store = new_data_store(DATA_STORE_ID, url=self.url_netcdf)
+        data_id = (
+            "collections/ENMAP_HSI_L2A/items/ENMAP01-____L2A-DT0000080454_202406"
+            "30T082045Z_001_V010402_20240701T122237Z?f=application%2Fgeo%2Bjson"
+        )
+        self.assertTrue(store.has_data(data_id))
+        self.assertFalse(store.has_data(data_id, data_type=str))
+        self.assertTrue(store.has_data(data_id, data_type="mldataset"))
 
     @pytest.mark.vcr()
     def test_get_data_opener_ids(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
+        opener_ids = (
+            "dataset:netcdf:https",
+            "dataset:zarr:https",
+            "dataset:geotiff:https",
+            "mldataset:geotiff:https",
+            "dataset:levels:https",
+            "mldataset:levels:https",
+            "dataset:netcdf:s3",
+            "dataset:zarr:s3",
+            "dataset:geotiff:s3",
+            "mldataset:geotiff:s3",
+            "dataset:levels:s3",
+            "mldataset:levels:s3",
+        )
+        self.assertCountEqual(opener_ids, store.get_data_opener_ids())
+        opener_ids = (
+            "mldataset:geotiff:https",
+            "mldataset:levels:https",
+            "mldataset:geotiff:s3",
+            "mldataset:levels:s3",
+        )
+        self.assertCountEqual(
+            opener_ids, store.get_data_opener_ids(data_type="mldataset")
+        )
+        opener_ids = (
+            "mldataset:geotiff:s3",
+            "dataset:geotiff:s3",
+        )
+        self.assertCountEqual(
+            opener_ids, store.get_data_opener_ids(data_id=self.data_id_nonsearchable)
+        )
+        opener_ids = ("dataset:geotiff:s3",)
         self.assertEqual(
-            ("dataset:zarr:stac",),
-            store.get_data_opener_ids()
+            opener_ids,
+            store.get_data_opener_ids(
+                data_id=self.data_id_nonsearchable, data_type="dataset"
+            ),
         )
 
     @pytest.mark.vcr()
@@ -133,7 +217,7 @@ def test_get_data_opener_ids_optional_args(self):
         with self.assertRaises(DataStoreError) as cm:
             store.get_data_opener_ids(data_type=str)
         self.assertEqual(
-            "Data type must be 'dataset', but got <class 'str'>",
+            "Data type must be 'dataset' or 'mldataset', but got <class 'str'>.",
             f"{cm.exception}",
         )
 
@@ -141,55 +225,254 @@ def test_get_data_opener_ids_optional_args(self):
     def test_get_open_data_params_schema(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
         schema = store.get_open_data_params_schema()
+        # no optional arguments
+        self.assertIsInstance(schema, JsonObjectSchema)
+        self.assertIn("asset_names", schema.properties)
+        self.assertIn("open_params_dataset_netcdf", schema.properties)
+        self.assertIn("open_params_dataset_zarr", schema.properties)
+        self.assertIn("open_params_dataset_geotiff", schema.properties)
+        self.assertIn("open_params_mldataset_geotiff", schema.properties)
+        self.assertIn("open_params_dataset_levels", schema.properties)
+        self.assertIn("open_params_mldataset_levels", schema.properties)
+
+        # test opener_id argument
+        schema = store.get_open_data_params_schema(opener_id="dataset:zarr:https")
         self.assertIsInstance(schema, JsonObjectSchema)
         self.assertIn("asset_names", schema.properties)
+        self.assertIn("open_params_dataset_zarr", schema.properties)
+        self.assertNotIn("open_params_dataset_netcdf", schema.properties)
+        self.assertCountEqual(
+            [
+                "log_access",
+                "cache_size",
+                "group",
+                "chunks",
+                "mask_and_scale",
+                "decode_cf",
+                "decode_times",
+                "decode_coords",
+                "drop_variables",
+                "consolidated",
+            ],
+            schema.properties["open_params_dataset_zarr"].properties.keys(),
+        )
+
+        # test data_id argument
+        schema = store.get_open_data_params_schema(data_id=self.data_id_nonsearchable)
+        self.assertIn("asset_names", schema.properties)
+        self.assertIn("open_params_dataset_geotiff", schema.properties)
+        self.assertIn("open_params_mldataset_geotiff", schema.properties)
+        self.assertNotIn("open_params_dataset_zarr", schema.properties)
 
     @pytest.mark.vcr()
-    def test_open_data(self):
-        store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
+    def test_open_data_tiff(self):
+        store = new_data_store(DATA_STORE_ID, url=self.url_time_range)
 
         # open data without open_params
-        assets = store.open_data(self.data_id_nonsearchable)
-        self.assertTrue(1, len(assets))
-        self.assertEqual("znz001_previewcog", assets[0].title)
-        self.assertEqual(
-            ("https://oin-hotosm.s3.amazonaws.com/5afeda152b6a08001185f11a/"
-             "0/5afeda152b6a08001185f11b.tif"),
-            assets[0].href
+        ds = store.open_data(self.data_id_time_range, opener_id="dataset:geotiff:https")
+        self.assertIsInstance(ds, xr.Dataset)
+        self.assertCountEqual(
+            ["blue_p50", "blue_p25", "blue_p75", "qa_f"], list(ds.data_vars)
+        )
+        self.assertCountEqual([151000, 188000], [ds.sizes["y"], ds.sizes["x"]])
+        self.assertCountEqual(
+            [512, 512], [ds.chunksizes["x"][0], ds.chunksizes["y"][0]]
+        )
+        self.assertDictEqual(
+            dict(
+                AREA_OR_POINT="Area",
+                _FillValue=0,
+                scale_factor=1.0,
+                add_offset=0.0,
+                grid_mapping="spatial_ref",
+            ),
+            ds.blue_p25.attrs,
         )
 
         # open data with open_params
-        assets = store.open_data(self.data_id_nonsearchable, asset_names=["raster"])
-        self.assertTrue(1, len(assets))
-        self.assertEqual("znz001_previewcog", assets[0].title)
+        mlds = store.open_data(self.data_id_time_range, asset_names=["blue_p25"])
+        self.assertIsInstance(mlds, MultiLevelDataset)
+        ds = mlds.base_dataset
+        self.assertCountEqual(["blue_p25"], list(ds.data_vars))
+        self.assertCountEqual([151000, 188000], [ds.sizes["y"], ds.sizes["x"]])
+        self.assertCountEqual(
+            [512, 512], [ds.chunksizes["x"][0], ds.chunksizes["y"][0]]
+        )
+
+        # open data where multiple assets are stored in one mldataset
+        mlds = store.open_data(
+            self.data_id_time_range, asset_names=["blue_p25", "blue_p75"]
+        )
+        self.assertIsInstance(mlds, MultiLevelDataset)
+        ds = mlds.base_dataset
+        self.assertCountEqual(["blue_p25", "blue_p75"], list(ds.data_vars))
+        self.assertCountEqual([151000, 188000], [ds.sizes["y"], ds.sizes["x"]])
+        self.assertCountEqual(
+            [512, 512], [ds.chunksizes["x"][0], ds.chunksizes["y"][0]]
+        )
+
+    @pytest.mark.vcr()
+    def test_open_data_netcdf(self):
+        store = new_data_store(DATA_STORE_ID, url=self.url_netcdf)
+
+        # open data without open_params
+        ds = store.open_data(self.data_id_netcdf, asset_names=["data"])
+        self.assertIsInstance(ds, xr.Dataset)
+        self.assertCountEqual(
+            [
+                "data_radiometric_cloud_fraction",
+                "data_radiometric_cloud_fraction_precision",
+                "data_number_of_observations",
+                "data_quality_flag",
+            ],
+            list(ds.data_vars),
+        )
+        self.assertCountEqual([1800, 3600], [ds.sizes["lat"], ds.sizes["lon"]])
+
+    @pytest.mark.vcr()
+    def test_open_data_abfs(self):
+        store = new_data_store(
+            DATA_STORE_ID, url="https://planetarycomputer.microsoft.com/api/stac/v1"
+        )
+
+        # open data without open_params
+        data_id = "collections/era5-pds/items/era5-pds-2020-12-an"
+        with self.assertRaises(DataStoreError) as cm:
+            _ = store.open_data(data_id, asset_names=["surface_air_pressure"])
         self.assertEqual(
-            ("https://oin-hotosm.s3.amazonaws.com/5afeda152b6a08001185f11a/"
-             "0/5afeda152b6a08001185f11b.tif"),
-            assets[0].href
+            (
+                "Only 's3' and 'https' protocols are supported, not 'abfs'. The asset "
+                "'surface_air_pressure' has a href 'abfs://era5/ERA5/2020/12/"
+                "surface_air_pressure.zarr'. The item's url is given by "
+                "'https://planetarycomputer.microsoft.com/api/stac/v1/collections/"
+                "era5-pds/items/era5-pds-2020-12-an'."
+            ),
+            f"{cm.exception}",
+        )
+
+    # run server demo in xcube/examples/serve/demo by running
+    # "xcube serve --verbose -c examples/serve/demo/config.yml" in the terminal
+    @unittest.skipUnless(XCUBE_SERVER_IS_RUNNING, SKIP_HELP)
+    def test_open_data_xcube_server(self):
+        store = new_data_store(DATA_STORE_ID, url="http://127.0.0.1:8080/ogc")
+
+        # open data in zarr format
+        ds = store.open_data("collections/datacubes/items/local_ts")
+        self.assertIsInstance(ds, xr.Dataset)
+        self.assertCountEqual(
+            [
+                "analytic_c2rcc_flags",
+                "analytic_conc_chl",
+                "analytic_conc_tsm",
+                "analytic_kd489",
+                "analytic_lat_bnds",
+                "analytic_lon_bnds",
+                "analytic_quality_flags",
+                "analytic_time_bnds",
+            ],
+            list(ds.data_vars),
+        )
+        self.assertCountEqual(
+            [1000, 2000, 5], [ds.sizes["lat"], ds.sizes["lon"], ds.sizes["time"]]
+        )
+        # open data in zarr format with open_params
+        open_params_dataset_zarr = dict(chunks={"time": 5, "lat": 128, "lon": 128})
+        ds = store.open_data(
+            "collections/datacubes/items/local_ts",
+            open_params_dataset_zarr=open_params_dataset_zarr,
+        )
+        self.assertIsInstance(ds, xr.Dataset)
+        self.assertCountEqual(
+            [
+                "analytic_c2rcc_flags",
+                "analytic_conc_chl",
+                "analytic_conc_tsm",
+                "analytic_kd489",
+                "analytic_lat_bnds",
+                "analytic_lon_bnds",
+                "analytic_quality_flags",
+                "analytic_time_bnds",
+            ],
+            list(ds.data_vars),
+        )
+        self.assertCountEqual(
+            [1000, 2000, 5], [ds.sizes["lat"], ds.sizes["lon"], ds.sizes["time"]]
         )
+        self.assertCountEqual(
+            [128, 128, 5],
+            [
+                ds.chunksizes["lat"][0],
+                ds.chunksizes["lon"][0],
+                ds.chunksizes["time"][0],
+            ],
+        )
+
+        # open data in levels format
+        ds = store.open_data("collections/datacubes/items/local")
+        self.assertIsInstance(ds, xr.Dataset)
+        self.assertCountEqual(
+            [
+                "analytic_c2rcc_flags",
+                "analytic_chl_category",
+                "analytic_chl_tsm_sum",
+                "analytic_conc_chl",
+                "analytic_conc_tsm",
+                "analytic_kd489",
+                "analytic_lat_bnds",
+                "analytic_lon_bnds",
+                "analytic_quality_flags",
+                "analytic_time_bnds",
+            ],
+            list(ds.data_vars),
+        )
+        self.assertCountEqual(
+            [1000, 2000, 5], [ds.sizes["lat"], ds.sizes["lon"], ds.sizes["time"]]
+        )
+        # open data store in tif format
+        mlds = store.open_data(
+            "collections/datacubes/items/cog_local", data_type="mldataset"
+        )
+        ds = mlds.base_dataset
+        self.assertIsInstance(mlds, MultiLevelDataset)
+        self.assertEqual(3, mlds.num_levels)
+        self.assertIsInstance(ds, xr.Dataset)
+        self.assertCountEqual(
+            [
+                "analytic_multires_band_1",
+                "analytic_multires_band_2",
+                "analytic_multires_band_3",
+                "analytic_multires_spatial_ref",
+            ],
+            list(ds.data_vars),
+        )
+        self.assertCountEqual([343, 343], [ds.sizes["y"], ds.sizes["x"]])
 
