20231116 9531300 1.0 NAP BIO_VEG 6123217.106383 6462242.635352 1801473.050259 2075181.999888 1 VM_Sonoma_hierarchy_unique_mapclass Relationship BIO_VEG_RC Relationship 002 Projected GCS_North_American_1983 Linear Unit: Foot_US (0.304801) NAD_1983_StatePlane_California_II_FIPS_0402_Feet <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.7'><WKT>PROJCS["NAD_1983_StatePlane_California_II_FIPS_0402_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",6561666.666666666],PARAMETER["False_Northing",1640416.666666667],PARAMETER["Central_Meridian",-122.0],PARAMETER["Standard_Parallel_1",38.33333333333334],PARAMETER["Standard_Parallel_2",39.83333333333334],PARAMETER["Latitude_Of_Origin",37.66666666666666],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",2226]]</WKT><XOrigin>-115211800</XOrigin><YOrigin>-93821500</YOrigin><XYScale>3048.0060960121918</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102642</WKID><LatestWKID>2226</LatestWKID></ProjectedCoordinateSystem> 20220107 09260100 20170616 08404900 North American Profile of ISO19115 2003 FALSE 50000 5000 Allison Schichtel Sonoma County Agricultural Preservation & Open Space District Conservation GIS Analyst 747 Mendocino Ave, Suite 100 Santa Rosa CA 95401 Allison.Clark@sonoma-county.org 707.565.7353 Allison Schichtel The Sonoma County fine scale vegetation and habitat map is an 83-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. Static copy for Onsite Waste Treatment Systems (OWTS). <DIV STYLE="text-align:Left;"><DIV><DIV>The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8)A layer package containing the same spatial data as this download, but with the symbology seen in the thumbnail above can be found here: http://sonomaopenspace.maps.arcgis.com/home/item.html?id=ae914472d03b436a8a32e6dc2f8937b1. The symbology is also available as a layer file (without spatial data) here: http://sonomaopenspace.maps.arcgis.com/home/item.html?id=e11a2b6ffb334145bd8f4efba2a83d6f.The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels.The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary.The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup). </DIV></DIV></DIV> Sonoma County Water Agency, Sonoma County Agricultural Preservation and Open Space District, Sonoma County Vegetation Mapping and LiDAR Program Allison Schichtel Sonoma County Agricultural Preservation & Open Space District Conservation GIS Analyst 747 Mendocino Ave, Suite 100 Santa Rosa CA 95401 Allison.Clark@sonoma-county.org 707.565.7353 Allison Schichtel 2017-05-19T00:00:00 2017-05-01T00:00:00 2017-05-19T00:00:00 Sonoma County Vegetation & Habitat Sonoma County Vegetation Map Veg Vegetation vegmap habitat OWTS <DIV STYLE="text-align:Left;"><DIV><DIV>You are free to: Share - copy and redistribute the data in any medium or format. Adapt - You may make derivative works, transform, and build upon the data for any purpose, even commercial. The licensor cannot revoke these freedoms as long as you follow the license terms.License terms: Attribution - You must give appropriate credit (if supplied, you must provide the name of the creator and attribution parties, a copyright notice, a license notice, a disclaimer notice and a link to the material) and indicate if any changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you, your organization, or your use of the data. ShareAlike - if you modify, transform, or