diff --git a/demos/synthetic_clouds_demo.ipynb b/demos/synthetic_clouds_demo.ipynb
index a124c9f..22b08df 100644
--- a/demos/synthetic_clouds_demo.ipynb
+++ b/demos/synthetic_clouds_demo.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "[1] Matthew Lave, Matthew J. Reno, Robert J. Broderick, \"Creation and Value of Synthetic High-Frequency Solar Inputs for Distribution System QSTS Simulations,\" 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), Washington, DC, USA, 2017, pp. 3031-3033, doi: https://dx.doi.org/10.1109/PVSC.2017.8366378.\n",
     "\n",
-    "# Setup\n",
+    "## Setup\n",
     "Perform needed imports"
    ],
    "id": "d701625fd7e3ad13"
@@ -43,7 +43,7 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "# Load Sample Timeseries Data\n",
+    "## Load Sample Timeseries Data\n",
     "The model attempts to create representative variability to match that observed from a reference time series. In this case, we'll process one of the 1-second resolution timeseries from the HOPE-Melpitz campign. We will load the data and convert it to clearsky index. "
    ],
    "id": "e386bd79e13077f4"
@@ -111,7 +111,7 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "# Visualize the sensor layout in the CMV direction\n",
+    "## Visualize the sensor layout in the CMV direction\n",
     "We want to describe how the sensors are distributed in the cloud motion vector direction. So we'll rotate the positions of the entire field to align with the CMV in the +X direction. This will allow us to describe positions of sensors within the field with respect to the motion of clouds, which seldom aligns with the cardinal directions."
    ],
    "id": "e5770aab6c893bf3"
@@ -162,7 +162,7 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "# Compute Timeseries Statistics\n",
+    "## Compute Timeseries Statistics\n",
     "The scaling of the cloud field is based on variability as expressed through statistical properties of the time series. So we'll extract those in advance. We do so for a single sensor (number 40) that is centrally located in the field, though more detailed analysis could consider representing properties for the entire field.\n",
     "- `ktmean` - The mean clearsky index\n",
     "- `kt1pct` - The 1st percentile of clearsky index, used similar to a minimum\n",
@@ -226,7 +226,7 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "# Relate the Time and Space Scales\n",
+    "## Relate the Time and Space Scales\n",
     "Since we rotated the sensor positions, we can now calculate the overall spatial size of the distribution along and perpendicular to the cloud motion vector. We'll also look at the dureation of the time series (in this case 1 hour) and its temporal resolution (1 second). \n",
     "\n",
     "Using the cloud speed we can relate these spatial dimensions to time dimensions. When we generate the cloud field, we will assume that each pixel in the field represents a 1-second step in time. So moving 1 pixel within the field along the X axis represents either a 1 second shift upwind or downwind in space, or a 1 second shift of the time axis at a fixed spatial position as clouds transit the field. Moving 1 pixel along the Y axis will always represent a 1 second spatial shift perpendicular to the cloud motion vector, since no motion occurs in that direction."
@@ -275,7 +275,7 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "# Generating the Randomized Cloud Field\n",
+    "## Generating the Randomized Cloud Field\n",
     "The function `cloudfield_timeseries` generates a cloud field from which time series can be sampled. The field is generated by creating a random field of cloudiness, then scaling it to match the clear sky condition properties of the reference time series. The output field's first axis (rows) represents the spatial dimension perpendicular to the cloud motion vector. The second axis (columns) represent the spatial dimension along the cloud motion vector, which coincides with time axis. \n",
     "\n",
     "Each pixel represents a time step of 1 second, either in literal time, or associated with a spatial shift of the equivalent of 1 second of cloud motion. In this case, where the cloud velocity is around 20 m/s, this implies that a shift along either axis corresponds to a 20 m spatial shift.   "
@@ -319,7 +319,7 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "## Extracting the time series at a point\n",
+    "### Extracting the time series at a point\n",
     "We can extract the time series at points in the field. We need to first convert our spatial positions into a spatially based coordinate system. We can then choose that starting point as a location for a time series at that point. The time series will extend along the x-axis with each time series at a length of `t_extent` seconds.\n",
     "\n",
     "One consequence of this approach that is important to note is that any two points that are located precisely up/down-wind from each other will have identical time series, albeit with a delay associated with the cloud motion. This is visible in the results below in which the two sensors are nearly aligned with the cloud motion, but have an upwind/downwind separation.  "
@@ -436,11 +436,61 @@
    ],
    "execution_count": 9
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "It might also be interesting to compare the statistical distribution of the true and simulated timeseries. We can do this by comparing the histograms and CDFs of the two time series.",
+   "id": "38a56327324142ad"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-07T15:54:33.614197Z",
+     "start_time": "2024-11-07T15:54:32.921612Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# show histograms and CDFs\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(10, 8))\n",
+    "axs[0,0].hist(kt[40], bins=100, alpha=0.5, label='True')\n",
+    "axs[0,0].hist(sim_kt[40], bins=100, alpha=0.5, label='Simulated')\n",
+    "axs[0,0].legend()\n",
+    "axs[0,0].set_title('Hist - Sensor 40')\n",
+    "axs[0,1].ecdf(kt[40], label='True')\n",
+    "axs[0,1].ecdf(sim_kt[40], label='Simulated')\n",
+    "axs[0,1].set_title('CDF - Sensor 40')\n",
+    "axs[0,1].legend()\n",
+    "axs[1,0].hist(kt.values.flatten(), bins=100, alpha=0.5, label='True')\n",
+    "axs[1,0].hist(sim_kt.values.flatten(), bins=100, alpha=0.5, label='Simulated')\n",
+    "axs[1,0].legend()\n",
+    "axs[1,0].set_title('Hist - All Sensors')\n",
+    "axs[1,1].ecdf(kt.values.flatten(), label='True')\n",
+    "axs[1,1].ecdf(sim_kt.values.flatten(), label='Simulated')\n",
+    "axs[1,1].set_title('CDF - All Sensors')\n",
+    "axs[1,1].legend()\n",
+    "plt.show()"
+   ],
+   "id": "827cf67968b9ede8",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 4 Axes>"
+      ],
+      "image/png": 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pghEjRmD+/Plwc3ODkZERzp07h08++USsk56ejrCwMI0yIiKpvf/++1i6dClCQ0Ph5eX1TsdKSkrC1q1b4eTkJF6pKSx7e/scD9KtV69egY/j4uKCsmXLavRNgDrJLMgVtrcxMDBA27Zt0bZtW8ydOxczZszAd999h0OHDsHHxwfOzs64dOkSVCqVxhd8N27cAIBC909RUVG4ffu2OLLjdV988QUA9ZU3Kysr1K9fH4cOHUJiYqLGhBjZD5rOK/Gk0oX3aFGp9vTpU411c3NzVK1aVWM4QPaDdOPj4996vNq1a8PHx0d8eXh45Fk3JSUFoaGhuW7LfhBv9rC9Tz75BA8fPsSyZcty1H358iWSk5PfGtvrsrKykJCQoFFma2sLR0dH8dwbNWoEW1tbLFmyROPnsXv3bly/fh2dO3cuUJvZ3NzcsHXr1hyvOnXqoFKlSti6dSsGDhwIALC0tISPjw/Wrl2rMRxnzZo1SEpKwscff1yoGIiIisKYMWNQpkwZDBo0CLGxsTm2R0REYMGCBW89zsuXL9GvXz88e/YM33333Tt/wWZiYqLRN/n4+Lzxi8DTp0/n2q+cOXMGT58+FfsmDw8PuLq6Ys6cOeIwx9f9d7hifjx79ixHWXbSkt0XderUCTExMdi4caNYJzMzEz/99BPMzc3h7e1d4HYB4Pvvv8/RN02bNg2A+ne7detW8TPBRx99hKysLI2HT6elpWHlypXw9PTkjIMEgFe0qJSrXbs2WrVqBQ8PD1hbW+PcuXPilOfZspOlr776Cr6+vjA0NESvXr3eue2UlBQ0bdoUTZo0QYcOHeDk5IT4+Hhs27YNx44dQ7du3dCgQQMAQL9+/fDHH39g6NChOHToEJo1a4asrCzcuHEDf/zxB/bu3ZvrfWp5efHiBSpWrIiPPvoI9erVg7m5Ofbv34+zZ8/ixx9/BKC+sXjmzJkYMGAAvL290bt3b3F698qVK2PUqFGFOu9y5cqhW7duOcqzn6X1323Tp09H06ZN4e3tjSFDhuDBgwf48ccf0b59e3To0KFQMRARFQVXV1esX78ePXv2RK1ateDn5wc3Nzekp6fj5MmT4hTkr3v48CHWrl0LQH0V69q1a9i0aRNiYmLw9ddf4/PPP9f5eaxZswbr1q3Dhx9+CA8PD8jlcly/fh0rVqyAiYmJOFGEgYEBfv31V3Ts2BF16tTBgAEDUKFCBTx8+BCHDh2CUqnE33//XaC2p06diqNHj6Jz585wdnZGXFwcFi1ahIoVK6J58+YAgCFDhuCXX35B//79cf78eVSuXBmbN2/GiRMnMH/+/EJPSJJ9/NdlT87x3nvvafRPnp6e+PjjjxEUFIS4uDhUrVoVq1evxt27d7F8+fJCtU8lkMSzHhIVWvb07mfPns11e27Thf93evfvv/9eaNy4sWBlZSWYmpoKNWvWFKZPny6kp6eLdTIzM4Uvv/xSKF++vCCTybQ21XtGRoawbNkyoVu3boKzs7OgUCgEMzMzoUGDBsLs2bNzPLk+PT1dmDlzplCnTh1BoVAIZcuWFTw8PIQpU6YICQkJYj0AuU7b/vq5p6WlCaNHjxbq1asnWFhYCGXKlBHq1asnLFq0KMd+GzduFBo0aCAoFArB2tpa6Nu3r/DgwQONOv7+/kKZMmXe6eeR2+8r27Fjx4SmTZsKJiYmQvny5YWAgABx+l4iouLm5s2bwuDBg4XKlSsLcrlcsLCwEJo1ayb89NNPQmpqqljP2dlZnJ5dJpMJSqVSqFOnjjB48GDh9OnTuR47r7/x2nTp0iVh9OjRQsOGDQVra2vByMhIcHBwED7++GPhwoULOepfvHhR6N69u2BjYyMoFArB2dlZ+OSTT4QDBw6IdbKnd//vtO3ZfXlkZKQgCIJw4MABoWvXroKjo6Mgl8sFR0dHoXfv3sLNmzc19ouNjRUGDBgglCtXTpDL5YK7u3uOKe6zp3efPXt2oX8WeU3vLgiC8PLlS+Gbb74R7O3tBYVCIbz33nvCnj17Ct0WlTwyQdDx48aJiIiIiIhKON6jRUREREREpGVMtIiIiIiIiLSMiRYREREREZGWMdEiIiLKxdGjR9GlSxc4OjpCJpNh27Ztb93n8OHDaNiwIRQKBapWrYpVq1YVeZxERFQ8MdEiIiLKRXJyMurVq4eFCxfmq35kZCQ6d+6M1q1bIywsDCNHjsSgQYOwd+/eIo6UiIiKI846SERE9BYymQxbt27N9Rlw2caOHYudO3fiypUrYlmvXr0QHx+PPXv26CBKIiIqTkr1A4tVKhUePXoECwuLd37qOhER5Z8gCHjx4gUcHR1hYFAyBleEhobCx8dHo8zX1xcjR47Mc5+0tDSkpaWJ6yqVCs+ePYONjQ37JSIiHdN231SqE61Hjx7ByclJ6jCIiEqt+/fvo2LFilKHoRUxMTGws7PTKLOzs0NiYiJevnwJU1PTHPsEBwdjypQpugqRiIjyQVt9U6lOtCwsLACof5hKpVLiaIiISo/ExEQ4OTmJf4dLq6CgIAQGBorrCQkJqFSpEvslIiqerm0HbuwEBOHfl+rVO1T/rqsACMC9U4CQqbm/fV3AyASQGQAGBup3mSFgIAcaDQBcWuYrjNCIpxj82zmNsvpOVjA2lCG4uzvsLXN+sZUf2u6bSnWilT0sQ6lUskMjIpJASRoeZ29vj9jYWI2y2NhYKJXKXK9mAYBCoYBCochRzn6JiIqlo5OBlCf5qysHgH//xo+OAMqUe+fmH8a/RJefjuNZcjoMFGYAgIaVrLDli2bvfOzXaatvKtWJFhERkbZ4eXlh165dGmUhISHw8vKSKCIiIi3LSle/t/oWMLf994qU7N/3f1+QvSo3swZcWquX36VZlYDeS0/hzN1nGuVftamKwPY13unYRYmJFhERUS6SkpJw+/ZtcT0yMhJhYWGwtrZGpUqVEBQUhIcPH+K3334DAAwdOhQ///wzxowZg88++wwHDx7EH3/8gZ07d0p1CkRERcP9I8DGtcibUakEbDh7H99uvaxR3tTVBisHvAeFkWGRx/AumGgRERHl4ty5c2jdurW4nn0vlb+/P1atWoXo6GhERUWJ26tUqYKdO3di1KhRWLBgASpWrIhff/0Vvr6+Oo+diEjf7b8Wi0H/uQ/Lw7ksNg5pAiND/ZitlonWW6hUKqSnp0sdBmmRsbExDA2L9zcgRCS9Vq1a4U2Pmly1alWu+1y8eLEIo1LLyspCRkZGkbdDusO+ieiViMdJGkmWucIIP/VugNY1bSWMquCYaL1Beno6IiMjoVKppA6FtMzKygr29vYl6kZ8Iir5BEFATEwM4uPjpQ6FigD7JiK1vVdjxOVf/RrBp7bdG2oXX0y08iAIAqKjo2FoaAgnJ6cS80DN0k4QBKSkpCAuLg4A4ODgIHFERET5l51k2drawszMjB/ISwj2TUSvZGSpsPhwBABgTIcaeptkAUy08pSZmYmUlBQ4OjrCzMxM6nBIi7KnWY6Li4OtrS2HahCRXsjKyhKTLBsbG6nDIS1j30SkduL2E7xIVT9/65NGThJH8254mSYPWVlZAAC5XC5xJFQUspNn3uNARPoi++8Vv/wrudg3EQGXHiQAALyrl0c585zPGdQnTLTegsMySib+XolIX/HvV8nF3y0RsOSIethgI+eyEkfy7phoERERERGR5G7HvUBKunpUmb7NMJgbJlpERERERCQ5v+VnAAD1nKzgVsFS4mjeHSfDKKB5ITd12t6odtXzXfdtQw4mTZqEyZMnv2NERKQTh4JfLbcOki4OonfEvomI8uNGTCIeJaQCALyrlZM4Gu1golWCREdHi8sbN27ExIkTER4eLpaZm5uLy4IgICsrC0ZG/CdARERFh30TEeXHBz+fAAA4WpogsH0NiaPRDg4dLEHs7e3Fl6WlJWQymbh+48YNWFhYYPfu3fDw8IBCocDx48fRv39/dOvWTeM4I0eORKtWrcR1lUqF4OBgVKlSBaampqhXrx42b96s25MjIiK9xL6JiN5EEAR0WnAM6ZkqAMDQVq4SR6Q9/MqolBk3bhzmzJkDFxcXlC2bv9lcgoODsXbtWixZsgTVqlXD0aNH8emnn6J8+fLw9vYu4oiJiCgvgiDgZUaWJG2bGhtqbZY89k1EpdOdx0lo8+MRcd3S1Bj9mjhLGJF2MdEqZaZOnYp27drlu35aWhpmzJiB/fv3w8vLCwDg4uKC48eP45dffmFnRkQkoZcZWag9ca8kbV+b6gszuXY+RrBvIip9XqRmaCRZ9ZyssO2LpiXqMQdMtEqZRo0aFaj+7du3kZKSkqMDTE9PR4MGDbQZGhERlVLsm4hKl9SMLLhP3ieuT+pSGwOaVZEwoqLBRKuUKVOmjMa6gYEBBEHQKHv9ifRJSUkAgJ07d6JChQoa9RQK/X5aNxGRvjM1NsS1qb6Sta0t7JuISo+Fh25j9t5XE+J80756iUyyACZapV758uVx5coVjbKwsDAYGxsDAGrXrg2FQoGoqCgOxSAiKmZkMpnWhu8VJ+ybiEqeKw8T8P5PxzXKutZ3xPA21SSKqOiVvL/OVCBt2rTB7Nmz8dtvv8HLywtr167FlStXxKEXFhYW+OabbzBq1CioVCo0b94cCQkJOHHiBJRKJfz9/SU+AyIiKmnYNxGVLC/Ts9DlZ80ka/1gTzR1LRnPy8oLE61SztfXFxMmTMCYMWOQmpqKzz77DH5+frh8+bJYZ9q0aShfvjyCg4Nx584dWFlZoWHDhvj2228ljJyIiEoq9k1EJcuP+8KRPRr4+25u6OtZqURNepEXmfDfQdClSGJiIiwtLZGQkAClUqmxLTU1FZGRkahSpQpMTEwkipCKCn+/VOwdCn613DpIujiKyJv+/pZm7JdKN/6OqdgLdgLSEoEvLwA2+Xve1aUH8eLDiDu7O2Bh34ZFGeE70XbfJPkDi3/44QfIZDKMHDlSLEtNTUVAQABsbGxgbm6OHj16IDY2VmO/qKgodO7cGWZmZrC1tcXo0aORmZmp4+iJiIiIiOh1KpWAQzfi0GtpqJhkAcCM7u4SRqV7kg4dPHv2LH755RfUrVtXo3zUqFHYuXMnNm3aBEtLSwwfPhzdu3fHiRPqX1RWVhY6d+4Me3t7nDx5EtHR0fDz84OxsTFmzJghxakQEREREZVq+67GYNP5Bwi5FptjW8iolrA0NZYgKulIlmglJSWhb9++WLZsGb7//nuxPCEhAcuXL8f69evRpk0bAMDKlStRq1YtnDp1Ck2aNMG+fftw7do17N+/H3Z2dqhfvz6mTZuGsWPHYvLkyZDL5VKdFhERERFRiScIAq4+SsTZu8+wJvQe7jxJzrVe25q2+K5zLbiUN9dxhNKTLNEKCAhA586d4ePjo5FonT9/HhkZGfDx8RHLatasiUqVKiE0NBRNmjRBaGgo3N3dYWdnJ9bx9fXFsGHDcPXq1TwfVpiWloa0tDRxPTExsQjOjIiIiIioZIl7kQrLTBUUAFrNOYS7wo08637u7YJPPZ3hZG2muwCLIUkSrQ0bNuDChQs4e/Zsjm0xMTGQy+WwsrLSKLezs0NMTIxY5/UkK3t79ra8BAcHY8qUKe8YPRERERFRyReTkIqlR+9gxYlIAMAlRRYUuUwW6GRtiqYu5TDCpxocrUx1HGXxpfNE6/79+xgxYgRCQkJ0PqNOUFAQAgMDxfXExEQ4OTnpNAYiIiIiouIkJT0T6ZkqpGepEBrxFN9uuYzk9Kw86/dvWhlu7g3hVsESJsaGOoxUv+g80Tp//jzi4uLQsOGrqR2zsrJw9OhR/Pzzz9i7dy/S09MRHx+vcVUrNjYW9vb2AAB7e3ucOXNG47jZsxJm18mNQqGAQqHQ4tkQEREREemPuMRUXI1OxK5L0dh0/kG+9qlqa44FvepDudoYSHuJ/k2rADbWRRyp/tN5otW2bVuNBw4CwIABA1CzZk2MHTsWTk5OMDY2xoEDB9CjRw8AQHh4OKKiouDl5QUA8PLywvTp0xEXFwdbW1sAQEhICJRKJWrXrq3bEyIiIiIiKoZuxyXh6qME7LwUjWO3nuBlRt5XqQBAJgNMjQ2Rkp6FDxtUwJgONeBgyaGAhaXzRMvCwgJubm4aZWXKlIGNjY1YPnDgQAQGBsLa2hpKpRJffvklvLy80KRJEwBA+/btUbt2bfTr1w+zZs1CTEwMxo8fj4CAAF6xIiIiIqJSRxAERD5JxraLD/HXP49w92nKG+s7WJrAs4o1er5XCdXtzGFlJoehQS43YFGhSfocrbzMmzcPBgYG6NGjB9LS0uDr64tFixaJ2w0NDbFjxw4MGzYMXl5eKFOmDPz9/TF16lQJo9YfMpkMW7duRbdu3Yq0ncqVK2PkyJEaD6PWlVWrVmHkyJGIj4/XedtERFRw7JvojWIuA6ELARnvB3rdy4xM3H2agqhnKUhIyQAAVATwBaDxKd/B0gSZKgEVrExRycYMJkav/Rwv//vKjzTO2F0QxSLROnz4sMa6iYkJFi5ciIULF+a5j7OzM3bt2lXEkeXiULBu22sdVOBdHj9+jIkTJ2Lnzp2IjY1F2bJlUa9ePUycOBHNmjVDdHQ0ypYtWwTBvht2QEREJRf7Jiq0/VOA43OljqJYMgVQ69/XGz/VZz/i6iWA6HdtVQYolO96kFKhWCRapF09evRAeno6Vq9eDRcXF8TGxuLAgQN4+vQpgDdPGEJERFQU2DdRoaztAdze/2q9bi/AtqZ08UjoZYYK1x8l4MCNOGSpBI1tNuYKVLYxg5eLDcxNivjjvZ0bYF6+aNsoIZholTDx8fE4duwYDh8+DG9vbwDqq3+NGzcW67w+POPu3buoUqUKNm7ciJ9++gnnzp2Dm5sb1q1bh4SEBAwbNgw3btxAixYt8Ntvv6F8efV/rFatWqF+/fqYP3++eNxu3brBysoKq1atyjW2uXPnYuXKlbhz5w6sra3RpUsXzJo1C+bm5jh8+DAGDBggxgcAkyZNwuTJk5GWlobvvvsOv//+O+Lj4+Hm5oaZM2eiVatW4rFXrVqFiRMn4smTJ/D19UXz5s21+FMlIqJ3wb6JfVOh7BuvmWQFPQAUFtLFI4G0zCzM3hOO/ddjc73n6ut21fFl22oSREb5YSB1AKRd5ubmMDc3x7Zt25CWlpbv/SZNmoTx48fjwoULMDIyQp8+fTBmzBgsWLAAx44dw+3btzFx4sR3is3AwAD/+9//cPXqVaxevRoHDx7EmDFjAABNmzbF/PnzoVQqER0djejoaHzzzTcAgOHDhyM0NBQbNmzApUuX8PHHH6NDhw64desWAOD06dMYOHAghg8fjrCwMLRu3Rrff//9O8VKRKQXBAFIT5bmJQhvj+9f7JvYNxVY0mPg5E+v1ic+KzVJVnJaJraHPUS3hSdQY/we/Ho8UiPJqmprju+7uSEyuBOTrGKOV7RKGCMjI6xatQqDBw/GkiVL0LBhQ3h7e6NXr16oW7dunvt988038PX1BQCMGDECvXv3xoEDB9CsWTMA6pkg8/o2ML9ev/G4cuXK+P777zF06FAsWrQIcrkclpaWkMlkGsNHoqKisHLlSkRFRcHR0VGMdc+ePVi5ciVmzJiBBQsWoEOHDmLHWL16dZw8eRJ79ux5p3iJiIq9jBRghqM0bX/7CJCXyVdV9k3smwrszFL1u4ERMPYuYFDyJ8HYcyUaw9dfRKYq55cYDpYmmPxBHbSuYQu5Ea+T6Av+pkqgHj164NGjR/jrr7/QoUMHHD58GA0bNnxjZ/R6R2dnZwcAcHd31yiLi4t7p7j279+Ptm3bokKFCrCwsEC/fv3w9OlTpKTkPf3o5cuXkZWVherVq4vfiJqbm+PIkSOIiIgAAFy/fh2enp4a+2U/c42IiIoH9k3sm/It9ipwbI56uePMEn0l62lSGr7+4x9UHrcTQ9de0EiyFEYG8PNyxtHRrREa1Ba+deyZZOkZXtEqoUxMTNCuXTu0a9cOEyZMwKBBgzBp0iT0798/1/rGxsbicvY49P+WqVQqcd3AwADCf4aNZGRk5BnP3bt38f7772PYsGGYPn06rK2tcfz4cQwcOBDp6ekwMzPLdb+kpCQYGhri/PnzMDTU/DbL3Nw8z/aIiEoFYzP1lSWp2i4g9k30Vi/jgVWdAUEFmFgB7h9LHVGRuBX7Av2Wn0FMYmqObaN9a2BwCxcmVSUAE61Sonbt2ti2bZvWjle+fHlER7+aHzQrKwtXrlxB69atc61//vx5qFQq/PjjjzAwUP/h+OOPPzTqyOVyZGVpPrG8QYMGyMrKQlxcHFq0aJHrsWvVqoXTp09rlJ06darA50REpHdksnwP3yuO2DeRBpUK+Kkh8PK5ev2T3wATS2lj0qLktExM3H4Vf154kGNb4yrWCGxXHU1cbCSIjIoKE60S5unTp/j444/x2WefoW7durCwsMC5c+cwa9YsdO3aVWvttGnTBoGBgdi5cydcXV0xd+7cNz5jpGrVqsjIyMBPP/2ELl264MSJE1iyZIlGncqVKyMpKQkHDhxAvXr1YGZmhurVq6Nv377w8/PDjz/+iAYNGuDx48c4cOAA6tati86dO+Orr75Cs2bNMGfOHHTt2hV79+7lGHgiomKEfRP7prcSBGDqa89R8x4LuHhLF4+WCIKAU3eeYcuFB9h0PmeC1b1hBUzv5g5Tecm/B6004jXJEsbc3Byenp6YN28eWrZsCTc3N0yYMAGDBw/Gzz//rLV2PvvsM/j7+8PPzw/e3t5wcXHJ8xtDAKhXrx7mzp2LmTNnilP0BgdrPvy5adOmGDp0KHr27Iny5ctj1qxZAICVK1fCz88PX3/9NWrUqIFu3brh7NmzqFSpEgCgSZMmWLZsGRYsWIB69eph3759GD9+vNbOlYiI3g37JvZNb3V+5avlio2B1t9KF4sWpGeqsORIBKoE7ULvZac0kqwadhbYFtAMd3/ojLmf1GeSVYLJhP8OZi5FEhMTYWlpiYSEBCiVmk+4Tk1NRWRkJKpUqQITExOJIqSiwt8vFXuHXvuw1zpIujiKyJv+/pZm7JdKt1L9O/7FG4gOA1zbAv22SB1NoQmCgJ8P3saPITc1ysvIDRHUqRb6NK4EAwOZRNHR22i7b+LQQSIiIiKSzsvn6iQLUA8Z1ENpmVmYF3ILS45EaJTblJHjry+bo4KVqUSRkZSYaBERERGRdI7+O5V72cqAU2NJQymoE7efIHj3dVx5mKhRbiADdo1ogZr2vGJfmjHRIiIiIiJpXN4MhP57n17jz9UzaeqB47eeYMrfV3ErLkmjvLKNGRb2bYg6jiVntkQqPCZaRERERKR7lzcDfw5UL5vbA55DpY0nH5LSMtFn2SlcepAglpU1M4Z39fII6lQLdspSdm8dvRETrbcoxXOFlGj8vRKRvuLfr5Kr1Pxu74UCKzu8WjcyBYYeAwyK72TY6ZkqzNh1HatO3hXLjA1lWP1ZYzR1LSddYFSsMdHKQ/aT3tPT02FqyhsYS5qUlBQAgLGxscSREBHlT/bfq5SUFPZLJVSJ7psEAbi6Bdj8Wc5tn+0GzG11H1M+ZKkEfLTkJC5GxWuUd6hjj8WfNoRMT4Y6kjSYaOXByMgIZmZmePz4MYyNjcUnxpN+EwQBKSkpiIuLg5WVlZhQExHlZuHChZg9ezZiYmJQr149/PTTT2jcOO+b9efPn4/FixcjKioK5cqVw0cffYTg4GCtTNVtaGgIKysrxMXFAQDMzMz4Ia+EKPF905HZwKHvc5a/Pw9olEviVQwIgoCZe8JzzCLYxMUay/wawcKkBCbDpHVMtPIgk8ng4OCAyMhI3Lt3T+pwSMusrKxgb28vdRhEVIxt3LgRgYGBWLJkCTw9PTF//nz4+voiPDwctrY5v31fv349xo0bhxUrVqBp06a4efMm+vfvD5lMhrlz52olpuy/W9nJFpUsJa5vSngIzKutWWZkCnzwE1D3Y2liyoeH8S/R7IeDGmX1nKywcUgTmBiXsCSYihQTrTeQy+WoVq0a0tPTpQ6FtMjY2LjkfVtIRFo3d+5cDB48GAMGDAAALFmyBDt37sSKFSswbty4HPVPnjyJZs2aoU+fPgCAypUro3fv3jh9+rTWYsr+EtDW1hYZGRlaOy5Jr8T1TU9uAT83erVu4Qh8tAJw9pIupjdQqQT89c8jjNwYlmPb4r4N0cHNnleQqcCYaL2FgYFB6Xs6OxFRKZeeno7z588jKChILDMwMICPjw9CQ0Nz3adp06ZYu3Ytzpw5g8aNG+POnTvYtWsX+vXrl2c7aWlpSEtLE9cTExPzrPs6Q0PDkvWhnEqWmCvAkmav1puPAnwmSxbO2zxNSoPH9/tzlLetaYtlfo1gYMAEiwpHkhuPFi9ejLp160KpVEKpVMLLywu7d+8Wt7dq1QoymUzjNXSo5pSfUVFR6Ny5M8zMzGBra4vRo0cjMzNT16dCREQl0JMnT5CVlQU7OzuNcjs7O8TExOS6T58+fTB16lQ0b94cxsbGcHV1RatWrfDtt9/m2U5wcDAsLS3Fl5OTk1bPg0jnYq9qJlkDdhfbJOvHfeFo9P3+HEnWN+2rIzK4E5b3f49JFr0TSa5oVaxYET/88AOqVasGQRCwevVqdO3aFRcvXkSdOnUAAIMHD8bUqVPFfczMzMTlrKwsdO7cGfb29jh58iSio6Ph5+cHY2NjzJgxQ+fnQ0REdPjwYcyYMQOLFi2Cp6cnbt++jREjRmDatGmYMGFCrvsEBQUhMDBQXE9MTGSyRfrr1n5gXY9X6/47AOem0sWTC0EQcOzWE/itOJNj25gONfBFq6oSREUllSSJVpcuXTTWp0+fjsWLF+PUqVNiomVmZpbnDaH79u3DtWvXsH//ftjZ2aF+/fqYNm0axo4di8mTJ0Mulxf5ORARUclVrlw5GBoaIjY2VqM8NjY2z75pwoQJ6NevHwYNGgQAcHd3R3JyMoYMGYLvvvsu19lrFQoFFAqF9k+ASArHfny1POQI4FhfslD+KzNLhZUn7mL6rus5ts39pB46ujnAVM7huKRdks9ZnpWVhQ0bNiA5ORleXq9ukFy3bh3KlSsHNzc3BAUFic+WAIDQ0FC4u7trDOnw9fVFYmIirl69qtP4iYio5JHL5fDw8MCBAwfEMpVKhQMHDmj0Va9LSUnJkUxl30dVah5ES6VX0mPg/in1st9fkidZsYmp2B72EN0WnoDrt7tQ9bvdOZKsad3ccPeHzujesCKTLCoSkk2GcfnyZXh5eSE1NRXm5ubYunUratdWTwHap08fODs7w9HREZcuXcLYsWMRHh6OLVu2AABiYmJyHTefvS0vhb3pmIiISp/AwED4+/ujUaNGaNy4MebPn4/k5GRxFkI/Pz9UqFABwcHBANSjNebOnYsGDRqIQwcnTJiALl26cOIKKvlu7gYEFWDtCrh4SxLC06Q0LD16B78cvZNnHbmhAf4Y6oX6Tla6C4xKLckSrRo1aiAsLAwJCQnYvHkz/P39ceTIEdSuXRtDhgwR67m7u8PBwQFt27ZFREQEXF1dC91mcHAwpkyZoo3wiYiohOvZsyceP36MiRMnIiYmBvXr18eePXvEL/aioqI0rmCNHz8eMpkM48ePx8OHD1G+fHl06dIF06dPl+oUiIrey+fAjkDgqvrLcNR6X7fNp2dhdehdzNxzA7ldOK7jqERVW3N85FERnlVsIDeSfDAXlSIyoZiMZ/Dx8YGrqyt++eWXHNuSk5Nhbm6OPXv2wNfXFxMnTsRff/2FsLAwsU5kZCRcXFxw4cIFNGjQINc2crui5eTkhISEBCiVSq2fExFRoR0KfrXcOijvenoqMTERlpaW/Pv7H/y5kF6IuwHs+ga4eyzntlFXAcuKRR7C3SfJmLHrOvZdi82xzdZCgSEtXTCweRU++4oKRNt/g4vNc7RUKpVGEvS67ITKwcEBAODl5YXp06cjLi4Otra2AICQkBAolUpx+GFueNMxERERUSFlpgNHZwNHZ+XcVrsb0H0pYFR0n7MEQcD/DtzGvP03c2yzKSPHkJYuGNzChVOyU7EhSaIVFBSEjh07olKlSnjx4gXWr1+Pw4cPY+/evYiIiMD69evRqVMn2NjY4NKlSxg1ahRatmyJunXrAgDat2+P2rVro1+/fpg1axZiYmIwfvx4BAQEMJEiIiIi0ob0ZODKFuDccuDRxZzb3T8GGnwKVG4J5DKrpjb9cfY+xvx5KUd5j4YVMfmD2rAwMS7S9okKQ5JEKy4uDn5+foiOjoalpSXq1q2LvXv3ol27drh//z72798v3nTs5OSEHj16YPz48eL+hoaG2LFjB4YNGwYvLy+UKVMG/v7+Gs/dIiIiIqJCurwZ+HNg7tuMTIHBBwG7vEcRacv9Zyn4fM15XIt+NYFZBStTzOtZH42rWBd5+0TvQpJEa/ny5Xluc3JywpEjR956DGdnZ+zatUubYRERERHRte2aSVaNToCJFeD+EVDFGzAs+o+PiakZCPrzMnZejtYo3zTUC+9VZoJF+qHY3KNFRERERBJ7cgv4w+/V+ohLQFlnnTQtCAJ2Xo7Gzwdv40bMC41t3RtUwI+f1OPkFqRXmGgRERERkdrusa+WA87oJMlKSMnA8N8v4NitJzm2uZYvg3WDmsDe0qTI4yDSNiZaRERERATcOQxEHFQvf7oFKF+jSJsTBAHj/ryMjefua5QbGcjQtpYtvutUG5VszIo0BqKixESLiIiIiIDtwwEIQOUWQNW2RdbMo/iX6Lf8NCIeJ2uUd3Szx5SudWBrwatXVDIw0SIiIiIq7e4cARL+vbLkPabImolLTEXTHw5qlDlZm2L3iJYwV/BjKZUs/BdNREREVNqdXaZ+t3MDqrTU+uFVKgEz997AL0fuiGVd6zti+ofuTLCoxOK/bCIiIqLSLD0FuP63etmtu9YPH5uYCs8ZBzTKFvSqj671K2i9LaLihIkWERERUWn2z++vlj2Hau2wUU9T4L/yDCKfaN6LxWdhUWnBRIuIiIiotIqPAnYGqpcb+gHyMu98yMwsFUZsCMvxsOGgjjXxubfrOx+fSF8w0SIiIiIqjV7EAvPdX623/u6dDicIAnovO4VTd55plI/pUANDWrjAyNDgnY5PpG+YaBERERGVNoIAzK31ar3LAsDCvtCHW386Ct9uvaxR5lKuDDYMaQJbJadrp9KJiRYRERFRabP1c0DIUi+3nw549C/0oYauOY89V2PEdVsLBQ5+04qzCVKpx/8BRERERKXJ1mHApY3q5SYBQNPhhTpMdMJL+Px4BMnp6oTNQmGEP79oiup2FtqKlEivMdEiIiIiKi3++hL4Z716uc6HQIcZBT5EWmYWAjf+ozHZhYdzWfw5rKm2oiQqEZhoEREREZUGYb8DF35TL1f1AT5eVeBDhMe8gO/8oxpl33dzw6dNnLUQIFHJwkSLiIiIqKS7sQvY9u8zsiwcgT6bCrR73ItUfLjwJB7GvxTLXMqXwar+jVHJxkybkRKVGEy0iIiIiEqyjFRgQ+9X64NCAIP8T7WemJqBxtMPaJTN+bgePvKoqK0IiUokJlpEREREJdmGPq+WB+wGLPOfIJ2MeIJ+y8+I6y2qlcPSfo1gKjfUZoREJRITLSIiIqKS6vJmIOLfq1GtvwOc8zdhhSAIaDzjAB6/SBPLZnzojj6elYoiSqISiYkWERERUUmUngLsCVIvu7QGvMfke9egLZc1kqy/hjdD3YpWWg6QqGRjokVERERUEl3eBCTHAZABH63I1y6ZWSq8N30/nqdkAAB6N66E4O7uRRgkUcmV/zshtWjx4sWoW7culEollEolvLy8sHv3bnF7amoqAgICYGNjA3Nzc/To0QOxsbEax4iKikLnzp1hZmYGW1tbjB49GpmZmbo+FSIiIqLi6eIa9ft7AwEz67dWPxnxBFW/2y0mWZamxpjxoVtRRkhUoklyRatixYr44YcfUK1aNQiCgNWrV6Nr1664ePEi6tSpg1GjRmHnzp3YtGkTLC0tMXz4cHTv3h0nTpwAAGRlZaFz586wt7fHyZMnER0dDT8/PxgbG2PGjII/eI+IiIioRElLAh6cUy/X7fXGqgkpGeiw4CiiE1LFsqauNlg3yBMymawooyQq0WSCIAhSBwEA1tbWmD17Nj766COUL18e69evx0cffQQAuHHjBmrVqoXQ0FA0adIEu3fvxvvvv49Hjx7Bzs4OALBkyRKMHTsWjx8/hlwuz1ebiYmJsLS0REJCApRKZZGdGxFRgR0KfrXcOki6OIoI//7mjj8X0prw3cDvvQCzcsDo20AuCdOTpDR8se4CzkQ+0yj/e3hzuFe01FWkRMWGtv8GSzJ08HVZWVnYsGEDkpOT4eXlhfPnzyMjIwM+Pj5inZo1a6JSpUoIDQ0FAISGhsLd3V1MsgDA19cXiYmJuHr1ap5tpaWlITExUeNFREREVOJEHFS/V/fNNclKTstEo+/3ayRZXes7Ivz7DkyyiLREsskwLl++DC8vL6SmpsLc3Bxbt25F7dq1ERYWBrlcDisrK436dnZ2iImJAQDExMRoJFnZ27O35SU4OBhTpkzR7okQERERFTeXN6vfHRvk2JSSnokuPx8X152sTbHC/z1Us7PQVXREpYJkiVaNGjUQFhaGhIQEbN68Gf7+/jhy5EiRthkUFITAwEBxPTExEU5OTkXaJhEREZFOPb8HvPz3SpVrG41Nf5y9jzF/XhLXh7VyxdgONXUZHVGpIVmiJZfLUbVqVQCAh4cHzp49iwULFqBnz55IT09HfHy8xlWt2NhY2NvbAwDs7e1x5swZjeNlz0qYXSc3CoUCCoVCy2dCREREVIzc2KF+t3MHbFzF4nF/XsKGs/fF9SEtXZhkERUhye/RyqZSqZCWlgYPDw8YGxvjwIED4rbw8HBERUXBy8sLAODl5YXLly8jLi5OrBMSEgKlUonatWvrPHYiIiKiYiE1ETj4vXrZvYdYPGbzPxpJ1v7Alvi2Uy1dR0dUqkhyRSsoKAgdO3ZEpUqV8OLFC6xfvx6HDx/G3r17YWlpiYEDByIwMBDW1tZQKpX48ssv4eXlhSZNmgAA2rdvj9q1a6Nfv36YNWsWYmJiMH78eAQEBPCKFREREZVeqzoBGSmAgTHQ6DMAwJy94fjj3AOxyj+T2sPS1FiqCIlKDUkSrbi4OPj5+SE6OhqWlpaoW7cu9u7di3bt2gEA5s2bBwMDA/To0QNpaWnw9fXFokWLxP0NDQ2xY8cODBs2DF5eXihTpgz8/f0xdepUKU6HiIiISHqhC4GYy+rljjMBE0tsOncfPx+6LVa5PrUDTOWGEgVIVLoUm+doSYHPKyGiYovP0SqV+HOhQruxE9jQR71cviYQcBrXHiWi0/+OAVDP8H7627awtTCRMEii4k3bf4MlmwyDiIiIiLQgbD2wbZh62VAOfLYH0QkvxSTL1NgQZ8f7wFzBj31EusT/cURERET66sBU4NiPr9ZH38bjDBN4Be8Xi5b082CSRSQB/q8jIiIi0keb+gNXt75aHx2B+ynGaDHrVZL15zAveDhb6z42ImKiRURERKRXkuKA1R8Aj6+/Kvs2GkmCHC2m7RWLxneuxSSLSEJMtIiIiIj0xeXNwJ8DX627tAZ6b0CmgRwek14lWcHd3dG7cSUJAiSibMXmgcVERERE9AaX/tBMst4bDPTbChibYNBv55CWqQIAjO1Qk0kWUTHAK1pERERExd3LeGDLYPWyiRUw7ARgWREAMHDVWRwOfwwAGNS8Coa1cpUmRiLSwESLiIiIqDjLSAVmOr9a//I8UKYcAOCPc/dx4EYcAKBNTVt826mWFBESUS6YaBEREREVZ9lXsgCgzXgxydp5KRpjNl8CAFS3M8eK/u9JER0R5YH3aBEREeVh4cKFqFy5MkxMTODp6YkzZ868sX58fDwCAgLg4OAAhUKB6tWrY9euXTqKlkqkx+HA9b/Uy20nAS1HAwDiU9LxzaZ/xGprBnpKER0RvQGvaBEREeVi48aNCAwMxJIlS+Dp6Yn58+fD19cX4eHhsLW1zVE/PT0d7dq1g62tLTZv3owKFSrg3r17sLKy0n3wVHJcXKt+d24GtAgEAKRnqtB+3lG8zMgCABwd3Rp2ShOpIiSiPDDRIiIiysXcuXMxePBgDBgwAACwZMkS7Ny5EytWrMC4ceNy1F+xYgWePXuGkydPwtjYGABQuXJlXYZMJU1GKnBmqXq5Xi+xeG7ITcS9SAMAbBjSBJVszKSIjojegkMHiYiI/iM9PR3nz5+Hj4+PWGZgYAAfHx+Ehobmus9ff/0FLy8vBAQEwM7ODm5ubpgxYwaysrLybCctLQ2JiYkaLyLR2h5AZipgXAao2xMAkJaZhSVHIgAAw1q5oomLjZQREtEbMNEiIiL6jydPniArKwt2dnYa5XZ2doiJicl1nzt37mDz5s3IysrCrl27MGHCBPz444/4/vvv82wnODgYlpaW4svJyUmr50F6LPIocO+4etkrADBSAADG/jv5hbGhDGN8a0gVHRHlAxMtIiIiLVCpVLC1tcXSpUvh4eGBnj174rvvvsOSJUvy3CcoKAgJCQni6/79+zqMmIqtR2HAmu7qZYf6QJvvAAB3nyRjW9gjAEBHNwfIZDJp4iOifOE9WkRERP9Rrlw5GBoaIjY2VqM8NjYW9vb2ue7j4OAAY2NjGBoaimW1atVCTEwM0tPTIZfLc+yjUCigUCi0Gzzpt7PLgZ2Br9Z7rhEXh6w5BwAwNJBhXs/6Og6MiAqKV7SIiIj+Qy6Xw8PDAwcOHBDLVCoVDhw4AC8vr1z3adasGW7fvg2VSiWW3bx5Ew4ODrkmWUQ53D/7nyRrHWBVCQAwafsV3IxNAgAs6FUfhga8mkVU3DHRIiIiykVgYCCWLVuG1atX4/r16xg2bBiSk5PFWQj9/PwQFBQk1h82bBiePXuGESNG4ObNm9i5cydmzJiBgIAAqU6B9MntA8DyV5OvIOAsUOt9AMCaU/ewOvQeAKCGnQU6uztIESERFRCHDhIREeWiZ8+eePz4MSZOnIiYmBjUr18fe/bsESfIiIqKgoHBq+8rnZycsHfvXowaNQp169ZFhQoVMGLECIwdO1aqUyB9sfkz4Mqfr9b77wTKV4cgCGg/7yhuxamvZBnIgB1fNee9WUR6gokWERFRHoYPH47hw4fnuu3w4cM5yry8vHDq1KkijopKlEubNJOsT7cAlZsjKS0TDaeFID3z1VDUfaNawtiQg5GI9IUk/1uDg4Px3nvvwcLCAra2tujWrRvCw8M16rRq1QoymUzjNXToUI06UVFR6Ny5M8zMzGBra4vRo0cjMzNTl6dCREREVDjP7wFbBqmXTa2BCU+Aqm0hCAIGrDwjJlkd6tgjMrgTqtpaSBgsERWUJFe0jhw5goCAALz33nvIzMzEt99+i/bt2+PatWsoU6aMWG/w4MGYOnWquG5m9urJ51lZWejcuTPs7e1x8uRJREdHw8/PD8bGxpgxY4ZOz4eIiIiowEImvFr+8jxgaAwA+C30Hs7efQ4AmPB+bQxsXkWK6IjoHUmSaO3Zs0djfdWqVbC1tcX58+fRsmVLsdzMzCzPaXT37duHa9euYf/+/bCzs0P9+vUxbdo0jB07FpMnT+YMT0RERFR8xUcB17arl7svA8ysAQAHb8Ri0l9XAQBDvV2ZZBHpsWIx0DchIQEAYG1trVG+bt06lCtXDm5ubggKCkJKSoq4LTQ0FO7u7uJNyQDg6+uLxMREXL16Ndd20tLSkJiYqPEiIiIi0ilVFjDf/dW6+8cAgH1XY/DZqnNi8Tftq+s6MiLSIsknw1CpVBg5ciSaNWsGNzc3sbxPnz5wdnaGo6MjLl26hLFjxyI8PBxbtmwBAMTExGgkWQDE9ZiYmFzbCg4OxpQpU4roTIiIiIjyYf0nr5bfnwfIZFh98q54JQtQT3xhxIkviPSa5IlWQEAArly5guPHj2uUDxkyRFx2d3eHg4MD2rZti4iICLi6uhaqraCgIAQGvnoQYGJiIpycnAoXOBEREVFBRR4Fbu9XL1drDzT6DFcfJWgkWRcntEPZMrwFgkjfSfpVyfDhw7Fjxw4cOnQIFStWfGNdT09PAMDt27cBAPb29oiNjdWok72e131dCoUCSqVS40VERESkE4IA7PxavWxTFejzB27HJeHTX0+LVUJGtWSSRVRCSJJoCYKA4cOHY+vWrTh48CCqVHn7jZ5hYWEAAAcH9dPQvby8cPnyZcTFxYl1QkJCoFQqUbt27SKJm4iIiKjQ/vkdeHJTvdxrPY7dfgKfuUfwPCUDALBxSBNUs+MU7kQlhSRDBwMCArB+/Xps374dFhYW4j1VlpaWMDU1RUREBNavX49OnTrBxsYGly5dwqhRo9CyZUvUrVsXANC+fXvUrl0b/fr1w6xZsxATE4Px48cjICAACoVCitMiIiIiyp0gAMd+VC+7tEKihQuGLzokbv7x43rwdLGRKDgiKgqSJFqLFy8GoH4o8etWrlyJ/v37Qy6XY//+/Zg/fz6Sk5Ph5OSEHj16YPz48WJdQ0ND7NixA8OGDYOXlxfKlCkDf39/jeduERERERULu0YDT9W3P8A3GDN2XkfCS/WVrANfe8O1vLmEwRFRUZAk0RIE4Y3bnZyccOTIkbcex9nZGbt27dJWWERERETa9+QWcHaZetm4DMLSHbHh7AkAQO/GlZhkEZVQks86WNrNC7kpLo9qx+dlEBERlTjnVqrfZQZI+yYC3SYdFDdNfJ/3lROVVHxAAxEREVFRSU0Ezq1QL3ddiGazT4ibVvZ/D6ZyQ4kCI6KixkSLiIiIqKiELgQyXwJlbDEtyh1PktIAAP5ezmhd01bi4IioKDHRIiIiIioKyU+AEwsAABvl3bD8ZBQAoJ6TFaZ0dZMyMiLSASZaREREREXhz0FA5kukGZbBpGgvsXjzUK837EREJQUTLSIiIiJtu3cSuKN+Ttas1G5IhQLV7cxxZ0YnGBvy4xdRacD/6URERETadnweAOCcqjqWZ3VGBStT7PyqBQwMZBIHRkS6wkSLiIiISJueRQK39gEAZmT0AaB+KDGvZBGVLvwfT0RERKRNoQsBALdUFXBBqIYFverDxJjTuBOVNky0iIiIiLQlIxW4shkAsCjzAzhamqJr/QoSB0VEUmCiRURERKQt51YAL5/jhWCKXSpPNKtaTuqIiEgiTLSIiIiItEEQgCt/AgB2ZzVGGuQY17GmxEERkVSYaBERERFpw4OzwMNzAIAlWV3QtqYtbMwVEgdFRFIxkjoAIiIiohLh9BIAwK6sxrgjOOL37u4SB0REUuIVLSIiIiItyLi5HwCwN6sRmrhYw05pInFERCQlXtHSsXkhN6UOgYiIiLTs8b3rKJ+eAAA4qGqI4/0aSRwREUmNV7SIiIiI3sGVhwnYsmwaACBM5YIf+7WApamxxFERkdR4RYuIiIjoHUzYEoaNhnsAAGXqdkX7OvYSR0RExQETLSIiIqJCSEjJQL2p+7BXPgZygywIkKFa13FSh0VExQSHDhIREREV0PPkdNSbug9TjFaihsEDAICsoR9gzAkwiEhNkkQrODgY7733HiwsLGBra4tu3bohPDxco05qaioCAgJgY2MDc3Nz9OjRA7GxsRp1oqKi0LlzZ5iZmcHW1hajR49GZmamLk+FiIiISpnUjCx4fB+CyrJo+BuFvNrQZYF0QRFRsSNJonXkyBEEBATg1KlTCAkJQUZGBtq3b4/k5GSxzqhRo/D3339j06ZNOHLkCB49eoTu3buL27OystC5c2ekp6fj5MmTWL16NVatWoWJEydKcUpERERUCuy/FouaE/agNu7gsOJrdaFCCYx/DMhk0gZHRMWKJPdo7dmzR2N91apVsLW1xfnz59GyZUskJCRg+fLlWL9+Pdq0aQMAWLlyJWrVqoVTp06hSZMm2LdvH65du4b9+/fDzs4O9evXx7Rp0zB27FhMnjwZcrlcilMjIiKiEup2XBIG/XYODWU3sUUx+dWGXusAI37uICJNxeIerYQE9XMnrK2tAQDnz59HRkYGfHx8xDo1a9ZEpUqVEBoaCgAIDQ2Fu7s77OzsxDq+vr5ITEzE1atXdRg9ERERlXQv07PgM/cIJhit0UyyPl4NVGkpWVxEVHxJPuugSqXCyJEj0axZM7i5uQEAYmJiIJfLYWVlpVHXzs4OMTExYp3Xk6zs7dnbcpOWloa0tDRxPTExUVunQURERCVUeMwL+M4/inFG6zHQaPerDSMvA1aVpAuMiIo1yROtgIAAXLlyBcePHy/ytoKDgzFlypQib4eIiIj0n0olYMyfl7D5/AOURSKGGu14tTHoAaCwkC44Iir2JB06OHz4cOzYsQOHDh1CxYoVxXJ7e3ukp6cjPj5eo35sbCzs7e3FOv+dhTB7PbvOfwUFBSEhIUF83b9/X4tnQ0RERCXF5QcJcPl2FzafV0/dHmy8XL3ByEQ98QWTLCJ6C0kSLUEQMHz4cGzduhUHDx5ElSpVNLZ7eHjA2NgYBw4cEMvCw8MRFRUFLy8vAICXlxcuX76MuLg4sU5ISAiUSiVq166da7sKhQJKpVLjRURERPS6i1HP0eXnVyNtyhumoIPhWfVKl/9x4gsiyhdJhg4GBARg/fr12L59OywsLMR7qiwtLWFqagpLS0sMHDgQgYGBsLa2hlKpxJdffgkvLy80adIEANC+fXvUrl0b/fr1w6xZsxATE4Px48cjICAACoVCitMiIiIiPXfqzlP0WnpKXF854D20frkf2PZvgfvHksRFRPpHkkRr8eLFAIBWrVpplK9cuRL9+/cHAMybNw8GBgbo0aMH0tLS4Ovri0WLFol1DQ0NsWPHDgwbNgxeXl4oU6YM/P39MXXqVF2dBhEREZUgj1+kaSRZO79qjjqOlsDC/6kL6vcFDIrFhM1EpAckSbQEQXhrHRMTEyxcuBALFy7Ms46zszN27dqlzdCIiIiolJr01xVx+diY1nCyNgOiTgOPr6sL6/aUKDIi0kf8WoaIiIhKvZiEVOy6rL6Vodd7Tuok69wKYEV7dQXXtoCLt4QREpG+kXx6dyIiIiIp3XuaDO/ZhwEAFgojzOhaC/ihEpCa8KpS17xH2BAR5YaJFhEREZVq4/68LC6vHVAfBt+X06zwVRigdNBtUESk9zh0kIiI6A0WLlyIypUrw8TEBJ6enjhz5ky+9tuwYQNkMhm6detWtAFSoWWpBHy26ixC7zwFAAxqXgX1Hvz+qoJjA2BSPGBdJfcDEBG9ARMtIiKiPGzcuBGBgYGYNGkSLly4gHr16sHX11fjGY65uXv3Lr755hu0aNFCR5FSYQxcfRYHb6h/ly7lyuDrdtWBs/8+mNi5GTDkMCCTSRcgEek1JlpERER5mDt3LgYPHowBAwagdu3aWLJkCczMzLBixYo898nKykLfvn0xZcoUuLi46DBaKojO/zuGw+GPAQAtqpXDwW9awfTCUiAhSl3ho5USRkdEJQETLSIiolykp6fj/Pnz8PHxEcsMDAzg4+OD0NDQPPebOnUqbG1tMXDgwLe2kZaWhsTERI0XFb1fj93B1Ufqn7WFwgi/fdZYfSVrb5C6QrX2gIWdhBESUUnARIuIiCgXT548QVZWFuzsND9w29nZISYmJtd9jh8/juXLl2PZsmX5aiM4OBiWlpbiy8nJ6Z3jpjfLyFLhp4O3xfV/JrWH7MxSYGfgq0pdF0kQGRGVNEy0iIiItODFixfo168fli1bhnLlyr19BwBBQUFISEgQX/fv3y/iKGnPlRgkvMwAAJwKagsDIRM4NP1Vhe9iAfPyEkVHRCUJp3cnIiLKRbly5WBoaIjY2FiN8tjYWNjb2+eoHxERgbt376JLly5imUqlAgAYGRkhPDwcrq6uGvsoFAooFIoiiJ7yMumvqwCAzu4OsLc0AXaNUT8vy1AOfHMTMDaROEIiKil4RYuIiCgXcrkcHh4eOHDggFimUqlw4MABeHl55ahfs2ZNXL58GWFhYeLrgw8+QOvWrREWFsZhgcXAncdJeJacDgBoX8cOiL0GnPlFvdErADAtK2F0RFTS8IoWERFRHgIDA+Hv749GjRqhcePGmD9/PpKTkzFgwAAAgJ+fHypUqIDg4GCYmJjAzc1NY38rKysAyFFO0vgt9B4AwNhQhg/qOQJ7fnq10XusRFERUUnFRIuIiCgPPXv2xOPHjzFx4kTExMSgfv362LNnjzhBRlRUFAwMODhEHzxLTseqk3cBAKN9a0AGAGHr1BtbjweMTaUKjYhKKCZaREREbzB8+HAMHz48122HDx9+476rVq3SfkBUKNvDHorLPRtVAu4cAtL+nU6/7scSRUVEJRm/hiMiIqISLe5FKqb8fQ0A0LOREyzNjIErW9QbnZsDZStLFxwRlVhMtIiIiKhE+3L9RXH5a9/qQGYacP0vdUGdbtIERUQlHhMtIiIiKrHuP0vB6chnAIDZH9WFrYUJcPe4ekp3uTngMUDiCImopGKiRURERCXW3JCbAICa9hb4uNG/U+xf+VP9Xq0dYMjb1YmoaDDRIiIiohIpI0uFrRfVk2D0eu/fJCvq1KvZBhv6SxQZEZUGTLSIiIioRFp69A4AQCYDPqhfQV2482v1u6EccGklTWBEVCow0SIiIqIS6ejNxwCAVtXLw7qMHIiPAmKvqDd++qc6AyMiKiKSJFpHjx5Fly5d4OjoCJlMhm3btmls79+/P2QymcarQ4cOGnWePXuGvn37QqlUwsrKCgMHDkRSUpIOz4KIiIiKswfPXwIA+no6qwv2jVe/21QFqrSUKCoiKi0kSbSSk5NRr149LFy4MM86HTp0QHR0tPj6/fffNbb37dsXV69eRUhICHbs2IGjR49iyJAhRR06ERER6YHIJ8l4GK9OtOpXsgJiLgPXtqs3euX+AGoiIm2SZKqdjh07omPHjm+so1AoYG9vn+u269evY8+ePTh79iwaNWoEAPjpp5/QqVMnzJkzB46OjlqPudQ7FPxquXWQdHEQERHlw+y9NwAATtamKGeuAP6art5QwQNoxCndiajoFdt7tA4fPgxbW1vUqFEDw4YNw9OnT8VtoaGhsLKyEpMsAPDx8YGBgQFOnz6d5zHT0tKQmJio8SIiIqKS5VB4HHZdjgEA9HqvEpCRCtw5pN7Y+lsJIyOi0qRYPjyiQ4cO6N69O6pUqYKIiAh8++236NixI0JDQ2FoaIiYmBjY2tpq7GNkZARra2vExMTkedzg4GBMmTKlqMMvOV6/ikVERKQHVCoBA1aeFdcHNKsM3N4DZKYCZjaASxvpgiOiUqVYJlq9evUSl93d3VG3bl24urri8OHDaNu2baGPGxQUhMDAQHE9MTERTk5O7xQrERERFR/Zz80CgMV9G8JMbgRc3qQuqN4BMCi2g3mIqITRi782Li4uKFeuHG7fvg0AsLe3R1xcnEadzMxMPHv2LM/7ugD1fV9KpVLjRURERCVHdqLlWcUaHd0dgMfhwLVt6o3uH0sXGBGVOnqRaD148ABPnz6Fg4MDAMDLywvx8fE4f/68WOfgwYNQqVTw9PSUKkwiIiKS0O7L0Th++wkA4Ku21dSFlzaq3y0r8QHFRKRTkgwdTEpKEq9OAUBkZCTCwsJgbW0Na2trTJkyBT169IC9vT0iIiIwZswYVK1aFb6+vgCAWrVqoUOHDhg8eDCWLFmCjIwMDB8+HL169eKMg0RERKVQSnomhq27AAAwNpShWdVy6g0396rfm33FBxQTkU5JckXr3LlzaNCgARo0aAAACAwMRIMGDTBx4kQYGhri0qVL+OCDD1C9enUMHDgQHh4eOHbsGBQKhXiMdevWoWbNmmjbti06deqE5s2bY+nSpVKcDhEREUkoOS0TtSfuFdc3D22qXoi/D8ReUS/XfF+CyIioNJPkilarVq0gCEKe2/fu3ZvntmzW1tZYv369NsMiIiIiPSIIAqbvvI5fj0eKZT617FDPyUq9Er5L/e7YEFA66D5AIirViuWsg0RERERvM/mvq1gdek9cH+1bAwGtq76qcPB79XtVHx1HRkTERIuIiIj00Pl7zzWSrGNjWsPJ2uxVhQfngLRE9XJ1Xx1HR0TERIuIiIj0yPXoRHy26iyiE1LFsitTfGGu+M9Hmu3D1e/WrkDFRjqMkIhIjYkWERERFXtRT1Pw4aITeJqcrlH+c58GOZOsZ5HA4+vq5ffn6ShCIiJNTLSIiIioWLsQ9RzdF53UKPugniOmdXWDpZlxzh12j1W/l60MuHgXfYBERLlgokVERETFUlJaJtwmac5E7OfljCkf1IEsr2divXwORB5VL7f4pogjJCLKGxMtIiIiKnZUKiFHkvWrXyP41LZ7847/bAAyXwLm9kD9vkUYIRHRmzHRKm0OBavfWwdJGwcREVEeEl5moN6UfeJ6+9p2WOqXjwktBAHYN0G97OEPGBgUUYRERG/HRIuIiIiKhSyVgO1hDxH4xz9imZWZcf6SLACIvQKoMtTLtT4oggiJiPKPiRYRERFJJjNLhc3nHyBo62UIgua2T5tUwpQP3PJ/sJv/DjWs3AKwL8B+RERFgIkWERERSeJm7Au0n3c01235uh/rdYIAnFupXq7WTgvRERG9GyZaREREpFMvUjMwaPU5nI58Jpa5lC+D9rXtMayVKyxNc5my/W3uHAISH6iXa3fVUqRERIXHRIuIiIh05kZMIjrMP6ZR9n03N3zaxPndDnxohvrdsYH6+VlERBJjokVERERF7sjNx/j12B0cu/VELHMpXwZ/DW8Oc8U7fhyJjwIenFUvt+KsukRUPDDRIiIioiKRkaXCd1sv449zD3JsW+bXCO0Kcg/Wmxycrn63qQpUa6+dYxIRvSMmWkRERKR1uQ0RBICxHWrCv6kzzORa+giSlQGE71IvNx4CyGTaOS4R0TtiokVEVErNC7kpLo9qV13CSKikmbsvHP87eFtct7VQYPGnHmhYyQoybSdC/2wA0hIBuTngMUC7xyYiegdMtIi04VDwq+XWvD+AiEqvA9djNZKsxX0boqO7Q9E1eOvfZ2fV+gAwkhddO0REBcREqxjht8tERKTP7j9LwcDV58T13SNaoJaDsugaTE8Bbu1XLzf0K7p2iIgKwUDqAIiIiEj/ZakE+M5/9fDhjUOaFG2SBQD3TgCZLwFze6BSk6Jti4iogCRJtI4ePYouXbrA0dERMpkM27Zt09guCAImTpwIBwcHmJqawsfHB7du3dKo8+zZM/Tt2xdKpRJWVlYYOHAgkpKSdHgWREREBKj7bddvdyElPQsAMONDd3i62BR9wzd2qt9dW3MSDCIqdiRJtJKTk1GvXj0sXLgw1+2zZs3C//73PyxZsgSnT59GmTJl4Ovri9TUVLFO3759cfXqVYSEhGDHjh04evQohgwZoqtTICIion9tC3soLlcpVwZ9PCvppuGbe9TvtT7QTXtERAUgyT1aHTt2RMeOHXPdJggC5s+fj/Hjx6Nr164AgN9++w12dnbYtm0bevXqhevXr2PPnj04e/YsGjVqBAD46aef0KlTJ8yZMweOjo46Oxd6hfeYERGVPtceJWLUxn8AAEoTIxz82ls3DUf/A7yIBmQGQOXmummTiKgAit09WpGRkYiJiYGPj49YZmlpCU9PT4SGhgIAQkNDYWVlJSZZAODj4wMDAwOcPn1a5zETERGVVlP+viouhwa11f707Xm5FaJ+d24GmBTxvWBERIVQ7GYdjImJAQDY2Wk+Ld7Ozk7cFhMTA1tbW43tRkZGsLa2FuvkJi0tDWlpaeJ6YmKitsImIiIqdW7HJeF05DMAwEifaiij0OHHinsn1O9Vfd5cj4hIIsXuilZRCg4OhqWlpfhycnKSOiQiIiK9dfVRgrg8sHkV3TYee039XvE93bZLRJRPxS7Rsre3BwDExsZqlMfGxorb7O3tERcXp7E9MzMTz549E+vkJigoCAkJCeLr/v37Wo6+dGsStRRNopbqrsFDwa9eRESkcyM2hAEAmlW1gYWJse4afhkPJP07gsWutu7aJSIqgGKXaFWpUgX29vY4cOCAWJaYmIjTp0/Dy8sLAODl5YX4+HicP39erHPw4EGoVCp4enrmeWyFQgGlUqnxonfEZIeISriFCxeicuXKMDExgaenJ86cOZNn3WXLlqFFixYoW7YsypYtCx8fnzfW13cKI/XHiNY1bN9SU8ue/Dv5koUjYFpWt20TEeWTJIlWUlISwsLCEBYWBkA9AUZYWBiioqIgk8kwcuRIfP/99/jrr79w+fJl+Pn5wdHREd26dQMA1KpVCx06dMDgwYNx5swZnDhxAsOHD0evXr0442AxMS/kpvgiItJXGzduRGBgICZNmoQLFy6gXr168PX1zTGqItvhw4fRu3dvHDp0CKGhoXByckL79u3x8OHDXOvrs/RMFdIyVQCADxtU0G3jz++q3611PFyRiKgAJJkM49y5c2jdurW4HhgYCADw9/fHqlWrMGbMGCQnJ2PIkCGIj49H8+bNsWfPHpiYmIj7rFu3DsOHD0fbtm1hYGCAHj164H//+5/Oz0UXOG06EZE05s6di8GDB2PAgAEAgCVLlmDnzp1YsWIFxo0bl6P+unXrNNZ//fVX/Pnnnzhw4AD8/Px0ErMupGZkoe7kfQAAU2NDKE11OGwQAJ7eVr9b6eh5XUREhSBJotWqVSsIgpDndplMhqlTp2Lq1Kl51rG2tsb69euLIjwiIiKkp6fj/PnzCAoKEssMDAzg4+MjPm7kbVJSUpCRkQFra