     @pytest.mark.vcr()
     def test_open_data_wrong_opener_id(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
         with self.assertRaises(DataStoreError) as cm:
-            store.open_data(
-                self.data_id_nonsearchable,
-                opener_id="wrong_opener_id"
-            )
+            store.open_data(self.data_id_nonsearchable, opener_id="wrong_opener_id")
         self.assertEqual(
-            "Data opener identifier must be 'dataset:zarr:stac', "
-            "but got 'wrong_opener_id'",
+            "Data opener identifier must be one of ('dataset:netcdf:https', "
+            "'dataset:zarr:https', 'dataset:geotiff:https', 'mldataset:geotiff:https', "
+            "'dataset:levels:https', 'mldataset:levels:https', "
+            "'dataset:netcdf:s3', 'dataset:zarr:s3', 'dataset:geotiff:s3', "
+            "'mldataset:geotiff:s3', 'dataset:levels:s3', 'mldataset:levels:s3'), "
+            "but got 'wrong_opener_id'.",
             f"{cm.exception}",
         )
 
     @pytest.mark.vcr()
     def test_search_data(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
-        descriptors = list(store.search_data(
-            collections="zanzibar-collection",
-            bbox=[39.28, -5.74, 39.31, -5.72],
-            time_range=["2019-04-23", "2019-04-24"]
-        ))
+        descriptors = list(
+            store.search_data(
+                data_type="dataset",
+                collections="zanzibar-collection",
+                bbox=[39.28, -5.74, 39.31, -5.72],
+                time_range=["2019-04-23", "2019-04-24"],
+            )
+        )
 
         expected_descriptor = dict(
             data_id="zanzibar/znz001.json",
@@ -198,9 +481,9 @@ def test_search_data(self):
                 39.28919876472999,
                 -5.743028283012867,
                 39.31302874892266,
-                -5.722212794937691
+                -5.722212794937691,
             ],
-            time_range=["2019-04-23", None]
+            time_range=("2019-04-23T00:00:00Z", None),
         )
 
         self.assertEqual(1, len(descriptors))
@@ -210,22 +493,35 @@ def test_search_data(self):
     @pytest.mark.vcr()
     def test_search_data_searchable_catalog(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_searchable)
-        descriptors = list(store.search_data(
-            collections=["sentinel-2-l2a"],
-            bbox=[9, 47, 10, 48],
-            time_range=["2020-03-01", "2020-03-05"]
-        ))
+        descriptors = list(
+            store.search_data(
+                data_type="dataset",
+                collections=["sentinel-2-l2a"],
+                bbox=[9, 47, 10, 48],
+                time_range=["2020-03-01", "2020-03-05"],
+            )
+        )
 
         prefix = "collections/sentinel-2-l2a/items/"
         data_ids_expected = [
-            "S2A_32TMT_20200305_0_L2A", "S2A_32TNT_20200305_0_L2A",
-            "S2A_32UMU_20200305_0_L2A", "S2A_32UNU_20200305_0_L2A",
-            "S2A_32TMT_20200302_1_L2A", "S2A_32TMT_20200302_0_L2A",
-            "S2A_32TNT_20200302_1_L2A", "S2A_32TNT_20200302_0_L2A",
-            "S2A_32UMU_20200302_1_L2A", "S2A_32UMU_20200302_0_L2A",
-            "S2A_32UNU_20200302_1_L2A", "S2A_32UNU_20200302_0_L2A"
+            "S2A_32TMT_20200305_1_L2A",
+            "S2A_32TMT_20200305_0_L2A",
+            "S2A_32TNT_20200305_1_L2A",
+            "S2A_32TNT_20200305_0_L2A",
+            "S2A_32UMU_20200305_1_L2A",
+            "S2A_32UMU_20200305_0_L2A",
+            "S2A_32UNU_20200305_1_L2A",
+            "S2A_32UNU_20200305_0_L2A",
+            "S2A_32TMT_20200302_1_L2A",
+            "S2A_32TMT_20200302_0_L2A",
+            "S2A_32TNT_20200302_1_L2A",
+            "S2A_32TNT_20200302_0_L2A",
+            "S2A_32UMU_20200302_1_L2A",
+            "S2A_32UMU_20200302_0_L2A",
+            "S2A_32UNU_20200302_1_L2A",
+            "S2A_32UNU_20200302_0_L2A",
         ]
-        data_ids_expected = [prefix + id for id in data_ids_expected]
+        data_ids_expected = [prefix + data_id for data_id in data_ids_expected]
 
         expected_descriptor = dict(
             data_id=data_ids_expected[0],
@@ -234,25 +530,27 @@ def test_search_data_searchable_catalog(self):
                 7.662878883910047,
                 46.85818510451771,
                 9.130456971519783,
-                47.85361872923358
+                47.85361872923358,
             ],
-            time_range=["2020-03-05", None]
+            time_range=("2020-03-05T10:37:41.587000Z", None),
         )
 
-        self.assertEqual(12, len(descriptors))
+        self.assertEqual(16, len(descriptors))
         for d in descriptors:
             self.assertIsInstance(d, DatasetDescriptor)
         self.assertCountEqual(data_ids_expected, [d.data_id for d in descriptors])
         self.assertEqual(expected_descriptor, descriptors[0].to_dict())
 
     @pytest.mark.vcr()
-    def test_search_data_time_range(self):
+    def test_search_data_multi_level(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_time_range)
-        descriptors = list(store.search_data(
-            collections=["lcv_blue_landsat.glad.ard"],
-            bbox=[-10, 40, 40, 70],
-            time_range=["2000-01-01", "2000-04-01"]
-        ))
+        descriptors = list(
+            store.search_data(
+                collections=["lcv_blue_landsat.glad.ard"],
+                bbox=[-10, 40, 40, 70],
+                time_range=["2000-01-01", "2000-04-01"],
+            )
+        )
 
         expected_descriptors = [
             dict(
@@ -261,14 +559,15 @@ def test_search_data_time_range(self):
                     ".12.02..2000.03.20/lcv_blue_landsat.glad.ard_1999.12.02"
                     "..2000.03.20.json"
                 ),
-                data_type="dataset",
+                data_type="mldataset",
+                num_levels=9,
                 bbox=[
                     -23.550818268711048,
                     24.399543432891665,
                     63.352379098951936,
-                    77.69295185585888
+                    77.69295185585888,
                 ],
-                time_range=["1999-12-02", "2000-03-20"]
+                time_range=("1999-12-02", "2000-03-20"),
             ),
             dict(
                 data_id=(
@@ -276,15 +575,16 @@ def test_search_data_time_range(self):
                     ".03.21..2000.06.24/lcv_blue_landsat.glad.ard_2000.03.21"
                     "..2000.06.24.json"
                 ),
-                data_type="dataset",
+                data_type="mldataset",
+                num_levels=9,
                 bbox=[
                     -23.550818268711048,
                     24.399543432891665,
                     63.352379098951936,
-                    77.69295185585888
+                    77.69295185585888,
                 ],
-                time_range=["2000-03-21", "2000-06-24"]
-            )
+                time_range=("2000-03-21", "2000-06-24"),
+            ),
         ]
 
         self.assertEqual(2, len(descriptors))
@@ -292,6 +592,66 @@ def test_search_data_time_range(self):
             self.assertIsInstance(d, DatasetDescriptor)
         self.assertEqual(expected_descriptors, [d.to_dict() for d in descriptors])
 
+    @pytest.mark.vcr()
+    def test_describe_data(self):
+        store = new_data_store(DATA_STORE_ID, url=self.url_netcdf)
+        data_id = (
+            "collections/D4H/items/S1A_IW_GRDH_1SDV_20231031T030757_"
+            "20231031T030822_051003_062646_8C53?f=application%2Fgeo%2Bjson"
+        )
+        descriptor = store.describe_data(data_id, data_type="mldataset")
+        expected_descriptor = dict(
+            data_id=data_id,
+            data_type="mldataset",
+            num_levels=7,
+            bbox=[
+                36.211544470664904,
+                -15.48028,
+                37.86349678056911,
+                -14.169830229695073,
+            ],
+            time_range=("2023-10-31T00:00:00.000+00:00", None),
+        )
+        self.assertIsInstance(descriptor, MultiLevelDatasetDescriptor)
+        self.assertDictEqual(expected_descriptor, descriptor.to_dict())
+
+    # run server demo in xcube/examples/serve/demo by running
+    # "xcube serve --verbose -c examples/serve/demo/config.yml" in the terminal
+    @unittest.skipUnless(XCUBE_SERVER_IS_RUNNING, SKIP_HELP)
+    def test_describe_data_xcube_server(self):
+        store = new_data_store(DATA_STORE_ID, url="http://127.0.0.1:8080/ogc")
+        data_id = "collections/datacubes/items/local"
+        descriptor = store.describe_data(data_id, data_type="mldataset")
+        expected_descriptor = dict(
+            data_id=data_id,
+            data_type="mldataset",
+            num_levels=3,
+            bbox=[0, 50, 5, 52.5],
+            time_range=("2017-01-16T10:09:21Z", "2017-01-30T10:46:33Z"),
+        )
+        self.assertIsInstance(descriptor, MultiLevelDatasetDescriptor)
+        self.assertDictEqual(expected_descriptor, descriptor.to_dict())
+
+    @pytest.mark.vcr()
+    def test_describe_data_assert_log(self):
+        store = new_data_store(DATA_STORE_ID, url=self.url_netcdf)
+        with self.assertLogs("xcube.stac", level="INFO") as cm:
+            descriptor = store.describe_data(self.data_id_netcdf, data_type="mldataset")
+        self.assertIsInstance(descriptor, DatasetDescriptor)
+        self.assertEqual(1, len(cm.output))
+        msg = (
+            f"INFO:xcube.stac:The data ID {self.data_id_netcdf!r} contains not only "
+            f"assets in geotiff format. Therefore, data_type is set to 'dataset'"
+        )
+        self.assertEqual(msg, str(cm.output[-1]))
+        expected_descriptor = dict(
+            data_id=self.data_id_netcdf,
+            data_type="dataset",
+            bbox=[-180.0, -90.0, 180.0, 90.0],
+            time_range=("2024-06-19T00:00:00Z", None),
+        )
+        self.assertDictEqual(expected_descriptor, descriptor.to_dict())
+
     @pytest.mark.vcr()
     def test_get_search_params_schema(self):
         store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
@@ -302,95 +662,47 @@ def test_get_search_params_schema(self):
         self.assertIn("collections", schema.properties)
 
