diff --git a/examples/geotiff_nonsearchable_catalog.ipynb b/examples/geotiff_nonsearchable_catalog.ipynb
index 7ba71b2..1bd8887 100644
--- a/examples/geotiff_nonsearchable_catalog.ipynb
+++ b/examples/geotiff_nonsearchable_catalog.ipynb
@@ -33,8 +33,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3.43 s, sys: 320 ms, total: 3.75 s\n",
-      "Wall time: 1.94 s\n"
+      "CPU times: user 3.34 s, sys: 369 ms, total: 3.71 s\n",
+      "Wall time: 1.97 s\n"
      ]
     }
    ],
@@ -156,7 +156,7 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x76b61eb6c320>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7e2ff24b4d40>"
       ]
      },
      "execution_count": 2,
@@ -184,8 +184,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 36.1 ms, sys: 6.74 ms, total: 42.8 ms\n",
-      "Wall time: 352 ms\n"
+      "CPU times: user 54.2 ms, sys: 10.1 ms, total: 64.3 ms\n",
+      "Wall time: 257 ms\n"
      ]
     },
     {
@@ -219,8 +219,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.44 s, sys: 277 ms, total: 1.72 s\n",
-      "Wall time: 22.3 s\n"
+      "CPU times: user 1.7 s, sys: 271 ms, total: 1.97 s\n",
+      "Wall time: 24.2 s\n"
      ]
     },
     {
@@ -265,8 +265,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 28 µs, sys: 3 µs, total: 31 µs\n",
-      "Wall time: 33.9 µs\n"
+      "CPU times: user 47 µs, sys: 6 µs, total: 53 µs\n",
+      "Wall time: 59.6 µs\n"
      ]
     },
     {
@@ -327,7 +327,7 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x76b61e11b260>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7e2ff1a40080>"
       ]
      },
      "execution_count": 5,
@@ -357,8 +357,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 479 ms, sys: 90.2 ms, total: 569 ms\n",
-      "Wall time: 11.8 s\n"
+      "CPU times: user 488 ms, sys: 131 ms, total: 619 ms\n",
+      "Wall time: 10.5 s\n"
      ]
     },
     {
@@ -413,8 +413,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 62 µs, sys: 9 µs, total: 71 µs\n",
-      "Wall time: 76.8 µs\n"
+      "CPU times: user 28 µs, sys: 5 µs, total: 33 µs\n",
+      "Wall time: 35 µs\n"
      ]
     },
     {
@@ -436,7 +436,7 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x76b61e11b1a0>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7e2ff1b07fb0>"
       ]
      },
      "execution_count": 7,
@@ -468,8 +468,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.51 s, sys: 128 ms, total: 1.64 s\n",
-      "Wall time: 3.15 s\n"
+      "CPU times: user 1.41 s, sys: 126 ms, total: 1.54 s\n",
+      "Wall time: 3.6 s\n"
      ]
     },
     {
@@ -848,7 +848,9 @@
        "    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;</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-3daf2f6c-d68a-442e-8625-a2377ebd2f53' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3daf2f6c-d68a-442e-8625-a2377ebd2f53' 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-fb42a0a4-d133-4328-8f3d-b6c60075b26f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb42a0a4-d133-4328-8f3d-b6c60075b26f' 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-ddb0c746-135a-46a5-9868-5659aa66611f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ddb0c746-135a-46a5-9868-5659aa66611f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fb30bf2-dcbe-476a-a2dc-1326a02c7776' class='xr-var-data-in' type='checkbox'><label for='data-8fb30bf2-dcbe-476a-a2dc-1326a02c7776' 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-21cd6234-4bdb-4d8d-9f43-b5c9a3611f7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21cd6234-4bdb-4d8d-9f43-b5c9a3611f7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7514ea34-2764-442d-a304-46a72c41fdc7' class='xr-var-data-in' type='checkbox'><label for='data-7514ea34-2764-442d-a304-46a72c41fdc7' 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-dc8c6aa5-1e70-48fa-ac18-53d0386a24ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc8c6aa5-1e70-48fa-ac18-53d0386a24ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56828f00-c994-49e6-936a-9498189e5141' class='xr-var-data-in' type='checkbox'><label for='data-56828f00-c994-49e6-936a-9498189e5141' 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-52236c2d-1f37-40f5-b6f9-a6a04e45fb07' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52236c2d-1f37-40f5-b6f9-a6a04e45fb07' 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-64ea2c9e-ea9d-474f-919e-707534b3ac74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64ea2c9e-ea9d-474f-919e-707534b3ac74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eee99aae-c2a6-43c3-9a77-09d39c040bc2' class='xr-var-data-in' type='checkbox'><label for='data-eee99aae-c2a6-43c3-9a77-09d39c040bc2' 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",
+       "    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-5147bc73-0dad-4b27-8480-7ce37bb78b78' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5147bc73-0dad-4b27-8480-7ce37bb78b78' 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-ebdd6d42-0086-40d8-8bce-282295f29bbb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebdd6d42-0086-40d8-8bce-282295f29bbb' 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-d33875de-c932-4568-8892-b0e438c98f91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d33875de-c932-4568-8892-b0e438c98f91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-082a8fdb-631f-411e-a6d3-211bd3ef1e77' class='xr-var-data-in' type='checkbox'><label for='data-082a8fdb-631f-411e-a6d3-211bd3ef1e77' 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-08221d2a-7175-42bb-9233-3dbeb00c6c64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08221d2a-7175-42bb-9233-3dbeb00c6c64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dcba3ca-3d17-4022-985b-0b1bb9bab185' class='xr-var-data-in' type='checkbox'><label for='data-2dcba3ca-3d17-4022-985b-0b1bb9bab185' 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-0cef48dd-bf95-454c-9464-99e6462bdc4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cef48dd-bf95-454c-9464-99e6462bdc4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfdd8bd0-71a4-4fec-b258-1b7247189ff7' class='xr-var-data-in' type='checkbox'><label for='data-bfdd8bd0-71a4-4fec-b258-1b7247189ff7' 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-61b89c7f-e355-488d-b959-befc097fb640' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61b89c7f-e355-488d-b959-befc097fb640' 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-d2b02a50-e5f5-4a2e-8ca6-47042afc58e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2b02a50-e5f5-4a2e-8ca6-47042afc58e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d921e349-d515-4919-8681-0f240b77e1c9' class='xr-var-data-in' type='checkbox'><label for='data-d921e349-d515-4919-8681-0f240b77e1c9' 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",
@@ -939,7 +941,7 @@
        "</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-e616f46a-5d53-42be-8075-37d166449ccb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e616f46a-5d53-42be-8075-37d166449ccb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9d4c077-4c8e-482c-be86-e51acd9b641a' class='xr-var-data-in' type='checkbox'><label for='data-a9d4c077-4c8e-482c-be86-e51acd9b641a' 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",
+       "</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-424a6a1f-ad90-45fb-a170-ee273ddeb766' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-424a6a1f-ad90-45fb-a170-ee273ddeb766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5674e62d-483e-4a29-bb8b-11ed6da1acb5' class='xr-var-data-in' type='checkbox'><label for='data-5674e62d-483e-4a29-bb8b-11ed6da1acb5' 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",
@@ -1030,7 +1032,7 @@
        "</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-6c485edc-9ec3-4a0f-8286-086d88aa3853' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c485edc-9ec3-4a0f-8286-086d88aa3853' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4840ec1f-a18b-4aea-84a8-1a93458edc99' class='xr-var-data-in' type='checkbox'><label for='data-4840ec1f-a18b-4aea-84a8-1a93458edc99' 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",
+       "</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-4dbd66ce-7d35-408f-930b-66a03f4cdd4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dbd66ce-7d35-408f-930b-66a03f4cdd4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daca438d-01d1-4474-b487-35cae63e381d' class='xr-var-data-in' type='checkbox'><label for='data-daca438d-01d1-4474-b487-35cae63e381d' 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",
@@ -1121,7 +1123,7 @@
        "</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-3d835412-7a08-4973-89e3-4a4aa2ddbb06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d835412-7a08-4973-89e3-4a4aa2ddbb06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04cfb5e3-0422-4902-85ce-b24003522f7d' class='xr-var-data-in' type='checkbox'><label for='data-04cfb5e3-0422-4902-85ce-b24003522f7d' 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",
+       "</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-bc229502-cdbd-4eda-99ea-4a352901d900' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc229502-cdbd-4eda-99ea-4a352901d900' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaf9e9a7-8168-44f9-bf3a-e1d8c3932387' class='xr-var-data-in' type='checkbox'><label for='data-aaf9e9a7-8168-44f9-bf3a-e1d8c3932387' 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",
@@ -1212,17 +1214,17 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0ee6ec5-9e62-4ed6-8ecd-a50812e5adf3' class='xr-section-summary-in' type='checkbox'  ><label for='section-c0ee6ec5-9e62-4ed6-8ecd-a50812e5adf3' 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-4b233437-6dd6-4e51-ba4f-639a501af2e2' class='xr-index-data-in' type='checkbox'/><label for='index-4b233437-6dd6-4e51-ba4f-639a501af2e2' 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",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8eabe4a1-baef-47a3-9d14-14aabbda95af' class='xr-section-summary-in' type='checkbox'  ><label for='section-8eabe4a1-baef-47a3-9d14-14aabbda95af' 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-bfdd3ee5-de6f-4c1d-9e49-3a477dcb02f5' class='xr-index-data-in' type='checkbox'/><label for='index-bfdd3ee5-de6f-4c1d-9e49-3a477dcb02f5' 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-1c692a54-d881-4682-a206-e7efda0bb4e5' class='xr-index-data-in' type='checkbox'/><label for='index-1c692a54-d881-4682-a206-e7efda0bb4e5' 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",
+       "      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-09f02b3f-eb1c-4dfb-b011-ecd27285024a' class='xr-index-data-in' type='checkbox'/><label for='index-09f02b3f-eb1c-4dfb-b011-ecd27285024a' 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-6d718416-ba11-43e0-a733-184a88cd5cdd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6d718416-ba11-43e0-a733-184a88cd5cdd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=151000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd1e0ba0-2730-4c3c-b75e-e1fd68434fe9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd1e0ba0-2730-4c3c-b75e-e1fd68434fe9' 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",
@@ -1235,7 +1237,9 @@
        "    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>"
+       "    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": 8,
@@ -1265,14 +1269,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3.78 s, sys: 1.72 s, total: 5.5 s\n",
-      "Wall time: 39.7 s\n"
+      "CPU times: user 3.65 s, sys: 1.74 s, total: 5.39 s\n",
+      "Wall time: 1min 7s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x76b5f0476ba0>"
+       "<matplotlib.collections.QuadMesh at 0x7e2fc46345f0>"
       ]
      },
      "execution_count": 9,
@@ -1304,23 +1308,799 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 5 µs, sys: 2 µs, total: 7 µs\n",
-      "Wall time: 12.4 µs\n"
+      "CPU times: user 1.29 s, sys: 148 ms, total: 1.43 s\n",
+      "Wall time: 2.79 s\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "mlds = store.open_data(\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",
+    ")\n",
+    "mlds.num_levels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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-1c47ea80-221a-4ccb-9735-5113404cb3e8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1c47ea80-221a-4ccb-9735-5113404cb3e8' 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-dbf88f5a-b983-4c20-8fe9-c9454d5ad248' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dbf88f5a-b983-4c20-8fe9-c9454d5ad248' 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-c1a44cc8-720c-41c1-846f-ee150af56ce0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1a44cc8-720c-41c1-846f-ee150af56ce0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f64e35f7-2815-43f1-b0e1-695b4d70e52a' class='xr-var-data-in' type='checkbox'><label for='data-f64e35f7-2815-43f1-b0e1-695b4d70e52a' 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-89dd00c5-06ae-4768-97c4-6d7201e01b8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89dd00c5-06ae-4768-97c4-6d7201e01b8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffaebbf1-f9cb-44a4-9bfa-e0a2f9a51bf0' class='xr-var-data-in' type='checkbox'><label for='data-ffaebbf1-f9cb-44a4-9bfa-e0a2f9a51bf0' 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-d6b38033-7863-47d9-a0d5-b81816a03758' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6b38033-7863-47d9-a0d5-b81816a03758' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de605e0f-f3a1-4a75-bb4f-f203381100a1' class='xr-var-data-in' type='checkbox'><label for='data-de605e0f-f3a1-4a75-bb4f-f203381100a1' 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-ad9d7f81-dd95-4802-9386-6a0b4448aaa4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad9d7f81-dd95-4802-9386-6a0b4448aaa4' 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-597ceff6-c920-48b1-b13d-a7edeee40a04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-597ceff6-c920-48b1-b13d-a7edeee40a04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40cc4c9b-89e6-4e41-8381-7d11dfb7d607' class='xr-var-data-in' type='checkbox'><label for='data-40cc4c9b-89e6-4e41-8381-7d11dfb7d607' 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-ec5715f0-0e5c-4a06-a15c-200b275829a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec5715f0-0e5c-4a06-a15c-200b275829a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cafee53-9d41-42e3-8593-4c4f2c0fe79d' class='xr-var-data-in' type='checkbox'><label for='data-4cafee53-9d41-42e3-8593-4c4f2c0fe79d' 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-36fd8558-b951-4eb5-8c2a-e3ea33e8a06e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36fd8558-b951-4eb5-8c2a-e3ea33e8a06e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2370a49-d5ec-4552-a1e6-f3ca01494039' class='xr-var-data-in' type='checkbox'><label for='data-a2370a49-d5ec-4552-a1e6-f3ca01494039' 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",
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+       "  <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",
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+       "  <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-8a3fc90d-c3c1-4333-9d48-958576f45c75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a3fc90d-c3c1-4333-9d48-958576f45c75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-348b3670-87e9-43eb-b25f-452934e9297b' class='xr-var-data-in' type='checkbox'><label for='data-348b3670-87e9-43eb-b25f-452934e9297b' 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",
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+       "  <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",
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+       "  <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-d301d1cb-baa9-41d2-a1ab-9adee6ae40ab' class='xr-section-summary-in' type='checkbox'  ><label for='section-d301d1cb-baa9-41d2-a1ab-9adee6ae40ab' 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-56359bc9-8432-4509-ac89-8d70143dd9e8' class='xr-index-data-in' type='checkbox'/><label for='index-56359bc9-8432-4509-ac89-8d70143dd9e8' 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-4a935535-5028-435e-a3e7-fd3df1258f2d' class='xr-index-data-in' type='checkbox'/><label for='index-4a935535-5028-435e-a3e7-fd3df1258f2d' 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-750bad91-d864-419e-b8ac-60fd5dc771e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-750bad91-d864-419e-b8ac-60fd5dc771e9' 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": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = mlds.get_dataset(5)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 114 ms, sys: 1.12 ms, total: 115 ms\n",