+tct+vrbLhbLjxEepb6alavxk4wNtTxAJm46+p3OzfdtktEVADF7h4t0n/Zk2LodGIMIiIte/LkCbKyst74uJG3GTt2LBwdHTWeDfk6fZwN9+TtJ/h262UAQDlzOSZ1qaP7IO4eU7+Xr6H7tomI8omJFhWpJlFLOVkGEZVKP/zwAzZs2ICtW7dqDH1/nb7NhpuakYU+v54W13/p10j3QVzfAbx8rl6u4KH79omI8qnYPbCYJJafhIhJExGVAuXKlYOhoeEbHzeSlzlz5uCHH37A/v37Ubdu3TzrKRQKKBQKrcSrC2tP3ROXZ3zoDg9nCWb8u7VX/V65BWCW+5BMIqLigFe0iIiIciGXy+Hh4aHxuBGVSoUDBw6IjxvJzaxZszBt2jTs2bMHjRpJcMWniGRkqfD9TvW9UU7WpujjKcFEFCoVcPPfRKvxEN23T0RUALyiRaRrr18RbB2Udz0iklxgYCD8/f3RqFEjNG7cGPPnz0dycrI4C6Gfnx8qVKiA4GD1/+uZM2di4sSJWL9+PSpXrizey2Vubg5zc3PJzkMb5r42A+7/ejWQJogn4UBSLGBgBFTN/b43IqLigolWMcXnTxERSa9nz554/PgxJk6ciJiYGNSvXx979uwRJ8iIioqCgcGrwSGLFy9Geno6PvroI43jTJo0CZMnT9Zl6Fp38Lr62WE17S3QoJJEDwm+d1L97uQJyM2kiYGIKJ+YaBHvuSIieoPhw4dj+PDhuW47fPiwxvrdu3eLPiAJxCamIjz2BQDgx0/qSRdIdJj63VGiK2pERAXAe7SIiIjojb78/aK47FJOwiGQ2c/Psq0lXQxERPnERIuIiIjy9CQpDWcinwEABjWvAlO5oTSBZKQC0f+ol52aSBMDEVEBcOggvZPQO0+LRfunMm9iVLvqksZCRFQSfbH2grgc2F7Cv7PXtgFZ6YBpWcDGVbo4iIjyiVe0iIiIKFe3Yl/gzF311azO7g4wk0v4/ez1v9XvdboDMpl0cRAR5ROvaJUGxWWyC05rTkSkV2btDReXJZ0EQ6UC7hxWL9fpJl0cREQFwESrtCouydd/FTIZaxK1FDhkU+D98vL69PockkhEpdXRm48BAO/XdYCJsUT3ZgFA1EkgPQkwMlFP7U5EpAc4dJCIiIhyCI14irRMFQBgcAsXaYM5Mkv9XsUbMFJIGwsRUT7xihbpl/xcieMQxTfjz4eI8uHB8xRxuZ6TlXSB3NwLRB5RLzcZKl0cREQFxESLSozXZ0D0crGRMBIiIv331z+PAAAf1HOULghBALYNUy+XrQK4tpEuFiKiAmKipWdev3cI4P1DWqVxtayHZGHkC69KEVERO3brCQDA2FDCuwxuhQAp/36J9slv0sVBRFQIvEeLiIiINLw+bPBrqZ6dlfIM2DJIvVz/U8ChrjRxEBEVEhMtIiIi0hByLRYAYGwog6OVqTRB/NoWSE0ADIyAdlOkiYGI6B1w6CCVHhxuR0SUL1cfJQIAutSV6P6sxEfAszvq5eajgDLlpImDiOgdFMsrWpMnT4ZMJtN41axZU9yempqKgIAA2NjYwNzcHD169EBsbKyEEVNxNi/kJuaF3NSYLIOIiPL2KP4lAKBpVYkSnCtb1O/m9kCb8dLEQET0jortFa06depg//794rqR0atQR40ahZ07d2LTpk2wtLTE8OHD0b17d5w4cUKKUKkweHWJiKhYEgQBZ+8+AwA4WJpIE8Stvep3rwBp2ici0oJim2gZGRnB3t4+R3lCQgKWL1+O9evXo00b9TSvK1euRK1atXDq1Ck0adJE16FScXYoGE2ieCWLiCi/niWnIyNLAABUszPXfQCpCcC9k+rlqm113z4RkZYUy6GDAHDr1i04OjrCxcUFffv2RVRUFADg/PnzyMjIgI+Pj1i3Zs2aqFSpEkJDQ6UKl/Ip9M5TjRcRERUvN2OTAADmCiPYWkhwRevkz4AqE7CsBNjW1n37RERaUiyvaHl6emLVqlWoUaMGoqOjMWXKFLRo0QJXrlxBTEwM5HI5rKysNPaxs7NDTEzMG4+blpaGtLQ0cT0xMbEowqcSLvtZZk2ilvLByERU4tx7mgwAaOhcVveNJz0GTsxXL7t1B2Qy3cdARKQlxTLR6tixo7hct25deHp6wtnZGX/88QdMTQs/zWxwcDCmTOEUsfTK6w+AHlUs/zfoUPZ9c7xnjqhUe5mRBQBQmuj4j6JKBWzyB7LS1ZNgeI/VbftERFqmFx8traysUL16ddy+fRvt2rVDeno64uPjNa5qxcbG5npP1+uCgoIQGBgoricmJsLJyamowi60JlFLAQCnKg2ROBLteNMQwdA7T3EqU53sjGon0UMx3+L1ZCxfONEHEemxF6mZAAAzuaFuG74dAtz7d1Kr7ksBuZlu2yci0rJie4/W65KSkhAREQEHBwd4eHjA2NgYBw4cELeHh4cjKioKXl5ebzyOQqGAUqnUeBEREdEriw7fBgCUt1DotuHzq9TvLq0AF2/dtk1EVASK5RWtb775Bl26dIGzszMePXqESZMmwdDQEL1794alpSUGDhyIwMBAWFtbQ6lU4ssvv4SXlxdnHCSdy+tqnd7cu/X61TciKvUSUjKQmqECADRyttZdw2lJwK196uVWHAlARCVDsUy0Hjx4gN69e+Pp06coX748mjdvjlOnTqF8+fIAgHnz5sHAwAA9evRAWloafH19sWjRIomjJl3I70yFBZnRMHuoJqRIjjjMkIiKkYSXGeJy65q2ums4+h/1TIPGZYBK/NKUiEqGYplobdiw4Y3bTUxMsHDhQixcuFBHEZEkiiAJyU7Asu8LK3K8YkQS0JjkpZje+0jFU/ZEGDZl5LptOPKI+t2psW7bJSIqQsUy0aL80/UHKl09++r1dqQYhide5ULJmZSEiOhtUtLVE2GYGOt4IoyIg+p31za6bZeIqAgx0aK3kvrBwqF3ngJ3vpE0BqKSosCzaFKpEvdC/azJcrqcCCMzHXh4Xr1c1Ud37RIRFTEmWkQF8PqVrjeR+oocEVFhnPr3b1c5XQ4dvP4XIKgAuQVQvqbu2iUiKmJMtN4R74UgouKMV7CoIM5EPgOg46GDYevV7+4fAQZ68dQZIqJ8YaJFpVJ+r0wVNY0rX60lDISICEBKunoyjM51HXTTYHrKq4cU1+2pmzaJiHSEiRaJSvtwt6K6F+2/x9X6z5ZTxFMR+u8VMV65L9lepKonw6hSroxuGow4AGSmAmY2nNadiEocJlokOfHq0qHSl9zl5V2HpObYn9PME1E+JKepE60ych19PLgVon6v5gvIZLppk4hIR5hoERUTvJeGiKQUl5gqPkfL0sy46BsUBODadvVy7Q+Kvj0iIh1jolXKST11uz7Q9v1c7/oznxdyE02i1McojUM8iaho/HnhobhsodDBx4PYK0BqvHrZpVXRt0dEpGNMtEoojaFj/C0TEdFbrDgRCQBoV9sOBgY6GMZ3aaP6vXILwNi06NsjItIxfgTXc5pXW+bkWh76Wo3ifAWEV9dyl98hhaF3nuJUZskbfsjJGPJHm0NPOYy19Ln2KBGP/31Y8cDmVYq+wfQUIHSRerler6Jvj4hIAky0KFdMevRDfn5Prw81BPJOtrOP9d9kTduJTX4+xDOZ0j0mV6VbdMJLAICRgQxNivoLucRoYPX7gKC+HwzVOxZte0REEmGiVYK8/kGJk+RSacAHhhNpx/1nKQCAVjXKF21D6SnA3Jqv1psEAGWK70gLIqJ3wUSrBCkuD+El7Xr993qq0hCt1dWVwlwp4dWVgiuOv3vSH3uvxgIAyprJi6aBhAfAoRlA2LpXZW49gA4ziqY9IqJigImWDvBDIxUnpe3B1O96jxfvEaPS4MrDBACAndJEuwfOeAnMrgqkJ2mWt/4O8B6j3baIiIoZJlpEeqQgVy21dYXzTV8U5JV0FOWXC3kdO79t5jXckMMQqbRSqQS8+PdBxR/Ud9TegSMOAms+1Cyr0x1oOxGw1sGEG0REEmOiVcpwkgt6XW7/HjgEjah0efTvRBgA4FKuzLsfUBCArUOBSxtelb03GOg0G5DpYNp4IqJigokWEdF/6MtwX06AQ9rwPDkDAFDOXA4jQ4OC7SwIwIto4OYe4J+N6ocQ/3eY4BenAduaue9PRFSCMdGSEK8c0Ov08d+Dvg+3e9eJOt71nPNq/03H1ZckkPTH6Uj1le1y5oq3V1ZlAde2AZf/BB6eA5Ji865rWQn4bDdgWVE7gRIR6RkmWkTFEGeQLN1ef/ZZaZiwhKS17NgdAIDS1PjNFZ/cBn72yHu7fV1AWUE9m2CNDoDCQotREhHpHyZaxURuH6zz+rCtL1c7qHQpjVdaCnPOBb0KFnrnaY6HSEtJ369illaCICA5PQuPX6QhPCYREY+TIQgCVpy4i2fJ6QAAPy/nvA/w/F7OJMu1DdDgU/UDh+VmRRg9EZF+0vtEa+HChZg9ezZiYmJQr149/PTTT2jcuLEksejqAwivdhAR6U5B+5lNmzZhwoQJuHv3LqpVq4aZM2eiU6dOOowYSM9UYf3pe1hx4i6MDGS48yT5rfv41LLLe+P+Sa+W+/4JVPPRQpRERCWbXidaGzduRGBgIJYsWQJPT0/Mnz8fvr6+CA8Ph62traSxlcZv94mo+NLHewCLg4L2MydPnkTv3r0RHByM999/H+vXr0e3bt1w4cIFuLm56STmWXtuYNHhiDfWqW5nDhNjQ9RxtISBDBjtWwMmxoa5V762Hbi6Vb3cZxOTLCKifNLrRGvu3LkYPHgwBgwYAABYsmQJdu7ciRUrVmDcuHESR0dERPquoP3MggUL0KFDB4wePRoAMG3aNISEhODnn3/GkiVLijze3ZejcyRZ/l7O8Klth5r2StiUkcPA4C1TrN8/C4T+DNw7CSTHvSovUx6o1q4IoiYiKpn0NtFKT0/H+fPnERQUJJYZGBjAx8cHoaGhue6TlpaGtLQ0cT0hIQEAkJiYWOg4UpOT3l4pD8kv095eiUhC7/Lvm3IXvO1Cvuq9/vchP7+H1+u7h/8EADhbcUChj5cf7/K3M3tfQRC0EktRKEw/ExoaisDAQI0yX19fbNu2Ldf62u6X/j4XAVVaCgDgxLg2sHx9ggshDUlJb+h3ov8BVnXOe3u/34EXLwoVFxGRPtB236S3idaTJ0+QlZUFOzvNMeV2dna4ceNGrvsEBwdjypQpOcqdnJyKJEYi/fez1AEQgML/HvLaTzu/12+1cIwXL17A0tJSC0fSvsL0MzExMbnWj4mJybV+UfZLlea/8yE0/SDN/c9ERLqmrb5JbxOtwggKCtL4plGlUuHZs2ewsbGBTEdPq09MTISTkxPu378PpVKpkzbfhb7FCzBmXdC3eAH9i1nf4gUKFrMgCHjx4gUcHR11FF3xVBz6JUD//r3pW7yA/sWsb/ECjFkX9C1eQNq+SW8TrXLlysHQ0BCxsZoPS4yNjYW9vX2u+ygUCigUmg9ktLKyKqoQ30ipVOrNP1BA/+IFGLMu6Fu8gP7FrG/xAvmPubheycpWmH7G3t5eb/slQP/+velbvID+xaxv8QKMWRf0LV5Amr7JQGtH0jG5XA4PDw8cOHBALFOpVDhw4AC8vLwkjIyIiEqCwvQzXl5eGvUBICQkhP0SEVEppLdXtAAgMDAQ/v7+aNSoERo3boz58+cjOTlZnB2KiIjoXbytn/Hz80OFChUQHBwMABgxYgS8vb3x448/onPnztiwYQPOnTuHpUv5/EMiotJGrxOtnj174vHjx5g4cSJiYmJQv3597NmzJ8eNyMWJQqHApEmTcgwVKa70LV6AMeuCvsUL6F/M+hYvoJ8xv83b+pmoqCgYGLwaHNK0aVOsX78e48ePx7fffotq1aph27ZtOnuGVmHp2+9O3+IF9C9mfYsXYMy6oG/xAtLGLBOK89y6REREREREekhv79EiIiIiIiIqrphoERERERERaRkTLSIiIiIiIi1jokVERERERKRlTLSKwMKFC1G5cmWYmJjA09MTZ86cydd+GzZsgEwmQ7du3Yo2wP8oSLyrVq2CTCbTeJmYmOgwWrWC/ozj4+MREBAABwcHKBQKVK9eHbt27dJRtGoFiblVq1Y5fs4ymQydO3culvECwPz581GjRg2YmprCyckJo0aNQmpqqo6iVStIzBkZGZg6dSpcXV1hYmKCevXqYc+ePTqL9ejRo+jSpQscHR0hk8mwbdu2t+5z+PBhNGzYEAqFAlWrVsWqVauKPM5sBY03Ojoaffr0QfXq1WFgYICRI0fqJE7Knb71SwD7Jl3Qt34J0L++SZ/6JYB9k9YJpFUbNmwQ5HK5sGLFCuHq1avC4MGDBSsrKyE2NvaN+0VGRgoVKlQQWrRoIXTt2lU3wQoFj3flypWCUqkUoqOjxVdMTIzO4i1MzGlpaUKjRo2ETp06CcePHxciIyOFw4cPC2FhYcU25qdPn2r8jK9cuSIYGhoKK1euLJbxrlu3TlAoFMK6deuEyMhIYe/evYKDg4MwatQoncRbmJjHjBkjODo6Cjt37hQiIiKERYsWCSYmJsKFCxd0Eu+uXbuE7777TtiyZYsAQNi6desb69+5c0cwMzMTAgMDhWvXrgk//fSTYGhoKOzZs6dYxhsZGSl89dVXwurVq4X69esLI0aM0EmclJO+9UuCwL6pOMYrdb9UmJil7pv0rV8SBPZN2sZES8saN24sBAQEiOtZWVmCo6OjEBwcnOc+mZmZQtOmTYVff/1V8Pf312mHVtB4V65cKVhaWuooutwVNObFixcLLi4uQnp6uq5CzKEw/y5eN2/ePMHCwkJISkoqqhA1FDTegIAAoU2bNhplgYGBQrNmzYo0ztcVNGYHBwfh559/1ijr3r270Ldv3yKNMzf56RzGjBkj1KlTR6OsZ8+egq+vbxFGlrv8xPs6b29vJloS0rd+SRDYN+mCvvVLgqB/fZM+90uCwL5JGzh0UIvS09Nx/vx5+Pj4iGUGBgbw8fFBaGhonvtNnToVtra2GDhwoC7CFBU23qSkJDg7O8PJyQldu3bF1atXdREugMLF/Ndff8HLywsBAQGws7ODm5sbZsyYgaysrGIb838tX74cvXr1QpkyZYoqTFFh4m3atCnOnz8vDom4c+cOdu3ahU6dOhV5vIWNOS0tLcfQIlNTUxw/frxIYy2s0NBQjfMDAF9f33z/G6LSSd/6JYB9ky76Jn3rlwD965tKQ78EsG96GyZaWvTkyRNkZWXBzs5Oo9zOzg4xMTG57nP8+HEsX74cy5Yt00WIGgoTb40aNbBixQps374da9euhUqlQtOmTfHgwQNdhFyomO/cuYPNmzcjKysLu3btwoQJE/Djjz/i+++/10XIhYr5dWfOnMGVK1cwaNCgogpRQ2Hi7dOnD6ZOnYrmzZvD2NgYrq6uaNWqFb799ltdhFyomH19fTF37lzcunULKpUKISEh2LJlC6Kjo3URcoHFxMTken6JiYl4+fKlRFFRcadv/RLAvkkXfZO+9UuA/vVNpaFfAtg3vQ0TLQm9ePEC/fr1w7Jly1CuXDmpw8kXLy8v+Pn5oX79+vD29saWLVtQvnx5/PLLL1KHlieVSgVbW1ssXboUHh4e6NmzJ7777jssWbJE6tDyZfny5XB3d0fjxo2lDiVPhw8fxowZM7Bo0SJcuHABW7Zswc6dOzFt2jSpQ8vTggULUK1aNdSsWRNyuRzDhw/HgAEDYGDAP4tUeuljvwSwb9I1feiXAP3rm9gvlTxGUgdQkpQrVw6GhoaIjY3VKI+NjYW9vX2O+hEREbh79y66dOkilqlUKgCAkZERwsPD4erqWmzizY2xsTEaNGiA27dvF0WIORQmZgcHBxgbG8PQ0FAsq1WrFmJiYpCeng65XF7sYs6WnJyMDRs2YOrUqUUZoobCxDthwgT069dP/HbT3d0dycnJGDJkCL777rsi7yQKE3P58uWxbds2pKam4unTp3B0dMS4cePg4uJSpLEWlr29fa7np1QqYWpqKlFUVNzpW79UmJhzw75J+/Fmk6JfAvSvbyoN/RLAvultmCJrkVwuh4eHBw4cOCCWqVQqHDhwAF5eXjnq16xZE5cvX0ZYWJj4+uCDD9C6dWuEhYXBycmpWMWbm6ysLFy+fBkODg5FFaaGwsTcrFkz3L59W/ywAAA3b96Eg4NDkSdZhY0526ZNm5CWloZPP/20qMMUFSbelJSUHB1W9ocHQRCKLth/vcvP2MTEBBUqVEBmZib+/PNPdO3atajDLRQvLy+N8wOAkJCQfP9fpdJJ3/qlwsScG/ZN2o83mxT9EqB/fVNp6JcA9k1vVaRTbZRCGzZsEBQKhbBq1Srh2rVrwpAhQwQrKytxmtl+/foJ48aNy3N/Xc/uVNB4p0yZIuzdu1eIiIgQzp8/L/Tq1UswMTERrl69WmxjjoqKEiwsLIThw4cL4eHhwo4dOwRbW1vh+++/L7YxZ2vevLnQs2dPncWZraDxTpo0SbCwsBB+//134c6dO8K+ffsEV1dX4ZNPPim2MZ86dUr4888/hYiICOHo0aNCmzZthCpVqgjPnz/XSbwvXrwQLl68KFy8eFEAIMydO1e4ePGicO/ePUEQBGHcuHFCv379xPrZU+iOHj1auH79urBw4UKdTqFb0HgFQRDre3h4CH369BEuXryo078VpKZv/ZIgsG8qjvFmk6pfEgT965v0rV8SBPZN2sZEqwj89NNPQqVKlQS5XC40btxYOHXqlLjN29tb8Pf3z3NfKTq0gsQ7cuRIsa6dnZ3QqVMnnT7foTAxC4IgnDx5UvD09BQUCoXg4uIiTJ8+XcjMzCzWMd+4cUMAIOzbt0+ncWYrSLwZGRnC5MmTBVdXV8HExERwcnISvvjiC512DgWN+fDhw0KtWrUEhUIh2NjYCP369RMePnyos1gPHTokAMjxyo7R399f8Pb2zrFP/fr1BblcLri4uOj0+TWFiTe3+s7OzjqLmV7Rt35JENg3Fcd4pe6XBEH/+iZ96pcEgX2Ttsn+bZCIiIiIiIi0hPdoERERERERaRkTLSIiIiIiIi1jokVERERERKRlTLSIiIiIiIi0jIkWERERERGRljHRIiIiIiIi0jImWkRERERERFrGRIuIiIiIiEjLmGgRERERERFpGRMtIiIiIiIiLWOiRUREREREpGVMtIiIiIiIiLSMiRaVaJUrV0b//v2lDqNQDh8+DJlMhsOHD4tl/fv3R+XKlSWLiYiIqFWrVmjVqpW4fvfuXchkMqxatUqymIiKIyZapDdWrVoFmUyGc+fO5bq9VatWcHNze+d2du3ahcmTJ7/zcd7mk08+gUwmw9ixY7V+bJVKhd9++w2enp6wtraGhYUFqlevDj8/P5w6dUrr7RERlWYRERH4/PPP4eLiAhMTEyiVSjRr1gwLFizAy5cvxXqVK1eGTCaDTCaDgYEBrKys4O7ujiFDhuD06dO5Hju7/n9f9vb2RXIu169fh0wmg4mJCeLj47V+/Lt372LAgAFwdXWFiYkJ7O3t0bJlS0yaNEnrbRFJzUjqAIiKUnh4OAwMCvZ9wq5du7Bw4cIiTbYSExPx999/o3Llyvj999/xww8/QCaTae34X331FRYuXIiuXbuib9++MDIyQnh4OHbv3g0XFxc0adJEa20REZVmO3fuxMcffwyFQgE/Pz+4ubkhPT0dx48fx+jRo3H16lUsXbpUrF+/fn18/fXXAIAXL17g+vXr2LRpE5YtW4ZRo0Zh7ty5Odpo164d/Pz8NMpMTU2L5HzWrl0Le3t7PH/+HJs3b8agQYO0duzbt2/jvffeg6mpKT777DNUrlwZ0dHRuHDhAmbOnIkpU6ZorS2i4oCJFpVoCoVC6hBy9eeffyIrKwsrVqxAmzZtcPToUXh7e2vl2LGxsVi0aBEGDx6s0bkDwPz58/H48WOttCOVlJQUmJmZSR0GEREiIyPRq1cvODs74+DBg3BwcBC3BQQE4Pbt29i5c6fGPhUqVMCnn36qUTZz5kz06dMH8+bNQ7Vq1TBs2DCN7dWrV8+xT1EQBAHr169Hnz59EBkZiXXr1mk10Zo3bx6SkpIQFhYGZ2dnjW1xcXFaa0cKqampkMvlBf5yl0o2/mugEu2/92hlZGRgypQpqFatGkxMTGBjY4PmzZsjJCQEgPoeqIULFwLQHK6hbevWrUO7du3QunVr1KpVC+vWrdPasSMjIyEIApo1a5Zjm0wmg62trUZZfHw8Ro4cCScnJygUClStWhUzZ86ESqUS62SPv58zZw6WLl0KV1dXKBQKvPfeezh79qzG8WJiYjBgwABUrFgRCoUCDg4O6Nq1K+7evatRb9GiRahTpw4UCgUcHR0REBCQY5hK9nDQ8+fPo2XLljAzM8O3334LADh37hx8fX1Rrlw5mJqaokqVKvjss8/e4SdHRFQws2bNQlJSEpYvX66RZGWrWrUqRowY8dbjmJqaYs2aNbC2tsb06dMhCEJRhPtWJ06cwN27d9GrVy/06tULR48exYMHD7R2/IiICFSsWDFHkgUgR98EALt370aLFi1QpkwZWFhYoHPnzrh69apGnf79+8Pc3BwPHz5Et27dYG5ujvLly+Obb75BVlaWRt0NGzbAw8MDFhYWUCqVcHd3x4IFCzTq3LlzBx9//DGsra1hZmaGJk2a5EiWs++h3rBhA8aPH48KFSrAzMwMiYmJb/2cQaULr2iR3klISMCTJ09ylGdkZLx138mTJyM4OBiDBg1C48aNkZiYiHPnzuHChQto164dPv/8czx69AghISFYs2ZNUYSPR48e4dChQ1i9ejUAoHfv3pg3bx5+/vlnyOXydz5+dge2adMmfPzxx2+8+pOSkgJvb288fPgQn3/+OSpVqoSTJ08iKCgI0dHRmD9/vkb99evX48WLF/j8888hk8kwa9YsdO/eHXfu3IGxsTEAoEePHrh69Sq+/PJLVK5cGXFxcQgJCUFUVJQ4kcfkyZMxZcoU+Pj4YNiwYQgPD8fixYtx9uxZnDhxQjwWADx9+hQdO3ZEr1698Omnn8LOzg5xcXFo3749ypcvj3HjxsHKygp3797Fli1b3vnnR0SUX3///TdcXFzQtGnTdz6Wubk5PvzwQyxfvhzXrl1DnTp1xG2pqak5+j0LCwutj9pYt24dXF1d8d5778HNzQ1mZmb4/fffMXr0aK0c39nZGfv378fBgwfRpk2bN9Zds2YN/P394evri5kzZyIlJQWLFy9G8+bNcfHiRY2JobKysuDr6wtPT0/MmTMH+/fvx48//ghXV1fx6mBISAh69+6Ntm3bYubMmQDU96OdOHFCTIZjY2PRtGlTpKSk4KuvvoKNjQ1Wr16NDz74AJs3b8aHH36oEeO0adMgl8vxzTffIC0tDXK5/K2fM6iUEYj0xMqVKwUAb3zVqVNHYx9nZ2fB399fXK9Xr57QuXPnN7YTEBAgFOV/jTlz5gimpqZCYmKiIAiCcPPmTQGAsHXrVo16hw4dEgAIhw4dEsv8/f0FZ2fnt7bh5+cnABDKli0rfPjhh8KcOXOE69ev56g3bdo0oUyZMsLNmzc1yseNGycYGhoKUVFRgiAIQmRkpABAsLGxEZ49eybW2759uwBA+PvvvwVBEITnz58LAITZs2fnGVtcXJwgl8uF9u3bC1lZWWL5zz//LAAQVqxYIZZ5e3sLAIQlS5ZoHGPr1q0CAOHs2bNv/VkQERWFhIQEAYDQtWvXfO/j7Oz8xj5o3rx5AgBh+/btYlle/d3KlSvfIfqc0tPTBRsbG+G7774Ty/r06SPUq1cvR11vb2/B29tbXM/uI94W05UrVwRTU1MBgFC/fn1hxIgRwrZt24Tk5GSNei9evBCsrKyEwYMHa5THxMQIlpaWGuX+/v4CAGHq1KkadRs0aCB4eHiI6yNGjBCUSqWQmZmZZ3wjR44UAAjHjh3TiKVKlSpC5cqVxT4ru392cXERUlJSNI6Rn88ZVHpw6CDpnYULFyIkJCTHq27dum/d18rKClevXsWtW7d0EGnu1q1bh86dO8PCwgIAUK1aNXh4eGh1+ODKlSvx888/o0qVKti6dSu++eYb1KpVC23btsXDhw/Feps2bUKLFi1QtmxZPHnyRHz5+PggKysLR48e1Thuz549UbZsWXG9RYsWANRDLQD18Be5XI7Dhw/j+fPnuca2f/9+pKenY+TIkRpj2QcPHgylUpljiIZCocCAAQM0yqysrAAAO3bsyNeVTCIibUtMTAQA8W+5NpibmwNQT5Lxuq5du+bo83x9fbXWLqAepvf06VP07t1bLOvduzf++eefHMP1CqtOnToICwvDp59+irt372LBggXo1q0b7OzssGzZMrFeSEgI4uPj0bt3b42+ydDQEJ6enjh06FCOYw8dOlRjvUWLFmLfBKj7jeTk5DcO4du1axcaN26M5s2bi2Xm5uYYMmQI7t69i2vXrmnU9/f3zzEpSXH4nEHFBxMt0juNGzeGj49PjtfrCUBepk6divj4eFSvXh3u7u4YPXo0Ll26VOhYEhISEBMTI76ePXv2xvrXr1/HxYsX0axZM9y+fVt8tWrVCjt27BA77ndlYGCAgIAAnD9/Hk+ePMH27dvRsWNHHDx4EL169RLr3bp1C3v27EH58uU1Xj4+PgBy3pxcqVIljfXsn3l2UqVQKDBz5kzs3r0bdnZ2aNmyJWbNmoWYmBhxn3v37gEAatSooXEsuVwOFxcXcXu2ChUq5BhS6e3tjR49emDKlCkoV64cunbtipUrVyItLa3APysiosJQKpUAciZF7yIpKQlAzuStYsWKOfq83O4Jy5aVlaXRN8XExCA9Pf2Nba9duxZVqlSBQqEQ+yZXV1eYmZlp9YvA6tWrY82aNXjy5AkuXbqEGTNmwMjICEOGDMH+/fsBQExS2rRpk6N/2rdvX46+ycTEBOXLl9coK1u2rMYXfl988QWqV6+Ojh07omLFivjss8+wZ88ejX3u3buXo28CgFq1aonbX1elSpUcdbX9OYP0GxMtKlVatmyJiIgIrFixAm5ubvj111/RsGFD/Prrr4U63ogRI+Dg4CC+unfv/sb6a9euBQCMGjUK1apVE18//vgjUlNT8eeffxYqjjexsbHBBx98gF27dsHb2xvHjx8XOwuVSoV27drleoUwJCQEPXr00DiWoaFhrm0Ir924PXLkSNy8eRPBwcEwMTHBhAkTUKtWLVy8eLFQ8ec2hbFMJsPmzZsRGhqK4cOH4+HDh/jss8/g4eEhflAhIipKSqUSjo6OuHLlitaOmX2sqlWrvtNx7t+/r9E3OTg44OTJk3nWz37kSGRkpEbfVLt2baSkpGD9+vVan6DD0NAQ7u7uCAoKwtatWwFATOiyJ2Nas2ZNrn3T9u3bcxzrbWxtbREWFoa//voLH3zwAQ4dOoSOHTvC39+/0OeQW/+k7c8ZpN84GQaVOtbW1hgwYAAGDBiApKQktGzZEpMnTxansC3ILINjxozRmHL3TVfVhH+nzW3dujW++OKLHNunTZuGdevW5Rgmp02NGjXCkSNHEB0dDWdnZ7i6uiIpKUm8gqUtrq6u+Prrr/H111/j1q1bqF+/Pn788UesXbtWnKwjPDwcLi4u4j7p6emIjIwsUCxNmjRBkyZNMH36dKxfvx59+/bFhg0btDodMRFRXt5//30sXboUoaGh8PLyeqdjJSUlYevWrXBychKvoBSWvb19jiFy9erVy7P+li1bkJqaisWLF6NcuXIa28LDwzF+/HicOHFCY0idNjVq1AgAEB0dDUDdhwDq5Eib/ZNcLkeXLl3QpUsXqFQqfPHFF/jll18wYcIEVK1aFc7OzggPD8+x340bNwAg19kSc/O2zxlUejDRolLl6dOnsLGxEdfNzc1RtWpV3L9/XywrU6YMAPW059n3AuWldu3aqF27dr7azp42d+rUqfjoo49ybL958yYmTJiAR48ewdHRMV/HzE32EMb/xpWeno4DBw7AwMBA/Lb0k08+weTJk7F3794c4/3j4+Nhbm4OI6P8/5lISUmBgYEBTExMxDJXV1dYWFiIw/p8fHwgl8vxv//9Dx06dBAT2+XLlyMhIQGdO3d+azvPnz+HlZWVRlJcv359AODwQSLSmTFjxojPmjp48CDs7Ow0tkdERGDHjh1vneL95cuX6NevH549e4YZM2a882NFTExMCpSgrF27Fi4uLjnucwLUf1N/+OEHrFu37p0TrWPHjqFJkyYaM8sC6nujgFdDyn19faFUKjFjxgy0bt06R/3Hjx/nGCr4Nv/t/w0MDMR7u//f3n2HR1Xlfxx/z0wyE0oKLQmBQCjSpIOEgAgoGhRRVl0RERARVxdUzNpiAXuUBcQVlJ8oxVWkuIqusChEEIEAUiIdpBlKEppJSCBt5v7+GDMSCZAyyaR8Xs8zz52599xzv2eSzMl3zr3n5vUbt9xyC1OnTs2XOGdkZPDBBx8QFhZWqP6+MP9nSNWhREuqlDZt2tCnTx+6dOlC7dq12bRpE59//jljx451lenSpQsAjz32GJGRkVgslnzXNRXXp59+isViuWQicdttt/H8888zf/58oqKiin2co0eP0q1bN66//npuuOEGgoODOXHiBJ999hk///wz48aNc31j+dRTT/H1119z6623cv/999OlSxcyMjLYvn07n3/+OYcPH77o283L2bdvHzfccAN33303bdq0wcvLiy+//JLk5GTXe1ivXj2io6N5+eWX6d+/P7fddht79+7lvffe45prrinUTTnnzp3Le++9x1/+8heaNWvG2bNnmTlzJn5+ftxyyy3Fe+NERIqoWbNmzJs3j8GDB9O6dWuGDx9O27Ztyc7OZt26dSxatCjfvRwBjh075jqNPD09nV27drFo0SKSkpL4xz/+wd/+9rcybUPeLUcee+yxArfbbDYiIyNZtGgR//rXvy5KeorirbfeYvPmzdxxxx2uJGfLli18/PHH1K5dm3HjxgHO0zLff/99hg0bRufOnbnnnnuoV68eCQkJLFmyhJ49ezJt2rQiHfvBBx/kzJkzXH/99TRs2JBff/2Vd999l44dO7pGEJ999lk+++wzbr75Zh577DFq167N3LlzOXToEP/5z38KdTPiwvyfIVWIh2c9FCm0vOndLzWld+/eva84vftrr71mdOvWzQgICDCqVatmtGrVynj99deN7OxsV5nc3Fzj0UcfNerVq2eYTCa3TPWeN21ur169LluuSZMmRqdOnQzDKP707mlpacY777xjREZGGg0bNjS8vb0NX19fIyIiwpg5c6bhcDjylT979qwRHR1tNG/e3LBarUbdunWNHj16GJMmTXK9L3lT9xY0bTtgTJgwwTAMwzh16pQxZswYo1WrVkaNGjUMf39/Izw83Fi4cOFF+02bNs1o1aqV4e3tbQQFBRmPPPKI8dtvv+UrU9DP1DAMY8uWLcaQIUOMRo0aGTabzQgMDDRuvfVWY9OmTZd9b0RESsO+ffuM0aNHG2FhYYbVajV8fX2Nnj17Gu+++66RmZnpKte4cWPX9Owmk8nw8/Mzrr76amP06NHGhg0bCqwbMMaMGVNqsU+ePNkAjNjY2EuWmTNnTr5p54s7vfvatWuNMWPGGG3btjX8/f0Nb29vo1GjRsb9999vHDhw4KLyK1euNCIjIw1/f3/Dx8fHaNasmXH//ffn+6wfMWKEUaNGjYv2nTBhQr7++/PPPzduuukmIzAw0LBarUajRo2Mv/3tb0ZiYmK+/Q4cOGDcddddRkBAgOHj42N069bN+Oabby6KCzAWLVp00XEL83+GVB0mw/DQ7cdFREREREQqKc06KCIiIiIi4mZKtERERERERNxMiZaIiIiIiIibKdESEREpwOrVqxk4cCAhISGYTCYWL158xX1WrVpF586dsdlsNG/enDlz5pR6nCIiUj4p0RIRESlARkYGHTp0YPr06YUqf+jQIQYMGEDfvn2Jj49n3LhxPPjgg3z77belHKmIiJRHmnVQRETkCkwmE19++SWDBg26ZJlnnnmGJUuWsGPHDte6e+65h5SUFJYtW1YGUYqISHlSpW9Y7HA4OH78OL6+viW+C7uIiBSeYRicPXuWkJCQQt0EtCKIi4ujX79++dZFRka6bsJakKysLLKyslyvHQ4HZ86coU6dOuqXRETKmLv7piqdaB0/fpzQ0FBPhyEiUmUdOXKEhg0bejoMt0hKSiIoKCjfuqCgINLS0jh//jzVqlW7aJ+YmBhefvnlsgpRREQKwV19U5VOtHx9fQHnm+nn5+fhaEREqo60tDRCQ0Ndn8NVVXR0NFFRUa7XqampNGrUSP2SiJQPe5bAl3+7cjmvahDcHmrWA4vV+bD6Qo060PAaaNAZLN6cSc9i4aajLNyUwImz2RdVU8NmIdjPh0A/H3xtFo7+lknr+r54WUx4Wcx4W8x4m034+nhRw+aFl9mEl9mMl8WExWyiutWL8Ka1sXlZitVcd/dNVTrRyjstw8/PTx2aiIgHVKbT44KDg0lOTs63Ljk5GT8/vwJHswBsNhs2m+2i9eqXRKRcyDgMNpMzkaoWAGYvMFucS5MFLN5QrxXcPg28C/6cA0g9n8P//XCAuesOk5FtB7zw8/PhmrBadGlci+5N69C6vh81bOUjNXFX31Q+WiMiIlLBRUREsHTp0nzrli9fTkREhIciEhFxk05DYcDkYu26Ylcyz36xnVPpzutRrw7x48FeTbilXf1ijzxVFEq0RERECpCens7+/ftdrw8dOkR8fDy1a9emUaNGREdHc+zYMT7++GMAHn74YaZNm8bTTz/NAw88wPfff8/ChQtZsmSJp5ogIuIxDofB1BX7+Nf3zs/RpvVq8Ez/VtzYOgizufKczXA5SrREREQKsGnTJvr27et6nXct1YgRI5gzZw6JiYkkJCS4tjdp0oQlS5bwxBNP8M4779CwYUM+/PBDIiMjyzx2ERFPysjK5anPf2bp9iQARkQ0JvqW1vh4V+4RrD9TonUFDoeD7OyLL9aTisvb2xuLpWr9oYtI0fXp04fL3Wpyzpw5Be6zdevWUozKyW63k5OTU+rHkbKjvkkqi1PpWYyYtZGdx9Pwtph4fVA77r6mas7yrUTrMrKzszl06BAOh8PToYibBQQEEBwcXKkuxBeRys8wDJKSkkhJSfF0KFIK1DdJRZeWmcN9H25gT9JZatewMuO+LnRrUtvTYXmMEq1LMAyDxMRELBYLoaGhleaGmlWdYRicO3eOEydOAFC/fn0PRyQiUnh5SVZgYCDVq1fXP+SVhPomqQzSMnN4cM4m9iSdpZ6vjQUPdadpvZqeDsujlGhdQm5uLufOnSMkJITq1at7Ohxxo7xplk+cOEFgYKBO1RCRCsFut7uSrDp16ng6HHEz9U1SkaVn5TLsww38fDQVX5sXs++/psonWQAaprkEu90OgNVq9XAkUhrykmdd4yAiFUXe55W+/Ku81DdJRZRyLpvhHzmTrIDq3nz2UHfaNvD3dFjlgka0rkCnZVRO+rmKSEWlz6/KSz9bqWjSs3IZ9tFGth9Lxc/HizkjuynJuoASLRERERERKZKsXDsPzv2J7cdSqfX7SFarYD9Ph1Wu6NRBEREREREpkn8u28v6g2eoafNi7gPdlGQVQCNaRfT28n1lerwnbmxR6LJXOuVgwoQJvPTSSyWMSERKxcoY57JvtGfjEHEz9U0ilc/a/af4cM0hAN4e3JH2DQM8G1A5pUSrEklMTHQ9X7BgAePHj2fv3r2udTVr/jH7i2EY2O12vLz0KyAiIqVHfZNI5XIqPYuohfEADA1vxI1tgjwbUDmmUwcrkeDgYNfD398fk8nker1nzx58fX353//+R5cuXbDZbKxZs4b777+fQYMG5atn3Lhx9OnTx/Xa4XAQExNDkyZNqFatGh06dODzzz8v28aJiEiFpL5JpPLIznXw+PytJKdl0TywJs/d0trTIZVr+sqoinn22WeZNGkSTZs2pVatWoXaJyYmhk8++YQZM2Zw1VVXsXr1au677z7q1atH7969SzliERG5FMMwOJ9j98ixq3lb3DZLnvomkfIvK9fOY59tZe3+01S3Wph2bydq2JRKXI7enSrmlVde4cYbbyx0+aysLN544w1WrFhBREQEAE2bNmXNmjX83//9nzozEREPOp9jp834bz1y7F2vRFLd6p5/I9Q3iZRvhgHP/mc73+5MxmoxM31oZ01+UQhKtKqYrl27Fqn8/v37OXfu3EUdYHZ2Np06dXJnaCIiUkWpbxIp3345cZYv9x3DYjbxwfAu9GkZ6OmQKgQlWlVMjRo18r02m80YhpFv3YV3pE9PTwdgyZIlNGjQIF85m81WSlGKiEhhVPO2sOuVSI8d213UN4mUbxsOnQHgHze1UJJVBEq0qrh69eqxY8eOfOvi4+Px9vYGoE2bNthsNhISEnQqhohIOWMymdx2+l55or5JpHw4nnqeEJynDt7dtSGP9G7m6ZAqlMr36SxFcv311/PPf/6Tjz/+mIiICD755BN27NjhOvXC19eXJ598kieeeAKHw8G1115Lamoqa9euxc/PjxEjRni4BSIiUtmobxLxvO/3JLNry1HGmiEkwIchf2nntglwqgolWlVcZGQkL774Ik8//TSZmZk88MADDB8+nO3bt7vKvPrqq9SrV4+YmBgOHjxIQEAAnTt35rnnnvNg5CIiUlmpbxLxrEWbjhD9xXbGmg0wQ+8W9fC26K5QRWUy/nwSdBWSlpaGv78/qamp+PnlnzklMzOTQ4cO0aRJE3x8fDwUoZQW/Xyl3FkZ41z2jfZsHGXkcp+/VZn6papNP2PxNLvDYMryvUxfeQCA90K+5ZYzc+GaB2HAZA9HV/rc3TdpREtEREREpIo7k5HNo59tYe3+0wCM6duMm72DYbWHA6vAlGiJiIiIiFRRDofBvI0JTPpuLynncqjmbeH1v7Tljs4NYaWuySoJJVoiIiIiIlWMw2GwYncy78T+ws7jaQC0CKrJu0M60zLY18PRVQ5KtEREREREqojMHDtfbj3GnLWH2Zt8FoAaVgtPRrZkWPfGeGnSC7dRoiUiIiIiUsmlZebw77hfmbvuMCfOZgFQ0+bFsIjGPNSrKbVqWD0cYeWjREtEREREpJLacSyVBT8dYfHWY5zNygWgvr8PD/Rswt3XhOJfzdvDEVZeRRobjImJ4ZprrsHX15fAwEAGDRrE3r1785XJzMxkzJgx1KlTh5o1a3LnnXeSnJycr0xCQgIDBgygevXqBAYG8tRTT5Gbm5uvzKpVq+jcuTM2m43mzZszZ86ci+KZPn06YWFh+Pj4EB4ezsaNG4vSHBERERGRSic9K5cFPyVw+/S13PruGv69/lfOZuXSPLAmU+7uwA9P9WX0dU2VZJWyIiVaP/zwA2PGjGH9+vUsX76cnJwcbrrpJjIyMlxlnnjiCf773/+yaNEifvjhB44fP84dd9zh2m632xkwYADZ2dmsW7eOuXPnMmfOHMaPH+8qc+jQIQYMGEDfvn2Jj49n3LhxPPjgg3z77beuMgsWLCAqKooJEyawZcsWOnToQGRkJCdOnCjJ+yEiIiIiUiEd/e0cMUt3ExETyzP/2c7PR1Lwtpi4tX19/j2qG9+Nu447OjfE6qXrsMpCkU4dXLZsWb7Xc+bMITAwkM2bN3PdddeRmprKRx99xLx587j++usBmD17Nq1bt2b9+vV0796d7777jl27drFixQqCgoLo2LEjr776Ks888wwvvfQSVquVGTNm0KRJEyZPdt4YrXXr1qxZs4a3336byMhIAKZMmcLo0aMZOXIkADNmzGDJkiXMmjWLZ599tsRvjIiIiIhIRbDzeCrvrTzA0h2JGIZzXdO6Nfhr11Du6tKQer42zwZYRZXoGq3U1FQAateuDcDmzZvJycmhX79+rjKtWrWiUaNGxMXF0b17d+Li4mjXrh1BQUGuMpGRkTzyyCPs3LmTTp06ERcXl6+OvDLjxo0DIDs7m82bNxMdHe3abjab6devH3FxcZeMNysri6ysLNfrtLS04je+AjOZTHz55ZcMGjSoVI8TFhbGuHHjXD+3sjRnzhzGjRtHSkpKmR9bRESKTn2TlFs55yHxZ7DnXLDSuLic8ed1hSlTQLlClXEufjlxli+3HmPn8TRMGPQ2QZsQP/q1CaJjQy/MpgOQeAASi3msff8rYLsUVrETLYfDwbhx4+jZsydt27YFICkpCavVSkBAQL6yQUFBJCUlucpcmGTlbc/bdrkyaWlpnD9/nt9++w273V5gmT179lwy5piYGF5++eWiN/ZCK2NKtn9R9Y2+cpk/OXnyJOPHj2fJkiUkJydTq1YtOnTowPjx4+nZsyeJiYnUqlWrFIItGXVAIiKVl/omqZAOr4HPH4D05CuX9YCrgKcBLpww8DTwo5sPZLK4ucKqodiJ1pgxY9ixYwdr1qxxZzylKjo6mqioKNfrtLQ0QkNDPRhR6bjzzjvJzs5m7ty5NG3alOTkZGJjYzl9+jQAwcHBHo5QRESqGvVNUuEkboNP7oLc81CjHlSrnX+7yVTATgWsu6hccctAdq6DE2ezSD2fg/F7mVrVvanna8NqMbvxWBes864OHYdcXEauqFiJ1tixY/nmm29YvXo1DRs2dK0PDg4mOzublJSUfKNaycnJrg/Q4ODgi2YHzJuV8MIyf56pMDk5GT8/P6pVq4bFYsFisRRY5nIf1DabDZutcp+jmpKSwo8//siqVavo3bs3AI0bN6Zbt26uMheennH48GGaNGnCggULePfdd9m0aRNt27bl008/JTU1lUceeYQ9e/bQq1cvPv74Y+rVqwdAnz596NixI1OnTnXVO2jQIAICAgqcIRKc19XNnj2bgwcPUrt2bQYOHMjEiROpWbMmq1atcl1vZ/r9j3vChAm89NJLZGVl8fzzz/PZZ5+RkpJC27Zteeutt+jTp4+r7rwJVU6dOkVkZCTXXnutG99VEREpCfVN6psqHIcDvhnnTLKa9oUhn4F3NY+FcyYjm3dW7GPexgRy7M5T+25pF8w/bmpJg3o1PRaXXF6RphwxDIOxY8fy5Zdf8v3339OkSZN827t06YK3tzexsbGudXv37iUhIYGIiAgAIiIi2L59e77ZAZcvX46fnx9t2rRxlbmwjrwyeXVYrVa6dOmSr4zD4SA2NtZVpqqqWbMmNWvWZPHixfmuR7uSCRMm8MILL7Blyxa8vLy49957efrpp3nnnXf48ccf2b9/f76ZIYvDbDbzr3/9i507dzJ37ly+//57nn76aQB69OjB1KlT8fPzIzExkcTERJ588knAmdjHxcUxf/58tm3bxl//+lf69+/PL7/8AsCGDRsYNWoUY8eOJT4+nr59+/Laa6+VKFYRkQrBMCA7wzOPAq/tKJj6JvVNFc7ur+DYZvCuAX+Z4bEkKzvXwYc/HqTPP1cyN+5XcuwGva6qy3/HXst7Q7vQTElWuVakEa0xY8Ywb948vvrqK3x9fV3XVPn7+1OtWjX8/f0ZNWoUUVFR1K5dGz8/Px599FEiIiLo3r07ADfddBNt2rRh2LBhTJw4kaSkJF544QXGjBnjGm16+OGHmTZtGk8//TQPPPAA33//PQsXLmTJkiWuWKKiohgxYgRdu3alW7duTJ06lYyMDNc3T1WVl5cXc+bMYfTo0cyYMYPOnTvTu3dv7rnnHtq3b3/J/Z588knXjI6PP/44Q4YMITY2lp49ewIwatSoS34bWFgXXngcFhbGa6+9xsMPP8x7772H1WrF398fk8mUb1QyISGB2bNnk5CQQEhIiCvWZcuWMXv2bN544w3eeecd+vfv7+oYW7Rowbp16y6aJVNEpNLJOQdvhHjm2M8dB2uNQhVV36S+qUJx2GHVm87nPcaCr2dOa43dncz4r3ZyLOU8AK2CfRl/axt6NK/rkXik6Io0ovX++++TmppKnz59qF+/vuuxYMECV5m3336bW2+9lTvvvJPrrruO4OBgvvjiC9d2i8XCN998g8ViISIigvvuu4/hw4fzyiuvuMo0adKEJUuWsHz5cjp06MDkyZP58MMPXR+2AIMHD2bSpEmMHz+ejh07Eh8fz7Jlyy6aIKMquvPOOzl+/Dhff/01/fv3d938+XKd0YUdXd572K5du3zrSnqPshUrVnDDDTfQoEEDfH19GTZsGKdPn+bcuXOX3Gf79u3Y7XZatGjh+ka0Zs2a/PDDDxw4cACA3bt3Ex4enm+/qj6yKSJS3qhvUt9UYexaDCf3gE8AhD9c5odPSs3k4X9vZtTcTRxLOU89Xxsxd7RjyWO9lGRVMEUa0TIKcZqAj48P06dPZ/r06Zcs07hxY5YuXXrZevr06cPWrVsvW2bs2LGMHTv2ijFVRT4+Ptx4443ceOONvPjiizz44INMmDCB+++/v8Dy3t5/3Bk87zz0P69zOByu12az+aLfh5ycHC7l8OHD3HrrrTzyyCO8/vrr1K5dmzVr1jBq1Ciys7OpXr16gfulp6djsVjYvHkzFkv+GW9q1tRwuYhUcd7VnSNLnjp2EalvknLPnvvHDNPhD0P12pcv70bnsnOZ9v1+Zq89zPkcOxaziQevbcLj/a6iurVEd2QSD9FPrYpo06YNixcvdlt99erVIzHRdVMG7HY7O3bsoG/fvgWW37x5Mw6Hg8mTJ2M2OwdSFy5cmK+M1WrFbrfnW9epUyfsdjsnTpygV69eBdbdunVrNmzYkG/d+vXri9wmEZEKx2Qq9Ol75ZH6Jil3dnwOp3+B6nWh+yNldtg1v5wi+sttHDnjPE2wS+NavHp7W9qE+JVZDOJ+SrQqmdOnT/PXv/6VBx54gPbt2+Pr68umTZuYOHEit99+u9uOc/311xMVFcWSJUto1qwZU6ZMuew9Rpo3b05OTg7vvvsuAwcOZO3atcyYMSNfmbCwMNLT04mNjaVDhw5Ur16dFi1aMHToUIYPH87kyZPp1KkTJ0+eJDY2lvbt2zNgwAAee+wxevbsyaRJk7j99tv59ttvdQ68iEg5or5JfVOFsXmucxn+MFQLKPXDHTlzjn9+u5evf3aOTDcIqMb4gW24qU2QaxRXKq4iXaMl5V/NmjUJDw/n7bff5rrrrqNt27a8+OKLjB49mmnTprntOA888AAjRoxg+PDh9O7dm6ZNm17yG0OADh06MGXKFN566y3XFL0xMflv/tyjRw8efvhhBg8eTL169Zg4cSIAs2fPZvjw4fzjH/+gZcuWDBo0iJ9++olGjRoB0L17d2bOnMk777xDhw4d+O6773jhhRfc1lYRESkZ9U3qmyqEk3shYR2YzNBpaKkeKtfunE3w5nd+5Oufj2MywfCIxiwb14vIq4OVZFUSJqMwF15VUmlpafj7+5OamoqfX/6h2czMTA4dOkSTJk3w8fHxUIRSWvTzlXIn75qAvtGejaOMXO7ztypTv1S16WfsYStegjVvQ4v+cO+CKxYvrvgjKby4eAfbj6UC0LlRAK/c3pa2DfxL7ZhSOO7um3TqoIiIiIhUbdnn/jhtsOO9pXKItMwcXv56F//ZchQA/2reRN/ciru7hmI2awSrMlKiJSIiIiJV29Z/w/kzENAYWg5wa9WGYbBi9wkmfLWD46mZANzRqQFP929FsL9GLiszJVoiIiIiUnWdOwOrJzmf93gULO779/jE2Uye+2IHK3YnAxBauxpv392RrmFlN228eI4SLRERERGpmhwO+GYcZJyAui2h83C3Vf39nmSeWrSN0xnZeFtMjLq2KY/d0Fz3xKpC9JMWERERkarH4YCv/g67vnLONDjoPfCylbjarFw7MUv3MGfdYQBaBfsy9Z6OtArWxD9VjRKtK6jCkzJWag6Hw9MhiIgUiz6/Ki/9bMuQPQcWjoC9S5yvb3sXGnYtcbW7E9MYNz+evclnAbi/RxjP3twKH29LieuWikeJ1iV4e3tjMpk4efIk9erV0/0MKgnDMMjOzubkyZOYzWasVqunQxKRcmz69On885//JCkpiQ4dOvDuu+/SrVu3S5afOnUq77//PgkJCdStW5e77rqLmJgYt0zVbbVaMZvNHD9+nHr16mG1WtU3VRLqm8qYYcB/HvwjyRr4L+h0XwmrNPhkQwKvfrOL7FwHtWtYmfzXDvRtFeiGgKWiUqJ1CRaLhYYNG3L06FEOHz7s6XDEzapXr06jRo0wm3XPbhEp2IIFC4iKimLGjBmEh4czdepUIiMj2bt3L4GBF//zNG/ePJ599llmzZpFjx492LdvH/fffz8mk4kpU6aUOB6z2UyTJk1ITEzk+PHjJa5Pyh/1TWVk1Zuwa7HzdMEhC6DFTSWq7kxGNi9+tYMl2xIB6NuyHpP+2oE6NUt+GqJUbEq0LqNmzZpcddVV5OTkeDoUcSOLxYKXl5e+CRaRy5oyZQqjR49m5MiRAMyYMYMlS5Ywa9Ysnn322YvKr1u3jp49e3Lvvc578ISFhTFkyBA2bNjgtpisViuNGjUiNzcXu93utnrF89Q3lZFf4+CHN53PB0wpcZK14eBpHv1sKyfOZmExm3imf0tG92qqn6MASrSuyGKxYLHovFoRkaokOzubzZs3Ex0d7VpnNpvp168fcXFxBe7To0cPPvnkEzZu3Ei3bt04ePAgS5cuZdiwYZc8TlZWFllZWa7XaWlpV4zNZDLh7e2Nt7d3EVokImSlw38fcz7veB90HVnsqhwOg/d/OMDk7/biMKBZvRpMubsjHUID3BOrVApKtERERP7k1KlT2O12goKC8q0PCgpiz549Be5z7733curUKa699loMwyA3N5eHH36Y55577pLHiYmJ4eWXX3Zr7CJyCSsmwKl9UCMQIl8rdjVpmTk8MT+e2D0nALizc0Neuf1qatj0b7Xkp5OARURE3GDVqlW88cYbvPfee2zZsoUvvviCJUuW8Oqrr15yn+joaFJTU12PI0eOlGHEIlXIqf2wabbz+Z0zoVqtYlVz8GQ6d7y3jtg9J7B6mXn9L22ZfHcHJVlSIP1WiIiI/EndunWxWCwkJyfnW5+cnExwcHCB+7z44osMGzaMBx98EIB27dqRkZHBQw89xPPPP1/gBAc2mw2bTRfMi5S6HyeBYYcW/aFpn2JVEXfgNA9/spnU8zkE+dmYObwr7RsGuDVMqVw0oiUiIvInVquVLl26EBsb61rncDiIjY0lIiKiwH3OnTt3UTKVd42v7sko4kHZGbDra+fza6OKvLthGMxac4hhH20g9XwOnRoF8N+x1yrJkivSiJaIiEgBoqKiGDFiBF27dqVbt25MnTqVjIwM1yyEw4cPp0GDBsTExAAwcOBApkyZQqdOnQgPD2f//v28+OKLDBw4UJMqiXjSji8gJwNqhUHDa4q067nsXKK/2M5X8c5bKgzsEMLEO9tTzaq/abkyJVoiIiIFGDx4MCdPnmT8+PEkJSXRsWNHli1b5pogIyEhId8I1gsvvIDJZOKFF17g2LFj1KtXj4EDB/L66697qgkicuQn+N8zzudd7oci3KMsMfU8o+ZsYldiGhaziRcGtOb+HmGaul0KzWRU4fMZ0tLS8Pf3JzU1FT8/P0+HIyJV2UrnqAh9oy9frpLQ52/B9L6IuInDDt+9CBv/Dxy5ENYLhn0JlsLdFmFLwm/8/ZMtJKVlUqeGlelDO9O9aZ1SDlo8zd2fwRrREhEREZHKI+UIfP4AHN3ofH3VTXDX7EInWYu3HuPpz7eRbXfQrF4N5ozsRmjt6qUYsFRWSrREREREpOI7nwJL/gE7Pv9j3R0zof3dhdrd4TCY+O1eZvxwAIAb2wQx5e4O+Pro5uBSPEq0RERERKRiS9wGs2+G7PQ/1o38HzTuUajdz2fbiVoYz/92JAHw9z7N+MdNLbGYdT2WFJ8SLRERERGpuE7th7m3OpMs7xoQ+Rp0vr/QE1+cSs9i1NxN/HwkBavFzFt3teMvnRqWbsxSJSjREhEREZGKyTDg60chMxVCOsOwL6BarULvfvBkOiNmb+TImfMEVPfm/+7rQrgmvRA3UaIlIiIiIhXTkQ2QsA4sVhj87yIlWesPnmb0x5s4m5lLw1rVmPtAN5rVq1mKwUpVo0RLRERERCqmDf/nXHa4B/wLf7rfsh2JPPrZVnLsBp0bBTBzeFfq1LSVUpBSVSnREhEREZGKJzMN9i51Pu8ystC7fbHlKE99vg27w6D/1cFMvacjPt6WUgpSqjIlWiIiIiJS8RyIhdxMqNMcQjoVapfZaw/x8n93AXBn54ZMvKu9ZhaUUqNES0REREQqnv0rnMurIsF0+WTJMAymLN/Hu9/vB+CBnk14YUBrzEqypBQp0RIRERGRisUw4NBq5/Pm11+2qN1h8MbS3Xy05hAA4/pdxeM3XIXpCsmZSEkp0RIRERGRiuW3Q5CSAGZvCO1+yWKZOXYe+2wr3+1KBuDl265mRI+wMgpSqjolWiIiIiJSsez9n3MZGg62gqdkT8/K5YHZP7Hx8BmsXmb+eVd7bu/YoAyDlKpOiZaIiIiIVCzbFjiXbW4vcHPKuWzun/0T8UdS8PXx4sPhXXUjYilzSrREREREpOI4tgUSf3aeNtj2zos2n0rPYvhHG9mVmEZAdW8+fqAb7RsGlH2cUuUp0RIRERGRiiNumnPZ9g6okX+UKjH1PEM+WM/h0+eoW9PKJw+G0yrYzwNBiijREhEREZGKIv0k7PrK+TxiTL5NyWmZriSrQUA1/j2qG03rFXz9lkhZUKIlIiIiIhXD9kXgyIWQzlC/g2t1yrlshn20gcOnz9GwVjXmP9SdhrWqezBQETB7OgARERERkSty2GHjB87nnYa6Vp/NzGHYRxvZl5xOoK+Nz0YryZLyQYmWiIiIiJR/+5Y575/l4w8dhgDOKdyHz9rI9mOp1K5h5d+jwgmtrSRLygclWiIiIiJSvjkcsPIN5/OuD4C1BhlZuYya8xNbE1Lwr+acXbBlsK9n4xS5gBItERERESnf9v0PkneA1Rd6PEZmjp2H/r2JDYfOUNPmxccPdKNtA39PRymSjxItERERESnf1v0+pXu3B7H71OIfi35m7f7T1LBa+HhUNzqEBng0PJGCKNESERERkfLr+FZIWAdmL4xrRvPiVztYsi0RL7OJD4Z3pXOjWp6OUKRASrREREREpPz66SPnss0gJq0/y7wNCZhN8PbgjvRsXtezsYlchhItERERESmfcrNcNyj+rvotTF95AIDX/9KOgR1CPBmZyBUp0RIRERGR8mn/CshKI9MnkEd+tALw2A1XMaRbIw8HJnJlSrREREREpHza+SUAn53rit0wM7hrKE/0u8rDQYkUjhItERERESl/HA4c+5YD8N+cbvRrHcTrf2mLyWTycGAihVPkRGv16tUMHDiQkJAQTCYTixcvzrf9/vvvx2Qy5Xv0798/X5kzZ84wdOhQ/Pz8CAgIYNSoUaSnp+crs23bNnr16oWPjw+hoaFMnDjxolgWLVpEq1at8PHxoV27dixdurSozRERERGRcijz2DbMWSmcNaqRVqc979zTES+Lxgik4ijyb2tGRgYdOnRg+vTplyzTv39/EhMTXY/PPvss3/ahQ4eyc+dOli9fzjfffMPq1at56KGHXNvT0tK46aabaNy4MZs3b+af//wnL730Eh988IGrzLp16xgyZAijRo1i69atDBo0iEGDBrFjx46iNklEREREyhHDMFj234UAxJta8dHI7tSweXk4KpGiKfJv7M0338zNN9982TI2m43g4OACt+3evZtly5bx008/0bVrVwDeffddbrnlFiZNmkRISAiffvop2dnZzJo1C6vVytVXX018fDxTpkxxJWTvvPMO/fv356mnngLg1VdfZfny5UybNo0ZM2YUtVkiIiIiUk7M25hA08SVYIHQLjfTuE4NT4ckUmSlMv66atUqAgMDadmyJY888ginT592bYuLiyMgIMCVZAH069cPs9nMhg0bXGWuu+46rFarq0xkZCR79+7lt99+c5Xp169fvuNGRkYSFxdXGk0SERERkTLwS/JZPvzvD0RYdgEQdu09Ho5IpHjcPgbbv39/7rjjDpo0acKBAwd47rnnuPnmm4mLi8NisZCUlERgYGD+ILy8qF27NklJSQAkJSXRpEmTfGWCgoJc22rVqkVSUpJr3YVl8uooSFZWFllZWa7XaWlpJWqriIiIiLhPZo6dRz/byhCWAWCEXYepVmMPRyVSPG5PtO65549vHdq1a0f79u1p1qwZq1at4oYbbnD34YokJiaGl19+2aMxiIiIiEjB3vzfHo4nJXGvz/cAmCLGeDgikeIr9albmjZtSt26ddm/fz8AwcHBnDhxIl+Z3Nxczpw547quKzg4mOTk5Hxl8l5fqcylrg0DiI6OJjU11fU4cuRIyRonIiIiIm6x8dAZ5qw7zAjLt9TkPAS2gatu8nRYIsVW6onW0aNHOX36NPXr1wcgIiKClJQUNm/e7Crz/fff43A4CA8Pd5VZvXo1OTk5rjLLly+nZcuW1KpVy1UmNjY237GWL19ORETEJWOx2Wz4+fnle4iIiIiIZ53NzOHx+VsJNSXzqPUb58pe/wCzpnOXiqvIv73p6enEx8cTHx8PwKFDh4iPjychIYH09HSeeuop1q9fz+HDh4mNjeX222+nefPmREZGAtC6dWv69+/P6NGj2bhxI2vXrmXs2LHcc889hISEAHDvvfditVoZNWoUO3fuZMGCBbzzzjtERUW54nj88cdZtmwZkydPZs+ePbz00kts2rSJsWPHuuFtEREREZGy8sp/d5Gceo7p1T7AamRCWC+4+g5PhyVSIkVOtDZt2kSnTp3o1KkTAFFRUXTq1Inx48djsVjYtm0bt912Gy1atGDUqFF06dKFH3/8EZvN5qrj008/pVWrVtxwww3ccsstXHvttfnukeXv7893333HoUOH6NKlC//4xz8YP358vntt9ejRg3nz5vHBBx/QoUMHPv/8cxYvXkzbtm1L8n6IiIiISBlaviuZRZuPMtprCe0du8HqC7f9S6NZUuGZDMMwPB2Ep6SlpeHv709qaqpOIxQRz1oZ41z2jfZsHGVEn78F0/siVU3quRxufPsHGqX/zGc+b+FtZMHAd6DL/Z4OTaogd38G66sCEREREfGI15fuomH6dubYJjmTrJa3QKfhng5LxC3cPr27iIiIiMiVrPnlFOs3b+Y76+v4kOO8LuvOD3XKoFQa+k0WERERkTKVnpXLs19s4zGvL/Ex5UCjHnDvQrDW8HRoIm6jREtEREREytTrS3Zj/+0ot1vWOVfc+ApYq3s2KBE3U6IlIiIiImXmp8Nn+GxjAmO9FuNNLjS+FkKv8XRYIm6nREtEROQSpk+fTlhYGD4+PoSHh7Nx48bLlk9JSWHMmDHUr18fm81GixYtWLp0aRlFK1L+ZebYefrzbdTnNPd4rXKu7PucR2MSKS2aDENERKQACxYsICoqihkzZhAeHs7UqVOJjIxk7969BAYGXlQ+OzubG2+8kcDAQD7//HMaNGjAr7/+SkBAQNkHL1JO/Sv2Fw6dyuCZGuuw2O3QuCeE9fR0WCKlQomWiIhIAaZMmcLo0aMZOXIkADNmzGDJkiXMmjWLZ5999qLys2bN4syZM6xbtw5vb28AwsLCyjJkkXJt+9FUZvxwADC4r8ZPkAZ0vNfTYYmUGp06KCIi8ifZ2dls3ryZfv36udaZzWb69etHXFxcgft8/fXXREREMGbMGIKCgmjbti1vvPEGdrv9ksfJysoiLS0t30OkMnI4DF74agcOA55qdhTftP3gXR1a3erp0ERKjRItERGRPzl16hR2u52goKB864OCgkhKSipwn4MHD/L5559jt9tZunQpL774IpMnT+a111675HFiYmLw9/d3PUJDQ93aDpHy4sutx/j5SAo1bRZGm792ruw8HKoFeDQukdKkREtERMQNHA4HgYGBfPDBB3Tp0oXBgwfz/PPPM2PGjEvuEx0dTWpqqutx5MiRMoxYpGxkZOUy8ds9ALzVPhHrkbVg9oaIMR6OTKR06RotERGRP6lbty4Wi4Xk5OR865OTkwkODi5wn/r16+Pt7Y3FYnGta926NUlJSWRnZ2O1Wi/ax2azYbPZ3Bu8SDnz3qr9JKdl0ayWhZuPTXOu7P4IBDTybGAipUwjWiIiIn9itVrp0qULsbGxrnUOh4PY2FgiIiIK3Kdnz57s378fh8PhWrdv3z7q169fYJIlUhUcOpXBzNWHAJjWchvmM/uhZjBc96SHIxMpfUq0REREChAVFcXMmTOZO3cuu3fv5pFHHiEjI8M1C+Hw4cOJjo52lX/kkUc4c+YMjz/+OPv27WPJkiW88cYbjBmj06Ok6opZuptsu4Prr6pFq18/da687knw8fdsYCJlQKcOioiIFGDw4MGcPHmS8ePHk5SURMeOHVm2bJlrgoyEhATM5j++rwwNDeXbb7/liSeeoH379jRo0IDHH3+cZ555xlNNEPGoTYfP8N2uZMwmeCt0PaZ1h6F6XegwxNOhiZQJJVoiIiKXMHbsWMaOHVvgtlWrVl20LiIigvXr15dyVCLln2EYvPk/5wQYIzvWpN6Wqc4NN7wItpqeC0ykDOnUQRERERFxqx9/OcWmX3/D5mUmyvolZKZCcDvoNMzToYmUGSVaIiIiIuI2OXYHry3ZBcDDHbypsf0T54bIGDBbLrOnSOWiREtERERE3ObT9b+yLzmdWtW9ecT2P3DkQJProEkvT4cmUqaUaImIiIiIW/yWkc3bK34BILpPED7bfp9p8NonPBiViGco0RIRERERt3hv1X5Sz+fQKtiXO43vIPc8BLWDpn09HZpImVOiJSIiIiIllpSaycdxvwLw3A0NsWyY4dwQ8XcwmTwYmYhnKNESERERkRKb+eNBsnIddG1ci16ZK+HcKajdDNr91dOhiXiEEi0RERERKZHT6Vl8usE5mjW2bzNMm2c7N3QdCRZvD0Ym4jlKtERERESkRGavPUxmjoN2DfzpXe0gJG0HLx/oONTToYl4jJenAxARERGRiutsZg4fxx0GYEzfZph+HOvc0O4uqF7bc4GJeJhGtERERESk2OasPUxaZi5N69XgxuDzsH8FYIJe//B0aCIepURLRERERIolM8fOR2sPAfD4DVdh2fm5c0OzvlC7qQcjE/E8JVoiIiIiUiyLtx4j5VwODQKqcWu7+rBtgXND27s8G5hIOaBES0RERESKzO4w+GD1QQDu7xGGJSkeTu8Hr2rQ5jbPBidSDijREhEREZEi+25nEgdPZeDn48WQ8EawbaFzQ8ubwebr2eBEygElWiIiIiJSJIZhMOP30azhEWHU9DbD7v86N7a904ORiZQfSrREREREpEjWHTjNz0dSsHmZGdEjDBK3QtpRsNaE5v08HZ5IuaBES0RERESK5J0VvwAwpFsj6vna4MBK54amfcDbx3OBiZQjSrREREREpNA2//obGw+fwdti4uHezZwrf/nOuWzax2NxiZQ3SrREREREpNDeiXWOZg3q2IBgfx84/xsc3eTc2KK/ByMTKV+UaImIiIhIoew6nsbqfScxm+DR669yrjy4Cgw71G0JAaEejU+kPFGiJSIiIiKFMn3lfgBuaVefRnWqO1ce+N651CQYIvko0RIRERGRKzpwMp2lOxIBGHt9c+dKw3COaAE0uc4zgYmUU0q0REREROSK3lt5AMOAfq0DaRXs51yZ8iukJIDZC5r08myAIuWMEi0RERERuazE1PN8/fMxAMbmXZsFcOhH57JBV7DW8EBkIuWXEi0RERERuawZqw6QYzcIb1KbjqEBf2zIO20w7FpPhCVSrnl5OgCR0vD28n2u50/c2MKDkYiIiFRsiann+eynIwA8dsMFo1kO+x8TYTS73gORiZRvGtESERERkUuauvwXsnMddAurTY9mdf7YcGQDnD8DPv4QGu65AEXKKSVaIiIiIlKgI2fO8Z8tRwF45uaWmEymPzZu/9y5bHkLWHSSlMifKdESERERkQLN/PEguQ6DXlfVpUvj2n9syD4H2xY6n7cf7JngRMo5JVoiIiIicpEzGdks3OS8Nuvh3s3yb9zxH8g+CwGNoUlvD0QnUv4p0RIRERGRi8xee4jMHAftGvjnvzbL4YC4ac7nXR8As/6dFCmITqiVSuPCmQZFRESk+DKycvn3+l8BeKRPs/zXZu34D5zc45wEo8sID0UoUv7pKwgRERERyWfBT0dIOZdD4zrVibw6+I8NDjusfM35vMdjUK2WZwIUqQCKnGitXr2agQMHEhISgslkYvHixfm2G4bB+PHjqV+/PtWqVaNfv3788ssv+cqcOXOGoUOH4ufnR0BAAKNGjSI9PT1fmW3bttGrVy98fHwIDQ1l4sSJF8WyaNEiWrVqhY+PD+3atWPp0qVFbY6IiIiIXCA718HMHw8C8NB1TbGYL5xpcBH8dhiq1Ybwv3kmQJEKosiJVkZGBh06dGD69OkFbp84cSL/+te/mDFjBhs2bKBGjRpERkaSmZnpKjN06FB27tzJ8uXL+eabb1i9ejUPPfSQa3taWho33XQTjRs3ZvPmzfzzn//kpZde4oMPPnCVWbduHUOGDGHUqFFs3bqVQYMGMWjQIHbs2FHUJomIiIjI776KP0ZiaiaBvjbu7Nzwjw32XPjh9y++ezwKNl/PBChSQRT5Gq2bb76Zm2++ucBthmEwdepUXnjhBW6//XYAPv74Y4KCgli8eDH33HMPu3fvZtmyZfz000907doVgHfffZdbbrmFSZMmERISwqeffkp2djazZs3CarVy9dVXEx8fz5QpU1wJ2TvvvEP//v156qmnAHj11VdZvnw506ZNY8aMGcV6M0RERESqMofD4P9WO0ezRvZsgo+35Y+NuxbDmQPO0wW7PVRwBSLi4tZrtA4dOkRSUhL9+vVzrfP39yc8PJy4uDgA4uLiCAgIcCVZAP369cNsNrNhwwZXmeuuuw6r1eoqExkZyd69e/ntt99cZS48Tl6ZvOOIiIiISNGs2J3M/hPp+Pp4MbR7o/wbN812Lrv9DWw1yz44kQrGrbMOJiUlARAUFJRvfVBQkGtbUlISgYGB+YPw8qJ27dr5yjRp0uSiOvK21apVi6SkpMsepyBZWVlkZWW5XqelpRWleSIiIiKV2nurDgBwX/fG+Pl4/7EhLRF+XeN83nmYByITqXiq1KyDMTEx+Pv7ux6hoaGeDklERESkXNh+NJX4IylYLWYe6Jn/C28OxDqXIZ3Bv+HFO4vIRdyaaAUHO6f/TE5Ozrc+OTnZtS04OJgTJ07k256bm8uZM2fylSmojguPcakyedsLEh0dTWpqqutx5MiRojZRREREpFL6cI3z2qyb2wVTz9eWf+Pe/zmXV91YxlGJVFxuTbSaNGlCcHAwsbGxrnVpaWls2LCBiIgIACIiIkhJSWHz5s2uMt9//z0Oh4Pw8HBXmdWrV5OTk+Mqs3z5clq2bEmtWrVcZS48Tl6ZvOMUxGaz4efnl+8hIiIiUtUdSznPkm2JAIzu1TT/xvQT8Mty5/PWA8s4MpGKq8iJVnp6OvHx8cTHxwPOCTDi4+NJSEjAZDIxbtw4XnvtNb7++mu2b9/O8OHDCQkJYdCgQQC0bt2a/v37M3