     @pytest.mark.vcr()
-    def test_is_datetime_in_range(self):
-        class Item1():
-
-            def __init__(self) -> None:
-                self.properties = dict(
-                    datetime="2024-05-02T09:19:38.543000Z"
-                )
-
-        class Item2():
-
-            def __init__(self) -> None:
-                self.properties = dict(
-                    datetime="null",
-                    start_datetime="2023-12-02T09:19:38.543000Z",
-                    end_datetime="2024-05-02T09:19:38.543000Z"
-                )
-
-        item1_test_paramss = [
-            ("2024-04-30", "2024-05-03", self.assertTrue),
-            ("2024-04-26", "2024-05-02", self.assertFalse),
-            ("2024-04-26", "2024-05-01", self.assertFalse)
-        ]
-
-        item2_test_paramss = [
-            ("2024-05-05", "2024-05-08", self.assertFalse),
-            ("2024-04-30", "2024-05-03", self.assertTrue),
-            ("2024-04-26", "2024-04-29", self.assertTrue),
-            ("2023-11-26", "2023-12-31", self.assertTrue),
-            ("2023-11-26", "2023-11-30", self.assertFalse),
-            ("2023-11-26", "2024-05-08", self.assertTrue),
-        ]
-
-        store = new_data_store(
-            DATA_STORE_ID,
-            url=self.url_nonsearchable
-        )
-
-        item1 = Item1()
-        for (time_start, time_end, fun) in item1_test_paramss:
-            fun(
-                store._is_datetime_in_range(
-                    item1,
-                    time_range=[time_start, time_end]
-                )
-            )
-
-        item1 = Item2()
-        for (time_start, time_end, fun) in item2_test_paramss:
-            fun(
-                store._is_datetime_in_range(
-                    item1,
-                    time_range=[time_start, time_end]
-                )
-            )
+    def test_access_item_failed(self):
+        store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
+        with self.assertRaises(requests.exceptions.HTTPError) as cm:
+            store._access_item(self.data_id_nonsearchable.replace("z", "s"))
+        self.assertIn("404 Client Error: Not Found for url", f"{cm.exception}")
 
     @pytest.mark.vcr()
-    def test_do_bboxes_intersect(self):
-        class Item():
-
-            def __init__(self) -> None:
-                self.bbox = [0, 0, 1, 1]
-
-        item_test_paramss = [
-            (0, 0, 1, 1, self.assertTrue),
-            (0.5, 0.5, 1.5, 1.5, self.assertTrue),
-            (-0.5, -0.5, 0.5, 0.5, self.assertTrue),
-            (1, 1, 2, 2, self.assertTrue),
-            (2, 2, 3, 3, self.assertFalse)
-        ]
+    def test_get_s3_accessor(self):
+        store = new_data_store(DATA_STORE_ID, url=self.url_searchable)
 
-        store = new_data_store(
-            DATA_STORE_ID,
-            url=self.url_nonsearchable
+        opener = store._get_s3_accessor(root="datasets", storage_options={})
+        self.assertIsInstance(opener, S3DataAccessor)
+        self.assertEqual("datasets", opener.root)
+
+        with self.assertLogs("xcube.stac", level="DEBUG") as cm:
+            opener2 = store._get_s3_accessor(root="datasets2", storage_options={})
+        self.assertIsInstance(opener2, S3DataAccessor)
+        self.assertEqual("datasets2", opener2.root)
+        self.assertEqual(1, len(cm.output))
+        msg = (
+            "DEBUG:xcube.stac:The bucket 'datasets' of the S3 object storage "
+            "changed to 'datasets2'. A new s3 data opener will be initialized."
         )
+        self.assertEqual(msg, str(cm.output[-1]))
 
-        item = Item()
-        for (west, south, east, north, fun) in item_test_paramss:
-            fun(
-                store._do_bboxes_intersect(
-                    item,
-                    bbox=[west, south, east, north]
-                )
-            )
+    @pytest.mark.vcr()
+    def test_get_https_accessor(self):
+        store = new_data_store(DATA_STORE_ID, url=self.url_searchable)
 
-    def test_access_item_failed(self):
-        store = new_data_store(DATA_STORE_ID, url=self.url_nonsearchable)
-        with self.assertRaises(requests.exceptions.HTTPError) as cm:
-            store._access_item(self.data_id_nonsearchable.replace("z", "s"))
-        self.assertIn(
-            "404 Client Error: Not Found for url",
-            f"{cm.exception}"
+        opener = store._get_https_accessor(root="earth-search.aws.element84.com")
+        self.assertIsInstance(opener, HttpsDataAccessor)
+        self.assertEqual("earth-search.aws.element84.com", opener.root)
+
+        with self.assertLogs("xcube.stac", level="DEBUG") as cm:
+            opener2 = store._get_https_accessor(root="planetarycomputer.microsoft.com")
+        self.assertIsInstance(opener2, HttpsDataAccessor)
+        self.assertEqual("planetarycomputer.microsoft.com", opener2.root)
+        self.assertEqual(1, len(cm.output))
+        msg = (
+            "DEBUG:xcube.stac:The root 'earth-search.aws.element84.com' of the "
+            "https data opener changed to 'planetarycomputer.microsoft.com'. "
+            "A new https data opener will be initialized."
         )
+        self.assertEqual(msg, str(cm.output[-1]))
diff --git a/test/test_utils.py b/test/test_utils.py
new file mode 100644
index 0000000..6e8dbb5
--- /dev/null
+++ b/test/test_utils.py
@@ -0,0 +1,132 @@
+# The MIT License (MIT)
+# Copyright (c) 2024 by the xcube development team and contributors
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import datetime
+import unittest
+
+import pystac
+from xcube.core.store import DataStoreError
+
+from xcube_stac.utils import (
+    _convert_datetime2str,
+    _convert_str2datetime,
+    _do_bboxes_intersect,
+    _is_datetime_in_range,
+    _update_dict,
+)
+
+
+class UtilsTest(unittest.TestCase):
+
+    def test_convert_datetime2str(self):
+        dt = datetime.datetime(2024, 1, 1, 12, 00, 00)
+        self.assertEqual("2024-01-01T12:00:00", _convert_datetime2str(dt))
+        dt = datetime.date(2024, 1, 1)
+        self.assertEqual("2024-01-01", _convert_datetime2str(dt))
+
+    def test_convert_str2datetime(self):
+        dt = datetime.datetime(2024, 1, 1, 12, 00, 00, tzinfo=datetime.timezone.utc)
+        self.assertEqual(dt, _convert_str2datetime("2024-01-01T12:00:00.000000Z"))
+        self.assertEqual(dt, _convert_str2datetime("2024-01-01T12:00:00"))
+
+    def test_is_datetime_in_range(self):
+        item1 = pystac.Item(
+            "test_item",
+            geometry=None,
+            bbox=[0, 0, 1, 1],
+            datetime=datetime.datetime(2024, 5, 1, 9, 19, 38),
+            properties=dict(datetime="2024-05-02T09:19:38.000000Z"),
+        )
+        item2 = pystac.Item(
+            "test_item",
+            geometry=None,
+            bbox=[0, 0, 1, 1],
+            datetime=None,
+            properties=dict(
+                datetime="null",
+                start_datetime="2023-12-02T09:19:38.543000Z",
+                end_datetime="2024-05-02T09:19:38.543000Z",
+            ),
+        )
+        item3 = pystac.Item(
+            "test_item",
+            geometry=None,
+            bbox=[0, 0, 1, 1],
+            datetime=datetime.datetime(2024, 5, 1, 9, 19, 38),
+            properties=dict(),
+        )
+
+        item1_test_paramss = [
+            ("2024-04-30", "2024-05-03", self.assertTrue),
+            ("2024-04-26", "2024-05-02", self.assertFalse),
+            ("2024-04-26", "2024-05-01", self.assertFalse),
+        ]
+
+        item2_test_paramss = [
+            ("2024-05-05", "2024-05-08", self.assertFalse),
+            ("2024-04-30", "2024-05-03", self.assertTrue),
+            ("2024-04-26", "2024-04-29", self.assertTrue),
+            ("2023-11-26", "2023-12-31", self.assertTrue),
+            ("2023-11-26", "2023-11-30", self.assertFalse),
+            ("2023-11-26", "2024-05-08", self.assertTrue),
+        ]
+
+        for time_start, time_end, fun in item1_test_paramss:
+            fun(_is_datetime_in_range(item1, time_range=[time_start, time_end]))
+
+        for time_start, time_end, fun in item2_test_paramss:
+            fun(_is_datetime_in_range(item2, time_range=[time_start, time_end]))
+
+        with self.assertRaises(DataStoreError) as cm:
+            _is_datetime_in_range(
+                item3, time_range=[item1_test_paramss[0][0], item1_test_paramss[0][1]]
+            )
+        self.assertEqual(
+            "The item`s property needs to contain either 'start_datetime' and "
+            "'end_datetime' or 'datetime'.",
+            f"{cm.exception}",
+        )
+
+    def test_do_bboxes_intersect(self):
+        item = pystac.Item(
+            "test_item",
+            geometry=None,
+            bbox=[0, 0, 1, 1],
+            datetime=datetime.datetime(2024, 1, 1, 12, 00, 00),
+            properties={},
+        )
+
+        item_test_paramss = [
+            (0, 0, 1, 1, self.assertTrue),
+            (0.5, 0.5, 1.5, 1.5, self.assertTrue),
+            (-0.5, -0.5, 0.5, 0.5, self.assertTrue),
+            (1, 1, 2, 2, self.assertTrue),
+            (2, 2, 3, 3, self.assertFalse),
+        ]
+
+        for west, south, east, north, fun in item_test_paramss:
+            fun(_do_bboxes_intersect(item, bbox=[west, south, east, north]))
+
+    def test_update_nested_dict(self):
+        dic = dict(a=1, b=dict(c=3))
+        dic_update = dict(d=1, b=dict(c=5, e=8))
+        dic_expected = dict(a=1, d=1, b=dict(c=5, e=8))
+        self.assertDictEqual(dic_expected, _update_dict(dic, dic_update))
diff --git a/xcube_stac/accessor.py b/xcube_stac/accessor.py
new file mode 100644
index 0000000..10fff9e
--- /dev/null
+++ b/xcube_stac/accessor.py
@@ -0,0 +1,102 @@
+# The MIT License (MIT)
+# Copyright (c) 2024 by the xcube development team and contributors
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from typing import Union
+import warnings
+
+import xarray as xr
+from xcube.core.mldataset import MultiLevelDataset
+from xcube.core.store import DataTypeLike, new_data_store
+
+from .utils import _is_valid_ml_data_type
+
+
+class HttpsDataAccessor:
+    """Implementation of the data accessor supporting
+    the zarr, geotiff and netcdf format via the https protocol.
+    """
+
+    def __init__(self, root: str):
+        self._root = root
+        self._https_accessor = new_data_store("https", root=root, read_only=True)
+
+    @property
+    def root(self) -> str:
+        return self._root
+
+    def open_data(
+        self,
+        data_id: str,
+        format_id: str,
+        opener_id: str = None,
+        data_type: DataTypeLike = None,
+        **open_params,
+    ) -> Union[xr.Dataset, MultiLevelDataset]:
+        if format_id == "netcdf":
+            if _is_valid_ml_data_type(data_type):
+                warnings.warn(
+                    f"No data opener found for format {format_id!r} and data type "
+                    f"{data_type!r}. Data type is changed to the default data type "
+                    "'dataset'."
+                )
+            fs_path = f"https://{self._root}/{data_id}#mode=bytes"
+            return xr.open_dataset(fs_path, chunks={})
+        else:
+            return self._https_accessor.open_data(
+                data_id=data_id, opener_id=opener_id, data_type=data_type, **open_params
+            )
+
+
+class S3DataAccessor:
+    """Implementation of the data accessor supporting
+    the zarr, geotiff and netcdf format via the https protocol.
+    """
+
+    def __init__(
+        self,
+        root: str,
+        storage_options: dict,
+    ):
+        self._root = root
+        self._storage_options = storage_options
+        if "anon" not in storage_options:
+            storage_options["anon"] = True
+        self._s3_accessor = new_data_store(
+            "s3",
+            root=root,
+            read_only=True,
+            storage_options=storage_options,
+        )
+
+    @property
+    def root(self) -> str:
+        return self._root
+
+    def open_data(
+        self,
+        data_id: str,
+        opener_id: str = None,
+        data_type: DataTypeLike = None,
+        **open_params,
+    ) -> Union[xr.Dataset, MultiLevelDataset]:
+        return self._s3_accessor.open_data(
+            data_id=data_id, opener_id=opener_id, data_type=data_type, **open_params
+        )
diff --git a/xcube_stac/constants.py b/xcube_stac/constants.py
index a3772c8..b2c1891 100644
--- a/xcube_stac/constants.py
+++ b/xcube_stac/constants.py
@@ -19,23 +19,50 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
+import logging
 
 from xcube.util.jsonschema import (
     JsonArraySchema,
     JsonDateSchema,
     JsonNumberSchema,
-    JsonStringSchema
+    JsonStringSchema,
 )
 