build on the data, you must distribute your contributions under the same license as the original.No additional Restrictions - You may not apply legal terms or technological measures that legally restrict others form doing anything the license permits.Notices: You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the data.EXCEPT TO THE EXTENT REQUIRED BY APPLICABLE LAW, IN NO EVENT WILL LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY FOR ANY SPECIAL, INCIDENTAL, CONSEQUENTIAL, PUNITIVE OR EXEMPLARY DAMAGES ARISING OUT OF THIS LICENSE OR THE USE OF THE DATA, EVEN IF LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.The above is an easily understandable summary of and not a substitute for the license and disclaimer for the Attribution-ShareAlike 3.0 United States (CC BY-SA 3.0 US) the full text is available at creativecommons.org.https://creativecommons.org/licenses/by-sa/3.0/us/legalcode</DIV></DIV></DIV> Allison Schichtel Sonoma County Agricultural Preservation & Open Space District Conservation GIS Analyst 747 Mendocino Ave, Suite 100 Santa Rosa CA 95401 Allison.Clark@sonoma-county.org 707.565.7353 Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595 1 -123.539666 -122.345516 38.859930 38.098791 County of Sonoma 2013-10-01T00:00:00 Mimimum Mapping Units by Feature Type Agricultural Classes - 1/4 Acre Woody Upland Classes - 1/2 Acre Woody Riparian Classes - 1/4 Acre Uplan Herbaceous Classes - 1/2 Acre Wetland Herbaceaous Classes - 1/4 Acre Bare Land - 1/2 Acres Developed - 2/10 Acre Water - 400 Sq Ft BIO_VEG Feature Class 212392 Lifeform Classes Sonoma Veg MAp OBJECTID Internal feature number. Esri Sequential unique whole numbers that are automatically generated. OBJECTID OID 4 10 0 SHAPE Feature geometry. Esri Coordinates defining the features. Shape Geometry 4 0 0 MAP_CLASS Map Class String 255 0 0 National Vegetation Classification (NVCS) Map class. The full NVCS heirarchy is provided through a relationship class on the field. SCAPOSD SOURCE Source String 255 0 0 Source for label SCAPOSD Remotely Sensed Photogrametric interpretation SCAPOSD Field Field observation SCAPOSD REL_COV Relative Cover String 255 0 0 Relative sofetwoood and hardwood cover in 5-classes for forested stands SCAPOSD ALLIANCE Alliance String 255 0 0 Field validated alliance if a stand with a group/macrogroup level map class was validated to the alliance. SCAPOSD OID_COPY OID_COPY Integer 4 10 0 Unique polygon identifier. SCAPOSD Abbrv Abbrv String 255 0 0 Map class abbreviations for use in cartography and visualization. SCAPOSD LIFEFORM Lifeform String 255 0 0 19-class land-use-land-cover lookup, with more detail in ag and developed and less detail in forest. Sonoma Veg MAp LF_FOREST Forest Lifeform String 255 0 0 17-class land-use-land-cover lookup, with more detail in forest and less detail in agriculture and developed land. SCAPOSD SERP_FLAG Serpentine Flag SmallInteger 2 5 0 Flag for map classes highly correlated to serpentine soils. SCAPOSD 0 non-serpentine classes SCAPOSD 1 serpentine classes SCAPOSD TREE_HT_MN Mean LiDAR Tree Height Double 8 38 8 Mean stand height from LiDAR-derived canopy height model (CHM) SCAPOSD TREE_HT_MX Max LiDAR Tree Height Double 8 38 8 Max stand height from LiDAR-derived canopy height model (CHM) SCAPOSD TREE_HT_SD Standard Deviation LiDAR Tree Height Double 8 38 8 Standard deviation of stand height from LiDAR-derived canopy height model (CHM) SCAPOSD ABS_COVER Absolute % Tree Canopy Cover Double 8 38 8 Absolute canopy cover - represents percent of stand's LiDAR returns that are greater that 15' above the ground. SCAPOSD HDW_COV_LO Absolute Hardwood Cover Low End Double 8 38 8 Low end of estimated absolute hardwood cover. SCAPOSD HDW_COV_HI Absolute Hardwood Cover High End Double 8 38 8 High end of estimated absolute hardwood cover for forest stands SCAPOSD CON_COV_LO Absolute Conifer Cover Low End Double 8 38 8 Low end of estimated absolute conifer cover for forest stands SCAPOSD CON_COV_HI Absolute Conifer Cover High End Double 8 38 8 High end of estimated absolute conifer cover for forest stands. SCAPOSD PPT_IMPERV Proportion Impervious Double 8 38 8 Percent of stand with impervious cover SCAPOSD PPT_PERV Proportion Pervious Double 8 38 8 Percent of stand with pervious cover. SCAPOSD PPT_PVD_RD Proportion Paved Road Double 8 38 8 Percent of stand that is paved road. SCAPOSD PPT_DRT_RD Proportion Dirt Road Double 8 38 8 Percent of stand that is dirt road. SCAPOSD PPT_OT_IMP Proportion Other Impervious Double 8 38 8 Percent of stand that is 'other impervious' (not road or building) SCAPOSD PPT_BUILDG Proportion Buildings Double 8 38 8 Percent of stand that is buildings. SCAPOSD BM_MG_HA Aboveground Biomass (Metric Tons per Ha) Double 8 38 8 Aboveground biomass in metric tons per hectare. SCAPOSD BM_MG_AC Aboveground Biomass (Metric Tons per Acre) Double 8 38 8 Aboveground biomass in metric tons per acre. SCAPOSD BM_MG Aboveground Biomass (Metric Tons) Double 8 38 8 Aboveground Biomass in metric tons SCAPOSD CB_MG_HA Aboveground Carbon (Metric Tons per Hectare) Double 8 38 8 Aboveground carbon in metric tons per hectare. SCAPOSD CB_MG_AC Aboveground Carbon (Metric Tons per Acre) Double 8 38 8 Aboveground carbon in metric tons per acre. SCAPOSD CB_MG Aboveground Carbon (Metric Tons) Double 8 38 8 Aboveground carbon in metric tons. SCAPOSD CB_EQVT_HA Aboveground Carbon (Equivalents per Hectare) Double 8 38 8 Aboveground carbon in metric tons of CO2 equivalents (CO2e) per hectare. SCAPOSD CB_EQVT_AC Aboveground Carbon (Equivalents per Acre) Double 8 38 8 Aboveground carbon in metric tons of CO2 equivalents (CO2e) per acre. SCAPOSD CB_EQVTS Aboveground Carbon (Equivalents) Double 8 38 8 Aboveground carbon in metric tons of CO2 equivalents (CO2e) . SCAPOSD MN_P05 Mean of 5th Percentile Height Double 8 38 8 Lascanopy mean 5th percentile height for forest stands. SCAPOSD SD_P05 Standard Deviation of 5th Percentile Height Double 8 38 8 Lascanopy standar deviation 5th percentile height for forest stands. SCAPOSD MN_P10 Mean of 10th Percentile Height Double 8 38 8 Lascanopy mean 10th percentile height for forest stands. SCAPOSD SD_P10 Standard Deviation of 10th Percentile Height Double 8 38 8 Lascanopy standard deviation 10th percentile height for forest stands SCAPOSD MN_P25 Mean of 25th Percentile Height Double 8 38 8 Lascanopy mean 25th percentile height for forest stands SCAPOSD SD_P25 Standard Deviation of 25th Percentile Height Double 8 38 8 Lascanopy standard deviation 25th percentile height for forest stands SCAPOSD MN_P50 Mean of 50th Percentile Height Double 8 38 8 Lascanopy mean 50th percentile height for forest stands SCAPOSD SD_P50 Standard Deviation of 50th Percentile Height Double 8 38 8 Lascanopy standard deviation 50th percentile