+      "Wall time: 257 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7e2fc431f4d0>"
+      ]
+     },
+     "execution_count": 17,
+     "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",
-    "#mlds = store.open_data(descriptors[0].data_id)\n",
-    "#ds = mlds.get_dataset(0)\n",
-    "#ds"
+    "ds.blue_p50[3125:3750, 3125:3750].plot(vmax=20)"
    ]
   },
   {
diff --git a/examples/geotiff_searchable_catalog.ipynb b/examples/geotiff_searchable_catalog.ipynb
index b547408..d23f578 100644
--- a/examples/geotiff_searchable_catalog.ipynb
+++ b/examples/geotiff_searchable_catalog.ipynb
@@ -33,8 +33,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2.93 s, sys: 213 ms, total: 3.14 s\n",
-      "Wall time: 1.21 s\n"
+      "CPU times: user 3.73 s, sys: 303 ms, total: 4.03 s\n",
+      "Wall time: 2.12 s\n"
      ]
     }
    ],
@@ -60,8 +60,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 229 ms, sys: 28 ms, total: 257 ms\n",
-      "Wall time: 256 ms\n"
+      "CPU times: user 463 ms, sys: 75.8 ms, total: 538 ms\n",
+      "Wall time: 540 ms\n"
      ]
     },
     {
@@ -164,7 +164,7 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x72eee8cbc9e0>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7c33cb2ba0c0>"
       ]
      },
      "execution_count": 2,
@@ -194,8 +194,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 12.3 ms, sys: 0 ns, total: 12.3 ms\n",
-      "Wall time: 840 ms\n"
+      "CPU times: user 14.9 ms, sys: 3.88 ms, total: 18.8 ms\n",
+      "Wall time: 571 ms\n"
      ]
     }
    ],
@@ -221,23 +221,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 5.6 ms, sys: 342 µs, total: 5.94 ms\n",
-      "Wall time: 1.12 s\n"
+      "CPU times: user 18.9 ms, sys: 4.3 ms, total: 23.2 ms\n",
+      "Wall time: 1.2 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "['collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T052218_20240621T052234_054417_069F06',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T052128_20240621T052153_054417_069F06',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T052038_20240621T052103_054417_069F06',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T052013_20240621T052038_054417_069F06',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T051948_20240621T052013_054417_069F06',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T051923_20240621T051948_054417_069F06',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T051858_20240621T051923_054417_069F06',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T051808_20240621T051833_054417_069F06',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T051718_20240621T051743_054417_069F06',\n",
-       " 'collections/sentinel-1-grd/items/S1A_IW_GRDH_1SDV_20240621T051653_20240621T051718_054417_069F06']"
+       "['collections/sentinel-1-grd/items/S1A_EW_GRDM_1SDH_20240701T102449_20240701T102559_054566_06A43C',\n",
+       " 'collections/sentinel-2-c1-l2a/items/S2B_T36WWD_20240701T100557_L2A',\n",
+       " 'collections/sentinel-2-c1-l2a/items/S2B_T35WQV_20240701T100557_L2A',\n",
+       " 'collections/sentinel-2-c1-l2a/items/S2B_T37WCV_20240701T100557_L2A',\n",
+       " 'collections/sentinel-2-c1-l2a/items/S2B_T36WXE_20240701T100557_L2A',\n",
+       " 'collections/sentinel-2-c1-l2a/items/S2B_T37WDV_20240701T100557_L2A',\n",
+       " 'collections/sentinel-2-c1-l2a/items/S2B_T35XQA_20240701T100557_L2A',\n",
+       " 'collections/sentinel-2-c1-l2a/items/S2B_T35XNB_20240701T100557_L2A',\n",
+       " 'collections/sentinel-2-c1-l2a/items/S2B_T35XPB_20240701T100557_L2A',\n",
+       " 'collections/sentinel-2-c1-l2a/items/S2B_T37XDA_20240701T100557_L2A']"
       ]
      },
      "execution_count": 4,
@@ -267,8 +267,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 20 µs, sys: 2 µs, total: 22 µs\n",
-      "Wall time: 24.3 µs\n"
+      "CPU times: user 23 µs, sys: 2 µs, total: 25 µs\n",
+      "Wall time: 28.1 µs\n"
      ]
     },
     {
@@ -329,7 +329,7 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x72eee8ca4620>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7c33cb229ac0>"
       ]
      },
      "execution_count": 5,
@@ -359,8 +359,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 117 ms, sys: 18.5 ms, total: 136 ms\n",
-      "Wall time: 13 s\n"
+      "CPU times: user 240 ms, sys: 36.9 ms, total: 277 ms\n",
+      "Wall time: 14 s\n"
      ]
     },
     {
@@ -513,8 +513,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 24 µs, sys: 2 µs, total: 26 µs\n",
-      "Wall time: 28.6 µs\n"
+      "CPU times: user 87 µs, sys: 0 ns, total: 87 µs\n",
+      "Wall time: 95.1 µs\n"
      ]
     },
     {
@@ -536,7 +536,7 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x72eee8e1df40>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7c33caf2d460>"
       ]
      },
      "execution_count": 7,
@@ -566,8 +566,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 79.8 ms, sys: 782 µs, total: 80.5 ms\n",
-      "Wall time: 3.71 s\n"
+      "CPU times: user 140 ms, sys: 24.4 ms, total: 164 ms\n",
+      "Wall time: 3.34 s\n"
      ]
     },
     {
@@ -945,7 +945,9 @@
        "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;</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-b75c6d4f-77bc-44de-9aad-f06cfb6c6857' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b75c6d4f-77bc-44de-9aad-f06cfb6c6857' 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-c04cbd08-7f7f-4673-82ea-c23f7caa5ec3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c04cbd08-7f7f-4673-82ea-c23f7caa5ec3' 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-fdbe55c3-8784-47a6-9eeb-56958449a43b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdbe55c3-8784-47a6-9eeb-56958449a43b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6ee74b5-1ebc-4dec-a293-74b4defbdf65' class='xr-var-data-in' type='checkbox'><label for='data-d6ee74b5-1ebc-4dec-a293-74b4defbdf65' 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-bccbe498-b6aa-4111-b503-badd21c2e228' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bccbe498-b6aa-4111-b503-badd21c2e228' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59b58bfa-4f06-4c3b-bcca-fa6582fceff4' class='xr-var-data-in' type='checkbox'><label for='data-59b58bfa-4f06-4c3b-bcca-fa6582fceff4' 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-492a03e8-7fc5-4d20-a525-1b0cb1fbdd75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-492a03e8-7fc5-4d20-a525-1b0cb1fbdd75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15463729-00fa-4c0a-9560-d7b4889ce73d' class='xr-var-data-in' type='checkbox'><label for='data-15463729-00fa-4c0a-9560-d7b4889ce73d' 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-717a179f-e850-4294-8d95-93aa633c97b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-717a179f-e850-4294-8d95-93aa633c97b7' 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-ccd14720-ff16-4ec1-a23d-5837e5110960' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccd14720-ff16-4ec1-a23d-5837e5110960' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78611b40-8e2d-4229-841f-2a154a8dcbc2' class='xr-var-data-in' type='checkbox'><label for='data-78611b40-8e2d-4229-841f-2a154a8dcbc2' 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",
+       "    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-257895f3-0c54-4d81-81e1-a3659f05f3e0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-257895f3-0c54-4d81-81e1-a3659f05f3e0' 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-b419c61e-5897-4b2f-aeeb-966c60f42c20' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b419c61e-5897-4b2f-aeeb-966c60f42c20' 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-46a6e5a0-45f4-4e61-b2d0-5eba7d6993cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46a6e5a0-45f4-4e61-b2d0-5eba7d6993cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51562737-8fbd-4783-917c-d61491d7282a' class='xr-var-data-in' type='checkbox'><label for='data-51562737-8fbd-4783-917c-d61491d7282a' 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-a88c8ff4-7922-4c4b-a50e-6ed70a0301a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a88c8ff4-7922-4c4b-a50e-6ed70a0301a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc452efe-51f5-48ca-b8e0-e4fd932b5f5c' class='xr-var-data-in' type='checkbox'><label for='data-fc452efe-51f5-48ca-b8e0-e4fd932b5f5c' 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-32392be9-0e1e-47f8-b33b-7a50eb4de650' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32392be9-0e1e-47f8-b33b-7a50eb4de650' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fc1afc5-6e26-4492-8111-bea4c0d1c3bc' class='xr-var-data-in' type='checkbox'><label for='data-7fc1afc5-6e26-4492-8111-bea4c0d1c3bc' 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-dea98f1e-2f69-4da5-b513-35ded266c380' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dea98f1e-2f69-4da5-b513-35ded266c380' 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-361be6bb-f4bc-4b7d-8245-215cdbe93e2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-361be6bb-f4bc-4b7d-8245-215cdbe93e2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fce7842-4568-41aa-a478-1b2b734c6203' class='xr-var-data-in' type='checkbox'><label for='data-0fce7842-4568-41aa-a478-1b2b734c6203' 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",
@@ -1036,7 +1038,7 @@
        "</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-49447893-aeea-4515-bd22-e9a9d56484c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49447893-aeea-4515-bd22-e9a9d56484c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8baf8f6-ff49-4ded-b79e-14ab6661ddd4' class='xr-var-data-in' type='checkbox'><label for='data-c8baf8f6-ff49-4ded-b79e-14ab6661ddd4' 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",
+       "</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-8d496e03-f317-486a-85ff-8262bb429a0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d496e03-f317-486a-85ff-8262bb429a0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed3528c7-82e4-47aa-8370-b19b1aae5666' class='xr-var-data-in' type='checkbox'><label for='data-ed3528c7-82e4-47aa-8370-b19b1aae5666' 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",
@@ -1127,7 +1129,7 @@
        "</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-ffd5f17f-6a41-4590-b6bd-920eec1bf362' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffd5f17f-6a41-4590-b6bd-920eec1bf362' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f32d59b-e377-470f-909a-8a689ad5a965' class='xr-var-data-in' type='checkbox'><label for='data-9f32d59b-e377-470f-909a-8a689ad5a965' 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",
+       "</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-fe0f3da3-b492-46de-8a36-16946dc86230' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe0f3da3-b492-46de-8a36-16946dc86230' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f5c47c8-7b9b-4d5f-a986-9fb973144826' class='xr-var-data-in' type='checkbox'><label for='data-7f5c47c8-7b9b-4d5f-a986-9fb973144826' 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",
@@ -1218,17 +1220,17 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-21e4bf4f-1650-4687-83bd-af47ab3361e1' class='xr-section-summary-in' type='checkbox'  ><label for='section-21e4bf4f-1650-4687-83bd-af47ab3361e1' 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-62a7c47a-f19e-4c0f-8c19-0019a62575f3' class='xr-index-data-in' type='checkbox'/><label for='index-62a7c47a-f19e-4c0f-8c19-0019a62575f3' 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",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5bed506c-c099-4516-bb0c-91bcd4142878' class='xr-section-summary-in' type='checkbox'  ><label for='section-5bed506c-c099-4516-bb0c-91bcd4142878' 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-6bbbfc8e-629f-4ff5-83a9-d9d5e1186ea5' class='xr-index-data-in' type='checkbox'/><label for='index-6bbbfc8e-629f-4ff5-83a9-d9d5e1186ea5' 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-2f8ea44e-a5b3-47c8-bbfa-5fa10b0249ed' class='xr-index-data-in' type='checkbox'/><label for='index-2f8ea44e-a5b3-47c8-bbfa-5fa10b0249ed' 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",
+       "      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-58133b30-fd82-4ad0-b359-5a7733d31ca6' class='xr-index-data-in' type='checkbox'/><label for='index-58133b30-fd82-4ad0-b359-5a7733d31ca6' 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-973a646b-952d-4a54-b63f-5551aee7cfda' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-973a646b-952d-4a54-b63f-5551aee7cfda' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=10980))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35e83be3-62e6-43af-873a-40bf959c8bfc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35e83be3-62e6-43af-873a-40bf959c8bfc' 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",
@@ -1240,7 +1242,9 @@
        "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>"
+       "    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,
@@ -1270,14 +1274,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 814 ms, sys: 248 ms, total: 1.06 s\n",
-      "Wall time: 1min 38s\n"
+      "CPU times: user 1.14 s, sys: 441 ms, total: 1.58 s\n",
+      "Wall time: 1min 22s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x72eea0b5f770>"
+       "<matplotlib.collections.QuadMesh at 0x7c337b14af00>"
       ]
      },
      "execution_count": 9,
@@ -1309,15 +1313,679 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 57.6 ms, sys: 3.65 ms, total: 61.2 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",
+       "<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: 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-06f9844b-080b-41e2-b49f-b093d2a04890' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-06f9844b-080b-41e2-b49f-b093d2a04890' 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-e5406616-ef05-4ede-8dda-539181b19a6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e5406616-ef05-4ede-8dda-539181b19a6b' 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-7f0a8e8c-c7d1-40cd-8a41-d83d155ff236' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f0a8e8c-c7d1-40cd-8a41-d83d155ff236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fb56576-0573-48af-b6e1-8cafefa608ab' class='xr-var-data-in' type='checkbox'><label for='data-8fb56576-0573-48af-b6e1-8cafefa608ab' 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-95adcdde-e9af-4a4e-a38c-956891084fde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95adcdde-e9af-4a4e-a38c-956891084fde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ba5053c-3a85-4c8f-b2bd-8939650f2dce' class='xr-var-data-in' type='checkbox'><label for='data-1ba5053c-3a85-4c8f-b2bd-8939650f2dce' 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-ce2a5a51-18fe-46ac-a928-f6ecb05a792d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce2a5a51-18fe-46ac-a928-f6ecb05a792d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3b2b17e-a586-4dcf-bfe2-b12662737bfc' class='xr-var-data-in' type='checkbox'><label for='data-d3b2b17e-a586-4dcf-bfe2-b12662737bfc' 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-ad5469a2-1897-4e7b-b259-754e57420b2d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad5469a2-1897-4e7b-b259-754e57420b2d' 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-8ab05efe-04fa-42a5-87e0-3e7a38aee9c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ab05efe-04fa-42a5-87e0-3e7a38aee9c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50741eda-aa78-49e8-85a8-173d28386f6a' class='xr-var-data-in' type='checkbox'><label for='data-50741eda-aa78-49e8-85a8-173d28386f6a' 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-64d10b9e-e0d0-4d81-a5a5-54b5a97224a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64d10b9e-e0d0-4d81-a5a5-54b5a97224a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46274527-bbe1-4182-a7b6-5f4b8207512a' class='xr-var-data-in' type='checkbox'><label for='data-46274527-bbe1-4182-a7b6-5f4b8207512a' 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-46c57dd2-1563-4626-ad99-c441ab622059' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46c57dd2-1563-4626-ad99-c441ab622059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ec2268d-6835-4e72-8118-51bd1609a80d' class='xr-var-data-in' type='checkbox'><label for='data-1ec2268d-6835-4e72-8118-51bd1609a80d' 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-735b3e82-22b4-42da-823a-d7bb4b9c6d49' class='xr-section-summary-in' type='checkbox'  ><label for='section-735b3e82-22b4-42da-823a-d7bb4b9c6d49' 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-7ffdf34b-b63b-4bf5-a8a0-136e7f2285e2' class='xr-index-data-in' type='checkbox'/><label for='index-7ffdf34b-b63b-4bf5-a8a0-136e7f2285e2' 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-4c005ffc-8bf8-4458-9e34-21a8d71798aa' class='xr-index-data-in' type='checkbox'/><label for='index-4c005ffc-8bf8-4458-9e34-21a8d71798aa' 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-83ebd812-d2fd-458c-8639-7313a0ebd5e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83ebd812-d2fd-458c-8639-7313a0ebd5e5' 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": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 52.9 ms, sys: 4.14 ms, total: 57.1 ms\n",