 
 DATA_STORE_ID = "stac"
-DATASET_OPENER_ID = f"dataset:zarr:{DATA_STORE_ID}"
+LOG = logging.getLogger("xcube.stac")
 
-MIME_TYPES = [
-    "application/zarr",
-    "image/tiff",
-    "application/octet-stream"
-]
+MAP_MIME_TYP_FORMAT = {
+    "application/netcdf": "netcdf",
+    "application/x-netcdf": "netcdf",
+    "application/vnd+zarr": "zarr",
+    "application/zarr": "zarr",
+    "image/tiff": "geotiff",
+}
+
+MAP_FILE_EXTENSION_FORMAT = {
+    ".nc": "netcdf",
+    ".zarr": "zarr",
+    ".tif": "geotiff",
+    ".tiff": "geotiff",
+    ".geotiff": "geotiff",
+    ".levels": "levels",
+}
+
+DATA_OPENER_IDS = (
+    "dataset:netcdf:https",
+    "dataset:zarr:https",
+    "dataset:geotiff:https",
+    "mldataset:geotiff:https",
+    "dataset:levels:https",
+    "mldataset:levels:https",
+    "dataset:netcdf:s3",
+    "dataset:zarr:s3",
+    "dataset:geotiff:s3",
+    "mldataset:geotiff:s3",
+    "dataset:levels:s3",
+    "mldataset:levels:s3",
+)
 
 STAC_SEARCH_PARAMETERS = dict(
     time_range=JsonArraySchema(
@@ -48,7 +75,7 @@
             "Time range given as pair of start and stop dates. "
             "Dates must be given using format 'YYYY-MM-DD'. "
             "Start and stop are inclusive."
-        )
+        ),
     ),
     bbox=JsonArraySchema(
         items=(
@@ -63,10 +90,8 @@
         items=(JsonStringSchema(min_length=0)),
         unique_items=True,
         title="Collection IDs",
-        description=(
-            "Collection IDs to be included in the search request."
-        )
-    )
+        description="Collection IDs to be included in the search request.",
+    ),
 )
 
 STAC_OPEN_PARAMETERS = dict(
@@ -74,8 +99,6 @@
         items=(JsonStringSchema(min_length=0)),
         unique_items=True,
         title="Names of assets",
-        description=(
-            "Names of assets which will be included in the data cube."
-        )
+        description="Names of assets which will be included in the data cube.",
     )
 )
diff --git a/xcube_stac/href_parse.py b/xcube_stac/href_parse.py
new file mode 100644
index 0000000..5776db4
--- /dev/null
+++ b/xcube_stac/href_parse.py
@@ -0,0 +1,218 @@
+# The MIT License (MIT)
+# Copyright (c) 2024 by the xcube development team and contributors
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import re
+from typing import Tuple
+
+from xcube.core.store import DataStoreError
+
+
+# Bucket naming rules:
+# https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html
+AWS_REGEX_BUCKET_NAME = (
+    r"(?!^([0-9]{1,3}\.){3}[0-9]{1,3}$)"
+    r"(?!(^xn--|^sthree-|^sthree-configurator|"
+    r".+--ol-s3$|.+-s3alias$))"
+    r"^[a-z0-9][a-z0-9.-]{1,61}[a-z0-9]$"
+)
+# Region names: https://docs.aws.amazon.com/general/latest/gr/s3.html
+AWS_REGION_NAMES = [
+    "us-east-1",
+    "us-east-2",
+    "us-west-1",
+    "us-west-2",
+    "af-south-1",
+    "ap-east-1",
+    "ap-south-1",
+    "ap-south-2",
+    "ap-southeast-1",
+    "ap-southeast-2",
+    "ap-southeast-3",
+    "ap-southeast-4",
+    "ap-northeast-1",
+    "ap-northeast-2",
+    "ap-northeast-3",
+    "ca-central-1",
+    "ca-west-1",
+    "eu-central-1",
+    "eu-central-2",
+    "eu-west-1",
+    "eu-west-2",
+    "eu-west-3",
+    "eu-south-1",
+    "eu-south-2",
+    "eu-north-1",
+    "il-central-1",
+    "me-south-1",
+    "me-central-1",
+    "sa-east-1",
+    "us-gov-east-1",
+    "us-gov-west-1",
+]
+
+
+def _decode_href(href: str) -> Tuple[str, str, str, dict]:
+    """Decodes a href into protocol, root, remaining file path,
+    and region name if given.
+
+    Note:
+        The aws s3 URI formats are given by
+        https://docs.aws.amazon.com/quicksight/latest/user/supported-manifest-file-format.html.
+        Furthermore, the format
+        'https://<bucket-name>.s3.<region-name>.amazonaws.com/<filename>'
+        is encountered and will be supported as well.
+        The bucket naming rules are given by
+        https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html.
+        The region names are given by
+        https://docs.aws.amazon.com/general/latest/gr/s3.html.
+
+    Args:
+        href: href string of data resource
+
+    Returns: protocol, root, remaining file path, and storage options.
+
+    Raises:
+        DataStoreError: Error, AWS S3 root cannot be decoded since
+            it does not follow the uri pattern mentioned in Note.
+    """
+    protocol, root, fs_path, storage_options = _decode_aws_s3_href(href)
+    if root is None:
+        protocol, remain = href.split("://")
+        root = remain.split("/")[0]
+        fs_path = remain.replace(f"{root}/", "")
+        storage_options = {}
+
+    return protocol, root, fs_path, storage_options
+
+
+def _decode_aws_s3_href(href: str):
+    """Decodes an AWS S3 href into protocol, root, remaining file path,
+    and storage options needed for the S3 data store. If href does not fit to
+    the AWS S3 pattern, root, fs_path and region_name will be None.
+
+    Note:
+        The aws s3 URI formats are given by
+        https://docs.aws.amazon.com/quicksight/latest/user/supported-manifest-file-format.html.
+        Furthermore, the format
+        'https://<bucket-name>.s3.<region-name>.amazonaws.com/<filename>'
+        is encountered and will be supported as well.
+        The bucket naming rules are given by
+        https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html.
+        The region names are given by
+        https://docs.aws.amazon.com/general/latest/gr/s3.html.
+
+    Args:
+        href: href string of data resource
+
+    Returns: protocol, root, remaining file path, and storage options
+
+    Raises:
+        DataStoreError: Error if AWS S3 bucket name or region name does not follow
+            the prescribed naming rules given in the notes above.
+    """
+    # check for aws s3; see notes for different uri formats
+    protocol = "s3"
+    fs_path = None
+    region_name = None
+    root = None
+    if re.search(r"^https://s3\.amazonaws\.com/.{3,63}/", href) is not None:
+        tmp = href[8:].split("/")
+        root = tmp[1]
+        fs_path = "/".join(tmp[2:])
+    elif re.search(r"^s3://.{3,63}/", href) is not None:
+        tmp = href[5:].split("/")
+        root = tmp[0]
+        fs_path = "/".join(tmp[1:])
+    elif re.search(r"^https://.{3,63}\.s3\.amazonaws\.com/", href) is not None:
+        tmp = href[8:].split("/")
+        root = tmp[0][:-17]
+        fs_path = "/".join(tmp[1:])
+    elif re.search(r"^https://s3-.{9,14}\.amazonaws\.com/.{3,63}/", href) is not None:
+        tmp = href[8:].split("/")
+        region_name = tmp[0][3:-14]
+        root = tmp[1]
+        fs_path = "/".join(tmp[2:])
+    elif re.search(r"^https://.{3,63}\.s3-.{9,14}\.amazonaws\.com/", href) is not None:
+        tmp = href[8:].split("/")
+        region_name = tmp[0].split(".s3-")[-1][:-14]
+        root = tmp[0].replace(f".s3-{region_name}.amazonaws.com", "")
+        fs_path = "/".join(tmp[1:])
+    elif re.search(r"^https://.{3,63}\.s3\..{9,14}\.amazonaws\.com/", href) is not None:
+        tmp = href[8:].split("/")
+        region_name = tmp[0].split(".s3.")[-1][:-14]
+        root = tmp[0].replace(f".s3.{region_name}.amazonaws.com", "")
+        fs_path = "/".join(tmp[1:])
+
+    if root is not None:
+        _assert_aws_s3_bucket(root, href)
+    if region_name is not None:
+        _assert_aws_s3_region_name(region_name, href)
+
+    if region_name is None:
+        storage_options = {}
+    else:
+        storage_options = {"client_kwargs": {"region_name": region_name}}
+
+    return protocol, root, fs_path, storage_options
+
+
+def _assert_aws_s3_bucket(bucket: str, href: str):
+    """Test if bucket name follows the prescribed AWS S3 naming rules.
+
+    Note:
+        The bucket naming rules are given by
+        https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html.
+
+
+    Args:
+        bucket: bucket name
+        href: href string of data resource
+
+    Raises:
+        DataStoreError: Error, if the bucket name does not follow the naming rules
+    """
+    if re.search(AWS_REGEX_BUCKET_NAME, bucket) is None:
+        raise DataStoreError(
+            f"Bucket name {bucket!r} extracted from the href {href!r} "
+            "does not follow the AWS S3 bucket naming rules."
+        )
+
+
+def _assert_aws_s3_region_name(region_name: str, href: str):
+    """Test if region name is a valid AWS S3 region name.
+
+    Note:
+        The region names are given by
+        https://docs.aws.amazon.com/general/latest/gr/s3.html.
+
+
+    Args:
+        region_name: region name
+        href: href string of data resource
+
+    Raises:
+        DataStoreError: Error, if the bucket name does not follow the naming rules
+    """
+    if region_name not in AWS_REGION_NAMES:
+        raise DataStoreError(
+            f"Region name {region_name!r} extracted from the "
+            f"href {href!r} is not supported by AWS S3"
+        )
diff --git a/xcube_stac/plugin.py b/xcube_stac/plugin.py
index a03bd2f..db81736 100644
--- a/xcube_stac/plugin.py
+++ b/xcube_stac/plugin.py
@@ -19,16 +19,10 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-from xcube.constants import (
-    EXTENSION_POINT_DATA_OPENERS,
-    EXTENSION_POINT_DATA_STORES
-)
+from xcube.constants import EXTENSION_POINT_DATA_STORES
 from xcube.util import extension
 