height for forest stands SCAPOSD MN_P75 Mean of 75th Percentile Height Double 8 38 8 Lascanopy mean 75th percentile height for forest stands SCAPOSD SD_P75 Standard Deviation of 75th Percentile Height Double 8 38 8 Lascanopy standard deviation 75th percentile height for forest stands SCAPOSD MN_P90 Mean of 90th Percentile Height Double 8 38 8 Lascanopy mean 90th percentile height for forest stands SCAPOSD SD_P90 Standard Deviation of 90th Percentile Height Double 8 38 8 Lascanopy standard deviation 90th percentile height for forest stands SCAPOSD MN_QAV Mean LiDAR Average Square Height Double 8 38 8 Lascanopy mean average square height (QAV) for forest stands SCAPOSD SD_QAV Standard Deviation of LiDAR Average Square Height Double 8 38 8 Lascanopy standard deviation average square height for forest stands SCAPOSD MN_SKE Mean LiDAR Skewness Double 8 38 8 Lascanopy mean LiDAR skewness for forest stands SCAPOSD SD_SKE Standard Deviation of LiDAR Skewness Double 8 38 8 Lascanopy standard deviation LiDAR skewness for forest stands SCAPOSD MN_AVG Mean of LiDAR Returns Double 8 38 8 Lascanopy mean of all LiDAR returns for forest stands SCAPOSD MN_KUR Mean LiDAR Kurtosis Double 8 38 8 Lascanopy mean LiDAR kurtosis for forest stands SCAPOSD SD_KUR Standard Deviation of LiDAR Kurtosis Double 8 38 8 Lascanopy standard deviation LiDAR kurtosis for forest stands SCAPOSD MN_STD Mean of the Standard Deviations of LiDAR Returns Double 8 38 8 Lascanopy standard deviation of LiDAR returns from lascanopy for forest stands SCAPOSD Shape.STArea() Shape.STArea() Double 0 0 0 Area of feature in square feet. ESRI Shape.STLength() Shape.STLength() Double 0 0 0 Length of permimeter in feet. ESRI Here are some terms that may not be familar to some users of this data: Lascanopy: A tool that computes common forestry metrics from height-normalized LiDAR point clouds. It can compute canopy density or canopy cover (or gap fractions), height or intensity percentiles, averages, minima, maxima, kurtosis, skewness, standard deviation, and many more. https://rapidlasso.com/lastools/lascanopy/ Feature Class dataset EPSG 5.3(9.0.0) 212392 Simple FALSE 212392 TRUE FALSE 20231116 N/A Yes N/A N/A N/A Yes N/A Yes Main /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDpXvSz xIu1JVx82AC544OMg/17+tWJT5djLPZv5Q3sGV1ygVQMAcdST29Oc1j7Wd53JB2YLFeVBB45P5d+ D3zUoAitgk0ZgmbA2lmxgkffXkkY7cdiPflcEUTNcQFozJGjyoSxwMxsQc8evXnAwQfxqJpmUrE0 cbbIvLbIHTJ6D8fr79BU0FzELVYkDicSfxLhV44+bPOCD+p4pbhYndgIvmniLhUG5V+hXg45OT0x jnHItHYC5Daz3sAVYXn+ztudFbZLtwFZVxnIz6envVG7spJglxZ2mLeWdUSMNvUEqhC/MO5GM559 qnsLoJeMySeWqKYwIGZWIPOCc5GDjPXpU5mR4ZbuG3AdGEruh3KSrD724nHfGM8HnIJqLuLAJfCV 8u2SaS1j2HDMzkknbuHY8AADr1qG70eS0eS0miELiPMbZVFkBZsb2LYBPT/gI96mS8ihaWeTJuZ2 JWJQVEW4sM8A7uinGQOfxp8GsPqSz2c9n9qu5UCZRSxbHzdydo68+w4FLmqddg0K8Nu1vDYqbfzh PbkSoBt2/Nw4Yd87fXP6VpzS2+nTzBrKySJobYKkL7lc5crkmP7xA9B259Ib/RYrCxikvrlxfyMC kEIUqqhgW4I54ycZ5OBz1qpZ+R9otbg3FteNLnzEumBKDcB82SQePmyMd+2BU6TVwK7aZIsEt9cM hEhZ0SOItnk7gABtAznnOMA1BZwXBQeUI3eZGAiRiGDZC9CMZOSO/BPcVs6ppgEbuZSssjRxyRRx OiwA4wxOMHAB/OrelhdH0mUXAAleXzN0Rw7KAGAHHYjnPqfXNV7X3brULELSwiAWl5pxN3vzNKHH ytg/KTtOPoOBkck5pyRwi5hZLcLk7CUmaMYAIC7ccEYIGeTuHc8ZlpqWo3dzL5G+eUxOUbzgrrg5 OSuOgJ4z34rW0S1e/wBJP2jT/OMu/kttwd/XB9CAMdtvvUTXIrv8xjLlZJru+aztJFktUIaUKrkO MHpjnkYxzwTgcVZtTKqMy2peUsk4JnLqZPnK4UAYUnPT1HbpFdWLXUgi0y2kDqPuvO5SWLtn5uBj pj1xSWGnyaYpuLu0i823BDHaXQnGQWK5wcAfw/xZyahtOP8AX+YG/c6HZ6hZMXtIYbqROZEXaQ+O