diff --git a/city_metrix/layers/__init__.py b/city_metrix/layers/__init__.py
index 65f826da..1f407e7e 100644
--- a/city_metrix/layers/__init__.py
+++ b/city_metrix/layers/__init__.py
@@ -3,7 +3,7 @@
 from .land_surface_temperature import LandSurfaceTemperature
 from .tree_cover import TreeCover
 from .high_land_surface_temperature import HighLandSurfaceTemperature
-from .smart_cities_lulc import SmartCitiesLULC
+from .smart_surface_lulc import SmartSurfaceLULC
 from .open_street_map import OpenStreetMap, OpenStreetMapClass
 from .urban_land_use import UrbanLandUse
 from .natural_areas import NaturalAreas
diff --git a/city_metrix/layers/smart_cities_lulc.py b/city_metrix/layers/smart_surface_lulc.py
similarity index 99%
rename from city_metrix/layers/smart_cities_lulc.py
rename to city_metrix/layers/smart_surface_lulc.py
index 18354a3d..d1540633 100644
--- a/city_metrix/layers/smart_cities_lulc.py
+++ b/city_metrix/layers/smart_surface_lulc.py
@@ -15,7 +15,7 @@
 from .building_classifier.building_classifier import BuildingClassifier
 
 
-class SmartCitiesLULC(Layer):
+class SmartSurfaceLULC(Layer):
     def __init__(self, land_cover_class=None, **kwargs):
         super().__init__(**kwargs)
         self.land_cover_class = land_cover_class
@@ -125,6 +125,7 @@ def get_data(self, bbox):
         # use chunk 512x512
         aligned_datasets = [ds.chunk({'x': 512, 'y': 512}) for ds in aligned_datasets]
         lulc = xr.concat(aligned_datasets, dim='Value').max(dim='Value')
+        lulc = lulc.compute()
 
         return lulc
 
diff --git a/notebooks/tutorial/get layers.ipynb b/notebooks/tutorial/get layers.ipynb
index f4438504..a6602a16 100644
--- a/notebooks/tutorial/get layers.ipynb	
+++ b/notebooks/tutorial/get layers.ipynb	
@@ -26,14 +26,15 @@
     "| Layer name | class name | Parameter defaults | Layer metadata |\n",
     "| ---- | ---- | ---- | ---- |\n",
     "| Tropical Tree Cover | `TreeCover()` | `min_tree_cover=None`: a threshold to use to filter the minimum percent of tree cover| |\n",
-    "| EsaWorldCover | `EsaWorldCover()` | `land_cover_class=None`; `EsaWorldCoverClass`: a specific class of land cover| |\n",
+    "| EsaWorldCover | `EsaWorldCover()` | `land_cover_class=None`; `EsaWorldCoverClass`: a specific class of land cover; `year=2020`| |\n",
     "| Land Surface Temeprature | `LandSurfaceTemperature()` | `start_date=\"2013-01-01\", end_date=\"2023-01-01\"` | |\n",
     "| Albedo | `Albedo()` | `start_date=\"2021-01-01\", end_date=\"2022-01-01\"` | |\n",
     "| Natural Areas | `NaturalAreas()` | `none` | |\n",
     "| Open Street Map | `OpenStreetMap()` | `osm_class=None`; `OpenStreetMapClass`: Groupings of OSM Tags for various land uses | |\n",
-    "| Building Hight | `AverageNetBuildingHeight()` | `none` | |\n",
+    "| Building Height | `AverageNetBuildingHeight()` | `none` | |\n",
     "| Building Footprints | `OpenBuildings()` | `country='USA'` | |\n",
-    "| 1m Global Tree Canopy Height  | `TreeCanopyHeight()` | | |"
+    "| 1m Global Tree Canopy Height  | `TreeCanopyHeight()` | | |\n",
+    "| Smart Surface LULC | `SmartSurfaceLULC()` | | |"
    ]
   },
   {
@@ -48,22 +49,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "7ed2c665-e6d8-4e98-95ac-41aab749493f",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'rasterio'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgeopandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgpd\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrasterio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mplot\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m show\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrasterio\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m \n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rasterio'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import geopandas as gpd\n",
@@ -74,51 +63,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "602a6217-fd80-4cec-b40b-20de68b8f62b",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <style>\n",
-       "                .geemap-dark {\n",
-       "                    --jp-widgets-color: white;\n",
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-       "                    --jp-layout-color2: #454545;\n",
-       "                    background-color: #383838;\n",
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-       "\n",
-       "                .geemap-dark .jupyter-button {\n",
-       "                    --jp-layout-color3: #383838;\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab {\n",
-       "                    background-color: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "\n",
-       "                .geemap-colab .jupyter-button {\n",
-       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-       "                }\n",
-       "            </style>\n",
-       "            "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/plain": [
-       "'/Users/Chris.Rowe'"
+       "'/home/weiqi_tori/GitHub/wri/cities-cif'"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -195,10 +150,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "53554a74-6fa9-4030-8ee7-dd1df79f0d75",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geo_id</th>\n",
+       "      <th>geo_level</th>\n",
+       "      <th>geo_name</th>\n",
+       "      <th>geo_parent_name</th>\n",
+       "      <th>creation_date</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BRA-Salvador_ADM4-union_1</td>\n",
+       "      <td>ADM4-union</td>\n",
+       "      <td>BRA-Salvador</td>\n",
+       "      <td>BRA-Salvador</td>\n",
+       "      <td>2022-08-03</td>\n",
+       "      <td>MULTIPOLYGON (((-38.50135 -13.01134, -38.50140...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      geo_id   geo_level      geo_name geo_parent_name  \\\n",
+       "0  BRA-Salvador_ADM4-union_1  ADM4-union  BRA-Salvador    BRA-Salvador   \n",
+       "\n",
+       "  creation_date                                           geometry  \n",
+       "0    2022-08-03  MULTIPOLYGON (((-38.50135 -13.01134, -38.50140...  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# load boundary from s3\n",
     "boundary_path = 'https://cities-indicators.s3.eu-west-3.amazonaws.com/data/boundaries/boundary-BRA-Salvador-ADM4union.geojson'\n",
@@ -4728,6 +4739,675 @@
     "city_TreeCanopyHeight.plot()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "44a16b52",
+   "metadata": {},
+   "source": [
+    "# Smart Surface LULC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "76a643de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Authenticating to GEE with configured credentials file.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from city_metrix.layers import SmartSurfaceLULC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ec06484e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
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+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get smaller area\n",
+    "city_centroid = city_gdf.centroid\n",
+    "city_centroid_buffer = city_centroid.buffer(0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b974a2d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
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+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting layer ESA world cover from Google Earth Engine for bbox [-38.43996975 -12.92755175 -38.41996975 -12.90755175]:\n",
+      "[########################################] | 100% Completed | 1.12 ss\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/weiqi_tori/anaconda3/envs/fenv/lib/python3.10/site-packages/xee/ext.py:683: UserWarning: Unable to retrieve 'system:time_start' values from an ImageCollection due to: No 'system:time_start' values found in the 'ImageCollection'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting layer urban land use from Google Earth Engine for bbox [-38.43996975 -12.92755175 -38.41996975 -12.90755175]:\n",
+      "[########################################] | 100% Completed | 920.33 ms\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/weiqi_tori/anaconda3/envs/fenv/lib/python3.10/site-packages/xee/ext.py:683: UserWarning: Unable to retrieve 'system:time_start' values from an ImageCollection due to: No 'system:time_start' values found in the 'ImageCollection'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting layer average net building height from Google Earth Engine for bbox [-38.43996975 -12.92755175 -38.41996975 -12.90755175]:\n",
+      "[########################################] | 100% Completed | 512.02 ms\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/weiqi_tori/anaconda3/envs/fenv/lib/python3.10/site-packages/geopandas/geodataframe.py:1528: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  super().__setitem__(key, value)\n"
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+       "  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.DataArray &#x27;reclassify&#x27; (y: 2210, x: 2170)&gt; Size: 38MB\n",
+       "array([[ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       ...,\n",
+       "       [ 2,  2,  2, ..., 30, 30,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2]])\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 17kB 5.607e+05 5.608e+05 ... 5.629e+05 5.629e+05\n",
+       "  * y            (y) float64 18kB 8.573e+06 8.573e+06 ... 8.571e+06 8.571e+06\n",
+       "    spatial_ref  int64 8B 0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'reclassify'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 2210</li><li><span class='xr-has-index'>x</span>: 2170</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d151424a-a024-4a35-8712-4236518e1e5d' class='xr-array-in' type='checkbox' checked><label for='section-d151424a-a024-4a35-8712-4236518e1e5d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ... 30 30 30 30 30 30 30 2 2 2 2 2 2 2 2</span></div><div class='xr-array-data'><pre>array([[ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       ...,\n",
+       "       [ 2,  2,  2, ..., 30, 30,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2]])</pre></div></div></li><li class='xr-section-item'><input id='section-314c142c-d70a-4c9a-9bb2-c16efcfac149' class='xr-section-summary-in' type='checkbox'  checked><label for='section-314c142c-d70a-4c9a-9bb2-c16efcfac149' 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'>5.607e+05 5.608e+05 ... 5.629e+05</div><input id='attrs-c3647d0d-d3e5-43cc-a559-e72f5f834e06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3647d0d-d3e5-43cc-a559-e72f5f834e06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8eeb523b-7dfa-46e2-ab94-13a76980c761' class='xr-var-data-in' type='checkbox'><label for='data-8eeb523b-7dfa-46e2-ab94-13a76980c761' 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>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([560749.386974, 560750.386974, 560751.386974, ..., 562916.386974,\n",
+       "       562917.386974, 562918.386974])</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'>8.573e+06 8.573e+06 ... 8.571e+06</div><input id='attrs-5760a242-efa1-4f5f-9c63-fd02fd294573' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5760a242-efa1-4f5f-9c63-fd02fd294573' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab56b809-abfe-4a41-9702-5918acb3e6e7' class='xr-var-data-in' type='checkbox'><label for='data-ab56b809-abfe-4a41-9702-5918acb3e6e7' 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>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([8573019.092218, 8573018.092218, 8573017.092218, ..., 8570812.092218,\n",
+       "       8570811.092218, 8570810.092218])</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-0ff31202-b479-4b92-9c85-ebccfbcb9aae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ff31202-b479-4b92-9c85-ebccfbcb9aae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-937523f0-b00b-426c-80fa-ca782197e0cd' class='xr-var-data-in' type='checkbox'><label for='data-937523f0-b00b-426c-80fa-ca782197e0cd' 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 24S&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;,-39],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],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;32724&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 24S</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>-39.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>10000000.0</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 24S&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;,-39],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],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;32724&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>560748.8869744056 1.0 0.0 8573019.592217797 0.0 -1.0</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-9a1c7537-2172-42d3-86c7-52a54cbe3c8c' class='xr-section-summary-in' type='checkbox'  ><label for='section-9a1c7537-2172-42d3-86c7-52a54cbe3c8c' 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-d0d8e415-3237-43fa-8b4e-5fdb900131bf' class='xr-index-data-in' type='checkbox'/><label for='index-d0d8e415-3237-43fa-8b4e-5fdb900131bf' 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([560749.3869744056, 560750.3869744056, 560751.3869744056,\n",
