20201215
13541200
1.0
FGDC CSDGM MetadataVerify - ISD GIS
FALSE
Lidar_Highest_Hit_HS_2013
Local Area Network
6119016.656046
6497017.168888
1797201.893708
2250801.921360
1
002
Projected
GCS_NAD_1983_2011
Linear Unit: Foot_US (0.304801)
NAD_1983_StatePlane_California_II_FIPS_0402_Feet_2011
<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_2011",GEOGCS["GCS_NAD_1983_2011",DATUM["D_NAD_1983_2011",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]]</WKT><XOrigin>-115211800</XOrigin><YOrigin>-93821500</YOrigin><XYScale>36983428.057351544</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision></ProjectedCoordinateSystem>
32
FALSE
Mixed
1
Cache/Mixed
TRUE
continuous
unsigned integer
20230112
05162900
20230112
05162900
50000
5000
FGDC
Watershed Sciences, Inc. (WSI)
503-505-5100
503-546-6801
421 S.W. 6th Ave., Suite 800
Portland
OR
97204
US
ArcGIS Metadata
1.0
Sonoma County Vegetation Mapping and LiDAR Program
False
Sonoma County Vegetation Mapping and LiDAR Program
Raster Dataset
2014-02-19T00:00:00
2013-09-28T00:00:00
Sonoma County Vegetation Mapping and LiDAR Program
raster digital data
Lidar_Highest_Hit_HS_2013
Sonoma County Vegetation Mapping and LiDAR Consortium, NASA, University of Maryland, Watershed Sciences, Inc., Tukman Geospatial LLC
Sonoma County, Lake Mendocino, Sonoma Lake Watershed
LiDAR, Light Detection And Ranging
LiDAR
Light Detection And Ranging
Sonoma County
Lake Mendocino
Sonoma Lake Watershed
Elevation
Canopy Height
Highest Hit
First Return
Hillshade
Topography.
Data were provided by the University of Maryland and the Sonoma County Vegetation Mapping and LiDAR Program (http://sonomavegmap.org) under grant NNX13AP69G from NASA’s Carbon Monitoring System (Dr. Ralph Dubayah, PI). This data is available for unrestricted public use. However, users should acknowledge the source of the data in any reports, publications, or presentations where the data is used.
<div style='text-align:Left;'><div><div><p><span>Data were provided by the University of Maryland and the Sonoma County Vegetation Mapping and LiDAR Program (http://sonomavegmap.org) under grant NNX13AP69G from NASA’s Carbon Monitoring System (Dr. Ralph Dubayah, PI).</span></p><p><span>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.</span></p><p><span>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.</span></p><p><span>No additional Restrictions - You may not apply legal terms or technological measures that legally restrict others form doing anything the license permits.</span></p><p><span>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.</span></p><p><span>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.</span></p><p><span>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.</span></p><p><span>https://creativecommons.org/licenses/by-sa/3.0/us/legalcode</span></p></div></div></div>
Ground Condition - LiDAR: Leica ALS50 & Leica ALS70
2013-09-28T00:00:00
2013-11-26T00:00:00
1
-123.565035
-122.224633
38.086873
39.342443
Sonoma Veg Map
Sonoma County Vegetation Mapping & LiDAR Program
staff
http://sonomavegmap.org/
747 Mendocino Ave, Suite 100
Santa Rosa
CA
95401
openspace@sonoma-county.org
US
707-565-7360
707-565-7359
M-F 9am to 4pm
Watershed Sciences, Inc. (WSI)
503-505-5100
503-546-6801
421 S.W. 6th Ave., Suite 800
Portland
OR
97204
US
Watershed Sciences, Inc. (WSI)
Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595
1
-123.565033
-122.224632
39.342439
38.086871
A high resolution LiDAR derived hillshade facilitates the visualization of the topography of a landscape at a variety of scales. This layer may be used on its own or in conjunction with other data. This hillshade which was created from a LiDAR derived highest hit digital elevation model shows the signal returns that were the highest above the ground in a given location. This provides the viewer a hillshade display of the tree canopy or structures at the time of data capture. The Sonoma County Vegetation Mapping and LiDAR Program (http://sonomavegmap.org) and the University of Maryland (under grant NNX13AP69G from NASA’s Carbon Monitoring System, Dr. Ralph Dubayah, PI) contracted LiDAR and orthophoto data collection for all of Sonoma County in late 2013. Also included in the data collection were two areas in Mendocino County - the Soda Spring Creek-Dry Creek Watershed and Lake Mendocino. This fine scale data will help provide an accurate, up-to-date inventory of the county’s landscape features, ecological communities and habitats. Project funders include: NASA, the University of Maryland, the Sonoma County Agricultural Preservation and Open Space District, the Sonoma County Water Agency, the California Department of Fish and Wildlife, the United States Geological Survey, the Sonoma County Information Systems Department, the Sonoma County Transportation and Public Works Department, the Nature Conservancy, and the City of Petaluma.