p9Rn1rEitp/s0FlPp1peSxQF0KLtYqFwrBjwx+bp29+DW/o2/wAt98gclUYkKo5Iz2A9aqapo0W0 ywwHcigoSxZVPcMp7EY5HTrWMJtPlbHYwvLMVnaK8KG1d/NJWIRbVJ3bSW4GCO2eNw9M37JMoIp7 BkCxPI5ZQfm3dMiPkgdx61ztwb68vGhjguI0A8xoxgogIyuACAFzjB7DntXX6VbG4sPImt4Xa3ma ORmkG5+/JA4PI/Ktqvux1EjltQZNIvJbae3SVkVWt2dGXKdFUkBTxjGfbk4GKhluIJIzJHDIWGQ8 rg7lYkHOVwMn0H3dx5Pbdn8H3UtkzPcI12N2A0jMuM5AJPXHuKwLC5eEzmQyx+aCHMZJ3pgfNx24 Az3JGO4OsJRkrxd2hDbgy3Mix+WGjmIWMs2CoGB8gJztxg5xjI74NRSWP2MRSbfNKh5HUpjIyQCv OcZ7EDHOc9aRlXznC7JEQgpMUKhwMELzyMBTwfTikM/noWZJXlnlGwAfKFJIPyr1OAox6A9q1V1s BLps1uFgMqKwLFWKIN6ryQADxkfj17YoqqbczIAjxW0T4Vy7bQCfYckZA7cAUUpQjJ3bAkngUQgQ Hcr4EXlklumPmxxjGRk89fQ4qlJVkbz5nExYBmL7j6HkdenHOPersVoVmiuLlVby3CFd27ec8gHn kDHfAwOpzSTx3BRpCsKGLHzR7Qcnv8vfg8j15NWpdBFqy2s/2O/tw0ATO5VVZEUA5IJGSB6HI49M 4n1HS5LeJL+zYXEUaKXbcS0I/unHHAGCccZ6Yqg5jefMQeZmU4CkBg2eqEZwAcZz70JcIomtnMsL yx4WaJdoZiOVZe/3iMjB+vFZ2d7oZAFSSNJIkjJY5IzzzjpnrjOOSeasRR3LRhkQo0jGIzSIdoyM uPbAXHA9aZYRpDdqjXRtoiGVrgRllGQ/p24Xg4/SrlxNDYiO0gSWdZyUkuREyiXoMIueeQme+RyT VSetkA2xsrye0ea2gAlV8s2cFsseCPfkDH06mmXkCWUxhDecRIPLnCBATwTgg5I5I/UdxVzSni0T UWMmWW5UqAI2zk8gFeQcgqCCeM9upnvpbNZknimt/NiKmO3Vt6oTnPJOOCOTjGG7nArPmfN5ALeQ WMWnRXVugAklMc0rSkyHKHcN3oOAOOv6595bWMF9i2+1RwRB41kx8zsFyTnBBAwc8Z/pYkSW90+P UoRHEcMjxqgjQMF75HzMR3Ax2qrHEGiCyyyO0IEnlK2CR3wQeOM84Pv60R03YGulk1xeXNxeXD2d m0UcpWUr84IO0AbiOoYjOeuMVnxJDHdQPMqvFIFxtZhGgIHBKk5yC27jv2qeKTUnux513ulEC7HZ gBEWxtyBgHhgOf75q7OGeytnW2jVyEiLeepyWKg/KOew4+vTrUXadmMh06DSppIf+JerJ5jCYli2 wYONg4O0HBzjvnIxWnpQSfS44LQBnlkmm+cnKIWYYJz3zj8z1GaztMuBNPaky3UDsnzSRAvuLB2w FCnBHPXPABrUuGsmiN3p2om13yAzOoxkYAAK7c9wRx0JPIrKpe9holm82zuob+VQrrIIZ0jlLqFO MHnpznA469DWzIY42KiEu0xJYKBzwBk59gBXJwy292He7vPJWZ9kaXEQJZP7wkK9CCT0HHcdahut Sv4pXRNt/DaRZLQrIC+QO4yuB8p6cjGah0nJ2C50ljNCHnjtDEkQ2FFzwOm7jqDzj6/jWdquq2tp qMmzU5hKpAeAZIVgBjaMYPUZXnqD25w7e9mmuliaeSUzRZ8q1QBlf+6zH7o+b369fWa+lmh0mBEt Iop7e6CxAHkBuP3me5I5PfB7cm1RtLULi+IJ5nsYZb7y7SdIk27/AJpHPU4wPlPGME9zmtDw9qN3 JpjXFvZ2rRNKfljYozNwOc5APTv7cVJYXA1eGGJ4IrR0TzFmiZGBwQGG0jjoMjHHFVI7V20uNtNa aOQAqfKQgShWPPDYJHPPv9CB25eRoC1qfiOxu7caesjQyzsYplliYmJcfMCo6k9OD3zVLWtPhngk mtbgSrFEqTkTBXBGcEgg5HI6ntW/pd3pd1Ct1DLE8jgbpHIDMVAGeefSpr/SodTiBaV4pSm0ywNg sp6g+o+tZqahJKzQWueUh57uUOxBldgEDHAyMDBHoAcc+5z1q1M88sWPJiAn3BCqgBznqPQg4710 uteG4LWzaVHJu5BsjiggUKxJAJIAzjHHYDI9ecr7GDAk+Y5ZIfLZvlB+UcEhcZwMA8jG3B5JxXcq sZK6JsZZaLzJjMjtHHGy43fcfkDqOxI4P/6ilntZontlYYJ+bCwEYUjljg/y96K1UkhGl5H2mbZa Sh7oDGEzhsAAFRnv78cdR0EguHS02SWGQzBiFiJfjgKXBG72A789OKqRhncmCBXKsQ2cgNgAHbgg 55BAOP5im3NpdQRN5uyZVUly/oRgYIxkAjpnGVOB1rKy2bGLDAbm6X7PvaJHZhJCgJx644BIx39f oDVntzLdmZ3VsN825gec5x8oI53cduvoc2nubm6hkuofJi2/KTvIYfKBzk/nz1OTxmorqWeZgJUe TEZ2sFOeBkZBOcAevQVSvcBLBpIpY0j82QurbUjYgqxU545yNvfB4Parljc+fq26eV2lZxKZIySE BOSQFHHJAz23dOTjLVkWXPnKrCQIoiUkMCCCR0PA28HB/pZg064eFJfsbSRxZAljiyrfN649R6d6 ckuojqXWPVIBNd6Q81t57P5ocbmG3aAc7WH8Jz6AVHD4c3RO8hWBVRnEUZJzt2suSO/PUDPuec8t 9phk2w7Y/J2bcsQA3vz3JHXPH066kOpXlvcvDFeSrAI8vG/OQfvBSQWAGPQ42/ng6c46RY7iXMZt /Is2t4gbsxPGOSOTnOM454GAAMjoODUN9pz28SvBKVBGJCSF9eAOAxyD6dB3zVe8ijNxIu64byzy GVWYJwRzjIwPXGPanbp5t2mXVw53FpAqjKnAyV64XAHXAAx6dNUmrO4F/R1ju78RpEsssUX3ow6l kyvLYPP3iOOOOOMVoPHdwCK2sYLiRyXJQs20YyAMPlf09e9ZNjerZ3Ti5URgr5JlkJYg7xl1OB0G Dz1wfpXVTXNrbx217K1p5e4JI6wELuKk8EkAjPf6VhVbUtho5q0069ljjjdJYCTIiNcxYVtyNggl fQY5OeTjFWEtZ47hY7gCbYqxKluSR8oB2liApAXtuzyDWpfammnwWVxGIjIpyqbAM/u2HbJ79P5V TutbiisLgQ5lWVyklxv2gybiQyjk42nnOOAADRzTlqkBYk0lv7JjUaPZ242oJZrkjeWOMnCg989T +AqLY9gu9knnjmZCQ5AO4gj7oP8AdBXB7iqM2vXup+TJGH8vDCTzBlB8p54YDjGQcA5x1pjzlJpZ 5b2V5VH711AGTvPKhDnGT1zjg0KEtpAXVt5YvMkmWKCdmwJ+XYkYHAyB1IOc888cYGeltdag6wPF MBHzIxUAquRu69cZHcc54qON5oryO+gt3kkjKlUVWyu3BK5/EjqeOvWrMy3mpTTXiQqkUxJMEZwW KnDA7h065JHXnoMi7NCFMOoz3SwQCf7NaSnMybXGQvTgYBI4PJHPtUFtdRNe2vllLVMlA08Stlvm wwAXGAwxn+WONCFp59t7JuCXCsqGFSMuMKpb5scs3YY4U9CaoaC8jTv5n2WYyxMVhuiMbi2PlUn6 Z7kD8KL6O4Eb3N9GYbmGZWtmHlbvKUiXYeOP7xC46c4Arv5tTtbKxEpLlUGCgHzgAc5BxjGOa4ey lu7dZLOO2t5fIBOLpVG1iQOCcEv157cYx0MupQ6lfyi+jSGKEz43bvvkAYY7uAB0HQ4rOpTU2k9E NOx0WvG1a0h1MS/Kv7rzkdmEasRlxt4yMf54rA0e7AuhNII5Ipyv765l2dycjA6bgQR3J6jkCaC/ 1G5ul0HIIaKRC0yAAYX5QCvBwcc8/rVCOOaQ3DTRv52xUiKBTtwdpJVSMHA9OMZ44NEIcsXFg2WN QstP0+faI42uRJ8zQzFVAZm2qQTzgjnHTH5lVLG2tjZ3M0yI0gCkPMm8ZyMnJ5PJwfTd19CtLdL3 ETyxXFu8ltZSqkKy75MbQd3ykKCScgcfUr1PBDmkfy/NlTzV5wcMzNwSQeRuznceew6YqrPIqsHg hEMu/ezoeCpJIUoPcDHJ6ZqKO+e5n+zmF5Fl+UBVO8McHHBG72BIHPTgU+VtAMuLdJV8tJJ/NaQe Wrj5pV6bvzJxz/Wqc5jlEQmzjaoeREG1QAAR7nHfPHpXRWd88VzNftbRSJcnykSJsttzkkKchQQv IJ44xxWdcJGL+SQ2Qghk4GEJERCjA/3uM5Hp3zVRm72YGbZ2RujEkJiaVyQo3/cC5YlsjBGB09qt P9iuLKSdPKtboS7oyhJ3gAAjnvnnOeRSaXsillfzTMiH5jGTlgyMrY9MAnnn8akfT7eO4dYpIyWk HkKkyfMuem7orAEZyO2auUveEU/siI4Mc6NufaC24SKAQMenVsc56H051lsrq8dw1zEJCF+aS4Bb Yec8Z6K3bjGevOZ4bGTT5rU3EEaqQquzEshXP3gEAUjkE856fSr0Gkix1BUlaMJxgwuFVcABo8nq SM8ZBxnJ9cp1R2OavIooi8gDMoLhm3Btw6H0PtnA78U7zbiPTQsYiyu4q2QQ24jIweOARyB24rc1 ESGI+RN5C2q4LrAyvubGEdjxkDbk55z0rnJrmM2KgyxOWwdrHLJkAEDgDBCj6cY6cXB8yQM3/Dia asm29ihmRtyB2TeN4xgDv939ScGi9iji8m1sZXFpLJIVSRcg7SflUdxk45HFVdLuJbFrWfynNsZh IThgAMEZ4+XHPpnnjNbdr9ulurKRba2uRLJNMpnieIx5zlSdp9fQ9PpWM7qXN/XUaOfbTLeKCKSd 7iJXYMWVcqoKk7eeOvGe45A6iqo06bzWaR448nZukBkyO3PPPbAPr7V2kFtLOturqixzSbQwdmKY hYYwy4PIJ/HFc9f2V5EYbbdEJYptkbCQ72yx4UHAPJ5wP4acKrbtcTRAy2BtJRKFMsTNErINrSYA O7leoJ5AyRxjvm7pUNvdLcwSC4hkdd8jiTaWUjDLyB0GDg5+6eeajk08z2pnSxlaSFvLeOZjtd8c tnA24Jz1wOa0pbYXtncX1tZpbiJMv5gI4CchMD9c96UpK1hkQ0qGwtHaM3Dt5DRmIIf3MnPzbgQG O4Ecdc1Tfw5cSPbXM10LlSeI5Y2B3ZAA642nOeceh5JrprxkaKWP+zbVg6Echs4IJyR5f+z+lcze y3FveedNaQSXaKd0wwin5ig3KQB+B7EGopzm+oMtShbC1+y6jBZpFMwJVc+aQGAIXAx/Bz0HP5VN JiuYpCIZfL81CYXkJ/dqTwxPRvT2ySPenca0NVlitZbNQFmw4GF3ZG0cdR64HHWrMa3eu2cVtEWK QbngjdMblVgOuMN1GRn8615Wo+98wNYaNdX0hkjeMv5hk851GwkP1BC5J4Ldccj0NbE/hu0u3hlu Jrhp4x80qvtZ+Mc4H8ql8PWs9polvDcKVlGSyE525J4rREql1TDAsCRlSOhAP8/xrinVlzWT2KSM WLSIY9TiVZrqZogWkknkLcHGEPrnGeemPcVi6tYPZapmJEFvGjSN5sj/AL0d8np1Y4H+ySffr4Fx dXLADll/PaP/AK1Z09lc6hPDchoY3ieRMlM5TcwwQc5zhfTHPrThValdsGjnreys7SxurtrqR4GJ DRJj90x/vDPQsAMgjOAc+hTLy2uLV2hNtLDPHNHIHhZ2jkzjJGfunIHXj5e9FdSfW/4klWxhaEPF 5UrxzxKqs44TdkYbOBjg/j64yGbLYSNFKY7d5hgYJMSp1wf7xIUfX14p0LyXFnIwjll3ufNChiqH aASwBzwWLdPzqtcqs26ULOpH96MAKdg2k4AGCuTgZ/GrWrELOkIiHl+W6yoGIJ+UHnO0t2G4dep7 nk1RvIDst7hUMvnoCX2/8tAcYx26Dtzn64uJZylnknjSd5jmNF4Ytx2IztwWyB+uKrrZXVsfOIhB kZjvZs+WQcnA/DrjqfcVpFpdQJtPa5hiubMiBBINjrINzg9Dswc5Ac9AeD9ags/KinW4F08M0RZy 4A3Z54AZgO36kcnFakXl2qx2kzrDcTMn+kAhjG2Dl9wPUH+ZzjNZV7tkvW+yOJl+b92sPlghVBDE HrkZOD/WlF3bA0B4gkulxJBFNGwbzWlYR7zgHnbtHX19uMjmbU7qdphBLJ5KiBVmFvtkQDJ4VQ2B glR2OOT1xWQ6h5SHtmaeMhpMARoYuvpwCCOTzyPpT43iuL8vHBFHbOV3RuyngkfxEDqAefbP1OSK d0guMS3e6e4MzEhD5rKxYl2zyDjvjcfwrpZNE06aAyRWjKjMpZxLI5ji2/3eCR1wcH8qxF0wsk1y 6eUEkyFRlDKGXK5HU5+UY4HOeKtWerrZmHynukZgQIY48qF9SMjklR+GTzUz5n8LBEWn2RmtpbiS 4tna3QMqygL8wILIQRu/Lg5610UzwW/2HVn0qCK3AYeXDncJCRgkBR02sPTJrl7WeRs2arGqz7QW WH5uOOBwc8Dgck9Mc46G1ju9VufLTVZLixeb/WouwhwgwR3GOvXr2rOqtbsaFv7y3u7KyksrM2zX