-from .constants import (
-    DATASET_OPENER_ID,
-    DATA_STORE_ID
-)
+from .constants import DATA_STORE_ID
 
 
 def init_plugin(ext_registry: extension.ExtensionRegistry):
@@ -39,10 +33,3 @@ def init_plugin(ext_registry: extension.ExtensionRegistry):
         name=DATA_STORE_ID,
         description="STAC DataStore",
     )
-    # xcube DataOpener extensions
-    ext_registry.add_extension(
-        loader=extension.import_component("xcube_stac.store:StacDatasetOpener"),
-        point=EXTENSION_POINT_DATA_OPENERS,
-        name=DATASET_OPENER_ID,
-        description="xarray.Dataset from STAC API",
-    )
diff --git a/xcube_stac/store.py b/xcube_stac/store.py
index 166427a..906a88b 100644
--- a/xcube_stac/store.py
+++ b/xcube_stac/store.py
@@ -19,50 +19,90 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-import datetime
-import itertools
 import json
-from typing import Any, Container, Dict, Iterator, List, Tuple, Union
+import warnings
+from typing import Any, Container, Dict, Iterator, Tuple, Union
 
-import pandas as pd
+import numpy as np
 import pystac
 import pystac_client
 import requests
-from shapely.geometry import box
+import xarray as xr
+from xcube.core.mldataset import MultiLevelDataset, CombinedMultiLevelDataset
 from xcube.core.store import (
     DATASET_TYPE,
+    MULTI_LEVEL_DATASET_TYPE,
     DatasetDescriptor,
+    MultiLevelDatasetDescriptor,
     DataStore,
     DataStoreError,
+    DataType,
     DataTypeLike,
+    new_data_store,
 )
+from xcube.core.store.fs.impl.fs import S3FsAccessor
 from xcube.util.jsonschema import JsonObjectSchema, JsonStringSchema
 
 from .constants import (
-    DATASET_OPENER_ID,
-    MIME_TYPES,
+    DATA_OPENER_IDS,
+    LOG,
     STAC_OPEN_PARAMETERS,
     STAC_SEARCH_PARAMETERS,
 )
+from .href_parse import _decode_href
+from .accessor import HttpsDataAccessor, S3DataAccessor
+from .utils import (
+    _assert_valid_data_type,
+    _assert_valid_opener_id,
+    _get_attrs_from_item,
+    _get_formats_from_assets,
+    _get_format_from_asset,
+    _get_formats_from_item,
+    _get_url_from_item,
+    _is_valid_data_type,
+    _is_valid_dataset_data_type,
+    _is_valid_ml_data_type,
+    _list_assets_from_item,
+    _search_nonsearchable_catalog,
+    _update_dict,
+    _xarray_rename_vars,
+)
 
 
 _CATALOG_JSON = "catalog.json"
 
+_HTTPS_STORE = new_data_store("https")
+_OPEN_DATA_PARAMETERS = {
+    "open_params_dataset_netcdf": _HTTPS_STORE.get_open_data_params_schema(
+        opener_id="dataset:netcdf:https"
+    ),
+    "open_params_dataset_zarr": _HTTPS_STORE.get_open_data_params_schema(
+        opener_id="dataset:zarr:https"
+    ),
+    "open_params_dataset_geotiff": _HTTPS_STORE.get_open_data_params_schema(
+        opener_id="dataset:geotiff:https"
+    ),
+    "open_params_mldataset_geotiff": _HTTPS_STORE.get_open_data_params_schema(
+        opener_id="mldataset:geotiff:https"
+    ),
+    "open_params_dataset_levels": _HTTPS_STORE.get_open_data_params_schema(
+        opener_id="dataset:levels:https"
+    ),
+    "open_params_mldataset_levels": _HTTPS_STORE.get_open_data_params_schema(
+        opener_id="mldataset:levels:https"
+    ),
+}
+
 
 class StacDataStore(DataStore):
     """STAC implementation of the data store.
 
     Args:
         url: URL to STAC catalog
-        data_id_delimiter: Delimiter used to separate
-            collections, items and assets from each other.
-            Defaults to "/".
+        storage_options_s3: storage option for 's3' data store
     """
 
-    def __init__(
-        self,
-        url: str,
-    ):
+    def __init__(self, url: str, **storage_options_s3):
         self._url = url
         url_mod = url
         if url_mod[-len(_CATALOG_JSON) :] == "catalog.json":
@@ -81,15 +121,16 @@ def __init__(
             catalog = pystac.Catalog.from_file(url)
             self._searchable = False
         self._catalog = catalog
-
-        # TODO: Add a data store "file" here when implementing
-        # open_data(), which will be used to open the hrefs
+        self._https_accessor = None
+        self._s3_accessor = None
+        self._storage_options_s3 = storage_options_s3
 
     @classmethod
     def get_data_store_params_schema(cls) -> JsonObjectSchema:
         stac_params = dict(url=JsonStringSchema(title="URL to STAC catalog"))
+        stac_params["storage_options_s3"] = S3FsAccessor.get_storage_options_schema()
         return JsonObjectSchema(
-            description=("Describes the parameters of the xcube data store 'stac'."),
+            description="Describes the parameters of the xcube data store 'stac'.",
             properties=stac_params,
             required=["url"],
             additional_properties=False,
@@ -97,94 +138,181 @@ def get_data_store_params_schema(cls) -> JsonObjectSchema:
 
     @classmethod
     def get_data_types(cls) -> Tuple[str, ...]:
-        return (DATASET_TYPE.alias,)
+        return DATASET_TYPE.alias, MULTI_LEVEL_DATASET_TYPE.alias
 
     def get_data_types_for_data(self, data_id: str) -> Tuple[str, ...]:
-        return self.get_data_types()
+        if self._are_all_assets_geotiffs(data_id):
+            return MULTI_LEVEL_DATASET_TYPE.alias, DATASET_TYPE.alias
+        elif self._is_xcube_server_item(data_id):
+            return MULTI_LEVEL_DATASET_TYPE.alias, DATASET_TYPE.alias
+        else:
+            return (DATASET_TYPE.alias,)
 
     def get_data_ids(
         self, data_type: DataTypeLike = None, include_attrs: Container[str] = None
     ) -> Union[Iterator[str], Iterator[Tuple[str, Dict[str, Any]]]]:
-        self._assert_valid_data_type(data_type)
+        _assert_valid_data_type(data_type)
+        ml_data_type = _is_valid_ml_data_type(data_type)
         for item in self._catalog.get_items(recursive=True):
+            if ml_data_type:
+                formats = _get_formats_from_item(item)
+                if not (len(formats) == 1 and formats[0] == "geotiff"):
+                    continue
             data_id = self._get_data_id_from_item(item)
             if include_attrs is None:
                 yield data_id
             else:
-                attrs = self._get_attrs_from_item(item, include_attrs)
-                yield (data_id, attrs)
+                attrs = _get_attrs_from_item(item, include_attrs)
+                yield data_id, attrs
 
     def has_data(self, data_id: str, data_type: DataTypeLike = None) -> bool:
-        if self._is_valid_data_type(data_type):
-            return data_id in self.list_data_ids()
+        if _is_valid_data_type(data_type):
+            in_store = True
+            try:
+                _ = self._access_item(data_id)
+            except requests.exceptions.HTTPError:
+                in_store = False
+            if not in_store:
+                return False
+            else:
+                if _is_valid_ml_data_type(data_type):
+                    return self._are_all_assets_geotiffs(data_id)
+                return True
         return False
 
     def get_data_opener_ids(
         self, data_id: str = None, data_type: DataTypeLike = None
     ) -> Tuple[str, ...]:
-        self._assert_valid_data_type(data_type)
-        if data_id is not None and not self.has_data(data_id, data_type=data_type):
-            raise DataStoreError(f"Data resource {data_id!r} is not available.")
-        return (DATASET_OPENER_ID,)
+        _assert_valid_data_type(data_type)
+
+        if data_id is not None:
+            if not self.has_data(data_id, data_type=data_type):
+                raise DataStoreError(f"Data resource {data_id!r} is not available.")
+            item = self._access_item(data_id)
+            assets = _list_assets_from_item(item)
+            formats = _get_formats_from_assets(assets)
+            if self._is_xcube_server_item(data_id):
+                protocols = np.array(["s3"])
+            else:
+                protocols = []
+                for asset in assets:
+                    protocol, _, _, _ = _decode_href(asset.href)
+                    protocols.append(protocol)
+                protocols = np.unique(protocols)
+
+        if data_type is None and data_id is None:
+            return DATA_OPENER_IDS
+        elif data_type is None and data_id is not None:
+            return tuple(
+                opener_id
+                for opener_id in DATA_OPENER_IDS
+                if opener_id.split(":")[1] in formats
+                and opener_id.split(":")[2] in protocols
+            )
+        elif data_type is not None and data_id is None:
+            data_type = DataType.normalize(data_type)
+            return tuple(
+                opener_id
+                for opener_id in DATA_OPENER_IDS
+                if opener_id.split(":")[0] == data_type.alias
+            )
+        elif data_type is not None and data_id is not None:
+            data_type = DataType.normalize(data_type)
+            return tuple(
+                opener_id
+                for opener_id in DATA_OPENER_IDS
+                if opener_id.split(":")[0] == data_type.alias
+                and opener_id.split(":")[1] in formats
+                and opener_id.split(":")[2] in protocols
+            )
 
     def get_open_data_params_schema(
         self, data_id: str = None, opener_id: str = None
     ) -> JsonObjectSchema:
-        self._assert_valid_opener_id(opener_id)
-        # ToDo: Further parameters needs to be added for actual data access.
+        _assert_valid_opener_id(opener_id)
+        properties = {}
+        if opener_id is not None:
+            key = "_".join(opener_id.split(":")[:2])
+            key = f"open_params_{key}"
+            properties[key] = _OPEN_DATA_PARAMETERS[key]
+        if data_id is not None:
+            item = self._access_item(data_id)
+            formats = _get_formats_from_item(item)
+            for form in formats:
+                for key in _OPEN_DATA_PARAMETERS.keys():
+                    if form == key.split("_")[-1]:
+                        properties[key] = _OPEN_DATA_PARAMETERS[key]
+        if not properties:
+            properties = _OPEN_DATA_PARAMETERS
         return JsonObjectSchema(
-            properties=dict(**STAC_OPEN_PARAMETERS),
+            properties=_update_dict(properties, STAC_OPEN_PARAMETERS, inplace=False),
             required=[],
             additional_properties=False,
         )
 
     def open_data(
-        self, data_id: str, opener_id: str = None, **open_params
-    ) -> List[pystac.Asset]:
-        # ToDo: Actual access of the data needs to be implemented.
+        self,
+        data_id: str,
+        opener_id: str = None,
+        data_type: DataTypeLike = None,
+        **open_params,
+    ) -> Union[xr.Dataset, MultiLevelDataset]:
         stac_schema = self.get_open_data_params_schema()
         stac_schema.validate_instance(open_params)
-        self._assert_valid_opener_id(opener_id)
+        _assert_valid_data_type(data_type)
+        _assert_valid_opener_id(opener_id)
         item = self._access_item(data_id)
-        assets = self._get_assets_from_item(item, **open_params)
-        return list(assets)
+        return self._build_dataset(
+            item, opener_id=opener_id, data_type=data_type, **open_params
+        )
 
     def describe_data(
         self, data_id: str, data_type: DataTypeLike = None
-    ) -> DatasetDescriptor:
-        self._assert_valid_data_type(data_type)
+    ) -> Union[DatasetDescriptor, MultiLevelDatasetDescriptor]:
+        _assert_valid_data_type(data_type)
         item = self._access_item(data_id)
 