+      "Wall time: 1.16 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7c337aea5760>"
+      ]
+     },
+     "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",
-    "#mlds = store.open_data(descriptors[0].data_id)\n",
-    "#ds = mlds.get_dataset(0)\n",
-    "#ds"
+    "ds.green[:312, :312].plot(vmax=2000)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/netcdf_searchable_catalog.ipynb b/examples/netcdf_searchable_catalog.ipynb
index 8ae43d8..cccd890 100644
--- a/examples/netcdf_searchable_catalog.ipynb
+++ b/examples/netcdf_searchable_catalog.ipynb
@@ -33,8 +33,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2.81 s, sys: 195 ms, total: 3 s\n",
-      "Wall time: 1.08 s\n"
+      "CPU times: user 3.75 s, sys: 370 ms, total: 4.12 s\n",
+      "Wall time: 2.35 s\n"
      ]
     }
    ],
@@ -60,8 +60,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 235 ms, sys: 11.9 ms, total: 247 ms\n",
-      "Wall time: 246 ms\n"
+      "CPU times: user 342 ms, sys: 64 ms, total: 406 ms\n",
+      "Wall time: 405 ms\n"
      ]
     },
     {
@@ -164,7 +164,7 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x7b9850184770>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7ca13676e060>"
       ]
      },
      "execution_count": 2,
@@ -211,23 +211,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 12.3 ms, sys: 0 ns, total: 12.3 ms\n",
-      "Wall time: 3.25 s\n"
+      "CPU times: user 33.4 ms, sys: 12.1 ms, total: 45.5 ms\n",
+      "Wall time: 1.64 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "['collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240621?f=application%2Fgeo%2Bjson',\n",
-       " 'collections/S5P_TROPOMI_L3_P1D_COT/items/S5P_DLR_NRTI_01_040201_L3_COT_20240621?f=application%2Fgeo%2Bjson',\n",
-       " 'collections/S5P_TROPOMI_L3_P1D_CTH/items/S5P_DLR_NRTI_01_040201_L3_CTH_20240621?f=application%2Fgeo%2Bjson',\n",
-       " 'collections/S5P_TROPOMI_L3_P1D_HCHO/items/S5P_DLR_NRTI_01_040201_L3_HCHO_20240621?f=application%2Fgeo%2Bjson',\n",
-       " 'collections/S5P_TROPOMI_L3_P1D_O3/items/S5P_DLR_NRTI_01_040201_L3_O3_20240621?f=application%2Fgeo%2Bjson',\n",
-       " 'collections/S5P_TROPOMI_L3_P1D_SO2/items/S5P_DLR_NRTI_01_040201_L3_SO2_20240621?f=application%2Fgeo%2Bjson',\n",
-       " 'collections/S5P_TROPOMI_L4_P1D_NO2surf/items/S5P_NRT_L4v1_NO2_20240620_UTC1500?f=application%2Fgeo%2Bjson',\n",
-       " 'collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240620?f=application%2Fgeo%2Bjson',\n",
-       " 'collections/S5P_TROPOMI_L3_P1D_COT/items/S5P_DLR_NRTI_01_040201_L3_COT_20240620?f=application%2Fgeo%2Bjson',\n",
-       " 'collections/S5P_TROPOMI_L3_P1D_CTH/items/S5P_DLR_NRTI_01_040201_L3_CTH_20240620?f=application%2Fgeo%2Bjson']"
+       "['collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240701?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_COT/items/S5P_DLR_NRTI_01_040201_L3_COT_20240701?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_CTH/items/S5P_DLR_NRTI_01_040201_L3_CTH_20240701?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_HCHO/items/S5P_DLR_NRTI_01_040201_L3_HCHO_20240701?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_O3/items/S5P_DLR_NRTI_01_040201_L3_O3_20240701?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_SO2/items/S5P_DLR_NRTI_01_040201_L3_SO2_20240701?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_CF/items/S5P_DLR_NRTI_01_040201_L3_CF_20240630?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_COT/items/S5P_DLR_NRTI_01_040201_L3_COT_20240630?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_CTH/items/S5P_DLR_NRTI_01_040201_L3_CTH_20240630?f=application%2Fgeo%2Bjson',\n",
+       " 'collections/S5P_TROPOMI_L3_P1D_HCHO/items/S5P_DLR_NRTI_01_040201_L3_HCHO_20240630?f=application%2Fgeo%2Bjson']"
       ]
      },
      "execution_count": 4,
@@ -257,8 +257,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 28 µs, sys: 0 ns, total: 28 µs\n",
-      "Wall time: 34.3 µs\n"
+      "CPU times: user 25 µs, sys: 0 ns, total: 25 µs\n",
+      "Wall time: 27.4 µs\n"
      ]
     },
     {
@@ -319,7 +319,7 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x7b9913c713a0>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7ca13674c3b0>"
       ]
      },
      "execution_count": 5,
@@ -349,8 +349,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 43.3 ms, sys: 482 µs, total: 43.8 ms\n",
-      "Wall time: 3.5 s\n"
+      "CPU times: user 68.1 ms, sys: 0 ns, total: 68.1 ms\n",
+      "Wall time: 2.49 s\n"
      ]
     },
     {
@@ -402,8 +402,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 22 µs, sys: 0 ns, total: 22 µs\n",
-      "Wall time: 23.8 µs\n"
+      "CPU times: user 23 µs, sys: 0 ns, total: 23 µs\n",
+      "Wall time: 24.1 µs\n"
      ]
     },
     {
@@ -425,7 +425,7 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x7b9859125a30>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7ca13674f380>"
       ]
      },
      "execution_count": 7,
@@ -455,8 +455,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 83.4 ms, sys: 13.4 ms, total: 96.8 ms\n",
-      "Wall time: 2.52 s\n"
+      "CPU times: user 145 ms, sys: 18.7 ms, total: 164 ms\n",
+      "Wall time: 2.98 s\n"
      ]
     },
     {
@@ -834,8 +834,22 @@
        "    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;</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-d27b691e-ce60-4ca3-b2e8-2780a9965b91' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d27b691e-ce60-4ca3-b2e8-2780a9965b91' 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-2a79753f-6a23-4e2a-9145-1a62ae86a26a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a79753f-6a23-4e2a-9145-1a62ae86a26a' 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-a7555670-fb6c-4e3c-89ba-e4e5eb866203' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7555670-fb6c-4e3c-89ba-e4e5eb866203' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f017d27-2cf2-453f-81f2-69a12222624f' class='xr-var-data-in' type='checkbox'><label for='data-7f017d27-2cf2-453f-81f2-69a12222624f' 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-fe8f9e14-135f-4196-a685-e2f8840fd4a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe8f9e14-135f-4196-a685-e2f8840fd4a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efdbf71f-c6cd-4877-bac5-d26c507b8a21' class='xr-var-data-in' type='checkbox'><label for='data-efdbf71f-c6cd-4877-bac5-d26c507b8a21' 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-53f069eb-ab4e-42d4-b935-ad4364861ce9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-53f069eb-ab4e-42d4-b935-ad4364861ce9' 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-aecdf6da-2f20-43df-9e1a-2c4904261b13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aecdf6da-2f20-43df-9e1a-2c4904261b13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da680db9-3a5c-4f6c-9ead-e980033dbad0' class='xr-var-data-in' type='checkbox'><label for='data-da680db9-3a5c-4f6c-9ead-e980033dbad0' 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",
+       "    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-ff89add3-6b9a-4674-9f6e-6b4d244ed4aa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ff89add3-6b9a-4674-9f6e-6b4d244ed4aa' 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-304012f8-2067-43f8-bd5f-aa66e67a76a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-304012f8-2067-43f8-bd5f-aa66e67a76a6' 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-7acdc818-5e04-4c29-a98a-968a9fceb85c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7acdc818-5e04-4c29-a98a-968a9fceb85c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-913da165-cdaf-453a-87d0-504e77371ea0' class='xr-var-data-in' type='checkbox'><label for='data-913da165-cdaf-453a-87d0-504e77371ea0' 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-473029d3-f040-41cc-a2c7-4a0eab1e7b14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-473029d3-f040-41cc-a2c7-4a0eab1e7b14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c1a4c42-f652-4d9f-9fd5-7dd99dea64dd' class='xr-var-data-in' type='checkbox'><label for='data-4c1a4c42-f652-4d9f-9fd5-7dd99dea64dd' 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-379ce8c8-94c0-4ac3-a703-e122fe35f417' class='xr-section-summary-in' type='checkbox'  checked><label for='section-379ce8c8-94c0-4ac3-a703-e122fe35f417' 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-761b4abf-9cf6-4b5d-ad22-48bdbf0cf1c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-761b4abf-9cf6-4b5d-ad22-48bdbf0cf1c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0fd5105-eba5-4552-b5b9-cca28b6efbcd' class='xr-var-data-in' type='checkbox'><label for='data-f0fd5105-eba5-4552-b5b9-cca28b6efbcd' 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",
@@ -892,7 +906,7 @@
        "</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-69498388-8f96-4f2f-bde5-d53e285d319d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69498388-8f96-4f2f-bde5-d53e285d319d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47202815-d4ec-43a9-b2e0-7f636837111a' class='xr-var-data-in' type='checkbox'><label for='data-47202815-d4ec-43a9-b2e0-7f636837111a' 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",
+       "</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-8b82c0eb-776f-44f5-b62e-504b9a0c5d7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b82c0eb-776f-44f5-b62e-504b9a0c5d7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91fad3ed-4cf2-4f5f-8c0c-d8308c11e9fd' class='xr-var-data-in' type='checkbox'><label for='data-91fad3ed-4cf2-4f5f-8c0c-d8308c11e9fd' 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",
@@ -949,7 +963,7 @@
        "</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-2d1e9f10-0cef-417a-b0de-9cede842bde1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d1e9f10-0cef-417a-b0de-9cede842bde1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-735ca561-9158-45b1-b194-eaf151096396' class='xr-var-data-in' type='checkbox'><label for='data-735ca561-9158-45b1-b194-eaf151096396' 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",
+       "</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-1892cbdd-253a-47d2-9a64-175f215d2a03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1892cbdd-253a-47d2-9a64-175f215d2a03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1478435-9581-4384-8b5e-1bb25fd83281' class='xr-var-data-in' type='checkbox'><label for='data-c1478435-9581-4384-8b5e-1bb25fd83281' 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",
@@ -1006,7 +1020,7 @@
        "</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-bcd3a7a9-b93e-456e-a968-96abdce00357' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcd3a7a9-b93e-456e-a968-96abdce00357' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dedfe5be-9622-4c3a-9da4-ad8ee4ef13a6' class='xr-var-data-in' type='checkbox'><label for='data-dedfe5be-9622-4c3a-9da4-ad8ee4ef13a6' 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",
+       "</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-b4a81d6b-29c2-4fbe-9aa4-21d29036ce94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4a81d6b-29c2-4fbe-9aa4-21d29036ce94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c613f91-16ad-4d81-9cb8-6c7ce7de9125' class='xr-var-data-in' type='checkbox'><label for='data-4c613f91-16ad-4d81-9cb8-6c7ce7de9125' 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",
@@ -1061,7 +1075,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a1b4721e-7d13-4e24-a085-08959a98033f' class='xr-section-summary-in' type='checkbox'  ><label for='section-a1b4721e-7d13-4e24-a085-08959a98033f' 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-81f8c33d-f406-4bf1-a984-3241afdc004f' class='xr-index-data-in' type='checkbox'/><label for='index-81f8c33d-f406-4bf1-a984-3241afdc004f' 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",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-bbaec5c7-1818-46f9-b769-6137bba29c3c' class='xr-section-summary-in' type='checkbox'  ><label for='section-bbaec5c7-1818-46f9-b769-6137bba29c3c' 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-97e917db-864f-4554-89bf-6ccc9c9d0f3b' class='xr-index-data-in' type='checkbox'/><label for='index-97e917db-864f-4554-89bf-6ccc9c9d0f3b' 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",
@@ -1070,7 +1084,7 @@
        "         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-2452f242-b3da-4ac0-9370-5fc94ba4ddac' class='xr-index-data-in' type='checkbox'/><label for='index-2452f242-b3da-4ac0-9370-5fc94ba4ddac' 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",
+       "      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-a5468bcf-09c8-4c3a-b7ee-4e48927ab8fa' class='xr-index-data-in' type='checkbox'/><label for='index-a5468bcf-09c8-4c3a-b7ee-4e48927ab8fa' 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",
@@ -1079,7 +1093,7 @@
        "        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-64566d01-20e7-449f-b2a0-42f99a54ead4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-64566d01-20e7-449f-b2a0-42f99a54ead4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=3600))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62e2265a-893d-441a-b048-a464a33b9cf1' class='xr-section-summary-in' type='checkbox'  ><label for='section-62e2265a-893d-441a-b048-a464a33b9cf1' 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 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/mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698615959/S5P_NRTI_L2__CLOUD__20240619T185024_20240619T185524_34637_03_020601_20240619T193408.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616039/S5P_NRTI_L2__CLOUD__20240619T185524_20240619T190024_34637_03_020601_20240619T193646.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616045/S5P_NRTI_L2__CLOUD__20240619T190024_20240619T190524_34637_03_020601_20240619T193842.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616048/S5P_NRTI_L2__CLOUD__20240619T190524_20240619T191024_34637_03_020601_20240619T194039.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616051/S5P_NRTI_L2__CLOUD__20240619T191024_20240619T191524_34637_03_020601_20240619T194148.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616120/S5P_NRTI_L2__CLOUD__20240619T191524_20240619T192024_34637_03_020601_20240619T210949.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616123/S5P_NRTI_L2__CLOUD__20240619T192024_20240619T192524_34637_03_020601_20240619T210821.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616126/S5P_NRTI_L2__CLOUD__20240619T192524_20240619T193024_34637_03_020601_20240619T211018.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616150/S5P_NRTI_L2__CLOUD__20240619T193024_20240619T193524_34637_03_020601_20240619T211051.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616156/S5P_NRTI_L2__CLOUD__20240619T193524_20240619T194024_34637_03_020601_20240619T211130.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616159/S5P_NRTI_L2__CLOUD__20240619T202024_20240619T202524_34638_03_020601_20240619T211308.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616162/S5P_NRTI_L2__CLOUD__20240619T202524_20240619T203024_34638_03_020601_20240619T211502.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616165/S5P_NRTI_L2__CLOUD__20240619T203024_20240619T203524_34638_03_020601_20240619T211705.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616168/S5P_NRTI_L2__CLOUD__20240619T203524_20240619T204024_34638_03_020601_20240619T211920.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616171/S5P_NRTI_L2__CLOUD__20240619T204024_20240619T204524_34638_03_020601_20240619T212218.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616216/S5P_NRTI_L2__CLOUD__20240619T204524_20240619T205024_34638_03_020601_20240619T212302.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616219/S5P_NRTI_L2__CLOUD__20240619T205024_20240619T205524_34638_03_020601_20240619T212408.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616286/S5P_NRTI_L2__CLOUD__20240619T205524_20240619T210024_34638_03_020601_20240619T224811.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616313/S5P_NRTI_L2__CLOUD__20240619T210524_20240619T211024_34638_03_020601_20240619T224924.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616316/S5P_NRTI_L2__CLOUD__20240619T211024_20240619T211524_34638_03_020601_20240619T225002.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616319/S5P_NRTI_L2__CLOUD__20240619T211524_20240619T212024_34638_03_020601_20240619T225209.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616322/S5P_NRTI_L2__CLOUD__20240619T212024_20240619T212524_34638_03_020601_20240619T225057.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616325/S5P_NRTI_L2__CLOUD__20240619T220024_20240619T220524_34639_03_020601_20240619T225255.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616328/S5P_NRTI_L2__CLOUD__20240619T220524_20240619T221024_34639_03_020601_20240619T225449.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616331/S5P_NRTI_L2__CLOUD__20240619T221024_20240619T221524_34639_03_020601_20240619T225832.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616373/S5P_NRTI_L2__CLOUD__20240619T221524_20240619T222024_34639_03_020601_20240619T225934.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616376/S5P_NRTI_L2__CLOUD__20240619T222024_20240619T222524_34639_03_020601_20240619T230148.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616379/S5P_NRTI_L2__CLOUD__20240619T222524_20240619T223024_34639_03_020601_20240619T230217.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616382/S5P_NRTI_L2__CLOUD__20240619T223024_20240619T223524_34639_03_020601_20240619T230532.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616519/S5P_NRTI_L2__CLOUD__20240619T223524_20240619T224024_34639_03_020601_20240620T004019.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616555/S5P_NRTI_L2__CLOUD__20240619T224024_20240619T224524_34639_03_020601_20240620T003914.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616493/S5P_NRTI_L2__CLOUD__20240619T224524_20240619T225024_34639_03_020601_20240620T004101.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616564/S5P_NRTI_L2__CLOUD__20240619T225024_20240619T225524_34639_03_020601_20240620T004222.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616567/S5P_NRTI_L2__CLOUD__20240619T225524_20240619T230024_34639_03_020601_20240620T004331.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616570/S5P_NRTI_L2__CLOUD__20240619T230024_20240619T230524_34639_03_020601_20240620T004436.nc /mnt/data/dbs/cache/nrt-l2c-processing/ingestion/PDU-698616575/S5P_NRTI_L2__CLOUD__20240619T234524_20240619T235024_34640_03_020601_20240620T004600.nc /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",