LsY54EPmLtDbsbOc8Guet0OqXMUckyXDvImH3uxGd3B38HoM4HcfStvXLBLWO3tLa0jE6sQ1yQI1 fI3EcdSeePpxzWJcTJZ2yxRpPDdqxZo0d1MJz1698LyQT09KdK3L7vUGWJ9Xlxahy8kH2hGk+fl2 UgMOvJJGemRkHvV+51GyNo8NtpaSG7UGEmMP5SYCkHPIxtJ4yORWMbq4ks52uGm8rA2IspAyMHlf 7pyWJ9RV9/E0clj5UdtHFjOIhuYepG3P68dehwcU4bWQrlq1sLrR5LhYrMT2m7Z+9Uh9wRsY68d+ g5I4FVryf7EsTXdg7XNzH5pMarkgtxlcYB4A6dvWmPezXShY2V7w7Io7lGLbyAd3zcEYBz6c+9SW 13Hf2TQXsMcziNo4WUM5Ur77u27tnjBqbO95AZmnQBofOWJCfmP71uR82S3p0GBwec+td34b0ezs 9PtruOL99LCrFyckAjOP1rlrC6tbPT55YFkaKWZyYCv3Uyu0A8nkEc/nnFdvpUkP9k6esfyq1shj QnJ27R/LIrLFTlbyKiUru/sNRkudNdrlJocsVj+Utjjgjjv0OK05LyKKDzHyG2bxGfvn2x69vrXP T6PbWXixtSmgBtpE3AthiZueFX7xOACMD1q+Jmkv4pGWMX8vEML8m3h/iYgHqcfngdqwlGNlbYZa tbBiHe92SvI5fy9uVQ9uvUgYGeOg4FXUhjiTZGiovooxVV7K4kjX/iYTpKOrRqoU8/3SD9P8ae1i r7TJPcsV7iZkz9QuBWTd92MjvLdXP2uKAG6jVkVjhTt5yMn8x/gTRSz6dHIuQPMcEFVnkd0z/uk4 /HtRVxkkhHnlzuUOCDHPJvEpUYOcqWABIBx1x7mnQXMmoHTbBYcQgqAM43k4JOOO5Xv055q1N9ra GV2wUidY4U3ZLMQfvj+LjPIP4Y6Zc4lgl3wiMSwTeaIljAQ7TxjH1OfXIHpXox1ViAVCYoZ1fyn2 5V4zzGuMAhs5HIxycc9uQFt50SeCa7BigEYG1SPmKDHG7IySPfrjioUvnaeSSVhG5j27liGGKnIJ B78MT7gd61LeytDZ2pudUK2mwszeW4AYsCQSDkk8jPTj1qpO24CWtvPdWgiijP2aQHEiqQEBUhgR nkbsNjg85x0qvbw2iySXMt15cO4iSJFdmLYPXB6ZGc559eOIJZpJ45BbTXDWqY8uB4SRhupwNwHQ 988HHStTTkmaHUbhpHtmt4FG10Yjg4c8jPBXsODnHrUu6TYFYBxdtDqLQQtE0ZK5HMZI+VSvtz05 /PNSVzG0QMwjtZSXOD1x8oz1J6A8ZHJx0qVDYMts32iIkKUcSyM4UlVO/H1Y/LgDI5yM1HO01jEb Z4WmgWJipkxlPmxuUZyB2/E9apbgWLGdLvRrmxkvXNwZFMe4nHUAZLEDAJ7j1qOTybuIeTLtkRVW DcMSFecknn1/L6VqaOkmlalbIR9nW5KbQ8gYiPIOOMdTkc/0NdLIsk0083zzWofjzpMRKAMNwuSw +uB/OsZ1eWWg0jgoLZnsJ5POeIwFWVWlw3IbnGecHHT+9njkHd0DW3guIfPtbqcvbBFkUGR2II46 9ByfxFWJ7ieO4ksbW7hm+14jCkMwxkA4JJOACeemB0qrDYreXkb3tlcSxKzJKMMzMRv3cgdnI6Nx RKSnF82wF/Ublxe2eoy211Au5lDOoDAsGCDB4HUenSud122nkZboafJH55D+dPKCu3jA+bgduT3J reNxZxNJDbX95uV8eRdwGVUOeynBHX8PpUsc1/DE8B0sfvZFb7RboQq5wDhW74A6cfSphJws0gOK WOe6KRbJW80rsRIwOSdpwBgDPc/z61qfY54gmmnTSJvJYsGIBZuWDE98c8Z9+ea0tSk0SOe3d7u7 E8DLiOSLaeHXIyE6YDd+1Nm8mRbu6MWpGGRl8uDyn8uU7QMkgD9OeOvTGrqN20FYoLKbW3gvbjRg UkIWH96V+bDcr14PXHQ47DFXZreVGnvlSVJoiEWNk+60jFc5yRwcducfnakgt7SdJHW9MESFVU2p XBxuA4GWxtByeeOTWlf3ttK8lu1nfP5kiswjtnPyjgngdjn8aylUd1ZDscpo1tcjU7aI+egmBVJG zhMkElfRuO/t6V6XbxtDbRRPI0rIgUyN1YgdT9a5SHUvLlga2jvbmaJUCwNbEbVKkHLbBwD3z69a 6H+z3mVRd3c0y9WiG1UJ/AZI9iT75rHEScmr6DRXNvDqC3N08CXTozxwq/RdvBx6EkHn9cVn2TXk V60MehpEXKtJOJhvC8Zzk5JwMcHitttOsFQk2sCBR95UClQPcdKWOyt90cymSTHzIWmdx06jJI6G slUSTX9fmOxFp4vQ7/aAPKOWXcTuBJ+79AO55yT2qW5v4rZxGQ0khBIRME8Y9/8AaH51YRxIu4Bh yRhhg1XuLMS3EU6KnmIwyWHVfr/n3qLpvUZaoooqAPM7swxGWJLqMeSGSIlGGH3fd2kd+TgZGT0H aiG865CrC0tyx3t5hI3N93G3BPqO3P0qOC9vAwRni4TpKwbCjI2gHgce30xmrkVwmYR5LSb0Ky8f OBkMTxxyeBx6dxXs2cTMrzI0VvHCFi3Fg5aVVXYc4IBzkgkjIOcYPSoJd0R2CJdx+QfuwcM2MnBB J4xjjPPHSta4W0W0W4jhLHeXBlwCynbg8HODknHXBHPesuO1kkR3kuYo1QsoLybjxjIwMnksfbJN VGV1dgW4rX+z75/MW2DSQECMNn7yMcgkbeDgH8hVzVZotShgn2TxxIJPMfjYASWRTt6HJxVdbu2k nlc+YIwokImQNuYIdq/TJORxx3zV14BIZbYgtEYjiR9w3ucZwEOWOS3THfg1m3qm9wEuLxb7TI5y 0Nu0UBMbNJuJKkZAG447duQTwcDGNJ50kvmiDz0hiCM8acYOTuPoeR6fgalCwreQBQjLbRZbCELI wzndu7HqSRzg8cVrRz2b3F5bXUcChkZ4GB5LKijG1SeDyRk9upo+DZAY1jcRxzRyllScFX2Jk7zk HkdAMAZyK3JdVu7yWCSzi8iQBWbDBlUgngrgDJH1I6VRstK0+K3ElzLessqD54McfLkqQRk/N6cd +1S2cEkYubSIRxzkhULSrjYV5I4OR8vGDxRPlbuCJbS2OqXZ1B71ZLtZGIBbLAAZAG0ED+IZ4xwe 1dFa3SjT7e4uZZrfypXSWR0K5GTgknjOduevORXDQ3dwJ4in7tmJDGIBXIbjlup4B+ldjLP5vhqw a1mkgBXYFCA5K9yPque3Xmsa0HomNEt1qcNxNBDBcQusqziV8AnhSEzj61V8/S4klllvZVuZZCHj