<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span>Hillshade of the highest hit digital elevation model using the Sonoma Veg Map LiDAR data. The Mosaic hillshade function was applied to generate this hillshade. The default values were used except for the Z value. A value of .4 was used for the Z value. The raster cache was generated from the prevous item.</span></p><p style='margin:0 0 11 0;'><span>The DEM used to create this hillshade is described as a Highest Hit or First Return digital elevation model (DEM) represents the earth’s surface with the base or bare-earth DEM values subtracted from the first returns, with the resulting raster being the height of any vegetation, structure, or the ground for those areas lacking in vegetation or structures for the subject area. Values are in feet. Each cell in the GRID is three feet and has a value that represents an average vegetation height at that location. The purpose of the data is to provide users with a very accurate view of the vegetation height in the subject area for the date of data capture.</span></p></div></div></div>
LiDAR flight lines have been examined to ensure that there was at least 60% sidelap, there are no gaps between flightlines, and overlapping flightlines have consistent elevation values. Shaded relief images have been visually inspected for data errors such as pits, border artifacts, gaps, and shifting. The data were examined at a 1:3000 scale.
LiDAR has been collected and processed for all areas within the project study Area. In some areas of heavy vegetation and forest cover, there may be relatively few ground points in the LiDAR data. Some elevation units have been interpolated across areas in the ground model where there are no elevation data (e.g., over water, over dense vegetation). TINing the points produces large triangles and hence the elevations may be less accurate within such areas. Some elevation units have been interpolated across areas in the ground model where there are no elevation data (e.g., over water, over dense vegetation).
Relative Accuracy measures the divergence between points from different flightlines. Relative Accuracy median is 0.05 meters (0.17 feet) out of 106,255,686,985 laser points over 4,133 flightlines. For more information regarding the internal consistency between ground-classified points from different overlapping flightlines please see LiDAR data report.
The Fundamental Vertical Accuracy (FVA) of the data set is 0.03 meters (0.09 feet). Accuracy was assessed using 9,685 ground control points (GCP). These ground control points are distributed through out the project study area.
The FVA was tested using 851 independent ground control points (GCPs) located in open terrain. The GCPs were distributed throughout the project area. Elevations from the unclassified LiDAR surface were measured for the x,y location of each check point. Elevations interpolated from the LiDAR surface were then compared to the elevation values of the surveyed control. The Root-Mean-Square (RMSE) was computed to be 0.03 m (0.09 ft.). AccuracyZ has been tested to meet 0.05 m (0.17 ft.) FVA at 95 Percent confidence level using RMSE(z) x 1.9600 as defined by the National Standards for Spatial Data Accuracy (NSSDA); assessed and reported using National Digital Elevation Program (NDEP)/ASRPS Guidelines.
0.05 meters RMSEz at 95 percent Confidence Interval.
Acquisition. LiDAR data acquisition was started September 28, 2013 and was completed on November 26, 2013. The survey is utilizing a Leica ALS50 or ALS70 laser system mounted in a Piper Navajo PA-31-325 or Cessna Caravan 208B. Near nadir scan angles were used to increase penetration of vegetation to ground surfaces. Ground level GPS and aircraft IMU were collected during the flight.
2014-02-19
LiDAR Data Processing. Flight lines and data were reviewed to ensure complete coverage of the study area and positional accuracy of the laser points. Laser point return coordinates were computed using ALS Post Processor software and IPAS Pro GPS/INS software, based on independent data from the LiDAR system, IMU, and aircraft. The raw LiDAR file was assembled into flight lines per return with each point having an associated x, y, and z coordinate. Visual inspection of swath to swath laser point consistencies within the study area were used to perform manual refinements of system alignment. Custom algorithms were designed to evaluate points between adjacent flight lines. Automated system alignment was computed based upon randomly selected swath to swath accuracy measurements that consider elevation, slope, and intensities. Specifically, refinement in the combination of system pitch, roll and yaw offset parameters optimize internal consistency. Noise (e.g., pits and birds) was filtered using ALS post-processing software, based on known elevation ranges and included the removal of any cycle slips. Using TerraScan and MicroStation, ground classifications utilized custom settings appropriate to the study area. The corrected and filtered return points were compared to the RTK ground survey points collected to verify the vertical and horizontal accuracies. Points were output as laser points, TINed and GRIDed surfaces.