@@ -1091,7 +1105,21 @@
        "    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>"
+       "    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,
@@ -1121,14 +1149,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.11 s, sys: 192 ms, total: 1.31 s\n",
-      "Wall time: 10.2 s\n"
+      "CPU times: user 1.37 s, sys: 197 ms, total: 1.57 s\n",
+      "Wall time: 2.68 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7b984d077620>"
+       "<matplotlib.collections.QuadMesh at 0x7ca134b9d520>"
       ]
      },
      "execution_count": 9,
diff --git a/examples/xcube_server_stac_s3.ipynb b/examples/xcube_server_stac_s3.ipynb
index ef70b94..0cad667 100644
--- a/examples/xcube_server_stac_s3.ipynb
+++ b/examples/xcube_server_stac_s3.ipynb
@@ -55,10 +55,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "id": "5ffc84bd-501e-45f8-8355-501c1740142f",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<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=(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\n"
+     ]
+    },
     {
      "data": {
       "application/json": {
@@ -159,10 +183,10 @@
        "type": "object"
       },
       "text/plain": [
-       "<xcube.util.jsonschema.JsonObjectSchema at 0x782501bfaa80>"
+       "<xcube.util.jsonschema.JsonObjectSchema at 0x7ac5fcf83440>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "1559a572-bd4d-477b-809e-f143681e7af0",
    "metadata": {},
    "outputs": [
@@ -189,8 +213,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 6.29 ms, sys: 0 ns, total: 6.29 ms\n",
-      "Wall time: 6.64 ms\n"
+      "CPU times: user 5.82 ms, sys: 0 ns, total: 5.82 ms\n",
+      "Wall time: 6.33 ms\n"
      ]
     }
    ],
@@ -210,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "bbe0f659-88b7-4774-ba3a-a5c6f31d97db",
    "metadata": {},
    "outputs": [
@@ -218,8 +242,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 7.09 ms, sys: 144 µs, total: 7.23 ms\n",
-      "Wall time: 43.6 ms\n"
+      "CPU times: user 14.5 ms, sys: 0 ns, total: 14.5 ms\n",
+      "Wall time: 61.2 ms\n"
      ]
     },
     {
@@ -231,7 +255,7 @@
        " 'collections/datacubes/items/cog_geotiff_file']"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -252,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "id": "cac9aacb-f999-4acf-b9a8-112ceae58a83",
    "metadata": {},
    "outputs": [
@@ -261,7 +285,7 @@
      "output_type": "stream",
      "text": [
       "CPU times: user 25.4 ms, sys: 0 ns, total: 25.4 ms\n",
-      "Wall time: 32 ms\n"
+      "Wall time: 32.3 ms\n"
      ]
     },
     {
@@ -631,7 +655,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 400MB\n",
-       "Dimensions:                 (lat: 1000, lon: 2000, time: 5, bnds: 2)\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",
@@ -645,10 +669,12 @@
        "    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;</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-7c4c4320-80a1-4c62-bd19-ea4b1d2b63cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c4c4320-80a1-4c62-bd19-ea4b1d2b63cd' 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>: 1000</li><li><span class='xr-has-index'>lon</span>: 2000</li><li><span class='xr-has-index'>time</span>: 5</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0a452180-ef95-47d0-b1da-877a9716bcfd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0a452180-ef95-47d0-b1da-877a9716bcfd' 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-57b8d4b6-8164-4606-9ef2-68ea8abffe10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57b8d4b6-8164-4606-9ef2-68ea8abffe10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90209c21-6eac-4913-9fae-fc7e2e3b960e' class='xr-var-data-in' type='checkbox'><label for='data-90209c21-6eac-4913-9fae-fc7e2e3b960e' 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-8bde488b-56ff-49ff-8461-bd79b1cc8513' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bde488b-56ff-49ff-8461-bd79b1cc8513' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61bcc4af-8509-41d3-bc0f-3038728d9fc7' class='xr-var-data-in' type='checkbox'><label for='data-61bcc4af-8509-41d3-bc0f-3038728d9fc7' 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-9183eb86-bcb6-46ee-82c5-c59fdc2aa2d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9183eb86-bcb6-46ee-82c5-c59fdc2aa2d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-981a8e0a-e064-408f-98c5-ac3d8de709e4' class='xr-var-data-in' type='checkbox'><label for='data-981a8e0a-e064-408f-98c5-ac3d8de709e4' 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",
+       "    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-be9f4601-57af-49d3-9b94-4afc462fd95a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-be9f4601-57af-49d3-9b94-4afc462fd95a' 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-0f618b8e-dafc-4424-a737-b939fb7ae7f7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f618b8e-dafc-4424-a737-b939fb7ae7f7' 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-6e67855f-0bc2-4fe1-bcac-78fea2e5b8f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e67855f-0bc2-4fe1-bcac-78fea2e5b8f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5566f66-6a53-4930-9290-56824fde87fc' class='xr-var-data-in' type='checkbox'><label for='data-f5566f66-6a53-4930-9290-56824fde87fc' 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-8e851be6-e7fc-42ac-9bac-4c3c44df5114' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e851be6-e7fc-42ac-9bac-4c3c44df5114' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c7668e5-0966-434e-bbf7-7c0e93f7d6f3' class='xr-var-data-in' type='checkbox'><label for='data-0c7668e5-0966-434e-bbf7-7c0e93f7d6f3' 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-0d430e87-b116-4587-bf72-85d6bd5343a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d430e87-b116-4587-bf72-85d6bd5343a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4068bf70-57bc-4214-a558-1b5d0eac6f7a' class='xr-var-data-in' type='checkbox'><label for='data-4068bf70-57bc-4214-a558-1b5d0eac6f7a' 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-a065705b-d252-4b5c-8401-8eb5f17e9067' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a065705b-d252-4b5c-8401-8eb5f17e9067' 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=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-18b6130a-0277-4ccf-b0dc-68384f0fba9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18b6130a-0277-4ccf-b0dc-68384f0fba9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed2fd707-5f31-4f62-92d3-9a06d5d192c4' class='xr-var-data-in' type='checkbox'><label for='data-ed2fd707-5f31-4f62-92d3-9a06d5d192c4' 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",
+       "       &#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-26e590e7-b7b1-45c1-9db1-534481342d3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26e590e7-b7b1-45c1-9db1-534481342d3b' 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=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-f4737929-9789-4028-a933-752959cf8ea9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4737929-9789-4028-a933-752959cf8ea9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2038799e-754d-46b4-803f-30b75bad85a4' class='xr-var-data-in' type='checkbox'><label for='data-2038799e-754d-46b4-803f-30b75bad85a4' 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",
@@ -754,7 +780,7 @@
        "</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-393a2ed4-4ae0-4738-bea9-d6ae792f4cd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-393a2ed4-4ae0-4738-bea9-d6ae792f4cd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89a3c4e0-692f-48e3-880d-b4ac264ce348' class='xr-var-data-in' type='checkbox'><label for='data-89a3c4e0-692f-48e3-880d-b4ac264ce348' 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",
+       "</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-0dc81035-694f-468f-acfe-ca1e2c8791ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dc81035-694f-468f-acfe-ca1e2c8791ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bba73f8-9cb3-4c93-81b0-260b92cdec3d' class='xr-var-data-in' type='checkbox'><label for='data-2bba73f8-9cb3-4c93-81b0-260b92cdec3d' 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",
@@ -860,7 +886,7 @@
        "</svg>\n",
        "        </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-e59096bf-5e88-4c7e-988f-0efcc3cf61bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e59096bf-5e88-4c7e-988f-0efcc3cf61bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5b430d4-23c3-411f-831c-552cc908e7cf' class='xr-var-data-in' type='checkbox'><label for='data-f5b430d4-23c3-411f-831c-552cc908e7cf' 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",
+       "</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-6ef237e7-d193-4963-9854-a0dc8a6bb097' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ef237e7-d193-4963-9854-a0dc8a6bb097' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09027ade-9a51-42a7-b733-3dbd1596e72b' class='xr-var-data-in' type='checkbox'><label for='data-09027ade-9a51-42a7-b733-3dbd1596e72b' 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",
@@ -966,7 +992,7 @@
        "</svg>\n",
        "        </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=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-ec95f8c8-1a79-4520-9f20-ad6d95af5512' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec95f8c8-1a79-4520-9f20-ad6d95af5512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1df27c85-095f-4ad6-be56-267e7c2a39a7' class='xr-var-data-in' type='checkbox'><label for='data-1df27c85-095f-4ad6-be56-267e7c2a39a7' 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",
+       "</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-f5f7f46f-7f80-4ace-8da2-864b61012eb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5f7f46f-7f80-4ace-8da2-864b61012eb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-585451f5-94b8-4894-a676-171a618d1bfd' class='xr-var-data-in' type='checkbox'><label for='data-585451f5-94b8-4894-a676-171a618d1bfd' 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",
@@ -1072,7 +1098,7 @@
        "</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-721d288c-fe5a-44a1-b0c8-3f8ab875c39f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-721d288c-fe5a-44a1-b0c8-3f8ab875c39f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8238725c-c856-4760-b8f0-6d6a943a94f2' class='xr-var-data-in' type='checkbox'><label for='data-8238725c-c856-4760-b8f0-6d6a943a94f2' 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",
+       "</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-0316e1a6-f671-41dd-b505-7c260d6de61e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0316e1a6-f671-41dd-b505-7c260d6de61e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37e81258-4605-4686-8639-c25b2f765d13' class='xr-var-data-in' type='checkbox'><label for='data-37e81258-4605-4686-8639-c25b2f765d13' 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",
@@ -1127,7 +1153,7 @@
        "</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-cf19b592-bdd3-4bb0-8a45-d3fc53ec3633' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf19b592-bdd3-4bb0-8a45-d3fc53ec3633' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c73114c9-233a-4266-8396-51d122bf88c5' class='xr-var-data-in' type='checkbox'><label for='data-c73114c9-233a-4266-8396-51d122bf88c5' 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",
+       "</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-c8e64285-32f4-4bf2-9f80-0c27568d7a12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8e64285-32f4-4bf2-9f80-0c27568d7a12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e9fe72b-4ad5-4677-8201-a3c9979150ec' class='xr-var-data-in' type='checkbox'><label for='data-7e9fe72b-4ad5-4677-8201-a3c9979150ec' 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",
@@ -1182,7 +1208,7 @@
        "</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=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-03446473-cd9f-465a-92b4-de53e7b4c3eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03446473-cd9f-465a-92b4-de53e7b4c3eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-723d382d-fe69-47d9-b9cc-c506aa3c2c03' class='xr-var-data-in' type='checkbox'><label for='data-723d382d-fe69-47d9-b9cc-c506aa3c2c03' 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",
+       "</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-d582f58c-0802-4dd6-be88-23446d87d60e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d582f58c-0802-4dd6-be88-23446d87d60e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d9ca883-46f5-44b6-b1ed-3c6656e6a4db' class='xr-var-data-in' type='checkbox'><label for='data-7d9ca883-46f5-44b6-b1ed-3c6656e6a4db' 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",
@@ -1288,7 +1314,7 @@
        "</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-024ded1c-a678-47a0-b0e0-d1b6d7580d04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-024ded1c-a678-47a0-b0e0-d1b6d7580d04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ba2dfd3-62e6-40bc-bd1f-f8618cfe9c63' class='xr-var-data-in' type='checkbox'><label for='data-5ba2dfd3-62e6-40bc-bd1f-f8618cfe9c63' 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",
+       "</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-fee25935-513f-4602-8c2d-474b3902aec4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fee25935-513f-4602-8c2d-474b3902aec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f92775f6-bfe4-41b4-be0a-f311e9e590f3' class='xr-var-data-in' type='checkbox'><label for='data-f92775f6-bfe4-41b4-be0a-f311e9e590f3' 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",
@@ -1343,12 +1369,12 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-90eb6bb1-4f0b-4fd8-b7ed-07350168847d' class='xr-section-summary-in' type='checkbox'  ><label for='section-90eb6bb1-4f0b-4fd8-b7ed-07350168847d' 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-db4a8e56-671c-4b68-9601-db6b6b184ebd' class='xr-index-data-in' type='checkbox'/><label for='index-db4a8e56-671c-4b68-9601-db6b6b184ebd' 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",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5072fb19-74b6-45eb-b7d6-0cb81d182c05' class='xr-section-summary-in' type='checkbox'  ><label for='section-5072fb19-74b6-45eb-b7d6-0cb81d182c05' 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-0e59885f-f505-49db-9aed-50e7e93de54a' class='xr-index-data-in' type='checkbox'/><label for='index-0e59885f-f505-49db-9aed-50e7e93de54a' 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-b30a865b-36cc-4881-9579-e165ce45db95' class='xr-index-data-in' type='checkbox'/><label for='index-b30a865b-36cc-4881-9579-e165ce45db95' 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",