VwCAOc9BgAd+mT3qK2lee+M0cMDvHvRiASJUwS2Xxgn/AIEe3XFVtUm0aSeRjdWzuH3YQY3YOCC2 WAGBwMc+h7xGCvy6jMy6hYfa4bWQRWpm2LGxAXO4AMzEnBGBwOwJ460lvcoIo/M+yxrBG481GO6R 9m4A8+wXj8MV0Md1G8LTXt1bziRQIbczjEwyRulzwD8w6emO2KXWWsJtGuLa2aKHZ877pNu/5eqn nd3H/Aa19p9loVjJk1R5bCS1e6h3O7bE35EXy4/vFjkbuOc59zgkn+waorrOJbiN2CzIihH7nJJJ PT1/Ssw3UDRypLcNLLGzFpklYJMuemMDHJ9hxWtIsEeji+hv5ZZUVcRA8K/zbgRkkHGBkH371bio 9NxF/RpTd3Mc95K6Spa/LMkZibhuM/wuSPqOnFXYbzV/tTQS3GzEaNveBfvFQSPvAHnOfT8ayNE1 CVtWtBPNOqIhjEcZZlUkAhcc5HT3GB+HeVyVnyS1RSOPudRurlH+1Ro8CxNGHx5fzsu5cqGb0I69 TiteDW7K3lktbia2i2McMrKqnJPOM/X8frWrPBFcwtDMgeNuqn86peVp2koP3WxWbcZCrOc8cs3J 9OSaz54yVrDsSDVrHzAhn2ZOFd0ZUY/7LEbW/A1dqP8AdXVuMhZIZF6EcMD7Vzlss8eoRaXdPqLo sYClX2JjHXem0nGD1P4esqCle3QDp6KpLZSW6t9kuCrMOfP3SjOevLA/r6elFRZdxnl88c15IWFs IjHuMzAMFD4LZIxwSF6D09qsidY5pGeaSV5QAmAFBHQjjOeB8uOhHGealubybUp5TFbyWhZR55jc vuY5HzEjHRzwT2xmqkAiubZkvbp0iQ42ogywOTu9ByFznP3utexutTMVrgMsbRiSNiyjy9xwEOR3 7kZ/A/m82jh1mvFna3CfIoUngjKjIPHGDxwcD0OHzRszmFY7R2XBLxptyo4yRnGDwfmHXrUL3kzW 1u26LcuIiwT5hnkcd8BeuO/vQvICaWG0mZy0jRqYQ5RRkhs52jPqeSRxxjvVqC4aLUBGkzNAhKl4 wYSSvRsMck5JXke3PSqAljRhJG73bRDbEWG4jII9eOnAwfwqCK48yCVJY0LRqGRnYnGcFiBnBzwe Qfwpct0A+XUJDfs32qWS3JGcbispGcZDdevQ/rWlazSGeSe3djAX3C3RCEJwAQATx1Ixnowx6VQu ZLm8swyRTR2tuzk5C7V3MTxgAdsfhnjNSx6nbXSvG8KyXMr72lfO18MTgIMDucZz1xxQ43WiAvxa pBBpgt7RZDdyZH+jEnnzFOe2OB6Z6AnsLU0U17HagRajLdOTEyXDq8anBzkdcbgM56beoIFY7gRa 1uk8h1Cny2jfI3qCABlsjnpzmtjTfs0mqXl4sySQIDLsUEcAAbgu/ockdScispJR1XqM568vLyZh I6CNIlCFccALhTxnPUY7fqTWjHq039kJ5NtHbxpGYi0TlWbkfe9QdpGRnHtmqF+Y7e7eCCVZCkpC xtGec/MMZ6c8YzyfxzWsmt4riVpoJWDRME2El1Y52t9exrflTinYk6mynV7q4863nlkWZ/M/d+bE XBcnt05XpTf7Rje5VwUjEk8kkSRvliy7McKDxwRyOnNRxapbXlnFeyu76rE+AQqYYHGAcAbgQRxy c/nXT6ra/bNPZYdMRpSvyl1TKj256/jXJJqMveRRy9pY3dzdh1uZ7edX2jeMKzBgMgHGcE5PGeO3 Si4W6ig1KO7GXCticBpA20qOrHIxkj68Cta2tYLu9hlto4Y71QrTRINqKBt3A4U5J5HXAx3xUN5r drZF2l8OReUHdEkGzD7TgkcZxyO1NTk5WSAybF9LudNkN5LJNPAdylFw0jchcke+Cc9+eeasQzNa PD9suEaaX5WCxhpVzwWy5yDx1IH3hx6Nujpt5YW/m2lnDdylGG18Z3ZOSBjCjPXP86H0G6uJxDZ2 1mZPmEsqKVjX05A+nA57nrmrbX2tAKT39lamdLeIxSR/NGYnUZJ6buDwAMkDI56iuj0LU7u7vfsU 1/I/7tmEnlAbipAJXI6c+/Tt0rlbVS9uqO0wfaqRqsoBaQj7p5B249Ohz3Iq9bWd8DFfWzxRyxW6 bh9p28FixBJ7/cHX+IdadSEWrMSZ00lxfya2+mx3smBGSX8tRtO0Educ57dMVqaWrraDfeG6JOd7 DkHHIPT+QrntCkf+05pppC8mI/NJBHJBXv6Y/wDHT6V0djBLbxssjAg4IwxbnGDzgccDFcVVcvu+ haLdV5L2CNtgcPKc4jj+Zjj2HT8ar6xfzabYtcQ2huSuMqGxjJAHYk9e1ZfhOeS4hmmmD73AI3Ht luAMnH6dRURp+45vYL9DYhku7mXc0T2sCgEBtpdznkHBIA/Xntiio59SaDU1tTbyMhTO9RnLHcQv 5Kf89SpcX2GcH5aWum27bEcOzRkYwTjHLEfe+/x6Y70txBA4jIhVWWGNgRz8pSM7cHPr1/pwCivT T6+pmS/ZzZ3VvFbzSRtJBHMJVc7xubYQecHgdgKzbu1lh1SWMz79szo2QcNjqSM98D8h1ooohJ3A bfReTZC7idlkWXacnOSOM89umB255Oaqy3krxS7TtjjYbk7MeRn2+n09BRRW8NVqJmhaaoUhltJY EmiigLYfvuCAfQjjkcn2rTutEt7Lwla6mFR5Y1WZxtK+ZuKgLnPCjPOOvXjNFFYVPdlFLq0NGJJd zR3iS/u2ZTJgPGCv3f7vT+L9KVZ57CW4+zzSRkOrHYcA/MT06dQKKK2shBaXjzXb6peKt0xcK6SK Pm+RsYOMgjYOlMn1K5t28yF/LRJGWNFH3Bk4wfbA7dqKKfKuYC9pQsljZprPz1lKgBn5UYBIBx3y O2cDFdc1jDpaw3FnvhgnPzwq7dQhIwc8fdx0P4UUVx4hvmS7lIvDQLH7TLNIjSCRVXYztgAeozg8 knn1rz7WFUa49uiRpHHOY4wq4CAsTRRUYSTcnd9ByHRO9zdK80juASg3HLEY7t9M/wCc56srITqG n2s8kEUKiFc4ccruJwR6cfrmiitK2/8AXdCRV0yK1uNUkSW1ikWGJFiWRQwQl9vAx06cfhQNOXTN Bm1WIp50cBVdqEHOQN3UjPGc4oorOTaml5ofQYJo7DSkvbS2jRpInlkRmYhsKvoRg4cjPuTit+EX flpGLlI03CNRFFjHy5z8xP0ooqKu1/UEYviWe4tL22Q3dzImx2A3KuGAzn5VHPp6dab4duZLLSJL pQHV592xskqG4wDn/ZHaiitEk6K8xdTT02//ALU1IyvCsZi2bcEk/Mj9fpz+dFFFc1ZJSsikf//Z