2014-10-24
385873
0.979598
463047
0.979598
2
1
0
6119016.656046 1797201.893708
6119016.656046 2250801.921360
6497017.168888 2250801.921360
6497017.168888 1797201.893708
6308016.912467 2024001.907534
Band_1
32
3 foot pixel resolution raster cache.
False
False
False
False
False
This raster data set represents ground surface elevations derived from ground classified LiDAR point data.
Sonoma County Vegetation Mapping and LiDAR Program
dataset
EPSG
8.8(9.3.1.2)
20230112
/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a
HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy
MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIA
AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA
AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3
ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm
p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA
AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx
BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK
U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3
uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwC3Z6cm
t2Ns+q3FzfpCVa2byY0jb5TuB3AAty2SecrnaB1jtTeX94BZ3QsNFuF22qLCyGYKApeTAVkBDALg
jO0/Q1/KvfEUTxSah/oBRHkhIKwQQD5xtJVSzPzjsEJwCNtXLOzWyuC2l+bqU1pJHbNtjC+WgULt
XJGxQAQeSzY96ANqZrXyhHJYm5lgjkeS7ZmRbdFwQE5ZvukAAc8DPJrlbm2lvZLG+vLW0tbFmZlj
EJkeTc2QCdq4VSVLOXAY+ta2u3UkMtnp00N35epSOClttijtwPmd9xPznDAckA8n0Fc7c3VzbaLI
L6I6bayF1tre+k3JJEm3aG53ZJdiT3x3ycgGbeay97rpuLe5G22Hks1ijOWLPwo2leixKMBx0HPL
ETaLBAmo/Z7a2uza2zmPzprhg12Q21Vj25TI8x8nJABJGMA1Hpt7pskcIv7v7TYgyC6e3QxtNJyq
uAGACqiuQRg/MeOlJc6xplpeSQ6fa3UcNsohtAx8wyJIc53EMEXOANoU4BPU8AGxq2r2Fxu066kN
rptq/lvLeHfJNPK21tvzfcUbssuMcc4qvdabqNzbw3F9q1lZrOr7mhWMeTakBUxgEgbW6bgTnHXm
oVaPUtcd7mVb6ZbJNtgsRh3zMCAFAXOAFJyoGFx1JbOJPNPrelorTTpbvPMZLgTNEHVFJBCBdoQO
6rnPUY460AbF6EtNLutTu9WMtnpkpjtrSRWLtN5aqpDghsDLDqBxkYGAElgm0uCyvIYBa/ZHU2lt
LCv7x5ZCFkMagktgt8jEYIUg9jHeapYzx6fo2kXM8scUsE8/kFXiL53ySyswzIBlTg7R97pilu9V
0aG+byLy8u7uR/OknuVWIbuhcSLn5VRGwBgfNnOcUAEDC68ShL3VXaW2imEcctv5QeUseT82QzZZ