+       "      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-ada23fca-ed98-409a-8577-3d86f462b86d' class='xr-index-data-in' type='checkbox'/><label for='index-ada23fca-ed98-409a-8577-3d86f462b86d' 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",
@@ -1357,16 +1383,16 @@
        "                    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-cbf18725-da18-43da-a7fd-72e78ace58e1' class='xr-index-data-in' type='checkbox'/><label for='index-cbf18725-da18-43da-a7fd-72e78ace58e1' 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",
+       "      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-ef567234-3bee-41c6-a715-beb0f5a8af3b' class='xr-index-data-in' type='checkbox'/><label for='index-ef567234-3bee-41c6-a715-beb0f5a8af3b' 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-b97f111b-a2a2-4370-be4a-9c4c132d6d0d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b97f111b-a2a2-4370-be4a-9c4c132d6d0d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "              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-1f0b9f9a-9998-4d83-af24-eb98c117110f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1f0b9f9a-9998-4d83-af24-eb98c117110f' 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:                 (lat: 1000, lon: 2000, time: 5, bnds: 2)\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",
@@ -1380,10 +1406,12 @@
        "    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>"
+       "    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": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1404,7 +1432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "id": "4a77d253-232d-4db4-9f26-819d5dcb8671",
    "metadata": {},
    "outputs": [
@@ -1412,17 +1440,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 229 ms, sys: 28 ms, total: 257 ms\n",
-      "Wall time: 292 ms\n"
+      "CPU times: user 427 ms, sys: 75.9 ms, total: 503 ms\n",
+      "Wall time: 518 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x782504152f90>"
+       "<matplotlib.collections.QuadMesh at 0x7ac5d06d5760>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1460,7 +1488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "id": "2642bcb5-499f-49d4-a8b7-8103d2ee714e",
    "metadata": {},
    "outputs": [
@@ -1468,8 +1496,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 36 ms, sys: 361 µs, total: 36.3 ms\n",
-      "Wall time: 42.8 ms\n"
+      "CPU times: user 26.1 ms, sys: 11 ms, total: 37.1 ms\n",
+      "Wall time: 43.9 ms\n"
      ]
     },
     {
@@ -1839,20 +1867,191 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 400MB\n",
-       "Dimensions:                 (lat: 1000, lon: 2000, time: 5)\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",
+       "    lat_bnds                (lat, bnds) float64 16kB dask.array&lt;chunksize=(1000, 2), meta=np.ndarray&gt;\n",
        "  * lon                     (lon) float64 16kB 0.00125 0.00375 ... 4.996 4.999\n",
+       "    lon_bnds                (lon, bnds) float64 32kB dask.array&lt;chunksize=(2000, 2), meta=np.ndarray&gt;\n",
        "  * time                    (time) datetime64[ns] 40B 2017-01-16T10:09:21.834...\n",
+       "    time_bnds               (time, bnds) datetime64[ns] 80B dask.array&lt;chunksize=(5, 2), meta=np.ndarray&gt;\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_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_quality_flags  (time, lat, lon) float64 80MB dask.array&lt;chunksize=(1, 250, 250), meta=np.ndarray&gt;</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-73076db1-e3f5-45fa-84bd-dbecb48afa8d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-73076db1-e3f5-45fa-84bd-dbecb48afa8d' 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>: 1000</li><li><span class='xr-has-index'>lon</span>: 2000</li><li><span class='xr-has-index'>time</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a9f8abec-8203-4bc7-aee5-212b3cd20810' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9f8abec-8203-4bc7-aee5-212b3cd20810' 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-e59132e9-c23d-4e08-ba9c-1ee6873506d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e59132e9-c23d-4e08-ba9c-1ee6873506d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95fb728b-9a31-4866-be00-36e3bcd3f09d' class='xr-var-data-in' type='checkbox'><label for='data-95fb728b-9a31-4866-be00-36e3bcd3f09d' 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-4a1555cc-dda6-485d-b4c3-075f072032be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a1555cc-dda6-485d-b4c3-075f072032be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6486530-6736-4b50-b4a1-7424578adfae' class='xr-var-data-in' type='checkbox'><label for='data-d6486530-6736-4b50-b4a1-7424578adfae' 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-95e2adfa-f6ef-451f-bac8-e4ae904f5622' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95e2adfa-f6ef-451f-bac8-e4ae904f5622' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76cee821-c4ca-4bee-858e-b69cc77be2e0' class='xr-var-data-in' type='checkbox'><label for='data-76cee821-c4ca-4bee-858e-b69cc77be2e0' 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",
+       "    analytic_quality_flags  (time, lat, lon) float64 80MB dask.array&lt;chunksize=(1, 250, 250), 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-b129e20c-ef2b-4660-a129-5c5af6ef55cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b129e20c-ef2b-4660-a129-5c5af6ef55cf' 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-17acbc2f-c2ad-42ee-b82d-6021563836e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17acbc2f-c2ad-42ee-b82d-6021563836e2' class='xr-section-summary' >Coordinates: <span>(6)</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-b0f1a697-a691-456c-9941-6d8d82ef7c30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0f1a697-a691-456c-9941-6d8d82ef7c30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b4ccfe6-68ac-4fe7-aaed-90243ae83ce1' class='xr-var-data-in' type='checkbox'><label for='data-2b4ccfe6-68ac-4fe7-aaed-90243ae83ce1' 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>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-a758d2a6-b87a-4de9-be9e-9168386e2131' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a758d2a6-b87a-4de9-be9e-9168386e2131' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b42020a2-504b-4d90-ab45-9641501e236c' class='xr-var-data-in' type='checkbox'><label for='data-b42020a2-504b-4d90-ab45-9641501e236c' 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",
+       "        <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=\"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",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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)\">1000</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></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-6dfd7416-8bf2-49a0-875b-c00753037bcb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dfd7416-8bf2-49a0-875b-c00753037bcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-766c38ed-149a-4e68-b670-5a51200ede71' class='xr-var-data-in' type='checkbox'><label for='data-766c38ed-149a-4e68-b670-5a51200ede71' 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>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-399ae8d2-eed7-4bb2-9a80-0e4284e50f62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-399ae8d2-eed7-4bb2-9a80-0e4284e50f62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7572f611-208b-4fa7-8cdf-975fc4aa329b' class='xr-var-data-in' type='checkbox'><label for='data-7572f611-208b-4fa7-8cdf-975fc4aa329b' 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",
+       "        <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=\"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",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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 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-3b062103-7842-4a17-bc8d-0e6368fe8a16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b062103-7842-4a17-bc8d-0e6368fe8a16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f6a776a-6797-4061-be2b-1c31cde3e62a' class='xr-var-data-in' type='checkbox'><label for='data-5f6a776a-6797-4061-be2b-1c31cde3e62a' 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-87557b60-86c6-4dbc-8e91-82e58e4cdcfc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87557b60-86c6-4dbc-8e91-82e58e4cdcfc' class='xr-section-summary' >Data variables: <span>(5)</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-8b0a4f3d-f6d9-4f75-ad4f-a7d72b30a22c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b0a4f3d-f6d9-4f75-ad4f-a7d72b30a22c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b93ab30d-048e-41c8-a924-65e546a10eda' class='xr-var-data-in' type='checkbox'><label for='data-b93ab30d-048e-41c8-a924-65e546a10eda' 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",
+       "       &#x27;2017-01-30T10:46:33.836892416&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>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-7601f2c3-7bf9-4e56-8406-a8c8bb7c578f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7601f2c3-7bf9-4e56-8406-a8c8bb7c578f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e118a55-c74d-4d9b-89b3-05a78448dfcc' class='xr-var-data-in' type='checkbox'><label for='data-6e118a55-c74d-4d9b-89b3-05a78448dfcc' 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-da47afbf-4148-483d-88e5-2c3ce4bd98a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da47afbf-4148-483d-88e5-2c3ce4bd98a7' class='xr-section-summary' >Data variables: <span>(5)</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-c52502b8-f667-44f4-95ff-0ad34505038c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c52502b8-f667-44f4-95ff-0ad34505038c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37adc5e5-fe4f-4050-8642-210441248f71' class='xr-var-data-in' type='checkbox'><label for='data-37adc5e5-fe4f-4050-8642-210441248f71' 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",
@@ -1958,7 +2157,7 @@
        "</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-f14298fa-b02e-4597-8cc1-1713fecf65ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f14298fa-b02e-4597-8cc1-1713fecf65ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed4428f9-06e0-48f9-b33d-82630ec1f933' class='xr-var-data-in' type='checkbox'><label for='data-ed4428f9-06e0-48f9-b33d-82630ec1f933' 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",
+       "</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-72521e62-7b60-4a0d-9e0d-3c737bee57a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72521e62-7b60-4a0d-9e0d-3c737bee57a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0da0f9fd-1657-4cea-85e9-f9f92c4557fe' class='xr-var-data-in' type='checkbox'><label for='data-0da0f9fd-1657-4cea-85e9-f9f92c4557fe' 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",
@@ -2064,7 +2263,7 @@
        "</svg>\n",
        "        </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-618fffd8-9f04-4568-b8d1-7819769a7777' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-618fffd8-9f04-4568-b8d1-7819769a7777' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64f86e1d-86e7-4aae-a509-66960c8d0f74' class='xr-var-data-in' type='checkbox'><label for='data-64f86e1d-86e7-4aae-a509-66960c8d0f74' 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",
+       "</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-9d23df92-4e26-4db2-8fcb-4aa47d48f4a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d23df92-4e26-4db2-8fcb-4aa47d48f4a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b31ec414-12db-4f88-8b02-d584749c9a11' class='xr-var-data-in' type='checkbox'><label for='data-b31ec414-12db-4f88-8b02-d584749c9a11' 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",
@@ -2170,7 +2369,7 @@
        "</svg>\n",
        "        </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=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-bb1d537d-69b9-4285-ba13-050ff9c9c120' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb1d537d-69b9-4285-ba13-050ff9c9c120' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0a345c5-a250-4aab-aed1-e4980195d5d3' class='xr-var-data-in' type='checkbox'><label for='data-a0a345c5-a250-4aab-aed1-e4980195d5d3' 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",
+       "</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-5751dfba-5fbd-4973-8d3c-fcab7bf55a38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5751dfba-5fbd-4973-8d3c-fcab7bf55a38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc3147a0-aa50-434f-8fe4-6e60bbc585df' class='xr-var-data-in' type='checkbox'><label for='data-cc3147a0-aa50-434f-8fe4-6e60bbc585df' 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",
@@ -2276,7 +2475,7 @@
        "</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=(1, 250, 250), meta=np.ndarray&gt;</div><input id='attrs-8b59c228-5c45-408b-b7ab-b699f0d4bb84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b59c228-5c45-408b-b7ab-b699f0d4bb84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d1aa8cb-6de7-433a-980b-3b995dfc9992' class='xr-var-data-in' type='checkbox'><label for='data-9d1aa8cb-6de7-433a-980b-3b995dfc9992' 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",
+       "</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-ecb5be78-6679-4c45-98fd-3cf223860500' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ecb5be78-6679-4c45-98fd-3cf223860500' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-639b5d2c-d51c-43ad-826a-951e36a7d504' class='xr-var-data-in' type='checkbox'><label for='data-639b5d2c-d51c-43ad-826a-951e36a7d504' 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",
@@ -2382,12 +2581,12 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b99867e8-012e-4759-b8fd-95718d48abba' class='xr-section-summary-in' type='checkbox'  ><label for='section-b99867e8-012e-4759-b8fd-95718d48abba' 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-08f466cd-0c42-4eed-9f4c-1ee002009fdf' class='xr-index-data-in' type='checkbox'/><label for='index-08f466cd-0c42-4eed-9f4c-1ee002009fdf' 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",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-87ccc2f3-f14a-4bce-bb0e-139e786c74db' class='xr-section-summary-in' type='checkbox'  ><label for='section-87ccc2f3-f14a-4bce-bb0e-139e786c74db' 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-b519056f-acd2-4c3c-b8f5-f3abbd04d2f8' class='xr-index-data-in' type='checkbox'/><label for='index-b519056f-acd2-4c3c-b8f5-f3abbd04d2f8' 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-20dc5325-2188-42dd-9378-e970cb369714' class='xr-index-data-in' type='checkbox'/><label for='index-20dc5325-2188-42dd-9378-e970cb369714' 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",
+       "      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-0a782a53-3fed-4ed7-967e-5d8b74730876' class='xr-index-data-in' type='checkbox'/><label for='index-0a782a53-3fed-4ed7-967e-5d8b74730876' 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",
@@ -2396,29 +2595,35 @@
        "                    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-632e139f-9eba-44aa-aaad-e868b6aac295' class='xr-index-data-in' type='checkbox'/><label for='index-632e139f-9eba-44aa-aaad-e868b6aac295' 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",
+       "      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-36d85548-3bf5-45f9-bd57-c1f263a55671' class='xr-index-data-in' type='checkbox'/><label for='index-36d85548-3bf5-45f9-bd57-c1f263a55671' 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-2acafad3-7271-4c07-943b-e878be356f33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2acafad3-7271-4c07-943b-e878be356f33' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "              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-74116e03-f791-4e46-b6b7-7221546416a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74116e03-f791-4e46-b6b7-7221546416a3' 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:                 (lat: 1000, lon: 2000, time: 5)\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",
+       "    lat_bnds                (lat, bnds) float64 16kB dask.array<chunksize=(1000, 2), meta=np.ndarray>\n",