+       "       560752.3869744056, 560753.3869744056, 560754.3869744056,\n",
+       "       560755.3869744056, 560756.3869744056, 560757.3869744056,\n",
+       "       560758.3869744056,\n",
+       "       ...\n",
+       "       562909.3869744056, 562910.3869744056, 562911.3869744056,\n",
+       "       562912.3869744056, 562913.3869744056, 562914.3869744056,\n",
+       "       562915.3869744056, 562916.3869744056, 562917.3869744056,\n",
+       "       562918.3869744056],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=2170))</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-3b01283b-baed-442b-ab00-43caa95685cc' class='xr-index-data-in' type='checkbox'/><label for='index-3b01283b-baed-442b-ab00-43caa95685cc' 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([8573019.092217797, 8573018.092217797, 8573017.092217797,\n",
+       "       8573016.092217797, 8573015.092217797, 8573014.092217797,\n",
+       "       8573013.092217797, 8573012.092217797, 8573011.092217797,\n",
+       "       8573010.092217797,\n",
+       "       ...\n",
+       "       8570819.092217797, 8570818.092217797, 8570817.092217797,\n",
+       "       8570816.092217797, 8570815.092217797, 8570814.092217797,\n",
+       "       8570813.092217797, 8570812.092217797, 8570811.092217797,\n",
+       "       8570810.092217797],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=2210))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41c4ac96-552d-4ed3-a3f2-d9d59265c1dd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-41c4ac96-552d-4ed3-a3f2-d9d59265c1dd' 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>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'reclassify' (y: 2210, x: 2170)> Size: 38MB\n",
+       "array([[ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       ...,\n",
+       "       [ 2,  2,  2, ..., 30, 30,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2]])\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 17kB 5.607e+05 5.608e+05 ... 5.629e+05 5.629e+05\n",
+       "  * y            (y) float64 18kB 8.573e+06 8.573e+06 ... 8.571e+06 8.571e+06\n",
+       "    spatial_ref  int64 8B 0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load Smart Surface LULC layer\n",
+    "city_SmartSurfaceLULC = SmartSurfaceLULC().get_data(city_centroid_buffer.total_bounds)\n",
+    "city_SmartSurfaceLULC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "82163580",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/weiqi_tori/anaconda3/envs/fenv/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fd3d4c8d0f0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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zIwTT09NRvnx5PPjgg55lvXv39iozc+ZMvPfee/j666+RlJTkYwYFgKVLl6JatWpYvnw5hg8fjtzcXKSmpuKjjz5C165dAZRMeFyzZk1s2rQJ99xzj+FxEL92B/REGfhOLhyh8dWgx4l3ElF/ZJbUNsX5+ULlTn7QDAC86jfan9Z60X2KwjtXJ965PpuAmTbSdfDWs9vplRWpS6StvPJ61B+ZxS0reo8YtUvm/IjWySunVZ7dB31fnXumjWbdelR/a5ep7fQw2xYzONF+8nyx15v3HJvpt4rz87n9ixZa95BRvyKzD1GM+g/2GbTSPxfiGrbjM0djogoKCnDufBFO7q2NShXNW90uXipG3VancObMGVSqVMmzXMsaRnukmjZtinbt2qF+/fpYunQp2rZtCwA+1jU3x+Lmb0JaiHXo0AELFizA8ePH0aBBAxw4cADbt2/H/PnzDbdNTk5GXl4eGjdujKlTp3LdlYTU1FQMGDAAFSpU4K4vKirCxx9/jN9//x3t2rUDACEz6N69e3Ht2jWvMvHx8WjSpAl27NjBFWL5+fnIpx6qS5cuAQDKoCzKuDhCzFXO4Ez4kjjqJLIXN0fCsGPC2xS7inXXn1iWDABIGJSJiKhogGqX0f4iykejDOc4jPYpC+9cJY46iexFjQAAJxc3BwCfdmYvaoQIgNtGUgcAr2Mm2yUOO+a1PKJ8NKBRF9kPbz29jrTV6Pp52vXH9gSf41vc3FN3wrBjXudD5B4hx5TAHCvbbt4xa90bpE7SDq22k2Okj4/dD7sP+r669e19usemCedZtMqtb+/D2Yntba+XB68vsUxByXlNePIYTixLvn7/RMv3UQBQnJ/n9XeEq6zu88NCP9tlXOU89UUYHLvMPkTR6uPoPkLvOTLCq690l/zjD/FRqWKEJSHmqadSJS8hJkqFChXQtGlTZGdne2aZIWFMhPPnz6NatWqW22iFkA7Wnzx5MgYOHIiGDRuibNmySE5OxtixYzFw4EDNbeLi4rBw4UKsXr0aa9asQWJiIrp06YJt27Zxy+/evRuHDx/GE0884bPu0KFDuOGGGxAVFYW//vWv+OSTT9C4cWMA8AyXZS8wPZT23LlzKFeuHG666SbNMiyzZ89GbGys51ezZk3NYwVKOiu2w7ITkfqJCKs/KFO3HP2ylFnnNAnDjhkKDhnRahW9fdHrzJwzkeOwug8z++Xth3ddshc1Ei6rRURUdMnHgp8g++P9WOLm7UDcvB2OtcXp+gmkL7By//jrOhldE6tkL2pkS/9B+mLeL1AUuYst/6yQn5+PY8eOIS4uDnXr1kX16tWxceNGz/qCggJs3boV7dv75wNHi5AWYitXrkRaWhqWL1+Offv2YenSpZg3bx6WLl2quU1iYiKefPJJtGzZEu3atcO7776LXr16Yd68edzyqampaNKkCe644w5uXfv378fXX3+Np59+GoMHD8bRo0e9ypgxg+qVmTJlCnJzcz2/M2fO6NYVaERFmB7+FDn+gIgFnoiwAyeEktl92LF/ERGsJchk9h9IsS+L3YLJXwKMxg4xxqszXPqLULoftSiG2/JPhokTJ2Lr1q04efIkdu3ahb/85S+4ePEiBg8eDJfLhbFjx2LWrFn45JNPcPjwYQwZMgTly5fHww8/7NAZECOkXZPPPvssnnvuOQwYMABAiU/41KlTmD17NgYPHixcT9u2bZGWluaz/MqVK0hPT8crr7zC3a5cuXK47bbbAACtW7fGN998gzfffBPvv/8+qlevDkDfDFq9enUUFBTg119/9bKKnT9/XlOh640WsRPigrLSqYmIMLazsevr0EnYNsu0l2wratXS2qfROtqF6NQ5ZfdBlvHKWN2/SD1a4lD0nLPnkVg/XNHaz1tRbq5+ww0gz4hW+0gbtKwacfN2WHJX+lt8sdQflOl1DgIJe679aRXVgtwT4SDK/MGPP/6IgQMH4ueff8Ytt9yCtm3b4uuvv/bMlTxp0iRcvXoVI0aM8GQy2LBhQ0BziAEhLsSuXLniM/w2MjISxcVy5szMzEwvsURYtWoV8vPz8cgjjwjV43a7PfFbtBk0ObmkoyFm0Dlz5gAAWrVqhbJly2Ljxo3o378/AODs2bM4fPgw5s6dK3UMTmHlJSojwEKpw2Ff+Fpt5gkDOwSJ6DqeuLAb9rrx7hetNltpm5Ew491PrGgMdkSfPSKmRAVZoMUXi11i3Qoy+7eznVr3okifEgoUoxhWnIuyW6enp+uud7lcmDZtWtBNdRbSQqx3796YOXMmatWqhaSkJGRmZiIlJQXDhg3zlJkyZQpycnLw4YcfAgDmz5+POnXqICkpCQUFBUhLS8Pq1auxevVqn/pTU1PRt29fVKlSxWfd888/jx49eqBmzZq4dOkS0tPTkZGRgc8//xwAvMygCQkJSEhIwKxZs7zMoLGxsXj88ccxYcIEVKlSBZUrV8bEiRPRtGlTzyhKu/AJbBX42nPqRa4XGE7WO2VZcQr2xc+eO1mrmV55YqFx54mPGrXLKqW3jj5engWHved411TL8hMRFc0tb8ZKplde9DyZsYRFREV72sD7SKGP3YyVyEiQBZsAozF6vulzY2QlpC1aRv2eniU00PDORXF+nk/7AhkDZkSR240iC7MoWtk2lAhpIfb222/jxRdfxIgRI3D+/HnEx8dj+PDheOmllzxlzp49i9OnT3v+LigowMSJE5GTk4OYmBgkJSVh/fr16Nmzp1fdx48fx/bt27Fhwwbuvv/3v//h0UcfxdmzZxEbG4tmzZrh888/R7du3TxlRMygb7zxBsqUKYP+/ft7ErouWbLEa1oGJxAVZnYLICNXjD+sOP5C9pz5w31oZR9GQiZh2LGS4GCN7XkvjPqDMlGM6/ef0UuW3Z/svWLm+InoMvOC1tqGXU6EGS3WyPmUhXVXBrMAoxHta8ycE637yi7RpdcmvX2IeALodXRdwSzAFHKEtBCrWLEi5s+fr5uuYsmSJV5/T5o0CZMmTTKsu0GDBnDrqPHU1FTDOkTMoNHR0Xj77bfx9ttvG9YXDpCYkPqDMgE/iT+n8JdgtBoMb3e8WDBdm1C5V1i02myXCztUxBcPf1/PYLh3RAa/BEM7ZTETcM9uXxoI6VGTCv9gt5VKdARluFjG7IJ0xEW5udKuMTtGUurVYdcweaN6tO5Fq/cKvU+rdZ1YlqyZ6kDrZUqXD8UXrl1oWYjMpo4g22ndV1ZTPIhs69S+tXAy1YYsxXCjyMJPCTGFgsLfLsNQehk53VbRHFhGKRuMYqkClUPMKqLnxq66jKg/KFM6dQYQXJbGQCIzcOfsxPaenyL48Hf6ilBFCTEFAH4yQB52fakaQbvStPJulVbIOdfNhm8gxqyOWLQTmcB0veMyOiZPDJsfEl7KnufSfH/zzr+Z+5QWZWcntpe+vjL3hpn7Rq9ereVWP3acTkarsIeQjhFTOAvbKZD4LjszQQPeAah2xcgEAn9ZiNjrQs6VUy9/rUEVsvBeBFoj1vRyOcnuW/Q49UZsmoGIQ5H9s+tVILbc+ePBs5LJxM4FYvQp/VFC3wPsoBY9eM9OoO4nNWpSDCXEFFLYLcZoQlmEBQKj86R1jewO3jfCMzjDIiICk5fewp+wx8pLa6IQx0iM0aJIxD1phwvz7MT2jg+GCKZEt1Yo/uNnZfvSgHJNhhEyc9ZZgTc1iZX90F9rgXpRBavZXs9VouUeMRIisuJEbw47IxcNeZkYnV8nYrjYNBCiVgEjN6dWjjRyjORDha2TbpPCG/Z+ckVHeX4NRnzvuSZ695G/5uH0x4hU+jiVeA9/lEVMYQr6xVIaOgqZF2gwnA/W6qWHP66hiHXOzPa8+kh5+m9/Qe+fbYPWsQbD/RIK0OdPz8IqayUTqccfhIMFjIWMfrSyfWlACbEwIcJg/kmZRJkikHrqD8pE9h8dSMKwY6azPgeDpUAkRkg0i7tdbdCzAJCybEwIfW3YoH62rWbPO9mX0ewBZkfbsnGDMm5Yf4gwIwufiCALhns+2GHTtJBzJzKPLcAXU3riLBD519gZAOg+FbieTd/sTB2BpMhd8rOyfWlACTGFZUhnyAsm5U0zEkzoCVR/ZN3W2g8rdOiO12hqF3YZHW9ipgPX29bOtCY8wSUqwuh/SXmr953WvnlB0FovSlaQmR1YUdrQypNH7meg5L6k703R6x0syW7p+4i2hkXGxnr+nzDsGPDHvcXeMyJTPZG66BikCHcEID47msIPKCEWRogM31fI42SmaysCSQazgf12IuOeNGvVCtQ9TreXZwULZNvCFVqQibgsncDInSjbngYjvsfxd+t5LQvl8A8VrC+GEmJhwol3EhEB4wmNCU51WGatEJ4BAAZzUerBc5fxMPNyl+0MzZ4HLSuc1c5Yy+UpY9EymmzcahwYqYNsI1teb55LGVgroh48t7WWW1IvJYfCPF6DU6hrZncfp3U/sINjeO2h28Jef/rZoEWYUb8TzJ4GQjFcKILL0valASXEwoT6I7NQxlVOtwx5GRCTvr+/HkWwI/WAqCtLtLxoGSvYIWKc5vi79dBgxPc+y622UTb2JdhiZcwmmQ1lS0ewQgtennAS7fP0RDiJvZRpD1uf7H1OL1P3TPihhFgpgQ0GDWYxBnjHggBiQeuynSPZxh+WQh4RUdGmRAwbGyISuEyutd45EhGDWtYwWcyIYS1rmRNz9cnsX2uZyDqFdbSuPy/eUHRkotY1M0qhQcrQ24skMib7NPNc6D3/dj2vZil2l/ysbF8aUEKslELHVwSTGLPyUjUTcK8V/Ft/UKZQW8xORGwGow7+xLJkrguMdO5kPYvTAffsenq/onUSSIbxYEHvePWul9H5tnuUc2mENzOI1nm1y10s+yzpeQBEnw+6/5Ld1mmKLLomrWwbSighVsqhY2GCSZAFAvr42S9nf5wbs+5Jq+udaqORy07PwsQKtmASJOQcyIgwHsHyslTIYYd70EwKE6OypI8KplQXSoiJoYSYIiRclXrYYZXifT0TaEsZu07GLapVxu44MCtf93YG3JPt9JZrvYzoFBTBYgGjz4GoCCM5oNj14UIgLXdm9m21nTIfHFrb0SKeZwXTu8dEP0qMnjtFcKGEmAIA37wdiqLMCdjzYCYDtl3nUsT1Z2a9GTFoNq2HVj6tUHhJWAnAZ1/EitCCvmZ64kbmg4W9F5y4R1hRVnwlD3jClqoNKXa7UOy2MGrSwrahhBJiYQjvZSD69agVLxUognGoP+98GJ1fPfGmN/ydLhNM7km6XnZ5KIwAtQOfVAU6zx3BbEqWYHLN8qDdYU4MpuDNEEH2Y8c54mWvZ5H5aGCtory6tKxhenXbYdErdBfglKVaxFGuSTGUEAsj7OysteKl6DgEf+LEC5wNpLZ7oACN3ojFYBMnWm4TGlGhxXPNGe2XbBes8NxHTud74mXzDxWsCCU9gWe3FTV7USOp1BRayFhNZa1nem76YPxoVYihhJjCEL0gdn9hd4dL/0sIlOWP7ZT1gthFMOOe1Ip7sep2EV0frNYyEeueHYJAL04ulLC7zXruQLv3JfP8m7nmwXqPO0kRIlDkNcGS7PalAyXEwoTi/HyUia7o+H7s+GIkaHVmdn7pyrzgjLL6O22JEBU9Wi4arY5eZJmIe5TdXsQqxnPNGVkLgMC6w9k4HSvnQwat62/k7iQEq6VMpl28FB+sxdFoxK1ZZPIQyox4lJkgnkVvH0b3QTBYyNwWY8TcKkZMEYqEStCzEXYeh2w+nmAZ/s1zefFeQEYduNH2ZtpkB6wL1KudAYhP5M37qffS5VkyZc6rUflQG8hgJ3ojDO28n0XaoIWRxVekbt6oSJH6FeGFEmJhQkRUlN/2ZfXLmxU6vNQRJ5Yla8YXie5fK4Epr46IqGjuvIEAP6dVoK0SvJGHZDnByuhKO9CKuWHdemQZr4w/UqoYTbxuFANm5Kbk3SsyglYkiDucoK2iRJDz4qpo96Rd8YWk7vHJm5CSaTwC1qguJ0IqnBgM4RQqWF8MJcRKGSLmaqcfbLp+2X1ZbZve9noTYxP0XHr0NnacQ15H7o/Rk3a++I2mdxGyCFFCSe+8ipx7rZxeRscqI8ZECaTAcmo0poyFSu/eoMU3T4DRf7PbWxHupO5PUQMJMH9dRNyXZmLMAOM+iFxT+oPRq891X5ParxWK3BEocluIEVNTHCkUChaROK5QsVxYFQGicWJ0ebqMKPRLzUpON3pbsy4tJ+LFwimAW08kEbSEEn1NinH92pLzKSPInEZEROv1FVaeO17MHIvKARlaKCGm8MEo67zT0O4ovTnh7MyozVsvYlnhdbZ2iDO7RuLx3DX0tFY0okG+stYfLdEjc4ysdYy3jlev1+TuzLZ2C2YjF6XRNlaJjI31+rsoN9enDHttnbKM6brFNcQzvQ3rLuaJGHYZ+ZuEPhhZUM1g9loZiTArzzvv/g/EFG08iuFCsYVRk8UoHSYxJcQUAUOrcwjlrznRmBI7RICdnbfd9YvWIbsPraBtkfqMttVrI72NmXixYLF6BUOsmYhFkZTjnXs9i5id7l6jDyrSFtn71w70jjOY+k8VIyaGEmJhRLB09qLYPVecniVHy8pnZjCAmWHhRi5NvTg0Xl2ycWBGx88es+g+2JcB77j0Yu/MvMzItiJoBcqLYIfg0xpUoVVeC7OWM3+JLifEj5aQZeOv9M6NTB/Dewb00Lt/vSyxBtctGMSxU1iPEVMWMYUCgFxnpidSnMpvY9ek32SZP/LvyAgzO+rXwi7XjZabCPC9f3huUjb+y+w1sMMiYmQ1ExGQrBgzS2Eba25uJyyxgDPPCO+8Gy0zsob5K/efnvudLau13sy+efdgqEyLpbiOEmJhwol3Ej2eeJGHO1y/wEIV9qVi1IEHk6WD9xKSseAQMSaSqkI0BktvvdY6GbelrBgLFDKW1kDCu/d5AfpkOb0sGI6Ldz+cMEgQLbIu1CmJEbMw6bdyTSpCifojs1DGVU6orNmAcpFAeT2rl9ZXtIgVTXa7QGL2xeDP+DKRr2bWBaRXTm97kfayAwjY+0grVkhLpGq1mffCNBM3JopIAH8gXVNm53+0EyNrEi3I6OVkHTl/7DU3axEyOj6teknOPNHnVe+6yw6cCVYxV2xxiiMVrB8CFBYWYtq0aVi2bBnOnTuHuLg4DBkyBFOnTkVEBP/iZ2RkoHPnzj7Ljx07hoYNGwIAOnXqhK1bt/qU6dmzJ9avXw8AmD17NtasWYNvv/0WMTExaN++PebMmYPExERPeZeLr+bnzp2LZ599VnNfDz30ENLT0wXOgDm0XgZGVphQQcuyxGJHILpMvVoduFEcidUAYbKdmW3Y/WuV0cKsGDNKNWFkkdLbLy/GSBTZZ4QVCbLX4eRw/TaItiNY0bsGWtYwI6EdTJgRYYrSR0gLsTlz5mDBggVYunQpkpKSsGfPHgwdOhSxsbEYM2aM7rZZWVmoVKmS5+9bbrnF8/81a9agoKDA8/eFCxfQvHlzPPjgg55lW7duxciRI3H77bejsLAQL7zwArp3746jR4+iQoUKAICzZ8967fM///kPHn/8cfz5z3/2Wv7kk0/ilVde8fwdExMjcRbsw99uDKf2IepKs7p/LTdaMfV/mQS6Ip2zlQB3M/CC7VmM2kIypIuM5mItYyKuP7NijNQv436ltzVCK25O9NqJtsvMfWwm3pOXKJRtCw8jS6/IPU/Ki1jDrFrCnI6t0gvuZxFNoROswk4F64sR0kJs586d6NOnD3r16gUAqFOnDlasWIE9e/YYblu1alXceOON3HWVK1f2+js9PR3ly5f3EmKff/65V5nFixejatWq2Lt3Lzp27AgAqF69uleZtWvXonPnzqhXr57X8vLly/uUDTS8r1Gjsv7CatA/3XHz2m7FXWO2bVq5vdg20fmVjDpfvamYePAmXDZC9CVIjk9WjNkhTI3EHCnD259ZK7HVuDlRzFrznEDr3Bnt14wgM7O9EXbNhkEwE5wv4xYNpsm9tShGhMojJkBIC7EOHTpgwYIFOH78OBo0aIADBw5g+/btmD9/vuG2ycnJyMvLQ+PGjTF16lSuu5KQmpqKAQMGeCxdPHL/SJ7IijjC//73P6xfvx5Lly71Wbds2TKkpaWhWrVq6NGjB15++WVUrFiRW09+fj7y8/M9f1+8eFGzTXagFyxLMBtDFEiCIZBaFlacyH4Jy341i5YVbYeoGJPJlk/vX3YdWw7wdSXS60RhXePBFhdmNzLuf95xa8V48banl/PiBoHA5dGyMzhfVlwGs1VMYUxIC7HJkycjNzcXDRs2RGRkJIqKijBz5kwMHDhQc5u4uDgsXLgQrVq1Qn5+Pj766CN06dIFGRkZHksWze7du3H48GGkpqZq1ul2uzF+/Hh06NABTZo04ZZZunQpKlasiH79+nktHzRoEOrWrYvq1avj8OHDmDJlCg4cOICNGzdy65k9ezamT5+u2RYWu+K+eO4BWhCItkGr3mDCaELviKhoza9RkVxmZnIaaVmI9DpgdhJznuWIbhN9THS9Il/cpLzRS9BIjLHrsnUmbuftX9ZFSUNbcOg2FDPl9M4H/WzwXJFW4sWCCfb+t+M42NAIniBmz5kTFkczLkoREaZ3vdl7Ss/SG8wWMJYitwtFbgsJXS1sG0qEtBBbuXIl0tLSsHz5ciQlJWH//v0YO3Ys4uPjMXjwYO42iYmJXgH17dq1w5kzZzBv3jyuEEtNTUWTJk1wxx13aLZj1KhROHjwILZv365ZZtGiRRg0aBCio70foieffNLz/yZNmiAhIQGtW7fGvn370LJlS596pkyZgvHjx3v+vnjxImrWrOlTjvdFJfPFqYWeIDPahkeoiTRR7HKXAPAOYNfohJ2ILzPzlS1i8eKJMV6QvmwbjMrqWafI9oB5FxXvA0VEjInUKUooPzM8tPovp0WsyEcFwaoI08Ifz6vTFFkcNVmkXJPBz7PPPovnnnsOAwYMAAA0bdoUp06dwuzZszWFGI+2bdsiLS3NZ/mVK1eQnp7uFUjPMnr0aKxbtw7btm3Drbfeyi3z5ZdfIisrCytXrjRsS8uWLVG2bFlkZ2dzhVhUVBSioqIM6wH41hMWs52bGUEm0kattslu6wT0l6iMVcSOduq9FGQ6bNYFZ4QZISQrxrysfZzkryTY30gosZY8XtvYdeTv4vw8H8uXLHrWOHpf7HIzsUS8fZoN3Gcxk2bGn/gjpCBh2DHPnJiyfSKLlfay961szKdK6hoahLQQu3Llik+aisjISBQXy3WpmZmZiIuL81m+atUq5Ofn45FHHvFZ53a7MXr0aHzyySfIyMhA3bp1NetPTU1Fq1at0Lx5c8O2HDlyBNeuXeO2xwm04r38LchE2kYj8tIJxJehXgwMgef60BMRItjx9azVyTslxmRedqwYE2mDnhijl9kJ78Wn5TLjtUerXbx6yYwEIogcp51Cy0mxZJcLUgtyrum+jf6bxq6BCHrIfmgFk1Ws2B2BYgujJovVqMngp3fv3pg5cyZq1aqFpKQkZGZmIiUlBcOGDfOUmTJlCnJycvDhhx8CAObPn486deogKSkJBQUFSEtLw+rVq7F69Wqf+lNTU9G3b19UqVLFZ93IkSOxfPlyrF27FhUrVsS5c+cAALGxsV7pJy5evIiPP/4Yr7/+uk8dJ06cwLJly9CzZ0/cfPPNOHr0KCZMmIDk5GTceeedls+PmU6A7XxEt+e5EJzsDETqFumsnf66tuOlIRrk7qQrwwkxphekrbeNiMvISIwF6kVF79vqc8KeBz0XmT+eSTOYdb368zpq9YmifaQdbZR9toNFjCnXpBgutzt0JeelS5fw4osv4pNPPsH58+cRHx+PgQMH4qWXXkK5ciVZ5ocMGYIffvgBGRkZAEqSqS5cuBA5OTmIiYlBUlISpkyZgp49e3rVffz4cSQmJmLDhg3o1q2bz761krUuXrwYQ4YM8fy9cOFCjB07FmfPnkVsbKxX2TNnzuCRRx7B4