ySQF3DoBitC1kurPQWhW3tbK1cj7O0KedNd9WwidlJDHc38O3OTyc/RrrTNM23VlPbNf3V3HHBPH
b+a0YCfMBnaT82FLFvmIJOBT9O0FDbvquv38NrFJL5L3FzLsllUMd4XY2FPyouw54B57UAZUEcMu
pfZtJmmhlCmBJJ41ZlgcEs21QFUhAzZzzv7HJHRQw3Wt3dtoEOlra2FlbxPes5ZZJY872BUAEl/k
OCT9Tmrei2Nm8dxDotncwaXMuJLmTzEMzcgquAxVCPbPIHOeH3tvLo9jrbJdzXF3f/vZWS3d2WPh
QuJDuKhVYlzxwASKADXdZubrTm1J7C3tQYDJY3EyxyMIWVRjbkENgSvz2PGeCOJvtSl1M29z9pnn
/eiCCJxw8CsfmJVABk7gSGY/MR9dC6v0vHsrFdYgZ1jCTXM8IeOFZXPEakkFlCxhcE+xwCab4I0k
apbBWuLhoEJO7cVJQSEqqqPuqWDk88kngYBoAt6RoKNA1/d+Rt2YbETqFT7oVSxAyf8AaJJ2knr8
1y9CaRLZSvArRzOZ5WlJXzCnHLbCQMhcEJ82OCdwNdPdywRagsTQjyoI1LOBja4Y4BOeABwCOcng
HmsjXoYGmtpFtnX938kkqgAuCCispIZjucEjoCik9OADn5LZ724vL57Wxt7AqLi4hlnZZljTdlVJ
AI3Zdv8AvnqQMmnW8djfT+IDOtxYRBkhlaCNlMm0H5YwxJUDaOSDk9uaTUro6kPsOp3EMkcSnddt
L5ssiR7WYjIAZiRtGTnPtirWlXt1DDbvEr+erp5Fi0abS0hLMwOwliyqiqQOhcgkAkgGBFa6jc6f
cTSzIpkcNdvDxMdyYjtwpIxgAjGCFyevSt+aBNS1W1023hnsNJsLRnSSSN3VpHJbLhjzj5s/NyQc
egpXlvbiQrdm8e3hO6YIArTzyq+yMHOWYFUJwTksxxjmrd3qlhpfhyH7MUFyFktAhgLuzFlR5GBI
2nPrjcVIoATUL6ybQLzyrqDTIYre3SRhKrXNyhfd8gUbUyDuwMnP0NQWE89ve6hZ6SftMUskck4T
fjyQjEB1GXOT95snjIG0EZj8M6ALrTLW/vbaFopjiRrj50T5ztCLhuBj5iSP7vY51dKsrG+mv7TT
L9BYROUv50c7WgCqzHjhcsv3jkgcDAyaAMfRNLvNXmnt7g3aWdlG9zdyWsaW8cZeM/dXrIcLjsfc
daKsW0l/4isLsWlxFDp+9oYVUkRxR4A3SSYUuSWChR/eYkY6lAHYbVQtHewnzbNkZ5SyokUpUKkS
K5CuoTJy2SAQe2RM2pWVpoVzLqOrQRsxVVMMLB8q7Bgpdsk55LZJGSc9DWm6WG77KY1ngtwbiSV5
Am4psA3EcAZIwOny9Dg45TV9QgW0mkdE1EW5cW1pcKEhUnGWKrngbioJwBg/d4oArCC+1IS39zfz
WFr9oWK2E8B85m25TfghgAMv0GQATmsyS00uaaGWztru4WKKWCKA3DF5xu2ru2neHc5JUAYGM8ZN
Y9tq11p6gvYWRu2uWnW4MAfawxxtfAABIORk9BkA1Zjnu2SxhvYbq3gSIXVzci88tp5CxIYksAgO
OM8nacYoAs6df2ls51XVLVIUSNkW2NwmxXG8jahJI+cj5SME5JqbQNOmnE+u3FxGlrEPtkTPDtW5
aFcZLcEEMAcA9+mDxFPfr4f07ZHOqI100qWVzGXmjGXCFCQQCepcDk4OWxmtaz0OztNDtpbpoTfy
xeRJNfyeYkW6QptEbsNu0EtnA5HQAmgCtJqurXd/GZ7+IGYR3jQaawZwChZdwbCEneNxYHkDGAGN
XtF0250+dhe2s6aVagRxiWBS1zIxLOW5ChQzcdjtG3nk10ey1i0E9rpskVnA8Su1iH8yQlySF2AA
sEPLA4OP93EjxQyahLataySqVbfb26mcwiMgnezH5sMxHzA84GewAK9gPEF54MtobFyGvBM5UWyx