        "  * lon                     (lon) float64 16kB 0.00125 0.00375 ... 4.996 4.999\n",
+       "    lon_bnds                (lon, bnds) float64 32kB dask.array<chunksize=(2000, 2), meta=np.ndarray>\n",
        "  * time                    (time) datetime64[ns] 40B 2017-01-16T10:09:21.834...\n",
+       "    time_bnds               (time, bnds) datetime64[ns] 80B dask.array<chunksize=(5, 2), meta=np.ndarray>\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_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_quality_flags  (time, lat, lon) float64 80MB dask.array<chunksize=(1, 250, 250), meta=np.ndarray>"
+       "    analytic_quality_flags  (time, lat, lon) float64 80MB dask.array<chunksize=(1, 250, 250), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.7"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2431,7 +2636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "4663093d-8190-4d3c-abd8-ab79b4a7fbb2",
    "metadata": {},
    "outputs": [
@@ -2439,17 +2644,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 224 ms, sys: 13.1 ms, total: 237 ms\n",
-      "Wall time: 225 ms\n"
+      "CPU times: user 240 ms, sys: 27.6 ms, total: 267 ms\n",
+      "Wall time: 255 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7824c865dc10>"
+       "<matplotlib.collections.QuadMesh at 0x7ac5d05a6f60>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2479,7 +2684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "id": "c5bac383-796e-4d12-8457-eee49d16eda2",
    "metadata": {},
    "outputs": [
@@ -2487,8 +2692,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 24.1 ms, sys: 337 µs, total: 24.4 ms\n",
-      "Wall time: 48.7 ms\n"
+      "CPU times: user 25.8 ms, sys: 156 µs, total: 26 ms\n",
+      "Wall time: 54.1 ms\n"
      ]
     },
     {
@@ -2858,7 +3063,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50MB\n",
-       "Dimensions:               (x: 1491, y: 1387)\n",
+       "Dimensions:               (y: 1387, x: 1491)\n",
        "Coordinates:\n",
        "  * x                     (x) float64 12kB 2.236e+05 2.236e+05 ... 2.592e+05\n",
        "  * y                     (y) float64 11kB 4.218e+06 4.218e+06 ... 4.185e+06\n",
@@ -2866,9 +3071,11 @@
        "    analytic_band_1       (y, x) float64 17MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
        "    analytic_band_2       (y, x) float64 17MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
        "    analytic_band_3       (y, x) float64 17MB dask.array&lt;chunksize=(512, 512), meta=np.ndarray&gt;\n",
-       "    analytic_spatial_ref  int64 8B ...</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-72272c18-3675-460e-ab0c-c420e6fcfc02' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-72272c18-3675-460e-ab0c-c420e6fcfc02' 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>: 1491</li><li><span class='xr-has-index'>y</span>: 1387</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f921dcfb-6d99-4d4f-95ff-56195f98c3ad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f921dcfb-6d99-4d4f-95ff-56195f98c3ad' 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'>2.236e+05 2.236e+05 ... 2.592e+05</div><input id='attrs-477136d3-f978-4d86-9725-19f705bed0d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-477136d3-f978-4d86-9725-19f705bed0d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-063e40f1-3303-4142-8d21-371b8168ef82' class='xr-var-data-in' type='checkbox'><label for='data-063e40f1-3303-4142-8d21-371b8168ef82' 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([223598.200055, 223622.127126, 223646.054197, ..., 259201.681604,\n",
-       "       259225.608675, 259249.535746])</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'>4.218e+06 4.218e+06 ... 4.185e+06</div><input id='attrs-720f60e5-c136-499b-9e23-4688fe09e201' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-720f60e5-c136-499b-9e23-4688fe09e201' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73d3eec3-5f6a-4de8-bf4a-bc603fc80c64' class='xr-var-data-in' type='checkbox'><label for='data-73d3eec3-5f6a-4de8-bf4a-bc603fc80c64' 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([4217895.013918, 4217871.086847, 4217847.159776, ..., 4184779.947746,\n",
-       "       4184756.020675, 4184732.093604])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4fd5f9f-de4c-4260-a7f8-5ccc14ee91ca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4fd5f9f-de4c-4260-a7f8-5ccc14ee91ca' 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=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-61a770e5-1d72-443f-b700-90d1c0995e48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61a770e5-1d72-443f-b700-90d1c0995e48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a9aa7c4-adf2-4d49-adce-1ca03133b8d9' class='xr-var-data-in' type='checkbox'><label for='data-9a9aa7c4-adf2-4d49-adce-1ca03133b8d9' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    analytic_spatial_ref  int64 8B ...\n",
+       "Attributes:\n",
+       "    source:   file:///home/konstantin/bc_kon/01_coding/01_github/xcube-stac/e...</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-ffd7ad3c-8e66-4485-91b6-2b4e27a01e15' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ffd7ad3c-8e66-4485-91b6-2b4e27a01e15' 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>: 1387</li><li><span class='xr-has-index'>x</span>: 1491</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fbf9482c-5ab5-4a35-a9b9-b7f10c416c02' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fbf9482c-5ab5-4a35-a9b9-b7f10c416c02' 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'>2.236e+05 2.236e+05 ... 2.592e+05</div><input id='attrs-75bff57d-dfa5-4876-9991-b452d408fdf9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75bff57d-dfa5-4876-9991-b452d408fdf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bedb6858-f477-43a6-ac87-50ce2cbe63e9' class='xr-var-data-in' type='checkbox'><label for='data-bedb6858-f477-43a6-ac87-50ce2cbe63e9' 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([223598.200055, 223622.127126, 223646.054197, ..., 259201.681604,\n",
+       "       259225.608675, 259249.535746])</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'>4.218e+06 4.218e+06 ... 4.185e+06</div><input id='attrs-3c04a9e4-2d73-4685-b34f-f16d136b680a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c04a9e4-2d73-4685-b34f-f16d136b680a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c205f788-5709-4f5c-9dc2-8137480153e8' class='xr-var-data-in' type='checkbox'><label for='data-c205f788-5709-4f5c-9dc2-8137480153e8' 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([4217895.013918, 4217871.086847, 4217847.159776, ..., 4184779.947746,\n",
+       "       4184756.020675, 4184732.093604])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-52a42cc5-9194-4362-b88e-f5fa39127703' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52a42cc5-9194-4362-b88e-f5fa39127703' 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=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-21605114-8c50-452d-8923-a2f5e0b30c61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21605114-8c50-452d-8923-a2f5e0b30c61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40b8d786-d4ce-4a4a-9b93-404f66ef132f' class='xr-var-data-in' type='checkbox'><label for='data-40b8d786-d4ce-4a4a-9b93-404f66ef132f' 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>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",
@@ -2927,7 +3134,7 @@
        "</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=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-5ec89553-53d5-44ae-bf3c-dfbae58dfcfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ec89553-53d5-44ae-bf3c-dfbae58dfcfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-532c8fc4-79b0-4df2-824d-f5e617cee371' class='xr-var-data-in' type='checkbox'><label for='data-532c8fc4-79b0-4df2-824d-f5e617cee371' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\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=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-4ebb1965-7e0d-4f74-8b60-d135f1ce2157' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ebb1965-7e0d-4f74-8b60-d135f1ce2157' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20f70efb-b26f-4929-b645-823dccb5d4e0' class='xr-var-data-in' type='checkbox'><label for='data-20f70efb-b26f-4929-b645-823dccb5d4e0' 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>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",
@@ -2986,7 +3193,7 @@
        "</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=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-933efb27-a282-48fc-ad04-46e1aafa3abd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-933efb27-a282-48fc-ad04-46e1aafa3abd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01ffbf83-a4f2-41ed-bbed-43e4c543ae88' class='xr-var-data-in' type='checkbox'><label for='data-01ffbf83-a4f2-41ed-bbed-43e4c543ae88' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\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=(512, 512), meta=np.ndarray&gt;</div><input id='attrs-f5bd415d-c1b6-4716-a7ff-0dff895c9de4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5bd415d-c1b6-4716-a7ff-0dff895c9de4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-847fbf47-1a5d-4c9b-8a4b-6824b12a8cc6' class='xr-var-data-in' type='checkbox'><label for='data-847fbf47-1a5d-4c9b-8a4b-6824b12a8cc6' 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>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",
@@ -3045,7 +3252,7 @@
        "</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-8c1d4d8c-030c-45eb-a32d-628cd4736efb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c1d4d8c-030c-45eb-a32d-628cd4736efb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d53b8737-034c-48e3-bdf3-e8882720b15b' class='xr-var-data-in' type='checkbox'><label for='data-d53b8737-034c-48e3-bdf3-e8882720b15b' 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;NAD83 / UTM zone 18N&quot;,GEOGCS[&quot;NAD83&quot;,DATUM[&quot;North_American_Datum_1983&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6269&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;4269&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],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;26918&quot;]]</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>298.257222101</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>NAD83</dd><dt><span>horizontal_datum_name :</span></dt><dd>North American Datum 1983</dd><dt><span>projected_crs_name :</span></dt><dd>NAD83 / UTM zone 18N</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>-75.0</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;NAD83 / UTM zone 18N&quot;,GEOGCS[&quot;NAD83&quot;,DATUM[&quot;North_American_Datum_1983&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6269&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;4269&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],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;26918&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>223586.23651964564 23.927070933333344 0.0 4217906.977453323 0.0 -23.927070933333344</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-dde18302-c43b-4ab1-8cb4-ad2df9e3ebbe' class='xr-section-summary-in' type='checkbox'  ><label for='section-dde18302-c43b-4ab1-8cb4-ad2df9e3ebbe' 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-c07dbb51-2312-4a44-8277-a68621c89a55' class='xr-index-data-in' type='checkbox'/><label for='index-c07dbb51-2312-4a44-8277-a68621c89a55' 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([ 223598.2000551123, 223622.12712604564, 223646.05419697898,\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-b78bebb4-5b1b-421a-9a1e-2e60b6889c51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b78bebb4-5b1b-421a-9a1e-2e60b6889c51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-712156e9-377f-4280-a65a-b4e02c0fe957' class='xr-var-data-in' type='checkbox'><label for='data-712156e9-377f-4280-a65a-b4e02c0fe957' 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;NAD83 / UTM zone 18N&quot;,GEOGCS[&quot;NAD83&quot;,DATUM[&quot;North_American_Datum_1983&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6269&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;4269&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],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;26918&quot;]]</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>298.257222101</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>NAD83</dd><dt><span>horizontal_datum_name :</span></dt><dd>North American Datum 1983</dd><dt><span>projected_crs_name :</span></dt><dd>NAD83 / UTM zone 18N</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>-75.0</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;NAD83 / UTM zone 18N&quot;,GEOGCS[&quot;NAD83&quot;,DATUM[&quot;North_American_Datum_1983&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6269&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;4269&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],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;26918&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>223586.23651964564 23.927070933333344 0.0 4217906.977453323 0.0 -23.927070933333344</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-4e23abe8-2b84-4120-94b0-7930fe480a33' class='xr-section-summary-in' type='checkbox'  ><label for='section-4e23abe8-2b84-4120-94b0-7930fe480a33' 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-26cf2ddc-c2f3-4c12-abf3-867fc76cf78c' class='xr-index-data-in' type='checkbox'/><label for='index-26cf2ddc-c2f3-4c12-abf3-867fc76cf78c' 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([ 223598.2000551123, 223622.12712604564, 223646.05419697898,\n",
        "        223669.9812679123, 223693.90833884565, 223717.83540977899,\n",
        "        223741.7624807123, 223765.68955164563, 223789.61662257896,\n",
        "        223813.5436935123,\n",
@@ -3054,7 +3261,7 @@
        "       259105.97332017898,  259129.9003911123, 259153.82746204565,\n",
        "         259177.754532979, 259201.68160391232, 259225.60867484566,\n",
        "         259249.535745779],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1491))</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-84c28b2f-be7a-494a-9dca-fe314e23aaa1' class='xr-index-data-in' type='checkbox'/><label for='index-84c28b2f-be7a-494a-9dca-fe314e23aaa1' 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([ 4217895.013917857,  4217871.086846923,   4217847.15977599,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1491))</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-f51d3c20-8ec4-4450-b2a1-ae23b9684509' class='xr-index-data-in' type='checkbox'/><label for='index-f51d3c20-8ec4-4450-b2a1-ae23b9684509' 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([ 4217895.013917857,  4217871.086846923,   4217847.15977599,\n",
        "        4217823.232705057,  4217799.305634123,   4217775.37856319,\n",
        "       4217751.4514922565,  4217727.524421323,  4217703.597350391,\n",
        "        4217679.670279457,\n",
@@ -3063,11 +3270,11 @@
        "       4184875.6560298568, 4184851.7289589234,   4184827.80188799,\n",
        "        4184803.874817057, 4184779.9477461237, 4184756.0206751903,\n",
        "        4184732.093604257],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1387))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b5ebd536-2b88-4a09-b9cf-cf65a3947469' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5ebd536-2b88-4a09-b9cf-cf65a3947469' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1387))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-068642c4-7d58-4a55-9310-524974d9bb25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-068642c4-7d58-4a55-9310-524974d9bb25' 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-stac/examples/xcube_server/sample-geotiff.tif</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 50MB\n",
-       "Dimensions:               (x: 1491, y: 1387)\n",
+       "Dimensions:               (y: 1387, x: 1491)\n",
        "Coordinates:\n",
        "  * x                     (x) float64 12kB 2.236e+05 2.236e+05 ... 2.592e+05\n",
        "  * y                     (y) float64 11kB 4.218e+06 4.218e+06 ... 4.185e+06\n",
@@ -3075,10 +3282,12 @@
        "    analytic_band_1       (y, x) float64 17MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
        "    analytic_band_2       (y, x) float64 17MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
        "    analytic_band_3       (y, x) float64 17MB dask.array<chunksize=(512, 512), meta=np.ndarray>\n",
-       "    analytic_spatial_ref  int64 8B ..."