         # prepare metadata
+        time_range = (None, None)
         if "start_datetime" in item.properties and "end_datetime" in item.properties:
             time_range = (
-                self._convert_datetime2str(
-                    self._convert_str2datetime(item.properties["start_datetime"]).date()
-                ),
-                self._convert_datetime2str(
-                    self._convert_str2datetime(item.properties["end_datetime"]).date()
-                ),
+                item.properties["start_datetime"],
+                item.properties["end_datetime"],
             )
         elif "datetime" in item.properties:
-            time_range = (
-                self._convert_datetime2str(
-                    self._convert_str2datetime(item.properties["datetime"]).date()
-                ),
-                None,
-            )
+            time_range = (item.properties["datetime"], None)
+        metadata = dict(bbox=item.bbox, time_range=time_range)
+
+        # decision between MultiLevelDatasetDescriptor and DatasetDescriptor
+        get_mldd = False
+        if self._is_xcube_server_item(data_id) and _is_valid_ml_data_type(data_type):
+            get_mldd = True
+        elif self._are_all_assets_geotiffs(data_id) and not _is_valid_dataset_data_type(
+            data_type
+        ):
+            get_mldd = True
         else:
-            DataStoreError(
-                f"The item with the data ID {data_id!r} cannot be described."
-                "The item`s property needs to contain either 'start_datetime' and "
-                "'end_datetime' or 'datetime'."
+            if _is_valid_ml_data_type(data_type):
+                LOG.info(
+                    f"The data ID {data_id!r} contains not only assets in geotiff "
+                    f"format. Therefore, data_type is set to {DATASET_TYPE.alias!r}"
+                )
+
+        if get_mldd:
+            assets = _list_assets_from_item(item)
+            mlds = self.open_data(
+                data_id,
+                data_type="mldataset",
+                asset_names=[assets[-1].extra_fields["id"]],
             )
-        metadata = dict(bbox=item.bbox, time_range=time_range)
-        return DatasetDescriptor(data_id, **metadata)
+            return MultiLevelDatasetDescriptor(data_id, mlds.num_levels, **metadata)
+        else:
+            return DatasetDescriptor(data_id, **metadata)
 
     def search_data(
         self, data_type: DataTypeLike = None, **search_params
-    ) -> Iterator[DatasetDescriptor]:
-        self._assert_valid_data_type(data_type)
+    ) -> Iterator[Union[DatasetDescriptor, MultiLevelDatasetDescriptor]]:
+        _assert_valid_data_type(data_type)
         if self._searchable:
             # rewrite to "datetime"
             time_range = search_params.pop("time_range", None)
@@ -192,7 +320,7 @@ def search_data(
                 search_params["datetime"] = "/".join(time_range)
             items = self._catalog.search(**search_params).items()
         else:
-            items = self._search_nonsearchable_catalog(self._catalog, **search_params)
+            items = _search_nonsearchable_catalog(self._catalog, **search_params)
         for item in items:
             data_id = self._get_data_id_from_item(item)
             yield self.describe_data(data_id, data_type=data_type)
@@ -210,180 +338,108 @@ def get_search_params_schema(
     ##########################################################################
     # Implementation helpers
 
-    @classmethod
-    def _is_valid_data_type(cls, data_type: DataTypeLike) -> bool:
-        """Auxiliary function to check if data type is supported
-        by the store.
+    def _are_all_assets_geotiffs(self, data_id: str) -> bool:
+        """Auxiliary function to check if all assets are tifs, tiffs, or geotiffs.
 
         Args:
-            data_type: Data type that is to be checked.
-
-        Returns:
-            bool: True if *data_type* is supported by the store, otherwise False
-        """
-        return data_type is None or DATASET_TYPE.is_super_type_of(data_type)
-
-    @classmethod
-    def _assert_valid_data_type(cls, data_type: DataTypeLike):
-        """Auxiliary function to assert if data type is supported
-        by the store.
+            data_id: An identifier of data that is provided by this
+                store.
 
-        Args:
-            data_type: Data type that is to be checked.
+        Returns: True, if all assets are tifs, tiffs, or geotiffs.
 
-        Raises:
-            DataStoreError: Error, if *data_type* is not
-                supported by the store.
         """
-        if not cls._is_valid_data_type(data_type):
-            raise DataStoreError(
-                f"Data type must be {DATASET_TYPE!r}, " f"but got {data_type!r}"
-            )
+        item = self._access_item(data_id)
+        formats = _get_formats_from_item(item)
+        return len(formats) == 1 and formats[0] == "geotiff"
 
-    @classmethod
-    def _assert_valid_opener_id(cls, opener_id: str):
-        """Auxiliary function to assert if data opener identified by
-        *opener_id* is supported by the store.
+    def _is_xcube_server_item(self, data_id: str) -> bool:
+        """Auxiliary function to check if the item is published by xcube server.
 
         Args:
-            opener_id: Data opener identifier
+            data_id: An identifier of data that is provided by this store.
 
-        Raises:
-            DataStoreError: Error, if *opener_id* is not
-                supported by the store.
-        """
-        if opener_id is not None and opener_id != DATASET_OPENER_ID:
-            raise DataStoreError(
-                f"Data opener identifier must be "
-                f"{DATASET_OPENER_ID!r}, but got {opener_id!r}"
-            )
-
-    def _search_nonsearchable_catalog(
-        self,
-        pystac_object: Union[pystac.Catalog, pystac.Collection],
-        recursive: bool = True,
-        **search_params,
-    ) -> Iterator[pystac.Item]:
-        """Get the items of a catalog which does not implement the
-        "STAC API - Item Search" conformance class.
+        Returns: True, if item is published by xcube server.
 
-        Args:
-            pystac_object: either a `pystac.catalog:Catalog` or a
-                `pystac.collection:Collection` object
-            recursive: If True, the data IDs of a multiple-collection
-                and/or nested-collection STAC catalog can be collected. If False,
-                a flat STAC catalog hierarchy is assumed, consisting only of items.
-
-        Yields:
-            An iterator over the items matching the **open_params.
         """
+        item = self._access_item(data_id)
+        assets = _list_assets_from_item(item)
+        return self._is_xcube_server_asset(assets)
 
-        if pystac_object.extra_fields[
-            "type"
-        ] != "Collection" or pystac_object.id in search_params.get(
-            "collections", [pystac_object.id]
-        ):
-            if recursive:
-                if any(True for _ in pystac_object.get_children()):
-                    iterators = (
-                        self._search_nonsearchable_catalog(
-                            child, recursive=True, **search_params
-                        )
-                        for child in pystac_object.get_children()
-                    )
-                    yield from itertools.chain(*iterators)
-                else:
-                    iterator = self._search_nonsearchable_catalog(
-                        pystac_object, recursive=False, **search_params
-                    )
-                    yield from iterator
-            else:
-                for item in pystac_object.get_items():
-                    # test if item's bbox intersects with the desired bbox
-                    if "bbox" in search_params:
-                        if not self._do_bboxes_intersect(item, **search_params):
-                            continue
-                    # test if item fit to desired time range
-                    if "time_range" in search_params:
-                        if not self._is_datetime_in_range(item, **search_params):
-                            continue
-                    # iterate through assets of item
-                    yield item
-
-    def _convert_str2datetime(self, datetime_str: str) -> datetime.datetime:
-        """Converting datetime string to a datetime object, which can handle
-        the ISO 8601 suffix 'Z'.
+    def _is_xcube_server_asset(
+        self, asset: Union[pystac.Asset, list[pystac.Asset]]
+    ) -> bool:
+        """Auxiliary function to check if the asset(s) is/are published by xcube server.
 
         Args:
-            datetime_str: datetime string
-
-        Returns:
-            dt: datetime object
-        """
-        dt = pd.Timestamp(datetime_str).to_pydatetime()
-        if dt.tzinfo is None:
-            dt = dt.replace(tzinfo=datetime.timezone.utc)
-        return dt
+            asset: a list or single pystac.Asset object
 
-    def _convert_datetime2str(self, dt: datetime.datetime) -> str:
-        """Converting datetime to ISO 8601 string.
+        Returns: True, if the asset(s) is/are published by xcube server.
 
-        Args:
-            dt: datetime object
-
-        Returns:
-            datetime string
         """
-        return dt.isoformat()
+        if isinstance(asset, list):
+            asset = asset[0]
+        return "xcube:data_store_id" in asset.extra_fields
 
-    def _is_datetime_in_range(self, item: pystac.Item, **open_params) -> bool:
-        """Determine whether the datetime or datetime range of an item
-        intersects to the 'time_range' given by *open_params*.
+    def _select_xcube_server_asset(
+        self,
+        assets: list[pystac.Asset],
+        asset_names: list[str] = None,
+        data_type: DataTypeLike = None,
+    ) -> list[str]:
+        """Selects the asset from item published by xcube server according to
+        the data type. If no data type is given, the asset linking to 'dataset'
+        will be returned.
 
         Args:
-            item: item/feature
-            open_params: Optional opening parameters which need
-                to include 'time_range'
-
+            assets: list of asset object
+            asset_names: asset names given in *open_params* in :meth:`open_data`
+            data_type: required data type of the return value in :meth:`open_data`
 
         Returns:
-            True, if the datetime of an item is within the 'time_range',
-            or if there is any overlap between the 'time_range' and
-            the datetime range of an item; otherwise False.
+            assets: selected asset
 
+        Raises:
+            DataStoreError: Error, if "analytic" and "analytic_multires" is selected
+                in *asset_names*
         """
-        dt_start = self._convert_str2datetime(open_params["time_range"][0])
-        dt_end = self._convert_str2datetime(open_params["time_range"][1])
-        if "start_datetime" in item.properties and "end_datetime" in item.properties:
-            dt_start_data = self._convert_str2datetime(
-                item.properties["start_datetime"]
-            )
-            dt_end_data = self._convert_str2datetime(item.properties["end_datetime"])
-            return dt_end >= dt_start_data and dt_start <= dt_end_data
-        elif "datetime" in item.properties:
-            dt_data = self._convert_str2datetime(item.properties["datetime"])
-            return dt_start <= dt_data <= dt_end
-        else:
-            DataStoreError(
-                "The item`s property needs to contain either 'start_datetime' and "
-                "'end_datetime' or 'datetime'."
+        if asset_names is None:
+            if _is_valid_ml_data_type(data_type):
+                assets = [assets[1]]
+            else:
+                assets = [assets[0]]
+        elif "analytic_multires" in asset_names and "analytic" in asset_names:
+            raise DataStoreError(
+                "Xcube server publishes data resources as 'dataset' and "
+                "'mldataset' under the asset names 'analytic' and "
+                "'analytic_multires'. Please select only one asset in "
+                "<asset_names> when opening the data."
             )
+        return assets
 
-    def _do_bboxes_intersect(self, item: pystac.Item, **open_params) -> bool:
-        """Determine whether two bounding boxes intersect.
+    def _extract_params_xcube_server_asset(
+        self, asset: pystac.Asset
+    ) -> tuple[str, str, str, dict]:
+        """Extracts the data store parameters and the data ID from an asset
+        published by xcube server.
 