wrGW+8SB8xJ2nv0wRjNWp9P0XTNMtrrXoZrjWHdD5DSvsH77LsctjJIIHQHbkA03W4hpUC6JBBHG
Zo/tRs4gzzLFjBjLZIXlt2cbeW6AZrc07wfe6pe22r65dXESlFP9nKdiggkpn2AAOfXp0BoAyLMe
IPFuqRy2W7StNT/j6uZMJJLlTwOOcBiF78/e4yOr/sLQNDVJ7axgM0AwLiSMMwHIyOMDv0GevNdH
MqqEbacAEHA6Djv9fzPJrjdavgGkCZYgY47d/U+w/LmgCt/ask0kqI5IWU9WJ754JOe5749OKkub
aHV71472MSILeTKLKAWC7Wzkfd6nr2H5ZtkG+3RlCFDfIAz7WJAJ9MHufpVzVoHlhngWUrKwcJID
sK4XHVVY91GcZ5wAOKAOL125tzc6nJFIIZF1CKCIWMRP+rBAIYjaOrHBOflA6CpBby6T4dhtbeKb
yZna7lhTIPl9I0kccsuQSeBnJGeAKk0+OytbOZbVbN9QlVA11c7cuzkgooJyRllBLAE5PTbWlrj3
MtlFb6cls1hLfRW0moOBg7SSEHB+XKjPPHPFACSXlxBteQxNcT3EkkFrbjyRvT5iHGN7YboODnjt
Vhru8v5n+0PJ5tzb71VyscZUMT5uVYkv+7XsR2APWsvSHuZNdgvb2Y289pJuisdgiXOdzGaRy3y/
LjucKCAMYqIw2era3BHcS6hqN5LG0gkSUOBHjJQBWbam5jlmBwFPc4ABpm3s9VSCynMkt7LPhYJS
UilwEY5IULt2hTgZwNvJzk5s097ZavPYaa8nnT3TGS5dlVyfJIEUXIbAJZeMdAO+KfCt2mlX8sEc
0LAR209zdyGMWchbD7Qq5YoNgJJ4HBxk1z+l3Up8Sx6pc+XLZ6eqrDFcKFDR4bYOmADySe/PXJoA
6+80CGGzvr26vL2XY6fNMqwrhZCzg7huAGSAThicgKcEnnobaxntrG6XIffLK0yzEI7GQorruOc5
3YbaNuM/McA6MusXWoWMGnteRaZaagHu7kpBujjgIOMNngnJBGOr/hWdBJa/a/tv2NY4DZNdRQRB
2V0RioUpkYGEXLZI4Zs5IFAFu90mf7ZHpupRzi4ABjtTLtt0iXYisQBwu4MSWI+Ucc4FWJ7a9gsr
vTLGFJLe3XLRXQKRfLvkACR8Mx2kbT3Az3NY8cmrzwwlwyNLP9quZbi2KtMYwpVF3Y+TD9BgcnsQ
K3F1hzruoWunX3kRykwvst9z7yh8x3Z37YG4kjqvqRQBUQXdpb3WnaksjWgxEFjlfZdTHa6hpuFG
SWJAx159aKka4jtxbWOnXXn3LQ+XdzyfKke58opB+8+4kk7Xb5gVxgYKAOmvdXlv7OexD7o2uIlt
FCFWu8AO/mHqfnLZ7D5uRis3WNZF8zxpbQzW73cZiMsjO9ztd15OAArMAMKCcK2ccGr9r5dtBHp4
0a82RKEt4HhBmIywMkjHgM2fl7YY45xXNjSJJ4bq6XyI1sHkiI1G8Lm3jB52qVyCW3nePQ4PQkAg
XUdMsNRj09IYPPmJaS7eImJ3eXKqrN8yxhVIyFyTnA5zW/HqUMfh2K5t767v7maTyoYpJSkUhj5a
YZAKjEZYZPO3GK5jRNRnuNZRrS0tPsmVaV42MSMABtRWk5ZiIto6nJb61Z16CVLm5ulsriJ4ykbL
GsU+2Z8rtduxbGcAsFz3JoAoWll9v1GfUDPb232QSyNKXifMqiPCxgYJbexwSemMEnOd26uTHNG1
7qFzK8jhsrGiRQjy14fIJON2AoJOWzyTg1NLsvs1nHHBp8t4I3wHtroRNEpbMny7CRkhVLDJ+Q42
ggHsUtpbjSokYxRQrCsu1XIRVGGGIflXHAGOFJ+8uRggGLpVvp9za2rT2liwjmnZIY5UZUiJyEAU
NgZ27nPzc4HoNKDWIbTWr+Dw+0Rhit408sQP5ZyzlxuEeUOWHJOARyfR95pt/frcG5kUIIEEVzdB