+       "    analytic_spatial_ref  int64 8B ...\n",
+       "Attributes:\n",
+       "    source:   file:///home/konstantin/bc_kon/01_coding/01_github/xcube-stac/e..."
       ]
      },
-     "execution_count": 15,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3091,7 +3300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "id": "d1989bcf-a5c7-426b-a41c-8939bc6f93ec",
    "metadata": {},
    "outputs": [
@@ -3099,17 +3308,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 214 ms, sys: 19.8 ms, total: 234 ms\n",
-      "Wall time: 298 ms\n"
+      "CPU times: user 189 ms, sys: 8.39 ms, total: 197 ms\n",
+      "Wall time: 196 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7824c86cdc10>"
+       "<matplotlib.collections.QuadMesh at 0x7ac5d0335d00>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3139,7 +3348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "id": "e7ea4684-7a96-460f-9f65-9827b8b69ad9",
    "metadata": {},
    "outputs": [
@@ -3147,8 +3356,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 27.3 ms, sys: 267 µs, total: 27.6 ms\n",
-      "Wall time: 32.6 ms\n"
+      "CPU times: user 25.1 ms, sys: 84 µs, total: 25.2 ms\n",
+      "Wall time: 30.7 ms\n"
      ]
     },
     {
@@ -3518,7 +3727,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
-       "Dimensions:               (x: 343, y: 343)\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",
@@ -3526,7 +3735,9 @@
        "    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 ...</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-9c1c62e4-b97a-4ab6-933b-6f05fe67f13b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c1c62e4-b97a-4ab6-933b-6f05fe67f13b' 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>: 343</li><li><span class='xr-has-index'>y</span>: 343</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-88da62cd-dcfd-40de-96b5-fbe63561b72c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-88da62cd-dcfd-40de-96b5-fbe63561b72c' 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-118d3541-99aa-4ad8-a4f4-4bc612bc7631' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-118d3541-99aa-4ad8-a4f4-4bc612bc7631' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94404bce-2e20-41b7-a66d-ee516c68faa3' class='xr-var-data-in' type='checkbox'><label for='data-94404bce-2e20-41b7-a66d-ee516c68faa3' 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-b1dac970-39ab-4512-a5cb-9336667faf67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1dac970-39ab-4512-a5cb-9336667faf67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66132693-e666-42aa-bee4-1142fdbca4f7' class='xr-var-data-in' type='checkbox'><label for='data-66132693-e666-42aa-bee4-1142fdbca4f7' 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-006a90f5-920b-4dc5-84dd-2895a3381b63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-006a90f5-920b-4dc5-84dd-2895a3381b63' 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-ab98cd3d-9ce2-46b6-8ce2-2322ec75cb92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab98cd3d-9ce2-46b6-8ce2-2322ec75cb92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5039a8ec-85c1-4fd1-b1ac-82c018b3522e' class='xr-var-data-in' type='checkbox'><label for='data-5039a8ec-85c1-4fd1-b1ac-82c018b3522e' 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",
+       "    analytic_spatial_ref  int64 8B ...\n",
+       "Attributes:\n",
+       "    source:   file:///home/konstantin/bc_kon/01_coding/01_github/xcube-stac/e...</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-d5ee29b6-6d03-44d5-b0bc-66d89699fc35' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5ee29b6-6d03-44d5-b0bc-66d89699fc35' 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-5730dea3-1297-4375-a6b8-cd9671781178' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5730dea3-1297-4375-a6b8-cd9671781178' 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-91aaf68e-7860-48d3-be18-1def9d82b54d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91aaf68e-7860-48d3-be18-1def9d82b54d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe468b04-0906-4d3e-9b54-42e7efa2e4d5' class='xr-var-data-in' type='checkbox'><label for='data-fe468b04-0906-4d3e-9b54-42e7efa2e4d5' 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-0f6115e4-94f7-43d8-9bb9-88a41f3eb046' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f6115e4-94f7-43d8-9bb9-88a41f3eb046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-674fa28d-0075-439e-ac16-f4ce2fd82009' class='xr-var-data-in' type='checkbox'><label for='data-674fa28d-0075-439e-ac16-f4ce2fd82009' 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-9c2a4568-247e-486a-9fde-d1d574ad8410' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c2a4568-247e-486a-9fde-d1d574ad8410' 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-1430a32d-6c86-4903-af96-09b74fcd3d1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1430a32d-6c86-4903-af96-09b74fcd3d1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-840f2926-d760-4c6a-acf0-5a08a4272575' class='xr-var-data-in' type='checkbox'><label for='data-840f2926-d760-4c6a-acf0-5a08a4272575' 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",
@@ -3581,7 +3792,7 @@
        "</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-5dab3da6-322e-4d73-a8fb-76d343d1bd3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dab3da6-322e-4d73-a8fb-76d343d1bd3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2dae7e7-6112-4b5c-a083-614724b4bf72' class='xr-var-data-in' type='checkbox'><label for='data-c2dae7e7-6112-4b5c-a083-614724b4bf72' 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",
+       "</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-8a2a28df-cb53-426e-b02b-0b4a2917b24a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a2a28df-cb53-426e-b02b-0b4a2917b24a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47cbef6e-100d-4cde-937f-360e6da325c5' class='xr-var-data-in' type='checkbox'><label for='data-47cbef6e-100d-4cde-937f-360e6da325c5' 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",
@@ -3636,7 +3847,7 @@
        "</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-0161b93e-7733-431f-b560-81804b891499' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0161b93e-7733-431f-b560-81804b891499' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30a56699-f36c-4b47-a346-b2fde0aeabde' class='xr-var-data-in' type='checkbox'><label for='data-30a56699-f36c-4b47-a346-b2fde0aeabde' 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",
+       "</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-a9c3eaee-feee-4e52-80fe-6edb6c4cbb88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9c3eaee-feee-4e52-80fe-6edb6c4cbb88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-446b38d5-c498-4d2e-be7f-da1b9661ed83' class='xr-var-data-in' type='checkbox'><label for='data-446b38d5-c498-4d2e-be7f-da1b9661ed83' 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",
@@ -3691,21 +3902,21 @@
        "</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-5b1524dc-f878-46aa-b05b-9004ed51b10f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b1524dc-f878-46aa-b05b-9004ed51b10f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78fabff5-6178-44ed-b8e4-766ab9dc13b9' class='xr-var-data-in' type='checkbox'><label for='data-78fabff5-6178-44ed-b8e4-766ab9dc13b9' 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-0d21dc56-258a-49ec-b1f6-d545bd715d79' class='xr-section-summary-in' type='checkbox'  ><label for='section-0d21dc56-258a-49ec-b1f6-d545bd715d79' 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-59994663-b93b-45be-8319-a33f29d02b43' class='xr-index-data-in' type='checkbox'/><label for='index-59994663-b93b-45be-8319-a33f29d02b43' 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",
+       "</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-c0cd527e-69e1-4da1-a293-a56ff5648225' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0cd527e-69e1-4da1-a293-a56ff5648225' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f23c285-2c39-4f20-b4de-be86f35af444' class='xr-var-data-in' type='checkbox'><label for='data-4f23c285-2c39-4f20-b4de-be86f35af444' 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-8ad5a63d-b88a-4c98-b482-3c7de4203bb6' class='xr-section-summary-in' type='checkbox'  ><label for='section-8ad5a63d-b88a-4c98-b482-3c7de4203bb6' 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-450bb9b6-c323-45ef-b993-9d29716433a2' class='xr-index-data-in' type='checkbox'/><label for='index-450bb9b6-c323-45ef-b993-9d29716433a2' 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-f80ec743-81ac-475c-8e53-6e4ea821fe9a' class='xr-index-data-in' type='checkbox'/><label for='index-f80ec743-81ac-475c-8e53-6e4ea821fe9a' 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",
+       "      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-cf979dd3-ff76-460f-8ef4-d890ac3db3be' class='xr-index-data-in' type='checkbox'/><label for='index-cf979dd3-ff76-460f-8ef4-d890ac3db3be' 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-1618187e-7649-49f1-898a-e3543493a5d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1618187e-7649-49f1-898a-e3543493a5d6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=343))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9630f10-9543-48e5-a2c2-96652a1ef46e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9630f10-9543-48e5-a2c2-96652a1ef46e' 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-stac/examples/xcube_server/sample-cog.tif</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 3MB\n",
-       "Dimensions:               (x: 343, y: 343)\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",
@@ -3713,10 +3924,12 @@
        "    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 ..."
+       "    analytic_spatial_ref  int64 8B ...\n",
+       "Attributes:\n",
+       "    source:   file:///home/konstantin/bc_kon/01_coding/01_github/xcube-stac/e..."
       ]
      },
-     "execution_count": 18,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3729,7 +3942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "id": "bd4ac8db-6b25-4aca-a49d-b1689e2a62ac",
    "metadata": {},
    "outputs": [
@@ -3737,17 +3950,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 32.8 ms, sys: 0 ns, total: 32.8 ms\n",
-      "Wall time: 36.1 ms\n"
+      "CPU times: user 35.4 ms, sys: 0 ns, total: 35.4 ms\n",
+      "Wall time: 36.7 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7824b0dfaf90>"
+       "<matplotlib.collections.QuadMesh at 0x7ac5d0361a90>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
diff --git a/test/cassettes/test_store/StacDataStoreTest.test_open_data_abfs.yaml b/test/cassettes/test_store/StacDataStoreTest.test_open_data_abfs.yaml
index 0c24f2f..cd7d78b 100644
--- a/test/cassettes/test_store/StacDataStoreTest.test_open_data_abfs.yaml
+++ b/test/cassettes/test_store/StacDataStoreTest.test_open_data_abfs.yaml
@@ -84,12 +84,12 @@ interactions:
         Level-2A","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-2-l2a"},{"rel":"child","type":"application/json","title":"MODIS
         Leaf Area Index/FPAR 8-Day","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-15A2H-061"},{"rel":"child","type":"application/json","title":"MODIS
         Land Surface Temperature/Emissivity Daily","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-11A1-061"},{"rel":"child","type":"application/json","title":"MODIS
-        Leaf Area Index/FPAR 4-Day","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-15A3H-061"},{"rel":"child","type":"application/json","title":"ECMWF
-        Open Data (real-time)","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast"},{"rel":"child","type":"application/json","title":"MODIS
+        Leaf Area Index/FPAR 4-Day","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-15A3H-061"},{"rel":"child","type":"application/json","title":"MODIS
         Vegetation Indices 16-Day (500m)","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-13A1-061"},{"rel":"child","type":"application/json","title":"Daymet
         Daily North America","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-daily-na"},{"rel":"child","type":"application/json","title":"Land
         Cover of Canada","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/nrcan-landcover"},{"rel":"child","type":"application/json","title":"MODIS
-        Snow Cover 8-day","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-10A2-061"},{"rel":"child","type":"application/json","title":"NOAA
+        Snow Cover 8-day","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-10A2-061"},{"rel":"child","type":"application/json","title":"ECMWF
+        Open Data (real-time)","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast"},{"rel":"child","type":"application/json","title":"NOAA
         MRMS QPE 24-Hour Pass 2","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-24h-pass2"},{"rel":"child","type":"application/json","title":"Sentinel
         1 Level-1 Ground Range Detected (GRD)","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd"},{"rel":"child","type":"application/json","title":"NASADEM
         HGT v001","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/nasadem"},{"rel":"child","type":"application/json","title":"Esri
@@ -159,11 +159,11 @@ interactions:
       Connection:
       - keep-alive
       Content-Length:
-      - '3324'
+      - '3321'
       Content-Type:
       - application/json
       Date:
-      - Fri, 28 Jun 2024 14:17:20 GMT
+      - Mon, 01 Jul 2024 06:55:56 GMT
       Strict-Transport-Security:
       - max-age=31536000; includeSubDomains
       X-Cache:
@@ -173,7 +173,7 @@ interactions:
       vary:
       - Accept-Encoding
       x-azure-ref:
-      - 20240628T141720Z-r16b4c9c64d9ndrgwr6tpuhs7c000000058g000000002gwg
+      - 20240701T065556Z-1586678d48fvb4sdmzpm9r2n1s00000009f000000000nk4u
     status:
       code: 200
       message: OK
@@ -244,7 +244,7 @@ interactions:
       Content-Type:
       - application/geo+json
       Date:
-      - Fri, 28 Jun 2024 14:17:21 GMT
+      - Mon, 01 Jul 2024 06:55:56 GMT
       Strict-Transport-Security:
       - max-age=31536000; includeSubDomains
       X-Cache:
@@ -254,7 +254,7 @@ interactions:
       vary:
       - Accept-Encoding
       x-azure-ref:
-      - 20240628T141720Z-r16b4c9c64dpf7xzq9mfyssp98000000052g00000000hwca
+      - 20240701T065556Z-16dbcf9f44cgmttlasnpk649ss0000000a9g00000000bb13
     status:
       code: 200
       message: OK
@@ -273,18 +273,18 @@ interactions:
     uri: https://planetarycomputer.microsoft.com/api/sas/v1/token/cpdataeuwest/era5
   response:
     body:
-      string: '{"msft:expiry":"2024-06-28T15:02:21Z","token":"st=2024-06-27T14%3A17%3A21Z&se=2024-06-28T15%3A02%3A21Z&sp=rl&sv=2024-05-04&sr=c&skoid=9c8ff44a-6a2c-4dfb-b298-1c9212f64d9a&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2024-06-27T16%3A29%3A39Z&ske=2024-07-04T16%3A29%3A39Z&sks=b&skv=2024-05-04&sig=mhOqQXY8zcfKPArTB4hGFRQQEbcqlx5ftzKtxA8fh6w%3D"}'