r0aDZu3MjatWsZO3Ys99xzDyEhIQDce++9WK1WRo0axc6dO1mwYAHvvPMOUVFRrjgef/xxli1bxuTJk9mzZw8vvfQSmzZtYuzYsSV/V0RERESqkFlrDpHrMIhoWoe2Dfzzb4x9BexZENIJgtp6JkCRCqjIk2Fs2rSJvn37ul7nJT8jRoxgzpw5PP3002RkZPDQQw+RkpLCtddey7Jly/Dx8XHt8+mnnzJ27FhuuOEGzGYzd955J//6179c2/39/fnuu+8YM2YMXbp0oW7duowfPz7fvbZ69OjBvHnzeOGFF3juuee46qqrWLx4MW3b6gNAREREpLDSMnNYuMl5OcVDvf80mrV5Dmz9N2CCyBgwmS7aX0QKZjIMw/B0EJ6SlpaGv78/qampOo2wEnh7+b4C1z9xY4syjkSkGFbGOJd9oz0bRxnR52/B9L6IJ8xac4hXvtlF03o1WPFEb8zm35OpMwdhejjYs6H3M9D3Oc8GKlLK3P0ZXKVmHRQRESmq6dOnExYWho+PD+Hh4WzcuLFQ+82fPx+TyeQ6dV6kPHI4DObGHQacNyh2JVkAq950JllN+0KfqvElkIg7KdESERG5hAULFhAVFcWECRPYsmULHTp0IDIy8qLZc//s8OHDPPnkk/Tq1auMIhUpnpV7T/Dr6XP4+nhxZ+cGf2w4dwZ2ful8fv0LOmVQpBiUaImIiFzClClTGD16NCNHjqRNmzbMmDGD6tWrM2vWrEvuY7fbGTp0KC+//DJNmza9ZDmR8mDW2kMADOnWiOrWCy7d37XYOZoV3A4advVMcCIVnBItERGRAmRnZ7N582b69evnWmc2m+nXrx9xcXGX3O+VV14hMDCQUaNGXfEYWVlZpKWl5XuIlJVtR1NYu/80FrOJ4RGN82/c/h/nsu2dZR+YSCWhREtERKQAp06dwm63ExQUlG99UFAQSUlJBe6zZs0aPvroI2bOnFmoY8TExODv7+96hIaGljhukcKas/YwALd1CKFhrep/bDixB35dAyYztPurZ4ITqQSUaImIiLjB2bNnGTZsGDNnzqRu3bqF2ic6OprU1FTX48iRI6UcpYjTbxnZfLPdeYPiET3C8m/cMte5bHkL+Dcs28BEKpEi30dLRESkKqhbty4Wi4Xk5OR865OTkwkODr6o/IEDBzh8+DADBw50rXM4HAB4eXmxd+9emjVrlm8fm82GzWYrhehFLu/LrcfIznXQur4fHRpecINiey7s+P20wU73eSY4kUpCI1oiIiIFsFqtdOnShdjYWNc6h8NBbGwsERERF5Vv1aoV27dvJz4+3vW47bbb6Nu3L/Hx8TotUMoNh8Pg49+ndL+3WyimC2cU/HUtpCdDtVrQ7AbPBChSSWhES0RE5BKioqIYMWIEXbt2pVu3bkydOpWMjAxGjhwJwPDhw2nQoAExMTH4+PjQtm3bfPsHBAQAXLRexJN+2HeSw79P6X5H5z+dGrjlY+ey9W3gZS374EQqESVaIiIilzB48GBOnjzJ+PHjSUpKomPHjixbtsw1QUZCQgJms04OkYolbzTr7q6h1LBd8K9gVjrsXep83nlE2QcmUsko0RIREbmMsWPHMnbs2AK3rVq16rL7zpkzx/0BiZTA0d/OsWrfSQDu6/6nKd1/XQc558C/ETTo7IHoRCoXfQ0nIiIiUkV8HPcrhgE9m9ehSd0a+Tce+sG5bNobLrxuS0SKRYmWiIiISBVwPtvOgp+ctxC4v0eTiwsczEu0+pRdUCKVmE4dlErv7eX7XM+fuLGFByMRERHxnC+2HiX1fA6htatxfavA/BtTj0HydsAETXp7JD6RykYjWiIiIiKVnGEYzN/oHM0a3j0Mi/lPpwbuWeJchnaDmvXKODqRykmJloiIiEgltyUhhe3HUrF6mbmjc4OLC2xb4Fy2HnjxNhEpFiVaIiIiIpXcv3+f0n1g+xDq1LTl33jqFzi2CUwWaHd32QcnUkkp0RIRERGpxE6ezWLp9iQAhkc0vrjArsXOZbO+4BtUdoGJVHJKtEREREQqsU/W/0q23UHH0AA6hAZcXGD3N85l69vKNC6Ryk6JloiIiEglZXcYLNzknARjZM+wiwv89iskxoPJDC1vKdPYRCo7JVoiIiIildR3O5NITM2kVnVv+rcNvrjAzi+cy8Y9NdugiJsp0RIRERGphAzD4P9WHwRgaHhjbF6Wiwvt+tq5bHtHGUYmUjUo0RIRERGphL7fc4L4IynYvMwM71HAJBhpiXB8i/O5ThsUcTslWiIiIiKVjMNhMGX5PgDu7xlGoK/PxYV++da5bNAVfAs4rVBESkSJloiIiEgl899tx9l5PI3qVgt/u65ZwYUOr3Eum99QdoGJVCFKtEREREQqkdPpWbz6zW4AHu7djNo1rAUXTNjgXDbuUUaRiVQtSrREREREKpHoL7ZzKj2L5oE1+VvvpgUXSj0KqQlgskCDLmUboEgVoURLREREpJL4z+ajfLcrGS+ziXfu6VjwTIMARzY6l8HtwOZbdgGKVCFKtETcZWWM8yEiIuIB+0+c5cWvdgDw6PVXcXWI/6ULH/n9tMGGXcsgMpGqSYmWiIiISAV3NjOHRz7ZwrlsO92b1mbs9c0vv0PCeudS12eJlBolWiIiIiIVmMNh8MSCeH45kU6gr41/3dMJi9l06R3O/wZJ25zPG0WUTZAiVZASLREREZEKbNrK/azYfQKrxcyHI7oS6FfAPbMulLABDAfUuQr8QsomSJEqSImWiIiISAUVuzuZt1c4b0z86qCrad8w4Mo7JaxzLkPDSy8wEVGiJSIiIlIRHTlzjqiFP2MYMDS8EYOvaVS4HQ+sdC6bXFd6wYmIEi0RERGRiuZUehb3z95I6vkc2jf0Z8LAqwu3Y/qJP67Patqn1OITESVaIiIiIhVKWmYOo+b8xIGTGdT39+GDYV2xehXyX7pDq53L4PbgG1R6QYoIXp4OQEREREQK53R6FvfP/ontx1IJqO7Nv0eFE+x/hckvLnR4jXMZ1qt0AhQRFyVaIiIiIhVAyrlshn64gT1JZ6lV3ZtPHgyneWDNolXy6+8TYYT1dH+AIpKPEi0RERGRci4tM4fhszayJ+ks9XxtfDY6nOaBvkWr5NwZOLXX+VwzDoqUOl2jJSIiIlKOpWflct+HG9h2NJXaNazMGXlN0ZMs+OP6rHqtoEZd9wYpIhfRiJaIiIhIOZWZY+dv/97EtqPOa7I+GRVOmxC/4lV2INa5bNrXfQGKyCVpREvE3VbGOB8iIiIlYBgGz/xnG2v3n6a61cJHI64pfpIFf0yE0ex69wQoIpelREtERESkHPpozSG+ij+OxWxi5vCudGlcq/iVZZyCMwedz0OvcU+AInJZSrREREREypl9yWd5a9keAF4c0JqezUt4TdWRDc5lvdZQrQQJm4gUmhItERERkXLEMAwmfLWTHLtB35b1GB4RVvJKj291Lht0LnldIlIoSrREREREypHY3SeIO3gaq8XMq4PaYjabSl7p8XjnMqRTyesSkUJRoiUiIiJSTuTYHbyxdDcAI68No2Gt6iWv1DDg2Gbnc41oiZQZJVoiIiIi5cRX8cc5eCqDWtW9Gdu3uXsq/e0QnD8DFisEtXNPnSJyRUq0RERERMoBu8PgvZX7ARh9XVN8fbzdU3HeaYNBbcHL6p46ReSKlGiJFIXukSUiIqXkq/hjHDyVQUB1b4Z1b+y+ihN/di7rd3BfnSJyRUq0RERERDzM4TCY9v3vo1m93DiaBXBil3MZ3NZ9dYrIFbk90XrppZcwmUz5Hq1atXJtz8zMZMyYMdSpU4eaNWty5513kpycnK+OhIQEBgwYQPXq1QkMDOSpp54iNzc3X5lVq1bRuXNnbDYbzZs3Z86cOe5uikjhaJRLRERKaNnOJA6eysDXx4sRPcLcV7Fh/DGiFdzeffWKyBWVyojW1VdfTWJiouuxZs0a17YnnniC//73vyxatIgffviB48ePc8cdd7i22+12BgwYQHZ2NuvWrWPu3LnMmTOH8ePHu8ocOnSIAQMG0LdvX+Lj4xk3bhwPPvgg3377bWk0R+RiSqxERMRNDMNg+u/XZo3sEUZNm5f7Kk8/AenJYDI7r9ESkTLjxr/kCyr18iI4OPii9ampqXz00UfMmzeP66+/HoDZs2fTunVr1q9fT/fu3fnuu+/YtWsXK1asICgoiI4dO/Lqq6/yzDPP8NJLL2G1WpkxYwZNmjRh8uTJALRu3Zo1a9bw9ttvExkZWRpNEhERESkVWxJS2Hk8DZuXmZE9m7i38rzTBms1AasbpooXkUIrlRGtX375hZCQEJo2bcrQoUNJSEgAYPPmzeTk5NCvXz9X2VatWtGoUSPi4uIAiIuLo127dgQFBbnKREZGkpaWxs6dO11lLqwjr0xeHZeSlZVFWlpavoeIiIiIJ32y/lcABnYIoVYNN88KeHKPcxnY2r31isgVuT3RCg8PZ86cOSxbtoz333+fQ4cO0atXL86ePUtSUhJWq5WAgIB8+wQFBZGUlARAUlJSviQrb3vetsuVSUtL4/z585eMLSYmBn9/f9cjNDS0pM0VuTRduyUiIleQej6HJdsTARga3sj9Bzi1z7ms28L9dYvIZbk90br55pv561//Svv27YmMjGTp0qWkpKSwcOFCdx+qyKKjo0lNTXU9jhw54umQpJKKO3ja9RCRim369OmEhYXh4+NDeHg4GzduvGTZmTNn0qtXL2rVqkWtWrXo16/fZcuLLN56jOxcBy2DfOkYGuD+A5x2XvtF3avcX7eIXFapT+8eEBBAixYt2L9/P8HBwWRnZ5OSkpKvTHJysuuaruDg4ItmIcx7faUyfn5+VKtW7ZKx2Gw2/Pz88j1ESlvcR08S99GTvL18H28v3+fpcESkCBYsWEBUVBQTJkxgy5YtdOjQgcjISE6cOFFg+VWrVjFkyBBWrlxJXFwcoaGh3HTTTRw7dqyMI5eK4j9bjgIw+JpQTCaT+w9w6hfnso4SLZGyVuqJVnp6OgcOHKB+/fp06dIFb29vYmNjXdv37t1LQkICERERAERERLB9+/Z8ndjy5cvx8/OjTZs2rjIX1pFXJq8OkUvJS3aU9IhIYUyZMoXRo0czcuRI2rRpw4wZM6hevTqzZs0qsPynn37K3//+dzp27EirVq348MMPcTgcF/VZIgC/ns5g29FULGYTt3UMcf8Bzp2Bs87TEglsdfmyIuJ2bp918Mknn2TgwIE0btyY48ePM2HCBCwWC0OGDMHf359Ro0YRFRVF7dq18fPz49FHHyUiIoLu3bsDcNNNN9GmTRuGDRvGxIkTSUpK4oUXXmDMmDHYbDYAHn74YaZNm8bTTz/NAw88wPfff8/ChQtZsmSJu5sj4jbdEz4A4O3lD7nWPXGjzpkXKa+ys7PZvHkz0dHRrnVms5l+/fpdcfKlPOfOnSMnJ4fatWsXuD0rK4usrCzXa03SVLUs3+U8Oye8SW3q1rS5/wB5E2H4NwKbr/vrF5HLcvuI1tGjRxkyZAgtW7bk7rvvpk6dOqxfv5569eoB8Pbbb3Prrbdy5513ct111xEcHMwXX3zh2t9isfDNN99gsViIiIjgvvvuY/jw4bzyyiuuMk2aNGHJkiUsX76cDh06MHnyZD788ENN7S4iIm5z6tQp7HZ7gZMv5U3OdCXPPPMMISEhF82Um0eTNFVt3/2eaN3YJugKJYsp2Tlbs0azRDzD7SNa8+fPv+x2Hx8fpk+fzvTp0y9ZpnHjxixduvSy9fTp04etW7cWK0aR8uLC0xc1uiVSubz55pvMnz+fVatW4ePjU2CZ6OhooqKiXK/T0tKUbFURGVm5bPn1NwBuaFVKidaJ3c6lpnYX8YhSuWGxSJWgqdtFKrW6detisVgKnHwpb3KmS5k0aRJvvvkmK1asoH379pcsZ7PZXKfFS9USd+A0uQ6D0NrVaFSnlG4knLTduQy+9O+giJQeJVoiblJaU7lfOOqVd53X+kbO67w0CiZSeqxWK126dCE2NpZBgwYBuCa2GDt27CX3mzhxIq+//jrffvstXbt2LaNopaJZd8DZZ1zbvF7pHMDhgBO7nM+Dri6dY4jIZSnREiknNAuiSPkTFRXFiBEj6Nq1K926dWPq1KlkZGQwcuRIAIYPH06DBg2IiXGOcL/11luMHz+eefPmERYW5rqWq2bNmtSsWdNj7ZDyZ/3vX871aFandA6Qdgyy08HsBXWal84xROSylGhJhVZVk5O8kS2Y5NE4RCq7wYMHc/LkScaPH09SUhIdO3Zk2bJlrgkyEhISMJv/mFfq/fffJzs7m7vuuitfPRMmTOCll14qy9ClHMvMsbMnyTnDZNewWqVzkOQdzmXdFmDxLp1jiMhlKdES8ZDuCR+4TgEUkfJr7NixlzxVcNWqVfleHz58uPQDkgpvb9JZHAbUrmEl2K/giVJKLPFn57J+h9KpX0SuqNRvWCwiIiIif9iXfBaAlkG+mEym0jnIsc3OZUin0qlfRK5II1oihVHVZxjMa3/f6MuXExGRK9p53HnaYKv6pXQTYYcDjmx0Pm+oCVlEPEWJllRpuo+ViIiUtW1HUwDoGBpQOgc4/QtkpoBXNQjWqYMinqJES6QCc2uiuDJGI1YiIqXM7jDYm+Q8dbB1fb/SOcjhNc5lgy5g0b96Ip6ivz6RSkKjcyIi5d/Bk+lkZNupbrXQrF4pTfm/P9a5bNandOoXkUJRoiVSxv6Yml1ERKqavb9PhHFVkC8WcylMhGHPhUM/OJ83v9H99YtIoWnWQZFyqHvCB0rIREQqoc2//gZA+wb+pXOAxHjnjYpt/hDcrnSOISKFokRLREREpIzkzTjYqVFA6Rwg7/qssGvBbCmdY4hIoejUQZFKSNdriYiUP4ZhsCfRmWi1CCqlqd0PrnQum/YunfpFpNA0oiVSgbn9FMOVMbpnmIhIKTmWcp60zFy8LabSSbQy0+DwWufzZte7v34RKRIlWiIepGuxRESqjn2/T4QRVqcGVq9S+Bds/3Jw5ECdq6DuVe6vX0SKRKcOilRyF55GCDqVUETEU+KPpALQrmEpTYSx71vnstUtpVO/iBSJEi2RKqbcXL+Vd4qibpIsIlXEruO/J1qlNeNg3kQYzW4onfpFpEiUaImUQNzB054OoXTpei0REbfZneg8dfDqkFJItM4mQdoxMJmhQRf31y8iRaZES6QcyLtOa32jhy673d3yjW7p00BEpNSczczhWMp5AFqWxkQYRzY6l/Vaga2m++sXkSLTZBgilcDlEjFNuCEi4nl7k5yjWfX9ffCv7u3+Axzb5FyGdnN/3SJSLPoOW0QKr7DXVa2M0bVXbqLJTEQqh72/zzhYavfPOvp7oqXTBkXKDSVaIkVUXq/LutLphyIi4jk7jztvVNwquBQSrdysPxKtRj3cX7+IFIsSLZEq4kqJ2IUJZASaBENExJ1+PpICQIfQAPdXfvQnsGdBjUCo08z99YtIsSjREvlduZn2XEREKhW7w+CXE+kAXB3i5/4DHFrtXIZdCyaT++sXkWJRoiVSjlWJSSx0P62L/Pm6LBGp2I7+do7sXAdWLzMNa1V3/wHyEq2mfdxft4gUmxItkSt4e/k+uieUz+uyLqckSVq+0wib1nFHOCIiVVbe9Vktg3yxmN084pRzHo5tdj4Pu9a9dYtIiSjREqnirpSQ/XnyDyVeIiJFs+/3GQdLZSKMpO1gz4aaQbo+S6ScUaIlUsmU9umGcQdPsz7XeWqbrmUTEbmyvHtolcrU7sk7ncugtu6vW0RKRImWSAF0jYyUtbyRw7wk9lI0aYtIxZM3EUbLUhnR2uZcBl3t/rpFpESUaIlIsemffs/S+y9S/mXnOjh8KgOApvVquP8Ax+Ody5CO7q9bREpEiZbIZVSJWf/c5MJ/+rsnnL7kyMwTXv8pq5BERDxu/4l0ch0Gvj5eNAio5t7KczIheYfzeUgn99YtIiWmREtESsWlbpB8yRkNNc27iFRCeRNhtAzyxeTue1wd/en3iTCCoVYT99YtIiWmREukHCnLEbTyNlp34TVKVeU0uHyjgB6MQ0RKz/ZjqUAp3aj48I/OpW5ULFIuKdGSKuVSoyziGX+eOj7P5SYjqSpJWFHpei2R8smVaDXwd3/lh9c6l02uc3/dIlJiZk8HICIiIlIZ5dod/HwkBYDOjWq5ufJsOLbJ+byRxsRFyiONaIlIhXKp0S6N4ohIeXP4dAZZuQ6qeVtoWtfNMw4mxkNuJlSvA3X1+SdSHinREpFKQafOiUh5s+XXFADaNfDHbHbzNVT7VziXjXvq+iyRckqJlkgVU9aTYHjiujhd4yUi5cG2YykAdGwU4N6KDQN2fe183qK/e+sWEbdRoiUiVYpOPRSRsrI1IQWA9g3dPBHG4TVwcjd4VYNWt7i3bhFxGyVaUqFcbqRCyrfyNp38n5XVqYf6HRapGrJy7exNct5Dq5M7J8IwDFj1+30HOw6Bam6eZENE3EaJlkgByntSUF5VlvdNpx6KSEntOp5GrsOgVnVvQvx93FfxL8vh17Xg5QO9/uG+ekXE7ZRoSZVRmCSgsiQKpa0q34+sqCNfnhjB0sQgIp6Xd5/Aa8JqY3LXZBVnDsHiR5zPu44C/4buqVdESoUSLREpMSWo+en0QBHZfPg3ALo1qe2eCo/8BPOHwLlTENwO+j7nnnpFpNQo0ZIqqSqPyIj7KKESkYKkns9h7YFTAHRvWqfkFe77FhaOgNzzENAIhn4Otpolr1dESpUSLSn39M+siIhUJDN+OEBmjoOWQb5cHeJX/Iqyz8EPb8GmWc4kq9n1cPfHYPN1X7AiUmqUaImIiIi4yU+HzzDjhwMAPN7vqqJfn+Www4HvYeXrkLgNDLtzfVA7uHchWLzdHLGIlBYlWiJSbLo2q/z784iwJscQKT1ZuXae/3I7hgF/6dSAm9sGF62C1f+En2bB2eN/rPOuAde/AOF/A7PFvQGLSKlSoiWCEgapOHR9oUj55HAYvPT1LvYlp1OnhpUXb21TtNGslTHww5t/vG7eDzreC61v0yiWSAWlREsqvcslUUqwyh8lEiJSkaSez+Hr+GMs2HSEHcfSAHjjjnbUrmEtfCVbP/0jyer1JFz3FHi78d5bIuIRZk8HUFLTp08nLCwMHx8fwsPD2bhxo6dDEhEpt95evs/1kMIpaj+zaNEiWrVqhY+PD+3atWPp0qVlFKmUFcMw2H8inee/3E6311fw4lc72XEsDR9vMxPvak/k1UU4ZTAtEb6Ndj7v8xzc8KKSLJFKokKPaC1YsICoqChmzJhBeHg4U6dOJTIykr179xIYGOjp8KQE9E+gSH4affWMovYz69atY8iQIcTExHDrrbcyb948Bg0axJYtW2jbtq0HWlC55dodpGflci7bztnMXByGgd1hkOswsDsc5Nqdz3MdBrl2x+/rna8dvy/TM3M4n+OgutVCzu9lcu0OcuwGOXYH2bkOsnKdy4zsXH47l80vyemcOJvliqNFUE3u7hrKwA4hBPkVIUk6dwYWjYDMVKjfAa59ohTeJRHxFJNhGIangyiu8PBwrrnmGqZNmwaAw+EgNDSURx99lGefffaK+6elpeHv709qaip+fiWYfrUcujBRqYgXv7sz0dI/qBVTVTt18EqnTP759/jCcu443bKsPycqyudvUfuZwYMHk5GRwTfffONa1717dzp27MiMGTOueLyK8r4UhmE4E5m8ROVcdi6n0rP5LSOblPPZnMu2cz7bTmaO3ZXIZP3+yMyxcyo9i8TUTAJ9bb+vt5OZ88cyM9vO2axcj7XPajHTvVkdRvdqwrXN6xbteqzsc7DrK1j6FGSfBbMXPLTKeSNiEfEYd38GV9gRrezsbDZv3kx0dLRrndlspl+/fsTFxRW4T1ZWFllZf3wDlZqaCjjf1Ipq+vf7r1jmcu271P5jrm9eqGNcWM6dMjPSS1zHNUdnA5BR4prEE9zxO1CRZJx3fjZdqt152/NcWO5K+xbGpT4n/vz3766/+bzjlefv+orTz8TFxREVFZVvXWRkJIsXLy6wvLv7pbgDp/lg9QGycx0YgGGA6x02jAvW/fHcuemPn4Nr++/7Gr/v59yYf12+sgYkpmZSt6aVzBw753Mc2B0l//kW5ms3b4sZH28TZzPtBPra8LKY8DKbsJhNeJnNmM0mvM0mvCxmLCbneovFhMUMXmbnfrl2Bw1qVcNiNuFtNuNlMeFtMWP1NmMzm/H2MuPjbSagmpUGtapxdYg/1azOWQDPnj175SBPH4DvXoDDP+Zfb/aG4V9B9cZQgf8fEakM3N03VdhE69SpU9jtdoKCgvKtDwoKYs+ePQXuExMTw8svv3zR+tDQ0FKJsbx4rhT3KU7dIoUzzdMBeEhh211QueK/Z576mz979iz+/v5urtU9itPPJCUlFVg+KSmpwPKVsV86UsWPXyxv9PJ0BCJyAXf1TRU20SqO6OjofN80OhwOzpw5Q506dTCZTKSlpREaGsqRI0cq/Ckbl1MV2lkV2ghqZ2VT1dq5a9cuQkJCPB2OR6lfclI7Kxe1s3KpCu3Ma2NCQgImk8ltfVOFTbTq1q2LxWIhOTk53/rk5GSCgwue7cdms2Gz2fKtCwgIuKicn59fpf1FulBVaGdVaCOonZVNVWlngwYNMJvL7+S3xelngoOD1S+VgNpZuaidlUtVaKe/v79b21h+e7grsFqtdOnShdjYWNc6h8NBbGwsERERHoxMREQqg+L0MxEREfnKAyxfvlz9kohIFVRhR7QAoqKiGDFiBF27dqVbt25MnTqVjIwMRo4c6enQRESkErhSPzN8+HAaNGhATEwMAI8//ji9e/dm8uTJDBgwgPnz57Np0yY++ECzn4qIVDUVOtEaPHgwJ0+eZPz48SQlJdGxY0eWLVt20YXIhWWz2ZgwYcJFp3FUNlWhnVWhjaB2VjZqZ/lzpX4mISEh3+mPPXr0YN68ebzwwgs899xzXHXVVSxevLjY99CqSO9VSaidlYvaWblUhXaWVhsr9H20REREREREyqMKe42WiIiIiIhIeaVES0RERERExM2UaImIiIiIiLiZEi0RERERERE3q3KJ1vTp0wkLC8PHx4fw8HA2btx42fKLFi2iVatW+Pj40K5dO5YuXVpGkRZfUdo4c+ZMevXqRa1atahVqxb9+vW74ntSXhT1Z5ln/vz5mEwmBg0aVLoBuklR25mSksKYMWOoX78+NpuNFi1aVLrfW4CpU6fSsmVLqlWrRmhoKE888QSZmZllFG3RrV69moEDBxISEoLJZGLx4sVX3GfVqlV07twZm81G8+bNmTNnTqnHWVJFbecXX3zBjTfeSL169fDz8yMiIoJvv/22bIItJ6pCvwTqm65EfVP5pL7pYuqbisCoQubPn29YrVZj1qxZxs6dO43Ro0cbAQEBRnJycoHl165da1gsFmPixInGrl27jBdeeMHw9vY2tm/fXsaRF15R23jvvfca06dPN7Zu3Wrs3r3buP/++w1/f3/j6NGjZRx50RS1nXkOHTpkNGjQwOjVq5dx++23l02wJVDUdmZlZRldu3Y1brnlFmPNmjXGoUOHjFWrVhnx8fFlHHnRFLWdn376qWGz2YxPP/3UOHTokPHtt98a9evXN5544okyjrzwli5dajz//PPGF198YQDGl19+ednyBw8eNKpXr25ERUUZu3btMt59913DYrEYy5YtK5uAi6mo7Xz88ceNt956y9i4caOxb98+Izo62vD29ja2bNlSNgF7WFXolwxDfZP6JvVN5ZX6poK5q2+qUolWt27djDFjxrhe2+12IyQkxIiJiSmw/N13320MGDAg37rw8HDjb3/7W6nGWRJFbeOf5ebmGr6+vsbcuXNLK0S3KE47c3NzjR49ehgffvihMWLEiArRmRW1ne+//77RtGlTIzs7u6xCdIuitnPMmDHG9ddfn29dVFSU0bNnz1KN010K8yH/9NNPG1dffXW+dYMHDzYiIyNLMTL3Kkw7C9KmTRvj5Zdfdn9A5VBV6JcMQ32T+ib1TRWB+qbLK07fVGVOHczOzmbz5s3069fPtc5sNtOvXz/i4uIK3CcuLi5feYDIyMhLlve04rTxz86dO0dOTg61a9curTBLrLjtfOWVVwgMDGTUqFFlEWaJFaedX3/9NREREYwZM4agoCDatm3LG2+8gd1uL6uwi6w47ezRowebN292ncJx8OBBli5dyi233FImMZeFivb54y4Oh4OzZ8+W688gd6kK/RKob1LfpL5JfVPFV9y+yauU4il3Tp06hd1uJygoKN/6oKAg9uzZU+A+SUlJBZZPSkoqtThLojht/LNnnnmGkJCQi/6IypPitHPNmjV89NFHxMfHl0GE7lGcdh48eJDvv/+eoUOHsnTpUvbv38/f//53cnJymDBhQlmEXWTFaee9997LqVOnuPbaazEMg9zcXB5++GGee+65sgi5TFzq8yctLY3z589TrVo1D0VWuiZNmkR6ejp33323p0MpdVWhXwL1Teqb1Depb6r4its3VZkRLbmyN998k/nz5/Pll1/i4+Pj6XDc5uzZswwbNoyZM2dSt25dT4dTqhwOB4GBgXzwwQd06dKFwYMH8/zzzzNjxgxPh+ZWq1at4o033uC9995jy5YtfPHFFyxZsoRXX33V06FJCcybN4+XX36ZhQsXEhgY6OlwpJxQ31TxqW9S31SRlaRvqjIjWnXr1sVisZCcnJxvfXJyMsHBwQXuExwcXKTynlacNuaZNGkSb775JitWrKB9+/alGWaJFbWdBw4c4PDhwwwcONC1zuFwAODl5cXevXtp1qxZ6QZdDMX5edavXx9vb28sFotrXevWrUlKSiI7Oxur1VqqMRdHcdr54osvMmzYMB588EEA2rVrR0ZGBg899BDPP/88ZnPF/w7pUp8/fn5+lfIbw/nz5/Pggw+yaNGicj1q4U5VoV8C9U3qm9Q3qW+quEraN1X8n3ghWa1WunTpQmxsrGudw+EgNjaWiIiIAveJiIjIVx5g+fLllyzvacVpI8DEiRN59dVXWbZsGV27di2LUEukqO1s1aoV27dvJz4+3vW47bbb6Nu3L/Hx8YSGhpZl+IVWnJ9nz5492b9/v6uzBti3bx/169cvlx0ZFK+d586du6jDyuvAnde5VnwV7fOnJD777DNGjhzJZ599xoABAzwdTpmpCv0SqG9S36S+CdQ3VURu6ZuKPOVGBTZ//nzDZrMZc+bMMXbt2mU89NBDRkBAgJGUlGQYhmEMGzbMePbZZ13l165da3h5eRmTJk0ydu/ebUyYMKHcT6Nb1Da++eabhtVqNT7//HMjMTHR9Th79qynmlAoRW3nn1WUmZ2K2s6EhATD19fXGDt2rLF3717jm2++MQIDA43XXnvNU00olKK2c8KECYavr6/x2WefGQcPHjS+++47o1mzZsbdd9/tqSZc0dmzZ42tW7caW7duNQBjypQpxtatW41ff/3VMAzDePbZZ41hw4a5yudNofvUU08Zu3fvNqZPn14hptAtajs//fRTw8vLy5g+fXq+z6CUlBRPNaFMVYV+yTDUN6lvUt9UXqlvKt2+qUolWoZhGO+++67RqFEjw2q1Gt26dTPWr1/v2ta7d29jxIgR+covXLjQaNGihWG1Wo2rr77aWLJkSRlHXHRFaWPjxo0N4KLHhAkTyj7wIirqz/JCFaUzM4yit3PdunVGeHi4YbPZjKZNmxqvv/66kZubW8ZRF11R2pmTk2O89NJLRrNmzQwfHx8jNDTU+Pvf/2789ttvZR94Ia1cubLAv7W8do0YMcLo3bv3Rft07NjRsFqtRtOmTY3Zs2eXedxFVdR29u7d+7Llq4Kq0C8Zhvomw1DfpL6p/FHfNMIwjNLrm0yGUUnGMkVERERERMqJKnONloiIiIiISFlRoiUiIiIiIuJmSrRERERERETcTImWiIiIiIiImynREhERERERcTMlWiIiIiIiIm6mREtERERERMTNlGiJiIiIiIi4mRItERERERERN1OiJSIiIiIi4mZKtERERERERNxMiZaIiIiIiIib/T+L4r0kb+kwqwAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 19
+  },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "# Appendix - Internal methodology of the cloud field generation\n",
+    "## Appendix - Internal methodology of the cloud field generation\n",
     "It's worth looking in more detail at the internal processes of the cloud generation methodology to better understand what's happening. \n",
     "\n",
     "Cloud field relies on generating various scales of random noise and adding them together. The job of the function `_random_at_scale` is to generate a random field at a given scale and then interpolate it to a higher resolution. This function will be called at each level of the wavelet decomposition to generate the cloud field with different scaling factors. "
diff --git a/demos/synthetic_clouds_demo.py b/demos/synthetic_clouds_demo.py
index cf9ea70..c6306bb 100644
--- a/demos/synthetic_clouds_demo.py
+++ b/demos/synthetic_clouds_demo.py
@@ -204,4 +204,25 @@
 axs[1].set_aspect('equal')
 axs[1].set_xlabel('X Position')
 axs[1].set_ylabel('Y Position')
+plt.show()
+
+# It might also be interesting to compare the statistical distribution of the true and simulated timeseries. We can do this by comparing the histograms and CDFs of the two time series.
+# show histograms and CDFs
+fig, axs = plt.subplots(2, 2, figsize=(10, 8))
+axs[0,0].hist(kt[40], bins=100, alpha=0.5, label='True')
+axs[0,0].hist(sim_kt[40], bins=100, alpha=0.5, label='Simulated')
+axs[0,0].legend()
+axs[0,0].set_title('Hist - Sensor 40')
+axs[0,1].ecdf(kt[40], label='True')
+axs[0,1].ecdf(sim_kt[40], label='Simulated')
+axs[0,1].set_title('CDF - Sensor 40')
+axs[0,1].legend()
+axs[1,0].hist(kt.values.flatten(), bins=100, alpha=0.5, label='True')
+axs[1,0].hist(sim_kt.values.flatten(), bins=100, alpha=0.5, label='Simulated')
+axs[1,0].legend()
+axs[1,0].set_title('Hist - All Sensors')
+axs[1,1].ecdf(kt.values.flatten(), label='True')
+axs[1,1].ecdf(sim_kt.values.flatten(), label='Simulated')
+axs[1,1].set_title('CDF - All Sensors')
+axs[1,1].legend()
 plt.show()
\ No newline at end of file
diff --git a/docs/sphinx/source/demos/synthetic_clouds_demo.nblink b/docs/sphinx/source/demos/synthetic_clouds_demo.nblink
new file mode 100644
index 0000000..e0927ff
--- /dev/null
+++ b/docs/sphinx/source/demos/synthetic_clouds_demo.nblink
@@ -0,0 +1,3 @@
+{
+  "path":"../../../../demos/synthetic_clouds_demo.ipynb"
+}
\ No newline at end of file
diff --git a/docs/sphinx/source/examples.rst b/docs/sphinx/source/examples.rst
index a5d59d6..f132c56 100644
--- a/docs/sphinx/source/examples.rst
+++ b/docs/sphinx/source/examples.rst
@@ -21,6 +21,16 @@ Field Analysis Examples
    demos/field_demo_detailed
    demos/field_reassignment_demo
 
+.. _synthirrad-examples:
+
+Synthetic Irradiance Examples
+-----------------------------
+
+.. toctree::
+   :maxdepth: 1
+
+   demos/synthetic_clouds_demo
+
 Other Examples
 --------------
 
diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst
index 71aaf6e..f1efddf 100644
--- a/docs/sphinx/source/index.rst
+++ b/docs/sphinx/source/index.rst
@@ -26,6 +26,8 @@ The :mod:`solarspatialtools.cmv` module contains functions for calculating the c
 
 The :mod:`solarspatialtools.field` module contains functions for validating the layout of a PV plant or measurement network by calculating the relative delays between each sensor in the network subject to cloud motion.
 
+The :mod:`solarspatialtools.synthirrad` package contains functions for downscaling and generation of synthetic irradiance timeseries.
+
 The best starting point is to read through the :ref:`cmv-examples` and :ref:`field-examples` sections to see some sample Jupyter notebooks that demonstrate how these functions can be used in practice.
 
 
@@ -38,6 +40,7 @@ Contents
 
    cmv
    field
+   synthirrad
    othermods
 
 .. toctree::
diff --git a/docs/sphinx/source/synthirrad.rst b/docs/sphinx/source/synthirrad.rst
new file mode 100644
index 0000000..40eebfd
--- /dev/null
+++ b/docs/sphinx/source/synthirrad.rst
@@ -0,0 +1,25 @@
+.. currentmodule:: solarspatialtools
+
+Synthetic irradiance generation
+----------------------------------
+The `solarspatialtools.synthirrad` package contains tools for generating synthetic irradiance timeseries and performing downscaling of timeseries. The package implements the following approaches:
+
+cloudfield
+==========
+
+Generate a simulated field of clouds from which spatially distributed timeseries of kt can be extracted. The field distributions are based on the properties of a time series of kt values. This is an implementation of the method described by Lave et al [1]. Some aspects of the implementation diverge slightly from the initial paper to follow a subsequent code implementation of the method shared by the original authors.
+
+ [1] Matthew Lave, Matthew J. Reno, Robert J. Broderick, "Creation and Value of Synthetic High-Frequency Solar Inputs for Distribution System QSTS Simulations," 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), Washington, DC, USA, 2017, pp. 3031-3033, doi: https://dx.doi.org/10.1109/PVSC.2017.8366378.
+
+.. automodule:: solarspatialtools.synthirrad.cloudfield
+
+
+    .. rubric:: Functions
+
+    .. autosummary::
+        :toctree: generated/
+
+            get_timeseries_stats
+            cloudfield_timeseries
+
+
diff --git a/src/solarspatialtools/spatial.py b/src/solarspatialtools/spatial.py
index ecc5023..7855015 100644
--- a/src/solarspatialtools/spatial.py
+++ b/src/solarspatialtools/spatial.py
@@ -314,7 +314,7 @@ def rotate_vector(vector, theta):
     vector : (x, y) numeric
         A tuple (or numpy array) containing the input vector.
         To operate on multiple points, vector should be of the form:
-            ((x1, x2, x3, x4), (y1, y2, y3, y4))
+        ((x1, x2, x3, x4), (y1, y2, y3, y4))
 
     theta : numeric
         Angle of rotation in radians
diff --git a/src/solarspatialtools/synthirrad/cloudfield.py b/src/solarspatialtools/synthirrad/cloudfield.py
index 0bce911..980aa4b 100644
--- a/src/solarspatialtools/synthirrad/cloudfield.py
+++ b/src/solarspatialtools/synthirrad/cloudfield.py
@@ -570,9 +570,15 @@ def get_positional_ts(tgt_position, field, cloud_speed, duration=3600, pixres=1)
 
 
 
-def cloudfield_timeseries(weights, scales, size, frac_clear, ktmean, ktmax, kt1pct, edgesmoothing=3):
+def cloudfield_timeseries(weights, scales, size, frac_clear, ktmean, ktmax, kt1pct, scaling='original', edgesmoothing=3):
     """
-    Generate a time series of cloud fields based on the properties of a time series of kt values.
+    Generate a time series of cloud fields based on the properties of a time series of kt values. This is an
+    implementation of the method described by Lave et al [1]. Some aspects of the implementation diverge slightly from
+    the initial paper to follow a subsequent code implementation of the method shared by the original authors.
+
+    [1] Matthew Lave, Matthew J. Reno, Robert J. Broderick, "Creation and Value of Synthetic High-Frequency Solar Inputs
+    for Distribution System QSTS Simulations," 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC),
+    Washington, DC, USA, 2017, pp. 3031-3033, doi: https://dx.doi.org/10.1109/PVSC.2017.8366378.
 
     Parameters
     ----------
@@ -590,6 +596,8 @@ def cloudfield_timeseries(weights, scales, size, frac_clear, ktmean, ktmax, kt1p
         The maximum of the kt values
     kt1pct : float
         The 1st percentile of the kt values
+    scaling : str
+        The scaling method to use. Either 'original' or 'basic'
     edgesmoothing : int
         The size of the uniform filter for edge smoothing
 
@@ -604,5 +612,11 @@ def cloudfield_timeseries(weights, scales, size, frac_clear, ktmean, ktmax, kt1p
 
     edges, smoothed = _find_edges(clear_mask, edgesmoothing)
 
-    field_final = _scale_field_lave(cfield, clear_mask, smoothed, ktmean, ktmax, kt1pct, plot=False)
+    if scaling == 'original':
+        field_final = _scale_field_lave(cfield, clear_mask, edges, ktmean, ktmax, kt1pct, plot=False)
+    elif scaling == 'basic':
+        field_final = _scale_field_basic(cfield, clear_mask, smoothed, ktmean, ktmax, kt1pct, plot=False)
+    else:
+        raise ValueError("Scaling method must be either 'original' or 'basic'.")
+
     return field_final