0hVgrByp4JyzL91VBDMBnFXPC0+lQ32pw2sbvcWohH2icYkfKfK4B+bkNgcZ7A9MAEPhzRL6PVbz
XdWjiXUrs5EIO/yQCdu4/wARUEDAwMqDhj06/wAxwAxZFYnJyO31PXsPbjPes8XCSy7lHGPk2Hpg
ADG0c+gx/wABzyaa1xG6+XsDxdwcjHYYwfr9OnJ6gCX97IkMm9lKoDgeh9cfrz6jpXE3dw9/qPlI
SwPX5evQcnv/APX9uOj1R1WNipDE7zvODj+8SB2ye2ec+wqvoOjytL58oZC/AZRkj/I/zzQAo0/y
NPU7m820InZBnLYGSPl9t3A96twSLJaytMyRxTJI6lGwRHxlic+h+mBW1eJDbWkq7HYshyE+8c9l
HryMDvx61xutavLpx0Wxt7EvZRKyRygkxuOYix6DaCzY5ORtJx1ABmW11MtzcancRQxXAt0ngtkh
KtCrJshA67c7izZ4zGPUZksNPhvZ1jtCV1C0crbs7nAKsV3tg7d3BPH93g8E1DvuZdQt0MUdokyr
HBAXKtdyRbgp29cEsRzyTnnAzV+8hFvpVzHPcQvbWoCFo5wN3ODt28jBQgEAkBQMcHcAYd3NeXF7
DZWOnvc6i926CeLdGHUFgwZ2JIbIbJBPHQjHLdL1G2s714LOcBDEkV3efO7MojZViTG4gFl3gY5y
M8Cp724s3tbS0AtILCNGit4Yi0RnYgoHeRuka7iTzzyapTCGyTzrbRIrIxOmxPLl3wIrOfMZgM5L
ocdGA4z6AGa9ms1kun3moG3SVkljtXXBVGdiWKoTuY8YB59ulXbuwtEuIoABE91EoaVcxhcIyPl2
zjgLnoBuIAxml0HSbS0jke5ivZHilUIFVi11OxcAdMhV2sDjJO45GOKba2tkNMi0y0KpfTNbqbe3
ZP37GWRtsrkHaAEU8Fx8vI5oAt20Ed1dRWl//Z0lvDDCstlCrM77UCopYIWZsuDt+UZbrnOKF7Ja
veapJLBHZ28gWOUO5uJxAMYiRegdgmeoCgNyMYF02cun3915ceVku3junkLKrYZWVAG8xt7sM7iS
wB7CoQuktHqMNrZR6hrIC+VDYxM8Eal92xSMjavUlsA5I+gBTOoWq2/2hJYUv7+RBcqglDwxMVKo
ijHyIiqSd2SSADgHOhq89tYmxijspH0uKSRJDcBV3yKxBYQDlhkPw2fmI3cDFXdd16O4hFvfXMd7
dXNx5sdlbCRA2I1WMtuwwLEKeo6HqKyxINT0u30u3WK2iyyyvG7OjbSnmSlRncS4fA44b2BIBd1K
WW30uVNH0+LTJF8uFXRo5APMB+RnLjafmYscHGRnAxtKsX2rWCaAkxkRYnmSIvf3CyTRJ5e47ERf
lbOckDPqfmABQBXunhYzwWd/pUEoh3sX8yEqAQrs0eNpbavbHcA5wDSs4dDmttWvLu8vdRXz2QWl
mFgV8nhyNzPtJK88d+TkZmuNPu3i1G81aZINPVRG5vbVoQ/zb1W3jYggkMe3A9SxAv6P5T30un2V
1DczQL54hliRbKGV95JTGdjKcKg6lsZAHQApeH9Qs/szx3UEs9z5vnpbzPD5UjbW2ISwBGA5JAIA
wwPJq1aWkEFtdXOmwot5AFncgHy7eV2OF9AygqBzkkrjjJGiLWPT/D8gvLiO4ayg8mazaESh5pX3
NJhRwFJ4DZypP3c02CGy0DQ7G31FfswvHaaO2k3xSMUB2MQADyQdzHB6dAxFAGtD4b1WCOG51HW4
is6mOa2S3CoAoJckpnduww3dcOD3pdX8S6leR3dj5DxQ3EqxefFCZiY2DksFK/KMRlVDdd27kDFX
kuRrDvJrETLbwwhUglQOlxOXPMYAyAm0dRk5Jx8pzy2hxW91qLx3Ey25nkaZLMxvIoO7kZLqzvlU
ViBjC44NAF/UZLmxstP1O7dkklHmCC6ZWmZ9vyxIDGyRDbn3yADjrWX8PoLiLSZdTntolkvN5DKA