+      string: '{"msft:expiry":"2024-07-01T07:40:56Z","token":"st=2024-06-30T06%3A55%3A56Z&se=2024-07-01T07%3A40%3A56Z&sp=rl&sv=2024-05-04&sr=c&skoid=9c8ff44a-6a2c-4dfb-b298-1c9212f64d9a&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2024-06-30T03%3A12%3A52Z&ske=2024-07-07T03%3A12%3A52Z&sks=b&skv=2024-05-04&sig=UmlrxTFBYe39lA/B4%2BTzD5xpiTrVIv%2BUPv6dPBgd6tI%3D"}'
     headers:
       Accept-Ranges:
       - bytes
       Connection:
       - keep-alive
       Content-Length:
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       Content-Type:
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     status:
       code: 200
       message: OK
diff --git a/test/cassettes/test_store/StacDataStoreTest.test_open_data_tiff.yaml b/test/cassettes/test_store/StacDataStoreTest.test_open_data_tiff.yaml
index afc2f99..7625159 100644
--- a/test/cassettes/test_store/StacDataStoreTest.test_open_data_tiff.yaml
+++ b/test/cassettes/test_store/StacDataStoreTest.test_open_data_tiff.yaml
@@ -416,17 +416,19 @@ interactions:
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       - application/json
       Date:
-      - Tue, 25 Jun 2024 12:52:55 GMT
+      - Mon, 01 Jul 2024 13:47:23 GMT
       ETag:
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       Last-Modified:
       - Fri, 15 Jul 2022 14:06:49 GMT
       Server:
-      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head21)
+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (F8-R407-U17)
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       message: OK
@@ -847,17 +849,19 @@ interactions:
       Content-Type:
       - application/json
       Date:
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+      - Mon, 01 Jul 2024 13:47:23 GMT
       ETag:
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       Last-Modified:
       - Fri, 15 Jul 2022 14:06:49 GMT
       Server:
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+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (F8-R407-U16)
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       message: OK
@@ -923,17 +927,17 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Tue, 25 Jun 2024 12:52:55 GMT
+      - Mon, 01 Jul 2024 13:47:23 GMT
       ETag:
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       Last-Modified:
       - Thu, 19 May 2022 19:07:42 GMT
       Server:
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+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head12)
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@@ -999,17 +1003,93 @@ interactions:
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       - application/json
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+      - Mon, 01 Jul 2024 13:47:25 GMT
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       Server:
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+      - WasabiS3/7.20.2951-2024-06-14-7fcea49969 (head22)
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       x-amz-request-id:
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+      - 3160D5CD17868DDA:B
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+      Accept-Encoding:
+      - gzip, deflate, br
+      Connection:
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+      User-Agent:
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+    method: GET
+    uri: https://s3.eu-central-1.wasabisys.com/stac/odse/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
+  response:
+    body:
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+        \   \"start_datetime\": \"1999-12-02\",\n    \"end_datetime\": \"2000-03-20\",\n
+        \   \"gsd\": \"30\",\n    \"instruments\": [\n      \"TM\",\n      \"ETM+\",\n
+        \     \"OLI\"\n    ],\n    \"platform\": \"landsat\",\n    \"datetime\": \"1999-12-02T00:00:00Z\"\n
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+        \       ]\n      ]\n    ]\n  },\n  \"links\": [\n    {\n      \"rel\": \"root\",\n
+        \     \"href\": \"../../catalog.json\",\n      \"type\": \"application/json\",\n
+        \     \"title\": \"OpenDataScience Europe\"\n    },\n    {\n      \"rel\":
+        \"collection\",\n      \"href\": \"../collection.json\",\n      \"type\":
+        \"application/json\",\n      \"title\": \"Quarterly blue band of GLAD landsat
+        ARD (2000\\u20132020)\"\n    },\n    {\n      \"rel\": \"parent\",\n      \"href\":
+        \"../collection.json\",\n      \"type\": \"application/json\",\n      \"title\":
+        \"Quarterly blue band of GLAD landsat ARD (2000\\u20132020)\"\n    }\n  ],\n
+        \ \"assets\": {\n    \"blue_p50\": {\n      \"href\": \"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
+        \     \"type\": \"image/tiff; application=geotiff\",\n      \"main\": true,\n
+        \     \"roles\": [\n        \"data\"\n      ]\n    },\n    \"blue_p25\": {\n
+        \     \"href\": \"https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsat.glad.ard_p25_30m_0..0cm_1999.12.02..2000.03.20_eumap_epsg3035_v1.1.tif\",\n
+        \     \"type\": \"image/tiff; application=geotiff\",\n      \"roles\": [\n
+        \       \"data\"\n      ]\n    },\n    \"blue_p75\": {\n      \"href\": \"https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_blue_landsat.glad.ard_p75_30m_0..0cm_1999.12.02..2000.03.20_eumap_epsg3035_v1.1.tif\",\n
+        \     \"type\": \"image/tiff; application=geotiff\",\n      \"roles\": [\n
+        \       \"data\"\n      ]\n    },\n    \"qa_f\": {\n      \"href\": \"https://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_qa_landsat.glad.ard_f_30m_0..0cm_1999.12.02..2000.03.20_eumap_epsg3035_v1.1.tif\",\n
+        \     \"type\": \"image/tiff; application=geotiff\",\n      \"roles\": [\n
+        \       \"data\"\n      ]\n    },\n    \"thumbnail\": {\n      \"href\": \"https://s3.eu-central-1.wasabisys.com/stac/odse/lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20/lcv_blue_landsat.glad.ard_p50_30m_0..0cm_1999.12.02..2000.03.20_eumap_epsg3035_v1.1.png\",\n
+        \     \"type\": \"image/png\",\n      \"roles\": [\n        \"thumbnail\"\n
+        \     ]\n    },\n    \"blue_p25_preview\": {\n      \"href\": \"https://s3.eu-central-1.wasabisys.com/stac/odse/lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20/lcv_blue_landsat.glad.ard_p25_30m_0..0cm_1999.12.02..2000.03.20_eumap_epsg3035_v1.1.png\",\n
+        \     \"type\": \"image/png\",\n      \"roles\": []\n    },\n    \"blue_p75_preview\":
+        {\n      \"href\": \"https://s3.eu-central-1.wasabisys.com/stac/odse/lcv_blue_landsat.glad.ard/lcv_blue_landsat.glad.ard_1999.12.02..2000.03.20/lcv_blue_landsat.glad.ard_p75_30m_0..0cm_1999.12.02..2000.03.20_eumap_epsg3035_v1.1.png\",\n
+        \     \"type\": \"image/png\",\n      \"roles\": []\n    },\n    \"qa_f_preview\":
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+        \   77.69295185585888\n  ],\n  \"stac_extensions\": [\n    \"https://stac-extensions.github.io/eo/v1.0.0/schema.json\"\n
+        \ ],\n  \"collection\": \"lcv_blue_landsat.glad.ard\"\n}"
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+      Server:
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+      x-amz-request-id:
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diff --git a/test/test_store.py b/test/test_store.py
index 02a092e..71101be 100644
--- a/test/test_store.py
+++ b/test/test_store.py
@@ -26,6 +26,7 @@
 import pytest
 import requests
 import xarray as xr
+from xcube.core.mldataset import MultiLevelDataset
 from xcube.core.store import DatasetDescriptor, DataStoreError
 from xcube.core.store.store import new_data_store
 from xcube.util.jsonschema import JsonObjectSchema
@@ -238,13 +239,27 @@ def test_open_data_tiff(self):
         )
 
         # open data with open_params
-        ds = store.open_data(self.data_id_time_range, asset_names=["blue_p25"])
+        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)
@@ -272,7 +287,7 @@ def test_open_data_abfs(self):
         # 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)
+            _ = store.open_data(data_id, asset_names=["surface_air_pressure"])
         self.assertEqual(
             (
                 "Only 's3' and 'https' protocols are supported, not 'abfs'. The asset "
@@ -339,7 +354,26 @@ def test_open_data_xcube_server_zarr(self):
         )
         self.assertCountEqual([1387, 1491], [ds.sizes["y"], ds.sizes["x"]])
         # open data store in cloud-optimized tif format
-        ds = store.open_data("collections/datacubes/items/cog_geotiff_file")
+        ds = store.open_data(
+            "collections/datacubes/items/cog_geotiff_file",
+            opener_id="dataset:geotiff:s3",
+        )
+        self.assertIsInstance(ds, xr.Dataset)
+        self.assertCountEqual(
+            [
+                "analytic_band_1",
+                "analytic_band_2",
+                "analytic_band_3",
+                "analytic_spatial_ref",
+            ],
+            list(ds.data_vars),
+        )
+        self.assertCountEqual([343, 343], [ds.sizes["y"], ds.sizes["x"]])
+        # open data store in cloud-optimized tif format in mldataaset
+        ds = store.open_data(
+            "collections/datacubes/items/cog_geotiff_file",
+            opener_id="mldataset:geotiff:s3",
+        )
         self.assertIsInstance(ds, xr.Dataset)
         self.assertCountEqual(
             [
diff --git a/xcube_stac/opener.py b/xcube_stac/opener.py
index ec38c85..c6ac4d1 100644
--- a/xcube_stac/opener.py
+++ b/xcube_stac/opener.py
@@ -18,9 +18,11 @@
 # 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 xarray as xr
 from xcube.util.jsonschema import JsonObjectSchema
+from xcube.core.mldataset import MultiLevelDataset
 from xcube.core.store import DataOpener, new_data_store
 
 
@@ -41,12 +43,18 @@ def __init__(self, root: str, opener_id: str):
     def root(self):
         return self._root
 
+    @property
+    def opener_id(self):
+        return self._opener_id
+
     def get_open_data_params_schema(self, data_id: str = None) -> JsonObjectSchema:
         return self._https_accessor.get_open_data_params_schema(
             data_id=data_id, opener_id=self._opener_id
         )
 
-    def open_data(self, data_id: str, **open_params) -> xr.Dataset:
+    def open_data(
+        self, data_id: str, **open_params
+    ) -> Union[xr.Dataset, MultiLevelDataset]:
         stac_schema = self.get_open_data_params_schema()
         stac_schema.validate_instance(open_params)
         return self._https_accessor.open_data(
@@ -109,6 +117,10 @@ def __init__(
     def root(self):
         return self._root
 
+    @property
+    def opener_id(self):
+        return self._opener_id
+
     def get_open_data_params_schema(
         self, data_id: str = None, opener_id: str = None
     ) -> JsonObjectSchema:
@@ -116,7 +128,9 @@ def get_open_data_params_schema(
             data_id=data_id, opener_id=opener_id
         )
 
-    def open_data(self, data_id: str, **open_params) -> xr.Dataset:
+    def open_data(
+        self, data_id: str, **open_params
+    ) -> Union[xr.Dataset, MultiLevelDataset]:
         stac_schema = self.get_open_data_params_schema()
         stac_schema.validate_instance(open_params)
         return self._s3_accessor.open_data(
diff --git a/xcube_stac/store.py b/xcube_stac/store.py
index 86055db..383b3fd 100644
--- a/xcube_stac/store.py
+++ b/xcube_stac/store.py
@@ -28,6 +28,7 @@
 import pystac_client
 import requests
 import xarray as xr
+from xcube.core.mldataset import MultiLevelDataset, CombinedMultiLevelDataset
 from xcube.core.store import (
     DATASET_TYPE,
     MULTI_LEVEL_DATASET_TYPE,
@@ -56,6 +57,7 @@
     _get_opener_id,
     _search_nonsearchable_catalog,
     _update_nested_dict,
+    _xarray_rename_vars,
 )
 
 
@@ -177,13 +179,11 @@ def get_open_data_params_schema(
 
     def open_data(
         self, data_id: str, opener_id: str = None, **open_params
-    ) -> xr.Dataset:
+    ) -> 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)
         item = self._access_item(data_id)
-        if self._pc:
-            item = pc.sign(item)
         return self._build_dataset(item, opener_id=opener_id, **open_params)
 
     def describe_data(
@@ -346,7 +346,7 @@ def _get_data_id_from_item(self, item: pystac.Item) -> str:
 
     def _build_dataset(
         self, item: pystac.Item, opener_id: str = None, **open_params
-    ) -> Union[xr.Dataset,]:
+    ) -> 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
@@ -364,8 +364,10 @@ def _build_dataset(
         assets = _list_assets_from_item(item, asset_names=asset_names)
         formats = _get_formats_from_assets(assets)
 
-        ds = xr.Dataset()
+        list_ds_asset = []
         for asset in assets:
+            if self._pc:
+                asset = pc.sign(asset)
             if "xcube:store_kwargs" in asset.extra_fields:
                 store_kwargs = asset.extra_fields["xcube:store_kwargs"]
                 protocol = store_kwargs["data_store_id"]
@@ -395,13 +397,34 @@ def _build_dataset(
                     f"The asset '{asset.extra_fields['id']}' has a href '{asset.href}'."
                     f" The item's url is given by '{url}'."
                 )
+            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)
 
-            for varname, da in ds_asset.data_vars.items():
-                if len(ds_asset) == 1:
-                    key = asset.extra_fields["id"]
-                else:
-                    key = f"{asset.extra_fields['id']}_{varname}"
-                ds[key] = da
+        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_opener(
@@ -421,6 +444,10 @@ def _get_s3_opener(
                 self._s3_opener = S3DataOpener(
                     root, opener_id, storage_options=storage_options
                 )
+            if not self._s3_opener.opener_id == opener_id:
+                self._s3_opener = S3DataOpener(
+                    root, opener_id, storage_options=storage_options
+                )
         return self._s3_opener
 
     def _get_https_opener(self, root: str, opener_id: str) -> HttpsDataOpener:
@@ -434,4 +461,6 @@ def _get_https_opener(self, root: str, opener_id: str) -> HttpsDataOpener:
                     "A new https data opener will be initialized."
                 )
                 self._https_opener = HttpsDataOpener(root, opener_id)
+            if not self._https_opener.opener_id == opener_id:
+                self._https_opener = HttpsDataOpener(root, opener_id)
         return self._https_opener
diff --git a/xcube_stac/utils.py b/xcube_stac/utils.py
index 4990176..d891b21 100644
--- a/xcube_stac/utils.py
+++ b/xcube_stac/utils.py
@@ -28,6 +28,7 @@
 import pandas as pd
 import pystac
 from shapely.geometry import box
+import xarray as xr
 from xcube.core.store import DataStoreError
 
 from .constants import MAP_MIME_TYP_FORMAT
@@ -348,3 +349,9 @@ def _select_opener_id(asset: pystac.Asset, formats: np.array, protocol: str) ->
         asset_format = MAP_MIME_TYP_FORMAT[asset.media_type.split("; ")[0]]
         opener_id_asset = f"dataset:{asset_format}:{protocol}"
     return opener_id_asset
+
+
+def _xarray_rename_vars(
+    ds: Union[xr.Dataset, xr.DataArray], name_dict: dict
+) -> Union[xr.Dataset, xr.DataArray]:
+    return ds.rename_vars(name_dict)