cOHcfnyZdSsWRO9evXCyy+/rDn6kuXixYuIjY3F3VH9fTLra3Vysg+oWQtCsHb+gPWOSjTeSK88axHjlaPLnBC0COmdd94E3DLnQrYsTzTx9ql33CxE4IlYcETba9c8qnrbWInRFHUxGcUr6cXDmbWIiViB9Zbx2iqyvd4zaMfISa3zwV5HPZekbB/Dez55+zaqV+86F7qvIQNrkZub65VL007Ie+n9fa0Qc4N5e8/Vy4UY3nKvo20NBsxL1SCgYsWKmD9/Pk6dOoWrV6/ixIkTmDFjhkeEAcCSJUs8IgwAJk2ahO+++w5Xr17FL7/8gi+//NJHhAFAgwYN4Ha7uSIMKHFN8n60CAOAp556CleuXPERYQBQs2ZNbN26FRcuXEB+fj6+++47vPnmm8IizIiEYd5Tg/DciLL1yGxLtiPb+CO2I9A4KTrrD8oUOoe8L3ij8k6VZVNREHealbgoUo9oG4LlvmOfRSfuFd6MBLw22HVOAm15CfS1dSIuTGR/wRiWoTBPSAsxhRi0ICJ/A+bcAjxxZSSyzAq5cCciKtrzE0VGjJHrRFJTkASvol/6enXLirGIqGjdJK3sPanXTjuFpkgdbFvo80mn/TDCjIVYpn6Cv8WYVUSsYSLb0cjmojODkQhz6jrLiLFAX2OS0NXKrzRQOo5SAYAvyMwKI1qUseJMZJvSZCWTgRVPPETFGIEIIXbUJ5tnzEnLGO/lxB6jjEAwIyb0AuZ550ZGJBudX73j1/uZRSb1AhEtRvu0o13+wElrkJ2DI0TQOt+hIsbIFEdWfqWB0nGUpYAT7yRKdxK8wGEryLzMyX7rvg8vUSZqZbOCHfFh/tyOhxnLmCh2izGZ8+2UGNMrS45Dy4pi9boFY6wkHfQOmLMgBftHlN1WMZFYWX+6aoPpflJYQwmxMKH+yCwA8l9sZsSY0Ze77Iu/7vveP1KHlgvUKaFmR91aHbFRp2nG2kDEmOh2Tgg3I7cffT5kRbpWWfpcEcuPHWIM8H55s+0nsBYzI0sYGw/mtFWJtnLR+9PaJ33sIhZANs7PHx9QZgWOEy5Ku0WYlXvB6JkKdAxfMVyWf6WBkE5fofCGfdEYPYBacWN2PLha7iiWk8Oviy+terQQcYXqwW7Li1XRWqdVNzluLTEmcn7NTJAtktoCkB/1aLas3rUXqZe2UtmZtkOvLM+9btVyasezZEVMiCQCZmM4tdpMz34gMrUR79nUWuaEWKCTBVvFSFxaFZ9aM0uIINqvBAKr7sXS4ppUQiwMkXnpsg+xqIBi4Q27Zl9sWvUZiTG9tuth9KVoVIeosJDBiU6TvHBInUYpCUTvC7asVr08SxOvfmLBIhYVUYFlJMbYeq2IMb32a+0b4KepYOuQtXyYFRFaKUO0IOeBnsUA0PmwE4ybY58fGdFmhxgmx8wOtJDFqC2yfYNRahbAnCDTOsfsuS2+kgc8sVa6foVzhHQeMYV+HjFALu+MqPCQ7cxI3igCK/aMLFN2YlVQiWwvIga0BA7BTIcsmmdMtI2yZY3ODZsTDYBQPjC2XpkXmehUVOxyJ+4PM7nACFbzYunli+Mtt/OZtPrhIeN2ZvfJ7lum76Jz+/H2we5LFF4b2H3RyDyndHmtdhW6C/BF/iq/5BGbt6eD5TxiE1tvV3nEFKGNTAfBiiWeq8ZKO7Tij+h1/jCvW9mHUfwSKSPiNtVbb8VFIYodcWCkHjZmyAhyfCJWH3/Xa/a+t8Nqyra7/qBMyyKMngpJ60Wv9UyS/wcStn/g9RdOxKWJ1Gu3dZvXB4rei4G+TjyK3S7Lv9KAEmKlAJEXtJGLxkpHx0tTQNfrVCcqi2gqAVExJlrGTkTFmJYolq2XFR96x80GuMvcW3RZXrA8bfmyS4yJto2Us/ohQbfXLgFmlGlfBN45EEm9QQ+ocPoZl/0IMVM/i9n98QZ48PZnZKXk4fR5UDiDck2GOEauSRpZlxmhOD9PMwZJBpFEok5/1emdA6Pj0ppuBBALWpbdr6x7kjdazwhZKw7t8jHazok2yFw/vQSyom2w8zhFXKqAdTckT4CKxGtphSew2wPOxLvJuuB42/JCIMy6Jp2eu5MXX6iFrKtY7570p2vy1W/uQrQF12Te5UI8d/vWsHdNKiEW4pAbvvYHLyKivPcDbfalolXGKK5JFJ6gMbJA2CHQrIgwFj1RBojFHRnt04oQ09qnFrJlAevCSaROrbkxRcoC8mJMz/Jh9WVsd5A2ud6u6CgAwPF363HLicbA8eI2teKNzDz/ovNgGqElFvXKm5lLVLQvNEJkHlIRZMRYsAixWbs7WxZiz9+xRQkxRXDjxKTfeg8xWeeUENNaZoeLT0YMGb0YRQPM9dooIsTMvJjZdthllTJrBRBpA7G6At4B/FqTINstHMl+tAY88ISJXp1m7jVATIhpXWcaLQGl1z66HiPRaVaI2ZlSgmB0z5o5p3rWQNm4MKOJvOkystYxmWtI8KcQ+9vuuy0LsRfv+EIJMUVwI+OaJJh1UZLlgDX3ichQfzMvWh5GLhmzVinRr1krVjg7hJhRG0TLWnXFGLWBFkL0fvReYnaKMXZEp4jFRk+E8dbrWewA85ZPer+ioovXRpEXvFUhplW3VbT6ACvWZMCeEbV2CzG6fWbaqIRY8KHyiJVSzIoxJ4JBrXbMei9Eq20lX/CyosgqZqwGWp05L1ZGC15ZqyKMzk9lVIdWnjGtgGZRgUfqFREQWvWKxMbJin27LUSiYox9jlm3pN7xm70P6GvrBFbj2Aha7TPbblkXpAj09bNyTZym2B2BYgtJWa1sG0ooIVYKERFTemUShh1DsRMNY7DawcjGkmhhVYyZPQ47hZ8ZMUb/7c82yJxvmeOSmbFAT5BqEegXotYzK7JcT6TZiWyy2UDCu5bBJHh4YszoHsxe1OiPhK7+aWMRgCIL0xQV2deUoKZ0yE0FFytijB2CLQMZ1m417kt2zj4rnSidEsGqBUOk3VZEmFb9Zl6wdr54iGVMb55SAn2+je4zmePSs0Cx9zT7oiPL6L/J/7VegGZH6RmhZe3VOxe8NmthJN6sPP8sMqkwZH5m0Dt3RpZXq3GzZs6n7IcCcH1eYkXwoIRYKSVYvuystEOr83JqUmWS24nkRXLKamAVvU7dSLTQosIOy4jIdTASYyJtMCPGtPKQadVL7lVajFl129I0GPG9cFmRsAJeefb6ytZjtDxcCJb+UQSRa0hfc39CXJNWfqWB0nGUCtM46aYIZUQFmZMdn1nrnNY19bebSgSeBUqvrGhbRcvqued5//cnelYrFrPWL63y4Ugoi072I4Fg54eCGcik31Z+pQEVIxYmnHgn0SuPWDB3lnrz3rGYibvxZ8yJllDwx/nXyiAvM5JQJADdTJC2nRZJMwLL7MAEvXK8gHcrRMbGaub+EkVkYAH5G9AXFTIB+uxyJyzQgcIoNkzmWPXmlXSaQMcsKsQpHXKzFFB/ZJaX6Zm8OOifFQLxVchzAZnBHx0S6+qx67yLQKxzZvYt+sUcDJYxmf3bUZa+b2Qsc2aQcUvKImIhUy7K0IY3sCQYRJgbLhRb+LktBPoDwOzZs+FyuTB27NjrbXK7MW3aNMTHxyMmJgadOnXCkSNHLB6pNZRFLAzRegDNdpq8l6CVL2CZToIX22IHdn3B8+rhpV3IXiSWWNIqWq4J3noz4srfHTydkkPGggWItVXL2iViZQoW2PNCi2ve9TczYEN2YECwwSYIZv/W+iARvd9lrFyyOcNkCLQrksWqe9HKtt988w0WLlyIZs2aeS2fO3cuUlJSsGTJEjRo0AAzZsxAt27dkJWVhYoVK5renxWURawUQVttguVBFcWu4PFAYndGcRHYa85aymTOq9NWIRGciANjj0vv5RvM96BWULaoNUuvTwgHMeYvAnU+gk2EBZLLly9j0KBB+Mc//oGbbrrJs9ztdmP+/Pl44YUX0K9fPzRp0gRLly7FlStXsHz58oC1VwkxhV+hxYiZIel0gLyZ4epOjag02p8/k8HqoSfCg1mM0dfNCTelU4MC9KDjw4pyc1GUm2uqHl4smNbf7DIzFlEeImlGAgn73JO/I2NjNT+QtAQ5fZx2pvGwA61n27uvzPdbe4rdLss/oCRTP/3LNziGkSNHolevXujatavX8pMnT+LcuXPo3r27Z1lUVBTuuusu7Nixw/4TIIgSYqUUKy8Sq0KG5JKysj3ga2HiiSxynP4WYE7Cpl2w42Vgl2XMX+fZSSueTKC/ESLXSVak886x3vXTclHy6tT7sAnHUZS0+JU5jmASYMFsCStChOUfANSsWROxsbGe3+zZszX3mZ6ejn379nHLnDt3DgBQrVo1r+XVqlXzrAsESoiVYmQeXDusAHa65rTEGI9g6aDssoo5bY1yQuA44Za1U4zpxVXpYVRWa9CEnefD6v0tK1JlXJSBcMfLIGsNC1aCta12WcTOnDmD3Nxcz2/KlCnc/Z05cwZjxoxBWloaoqO1hbLL5T0IwO12+yzzJ0qIKYSx42G300VHRguy+bTYr/pwsYQR6Fgvu0WZrIuOlDWyhNk1pyLPEmSXGKNHSFqtUytei8Vul7UVq5gMWueHtf7ZPZemCKQ/EPkR/Clk7LSmGd2n4eIJqFSpktcvKiqKW27v3r04f/48WrVqhTJlyqBMmTLYunUr3nrrLZQpU8ZjCWOtX+fPn/exkvkTNWoyTDjxTiISR50MdDM0cbIz5okxel04ohfnY/WlojWSUK+sCDJzPcpi9whJK3UGg6tI1KJD2i9zXdhrzloTefu1ct1l+w6jyeKBEoFihzXM7mfPDMFqDQOAYkSg2IK9R3bbLl264NChQ17Lhg4dioYNG2Ly5MmoV68eqlevjo0bNyI5ueT6FxQUYOvWrZgzZ47pdlrF5Xa73QHbu8IyFy9eRGxsLO6O6o+Ti5v7rNcLzObFiQBiw6plv7KcegGXZnjXSfTFIPKykREUMuk5rN4Lesct2lZSVu88mKnTTBucsFicWJYsJS712qDVH/DOD7uM1OvENbeKlZQVWueLFXf1B2U6FktmdH9qtbHQfQ0ZWIvc3FxUqlTJkbaR99LTX/ZD1A1lTdeTf/ka3vvTGktt7dSpE1q0aIH58+cDAObMmYPZs2dj8eLFSEhIwKxZs5CRkRHQ9BXKIhZGaHW8ivCE7WhJri0aK1/sMpYxGWsXKSsrCLTK0DnGrFjGzJajy8qKNld0FODQvKjZkmJMC5ns8Ky1jJRLGHbM0x4zwtOu3Fu0MOQh2l8aHb/HOvrHfpyyWgWzNSxYmTRpEq5evYoRI0bg119/RZs2bbBhw4aAiTBAWcRCHtoiVsZVTmgbo69iJyxiCucxum5asUNGiFiGzFg+eGLMzH1FH7eoFUvGgiVrGROtj2TTN5u2wggtsctD9rzTiYpFxR5ZZqXvsFuImbGG6aFnZeMtt4JRW4PBIjZ8258tW8Te77ja0bYGAypYP0yQyQ0j0zlr/RShBwkcNxuUroeZ4Gw7RuLy6gTE5lQUfQ7sHl1M6jv+bj2485zL6URy7uk9r3Y8z1rHy7PGZi9qZClYPVj6Hr3+kPdxQQYWGfWhotdDRDBqpbmJ0Ah0dwK3OwLFFn7uUjLpd0gfZWFhIaZOnYq6desiJiYG9erVwyuvvILi4mLNbTIyMuByuXx+3377radMp06duGV69erlKTN79mzcfvvtqFixIqpWrYq+ffsiKyvLa19DhgzxqaNt27ZeZfLz8zF69GjcfPPNqFChAu6//378+OOPNp0hhcIYJ5KYBqsYY19gMmLUzrQW/nIpOR2XqXUcescX6HAJp1JWhFrKC0XwENJCbM6cOViwYAH+/ve/49ixY5g7dy5ee+01vP3224bbZmVl4ezZs55fQkKCZ92aNWu81h0+fBiRkZF48MEHPWW2bt2KkSNH4uuvv8bGjRtRWFiI7t274/fff/faz7333utV12effea1fuzYsfjkk0+Qnp6O7du34/Lly7jvvvtQVFRk8ezIESxfmgrzmLFcyqarECmnleNNK7EpXa8Za4meVYId3cd7UdotxkTPqRMiVAsnLdv+SplBsKvtdrSF3K96IkwmAXMwJYq1gyK4LP9KAyEtxHbu3Ik+ffqgV69eqFOnDv7yl7+ge/fu2LNnj+G2VatWRfXq1T2/yMhIz7rKlSt7rdu4cSPKly/vJcQ+//xzDBkyBElJSWjevDkWL16M06dPY+/evV77iYqK8qqrcuXKnnW5ublITU3F66+/jq5duyI5ORlpaWk4dOgQNm3aJH0+9DpZo6mDeGUVoY/Ii9dOMUa7KIHrU9+IvFzMihKt+unjsksU2S3GABi+nEMFf7ooAXP9lB3WsOL8PK+2+9sSZnRPkftd5L53mmK31aSuAW2+3whpIdahQwds3rwZx48fBwAcOHAA27dvR8+ePQ23TU5ORlxcHLp06YItW7bolk1NTcWAAQNQoUIFzTK5fwTc0kILKHGFVq1aFQ0aNMCTTz6J8+fPe9bt3bsX165d85r3Kj4+Hk2aNNGc9yo/P99n3i2FwipOWsbsFi4ysG5II0T3b9cx+dMq5iSh5qK0SzgF8hhYwUWP8GXjQRXBTUgLscmTJ2PgwIFo2LAhypYti+TkZIwdOxYDBw7U3CYuLg4LFy7E6tWrsWbNGiQmJqJLly7Ytm0bt/zu3btx+PBhPPHEE5p1ut1ujB8/Hh06dECTJk08y3v06IFly5bhiy++wOuvv45vvvkGd999t2fC0nPnzqFcuXJes8MD+vNezZ4922vOrZo1a2q2S6GQIVzFGF23HfuXPU9GZcNJjPnTRUnQs4y5oqM8P7vOMW0JA7yPxZ9zrWr9ggkrgfrkVxoI6aNcuXIl0tLSsHz5cuzbtw9Lly7FvHnzsHTpUs1tEhMT8eSTT6Jly5Zo164d3n33XfTq1Qvz5s3jlk9NTUWTJk1wxx13aNY5atQoHDx4ECtWrPBa/tBDD6FXr15o0qQJevfujf/85z84fvw41q9fr3tcevNeTZkyxWvOrTNnzujWpVDIINuR2y2wSFm7ZmIw6zayM9heRgSGM1ouSn9Dn2vafWemLf6+bqEm2IvhsvwrDYS0EHv22Wfx3HPPYcCAAWjatCkeffRRjBs3Tndmdh5t27ZFdna2z/IrV64gPT1d1xo2evRorFu3Dlu2bMGtt96qu5+4uDjUrl3bs6/q1aujoKAAv/76q1c5vXmvoqKifObd0kPFeykAuSBgJ+KgAiHGZF9aZtJaGO2DPH8klYRemVB7TnkxSHpWMS1k4gh5aG1bnJ8Hd14+3Hn5OP5uPW7bzVqStAS+P2L9Qkm0F7ldln+lgZAWYleuXEFEhPchREZG6qav4JGZmYm4uDif5atWrUJ+fj4eeeQRn3VutxujRo3CmjVr8MUXX6Bu3bqG+7lw4QLOnDnj2VerVq1QtmxZbNy40VOGjNJs37691DHwCLWOXWE/Zl8MdooxIjLMijEzI8xkE2iatdbIWHX0yoZasD4Rl1rwjpMnRp0cRak1gEPr3hC9ljwRJiukgyWYXhEchLQQ6927N2bOnIn169fjhx9+wCeffIKUlBQ88MADnjJTpkzBY4895vl7/vz5+Ne//oXs7GwcOXIEU6ZMwerVqzFq1Cif+lNTU9G3b19UqVLFZ93IkSM9btGKFSvi3LlzOHfuHK5evQoAuHz5MiZOnIidO3fihx9+QEZGBnr37o2bb77Z077Y2Fg8/vjjmDBhAjZv3ozMzEw88sgjaNq0Kbp27Wr36VIohJFx15gRWCJYfUnLijCzL2a7c4yFEmbiv/ztoqSvr6xA59Vlh3ikLXG8gHutfYcaKkZMjJCea/Ltt9/Giy++iBEjRuD8+fOIj4/H8OHD8dJLL3nKnD17FqdPn/b8XVBQgIkTJyInJwcxMTFISkrC+vXrfUZaHj9+HNu3b8eGDRu4+37vvfcAlCR/pVm8eDGGDBmCyMhIHDp0CB9++CF+++03xMXFoXPnzli5cqXXnFZvvPEGypQpg/79++Pq1avo0qULlixZ4pVOQ6EIFORFYfTyoV8oomXJ/2XKktQBeph5WeqN+tOygAC+0ytpuavINmTmAbpsKFnCROGdN3pOUIJVYWSEnfuSEUIy08Xx2qMlbu0YhVp8JQ/QjraxlWKUpKGwsn1pQM01GeKQOb06oQ/KuMzP6RVIZOYnVMih57oT6dRpa5dM+gfe9WTbIiOYSFkjIUbKib50RdqgNToOMJ7nkm4v7bqyMul5MKGXS0vvGmits3seShlrndE9o3evsPeDXZOUs23TQ/RZKnQX4Iv8VX6Za7L/5kdRroLYHMg8Cn4vwKouH6m5JhUKRWhip6VFNmaMF9vFxtGYia/SC+CnX5Ss28eovNG+SXkCL6ZLzxrGLqOPnQ7WF8m+HqyIiB76eugFu9txHszEo8mKML34NLufP/JvKKStILgtjph0lxKLmBJiioAjM1F0aeLEsmSvn9FLSvTlZSXWRDZmzOilKivGtNqg9aLUEmQi+6RfsqJxcGbEZagKL8B3IAYLfT5oAea0cKCvgcj+9K6Z1eB8O4Ly7YpN8zfWsupbc2uGEkqIKfxCqAutQLS//qBMr5dIoEda0aJGpKxMvVbEmGh7WEFmVxwZr1xpDd43sj76Q0ywIkwUkVgtM/CC8hUKmpAO1leEFiQuJljjYIxSIfBifZyG7IuOuSKTDBNkXjZsDJVZnArKF3UfsS80GZEEGLvIAP30B0axQrKDHMIBnvWLLHcSXpyezH6NrpNdcWwy9x4Ppwc2OIHVkY9q1KQOmzdvxubNm3H+/HmfnF2LFi2ypWGK8MLI/UjWB2vQvpXOjxy3ncdGt8dMLIzVfZsZISlar4wYI/+XxcpLUVZk2SlCgx2r18UKZkWY3jonjkHv2eWV4w2c4dUVjFh1L5YW16S0EJs+fTpeeeUVtG7dGnFxcZpT8SgUWvAsS2SZv61OTqRCYCECjBWidgkztmPX6qjZY5G1BLDrZMWYXRYKUoau26i9em0j9cnUo3VMWiP3EoYdM3Wv6T0PIjFl/nqO6FGCRtZCHk4EtrPtEilP4AkkJ9qvd69pPcdaQi7YRZlCG+n0FXFxcZg7dy4effRRp9qkkCCU0lcQl5rsyCTAuReKSFC7Xa4JGrOiTLbD17OWmb0WvPpFy9pRp9Z6OywxMi820f3ZUY730SJCsIYB2AE7+TaBfpYiY2O91rnz8j3/N7r/ZT6UnBxoIfvcELS28Wf6it4bHkdZC+krrv1egE+7p4Z9+gppi1hBQYEt0+8oFMEEz1oEOPeVyXbygbCWkWVsOdn67baM6dWp114tV44MMhYymYELIuconOLFAgE5tyIT3Im4JOUmyuPX6U8rlahl3J8o16QY0haxyZMn44YbbsCLL77oVJsUEoSCRUwk+7joesDepI88Fxf524n9i0ILMzs6UZHzrkWwWMZkBZZdYlrEQmf3cdtxzcPVGqZlCSPnzBUd5bMNawmjy9PQ60TOn9ZAAS3MXFc77ge6bXWHHvCbRazH509atoj9595/hL1FTFqIjRkzBh9++CGaNWuGZs2aoWxZ75d/SkqKrQ1U6BOKQgyw9lKzW4jJxE1Z3b8s7AhJQL4zt9KRa71cjF5idrXJjoEI/hBk/hZjwXaf+hOjUcMiQkyk/5EVYlr1GWH1OsuS9fe6OPXE35QQCyKkXZMHDx5EixYtAACHDx/2WqcC9xVG2OF+MRMnxXP1hYIbiMxPqPflbdbSJYJWMLwdo83MvGTMbGOHy9KoHlm3q9VysgMgwgWR1C1EdLmio7wEGGBPXCRphwharnW9Zbxt7LzO9Udm4ZRttemjXJNiSAuxLVu2ONEORRDgxIhFp7OFiwgNrdQZ7IvViGCwMPCymBt15E68rLVeFqQtRpY0+tzriXOe+DErqHjX22ocGVuHP8WY0f4ioqKD4p61AzOWYVkRxtun3ZgVZnZYhgOBEmJiWEro+uOPP8LlcqFGjRp2tUehMETmRcpOUcMjVDo1LYIpSNfI+qT34hENZLdq4ZIJyBeth26jqLAXCe7WE2OigwDCATvc83rrAnX+9Fz8euVCVZgp+EgLseLiYsyYMQOvv/46Ll++DACoWLEiJkyYgBdeeAEREaUjE244w5usOVDodUrF+XnSI5tCDfZaGH2li7oPnUZLJIi+ePTqpK1qVsSUU4JMtB6esNQ6F0bnSEtMBGI2CCuw6SaOv1vP628rgsNKbJ8/Ya3FemUIwSrM3ACKLUzcLRXAHsJIC7EXXngBqampePXVV3HnnXfC7Xbjq6++wrRp05CXl4eZM2c60U6FSYIxWz3vK5/uSAJp0QkXjFyEesiKOT23omiwskw7WTFGlpl9qdohyHiiStT1SMoalTdyrQWjqLCCXSKMd17sGsDhFLLPbbAKM+WaFENaiC1duhQffPAB7r//fs+y5s2bo0aNGhgxYoQSYkGISKyInqVF5qtaJK6CJxJkOgozX/da2wRTdnIaMi+nHVh144hsbzWoXLac1v7tsmyZqcdK8L4oRm5KLQJpGZOJtXLn5dsqImRc4Vb6SDvaRWA/MsygJ8yKr+QBT5iuWuEA0ukroqOjcfDgQTRo0MBreVZWFlq0aIGrV6/a2kCFPkbpK4wm2jbTuRTn5/l07EYTZtOY6WCcfoEEW2Cz1bQV/rSOiOxLtAxgfJwi5URftiIzK5jdF1kn6qY0486UrScYhBibXoIE1Yve70Z9DllPBuiIDKaQOS9mBZnI/WRXf6mHPzPrd/r30yhTIcp4Aw0Kf89Hxn3vqfQVLM2bN8ff//53vPXWW17L//73v6N58+a2NUxhHb1Jtp1EuRbtIVjdDWYxEgl2ujKdsJCZGfAhahkj+7FqQZPZX7Dg5H0tIsL8geh9aBS2IVJHMKFck2JIC7G5c+eiV69e2LRpE9q1aweXy4UdO3bgzJkz+Oyzz5xooyLIoL8IjbJchwrBZA3Tgu2cnRa8elZU1iohKrJ4uZ145ex0ZZL20X/LoFeHXSJLa3CDXlkjK51e8D7BH5ZmHmz8F0HE8mhUDvB17ds5cIX2CIggIwB5ZUMtvk0hj7QQu+uuu3D8+HG88847+Pbbb+F2u9GvXz+MGDEC8fHxTrRREcTwOsdAjtYrLRhZy8zgtLBrMOJ7HH+3nq2uOlGcEGQy24la1ewQYzIDBvyFSEyUETxhxdvWM0CJEUvBci60EL1evPswWI9NWcTEkI4RUwQXejFivBgJo/gKUazGqmgRCpYpWWgXsegIVjuuj0zMEYF3r+hBXnokps0ukWVXXVoxYXZYFWTrkImBsyNeTKYuGqM+ondmDtY2ruK1jI0ZNQqLsEs4sGLEyGppV/8i+nzaHSOotz3BqB5/xojduXaU5Rixr/r8XcWIASXTGjVp0gQRERE4ePCgbtlmzZrZ0jCFM1h5wTtl6QpH8UVDiy/2BUXWOT0DAUHGhSwz0o6UyV7UyGtKJi03jqh1yKmXGGtVYKeREt2nTNoIWcuYqFVMr6yWOGGhr5PIeehz9ALWNq6iGZqg1Ra7YeP42P04ZRUU+aAV7S/taKOWGzbQlrJiuCzlEbOybSghJMRatGiBc+fOoWrVqmjRogVcLhd4hjSXy4WioiLbG6kwh15gr8zoHK0H26ijCSbXSLDAijJWmNl5vvwxAouGTCVllHpDNCgfsC/AXwtyPXjWYxFk3YCyAxKsBu/TZex8HlMyuwLL9Mv489mnzxf9b6AJRP8XrKJMoY2Qa/LUqVOoVasWXC4XTp3Sny60du3atjVOYQzPNak3QTRBNADUrAtSpNMPd0uYLGZFGUkHIDO3nggyrkl2GWCPC060jNH+RHNEWXH3yQZl89zUvGfXTnevaH00vTNz8GlyDVvivJxCa6Qh2y6n+hzWMma3G9osdP2kTcVX8nDqib/5xTXZ5l/PWHZN7ur7lnJNAt7i6tSpU2jfvj3KlPHetLCwEDt27FBCLMiQCYSlO1qznbYa0WMe1j0W7CNRteKAiGXMDhecTBk7MAqEtssKnDDsGLINZr2wK3jfTH2E7EWNkJLZCFjkW1ewoDXSMNAWsWA6R8D19hS6C6BvTrEPt9sFt4WAeyvbhhLSE0N27twZv/zyi8/y3NxcdO7c2ZZGKeSJiIpCRFS0LUHepGOTjXsR3a44P69UW8PIdeJdL3JuivPzUH9Qptf5pM8zjTsvn5sSwh8vIy0hYfdLSNT9ZhfseWfXaW3DK0/g3fcnliV73QNsGaM6WYz2LSPAeMct2i+Yhb7/RfoIu6651vNohkALQIIKDQkdpNNXuN1uuFy+KvXChQuoUKGCLY1S+B+RWAKtjpF+GZNJuHkdWmkWXwSj+CmtFwFrlfFH7IdIsD49atIMWpYcOhjaH4JSa1AB/aEhgp7Via1fNnhfdN96uKKj0GDE95qTKZuNK7RrNLaZ7UXcj/7InK/XJrvQOw5/DfiRQaWvEENYiPXr1w9ASUD+kCFDEBV13e9bVFSEgwcPon379va3UOE4yp3oP+oPykS2ycBwglYcn956M/sS2Y64IXnlRAPORfdl50hLGcwG5NsVbE+3QXTfZkMKZPdnhd6ZOQCAT5NrCJX39+AThXWUa1IMYSEWGxsLoMQiVrFiRcTExHjWlStXDm3btsWTTz5pfwsVjuGkAFPWL22IBTGbirGycg30hJkZa5LsfUGLSzPpOPTaSO4jIvj8FWzNwo7IM0LmvNstIPXq4Q3oEN1WD62M/XamyxFtm92WoWBw8cmkkAlGy5hCH+mErtOnT8ezzz6L8uXLO9UmhQRkdMrdUf1RxlVOaBuzAkyJK3vh5WByMrbK7Kg63nVnR0vSk5TzBh3IWsW07jXeKE1eGdnRciL5oGQtY06WlYX3knbSCqYnCmQEqhVE7ieCHZO/s/uUbaMeZl2rvGfanwldW/5zPCItjJos+j0f+/6SokZNsjz22GPIyclBQkKC1/Ls7GyULVsWderUsattCgcwI8KUAHMGcl7ZYHx6mVVEgr21XjQi153u9BOGHUNxfp5P+goZi4/RPkVnJrBb1NB1mbGMBVqMyQgwOy0r9PWUmX0hmOCNmg3mY9Ab7OHvdrsBWJm7p7RM+yM9anLIkCHYsWOHz/Jdu3ZhyJAhdrRJmMLCQkydOhV169ZFTEwM6tWrh1deeQXFxcWa22RkZMDlcvn8vv32W0+ZTp06ccv06tXLU2b27Nm4/fbbUbFiRVStWhV9+/ZFVlaWZ/21a9cwefJkNG3aFBUqVEB8fDwee+wx/PTTT17t4e1rwIAB0ufixDuJuuvNjGos7aMb/U3CsGPX3ZYSAeKi8OqjO2jZTlovfQW7P5F7jrgfrSIq1gAYjpTTEg5Grkf6+ZGNA3NyYAJ7TeoPyvQ84+zzbkc/EBkb6/n5E3Ld7O7DyHOiNYLZKcyO6qRHYNcflInsRY0M3xUK/yNtEcvMzMSdd97ps7xt27YYNWqULY0SZc6cOViwYAGWLl2KpKQk7NmzB0OHDkVsbCzGjBmju21WVpaXqfOWW27x/H/NmjUoKCjw/H3hwgU0b94cDz74oGfZ1q1bMXLkSNx+++0oLCzECy+8gO7du+Po0aOoUKECrly5gn379uHFF19E8+bN8euvv2Ls2LG4//77sWfPHq+2PPnkk3jllVc8f9PxdzJovTRC4SsuXBBxnRlBu/bsunasGLCrXq17jo6Dk9mH1XNH1yO7b1mcsIw5NUI0EMH44QzPik0vl8EfI6AJ9QdlotB9zW95xIrhgktNcWSItBBzuVy4dOmSz/Lc3Fy/T2+0c+dO9OnTx2OpqlOnDlasWOEjdHhUrVoVN954I3dd5cqVvf5OT09H+fLlvYTY559/7lVm8eLFqFq1Kvbu3YuOHTsiNjYWGzdu9Crz9ttv44477sDp06dRq1Ytz/Ly5cujevXqhm0Wge7sZV+2yvplHdqqIyoq9M67lpXEbIctck8Y3Qf08dGxYXSdREg6NZLRbnjpK2SEk1aqCtZNpFcnK5TtOmci945MMDhg7LYk63n57bIXNcL45E34d7t6mtvztmMhGf959fvrfrNixeS1U0SUycwBG2jUqEkxpF2Tf/rTnzB79mwv0VVUVITZs2ejQ4cOtjbOiA4dOmDz5s04fvw4AODAgQPYvn07evbsabhtcnIy4uLi0KVLF2zZskW3bGpqKgYMGKCbJy03NxeAr4hjy7hcLh8BuGzZMtx8881ISkrCxIkTuUKXkJ+fj4sXL3r9WJyINVKIw85faAfEJWLFNULHbFmFFpl0m3jHHKh70Mp+Zc+rTHk9F7HZOkX25Q/XpyiiKStCBTtjOtlnPJQhecSs/EoD0haxuXPnomPHjkhMTMSf/vQnAMCXX36Jixcv4osvvrC9gXpMnjwZubm5aNiwISIjI1FUVISZM2di4MCBmtvExcVh4cKFaNWqFfLz8/HRRx+hS5cuyMjIQMeOHX3K7969G4cPH0ZqaqpmnW63G+PHj0eHDh3QpEkTbpm8vDw899xzePjhh71cooMGDULdunVRvXp1HD58GFOmTMGBAwd8rGmE2bNnY/r06T7L64/MwsnFzb2WKRHmf2hLEACvnGEyX7BsWV4yUIKstcwpiwHvmPXwxxe90T5ErJEiGFnG2HI8yzW7TyvWRK17wozA63P0guf/axtXAWBPQP/xd7UtYiLwpl4C7On37HAXWvFO0GW1tmXPPZlztugPo4AidJBOXwEAP/30E/7+97/jwIEDiImJQbNmzTBq1Chda5ATpKen49lnn8Vrr72GpKQk7N+/H2PHjkVKSgoGDx4sXE/v3r3hcrmwbt06n3XDhw/Hjh07cOjQIc3tR44cifXr12P79u249dZbfdZfu3YNDz74IE6fPo2MjAzdYbh79+5F69atsXfvXrRs2dJnfX5+PvLzr5vtL168iJo1a3qlrzDbiYSCqTsUYUeL2XWetV6CRsLM6OUu4po0yhdGp7IINYxmP9BC5mUrk5zU6Hrp5Y0zqlPkXuxz9ALWNq7i5RLTG4Upil0fA7QoZM+VHRn1Zc4nu42emJJN6yJ6fxkdc6H7GjKw1i/pK5JWPovI8hbSV1zJx5GHXlPpK3jEx8dj1qxZdrdFmmeffRbPPfecZ5Rh06ZNcerUKcyePVtKiLVt2xZpaWk+y69cuYL09HSvQHqW0aNHY926ddi2bZumCOvfvz9OnjyJL774wvBmatmyJcqWLYvs7GyuEIuKivKa1YCHUwG/CvPQ18RpcaIXd+IvWFdlKAoyWXjWLqOy9N9GZY3iBWUCx0mdIteFWMH06pIhFGIGabTOL73OaDuz0OfKinUtUKgYMTFMCbEvv/wS77//Pr7//nt8/PHHqFGjBj766CPUrVvXr3FiV65cQUSEd5hbZGSkbvoKHpmZmYiLi/NZvmrVKuTn5+ORRx7xWed2uzF69Gh88sknyMjIQN26dX3KEBGWnZ2NLVu2oEoV/Q4NAI4cOYJr165x22MGrU5PWb/8B50vjIgTs1/tvHqN3ENaI2nZ5XbOx+djpTHpovU3Zq1hBCtiTLSs1vWky4qid7xa91ewZG7n3UdODA7RO7dmYjRlt+PVo+KAwwtpIbZ69Wo8+uijGDRoEPbt2+dxk126dAmzZs3CZ599ZnsjtejduzdmzpyJWrVqISkpCZmZmUhJScGwYcM8ZaZMmYKcnBx8+OGHAID58+ejTp06SEpKQkFBAdLS0rB69WqsXr3ap/7U1FT07duXK6BGjhyJ5cuXY+3atahYsSLOnTsHoGQqqJiYGBQWFuIvf/kL9u3bh3//+98oKirylKlcuTLKlSuHEydOYNmyZejZsyduvvlmHD16FBMmTEBycjI3RYgMyioWnNBftaHYgRILl+joP62v+XC2kMlcY1kxxsOsCJMRjaGA3f2diNDhXT+r11Nm22C/fsoiJoa0EJsxYwYWLFiAxx57DOnp6Z7l7du313XhOcHbb7+NF198ESNGjMD58+cRHx+P4cOH46WXXvKUOXv2LE6fPu35u6CgABMnTkROTg5iYmKQlJSE9evX+4y0PH78OLZv344NGzZw9/3ee+8BKEnISrN48WIMGTIEP/74oyfmrEWLFl5ltmzZgk6dOqFcuXLYvHkz3nzzTVy+fBk1a9ZEr1698PLLLyMyMtLsafEQbh1tuEGuS0RUtCUrkWzgNHlRmL0v9NJW0PCWe5ZRbQ0mC1n9QZlebbOCqHWGV07kmmq5yUjQNmCcBkJPCAaL5UsPdpSuHR86VgLree0wi8i2we6uLHa74LIgptSoSQ2ysrK4owsrVaqE3377zY42CVOxYkXMnz8f8+fP1yyzZMkSr78nTZqESZMmGdbdoEED6I1jMBrjUKdOHcMyNWvWxNatWw3bYgdKjAUO3guNFclO5Abi5cbS2r9ou+nlvJcA7+URTEJLBK0RqzJ5tAgyzxx9LWRdv4C3AJPBCXeeHcgIEa3tZQfIOD2i2KnteO7KUHvuSjPSQiwuLg7fffedz5yS27dvR7161oYjK5wlGDvb0o7MaCg74I2ws3pf8L7+1X0mhuw5cypViVNiTEtMWRVZItuasUbJCmeZsk4/E6y7Mhjc/263xbkmS8lkk9JCbPjw4RgzZgwWLVoEl8uFn376CTt37sTEiRO9XIKK4ESJMWcx485hv2bJMiOMvnj1rGGA74tExgpjVC9PXLL1h8oXO6+dVgY2yFxnYuXi5dxqMOJ7gLKCaeWP0nvmWReoSFm6PNkGEHdR+6sPosWl3RZnLSuw1rHJjJK1iuc51MjnF+GOAIwnLrCFEiFmJUbMxsYEMdJCbNKkScjNzUXnzp2Rl5eHjh07IioqChMnTvT7XJMKceiOIFi+lsIR2dxABJHRWKKdd/Kuq8hs4ztfKX39tdYZ7U/UukLqLA3B+TKYsX6yIqzBiO+FtzUzMlNWKJWmgUEy108vzYjstmYI9vgxxXVMpa+YOXMmXnjhBRw9ehTFxcVo3LgxbrjhBrvbprAB1vVF/k8HuYp8ASvEIJNNE6zEhrCCiu1QeRas5F1XfZaZ7dR5HXjHPb8CALa1vslwe69j57RVL/YqXO49vRkRjGCvm5YAEw3I5+2bTdLKE1VGszwEM+yxa91fIsdkJGj0rGFmsEs4BXLQlho1KYYpIQaUTFTdunVrO9ui8AO8rzIta0ioupICDT3XJK/zFukQeYJKdkSYbDyR1rZWv85Z8WbF2ieD1dGodD2A+fufDaI3Ek686+biuCBl8nsZxYDR24qKl1DAjhGhIhalYLcI0n1H8ZU84An/7Nf9x8/K9qUBISHWr18/LFmyBJUqVUK/fv10y95www1ISkrCX//6V8TGxtrSSIX9sMKAxcpLXFECPQ2QmY6adS96xFkb3wB/WritOtLSaw4+M9dO6/5IPdgeANB/1z5ktolB8q6rqBiZp2sho9epGEU+/hCnsgH5Tl4rp+8D1gtgdn8ybj2jPlVvebiiLGJiCAmx2NhYuFwuz//1yM/Px4IFC/DVV19x525UBB+8TobtMNSwaPMU5+d5xUiJfI3zYryI8EnedRXJ2IdVR1p6vWBWHfGeEsvJF51H7LU5ho578tBxz68ewUVEITmG3pk5AICUzK5+EWH0+bXTmsO7blr18sqyljCtl7LeOTITkM/WLZPbTDSlhozIM4opszM+SmR/gK973ExcFe/4zbgqnRap9Udm4ZRjtSvMICTEFi9ezP2/FkePHsXtt99uvlUKU/TOzMGnyTV0y5gJyua5qVTwdWAg4iZ511X0TyqxSmUvaoTsI97l2GvKiiOe65OGiDqjEWDZixoh+yDweLMdmnV+mlzD5+VCC7eHD57A8mb1ddtjN49klYjDtET958Vu9ASBHeka7LR22RWEr7UvM0LUiXZYKWd220BA2lboz50q36QQpmPEAODHH3+Ey+VCjRrenVliYiJ27NhhqWEKe8le1AjjkzcByPEsS8ns6vk/sVrwhBwvrqw0TeZsNzwrip61gTcKkmcxA7QFGE32okZIxj6Rpvq8bB5vtgPYA3TEDmxrfZNnvcdlmXS9XnJPAUBKZiOP+CIxY6wYm77nPlMvsU6H2GO86uUOpffD8khWjkcEmgngZsuQWC5i/RIRXryXN1uPSLusWLuMyjohLvwpWEREJZuoWA+tVBRsGppgIWAC0aJrEso1yae4uBgzZszA66+/jsuXLwMoyXA/YcIEvPDCC4iIiEBkZCSaN29ue2MV+vBEVImVrORBTMns+ocY86ak48jxWU6gJ62+Xr4EJcjshRZOtNhiLVoErRcCK8DIdqQ868YUxSvgftH1/fLqJfdaSmbXEgHHqefhgycAANP33Oep144XBhF79N+0GFverL5n38Qix7oxZaeOAvj5vmhEj40VYORvkQne/SXGRGNLg1GYsMiOBNU6J+wodZpAiaFgttIpSpAWYi+88AJSU1Px6quv4s4774Tb7cZXX32FadOmIS8vDzNnznSinQoJaOsWLc4Shh3Dp6D+hrXs00qQ2QsrnmjxxbOKiYgwUcuZ6H2g9/JlX7rE4kqLMNYKBpSIIlrUyeBrDdOGHb3JE2NmMDp3/rY6yYofURch3UaR9gabdciJ86y33My+7D5fgRZhKrO+GNJCbOnSpfjggw9w//33e5Y1b94cNWrUwIgRI5QQCzC0COO5G2l3EWtB04ovO7Es2SdIn+QcAviCjKCEmTiZbWJQnJ+HVvu9ex+j3GBaHa2WCCPbswHNRhi9JNg20W7Lx5vt8HFLEiFk9uUjI8JoaDFI2kCLMJEAfyviy+rAAb2Xq50vciOLl1FAO69/CBRsu61eH9FZKMwIIV55vTbRsyKw1B+UiWK2Lvc1qfZYwd+jJt977z289957+OGHHwAASUlJeOmll9CjR48/6nNj+vTpWLhwIX799Ve0adMG77zzDpKSkky30Q6khdgvv/yChg0b+ixv2LAhfvnlF1sapTAPLaZoMcajd2YOUjIb+WxH4D3Y10XCdQuN3rBtVpgRlEDTZm+L650PLcqIVcxIhLFuSLocG2hPw9bFWpAeb7bDEwvGumD0BoFkL2pUst2iktgyVoyZtYZZgRVjIshYjmRfwGZGHRqlS+CNcBZ1wVm1qAXaFaZ3b5u5Plb26y+rFFfAOb7X4OLWW2/Fq6++ittuuw1AieGoT58+yMzMRFJSEubOnYuUlBQsWbIEDRo0wIwZM9CtWzdkZWWhYsWKAWu3y+2WM/61adMGbdq0wVtvveW1fPTo0fjmm2/w9ddf29pAhT4XL15EbGws7o7qjwf2/59nOSvCtFIHsF+1vE6VBGATgUDEAS0YtOaqE+3QlTDzJpJJE9Ni628AvGOwyHnWS6HAijGemGPr00P0euq5Pomrkg72J9uIWovMWsNYRGYIMHOueC9fI6uGbMoEI2EoYkXhtU02XkrknjATL2XFkiZat6z1SQ/2o5PtB82M2HQiVVCh+xoysBa5ubmoVKmS7fUD199LdVJfRER58zMxFF/Jww+P/81SWytXrozXXnsNw4YNQ3x8PMaOHYvJkycDKEm3Va1aNcyZMwfDhw833U6rSFvE5s6di169emHTpk1o164dXC4XduzYgTNnzuCzzz5zoo0KQYj4ItYtLyvXInrk5HWIRUz3ZbOr5J9W+91/WFtKXuYRUd7leeZ4XueTvOuqT7A4O+VSac9VxuaL8ojeZSX/iJ4j2lpFXJzJu64CbXyFt9HLgmcBE7GMkXWkDo9VDXKuUYIVEUaEF2/kJo2o9ctOZGO72PIiAkwPs1MXyQTxy3ykBdO5NkJkyjgjAh3P5QR2xYhdvHjRa3lUVBSioqI4W1ynqKgIH3/8MX7//Xe0a9cOJ0+exLlz59C9e3eveu666y7s2LEjoEJM2iIGAD/99BPeeecdfPvtt3C73WjcuDFGjBiB+Ph4J9qo0IG2iJVxlfMs5+UUIw86GydGd0jjkzf5WNM+Ta6hOWqP1MvDqoVFWcm8kRWr9IuVnGc6vQRvHku7EBF0vPIiIsIOIQZ4j6z0x/RLMhYqEbRc1GZinMhck05gZjCAP9DanxWLmF7snt1WVLP40yJW+wPrFrFTT/zNZ/nLL7+MadOmcbc5dOgQ2rVrh7y8PNxwww1Yvnw5evbsiR07duDOO+9ETk6Ol1Z56qmncOrUKfz3v/813U6rSFnErl27hu7du+P9999XQflBCBFftAjjBeyzAo08/Kz4ov82EmBE4LHCjkYrXoVY6dhteR1babeUAeLCgD5XxAKw6khLjxijxbVIYDVbRs8ypned6g/KNG0BtUuEkb+JGDMrwGRisPQGAcimyrDTEiazX3Y/Ittp3VvBZgGy2rfUH5TJndy+VGNTQtczZ854iUY9a1hiYiL279+P3377DatXr8bgwYOxdetWz3oyS5BnF263zzJ/IyXEypYti8OHDwe80Qo+Rln1ebCpLnpn5ngElWgA//jkTQDHeCXb0ZKcZ/R+ZONmFNrQYgyAlyDjDbwg6zzs0hdtTl8jO0UYPRDB38dhFZERs8EIzy1ZGp7vYBOc/sSuUZOVKlUStt6VK1fOE6zfunVrfPPNN3jzzTc9cWHnzp1DXFycp/z58+dRrVo10220gwjZDR577DGkpqY60RaFBYrz832WGY2YBOBjOaOXEcgoI/YFQCfslKXP0Qs+7WNznrFtAHyD2EsLWqNPRSnOz/OZ85KX1NUzl+UfPxFYt5iIZYFuh6hbNKOpdjoOM6QebO9zTwf6hSl6/gBvS1igLMVm982ea3+nuOBZ5wJJoPfvKG4LPzt273YjPz8fdevWRfXq1bFx40bPuoKCAmzduhXt27e3Z2cmkQ7WLygowAcffICNGzeidevWqFChgtf6lJQU2xqnECeCMtVq5RAj6Ak0Ul7rq9uz7R/vUTr4X8Yit7ZxFfQ5egHA9RGdrEuVZx3jvSjtnNg52LFDKNCuQY8YW+Rbt1EeMjvbwctVx4OIMVnrGBuUTwYMEKyeVxEXncz9qeWqpM97MN3vMpOf07BB8/60HjmR58zMbAxsexTmef7559GjRw/UrFkTly5dQnp6OjIyMvD555/D5XJh7NixmDVrFhISEpCQkIBZs2ahfPnyePjhhwPabmkhdvjwYbRsWdJ5Hz9+3GudclkGD96Chh+3xXNB8uK+WLcjmSqJrpcWZKIjkj5NruHJIcW2GaDcYm28c1+V1iD+x5vtwDYYp1sQgZxD2tKWvYiZPHwRHIcWYzLXVkSQaY2QtOraC5Q7zSfVh1/37hysILJTGIkOGgmn8xlM+Duh6//+9z88+uijOHv2LGJjY9GsWTN8/vnn6NatGwBg0qRJuHr1KkaMGOFJ6LphwwapHGJDhgzBsGHD0LFjR6m26WFq1KQieNAaNakV98L74uSKrz/QCv6n85KxaTFogaY3mouXO4m25JHYJa3cV1qdbDBZCuyADWx3IteQWden2cEUrIBiLVQigszIKsab+FtGhOlNvG3FcsN7DoyC/u0KzjcblG8FK3nJnBS79DW0u89gs92bGTHpRLsA/46arLngZUTEWBg1eTUPZ/463dG2yvLnP/8Z69evR82aNTF06FAMHjwYNWrIx2fTSMeI0Zw5cwY//vijpQYo7OHEO4meOK7emTkYn7zJE4cxPnmTjzuSjvsiZVj3IDs1Ei2SiPAi+yKYGTBA2kO2J3UQEQZct44FMq4k0DzebIfPZNZ2Qe4Vf1h5eALq8WY7vPYtIgwzmsboxo113POr53zx4sGcxq77M1SD880Sbs+4WRGmCE5Wr16NnJwcjBo1Ch9//DHq1KmDHj164J///CeuXTM3fZS0ECssLMSLL75YkjW3Th3Url0bsbGxmDp1qulGKKxTf2QWxidv8rgM9SAPPivS6H956S5okcT7lzelkl5HKvK1l9kmxvOj2y5Sf7ASGRvr9RNlW+ubvKw8dgZpy9ZTf1BmyTx2f7RBtC2sCGOtVjJiLCIqGhFR0T7nhQg0Xm4wEbHpio6CKzoK7rx8rjXMKqTd5Ke1jNd2f2B38D97j4j82Gfcqec8kAMd9AjWdsnjsuEXfFSpUgVjxoxBZmYmdu/ejdtuuw2PPvoo4uPjMW7cOGRnZ0vVJx0jNmrUKHzyySeYO3cu2rVrBwDYuXMnpk2bhp9//hkLFiyQrVJhA3qjJj2jGv+I+SHpJvRyi2lB5ypjt6WXicaJ0TFCRvmJtPJWhcoXpZboioyN9cmkTyDn4fFmO4A9JZYdp46VnH9/xeDRebzI3yTrP7m25N4QOebrYizPe9kf9z1vAmQekX+4JI0Cr52+75wQYWbnngwEvNgxu86DU8LO6vmUSdJslgh3BGD/9wUfm/KIBStnz57Fhg0bsGHDBkRGRqJnz544cuQIGjdujLlz52LcuHFC9UhbxFasWIElS5Zg+PDhaNasGZo1a4bhw4dj0aJFWLFihfSBKOyDF8dFRBixlultw7OM9c7MQfaiRkjJ7OrzL6mbzUVGkHV1kVGUemgJvFC0jIlAjiv1YHuheRGDGdoaxh7LpSLvF0z/pH1eswCYvb5kO7vFpdMCrLS5I/VwygLu9Hm1W6hbTWGjsIdr165h9erVuO+++1C7dm18/PHHGDduHM6ePYulS5diw4YN+Oijj/DKK68I1yltEYuOjkadOnV8ltepUwflypXz3YBi3bp1srtDt27dEBNjb/6gcOTkB80QUT4aKZmNPO7J8Zm+wgu4PuqRNw1S78ycEoG1CABKBJZeEOlalKShsDJsm25XfV5mWOhn5Wa/mEkbXNFRmpamYIO1lrEusXB6GZfEdeVR/y+Zx5QdnJG866qP5ROwPim2EYG6Z0Sz+4eHy0oOIwu46LW2e5CLVUTbTQf/h1RfEIYWsbi4OBQXF2PgwIHYvXs3WrRo4VPmnnvuwY033ihcp7QQGzlyJP72t79h8eLFnmkG8vPzMXPmTIwaNUp32759+0rty+VyITs7G/Xq1ZNtZqmj/sis66MmGS3jZaWCb94eIt7ISMjxmSVC7lOIuSvXNq5ire2Ue7LP0Que+mQ611BzU4qgdfy0O8/uJKdOYtRW3uAMLVc0WaYFbT0I5nQnWtc4XO5hO+HdA/S6UCWU226I21Xys7J9EPDWW2/hqaeeQnR0NMaMGYMJEyagfPnymuVvuukmnDx5Urh+aSGWmZmJzZs349Zbb0Xz5s0BAAcOHEBBQQG6dOmCfv36ecquWbPGZ/tz586hatWqQvuSye2huA6dciJ7USPdfFCe4P7MP/6PGl7b0/XwhnoTd6KeGJP98uSZ4Psn7dOdpJrXSQfzC1gGrY46lESYEXtbuNBq//W5Lz1QiWa18k2x58efIkxW+Bt9XPj7pWzVKnR2Ij8jedy8HZbq1YKNEw0W9NKNWKlHERyMHz8eAwYMQHR0NKZNm4bhw4frCjFZpIXYjTfeiD//+c9ey2rWrCm07eDBg6XcjI888kjQ5A4JFTyuReRwk60C3lMS0aMe6XkmgevWMjbrOrFa0dYrerkV6Jg2un2Z8L1v2Jcg+6I+sSw5KMSYmWmZ6I6cnheR/pcOSg91Wu339UFoiRxRQeb0tRcZkCLyQhZxP4ba7BGsQHNCmAWD9VtPOAW6bcGA213ys7J9MBAfH4/Vq1ejZ8+ecLvd+PHHH5GXx38Wa9WqJV2/tBBbvHix9E7Mbvvee+/pri8sLMS0adOwbNkyz0SeQ4YMwdSpUxERwR+HkJGRgc6dO/ssP3bsGBo2bAgA6NSpk9ds7YSePXti/fr1AIDZs2djzZo1+PbbbxETE4P27dtjzpw5SExM9JR3u92YPn06Fi5c6Mni+8477yApKclTJj8/HxMnTsSKFStw9epVdOnSBe+++y5uvfVW4xNEceKdRK8YMS0+Ta6BBBzzcV96jbBcBKRkendytEAjYosVY1ZEGHFPsgJMC/olSNxY9MTVwSbGrJB6sD0eb7bDc0xEmIWTRYyFvrZaUy05mZHdLKIWr2C06siiZQ1zmnAJRQjltgsTJjFiU6dOxejRozFq1Ci4XC7cfvvtPmXcbjdcLheKioqk65cWYnbz3Xff4cSJE+jYsSNiYmI8ByPCnDlzsGDBAixduhRJSUnYs2cPhg4ditjYWIwZM0Z326ysLC9r2y233OL5/5o1a1BQUOD5+8KFC2jevDkefPBBz7KtW7di5MiRuP3221FYWIgXXngB3bt3x9GjRz3zb86dOxcpKSlYsmQJGjRogBkzZqBbt27IysryuF3Hjh2LTz/9FOnp6ahSpQomTJiA++67D3v37kVkZKTQeWAh1i2SoPXf7erhvp3fA9BOaUEEnFdyVvimt2DnsLRqAWOhM/aLwIowAivGyLJgtyi48/I97X68WYkVYVvrm5C66HraitRFJcKMjEIMRUHWar/7D3dkyb90kD6BdUfzBpcA2nMGiohwEUuTSBoLLdj9F2vUE+z3JYusCDs7sb2QVYyXQ03LKioTvM+zllr5SNOzhAXDB0HQECYxYk899RQGDhyIU6dOoVmzZti0aROqVLHv3RcwIXbhwgU89NBD+OKLL7yC8p944gnceOONeP311w3r2LlzJ/r06YNevXoBKBm5uWLFCuzZs8dw26pVq2qOaqhcubLX3+np6ShfvryXEPv888+9yixevBhVq1bF3r170bFjR7jdbsyfPx8vvPCCJ25u6dKlqFatGpYvX47hw4cjNzcXqamp+Oijj9C1a4kwSktLQ82aNbFp0ybcc889hsdB8ArWx3WhRIsw4PqIScDX/UdbyXi5wYjLMyWza4lVLYCQfFOrFrX0SnNAw5tQGAju2DHRTtw7/UPovMSJ8CL/J/9mtoFP0t5MxHDz1slARLid11wmzkvNX2ge8vzquahFLGOBsDoFq6Ure1EjFF/JA54IdEtCj4oVK6JJkyZYvHgx7rzzTs9gRTuwNMWRFcaNG4cyZcrg9OnTXkFvDz30kI/I0aJDhw7YvHmzZ/LxAwcOYPv27ejZs6fhtsnJyYiLi0OXLl2wZcsW3bKpqakYMGCAx9LFI/ePIe9ExJ08eRLnzp1D9+7dPWWioqJw1113YceOki/DvXv34tq1a15l4uPj0aRJE08Zlvz8fFy8eNHrx4N0YMffreexftEijKCVQZ/UQbYj25K6nPzq4710k3dd9RpRt+pIS08byAucLkPg5TI7sSzZbzl5ROPD3Hn5OP7u9dHBCcOOeWWMZ60+9PQ9gPG8i8EALbyIGAOuj5akrx35+8eCkueJzVFHU5yfZ5g9/8SyZJ/s9WSOR5EkmmReVK0UE2aniBJ9joIp07oZl6SRNYydUYBgdD7N5hgj+5LpB3gzHwQb9NR17P0aCEudy239F2wMHjzYVhEGBNAitmHDBvz3v//1iYVKSEjAqVOnhOqYPHkycnNz0bBhQ0RGRqKoqAgzZ87EwIEDNbeJi4vDwoUL0apVK+Tn5+Ojjz5Cly5dkJGRwZ1Nfffu3Th8+DBSU1M163S73Rg/fjw6dOiAJk2aACgZHQoA1apV8ypbrVo1z/GdO3cO5cqVw0033eRThmzPMnv2bEyfPt1nOYkRo6E7qU/hnVcsYdgxpCzq6llGXnIpmSXLtMQb2zE+nf0d3ku4jdtWWYi7gLW4ZS9qhGTsw6ojLYFFQPYR7+08FhSNWCK67XRn5IS1xA7oc0wmqyb/At4uSyA0RBgAjyuShXZJZi9q5GXhJMtZl7gW9Lkj15VcZ9kkqXovaasWD9GZJxT8ie556wHv6dtELJeBugb+3C+7r/ojsyD2hrWBMIkRq1y5Mo4fP46bb74ZN910k2741C+//CJdv5AQoxsxbNgwvPnmm5ZTS/z+++/c4Z8///yzsNpcuXIl0tLSsHz5ciQlJWH//v0YO3Ys4uPjMXjwYO42iYmJXgH17dq1w5kzZzBv3jyuEEtNTUWTJk1wxx13aLZj1KhROHjwILZv3+6zjr1gIjFwemWmTJmC8ePHe/6+ePEiatas