WZU2Lkk8/wB7oe57Egu8R3VvcwXljbeIrJ47ZI/MkkXa21VIESc4ALLyvUAgEmul0u2W20q2eO9a
6tVtyYcQBVTewcAtjG7seeMAYyQCATTXLecC4JyWz8p5AA6549T2464HBhlKnLkp8uFBGByO/HfH
HA+mASaYYsySKFIO4hun/jw55yc89D1y3AtJptxLErwzWqO4kihNyC+6THPAGeOf+BAZIPAAEtLV
5ry1hlkQsNjGCJgZD1xv6AL8vbjnAwOG3dPvbGae4trZy0lt8r/JtBG5hkfiP5fjwKJDYavcahpl
peSstm88l9cwGWR2AG0ICwbqynqSOMk5JEVxq9r4fubNtOttsl4ZYZLi7SSN3ZssrBBno3HXnHYH
NAHZ6hqkcLm7ZQ1vaSGTkEByo3NgeiqCc9A2zpyR5pr+o6nqN1bQJafZb2R1tI4gS32SDKxqGYDk
s24nB4wPQV115fWkCmCa5eS1s40kkNvKGRsncoJBBZ5GByOR8gyDzngtM1B5dVhnCCSKwV5pFeIR
F5dzEEjkHEhQZJ6KuQAMgA6rXUS71pNOtiDCsUcKpGxZljztI+fIDsrPjIXAI5zmqIkhvJW2wyiI
3J3wxTxo86lWCxqQfmJJBZlHy5bkkE1mSQPNc2tvPd+XcySSJJHFZMoiJcqT5m75/wC6CF4ZyMZH
Ny3klnksYLe4S1kn3SS2rHZDHaBH8vzpQMEZz8vTJIxnoAWb+eW0tLe/aW3K2w8uW0sslY49gO0e
6qD3JwRmoZBbW1tcardPAbqWeTZHMhmZlIUqBtAGDnBO3kufasu6ibVtTuNPhuYn3p5cTxuDsiLf
O5CDGWC428Z354ANdJ4ojht7K3mkVUM1wiM6SBZWjU7yo7/eC45ADY6nFAGBJfQaLp2nXBv7lr4M
JJ08uZpfMHlllCsyhRgYJPJxgcZrR02wSz0tL3iCS3QvcTRyNHtdWGyBTuO3Cs2SM555zuptp/ai
a15+qXMltIAR5c0HCSvgYGcLkBBgnOMbsZwDQtJ7nVNLtj5kxeW4eKFpbpRBG77t7FiQZGWJevq5
6HqAU9RNrBa3VjaRTXDJdzFEDtNJM0m1Cw6qqgAgE7mLHgnkjZdjAUvZLbUbOwvBsD2mYnnyCVUP
KTgAHbkLk4JyKrQW8jaezwToEukWQpI8gkuvKTeHZiw+TDZPGAFGOcmmx217r9xBqetam13bTXEc
cMVtIsRlmkKF0C9cBeOnJH5gENgb600CwhjvbyxkuLhRKxVWWNHG0tuyCSEwcEHr/DzWiIND0rV7
PQrPUhPaxiO4mb5SHG0ZyApJcljgbhtz6rkrcX0Gp61d3Vrp93Zwpc3Pn3n2UkW0ZiRS429JC0Zx
2Gec9aXTLSS8077XI7aRpUqGASBWlursDrkKCyqBET82BnJ5BNAGPfavaattt10+QW/2lPsbMzF4
7cA5IH0249eTzzRWnpk2patLpttp0vkJH500t7l3faG2KrBVy2A47sCHGTgcFADob9dfuXl1TW4b
eVIxMyIzbVyN5UErywO3Hzfw87hWzqlnG81zZ2y2oik8hBuwrxyESMpBYZzmRDk5P1xVKzli+wud
S1K1j0fTY1SNXgLm7cr5m9l3nPLBuuB6c1F4V0Qz6Xpep3UcjbpZZUUucuCrANHCo4YkRnPP3eoA
NAF6DQ9X1aObQYtTW1ivLnzNRunLO6/MUjgVuA3CDkAfd/A9NqcVtZ39vcXe+9gMcUMFxNIvBBHy
KOFIHLHcQW4HIFFnaf2GUtrPVEV7yWSeQIiGR127VJPAEa7myfRQABkiuSm8U3OoQzRWCR3l1JvW
CG0Xcbb52Cu0wXPPA2jG7PuaAJ9X1K7gNnothp9qUKKVso5REMyCTgg4PA5YkZHfqSNTTfB0tvcx
211ZadcCRHF5cRPLGS7ncFVgBtUEn7nUdVHUWND8Nx+H45WuvJknkKO6qpL7gMEFucIDwF749mB3