         Args:
-            item: item/feature
-            open_params: Optional opening parameters which need
-                to include 'bbox'
+            asset: asset object
 
         Returns:
-            True if the bounding box given by the item intersects with
-            the bounding box given by *open_params*. Otherwise False.
+            protocol: protocol needed for the data store ID
+            root: bucket for S3 or root url for https
+            fs_path: path to file which is equivalent to data ID needed
+                to open the data
+            storage_options: additional storage options
         """
-        return box(*item.bbox).intersects(box(*open_params["bbox"]))
-
-    def _access_item(self, data_id: str) -> pystac.Item:
+        protocol = asset.extra_fields["xcube:data_store_id"]
+        data_store_params = asset.extra_fields["xcube:data_store_params"]
+        root = data_store_params["root"]
+        storage_options = data_store_params["storage_options"]
+        fs_path = asset.extra_fields["xcube:open_data_params"]["data_id"]
+        return protocol, root, fs_path, storage_options
+
+    def _access_item(self, data_id: str) -> Union[pystac.Item, str]:
         """Access item for a given data ID.
 
         Args:
@@ -392,38 +448,20 @@ def _access_item(self, data_id: str) -> pystac.Item:
 
         Returns:
             item object
+
+        Raises:
+            DataStoreError: Error, if the item json cannot be accessed.
         """
-        response = requests.request(method="GET", url=self._url_mod + data_id)
+        response = requests.request(method="GET", url=f"{self._url_mod}{data_id}")
         if response.ok:
             return pystac.Item.from_dict(
                 json.loads(response.text),
-                href=self._url + data_id,
+                href=f"{self._url_mod}{data_id}",
                 root=self._catalog,
                 preserve_dict=False,
             )
         else:
-            DataStoreError(response.raise_for_status())
-
-    def _get_assets_from_item(
-        self, item: pystac.Item, **open_params
-    ) -> Iterator[pystac.Asset]:
-        """Get all assets for a given item, which has a MIME data type
-
-        Args:
-            item: item/feature
-
-        Yields:
-            An iterator over the assets
-        """
-        for k, v in item.assets.items():
-            # test if asset is in 'asset_names' and the media type is
-            # one of the predefined MIME types; note that if asset_names
-            # is ot given all assets are returned matching the MINE types;
-            if k in open_params.get("asset_names", [k]) and any(
-                x in MIME_TYPES for x in v.media_type.split("; ")
-            ):
-                v.extra_fields["id"] = k
-                yield v
+            raise DataStoreError(response.raise_for_status())
 
     def _get_data_id_from_item(self, item: pystac.Item) -> str:
         """Extracts the data ID from an item object.
@@ -435,38 +473,167 @@ def _get_data_id_from_item(self, item: pystac.Item) -> str:
             data ID consisting the URL section of an item
             following the catalog URL.
         """
-        links = [link for link in item.links if link.rel == "self"]
-        assert len(links) == 1
-        return links[0].href.replace(self._url_mod, "")
+        return _get_url_from_item(item).replace(self._url_mod, "")
 
-    def _get_attrs_from_item(
-        self, item: pystac.Item, include_attrs: Container[str]
-    ) -> str:
-        """Extracts the desired attributes from an item object.
+    def _build_dataset(
+        self,
+        item: pystac.Item,
+        opener_id: str = None,
+        data_type: DataTypeLike = None,
+        **open_params,
+    ) -> Union[xr.Dataset, MultiLevelDataset]:
+        """Builds a dataset where the data variable names correspond
+        to the asset keys. If the loaded data consists of multiple
+        data variables, the variable name follows the structure
+        '<asset_key>_<data_variable_name>'
 
         Args:
-            item: Item object
-            include_attrs: A sequence of names of attributes to be returned
-                for each dataset identifier. If given, the store will attempt
-                to provide the set of requested dataset attributes in addition
-                to the data ids. If no attributes are found, empty dictionary
-                is returned.
+            assets: iterator over assets stored in an item
+            opener_id: Data opener identifier. Defaults to None.
+            data_type: Data type assigning the return value data type.
+                May be given as type alias name, as a type, or as a
+                :class:`xcube.core.store.DataType` instance.
+
+        Returns:
+            Dataset representation of the data resources identified
+            by *data_id* and *open_params*.
+        """
+        asset_names = open_params.pop("asset_names", None)
+        assets = _list_assets_from_item(item, asset_names=asset_names)
+        if self._is_xcube_server_asset(assets):
+            assets = self._select_xcube_server_asset(
+                assets, asset_names=asset_names, data_type=data_type
+            )
+
+        list_ds_asset = []
+        for asset in assets:
+            if self._is_xcube_server_asset(asset):
+                protocol, root, fs_path, storage_options = (
+                    self._extract_params_xcube_server_asset(asset)
+                )
+            else:
+                protocol, root, fs_path, storage_options = _decode_href(asset.href)
+
+            if protocol == "s3":
+                self._storage_options_s3 = _update_dict(
+                    self._storage_options_s3, storage_options
+                )
+
+            format_id = _get_format_from_asset(asset)
+            if opener_id is not None:
+                key = "_".join(opener_id.split(":")[:2])
+                open_params_asset = open_params.get(f"open_params_{key}", {})
+            elif data_type is not None:
+                open_params_asset = open_params.get(
+                    f"open_params_{data_type}_{format_id}", {}
+                )
+            else:
+                open_params_asset = open_params.get(
+                    f"open_params_dataset_{format_id}", {}
+                )
+
+            if protocol == "https":
+                opener = self._get_https_accessor(root)
+                ds_asset = opener.open_data(
+                    fs_path,
+                    format_id,
+                    opener_id=opener_id,
+                    data_type=data_type,
+                    **open_params_asset,
+                )
+            elif protocol == "s3":
+                opener = self._get_s3_accessor(
+                    root, storage_options=self._storage_options_s3
+                )
+                ds_asset = opener.open_data(
+                    fs_path,
+                    opener_id=opener_id,
+                    data_type=data_type,
+                    **open_params_asset,
+                )
+            else:
+                url = _get_url_from_item(item)
+                raise DataStoreError(
+                    f"Only 's3' and 'https' protocols are supported, not {protocol!r}. "
+                    f"The asset {asset.extra_fields['id']!r} has a href {asset.href!r}."
+                    f" The item's url is given by {url!r}."
+                )
+
+            if isinstance(ds_asset, MultiLevelDataset):
+                var_names = list(ds_asset.base_dataset.keys())
+            else:
+                var_names = list(ds_asset.keys())
+            if len(var_names) == 1:
+                name_dict = {var_names[0]: asset.extra_fields["id"]}
+            else:
+                name_dict = {
+                    var_name: f"{asset.extra_fields['id']}_{var_name}"
+                    for var_name in var_names
+                }
+            if isinstance(ds_asset, MultiLevelDataset):
+                ds_asset = ds_asset.apply(
+                    _xarray_rename_vars, dict(name_dict=name_dict)
+                )
+            else:
+                ds_asset = ds_asset.rename_vars(name_dict=name_dict)
+            list_ds_asset.append(ds_asset)
+
+        if len(list_ds_asset) == 1:
+            ds = list_ds_asset[0]
+        else:
+            if all(isinstance(ds, MultiLevelDataset) for ds in list_ds_asset):
+                ds = CombinedMultiLevelDataset(list_ds_asset)
+            else:
+                ds = list_ds_asset[0].copy()
+                for ds_asset in list_ds_asset[1:]:
+                    ds.update(ds_asset)
+        return ds
+
+    def _get_s3_accessor(self, root: str, storage_options: dict) -> S3DataAccessor:
+        """This function returns the S3 data accessor associated with the
+        bucket *root*. It creates the S3 data accessor only if it is not
+        created yet or if *root* changes.
+
+        Args:
+            root: bucket of AWS S3 object storage
+            storage_options: storage option of the S3 data store
 