6eOa5Jn06ZcYL0EqgYgwsi2bT4yu9/0mTRBBXS67vtrZ9q060tLzf7PJHOltg0GQkcShxNpC2kC7szKaxqDToRIR5jnve+AlzkIpRownxnguSSLO6PtJS4TpCaaIqGhPbCBdzsyLUEZ8icSchSJOBOiLWJhkxJgMCcOOIdvGAT0yAfh2pj3Rq5P9AMl6J1G5JiV54403PJrnjTfeEI5jF0VIiBUUFODixYu4+eabsXTpUsyZM8eyEOvYsSM+/PBD/O1vfwNQIliKi4vx2muvcUc18nj22Wfx3HPPYcCAAQCApk2b4tSpU5g9e7amEOPRtm1bpKWl+Sy/cuUK0tPTdacqGD16NNatW4dt27Z5WfeqV68OAJ7RnITz5897rGTVq1dHQUEBfv31Vy+r2Pnz59G+Pb/Di4qKMhSqekGu7LqURV291hs9yPQyXufoxDB7upM0M+8fbx2v42bnNbTjGIpyczXdk3S2bJqns7/z/P+9hNs8mejJfJMAvCxkoRq0T9yTEVG+oyRZ62bJoBDvoHmR60OXoRMHi6KVid1O91QoCDMrAsyu1BVGYozAlgmn0YlmjiOgxx4mwfq0nhgyZIjt9QsJsXbt2qFv375o1aoV3G43nnnmGc18YIsW6WQPpXjttdfQqVMn7NmzBwUFBZg0aRKOHDmCX375BV999ZVQHVeuXPFJUxEZGYniYrkQ2czMTC+xRFi1ahXy8/PxyCOP+Kxzu90YPXo0PvnkE2RkZKBu3bpe6+vWrYvq1atj48aNSE4u6fwLCgqwdetWzJkzBwDQqlUrlC1bFhs3bkT//v0BlEwievjwYcydO1fqGGjXpIgAI2glSNT7AuOlDbDrYZf5MvXEFLW5HtQrg9ZXtD87bnr/9Qdl+ogwGiK0Oh266hFh3jnGgvuFTgtGOk6MuCMz28QAu/gpK7QSB5sRVvT9Tn9QkGX+JlhTrAQqPYUeemKM56J02gWo1X+Y3VZmGzP3avaiRsAVP/YTYeKapNm3bx/Kli2Lpk2bAgDWrl2LxYsXo3Hjxpg2bZrhVI88hIRYWloa3njjDZw4cQIulwu5ubmaycxEady4MQ4ePIj33nsPkZGR+P3339GvXz+MHDmSK4p49O7dGzNnzkStWrWQlJSEzMxMpKSkYNiwYZ4yU6ZMQU5ODj788EMAwPz581GnTh0kJSWhoKAAaWlpWL16NVavXu1Tf2pqKvr27csdpjpy5EgsX74ca9euRcWKFT0xXbGxsYiJiYHL5cLYsWMxa9YsJCQkICEhAbNmzUL58uXx8MMPe8o+/vjjmDBhAqpUqYLKlStj4sSJaNq0qWcUpQx6FiG99fToJF45mYBZf8KLCeO5LfXaJyLGzFgseFYwdg5DXgb49xJuM4y7o+dq5P0djNAirNOhq8hoGuNxyRJ3JHD9mrLnnOSss8MSpTWa1ug+tjvtBG2hC6aErYEWYGasXgStkZRa5a2KoUASLO0obQwfPhzPPfccmjZtiu+//x4PPfQQ+vXrh48//hhXrlzB/Pnzpet0ud1yuWvr1q2LPXv2WMqhQUYKvv/++2jQoIHpei5duoQXX3wRn3zyCc6fP4/4+HgMHDgQL730kkeVDhkyBD/88AMyMjIAlOT2WrhwIXJychATE4OkpCRMmTLFZ6Tl8ePHkZiYiA0bNqBbt24++9byES9evNhjuiQJXd9//32vhK4koB8A8vLy8Oyzz2L58uVeCV1FZyu4ePEiYmNjcXdUf68YMYLIS4a1ENAYiTutMk69UIzy95h1SWh1amZzjxkJsVCZC1EWeuAA7Spl3aesG5hF65zz8kyx6AlodvJkq5Ywq/e5E+5wO7BLjJnJHSZyLYyuG69/YreRTWejZX3leRLs/jjlnRezQqz4Sh5OPfE35ObmOjZzDXkv1Xz9b4iIMf/xVHw1D2cmvOhoW2WJjY3Fvn37UL9+fcyZMwdffPEF/vvf/+Krr77CgAEDcObMGek6pYWYXdxyyy3YsWMHEhISArH7sEFEiImIMEA7MSaNVqcmgh0vGRLoTaesIKw60tJS+4yOW6T9Zi1h4QIRXNta3+R1bfa2cHmJIDIykrVqsueYuCSB68H6pAwrYvSuHznPPCHG295fHxf0vWBW9DuFHWJMRojJhgSY6dvYQRtmRZhZ74NZ7AqXyF7UyL9CbJ4NQmxicAmxSpUqYe/evUhISEC3bt1w3333YcyYMTh9+jQSExNx9ar8KHZTecS2bt2K3r1747bbbkNCQgLuv/9+fPnll1J1PPbYY7opIRRyFOfn+ywzM4KH7bzo7VkXJimvF59lJnZLDzquKHtRI6w60tLzY/cn23EZdeoieYSKcnO9fu68fK/twlmEAfDkPmPzuZFzlrzrqlfGfF7uNxY2uTB9HkVEGHA9j5gWets7mcMrHO8BUdhnSTamy6i8Vsxr/UGZnp8RbM5Btk/UIljdhgnDjqH+yKxANyOkad26NWbMmIGPPvoIW7du9SSUP3nypE+6KlGk84ilpaVh6NCh6NevH5555hm43W7s2LEDXbp0wZIlSzzxT0YUFBTggw8+wMaNG9G6dWufZKkpKSmyTVNQiHQEop0Yu1zLxM+rz+pXHJnLstV+t0dsnWDyiNF5p0gZ0f2yE2qznTsbcyJTNwtbb2nJup7ZJub6qEh45wgzEmE0bKJhgpE7XWZQCf2xES4j7cwSN29HQEZLyn5A6pVnn1+zFrD6gzKl0mwo/iBMRk3SzJ8/H4MGDcK//vUvvPDCC7jttpJ43n/+85+a2Q6MkBZiM2fOxNy5czFu3DjPsjFjxiAlJQV/+9vfhIXY4cOH0bJlyUuTZMZXWIN12bA5qQB+ygRR8z7vbxorbkEjiMDqn1SS2JXuUDOjKPfWInuTbeqNiqJdSUadNPtFHk4ijM4LltmG/KsxkGKX9zItMUbOJz29ETvXpNZLzxVdkt7FnZevW44HXc7fbkLeABHi6mbd2/7CqhiTxYwQlhFjWrGJRvGGCOJs+ixG922x+5qfWmI9O34wZtZv1qwZDh065LP8tddeMz0/tLQQ+/7779G7d2+f5ffffz+ef/554XqMphVSWEP0y0/WdWm0Heu2tEOMtdrvRivsK3FL7tc/NqNOmYaeIoiFZK9PPaj9EhIRszxrTTiJMMA7SSstqthErby0FHQ5LbQSEWu9PI+/Ww8NRnzPXccj0BYMM/nNgh0rucPMWJVkxBhgfM3tiscq7VbVcExfcebMGbhcLk/e0N27d2P58uVo3LgxnnrqKVN1SseI1axZE5s3b/ZZvnnzZuGRfgAwbNgwXLp0yWf577//7pV+QiGHUYeuFe/CztcnitE2TsWIOQ2JdZIZdcn70YRzp8xeFy0RpuWK5N2XvTNzPD9ZiDWMtMEMgRRHkbGxwvOUOo1dCVllMOviM7LYG6Wyof81u18roQsyx0yeGfancJaHH37YY0g6d+4cunXrht27d+P555/XTf6uh7RFbMKECXjmmWewf/9+tG/fHi6XC9u3b8eSJUvw5ptvCtezdOlSvPrqqz4Z+q9evYoPP/xQODGswhcjaxj9guE9uFr5dvRSQzglMtgEnnoQC1f2Qe0y5HjpNAskPsyOzlSLUBVheln7SVJWeiQrO28kOW52HY3Wy4MN0hd9SWmd6/qDMrkuJr2AfzIxuL8g54wISeJmLY3IWLfp8qKxgFax2l9ofagZtV+JrcBy+PBhz5SHq1atQpMmTfDVV19hw4YN+Otf/4qXXnpJuk5pIfb000+jevXqeP3117Fq1SoAQKNGjbBy5Ur06dPHcPuLFy/C7XbD7Xbj0qVLiI6+3jEWFRXhs88+Q9WqVWWbpYC5LOPFzN924m/xQQQVFll3C8i89PXKeiboxk2aZYIdNh8YnRnfaN5I3joWOss++Ru46tmGN8ek3qAKswTSRRjsQd4ysWJ2WtDMijErdYrsT7RNvA9Yuh3hjgsWY8Rsa4l9XLt2zTPN4KZNm3D//fcDABo2bIizZ8+aqlNaiAHAAw88gAceeMDUDm+88Ua4XC64XC5uMleXy4Xp06ebqlshFhtGyojEKtnxwvPXF5xIu0gQ+LbW3pYRo2PU6ky1OtZgfqnKwFrCLhVFe8RXq/1urrsxe9EfecJ2+Qbjs2IseddVLxFWcn183ZdmX2DsddC7F3kDLmRG2tkF71jpthkluw0Vi4noQBfA3pGUIttqtYEuJ1pPaRNf4U5SUhIWLFiAXr16YePGjZ75sn/66SfTie5NCTErbNmyBW63G3fffTdWr16NypUre9aVK1cOtWvXRnx8vL+bFRYEIicRL4jfnyJEz4JhpjNm40S0UnIQNygdzM+WJdawYIZYuFiI2GJjv4ysYIB3OpHkXVdxa7lfAAA/FlT2GflYsm2eV3mtegHxQREses9GIIOqjSxwToxANouIVcyJeDInRlIaYTiKUhKZ7Xhtt3o8ASMM01fMmTMHDzzwAF577TUMHjwYzZs3BwCsW7fO47KUxe9C7K677gJQkvysVq1amlMFKfwP+yVtdl4/pzsL3svr8WY7kHqwva1WKb2X4LbWN6Hjnl+9xBYtykJBhAHecz/SEMHFii56PQBE/BHC5HWudnnHhN36hwbKbBODTNTwEWMRUdGeODCynBfUL3Jfydx7xfl5Pu5IvQ8Ju+eaNMrWzrO8Jgw7xk1BI4IdH2o8MWZVfOlNRUWwU4w5MWKSdwz0vVWcn2fLPKkhRxiOmuzUqRN+/vlnXLx4ETfddD3k5KmnnkL58uVN1el3IUaoXbs2vvzyS7z//vv4/vvv8fHHH6NGjRr46KOPULduXXTo0CFQTVP8AelcRDqmQLhDxidvQm7R9RtfyzonEu8hw8MHT3j+n5ZYIhw6HbrKtaIRwQYEp8uIHrTAwlq/aLciT4Al77rqEWGkPG/EI8kNRgfjk0EZpF6eNYzGnyNSecKfdRnKXFvR6XJ495OVjws6v6BVnB5JyXNbWhlJKZp+Rxa7nmmZ+znUXNDhSGRkpJcIA4A6deqYri9gQmz16tV49NFHMWjQIOzbtw/5f0zRc+nSJcyaNQufffZZoJqmMCDQ5nHPi+RoiUVH70tT1qRPymp1+LQIA4BHskqExvJm9T3LWGuYZxBBEEIsYezoSC33I4F2Q7IjIun/0wlZaWiL2KfJNdDnaA53HcFIrFih/qBMZC9LthR7xtZHMHI9ylj59EbZGdXFzrFoBRERVtwxGRHb7A+VsDqSMlDuvRPM/WX2QyLk3JNhaBEDSrLor1q1CqdPn0ZBQYHXun379mlspY2puSaBkimKsrKyUFhYaGr7GTNmYMGCBfjHP/6BsmXLepa3b9/e1IEonEMrX02gc9cQKwqvLR33/OqxRBl9Seu5LmQ7PbItyUMWKtCCjAThA3+4EykRRl5q/ZP2ec4NnSOMTVFBLF9sKgqWtY2r+KQqEbWG2QW5f3jWE/anB5mf0O4RmLw2GIkwckz+Gg1a3DEZxR2TPf+3XB+nf5G1jImmhZBtk0i/x1of6VxhovcTIaQE2B+QzPpWfsHGW2+9haFDh6Jq1arIzMzEHXfcgSpVquD7779Hjx49TNUpbRG7cuUKRo8ejaVLlwIomZ6oXr16eOaZZxAfH4/nnntOqJ6srCx07NjRZ3mlSpXw22+/yTZLofCCFUF0583r0NivVY9Va8/1ulhrGCEtsYbHpUbouOfXkBJigK81TM8KBsBHdOklbGUFGb2Mxt/WMJoTy5JNz0hBv5S1JnYnCVrdefleI+rMin0zODGgxw7BJYuMdSuYBjwEQxv8ShhaxN59910sXLgQAwcOxNKlSzFp0iTUq1cPL730En755RdTdUpbxKZMmYIDBw4gIyPDKwdY165dsXLlSuF64uLi8N133/ks3759O+rVqyfbLIXCg9bXqt5ISF45UdjgadYaFqwZrzsduuoTI0Zbwwi0a4ceESkyXRE7VyS7jtDn6AX0OXrBa5/+hBYoJIhfBFd0FFzRUV6Z8OsPyvT8yHI6S74rOsprCiaRYyXXQGsULw9iCXPCGkYsX0YizGmRpnfu6PvWbAB+sFj/CfQHZURUdOkcABBgTp8+7ZncOyYmxjND0KOPPooVK1aYqlPaIvavf/0LK1euRNu2bb1GPDZu3BgnTvAtBjyGDx+OMWPGYNGiRXC5XPjpp5+wc+dOTJw40VRmWoWCRWskEwBkGwQue48iFO98H2+2wyt5a6A7bi14AowHG4xPW71oi5hebiuelYsN1gdK3JOsZdHKBOn0S9pKgDPP8uKph5mcW2RaIjOTkbPIxoPZZQkLhPUL8L5u5FrS8V/kbwK7TGtEeLA+n2FDGFrEqlevjgsXLqB27dqoXbs2vv76azRv3hwnT56E222uwdJC7P/+7/+4me9///13qVQUkyZNQm5uLjp37oy8vDx07NgRUVFRmDhxIkaNGiXbLIVCCjK0XNQdRYLyWXhB+qHmlqSz5bPxYB4BhuuB+GahLWI8cUbvF+BPkO5krBO5J8j94I+ceHbHB/Haa1WEWRVfTgXuA9pJlEudCzBIsRrnFYwxYnfffTc+/fRTtGzZEo8//jjGjRuHf/7zn9izZw/69etnqk5pIXb77bdj/fr1GD16NAB4xNc//vEPtGvXTqqumTNn4oUXXsDRo0dRXFyMxo0b44YbbpBtkkJhCvKCkokN0iJ7USN0xB/TGQW5CCPCix4pqSXCaHhZ8YlVTMRFQoLx+xy94ElZ0enQVeQWlce21jchIsr7ZapnrZBNkKlVl6iwM8qwX5SbC0DMImZXvj4auwVYoCxfdkDfG1rXPdQtYSE3ejKMWLhwIYqLSz4R//rXv6Jy5crYvn07evfujb/+9a+m6pQWYrNnz8a9996Lo0ePorCwEG+++SaOHDmCnTt3YuvWrdINKF++PFq3bi29nUJhF3TiRdmX18MHT3hZxUIFOokrPVVRZpsYrymKAP0Ju2lXpdZckiz06EjSBp5L0ml4Apy1kvp7pggZtNpVGgVYMAXkO00w35M+hGFm/YiICEREXA+v79+/P/r372+pTmkh1r59e3z11VeYN28e6tevjw0bNqBly5bYuXMnmjZtKlxPXl4e3n77bWzZsgXnz5/3KEyCSmGh8CdGrkqSuBXwdlOGogijrWHXJ+++LsIIbF4w3ihJ8i8dN0amNNJzP/Kw8+Wilx2ffmkXaggPLZFmReQQqxndFll4x/Vy639j+p77rsdDSdbpD/HlpHtSyzoUbFYvp6xYQR3vFiYxYgcPHhQu26xZM+n6TSV0bdq0qSd9hVmGDRuGjRs34i9/+QvuuOMONdWRIuCIxo2VpKu4/hKlR0uGCsQdSc8lmbzrKrKPlAgUEhNGo2fxosXYp8k1/ngplLwYyGhI2jV5vYy4IDETH8ZmwKeFTGEbX1FT933g5HB4bcOOVJN94REBpnWcRhORa4kvGj33K71vUiZULV8s4eaikxkVq/AfLVq0gMvlMgzGd7lcKCoqkq5fWohFRkbi7NmzPgH7Fy5cQNWqVYUbsX79enz22We48847ZZugUDgGO/egKHReI1lXXaAhrkn2paZ3HLx1dFJXdrJwNlkri5kEnVao+75xmWCbDJwVXzQPHzzhZbU1ImJbpt/FmJNWsVBAdO5LdgBCKIvNcAnWP3nypKP1SwsxLUWYn5+PcuXKCddTo0YNVKxYUXb3CoXjsKPnrGDGguIvaLHEE0K8KYvYbVvtv+r19/XJwEssMPRISdoi1DszB/9u55svUCT+xciSZtXFSVvF2HYZBe2zsO5IGjYlg4jlS3GdkImT+gNyvWWmNwqpeDAeYeKarF27tqP1Cwuxt956C0CJ6e2DDz7wGt1YVFSEbdu2oWHDhsI7fv311zF58mQsWLDA8YNUKMwgK8Y8nWab0Ph6Ja7JvS1cwLI/5o3UcElqBeuzE4MT6NxgJFmr1ryTx9/1FWR65170uhhZHmRw6mUoa/myk3CwioWapYh85IVauxXXmT17NqpVq4Zhw4Z5LV+0aBH+7//+D5MnT5auU1iIvfHGGwBKLGILFixAZGSkZ125cuVQp04dLFiwQHjHrVu3Rl5eHurVq4fy5ct7zTcJwPRUAaWViKgooMBs6svgg7V2+NuqpBcvJhtkHYzBtLR4Ii8GVoSx7eW5HAFvyxo5ViK66Gz5tAgrSd6a71UPeTlpWQHMuo1ZRNySBJ71Qu8pE7WAOSHAHsnKMXRP0vs1OhaFwjJW54sMEosYzfvvv4/ly5f7LE9KSsKAAQOcFWLER9q5c2esWbMGN91kLTB54MCByMnJwaxZs1CtWjUVrK8IOoggkBFeMnPgBRKeoCKQHF+i5QG+K5JYw9i6SqZmKfm/WUuTllXBronC9UQffT8Y5QTjZYSnscP6RUbuigj9YHB1ldZYMXbwR7D3EbYQJq5JmnPnziEuLs5n+S233IKzZ8+aqlM6RmzLli2mdsSyY8cO7Ny5E82bN7elPoXCLtgXsF6nGeiXmlXIsdJTDhkF1vPgTeDNE2F60GkltOL0aIGkd+61Mq7LwgoXO16gpD6rIowIMHb0p6jIIfFu4TKCMlyQiSELesJQiNWsWRNfffUV6tat67X8q6++Qnx8vKk6TaWv+PHHH7Fu3TqcPn0aBQUFXutSUlKE6mjYsCGuXjU/XYpCGz1XmJkh9DL7lEGmff6AZwERsSCwHWWwf/GyAml88iaPK1E29xcNG/+lJcLIdWfPN29SdiMxxkNrcncZaxDrwjMrxuy+n73EVxuxbUpyxpX0tdta3+SbjsPWFipkCSvhVQp44oknMHbsWFy7dg133303AGDz5s2YNGkSJkyYYKpOaSG2efNm3H///ahbty6ysrLQpEkT/PDDD3C73WjZsqVwPa+++iomTJiAmTNnomnTpj4xYpUqVZJtmkIhjdYLvf6gzICKQidhXYYlf/NFmJZVi479AuATiG9kCeMJHa3Jm+myRJTpXR+jXEwnh+vHiZERk/Q2dlnGzFjDzIgvGjqBL48yu45xc6rZjRPuyGD+4NGDvp/Mtj8Ujj1c0lfQTJo0Cb/88gtGjBjhMURFR0dj8uTJmDJliqk6pYXYlClTMGHCBLzyyiuoWLEiVq9ejapVq2LQoEG49957heshZbt06eK13O12m06KVtphX0wyQiIQaRZk2wfYE/CuJ754mLFw8bYJ9AAEAhFRfY5eQAp1yHR7iADTElREzPGW8/ZFr6PPAzk/5NxrpXEgyAbrk3qLmb9JPZ5s9IyVrv6gTCFLkU+iVJvmkaRnbJAVSXoxWOyE9E6/zJ2MBTOycAZz6hjAmuUrGGL9Sisulwtz5szBiy++iGPHjiEmJgYJCQmIiooy3lgDaSF27NgxrFixomTjMmVw9epV3HDDDXjllVfQp08fPP3000L12BVrplDIIjq3ZDB34lagxVJ9ZPpYvWRju4g1TE+Ekb8/Ta7BfYGwFjLAnvg7I+GmtQ/aJcrLsE+21XqZ6gkwI2vY8mb1bbdQsVYx9ljstIrxxBeJQ7NbmAV7GIAifDl37hx++eUXdOzYEVFRUR4jkhmkhViFChWQn18y7Dw+Ph4nTpxAUlISAODnn3/W3fbgwYNo0qQJIiIicNdddxnu68iRI0hMTESZMvxmFhYWYtq0aVi2bJlnJMOQIUMwdepUr0k5aTIyMtC5c2ef5ceOHfPkQevUqRN3AvOePXti/fr1AIBt27bhtddew969e3H27Fl88skn6Nu3r1d5rYsyd+5cPPvss5r7euihh5Cens7dNpwRDb62A39MKh0KLwkRq5YWJSkotAUHazVjXZ9G7hkybY8dKStYRK4/L4bNiovSnxaMQFjF2P35axBAKDxnpZYwDNa/cOEC+vfvjy1btsDlciE7Oxv16tXDE088gRtvvBGvv/66dJ3SQqxt27b46quv0LhxY/Tq1QsTJkzAoUOHsGbNGrRt21Z32+TkZJw7dw633HKL0L7atWuH/fv3o14934SPADBnzhwsWLAAS5cuRVJSEvbs2YOhQ4ciNjYWY8aM0a07KyvLKw6NbtOaNWu8BiFcuHABzZs3x4MPPuhZ9vvvv6N58+YYOnQo/vznP3P3wQ5l/c9//oPHH3/cp/yTTz6JV155xfN3TIz/p8bRC5wXmcPOCiKxEvRLjHUn+YP6gzKR/YeVJFDpLNiUDVaPnyfCZCnOz+PGH9H5xGi0RIzesfAm39bDLpHNm2qGLLd7JKU/cMIqpmf94mFH6gqe+1frOQvGHH6liXCMERs3bhzKli2L06dPo1Gj68/LQw89hHHjxvlHiKWkpODy5csAgGnTpuHy5ctYuXIlbrvtNk/SVy3cbjdefPFFlC9fXmhf7IhMlp07d6JPnz7o1asXAKBOnTpYsWIF9uzZY1h31apVceONN3LXVa5c2evv9PR0lC9f3kuI9ejRAz169NDdR/Xq1b3+Xrt2LTp37uwjLMuXL+9TVpYT7yQicZSz82E5gehLjHZXkZeHP6xaIhgdg5XAXCesQQQzaSpo6Jc6r66Oe34FwJ8M3YpwYV2GLE6MAmTvPzNiTDSu5+GDJ7C8mXxsGIuRVQwAsg/K1ysrvvyFsowp/MGGDRvw3//+F7feeqvX8oSEBJw6dcpUndJCjBYR5cuXx7vvviu8bceOHZGVlSVcvl27drrWoQ4dOmDBggU4fvw4GjRogAMHDmD79u2YP3++Yd3JycnIy8tD48aNMXXqVK67kpCamooBAwagQoUKwm1n+d///of169dj6dKlPuuWLVuGtLQ0VKtWDT169MDLL7+sOQ9nfn6+xzUMABcvXvT834prT8/K48ToQbPChH4hisR52YVVQcTGPSUMO6b5ta61r2B6wRARltE0hrt8W+tobGt9k+eFD2jfn2atFU5bOfQsL1bcjNP33Bd0c0iKCBi7XI9OJnQNpmeExWiO1LAlCK1aVvj999+5xqSff/7ZdMC+qTxiQIm16vz58ygu9v7+rFWrluY2GRkZZnfHZfLkycjNzUXDhg0RGRmJoqIizJw5EwMHDtTcJi4uDgsXLkSrVq2Qn5+Pjz76CF26dEFGRgY6duzoU3737t04fPgwUlNTLbV16dKlqFixIvr16+e1fNCgQahbty6qV6+Ow4cPY8qUKThw4AA2btzIrWf27NmYPn26z/L6I7NQxqU96Xqw5KqxOmSbwAoyO112eqKLl9Fd5kucl6rBaKRgML5cWAFGL+906KrnGmQ0jfFk0ScEwrVsB6S9WoMJQs0qxmsP654MVusXTajcR6VOgAFhGSPWsWNHfPjhh/jb3/4GoCQWvLi4GK+99pquQUcPaSF2/PhxPP7449ixY4fX8kCknVi5ciXS0tKwfPlyJCUlYf/+/Rg7dizi4+MxePBg7jaJiYlITEz0/N2uXTucOXMG8+bN4wqx1NRUNGnSBHfccYelti5atAiDBg1CdLT3w/jkk096/t+kSRMkJCSgdevW2LdvHzcv25QpUzB+/HjP3xcvXkTNmjUN9280AbLTL3tejJcd0MLGTL2ili427QFgPvia52bVKhOK8ERaOA6154kq0fswUFYxcm2I5bLjnl897km67WV2HfMSOHaLr9I6zZEi9Jk3bx7uuusu7NmzBwUFBZg0aRKOHDmCX375BV999ZWpOqWF2NChQ1GmTBn8+9//RlxcXEDniHz22Wfx3HPPYcCAAQCApk2b4tSpU5g9e7amEOPRtm1bpKWl+Sy/cuUK0tPTvQLpzfDll18iKysLK1euNCzbsmVLlC1bFtnZ2VwhFhUVZSlfCUFLmIm8RGRFj11WMC3YF6LVAF0zrk6zucYA50Sqv+h06Co3DowHGQkZytDtlxVjsoI0Ypv1KYjMWMXY7UX53x3aoSTVdquZVJwkGAcmhFuw/rVr1zBixAisW7cO//nPfxAZGYnff/8d/fr1w8iRI7lzUIogLcT279+PvXv3elI9BJIrV674pKmIjIz0cZcakZmZyT2Bq1atQn5+Ph555BFL7UxNTUWrVq2E5tU8cuQIrl27ZvqCmoUXw6RXVmakYzCLC6vxZTxBazbujWzPqzcY0crWzkuPoPA9D0ZWsYcPnkBaovkpp/Sg4/doqxgNb3opO1FWsVJAmLkmy5Yti8OHD6NKlSrcECGzSAuxxo0bG+YL8xe9e/fGzJkzUatWLSQlJSEzMxMpKSkYNmyYp8yUKVOQk5ODDz/8EAAwf/581KlTB0lJSSgoKEBaWhpWr16N1atX+9SfmpqKvn37okoV3xFhly9fxnfffef5++TJk9i/fz8qV67sFSd38eJFfPzxx9whrSdOnMCyZcvQs2dP3HzzzTh69CgmTJiA5ORk3HnnnZbOjVlEBZleORExQU+HY2V+Q39D0lho5b2yKjpkBHGgMvXTAuzLDmS0b75XGd6IyVAQl7IU5+dx574kgtwVHQV3nve5kblPnJoLUuteYUeERkRFAzpWOT0LmMIb+p4AgKLcXADhHTsWbhYxAHjssceQmpqKV1991bY6pYXYnDlzMGnSJMyaNSvgc0S+/fbbePHFFzFixAicP38e8fHxGD58OF566SVPmbNnz+L06dOevwsKCjBx4kTk5OQgJiYGSUlJWL9+PXr27OlV9/Hjx7F9+3Zs2LCBu+89e/Z4BeaRuK3BgwdjyZIlnuXp6elwu93cAQTlypXD5s2b8eabb+Ly5cuoWbMmevXqhZdffhmRkZGmzoldiMZeaQUui4ow+m+7BVmgpjixYwi9yPlnXZpOuiZ41q/rIswbYm3Z1vomrzYGk8vEbvTEGI9gCdongyu0JlInx6AlBpW7UR4izMNZgIUzBQUF+OCDD7Bx40a0bt3aJ5tCSkqKdJ0ut9stpTmJK5CNDVNzRAaGixcvIjY2FndH9dcdNSmLk8H8rBCjsSLGeC8+J17+ZOSk1ovWTvcivQ/RkZV2HrOoAGOtPgT6XISzEAP4Az/0LMWiE4DbNeWRkRuQNxE7+T9gXQyaaVM4wJu+yw60+h+j56zQfQ0ZWIvc3FzHDCfkvdRgwixEWhCcRfl5OP768462VRa9kZEulwtffPGFdJ3SFjE1R2TwopenRtZlxnuB2AURW6wgCyUXpZEosjKSk1eX3noWOyxjWvFfLFoCrDSiZVXSQsYqlpaY52jQPs+a5xSlQXzRyN4XYUWYxYgBzmggaSEmMkekIrDwOlErgsCpjNW0IAslESbyAuXlCzODP9OMiIovwp+2n/P8nzdqMpQGHtgF+9LlPTfssmBK8Mre23Zdu9ImvrRQmf8VPISEGD1Z98GD+nNiNGvWzJaGKeQ48U4iIsqXWEJC7UF3UoQFeki3TPA9jRUx7eSx0m7JLztUhzsvHx33/Ko7nVFpx46X7yNZOVjeLNrxBK+Atxhjc4vZMRdlacTIKmbnMxvoPo8mHIP1nSDCuAjQokULz0jJFi1aIDk5GS1atPD5JSeXUvNrEFB/ZBYShh1zVISpNATXMZOigraS6Z1LnhXMjhGZeshYw9x5+Z4fUCK+WAHGBukHw0vBX2ilfNAaaQuUWMWcpsyuYyiz65hukLjeB4PoNYzYlun5KXxhn2Onno2IqGjPL2C4bfhJMHv2bNx+++2oWLEiqlatir59+/pMq+h2uzFt2jTEx8cjJiYGnTp1wpEjRywcpHWEhNjJkydxyy23eP7//fff4+TJkz6/77//3tHGKgJHqFnZnEbUyqElqnjr2GVOC2uaba1v0hwFSTBab/fk3uGMvz5qiCB67B//xsMHT+Dhgye45USvk5a4UuLLGH/MiVva2bp1K0aOHImvv/4aGzduRGFhIbp3747ff//dU2bu3LlISUnB3//+d3zzzTeoXr06unXrhkuXLgWs3UKuydq1a3P/r1AotNGzYhmNhNRLWSGSRFerLqOv7y87VPeK/bJKabKE0WjFivHuCbuD9klmfC3RxUsUS18nuq3sOholuszjZKxYUD1zfg7W//zzz73+Xrx4MapWrYq9e/eiY8eOcLvdmD9/Pl544QXPvM9Lly5FtWrVsHz5cgwfPtxCY80jJMTWrVsnXOH9999vujEKZ7Aj+FYFmTqD1ovZqKyMRcWua/en7ecMrWJkfwpj7A7aJ8Lo4Szt9DB6iApCJcDMU9pGUNoVI3bx4kWv5aJT/eX+kTS3cuXKAEo8eufOnUP37t296rrrrruwY8eO4BZiffv29frb5XKBTj9G5xRTecSCh1Cfw1CGUBaKolNGEURGbJrFyBrGrlfB+fJYsYo9kpWDtMTr8z8aWb/069G3iilXmrNY7bPqD8r0SrQbzglia9as6fX3yy+/jGnTpulu43a7MX78eHTo0AFNmjQBAJw7V9J/VatWzatstWrVcOrUKfsaLImQEKPnbty0aRMmT56MWbNmoV27dnC5XNixYwemTp2KWbNmOdZQhTgiAoxnvtZ6kEPZwhEMZnqRDlfkRcxeV/bYrHbEducE08vIXhpwKpUFGUEpK75EKG0Wm0BAn2M7+if2uQ+mUZN2uSbPnDnjldBVxBo2atQoHDx4ENu3b/dZp5WQPlBI5xEbO3YsFixYgA4dOniW3XPPPShfvjyeeuopHDsWmlaJcMGp3E3BbG0KZqFo92jHYL4OCnGsWMUAeQuYIjhxemL1QGOXa7JSpUpSmfVHjx6NdevWYdu2bbj11ls9y6tXLwmtOHfuHOLi4jzLz58/72Ml8ydCoyZpTpw4gdjYWJ/lsbGx+OGHH+xok8IkPBFGUgewv3DD3wLF6XQSZtC7tiLXneQCswJ7TsLZXWIX7DlzMpXF8mb1sbxZfR+3JA9lGXOOQImvknQWxtYk2/Bz+gq3241Ro0ZhzZo1+OKLL1C3bl2v9XXr1kX16tWxceNGz7KCggJs3boV7du3N3OEtiBtEbv99tsxduxYpKWleRTluXPnMGHCBNxxxx22N1Ahxol3EpHogBgJ5dgrhbPoxYepe8YYf4l5WtglQPy6hLOlJpggglfvfBuJYpnn7cQ7icATwsVDipEjR2L58uVYu3YtKlas6IkJi42NRUxMDFwuF8aOHYtZs2YhISEBCQkJmDVrFsqXL4+HH344YO2WFmKpqano168fateujVq1agEATp8+jQYNGuBf//qX3e1T+Ak75qcMBHpCMVgsf05NEcXCu4aBOgfBcu7NEMlY/Iv+GHnlJHrpLcygZVUjdSuRFXxYsUBK3TNX/Phs+jl9xXvvvQcA6NSpk9fyxYsXY8iQIQCASZMm4erVqxgxYgR+/fVXtGnTBhs2bEDFihUtNNQa0kIsISEBBw4cwKZNm/Dtt9/C7XajcePG6Nq1a0CD3RR8UcK+nGUDvEPBshFIq52/9i1ybQlmRJBZt6Qr2o9ujgBBhJkTgkxrOiFaSImmtOCJL97gACD8Y5NCgdIwMML1x8/K9jLQ2Rw063S5MG3aNMNRl/5ESogVFhYiOjoa+/fvR/fu3b1ycSiCA7uC9UPJGhaotsqOdAxmN++21jd5RJVIQleeW/L4u/Vsb1dpQGuibbKMCCyeIOOJrwYjSmY44Y2CpfelxFhgMSvCgjE+VWENKSFWpkwZ1K5dW+UKCwGMXvoiQdTBKhp4aFmMnHCRFefnGZ4/niD227Q2VNtEj784P88zcoeILBErmdbLvjSnrbACfR9rCTItSHnaJsCzgLNijE6FIhKvpPAfesmdRWH7okJ3AfyWMcvPrslQRdo1OXXqVEyZMgVpaWmebLWK4IGXfT2UBJUo7Asr0FYxLauGE/vxF7TVixZlKomr/WjNmsDONyo6C4Ps/kqDmyxUCKfUQ3alrwh3pIXYW2+9he+++w7x8fGoXbs2KlSo4LV+3759tjVOIYeWMKEFmYgljBUWTj3IvTNz8Gmy8TB6UfwpVIgFgZxPkXOlJ6acEpJ2JXcsTeKLxIKxQftOw5t/lL2vWEuVrOWRvg9o8UWLMGUNcx4t0cv2DXZZ9FUameBGWoix0x0pghORTp2HXqyKEyKnd+b1efGIKDMSaKQ9dLlAWcXojlLk/OiJMdHpjcxcB6NBG/QyM502fe5DecQkjT9GS/LQe3bp9Sx23P9KhAUGct6dcunTz2Sx+5pDe+GgXJNCSAuxl19+2Yl2KCxSf2QW4Crns9xMp84usyrIiGCihRMtwIy245Ew7BiQeb0eUi4QAfGybh2zbkb6Ojh9jB33/IrUg+299iuCepHrY4dYcuKDQ103/8H2FWEfU1lKxJQVpIUYYe/evTh27BhcLhcaN26M5GQVXxCK0OLK6AVvRpARocQKLyLM6L9JOXYd296EYce46wM9moj+6hRJC2JW2JoRcqz41nNZssvYbWmRpggelJgKfrgiLEwsyArzSAux8+fPY8CAAcjIyMCNN94It9uN3NxcdO7cGenp6bjlllucaKfCJrQCf2UEjIwg03M38qxd9DI996SepSyQVjGZlyHPWimzrd75txrUndE0BvXha+nLXtQI2QclG6vwoJXXi7fOCPUCVwQ7KlhfDGkhNnr0aFy8eBFHjhxBo0YlncjRo0cxePBgPPPMM1ixYoXtjVTYB+3K0xJUokH0PCGh9TKRDcrnladjw0TrFomNCjRmXb9EjPGEnFEdoueBFpadDl1F6sH2mmIi7F0sFiCWD73zE4z3psI+eNawsEfFiAkhPen3559/jvfee88jwgCgcePGeOedd/Cf//zH1sYp7Ie2UJEf4N0p0K5BugyB/ZseYq8lDKyiJVJ4MWeBclFaTQGgZyHTctXSI2XpH4sdk71nNI3RtfgpIeEMxR2TdX+K0MMjzMP8mSEWMSu/0oC0Ray4uBhly5b1WV62bFkUF6vv4UDCi7kCfF/sPhakRb51kDJasVu8fWmJMStffmw9PEHCtsnOlBh2Iprigi2rN7BBZdgOL5SwCk9UjjaFHtJC7O6778aYMWOwYsUKxMfHAwBycnIwbtw4dOnSxfYGKsRhrUPXX+CUaIK3SGHnudMSMTxhRv5mXZl6cTBmkHF3suksAhUrpjUalYhUo3Z5CzJfVzE7yEKrTvaL2+oXeKdDVwFcBVCSW0wJQWs4mUg1MjY2YCk4FAoAyjUpiLQQ+/vf/44+ffqgTp06qFmzJlwuF06fPo2mTZsiLS3NiTYqBLh3+1ls+lNtAPoWIVpAsS9zthzP7aclyPT35y3a2LqM6qDFI1uH0baBmt9RL1eYqBgj5VMWdfWyWpLlALlO/rWMbWt9k8/0R2rEnkKhYFHB+mJIC7GaNWti37592LhxI7799lu43W40btwYXbt2daJ9CgvQYgsAxidv8ilDiwFapGmNXmQFmqgb0GgEJC9tBbsNKWNktSMEa6yYGTFmRlDaHX8SERWNba2jheagVAQeJ61hxR2TEbHtuvgmg2LIxPHKEncdp4L0Q2EgkkIM03nEunXrhm7dutnZFoUFPu8QhzKu638TQZOSed3qRVuWeAIlJbMrxidv4gos1gpFBJGRGBNJWUEv18ohRpZ13POr13Q7RlY1VsSIZI23o0OrPygT2RouSrpdVix2WsfMa7+VyZzJOWPnm1RuyeCAFUVmtjcL+zyRSeDJ9FBKkFkj5KcmUq5JIUwJsa1bt2LevHmehK6NGjXCs88+iz/96U92t09hEo/g+cOlxQtkp0UK+b+WtUvUCmVr25lltBAggszISkYIlItSD6tijCdatcSRk65DlbZCDqdiwswIKisCTiFPsPVBjqOEmBDSQiwtLQ1Dhw5Fv3798Mwzz8DtdmPHjh3o0qULlixZgocfftiJdipMQh78T3H9pa0XD0b+tovi/DxTE0/zzO4ZTWNMfSHaYX0yg5FVTLRtvHW8WD+2nF2uCuXysAcjARYRFR3ygpa4JhUlyIpuM/2bej5DH+k8YjNnzsTcuXOxcuVKPPPMMxgzZgxWrlyJV199FX/729+caKMmhYWFmDp1KurWrYuYmBjUq1cPr7zyim4ajYyMDLhcLp/ft99+6ynTqVMnbplevXp5ymzbtg29e/dGfHw8XC4X/vWvf/nsa8iQIT51tG3b1qtMfn4+Ro8ejZtvvhkVKlTA/fffjx9//NH0OdFy67HLiQtSC56FSWu6okBArGGiWMli7zS0RZLGSKCx1k06gN/Oa9Tp0NU/Rkt6t01hDvp6BoOFRFnFghPy/PN+oYLKIyaGtEXs+++/R+/evX2W33///Xj++edtaZQoc+bMwYIFC7B06VIkJSVhz549GDp0KGJjYzFmzBjdbbOyslCpUiXP3/TUTGvWrEFBQYHn7wsXLqB58+Z48MEHPct+//13NG/eHEOHDsWf//xnzf3ce++9WLx4sefvcuW8J+YeO3YsPv30U6Snp6NKlSqYMGEC7rvvPuzduxeRkZHGJ0EQ4saiH+JPk2t4uS6vj77TfonzYsV6Z+ZgbeMqmtuY+WLT2oZefn2fJcv0LG8RUdEBsYzJBNrzxBhvO1oQp2R29ZQhx722cRX0OXrBatM12db6Js99EwxCIhSg78lQepEaoSxg8mj1TwQ23yCv/wjGUAsuyjUphKlRk5s3b8Ztt93mtXzz5s2oWbOmbQ0TYefOnejTp4/HUlWnTh2sWLECe/bsMdy2atWquPHGG7nrKleu7PV3eno6ypcv7yXEevTogR49ehjuJyoqCtWrV+euy83NRWpqKj766CPPqNO0tDTUrFkTmzZtwj333GNYvyge9+Mi78B9kh5B5KFms/J7p7IIvHlcT/CRdWREY/aiRqg/KNPxYFhZESpyHX4sqIxby/2iuw89YSxLRtMYH4uY3r4VpQcSnE9Qoya10Xq2WREmKrBCRowpDJF2TU6YMAHPPPMMnn76aXz00UdIS0vDX//6V4wZMwYTJ050oo2adOjQAZs3b8bx48cBAAcOHMD27dvRs2dPw22Tk5MRFxeHLl26YMuWLbplU1NTMWDAAFSoUEG6jRkZGahatSoaNGiAJ598EufPn/es27t3L65du4bu3bt7lsXHx6NJkybYsWMHt778/HxcvHjR60ejFehuFMzOm8ZIy80pUp8oTlputCCB6/7Kdm1nGo3kXSWC6MeCykjJ9E4Z02q/M5+Prfa7cakoGpeKotVoSYVCEJn+RdZSHyoCzOV2W/6VBqSF2NNPP4309HQcOnQIY8eOxZgxY3D48GGsXLkSw4cPd6KNmkyePBkDBw5Ew4YNUbZsWSQnJ2Ps2LEYOHCg5jZxcXFYuHAhVq9ejTVr1iAxMRFdunTBtm3buOV3796Nw4cP44knnpBuX48ePbBs2TJ88cUXeP311/HNN9/g7rvvRn5+yVfkuXPnUK5cOdx0001e21WrVg3nzp3j1jl79mzExsZ6fqwVkp1DkiwDrn9B8eaRpEfgkRc8WcbGhvkzRswpoVZ/UGZJMP2iRj5WnYioaEcsZayAMTPXXGabGK+/A9khh8rLQKEIJkj/wvYzZp6noLdIu234lQJMpa944IEH8MADD9jdFmlWrlyJtLQ0LF++HElJSdi/fz/Gjh2L+Ph4DB48mLtNYmIiEhMTPX+3a9cOZ86cwbx589CxY0ef8qmpqWjSpAnuuOMO6fY99NBDnv83adIErVu3Ru3atbF+/Xr069dPczu32w2Xy8VdN2XKFIwfP97z98WLF1GzZk1PZn0tS1VJPFEjHxFF3JUlqSuABFy33vhDcBm50My62DoduoqMpjGG5ejkq04KCzutYpltYvziljCysHXc86vQOVaUEKgEw3pYzUGmEMPssxrq7keVWV8MaYvYN998g127dvks37Vrl1Bslp08++yzeO655zBgwAA0bdoUjz76KMaNG4fZs2dL1dO2bVtkZ2f7LL9y5QrS09NNWcN4xMXFoXbt2p59Va9eHQUFBfj1V+9M5efPn0e1atW4dURFRaFSpUpePz2IK5F2Y7HJUxOGHfOMouQJNbq8nW5Jp9GKa2IhrkrWOmaHZUwv+7XZ+snLnLgpST17W/DFu1lY6xsRgADweDO+61whByvMQkkUFXdM9lh1XdFRKnCfQtYtaVZsqcnEwwNpITZy5EicOXPGZ3lOTg5GjhxpS6NEuXLlCiIivA8hMjJSN30Fj8zMTMTFxfksX7VqFfLz8/HII49YaifhwoULOHPmjGdfrVq1QtmyZbFx40ZPmbNnz+Lw4cNo3769qX3whBRZNj55k2ZaCnYOSTpzPlvWyFLW5+iFgMR+0WQ0jZGy1ujFjWm5EvQgZenh5lYneKbrShh2DJltYjxizE7o42TFmELBoyg3VwXo24yR9TQk5ndVrkkhpF2TR48eRcuWLX2WJycn4+jRo7Y0SpTevXtj5syZqFWrFpKSkpCZmYmUlBQMGzbMU2bKlCnIycnBhx9+CACYP38+6tSpg6SkJBQUFCAtLQ2rV6/G6tWrfepPTU1F3759UaWKr3vs8uXL+O677zx/nzx5Evv370flypVRq1YtXL58GdOmTcOf//xnxMXF4YcffsDzzz+Pm2++2ePWjY2NxeOPP44JEyagSpUqqFy5MiZOnIimTZtKz935zoFOmJi53WfeRo8Fa5H3nI4Eo+z5olMU0dg5Ys+feCxjNrkq2WHoVushJAw75hFg/hRKrDVMuSUVCmOsPP+h7JYElGtSFGkhFhUVhf/973+oV6+e1/KzZ8+iTBnTU1ea4u2338aLL76IESNG4Pz584iPj8fw4cPx0ksvebXr9OnTnr8LCgowceJE5OTkICYmBklJSVi/fr3PSMvjx49j+/bt2LBhA3ffe/bsQefOnT1/k7itwYMHY8mSJYiMjMShQ4fw4Ycf4rfffkNcXBw6d+6MlStXomLFip7t3njjDZQpUwb9+/fH1atXPTMUmMkhlpLZFQk45mPJooP0tSbwpiGxLPUHZaLP0QvcrPsi80yGKqwgI4h0irTFi1eeZNs3Sp1Biy82GS1tBUvedRV7W7jQar/bdtckva/MNjGe3GGAEmGyhPycgQph/OWWVPdU+OByu+XGhw4YMADnzp3D2rVrEfvHxK6//fYb+vbti6pVq2LVqlWONFTB5+LFi4iNjcXcb/6E97LvxfjkTQC8LVfZixr5uCVFRBSJZ/LXRNnBCjl+0UBrLZFFztEJAyGm1TlnL2qE/kn7PFYw1i1plxCj29Vxz6+4VBSNVUdKrOB0bJgSY/rIuqEThh2z9ByZnbw7YlumpW1p1GTfvtddT2jpPetG25Jysi7KQvc1ZGAtcnNzDWOMzULeSy0HzERkOfOCsaggD/vSX3C0rcGAtAnr9ddfR8eOHVG7dm0kJ5fccPv370e1atXw0Ucf2d5AhTgkOSsRYwBfhPGghZnRy0CmrAx9jl7wuDXZLPlPZ3+H9xJu09zWSUgb2Az2ItvwxBaJFdPqZLU65oRhx4BdJQIss02MIxYwGjLJOmsNA5QIs5tQd0ERSrMA41Ga3ZKAck2KIi3EatSogYMHD2LZsmU4cOAAYmJiMHToUAwcOBBly5Z1oo0KAT7vEIe79/4KtP4jUz6OeVJWlIgbb/FAltHiR2RaIXpbs5BAfpFpkUj7AiXCnETU4sha4vwRF5a86yq2tb4JHff86rV/skxhjGxqFHKev+u8RHpfPeq2CeiE4Xqjg0sTTrslS+t5DXdMBXVVqFABTz31lN1tUViETIRN5oxMySzp2GmxRUOPbGSnKRIRS6Kw+9eqc23jKp6UE8TiQspqHUOoQl7SRm4F1kVBj5R0yiJGuzzpeSUfb7YD2KOsYWb4z0nflD++iJThk72oEeq+b3pzRQgi0n8EHDXXpBD+ja5XOA5xGdIB92uhLXwIfKuXNYiY+zS5BiKixL7myEueJ8gCLcb8+TWqFyPilACLiIr2GY1J2tE/aR8uFUX/sVx9lQcjZXYdQ2Eb5xLG8nKcqYDxEkiqGho7k7iGsiWstLgXraCEWJgS6AzetAgTgRVePKtLOFnEAN9YMd5ISX+iJcLIMidylin8D1dQhVAiWUWIWMMUwighFobQ1pRAxY1cdylqj86kpyFihZedrtFAIvIlKyPAnPoyjoiKRmab62KMblP/pH3ALpXcNZj5rvMS9EAblNllbeQlDWvtEnOvltCjbhsAoW3JkUHLGsbOpKFVPmxxu0t+VrYvBSghFqYEy4gbdjJxekQnAHQ6VPIvK8SIK9K7rNj8kaFIoK8XbQ3zEWFUGSXGQgM2aNzMAACFGGZnywj0M+8P1KhJMaSF2JAhQzBs2DDuBNmKwGPXJLFW47Horz+PRWyRrxijhRVtBdMSYeEoxkSul5OWMAAeaxhrncuEckmGCt4WqwMBawdQ0hZiFQtneDFyTguskHJLqmB9IaTnmrx06RK6d++OhIQEzJo1Czk5+vMOKvwP/TI1mh+RrGe/6vREGDv3Il0/+zeZFLg4P8/TedCTh9Pl1zau4tkv+T9Zrxc7prAH2tpFv0zIcmUNM89tW4YEugkKmzmxLNl2F6NMfTJz3yqCG2khtnr1auTk5GDUqFH4+OOPUadOHfTo0QP//Oc/ce3aNSfaqJAgYdgx6S8yu6xoItQflFl64iNCDL2RmkqEmSNkLBcKKbTckTL54szuN5TuKVex9V9pQFqIAUCVKlUwZswYZGZmYvfu3bjtttvw6KOPIj4+HuPGjUN2drbd7VRYhGfFIl90ZGoV1h1IYJfTVi4Wep2eO43tjHjtU197zsGeX1qEKVekQsHnxLJkrgjT+wDm9WV6gk1vHZ0kOCRw2/ArBZgSYoSzZ89iw4YN2LBhAyIjI9GzZ08cOXIEjRs3xhtvvGFXGxUOQL946QnCgevCS0uYWYWXrkEROHjXQYkx+yD3u3JPhgZEbPF+POz0JpSGAH6FL9LB+teuXcO6deuwePFibNiwAc2aNcO4ceMwaNAgVKxYEQCQnp6Op59+GuPGjbO9wQprsO6n4vw8r7gsvX9p2C88kSlO6DIk4ayoW5SNOxPdZ6hi97HoTTBOoLP3A76jJMPp/PoTdd4Cj1NWJKO+K1is+l5xvO4IIN8/+1WjJsWQFmJxcXEoLi7GwIEDsXv3brRo0cKnzD333IMbb7zRhuYp7IS1gn2aXENodKSZzoSetFtrez0xJpJbixVk4SzMzKJ37URzhanzaB3iUrptyxCVSsKPBEqA+YOQeC5VHjEhpIXYG2+8gQcffBDR0dod/E033YSTJ09aaphCjhPvJCJx1PVzznM30Z0Hnd+LEIgphGg3pdb/7RpMwGbvp3k6+zsA0Jxc/Ons7xybeNyfgyXofRLqD8rEXri8RJvKGeYcSow5jxMCLBjEFyHUgvYV+kgLsUcffdSJdigsMrJ5BmIyyyAlsysA3zgsXifyaXINn68qu8WYjDVNTzzSYkW0TtadqZf6wkhkOSXCnAy+FXFH0p05fS+UzGcZAl/cIUTIBVqHIEbnN9BiqrTFxSrXpBiWgvUVwYdo+gqtAP1ATSlEt5nXftqNaYU+Ry/YPgiBHtxgpm5ybP4g0PNZKkq4bcuQUpHw1F/oBdMD5tL6OIXVdoSUJUyNmhRCCbEw4fMOcdw5HUkWe/ZFT1yTfY5e8LKc8ISEUSoKOxEZ1m1GtJAh5HTSWCtEREV7zh1JTEsnqTWDXWJMK/WHEmGBh36JZi9qhHvimwewNc7yn5O7HA1WJyl47BBgbNqdkIi/ksD72PwUqa8QRgmxMKJ3Zo6PpYuGfhGTckS8pWR25Wa716rHqnXKqK1aWE19wctVRgRVp0NXPT+t46eFKyt8yfHQAlc0J5oVYSSyH1aEhePLJlSgxdiJZclhLcbshr7PRfoA0X4inHIYBtMxENeklV9pQAmxMOfT5BpeczvyOiYiILIXNZIWR2YEEb0Pet+iYsRuaw4RVLGRVxAbeQXbWt+kWZZY04iQ1DpfvMEQwYCyhAUHrBhTbkpxZD4C1f0eYMioSSu/UoB0sL4ieOFZaIiVKwFU4tZM37J0HaSclguPtUrpDQhgYcXJp8k1/BrASs4Jbxm7nHxR0m1e27iKlzWRXke2p62MWFSyLmHYMa+UHizkHIhYqWS/dOnzq6xgwQMdvF/ipszDf38K7GTdwQZv9gcRwl2AkXun/qBMzf4gGJ51FawvhhJiYYyW2CICgVjKPk2ugfGZm5CS2ZVxWeo/yGyQOc+qVdimEcrsKlmmZyGi6/JHJ8oKKFqgsWJtbeMq1+NQlgEpfxgzxmdu8qqLjFglyB5HwrBjsHtqNR+XpM31K6xBi7ESy1jJS/U/J3cFslkBwY4RpeEuwGjIvVOajjlccbndpcT2F6ZcvHgRsbGxmPvNnxBzQ4mu1hJghOxFjbxEGCsk6Kz7gJgFhv1aJXUUtrm+vMyuYz4CiBVvomLMyJUp+vU8PrlEgBrFnvG+PIlgY+PtWIsZez3o80r2JzMSStQixgvOD4avZIUvrAih7+1QFmX+mtYpFMSIlXyBes+tlhjT2qbQfQ0ZWIvc3FxUqlTJVHuMIO+ldve+gjJlzceqFV7Lw87PX3K0rcGAsoiVEmhBkDDsGD6Ft/WnOD8PWFZS1oxliidkSgRJDj75a1efNpCy7D549Yi2Q0sM6vEpSty2hvvjiB/WFcku54kwHn6xhCkBFtQQIU67Ksl1I8H84e625H2Q8dax63nPa2kbIRyIpNAiKNekGEqIhREiL33Ad8Rk78wcrG1cxWMdIlh5uEs6wpL9PLDg+mABIk6MLFZsZn3tffC3of8VOQYnOjHR62E3pe0lFE6wcWNkGQCf0ZVawowE/tttSROxbrGW1/oQs/QSKy/9zIo89yI5+LTWB+OzYeYjmNwzwSrGFMYoIRYmfN4hDg/s9xZYAF90kb9pNyHJg0XinoASV6Xsw23UMdJtsyKQ9NpF2qDnbpQ5JvYcyqA31yZdxggzQ9GVJSw0IW5wct8SYcZeT1qYEVF225YhngEiPeper7M4X24gAC+lhoioErHs6uW4YxM7sx9VIttplRFZ7pSQkRnlaWbwEhFjQfe8F7tLfla2LwUoIRYmnHgnESmZzTWnMgLgE5BO1vFG/vXOzPFYyMx+aWmlcPD3l5tV14VRKgpRF6Q/KG1TqIQzWhZewDem0COcll1fxoqUe+KbB80IOyNrthFmPw713J9ay6z2VUYDmnj7CxvrltXs+KVDhykhFi7UH5mFk4ub+zzArPCiRYNWjBMtOqyKMRp/p6ogbeZZtLQ6ZHo57UZNyWzksTSwrG2ch4go72VkW38mVfz/9s48Popi2+O/Sci+aUCyYIAAASFhCaARFQMKggvgxSuCqCCo+BAVEUWuV4N4gSdPER/iwn2AKCj6BFTkqRBlNREEJsgaQthDkCsXwpqEkHp/xGpqeqq3WTIzyfl+PvNJpru66kxNT/evT506JfscwXFxAIDLZWW1ZgfhWWQiRQzu56IsKCxcWlZvQog7AsyT62aafTCyhYfhclmZg+fN1d+YXtyZ0XarcaueSETNMQrcD6glkAgAJMTqFFaGBfWGL/lfdV3upJfwtMfI6KlR7Avu0dLyXPEZpFxwcrj44mKO16POryab8SZrx8osVI6rQ5LAlZsWEXiob7ZaIgvQFkTq64HsJl1bi5B7yrvjyvnsjWFBs5MJtLzx7njF1NcEvxuOFLDBzWB9j1ni35AQq2PIfsBGQ2dGIsnVuAW1wJM9pdeW+93U51flVlPsRxOVSHW88PGYHqtDlMrMOAufQ68uon5gNLymt8+TwisQhs489TBp5D2TTTKQtWH1Wmp1JMLvvGHuZsevJ9m1SIjVQdQ/XFe8UbInPisXBVHQ8PpkN5DaFmSyp8cBu04qs0VlqSiMVhrg9dbsd/R6ie3JsoTbwsMAIaBfbZ9VL5rYj+QNqzvIzlvRSybDzA3fld+dL0V/UFi45m9ED1m8nZmUGFrXKS0xbOZ6ZvZa6uoDMBF4BPRak1VVVfj73/+O1NRUREREoEWLFpg8eTKqq7V9DGvWrIHNZnN67dmzRynTo0cPaZm7775bKbNu3Tr069cPycnJsNls+OqrrxzauXTpEiZMmID27dsjKioKycnJeOSRR3Ds2DGHcrK2Bg8e7JkO8gDqWUxm4YuIA/JgY7PxGe5g9kLGBZbaiyiyPLMJBuw66bItfHo5f6WN2I3Wo/e7XJ8IXawJGaJoUP+O1dvqKi2H2h28RGoBZTQr08x1ytW+dHUYNJCgRb/NEdAesTfeeAMffPABFixYgPT0dGzevBmPPvoo4uLi8Oyzz+oeW1hY6JCp95prrlH+X7p0KSorK5X3J0+eRMeOHXH//fcr286fP4+OHTvi0UcfxX333edU/4ULF7B161a88sor6NixI06dOoWxY8eif//+2Lx5s0PZxx9/HJMnT1beR0REmO8EL2DlAmH0RK5+ghSfGN3J+2WlnFn4k/WAXScd4sq4p0vPI8YRPVta2dKrK8prhiOFJ3lZTJBV6sONlbiClrdUby1TrXPEn+OMRDxhp/oBTWv2pNa1y0y9RtdGsw+JnvaKBYWFI4gFARUeq1IfmjVpioAWYvn5+RgwYIDiqWrevDk+++wzJ6Ejo3Hjxrjqqquk++Lj4x3eL168GJGRkQ5C7M4778Sdd96pWX9cXBxWrVrlsG3WrFm44YYbcPjwYTRt2lTZHhkZicTEREOb3cWTQkfvgqN3QeL71Bc6q1O8rWCmvnGZucCuGu+YFeHF0VqcWHwa98Y6j7X5tMxnYIrQ8KfvMRInRfPaKudhfV1rVC1atcSVLHeZbDhSCyvCyUq4h145nnMubcRupxVAfJ0Gw8YYbG7EeblzbCAR0EOTt9xyC3788Ufs3bsXALBt2zZs2LABd911l+GxmZmZSEpKwu23347Vq1frlp07dy4GDx6MqKgot+wtKyuDzWZzEoCLFi1Co0aNkJ6ejvHjx+Ps2bOadVRUVODMmTMOL6tYDfCVYRQwrHfxkl0E1e59TwkMMxeh4kWZggBzHVFg1sbQj9kp/4T/U7wo0/TLSp2A75OUBgpa1yxXP6enhh6tpMpQv8QHX8J/CWiP2IQJE1BWVobrrrsOwcHBuHz5MqZMmYIhQ4ZoHpOUlIQ5c+agS5cuqKiowCeffILbb78da9aswa233upUftOmTdixYwfmzp3rlq3l5eV46aWX8OCDDzoMiQ4dOhSpqalITEzEjh07MHHiRGzbts3Jm8aZNm0aXnvtNZds0Hvak5W1Wp8VISXzjJlt1wiZh0A9g9PsEMeAXSelAk3mAUsbsfvKsKObWBmq1HtS9sRQDnnCvAcXS1Zmu5kVY+J5ERQWjqqstmiw0XEJIleQLcNkxRZXsIX/majPQ3nPZJ4rV4YjZXhjiNJsu95uwzLVcM8NW09cuAEtxD7//HMsXLgQn376KdLT01FQUICxY8ciOTkZw4YNkx7Tpk0btGnTRnnfrVs3HDlyBG+++aZUiM2dOxcZGRm44YYbXLbz0qVLGDx4MKqrq/Hee+857Hv88ceV/zMyMpCWloauXbti69at6Ny5s1NdEydOxLhx45T3Z86cQUpKimlbzMwesoLWBU1sy6wt3npybznUjhmLeiFtxG7LKSaMvGS1PeuTqDu4IsLE8uqZtmqxXpXlvZuvK2kY+HH+gJbtnnow9NYQpauI9lRfKAce81pTDtDQpDkCWoi98MILeOmll5RZhu3bt8ehQ4cwbdo0TSEm48Ybb8TChQudtl+4cAGLFy92CKS3yqVLlzBo0CAcOHAAP/30k4M3TEbnzp0REhKCoqIiqRALCwtDWFiY5MgrmEl5IHtqczdOy9WhMlcFmdWn+qJ5bbH8z4didd+I743SVcgWKPZ2sLM6DYbZi7xWWgx3kkKSN8w9XBVgatTfmTeFlwwrQ2auonjCPADvb7VnjOOtlDqeiAMzOs4MvO4qVolDllsJDNatW4f/+q//wpYtW1BaWoply5bh3nvvVfYzxvDaa69hzpw5OHXqFLKysjB79mykp6f7zmgEeIzYhQsXEBTk+BGCg4N101fIsNvtSEpKctr+xRdfoKKiAg899JBL9nERVlRUhNzcXDRsaByDtHPnTly6dElqj6uYmZqtNXNIfGkhC9Z3BR5XZaZN4M8ZQMJLrwzPa6blvRNZntlE6jUT2+EB0C2H2k2LGD07XcVsX1vpK05wXJzDsOTlsjISYW7iKREmUn1rJqpvrZ0M+a7gyZhJ2TC5VVoOtUvt8XRKHauhGkb7rVyD/QbmgZcFeDaDd999V7p/+vTpmDFjBt599138+uuvSExMRO/evXXjsmuDgPaI9evXD1OmTEHTpk2Rnp4Ou92OGTNmYMSIEUqZiRMnoqSkBB9//DEAYObMmWjevDnS09NRWVmJhQsXYsmSJViyZIlT/XPnzsW9994rFVDnzp3Dvn37lPcHDhxAQUEB4uPj0bRpU1RVVeGvf/0rtm7dim+//RaXL1/G8ePHAdTMygwNDUVxcTEWLVqEu+66C40aNcKuXbvw/PPPIzMzEzfffLNH+0rLW6UVOK/eJntv1JY7gozX46mn0+WZTZAGx7i0fvYSzLC75vnzZQZrCrwNPLwhwAjPYyV0w8wIgL8NUdY6tZxZXy+bAWMMM2fOxMsvv4yBAwcCABYsWICEhAR8+umnGDVqlOt2uklAC7FZs2bhlVdewejRo3HixAkkJydj1KhRePXVV5UypaWlOHz4sPK+srIS48ePR0lJCSIiIpCeno4VK1Y4zbTcu3cvNmzYgJUrV0rb3rx5M3r27Km853Fbw4YNw0cffYSjR4/im2++AQB06tTJ4djVq1ejR48eCA0NxY8//oh33nkH586dQ0pKCu6++27k5OQgODjY5X4x8rrIxI2ZQH4reXT06jGLWpAZ1aM1DAeIwfpX6pthty5ouB3uxJDKvh9Xhja9dcFWex3IC+Y6JMBcw5NDkjKMwjfcnazEt6nf1ymRVYuoswOYCdFRc+DAARw/fhx33HGHQz3Z2dnIy8vzqRCzMVZPouHqKGfOnEFcXBxuCxuEBrZQw/JGw5SyvDpm6xTLcuEzw95LGRYEXF/824qoky0rJLvwitnztRbpFmdNFi/KVNp3RTjpCWQr9XkjLUG1sMyS+iZ4uawM/1FU4/19P62Vx9qs69SWCLMyLOmJWZOAawmHraAnxDz5YOCJz6G+NunlJhPL6dUnK2N1uxZVrBI/VXyBsrIyw5hlV+H3peybXkGDBq73cVVVOdbmve60PScnB5MmTdI91mazOcSI5eXl4eabb0ZJSQmSk5OVck888QQOHTqEH374wWU73SWgPWLEFYpnt0GbMQcMy2nFLWhdTKzOgFTDRRhfy3GcPdclMWbFxS+7uKoX5VaLMKPZlO6IME/etLx9A9Rq88OMjIDJvu5rAsEL5s5kDatwUcXK5encxbY9EQNmBStruWqt98kxyqfojZACPRGmtc5treKhockjR444iEar3jARm82maoI5battSIjVY2RPcGJCUnGfq09jXOSIgmycPddhn1lh5m4MhdiOuA6mnjj8ul1DRYTxG0pweJjhU7nVC5/Z8q5czM0O64peiL3vtVD+p+EUZ7iHEAA+zMgAcOW78WcBpsZVAebKklwyT6uaQBsGr64od8irxtH7zZjNLebq764uPjDFxsa67b3jq9ccP37cYTLciRMnkJCQ4Fbd7hLQsyaJK7R8qtDlJy71DCItN7p6JqOZ9tSLZ4/LzMUMey/MsPf6M1i+l+Exaltd/ZxivXwdScB5WFLcJ3rC/Amr6UGs9BmJMGP4EG0gizDCc2gtLC5iZRalukygTtCxVbv/8hQ8cbqYLL2yshJr167FTTfd5LmGXIA8YoSCVsC+1nAl328GUexwjxiPH1uOK/u4ANLzlIl2uiMUlmc2AeZp7ytepH3xM5ufy4jaTHRp9JStHjpyJx6uLqL2gn2YkeH0/dWTROBOWBni46hTo9QV9H5nZh+KZOUC8qGolmdNGmUzGDt2LKZOnYq0tDSkpaVh6tSpiIyMxIMPPui6jR6APGL1HLWXy6x3zAyikJIFxmvVp5XHS0Sdc8wK3BPHjxM9YOq/AXnxk6AVSKwmUJ+8vUmXAoYuBQzvp7VSXoD/ZYsnfIvoFbPq8XKnjF9Ty3nENm/ejMzMTGRm1gwVjxs3DpmZmUomhRdffBFjx47F6NGj0bVrV5SUlGDlypWIiYlx95O6Bc2aDHDEWZMH5ncEYO3GoPfUpbXP6EnNaDYiLyPbJw4X8jJm4shkN0W9i5g4k3OGvZdynDjbU/25jIKOXcFqLIi7s0f1jleL7vruCetSwLClk035a6YPa7vPXEnkymdOAp63V+0RM5uGwp88YlpePbN9JU7W0KrL7O+YXx/MxOjq2SfaUfhuKg499nqtzJrscf3Lbs+aXPPrFK/a6g/Q0GQdw+rTud4sSaMZlFrbZTMT1RgJLCsiDKgRVsszm+iKxH72EiUVxfKwJk4xYxyZCAPkAsysOPO194RfpHlgMU9KqxYXvrTRn+hSwJS/9qwIBIX5lwDzV1zJ/+VPIsyT6MWXit58M2LMCqLo4uelK0PHnoDWmjQHCbE6hqtLaciSsWpNvdabWSm2YSSguNASy4riiyddTYP+hUoUa7LPJ7YxYJcgxlTta00e8BR6Gbr17OZ4yjb1mnvqxL71FS6+tnSqmcpuz4oA4HsRTQQWslmUepiJKQvYc6+WY8QCFRJidQwrT1fqskbeMa28Y3oizggugEQxJYoq8alRyzsmE3Tq/eL/QaqHdt6OK8sdmRmm9ETwrgwtLwx/6g0KC9f11IjesfoMH3rkBIWFw55lToAFqifMl3YHmgfMal+ZFWGezi2m5+1y8HxfCMxzti5Dwfp1hOLZbUyJCC6c9ALd1fWIYki2T6xXfYwZ9IYy00bsxrjMXKfAenVZMwH+MsSgfR43pjekahUzotTqBdmTT8ei1xOw9r3VBcQhyC2dbIogIy8Y4QqiCLOSykTvN6d3ffBWoliPwVAzldjVV/1wiJFHrD4j3nyNvGNiOaueM6ObmZ43i8Pzj/F29MSSrD7Rm6b2CjoE6GO3y8swqXElEN+VG7+ZJ2GO3tO9GPRbJHmqr0s5srgA48OPfImn+hAH5s3Pwb1dsgz5geIJc7V/XBFh6pRBrgp/rWN9llH/TyhGzBzkEasjtHyq0OVjjZ64RESPiRXvmBZmRBj3do3LzBWGER3TT6jrU7fBY85EG8WZk2ZstYLVi6pM+BoJwqCwcJcvtPxY3l51RbnyAmpuJOILqLnRWIl98TZGn5/vF19dCpj0GHECQ8uhdof+EPuFMMflsjKnFyHHXa8reW0DH/KIEQoy8SBuc8c7piXeZDFigFF6C0fv1gx7W2CeY93qY9Xtq2PS+DJMngiI15tqLkNrUkRtkTZit2EiUvEJPxDWUgSAzI0XNfdxTxhAWfEJ/0HvAc7doH2fDGEyuBms7zFL/BoSYgQA+XRq9Q/f1WB+9TEyMWYGMSj/iiftyn5FlAHKTEsxCL+fvQSwO7apnjWpZaMZzHgK1W2q+1w9S0q0y1tY9faoZ116U8CY8fjxYUbOFZF10WFb5saLUgFGOdMIT2B2tqTsXBOP5Z5Z4Mr578mHtJZPFeKQR2oyAc2aNAUJMUJBL4BfXQ5wFldGnjMjgaOXCNbMEKbWcKgsnYa6PsVDprHkkVlkNqhzmTl6AeVPqmoxBsBt22RtuCOivCnIroirGjHFhRRH/V4PLr4oHQXhbdz9PekJuYDzhhGmISFWDzGbTkG9DqReWVl8mNGwpBauiDB1We4Z4+3rpb8QcXV4UjaEK/aPbJbnldmfJZgxr5eTVyxQ8IQgU3u+7Fn6Q4tqRGHGPV+i9wuomwIsaB0Np/ozZj2t6vNfLxmsO6SN2I0qj9eqQzUAm2Ep/ePrASTE6ilGNyUxlYMZL5QnZlBqxYhZmcUoZteXCRq9XGTuYvZGL7NhXGaugxgz044ZxJmQrtZhFlcFmdUJB2oPl3q7iMwzShD+RlWW8zXL3Zgwf4BmTZqDhFg9QD3kp/dj5++Xw3l5Ib2bmpbosuIdc1V8iWjVz7Pmq2dKim2KmfXNXAT1+kN9YV2e6Ty7U52IVr2CgJYNnro4mwnSdwW1IBO3WcGM0BKRicu0EbsxascOAMCHyLBsA0F4C611Qr0txopntwEe82iV2lCMmClIiNURime3QVBkuOaPlt/0teKRtI4Ry6iTfhrlHrM6XCkLoDeDmJ5CXaeY7oIPO3LBIwuENzMsaEaAqd/XHKOf+0z8vJ4YnrSFh0kz/xcvyqxJ0eBW7cbIZlrqlXH382qd+x9mZChiTAu9VQjEyQD2rAgK7CdcxmiR9qqsth4fbg6kMIf6CgmxOobWDD3+V32DV7/ncWHqBKhijJV6gW0tQcY9TO4MV5rhyjqR+h4kcehPFocmS3lhNAOSoxZg6vZrvGLOC43XxqxIEV/lAZN5xGR5ybw1FPNhBnnDCN9hJMDUZdWzKPV+F2aFlk+GOckjZgpK6FpHUCd0lc1+FPNlAXBYOoi/V8dpFc1ri3GZuQ6CTm+BbfFYXidvTy3IxO0yW83CBZ/RhUY9e1FsRxREWole1cOtQI0A0xNhvAyvQ4ZsiSdvPsWavSCrk6F6Gp48lduT+uGV/hT7VDw/MjdedHq5wq2bTykvI/iyR1s62cgbRtQaVrzFernHxP9r3TvGhZg7r3oACbE6BBcJ4o9yhr2XEvck3uhlXiy1IODHqRfN5mW0PDkykcMRLwR8u1qoWY0PszLLUW2z7D33+nGbXBVgIqkf1vwV+8RTSylpIQ5LcjHlylOxLzLLN9got1MWN2ZGnBkNTd66+RR6bL+IHtvlwk5cFJwgrGJ1uNGd3ysReNDQZB1FHYCvFivKmop/5qYSh8nEvFVqUcfLy9JDcGQeHq1UF9wLN86e65YwsXLB0sriL3rFxP5SCzB3bOSB+2q8LcoCgQYbd2v2L/8O1ElZAe2cYuK2TedbOuxb1/Vqp/e3bj6FNe31JwQQRG1QldVWeRgR8ztauc5pxbJWscraS+hK6StMQUKsHqCVaJUjm0kozh4EoBl/pa6Px5JxHBOmOtsgEyVmMTskKeKUwBXOHj9ZHJg7AswTdfgi4La2vWBF89oqnkPAWTCLecI4Wqks9FAPR3JRZlaEdSlg5CEjLBO0zm4pVkyGKzG1vvSqUfoKc5AQq2PopYfQyjzPRZeDV2qe8zF6OIo951mP6rgyd2LCAPni3maOUbej9gKq7fKEABMxs8KAWLa+wPtFHJJUf1dmPF+8nPhXxExMmBn4TEoSZIS3kHnFAPnSc7I0RETgQDFidRTuytb6QapjngA45dASg/TNwmPN+GxJ9axNdX0y+/TaFBcI94Q3TNwuiwXTilVyFU8LO7PYwsMcXv6EzPOmFYPIM+bLMueLaMWNret6tcNLzwYjSIQRVnE1NUV1Rblh8L5WHj2fQsH6piAhVseQCSwrgkycJelqQHk/e8mVlBH2XoYzLkXbjIQft8nVJz6tzPrq+jyRx0uL1A9r/4n1clmZw0sPM7MlvTGTUuxz2XckznQUPVtmxBnfrhZnXIx1KWDoUsAcPnfxokzpZ9zSyYaC7KsQHBfn9mcmCD34g5sseF8WA+aTmZF6VDP3X/UAGpqsIxTPboM2Yw4o743iwmTZ9Pn/3DOmzvRulivDfY7xZurcZur9/FizmH3aE2dxOk1IgLYoEl3+vvJkqfH5E24toKzuYOJcsDIsadUGNWJi14Jst6on6jHuxoppDVPKckQSgQEJsTqEVmZ59X71/yKyH7krme454oxI7m0zWtpHr05XvGF6cWH8M3pzSREtXGlL73vjiMNs3KPD/+oNwel5v7wNt0srhhBwnumohZY4U/9vlINMb/UBI68iQXgKMVaMI5uFrkZre/WFclriyM8gIVZHaPlUIRrYQgFoB+zr3eTU5Rx/5PoLf6uRpYaoEVIlQgoM17LJyzLo601QUNsh2igTQoPStwIbr9ywveUVS/0QODDKY9XVOdTfr2zoGDDn+eIpL7jwknnNxP38++YijM+S3NLJRjMmCY/gildM7e3SWvWD79d60KvV9BVwN86LhBgRoMgEl56XTGuG5ZXUFb2ccoqZRS1+xtlzXarzyjJGvTRFmJ4gU3vh1E+Tg9K3arbN6xZTK8jgwsqonIhVb6PexVf0dhktZcTrcTdFhRlPmxVk9ciWRyrS+HxpI3YbijNZ+gv1/4DoCbyoDEvWlKHs+kTtoDysjTJ+CJR59mXXd1r02/8I6GD9qqoq/P3vf0dqaioiIiLQokULTJ48GdXV2lng1qxZA5vN5vTas2ePUqZHjx7SMnfffbdSZt26dejXrx+Sk5Nhs9nw1VdfObXFGMOkSZOQnJyMiIgI9OjRAzt37nQoU1FRgaeffhqNGjVCVFQU+vfvj6NHj7rfOXDMtO9KEKfeTd8qakHmSnCpllgRYyT0PqtWoL9MhMmGrbRmUB4YVfPi7fvC01U0r60SYG5mPUneR2J5owz64oxLXpa/1AH+3lwaCbiyPJL4Ej+XbNawOqBfJtjUNy7xPKAljghPYnQ9UV9HZCunqJE9pKp/D+rl8AjfE9AesTfeeAMffPABFixYgPT0dGzevBmPPvoo4uLi8Oyzz+oeW1hYiNjYWOX9Nddco/y/dOlSVFZWKu9PnjyJjh074v7771e2nT9/Hh07dsSjjz6K++67T9rG9OnTMWPGDHz00Udo3bo1/vGPf6B3794oLCxETEwMAGDs2LFYvnw5Fi9ejIYNG+L555/HPffcgy1btiA4ONilfpGhNSxp5JFSH+duDJUopmRizChprFlvnmiz4TDmRnl7YhZ3La9Yg427kfbn8Vey8rfFgVH6njF+kZWtQmAlFk9Mkqv+bsdl5jpNlBCPFSdJcDEm8z4FCmrbuQiUCv0s5/NGVk4rdxlBeBqtBzhXrrd6oyJV1k1znWoGt4YXadak/5Ofn48BAwYonqrmzZvjs88+w+bNmw2Pbdy4Ma666irpvvj4eIf3ixcvRmRkpIMQu/POO3HnnXdq1s8Yw8yZM/Hyyy9j4MCBAIAFCxYgISEBn376KUaNGoWysjLMnTsXn3zyCXr1qrkpLly4ECkpKcjNzUWfPn0MP4dVzMaJyY7zVkC71USEZkSbuF9dflxmLmC/Inj0brZcjPE6+DI8evnFRAGkhl9s1TOctMSXLN2GekkmcbkpsU9mzLtig9GQHxdi/K+sb8W1Kzm1EcjvKvwzGw1tyoa11Z4wgvAmDTbudhp+dOca6zezJ1l1zcud4+sBAT00ecstt+DHH3/E3r17AQDbtm3Dhg0bcNdddxkem5mZiaSkJNx+++1YvXq1btm5c+di8ODBiIqKMm3bgQMHcPz4cdxxxx3KtrCwMGRnZyMvLw8AsGXLFly6dMmhTHJyMjIyMpQyaioqKnDmzBmHlxX0Fv42ojYyN4vud72AVCu2i4JMXM5Jb+FyjijCeD1aIoyLIz70ykWXONQg+0yiHaI9WjnP1PvFvpJ9prQRuzFg10mHbV+3a+jwvuVQe618v/4CH840ii0kCFfhw/56oQINNu52yJ7vqXNQDNcg/J+A9ohNmDABZWVluO666xAcHIzLly9jypQpGDJkiOYxSUlJmDNnDrp06YKKigp88sknuP3227FmzRrceuutTuU3bdqEHTt2YO7cuZZsO378OAAgISHBYXtCQgIOHTqklAkNDcXVV1/tVIYfr2batGl47bXXLNniSWTTpr39Y/foxUky8sYFjTqIu2heW5cnKfD2tPpILXS01r3UQhRpWjNCxbIAEBwXp6RdGLDrpJMYU9vp7e9X9Kb5KvaKZywXb5bVFeXKDEmC8AT+4jmurihHNbtUew1SsL4pAlqIff7551i4cCE+/fRTpKeno6CgAGPHjkVycjKGDRsmPaZNmzZo06aN8r5bt244cuQI3nzzTakQmzt3LjIyMnDDDTe4ZKPN5jiswRhz2qZGr8zEiRMxbtw45f2ZM2eQkpJi2h6ttA1WUA+r1WbuLVcQ7RNnT3LEIUpe3h0BJiKrQ8ynJrYv7td7zz+DeohSNhkhbcRuLEcTp2WN1CJMdqPQi7Vz5cbiLzcjGS2H2lG0KBMth9oVO7d0osB8wn384dros0kmFCNmioAemnzhhRfw0ksvYfDgwWjfvj0efvhhPPfcc5g2bZqlem688UYUFRU5bb9w4QIWL16Mxx6zPtc3MTERAJw8WydOnFC8ZImJiaisrMSpU6c0y6gJCwtDbGysw8sXqN3o/jqUpTV7UvxflujWGxdPcVKCGEMmGx41WudTFuRvxD35+02XFXE1rjAQMTPjlCCsIJvJqze7l6h/BLRH7MKFCwgKctSSwcHBuukrZNjtdiQlJTlt/+KLL1BRUYGHHnrIsm2pqalITEzEqlWrkJlZc3GvrKzE2rVr8cYbbwAAunTpgpCQEKxatQqDBg0CAJSWlmLHjh2YPn265TbN4OkfvC+GKs2itke91NGVbSVKYLuR7UZ5v4zWshT7S+3REuuV1aG2zcwwJm9LjI0DrninuKdMFogva9vqrFuxLf5ULvOMeTofmSuohyi1PHiUwoIwg/p8MsLIE26Ev1x3HaChSVMEtBDr168fpkyZgqZNmyI9PR12ux0zZszAiBEjlDITJ05ESUkJPv74YwDAzJkz0bx5c6Snp6OyshILFy7EkiVLsGTJEqf6586di3vvvRcNGzrH0pw7dw779u1T3h84cAAFBQWIj49H06ZNYbPZMHbsWEydOhVpaWlIS0vD1KlTERkZiQcffBAAEBcXh5EjR+L5559Hw4YNER8fj/Hjx6N9+/bKLEpPIg7ReWPmoyyNgq8vDjyAXRQ8Yrb/GfZeGJeZazmxrN5+NVp9PcPeS1n+yZXF1WWIsyjF2ZtXEuG6LyJqM46stuFDlMpUf2E1Bb2ZsgQhQzZjVybwPfGA7JdeNQY3hZjHLPFrAlqIzZo1C6+88gpGjx6NEydOIDk5GaNGjcKrr76qlCktLcXhw4eV95WVlRg/fjxKSkoQERGB9PR0rFixwmmm5d69e7FhwwasXLlS2vbmzZvRs2dP5T2P2xo2bBg++ugjAMCLL76IixcvYvTo0Th16hSysrKwcuVKJYcYALz99tto0KABBg0ahIsXL+L222/HRx995JEcYkbrM3pCjIl1yLwmvrpRqz+b2BdcjHERZhWzoknvs3ORxMWYul6rGfc56gXUxZUMiua1RevRNcOTRh4ws+jlKwpkFO+dsBSNUdoSgjCD2dUjjBAFnV+KMMI0Nsbqie+vjnLmzBnExcXhtrBBylqTIurFrUXcFWJGIkvv4iA7xlXxIWvXqH5vTTAQ+xtwHBZVt6seNuSfW0yzIauLI7ugF6vyY/EyPH2FVoJXPcQhRaN+c3c4xV+G/YoNvGL+YidBcGQJjPk5LJ6vVewS1uBrlJWVeS3GmN+XeiU+gQZBzvcls1RVVyL3+Byv2uoPBLRHjDBGT3R4IoeSmeU2tLxxImox4ip6QkEUOt7y2mitoSnzSvL+5545Mb7M3X4Yl5mL5XCsY3lmkz8XXPdOH1ip01/jCkX45+FJfAHyihGBgXpmu8+orgbgRlJWi/HegUpAz5okjDG6OfLZj1Zn7pi96Yo3Wr2EhdxT484MIjM2efPipCcqtPaNy8x1iOUSA/nNorWuo2zZJL2Zrur1I43WnnQVraWn/OLG8SfqoSISXwThAjxY351XPYA8YnUYd5YxMhp+ciWzvdZ7WUoHqzFHVkSYVe+LmZmCDsOMdnOzHsVJA4Bq2NCNJR8dvYvlDm3x+j3tkXL1nFB7TYv/zOXlD/B+MlpRgSAIwh1IiNVxrHitxPJmM8Ib1WumfaM2jISQGZvcERx67TmJV4mG0OoHcRhynF17fUqrXJkh6dyWaDe3TfzrqSFC0ZNmJpGr2I9mxJgsLYCnBRz/DOrvj+LDCH/EL89LSl9hChJidRRPea3UAeUz7NaGJF2JRTIrzKzGuMnyibnClb7oZal9dbvqNBoiennK+H4tgSPzyolB+2ImfqvJWtVeIvWxPC9ZsEZ+Mr3zQUuMmc3FxMt5UpBZzQVFEIQAZdY3BQmxOognvVZiQDl/b7V9T04IkKXG4P/rzd408hDJ0ErOKoomdVCsXr3q9BT8PQ/Wl3my9OqwUo7PmjSaDCCb1ekpzNSnFmNayMQWL++N4U1vTvAgCKJ+Q8H6dRRPxVWJdVn1sKmD8z1xY9cL+JcFfMvatCpmxHpdiV0zg1qE8VxnsqB9o0B+2RCn7DOL9VgNnNeaAbr3vRbY+14LsPIKB28YP9e0+swWHua0Hqa6fvElTlDgL7Hu4kWZmpMYrMJFHZ+84M9rZhL1F/Fc95eJL4xVu/2qD5AQq2NYnc1otk532vfG7Di9m7rYlrp9q2jZamUo0kzeLXViWbOeNTNwb5hok9lZmWoBJPa7+rsU97v6PYv9YcYTWlvQECURCPid55axmuFFV18UI0bUddyJIZNhFP/Dy4h/3b1oqOPEZOLAE4zLzFXWowTMXfDEvF2yfVpDmkYrIlhB9N6o16bUE2Punhvq78KoPlZe4VCW50CTzfBU/69Xv6cDmP1pVidBEHUD8ojVIax4w8zeaD1dpzuzMI3qNHtzliG7YauFEvdaeSKGSi0WxWFIrbUntYYqjVDXJa636QqyWaOyvrDS/2rBZvWzytr3tAizOnmAIOo9lEfMFCTE6gjFs9uYKueNIUlXZmjKhis9OeTk7nCkTCipk66q0RIOak+OLF5KLb7UdaWN2O1QxkikyGyUzdpUt2GEzNvoqeB+3s8crVxssmFST7RvBvKGEYGCXwxRVle7/6oH0NBkHcLsD8+TQ5Lu3PxkN3BPDFm6cqw4hCcfRq0RCGLqCrUImGFvqzkUKftcrsRzyDxZoudHjGVSB5arhyatojfkq+4zUaSJ4lOrTvVEBRlm0l7UBiTGCILwJCTE6ggtnyoEJIt+i3h6SNKTcV5ifZ6s2x1Eu7g3TC/pqhVbazxsbZ22AcaB+mLAvV5ZceFqdTuy47TEjJUhX1lsmJZQkn3Hep/nuwMbcWdqluZ+giD8DOZmHrF6MjRJQqye4MncYiLeFkpWBKG3UHtrjIYojeB18MSrHFFkGXms3FkU3OhYreE+q+eEVU+YGb47sBEA0Gr1cP8YeiEIH6NOpyKbEMXL3Lr5FH7qEgU45ln2Gqy6Gszm+vBifUlfQUKsHuFO8LRsvydvhGZisoxu6N4WZFwkiakmxFxjZuGzKWWCSMx6L2tfFkumLisOT+p9T1YC9l35rrVmyurVpxaisn5ttXq4ZVsIoq6hJcBk8bfitpvWnsZPN9aOjeQRMwcF69cDrA5JAp4LwLbSJlAjcrTyb6mD6D05fCnGJWnFKBnNZHRlRqMZe7Ta1xJSA3adNBXHZCTCPPXdi9+PUf43rRmj+3p+pFknQdR3tK6F6vfrul5dazYR5iGPWB3HlZup1mw48b2nboRqL8lyOOa3EttUAuXn9XLa7w5mBZSuB0nDwyVD7D+toUi9tSb12uH7B+wqAZCrrIfJ2+OB/Vayw/uD6Gm1eriTGCMIwviBlI8W+OR3XM0AG3nEjCAhVg+w6g3TOt7T3jGtoSqttAVaIs0V29SCRkyeKs5+9NbFS0/8WVmCSUuYLc9sUiO6FtW850L263YNlfbdiTPzBeKQpHhjkeU1Ayj5KlG3sTIiIIqxvOyrvGyZAGMA3IjzqidCjIYm6zCuPAUZzYjzFHoJTfXg4sFbnjB1fipP5zjj34m4+LYVQaTOiC/LkN/PXoLqinIM2HXSqZ/4Goz+IMLEvjXqY734QD08ueYkQfgafh7zGFC966DeTGXCvyCPWB3FGz84dXyWJ8QQr8OVYT1PYWYWoTJ5YJ7jdm4TYM3LZDWeTPzcYkJW2ZAmH378ul1DFC+6snaj2dme6v2e/q71bNDydGl5vfTakc0eI4hAxuy5LPt91Ha+PQBg1QzMjaFJVk88YiTE6iCu3HiMbrZaM3KstiMeZxVPijC1gDEall2e2QRpMF6eyShHl9mcWWobZGJPPTQpa5sLMG6/O32vtt9VZN5Qs+tqym4m4rYHfysGAHzaoaWuIPP08kcE4U1ED1h1RbnDYB/39GZuvAgAsGdFaNaTNmI3Cme3AR7zmqmOsGq4NzRJ6SuIAEaWqVw2rOVqnfy9Xq4oK3UZ4elktCI8uaorn0FsS5lEIMmur67b7AxIAKjKcl4cXJbKgv8vDsGpE9BaFdvidvX/nkrka6ac1hO+FlyQAcCnHZxFM+8jEmSEP8LPT37e8lhHmSwRz2E9EUb4LyTE6hjK7EK7PJhbFlPEj1kOuRAQ6+R1cNQzGc24zL0pwlxBvVyRWaxknDfCTKZ7WSyYnpj2dPoJoyFLrRmgWueXbHKEDC0haJYaUXbFU0YQ/o76gcesXyhz40UHMab+jRbNawtcqL2HDxqaNAcJsTqE+OMVh6846mEtNbIbu/JDlogw8T0f9tITTf4owvRilcwc5+24o7QRu1GVVdMHM+bVZOM3MySproOjlb6iRkTVxMFp5TBTX9D1vHxiHTPsbV1KfCvab3QcL/Pa5nuU96JXjOPoKWtJnjHCLxB/j+5cW7Z0siEozPE349MAfRqaNAUJsQCHPzFUsUuovlCOlk8V4uKGKlSxSqeyF89Vodf6Q1jWKQlAzf7i2W3wVNr3uHgO+P6WK9v5PlwoRxWrxLJO1/y51blesWzLpwprYhDw5/qXkrrMYuUY/tmL/2zbbDv8uCqT283u9wTVf372qqpyVHVJRfWFcul3WPPdOH/e6j+ffMW+qGaXAABBzHHC9MVzVZi9rQeAcizrdA36bij983zgONaf+ug29HIqU0PfDaV/2vdnm7PL0Wv9IXx/S5Ji01Md1zgc8/0tSU7fnWh/6qPbAEApAwCF76Yq/7d8qhCpj25T9he+m4qcdanKvvvzDzjZ+Ze8mvPzf7ulKv1CEL4giAUp527LP8918dpi5vzc2LHmb6cfa34/Vbc5/2ZaPlWIKnYJh1A73qYqXHIrsX4V6sfv0sbqi++vjrJ//360bEnDLQRBEIR5jhw5gmuvvdYrdZeXlyM1NRXHjx93u67ExEQcOHAA4eF1N/0MCbEA5/Tp07j66qtx+PBhxMXF+doc05w5cwYpKSk4cuQIYmNjfW2OaQLR7kC0GSC7a5tAtDsQbQZ8azdjDGfPnkVycjKCgryXSrS8vByVleZHQLQIDQ2t0yIMoKHJgIf/kOLi4gLqQsSJjY0lu2uJQLQZILtrm0C0OxBtBnxnd208tIeHh9d5AeUpKLM+QRAEQRCEjyAhRhAEQRAE4SNIiAU4YWFhyMnJQVhYmK9NsQTZXXsEos0A2V3bBKLdgWgzELh2E96BgvUJgiAIgiB8BHnECIIgCIIgfAQJMYIgCIIgCB9BQowgCIIgCMJHkBAjCIIgCILwESTEvMykSZNgs9kcXomJiQ5ldu/ejf79+yMuLg4xMTG48cYbcfjwYWV/RUUFnn76aTRq1AhRUVHo378/jh49quxfs2aNUxv89euvvyrlZPs/+OADB1u2b9+OZs2auW3znDlz0KNHD8TGxsJms+H06dNOfXPq1Ck8/PDDiIuLQ1xcHB5++GGncocPH0a/fv0QFRWFRo0a4ZlnnnHK1rx9+3ZkZ2ejQYMGXrf74MGDGDlyJFJTUxEREYGWLVsiJyfHySazfV1bdgNA8+bNndp56aWX/Lq//e3c/ve//42nn34abdq0QWRkJJo2bYpnnnkGZWVlDnX427ltxm5/PLfN9rcnz+3aOEc8fV5nZ2cjIiICTZo0weTJk2tlHUnCgzDCq+Tk5LD09HRWWlqqvE6cOKHs37dvH4uPj2cvvPAC27p1KysuLmbffvst+/3335UyTz75JGvSpAlbtWoV27p1K+vZsyfr2LEjq6qqYowxVlFR4VB/aWkpe+yxx1jz5s1ZdXW1Ug8ANn/+fIdyFy5cUPaXlZWxhIQElp6ezlq1asX+53/+h0VFRbFXX33Vss1vv/02mzZtGps2bRoDwE6dOuXUN3379mUZGRksLy+P5eXlsYyMDHbPPfco+6uqqlhGRgbr2bMn27p1K1u1ahVLTk5mY8aMcbJ58ODB7D/+4z9YSkqKYrMrfW1k93fffceGDx/OfvjhB1ZcXMy+/vpr1rhxY/b88887lDPb17VlN2OMNWvWjE2ePNnBprNnz/p1f/vbub19+3Y2cOBA9s0337B9+/axH3/8kaWlpbH77rvPwW5/O7fN2O2P57bZ/vbkuV0b54inz+vBgwez7du3syVLlrCYmBj25ptvMiJwICHmZXJycljHjh019z/wwAPsoYce0tx/+vRpFhISwhYvXqxsKykpYUFBQez777+XHlNZWckaN27MJk+e7LAdAFu2bJlmW++99x6Li4tjL7/8smLztGnTWHJyssPFwchmkdWrV0tvsLt27WIA2C+//KJsy8/PZwDYnj17GGOM/d///R8LCgpiJSUlSpnPPvuMhYWFsbKyMgeby8vLlb6W2ewpu2VMnz6dpaamOmwz29e1aXezZs3Y22+/rXlsIPS3P53bnC+++IKFhoayS5cuMcb8/9zWsluGP53benZ78tyujXNEjbvndXl5ubJNq68J/4WGJmuBoqIiJCcnIzU1FYMHD8b+/fsBANXV1VixYgVat26NPn36oHHjxsjKysJXX32lHLtlyxZcunQJd9xxh7ItOTkZGRkZyMvLk7b3zTff4I8//sDw4cOd9o0ZMwaNGjXC9ddfjw8++ADV1dXKvvz8fGU4gds8a9YsHDt2DOvWrTNtsxny8/MRFxeHrKwsZduNN96IuLg45XPl5+cjIyMDycnJSpk+ffqgoqICW7ZscbCZJ0YsKirCW2+9hWPHjqF///6W+tpVysrKEB8f77TdTF/Xtt1vvPEGGjZsiE6dOmHKlCkOQzOB0N/+eG6XlZUhNjYWDRo0UNoKhHNbbbdWGX87t7Xs9tS5XRvniBp3z2sxMWyfPn1w7NgxHDx4UNcmwn8gIeZlsrKy8PHHH+OHH37AP//5Txw/fhw33XQTTp48iRMnTuDcuXP4z//8T/Tt2xcrV67EX/7yFwwcOBBr164FABw/fhyhoaG4+uqrHepNSEjA8ePHpW3OnTsXffr0QUpKisP2119/Hf/7v/+L3NxcDB48GM8//zymTp2q7D9+/DgSEhIcbH7rrbcAAAMHDjRtsxmOHz+Oxo0bO21v3Lix8rm4PSJXX301QkNDpWW43YsXLwYAlJSUWOprVyguLsasWbPw5JNPOmw329e1afezzz6LxYsXY/Xq1RgzZgxmzpyJ0aNHS23i+Ft/+9u5ffLkSbz++usYNWqUQ1v+fm7L7Fbjj+e2lt2ePLdr4xxR4+55LcLfa90fCD/E1y65+sa5c+dYQkICe+utt1hJSQkDwIYMGeJQpl+/fmzw4MGMMcYWLVrEQkNDnerp1asXGzVqlNP2I0eOsKCgIPbll18a2vLmm2+y2NhY5X3v3r3ZE0884VDm6NGjDACLj483bbOI1pDTlClTWOvWrZ3Kt2rVik2bNo0xxtjjjz/O7rjjDqcyISEh7LPPPjO0+aeffrLU12bsFikpKWGtWrViI0eO1CzDsdLX3rab8+WXXzIA7I8//mCM+X9/+9u5XVZWxrKysljfvn1ZZWWlst3fz20tu0X88dw2YzfH0+e2p88REW+d1/n5+Yb1Ef4BecRqmaioKLRv3x5FRUVo1KgRGjRogHbt2jmUadu2rTIDJzExEZWVlTh16pRDmRMnTjg9CQHA/Pnz0bBhQ/Tv39/QlhtvvBFnzpzB77//rrSlfoo6ceKEYpNZm82QmJiotCvyr3/9S/lcMntOnTqFS5cu6ZbhNjdv3txSX1vh2LFj6NmzJ7p164Y5c+YYlrfS1960W20TAOzbt0/TJn/pb8C/zu2zZ8+ib9++iI6OxrJlyxASEqLs8+dzW89ujj+e22bsVtsEeO7c9vQ5IuKt81p2fyD8ExJitUxFRQV2796NpKQkhIaG4vrrr0dhYaFDmb1796JZs2YAgC5duiAkJASrVq1S9peWlmLHjh246aabHI5jjGH+/Pl45JFHDC9UAGC32xEeHo6rrroKANCtWzesW7fOIbZi5cqVSEpKwsGDB03bbIZu3bqhrKwMmzZtUrZt3LgRZWVlyufq1q0bduzYgdLSUgd7wsLC0KVLF12bk5OTkZSUZKmvzVJSUoIePXqgc+fOmD9/PoKCjH9GZvvam3bLbAKApKQkxSZ/7G/Av87tM2fO4I477kBoaCi++eYbhIeHO5T313PbyG7AP89tM3bLbAI8c2574xzheOu8Tk5ORvPmzQ3rI/wEH3vk6jzPP/88W7NmDdu/fz/75Zdf2D333MNiYmLYwYMHGWOMLV26lIWEhLA5c+awoqIiNmvWLBYcHMzWr1+v1PHkk0+ya6+9luXm5rKtW7ey2267zSF9BSc3N5cBYLt27XKy45tvvmFz5sxh27dvZ/v27WP//Oc/WWxsLHvmmWeUMqdPn2YJCQnsuuuuY/PmzWPvv/8+i4yMZG3btrVsc2lpKbPb7eyf//wnA8DWrVvH7HY7O3nypFKmb9++rEOHDiw/P5/l5+ez9u3bS6f433777Wzr1q0sNzeXXXvttQ5TzrnNQ4YMYcOGDWOvv/46i46OZk8//bRLfW1kNx+yue2229jRo0cdppW70te1ZXdeXh6bMWMGs9vtbP/+/ezzzz9nycnJrH///n7d3/52bp85c4ZlZWWx9u3bs3379jl8/+Lv0d/ObTN2++O5bcZuT5/btXWOePK8HjJkCNu+fTtbunQpi42NpfQVAQYJMS/zwAMPsKSkJBYSEsKSk5PZwIED2c6dOx3KzJ07l7Vq1YqFh4ezjh07sq+++sph/8WLF9mYMWNYfHw8i4iIYPfccw87fPiwU1tDhgxhN910k9SO7777jnXq1IlFR0ezyMhIlpGRwWbOnOk0nfq3335jjRo1YgAYABYTE+OSzTk5OUod4mv+/PlKmZMnT7KhQ4eymJgYFhMTw4YOHeoUI3To0CF29913s4iICBYfH8/GjBnjMFWb29y9e3cWFBSkvFztayO758+fL90vPtNY6evasnvLli0sKyuLxcXFsfDwcNamTRuWk5PDzp8/79f9zfGXc5vHssleBw4cUMr527ltxm5/PLfN2O3pc7u2zhHGPHded+/enYWFhbHExEQ2adIkSl0RYNgYoxS8BEEQBEEQvoBixAiCIAiCIHwECTGCIAiCIAgfQUKMIAiCIAjCR5AQIwiCIAiC8BEkxAiCIAiCIHwECTGCIAiCIAgfQUKMIAiCIAjCR5AQIwjCJWw2G7766isAwMGDB2Gz2VBQUOBTm9zh+PHj6N27N6KiopQlZGqT4cOH49577/VqG3XheyKIukYDXxtAEETgk5KSgtLSUjRq1Mij9TZv3hxjx47F2LFjPVqvjLfffhulpaUoKChAXFyc19tT884778CT+bWHDx+O06dPK2IZ8N73RBCE65AQIwhCk0uXLplajDg4OBiJiYm1YJH3KC4uRpcuXZCWlubReisrKxEaGmpYrjbEX134ngiirkFDkwThA/71r38hMTERU6dOVbZt3LgRoaGhWLlypeZxR48exeDBgxEfH4+oqCh07doVGzduVPa///77aNmyJUJDQ9GmTRt88sknDscfPnwYAwYMQHR0NGJjYzFo0CD8/vvvyv5JkyahU6dOmDdvHlq0aIGwsDAwxlBUVIRbb70V4eHhaNeuHVatWuVQr3rIa82aNbDZbPjxxx/RtWtXREZG4qabbkJhYaFyTHFxMQYMGICEhARER0fj+uuvR25urrK/R48eOHToEJ577jnYbDbYbDZlX15eHm699VZEREQgJSUFzzzzDM6fP6/b53p907x5cyxZsgQff/wxbDYbhg8fLq2DDx++9tpraNy4MWJjYzFq1ChUVlY62D1mzBiMGzcOjRo1Qu/evQEAa9euxQ033ICwsDAkJSXhpZdeQlVVlVPdHMYYpk+fjhYtWiAiIgIdO3bEl19+6WDPzp07cffddyM2NhYxMTHo3r07iouLMWnSJCxYsABff/210ndr1qyRDk0a2dWjRw8888wzePHFFxEfH4/ExERMmjRJt68JgrCAT1e6JIh6zIoVK1hISAj79ddf2dmzZ1mrVq3Ys88+q1n+7NmzrEWLFqx79+5s/fr1rKioiH3++ecsLy+PMcbY0qVLWUhICJs9ezYrLCxkb731FgsODmY//fQTY4yx6upqlpmZyW655Ra2efNm9ssvv7DOnTuz7OxspY2cnBwWFRXF+vTpw7Zu3cq2bdvGqqqqWEZGBuvRowez2+1s7dq1LDMzkwFgy5YtY4wxduDAAQaA2e12xtiVhY+zsrLYmjVr2M6dO1n37t0dFjguKChgH3zwAfvtt9/Y3r172csvv8zCw8PZoUOHGGM1C2dfe+21bPLkyay0tJSVlpYyxmoWOY6OjmZvv/0227t3L/v5559ZZmYmGz58uGbfGfXNiRMnWN++fdmgQYNYaWkpO336tLSeYcOGsejoaPbAAw+wHTt2sG+//ZZdc8017G9/+5tSJjs7m0VHR7MXXniB7dmzh+3evZsdPXqURUZGstGjR7Pdu3ezZcuWsUaNGrGcnByHugcMGKC8/9vf/sauu+469v3337Pi4mI2RvMWPgAAC45JREFUf/58FhYWxtasWcMYY+zo0aMsPj6eDRw4kP3666+ssLCQzZs3j+3Zs4edPXuWDRo0iPXt21fpu4qKCqfvyYxd2dnZLDY2lk2aNInt3buXLViwgNlsNrZy5UrN/iYIwjwkxAjCh4wePZq1bt2aDR06lGVkZLCLFy9qlv3www9ZTEwMO3nypHT/TTfdxB5//HGHbffffz+76667GGOMrVy5kgUHB7PDhw8r+3fu3MkAsE2bNjHGaoRYSEgIO3HihFLmhx9+YMHBwezIkSPKtu+++86UEMvNzVWOWbFiBQOg+xnbtWvHZs2apbxv1qwZe/vttx3KPPzww+yJJ55w2LZ+/XoWFBSkWbdR3zDG2IABA9iwYcM0bWOsRizFx8ez8+fPK9vef/99Fh0dzS5fvswYqxEunTp1cjjub3/7G2vTpg2rrq5Wts2ePdvhOFGInTt3joWHhysimzNy5Eg2ZMgQxhhjEydOZKmpqayyslLTVlHYMeb8PZmxKzs7m91yyy0O9Vx//fVswoQJmv1EEIR5aGiSIHzIm2++iaqqKnzxxRdYtGgRwsPDNcsWFBQgMzMT8fHx0v27d+/GzTff7LDt5ptvxu7du5X9KSkpSElJUfa3a9cOV111lVIGAJo1a4ZrrrnGod6mTZvi2muvVbZ169bN1Ofr0KGD8n9SUhIA4MSJEwCA8+fP48UXX1RsiI6Oxp49e3D48GHdOrds2YKPPvoI0dHRyqtPnz6orq7GgQMHpMcY9Y0VOnbsiMjISOV9t27dcO7cORw5ckTZ1rVrV6f2u3Xr5jC8evPNN+PcuXM4evSoUxu7du1CeXk5evfu7fA5P/74YxQXFwOoOR+6d+9uKoZPC7N2id8jUPNd8u+RIAj3oGB9gvAh+/fvx7Fjx1BdXY1Dhw453fBEIiIiDOsTb6hATZwR3yb+r1UGAKKiopz2G7WjhSgS+DHV1dUAgBdeeAE//PAD3nzzTbRq1QoRERH461//6hBvJaO6uhqjRo3CM88847SvadOmmsfp9Y0nMOpDWfsyu4ArfbRixQo0adLEYV9YWBgAc+eDEWbtUos9m82m2EgQhHuQR4wgfERlZSWGDh2KBx54AP/4xz8wcuRIh8B5NR06dEBBQQH+/e9/S/e3bdsWGzZscNiWl5eHtm3bAqjxfh0+fNjBc7Nr1y6UlZUpZWTw444dO6Zsy8/PN/UZ9Vi/fj2GDx+Ov/zlL2jfvj0SExNx8OBBhzKhoaG4fPmyw7bOnTtj586daNWqldNLa3aiUd9YYdu2bbh48aLy/pdffkF0dLSDx1BNu3btkJeX5yBq8/LyEBMT4yS0ePmwsDAcPnzY6TNyj2aHDh2wfv16XLp0SdqmrO/ctYsgCM9DQowgfMTLL7+MsrIy/Pd//zdefPFFtG3bFiNHjtQsP2TIECQmJuLee+/Fzz//jP3792PJkiWKKHrhhRfw0Ucf4YMPPkBRURFmzJiBpUuXYvz48QCAXr16oUOHDhg6dCi2bt2KTZs24ZFHHkF2drbTUJpIr1690KZNGzzyyCPYtm0b1q9fj5dfftntz9+qVSssXboUBQUF2LZtGx588EEnL0vz5s2xbt06lJSU4I8//gAATJgwAfn5+XjqqadQUFCAoqIifPPNN3j66ac12zLqGytUVlZi5MiR2LVrF7777jvk5ORgzJgxCArSvpyOHj0aR44cwdNPP409e/bg66+/Rk5ODsaNGyc9LiYmBuPHj8dzzz2HBQsWoLi4GHa7HbNnz8aCBQsAAGPGjMGZM2cwePBgbN68GUVFRfjkk0+UmanNmzfHb7/9hsLCQvzxxx9SwWbVLoIgvICPYtMIol6zevVq1qBBA7Z+/Xpl26FDh1hcXBx77733NI87ePAgu++++1hsbCyLjIxkXbt2ZRs3blT2v/fee6xFixYsJCSEtW7dmn388ccOxx86dIj179+fRUVFsZiYGHb//fez48ePK/tzcnJYx44dndotLCxkt9xyCwsNDWWtW7dm33//valg/VOnTil12O12BoAdOHBAOaZnz54sIiKCpaSksHfffZdlZ2c7zBzNz89nHTp0YGFhYUy8XG3atIn17t2bRUdHs6ioKNahQwc2ZcoUzX4z0zdmg/UHDBjAXn31VdawYUMWHR3NHnvsMVZeXq6UUX8Gzpo1a9j111/PQkNDWWJiIpswYQK7dOmSU92c6upq9s4777A2bdqwkJAQds0117A+ffqwtWvXKmW2bdvG7rjjDhYZGcliYmJY9+7dWXFxMWOsZiYo7yMAbPXq1U7fkxm7ZJ/HTF8RBGEOG2MeTOVMEARRh5Flq/cUQ4YMQXBwMBYuXOjxugmC8F/I90wQBOFDqqqqsGvXLuTn5yM9Pd3X5hAEUcuQECMIgvAhO3bsQNeuXZGeno4nn3zS1+YQBFHL0NAkQRAEQRCEjyCPGEEQBEEQhI8gIUYQBEEQBOEjSIgRBOETevToAZvNBpvNhoKCAl+bY5rhw4crdntj9iRBEPULEmIEQfiMxx9/HKWlpcjIyPBaG2vWrIHNZsPp06c9Ut8777yD0tJSj9RFEARBa00SBOEzIiMjkZiY6GszANRkzNdaIkkkLi4OcXFxtWARQRD1AfKIEQThF3DP1Q8//IDMzExERETgtttuw4kTJ/Ddd9+hbdu2iI2NxZAhQ3DhwgXlOMYYpk+fjhYtWiAiIgIdO3bEl19+CQA4ePAgevbsCQC4+uqrYbPZMHz4cAA1Q6NjxozBuHHj0KhRI/Tu3RtAzfqbd911F6Kjo5GQkICHH35YWV6JIAjC05AQIwjCr5g0aRLeffdd5OXl4ciRIxg0aBBmzpyJTz/9FCtWrMCqVaswa9Yspfzf//53zJ8/H++//z527tyJ5557Dg899BDWrl2LlJQULFmyBABQWFiI0tJSvPPOO8qxCxYsQIMGDfDzzz/jww8/RGlpKbKzs9GpUyds3rwZ33//PX7//XcMGjSo1vuBIIj6AQ1NEgThV/zjH//AzTffDAAYOXIkJk6ciOLiYrRo0QIA8Ne//hWrV6/GhAkTcP78ecyYMQM//fQTunXrBgBo0aIFNmzYgA8//BDZ2dmIj48HADRu3BhXXXWVQ1utWrXC9OnTlfevvvoqOnfujKlTpyrb5s2bh5SUFOzduxetW7f25kcnCKIeQkKMIAi/okOHDsr/CQkJiIyMVEQY37Zp0yYANcOI5eXlyrAip7KyEpmZmYZtde3a1eH9li1bsHr1akRHRzuVLS4uJiFGEITHISFGEIRfERISovxvs9kc3vNt1dXVAKD8XbFiBZo0aeJQLiwszLCtqKgoh/fV1dXo168f3njjDaeySUlJ5j4AQRCEBUiIEQQRsLRr1w5hYWE4fPgwsrOzpWX4TMjLly8b1te5c2csWbIEzZs3R4MGdHkkCML7ULA+QRABS0xMDMaPH4/nnnsOCxYsQHFxMex2O2bPno0FCxYAAJo1awabzYZvv/0W//rXv3Du3DnN+p566in8+9//xpAhQ7Bp0ybs378fK1euxIgRI0wJOYIgCKuQECMIIqB5/fXX8eqrr2LatGlo27Yt+vTpg+XLlyM1NRUA0KRJE7z22mt46aWXkJCQgDFjxmjWlZycjJ9//hmXL19Gnz59kJGRgWeffRZxcXEICqLLJUEQnsfGGGO+NoIgiPpHjx490KlTJ8ycOdPXpriEzWbDsmXLcO+99/raFIIgAhh6xCMIwme89957iI6Oxvbt231timmefPJJ6axKgiAIVyCPGEEQPqGkpAQXL14EADRt2tTU8kL+wIkTJ3DmzBkANTMp1TMvCYIgrEBCjCAIgiAIwkfQ0CRBEARBEISPICFGEARBEAThI0iIEQRBEARB+AgSYgRBEARBED6ChBhBEARBEISPICFGEARBEAThI0iIEQRBEARB+AgSYgRBEARBED7i/wHrBlk1xGnVyQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "city_SmartSurfaceLULC.plot()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1276883d",
@@ -4767,9 +5447,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/tests/layers.py b/tests/layers.py
index 570f0646..095e56d7 100644
--- a/tests/layers.py
+++ b/tests/layers.py
@@ -1,6 +1,6 @@
 import ee
 
-from city_metrix.layers import LandsatCollection2, Albedo, LandSurfaceTemperature, EsaWorldCover, EsaWorldCoverClass, TreeCover, AverageNetBuildingHeight, OpenStreetMap, OpenStreetMapClass, UrbanLandUse, OpenBuildings, TreeCanopyHeight
+from city_metrix.layers import LandsatCollection2, Albedo, LandSurfaceTemperature, EsaWorldCover, EsaWorldCoverClass, TreeCover, AverageNetBuildingHeight, OpenStreetMap, OpenStreetMapClass, UrbanLandUse, OpenBuildings, TreeCanopyHeight, SmartSurfaceLULC
 from city_metrix.layers.layer import get_image_collection
 from .conftest import MockLayer, MockMaskLayer, ZONES, LARGE_ZONES, MockLargeLayer, MockGroupByLayer, \
     MockLargeGroupByLayer
@@ -103,4 +103,8 @@ def test_openbuildings():
 
 def test_tree_canopy_hight():
     count = TreeCanopyHeight().get_data(SAMPLE_BBOX).count()
-    assert count
\ No newline at end of file
+    assert count
+
+def test_smart_surface_lulc():
+    count = SmartSurfaceLULC().get_data(SAMPLE_BBOX).count()
+    assert count