         Returns:
-            dictionary containing the attributes defined by *include_attrs*
-            of data resources provided by this data store
+            S3 data opener
         """
-        attrs = {}
-        if "id" in include_attrs and hasattr(item, "id"):
-            attrs["id"] = item.id
-        if "bbox" in include_attrs and hasattr(item, "bbox"):
-            attrs["bbox"] = item.bbox
-        if "geometry" in include_attrs and hasattr(item, "geometry"):
-            attrs["geometry"] = item.geometry
-        if "properties" in include_attrs and hasattr(item, "properties"):
-            attrs["properties"] = item.properties
-        if "links" in include_attrs and hasattr(item, "links"):
-            attrs["links"] = item.links
-        if "assets" in include_attrs and hasattr(item, "assets"):
-            attrs["assets"] = item.assets
-        return attrs
+        if self._s3_accessor is None:
+            self._s3_accessor = S3DataAccessor(root, storage_options=storage_options)
+        else:
+            if not self._s3_accessor.root == root:
+                LOG.debug(
+                    f"The bucket {self._s3_accessor.root!r} of the "
+                    f"S3 object storage changed to {root!r}. "
+                    "A new s3 data opener will be initialized."
+                )
+                self._s3_accessor = S3DataAccessor(
+                    root, storage_options=storage_options
+                )
+        return self._s3_accessor
+
+    def _get_https_accessor(self, root: str) -> HttpsDataAccessor:
+        """This function returns the HTTPS data opener associated with the
+        *root* url and the *opener_id*. It creates the HTTPS data opener
+        only if it is not created yet or if *root* or *opener_id* changes.
+
+        Args:
+            root: root of URL
+
+        Returns:
+            HTTPS data opener
+        """
+        if self._https_accessor is None:
+            self._https_accessor = HttpsDataAccessor(root)
+        else:
+            if not self._https_accessor.root == root:
+                LOG.debug(
+                    f"The root {self._https_accessor.root!r} of the "
+                    f"https data opener changed to {root!r}. "
+                    "A new https data opener will be initialized."
+                )
+                self._https_accessor = HttpsDataAccessor(root)
+        return self._https_accessor
diff --git a/xcube_stac/utils.py b/xcube_stac/utils.py
new file mode 100644
index 0000000..0f6dce2
--- /dev/null
+++ b/xcube_stac/utils.py
@@ -0,0 +1,424 @@
+# The MIT License (MIT)
+# Copyright (c) 2024 by the xcube development team and contributors
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import copy
+import datetime
+import itertools
+from typing import Any, Container, Dict, Iterator, Union
+import warnings
+
+import numpy as np
+import pandas as pd
+import pystac
+from shapely.geometry import box
+import xarray as xr
+from xcube.core.store import (
+    DATASET_TYPE,
+    MULTI_LEVEL_DATASET_TYPE,
+    DataStoreError,
+    DataTypeLike,
+)
+
+from .constants import DATA_OPENER_IDS, MAP_FILE_EXTENSION_FORMAT, MAP_MIME_TYP_FORMAT
+
+
+def _get_assets_from_item(
+    item: pystac.Item,
+    asset_names: Container[str] = None,
+) -> Iterator[pystac.Asset]:
+    """Get all assets for a given item, which has a MIME data type
+
+    Args:
+        item: item/feature
+        asset_names: Names of assets which will be included
+            in the data cube. If None, all assets will be
+            included which can be opened by the data store.
+
+    Yields:
+        An iterator over the assets
+    """
+    for k, v in item.assets.items():
+        # test if asset is in 'asset_names' and the media type is
+        # one of the predefined MIME types; note that if asset_names
+        # is ot given all assets are returned matching the MINE types;
+        if (asset_names is None or k in asset_names) and v.media_type.split("; ")[
+            0
+        ] in MAP_MIME_TYP_FORMAT:
+            v.extra_fields["id"] = k
+            yield v
+
+
+def _list_assets_from_item(
+    item: pystac.Item,
+    asset_names: Container[str] = None,
+) -> list[pystac.Asset]:
+    """Get all assets for a given item, which has a MIME data type
+
+    Args:
+        item: item/feature
+        asset_names: Names of assets which will be included
+            in the data cube. If None, all assets will be
+            included which can be opened by the data store.
+
+    Yields:
+        An iterator over the assets
+    """
+    return list(_get_assets_from_item(item, asset_names=asset_names))
+
+
+def _search_nonsearchable_catalog(
+    pystac_object: Union[pystac.Catalog, pystac.Collection],
+    recursive: bool = True,
+    **search_params,
+) -> Iterator[pystac.Item]:
+    """Get the items of a catalog which does not implement the
+    "STAC API - Item Search" conformance class.
+
+    Args:
+        pystac_object: either a `pystac.catalog:Catalog` or a
+            `pystac.collection:Collection` object
+        recursive: If True, the data IDs of a multiple-collection
+            and/or nested-collection STAC catalog can be collected. If False,
+            a flat STAC catalog hierarchy is assumed, consisting only of items.
+
+    Yields:
+        An iterator over the items matching the **open_params.
+    """
+
+    if pystac_object.extra_fields[
+        "type"
+    ] != "Collection" or pystac_object.id in search_params.get(
+        "collections", [pystac_object.id]
+    ):
+        if recursive:
+            if any(True for _ in pystac_object.get_children()):
+                iterators = (
+                    _search_nonsearchable_catalog(
+                        child, recursive=True, **search_params
+                    )
+                    for child in pystac_object.get_children()
+                )
+                yield from itertools.chain(*iterators)
+            else:
+                iterator = _search_nonsearchable_catalog(
+                    pystac_object, recursive=False, **search_params
+                )
+                yield from iterator
+        else:
+            for item in pystac_object.get_items():
+                # test if item's bbox intersects with the desired bbox
+                if "bbox" in search_params:
+                    if not _do_bboxes_intersect(item, **search_params):
+                        continue
+                # test if item fit to desired time range
+                if "time_range" in search_params:
+                    if not _is_datetime_in_range(item, **search_params):
+                        continue
+                # iterate through assets of item
+                yield item
+
+
+def _get_attrs_from_item(
+    item: pystac.Item, include_attrs: Container[str]
+) -> Dict[str, Any]:
+    """Extracts the desired attributes from an item object.
+
+    Args:
+        item: Item object
+        include_attrs: A sequence of names of attributes to be returned
+            for each dataset identifier. If given, the store will attempt
+            to provide the set of requested dataset attributes in addition
+            to the data ids. If no attributes are found, empty dictionary
+            is returned.
+
+    Returns:
+        dictionary containing the attributes defined by *include_attrs*
+        of data resources provided by this data store
+    """
+    attrs = {}
+    if "id" in include_attrs and hasattr(item, "id"):
+        attrs["id"] = item.id
+    if "bbox" in include_attrs and hasattr(item, "bbox"):
+        attrs["bbox"] = item.bbox
+    if "geometry" in include_attrs and hasattr(item, "geometry"):
+        attrs["geometry"] = item.geometry
+    if "properties" in include_attrs and hasattr(item, "properties"):
+        attrs["properties"] = item.properties
+    if "links" in include_attrs and hasattr(item, "links"):
+        attrs["links"] = item.links
+    if "assets" in include_attrs and hasattr(item, "assets"):
+        attrs["assets"] = item.assets
+    return attrs
+
+
+def _convert_str2datetime(datetime_str: str) -> datetime.datetime:
+    """Converting datetime string to a datetime object, which can handle
+    the ISO 8601 suffix 'Z'.
+
+    Args:
+        datetime_str: datetime string
+
+    Returns:
+        dt: datetime object
+    """
+    dt = pd.Timestamp(datetime_str).to_pydatetime()
+    if dt.tzinfo is None:
+        dt = dt.replace(tzinfo=datetime.timezone.utc)
+    return dt
+
+
+def _convert_datetime2str(dt: Union[datetime.datetime, datetime.date]) -> str:
+    """Converting datetime to ISO 8601 string.
+
+    Args:
+        dt: datetime object
+
+    Returns:
+        datetime string
+    """
+    return dt.isoformat()
+
+
+def _is_datetime_in_range(item: pystac.Item, **open_params) -> bool:
+    """Determine whether the datetime or datetime range of an item
+    intersects to the 'time_range' given by *open_params*.
+
+    Args:
+        item: item/feature
+        open_params: Optional opening parameters which need
+            to include 'time_range'
+
+
+    Returns:
+        True, if the datetime of an item is within the 'time_range',
+        or if there is any overlap between the 'time_range' and
+        the datetime range of an item; otherwise False.
+
+    Raises:
+        DataStoreError: Error, if either 'start_datetime' and 'end_datetime'
+        nor 'datetime' is determined in the STAC item.
+    """
+    dt_start = _convert_str2datetime(open_params["time_range"][0])
+    dt_end = _convert_str2datetime(open_params["time_range"][1])
+    if "start_datetime" in item.properties and "end_datetime" in item.properties:
+        dt_start_data = _convert_str2datetime(item.properties["start_datetime"])
+        dt_end_data = _convert_str2datetime(item.properties["end_datetime"])
+        return dt_end >= dt_start_data and dt_start <= dt_end_data
+    elif "datetime" in item.properties:
+        dt_data = _convert_str2datetime(item.properties["datetime"])
+        return dt_start <= dt_data <= dt_end
+    else:
+        raise DataStoreError(
+            "The item`s property needs to contain either 'start_datetime' and "
+            "'end_datetime' or 'datetime'."
+        )
+
+
+def _do_bboxes_intersect(item: pystac.Item, **open_params) -> bool:
+    """Determine whether two bounding boxes intersect.
+
+    Args:
+        item: item/feature
+        open_params: Optional opening parameters which need
+            to include 'bbox'
+
+    Returns:
+        True if the bounding box given by the item intersects with
+        the bounding box given by *open_params*, otherwise False.
+    """
+    return box(*item.bbox).intersects(box(*open_params["bbox"]))
+
+
+def _update_dict(dic: dict, dic_update: dict, inplace: bool = True) -> dict:
+    """It updates a dictionary recursively.
+
+    Args:
+        dic: dictionary to be updated
+        dic_update: update dictionary
+        inplace: if True *dic* will be overwritten; if False copy if *dic* is
+            performed before it is updated.
+
+    Returns:
+        dic: updated dictionary
+    """
+    if not inplace:
+        dic = copy.deepcopy(dic)
+    for key, val in dic_update.items():
+        if isinstance(val, dict):
+            dic[key] = _update_dict(dic.get(key, {}), val)
+        else:
+            dic[key] = val
+    return dic
+
+
+def _get_url_from_item(item: pystac.Item) -> str:
+    """Extracts the URL an item object.
+
+    Args:
+        item: Item object
+
+    Returns:
+        the URL of an item.
+    """
+    links = [link for link in item.links if link.rel == "self"]
+    assert len(links) == 1
+    return links[0].href
+
+
+def _get_formats_from_item(
+    item: pystac.Item, asset_names: Container[str] = None
+) -> np.array:
+    """It transforms the MIME-types of selected assets stored within an item to the
+    format IDs used in xcube.
+
+    Args:
+        item: item/feature
+        asset_names: Names of assets which will be included
+            in the data cube. If None, all assets will be
+            included which can be opened by the data store.
+
+    Returns:
+        array containing all formats
+    """
+    assets = _list_assets_from_item(item, asset_names=asset_names)
+    return _get_formats_from_assets(assets)
+
+
+def _get_formats_from_assets(assets: list[pystac.Asset]) -> np.array:
+    """It transforms the MIME-types of multiple assets to the
+    format IDs used in xcube.
+
+    Args:
+        assets: list of assets
+
+    Returns:
+        array containing all format IDs
+    """
+    return np.unique(np.array([_get_format_from_asset(asset) for asset in assets]))
+
+
+def _get_format_from_asset(asset: pystac.Asset) -> str:
+    """It transforms the MIME-types of one assets to the format IDs used in xcube.
+
+    Args:
+        asset: one asset object
+
+    Returns: format ID
+
+    """
+    return MAP_MIME_TYP_FORMAT[asset.media_type.split("; ")[0]]
+
+
+def _xarray_rename_vars(
+    ds: Union[xr.Dataset, xr.DataArray], name_dict: dict
+) -> Union[xr.Dataset, xr.DataArray]:
+    """Auxiliary functions which turns the method xarray.Dataset.rename_vars and
+    xarray.DataArray.rename_vars into a function which takes the Dataset or DataArray
+    as argument.
+
+    Args:
+        ds: Dataset or DataArray
+        name_dict: Dictionary whose keys are current variable names and whose values
+            are the desired names.
+
+    Returns:
+        Dataset with renamed variables
+    """
+    return ds.rename_vars(name_dict)
+
+
+def _is_valid_data_type(data_type: DataTypeLike) -> bool:
+    """Auxiliary function to check if data type is supported
+    by the store.
+
+    Args:
+        data_type: Data type that is to be checked.
+
+    Returns:
+        True if *data_type* is supported by the store, otherwise False
+    """
+    return (
+        data_type is None
+        or DATASET_TYPE.is_super_type_of(data_type)
+        or MULTI_LEVEL_DATASET_TYPE.is_super_type_of(data_type)
+    )
+
+
+def _assert_valid_data_type(data_type: DataTypeLike):
+    """Auxiliary function to assert if data type is supported
+    by the store.
+
+    Args:
+        data_type: Data type that is to be checked.
+
+    Raises:
+        DataStoreError: Error, if *data_type* is not
+            supported by the store.
+    """
+    if not _is_valid_data_type(data_type):
+        raise DataStoreError(
+            f"Data type must be {DATASET_TYPE.alias!r} or "
+            f"{MULTI_LEVEL_DATASET_TYPE.alias!r}, but got {data_type!r}."
+        )
+
+
+def _is_valid_ml_data_type(data_type: DataTypeLike) -> bool:
+    """Auxiliary function to check if data type is a multi-level
+    dataset type.
+
+    Args:
+        data_type: Data type that is to be checked.
+
+    Returns:
+        True if *data_type* is a multi-level dataset type, otherwise False
+    """
+    return MULTI_LEVEL_DATASET_TYPE.is_super_type_of(data_type)
+
+
+def _is_valid_dataset_data_type(data_type: DataTypeLike) -> bool:
+    """Auxiliary function to check if data type is a dataset
+    data type.
+
+    Args:
+        data_type: Data type that is to be checked.
+
+    Returns:
+        True if *data_type* is a dataset data type, otherwise False
+    """
+    return DATASET_TYPE.is_super_type_of(data_type)
+
+
+def _assert_valid_opener_id(opener_id: str):
+    """Auxiliary function to assert if data opener identified by
+    *opener_id* is supported by the store.
+
+    Args:
+        opener_id: Data opener identifier
+
+    Raises:
+        DataStoreError: Error, if *opener_id* is not
+            supported by the store.
+    """
+    if opener_id is not None and opener_id not in DATA_OPENER_IDS:
+        raise DataStoreError(
+            f"Data opener identifier must be one of "
+            f"{DATA_OPENER_IDS}, but got {opener_id!r}."
+        )