rrUHmCbyCACWjXnaOpGPToD9OSelAFK08N6Np1vFALeHamVRWBZFLNktjksxxnLZbI+UqBmkmmkl
CrvyjspO9v1wD+GfYhe7UySd5DuZ8ADDKyAYB5PU8DGMg47bsDiqjNNcufk25O/5ucBuCSvB5UdC
QSME4XggFpIEjRQEbc+CMdee4wMDr1HTtk81ZAu9Nt4r21tFnkcvE3lOfOILEkKDlf7vBIycDk4J
LGBHR7m6kZLMOD5gPzOTnp0yceg5GdowButrHbSXLvDaR3SFTutgAvlv8oGSwGdqnGSOMd9wFAHJ
XtjqUomu9XUWlrbuFtLGeWLymySVB2Dn5V6AtySB3qzd6TqsOpW9/fWtl5lu+21t7OMygRgZLBiu
C7ELywIAx7mukgjR7iaWZ4fPjcRWcwMcTxEKxkQbgSqnGMjPfjgk5t9PfalEzxSvbwRRxr9rS4Rv
Mlycqu5iCMhBkc5zzjNAHB+ItTjjvgdStbqK6luVkkt1RcndGEXcc4JCBWPA/wBZxtyTVSxPmxXO
sqs0AvA6W8LzAhIUXK5ZhjczR9sH5c8ZzUPie3dLjSNGt7mLUJriVpJrgrvDSyY3Hfkh8BsjK5UA
dc1seIUkxB4U0tZsXEiQv5LqIwgVmAMg6k5DFTyQvX0AMSeLVYbiGWWOWy0qaRYIjOiJG+w/KNyE
cY3ksvYHls1JptlZaiBbPDIbRXk+0yo4iTYiptAy2Qo3DgjP7wnORw65QTaG80+oXS2/lh4QbUTO
UClflO8mMfLtBztw3sKPCdvpcUdxeS20Hm28ayhriXa0srKNiKp5YHa7cD5sgAcUAeg6XptlZO1v
YadbWn7tWLwoAHGCRlscn5W6+h+tcxq9rbTXP9rPfbpHtjPaRF2+VFYZKqpGXLAYDcHJ54FJdLc3
NzqdlLLbtPGoS4upJw0cZmj5+XPXAcBAOqj5uTnH1PTYLWbZpeo/ZbeyszbyzRhN0krxjjdkE8jH
ttOMUARS6d9p8XSwYklvLQo8y3MhliibIJ80nPC4wQMjgDJzSQ21jq880pWa707T48IJJin2iUAs
yRqvCxlsDhcnK924qavcabBcyw/2xd6jHNEzZiTYbic5XcxIyQGx1z90888bFxYXtjJBPeC1hW1W
aS1SZ40Zp9qso4+VVQspwcDIbIPOQCS50fi/1TWHtIESWO1+z2rsiR7kIZFQKSQX+UlODtbHAoud
Qsrfw+lrLaC4uRgWdrbg74woCsMlecDapOckx56ZJwooblIHm0vTleNII7ZLmRPLaWWXI81AAGbA
3AAAnBLHry++GoWtvp0I1GKSWXT9ttCpdycyE7cYPJ+XngcEdKANGOWzvtLsIFRpbeQo93BbMyny
0Qbw0vyoDhuQPUjjgVfMRbVV1fW5DZaXbuLO0sonIkKgkhcqu8fNGBkYPJGQOKqGzstO0hNPuJrS
SGeITJDZyCXe5RUVGcAfxLvz93IHYEhLe/xf+TbuL2/hgYCWW5xFbbZBjG4chhycc8qMnJyAOkS7
CLfRCaCAk2enKsiu9wQ2VYpjYF+XH3Rnlj0zRTdYSDRXsZraU3F4kfn+RDuCo4ZlciMjptYBRgcZ
PrkoAsXGl6bpljFZ2ekS6jJGqApdybUjuJVAVWz8rNgEgKOBjuSRuRyXkGkNDYxQTs8iWc0kAYRx
xrKAyq+7JTa3JUHBY555FGy0qS71q5QxyweSomkaRBdyrt+SMKo5Qtl2yRyeQAKsvq9raaoLmbWL
TfBN5iR+RIJfLc/6pYhySWU7mIBOFyMYoAo6tZ3lk1pZwX9/5ZtfOcSZNurfKEUFmLMpdQABz8o4
xyN/Qbb/AIR/SoLPz5ricrlEEeCTjsMZyd3OTxk8gE5wP7M1W51e0spzHaRCX7Y9pZbizgAsnQnG
AxAGQBkdcg1ua9q0+nSJpturTatcW7KbYTBcBhiPexHz5JYgZOMZ9TQBcikuJyzmyuZFX+4FcZ+o
fnPT/wCIA51INOvZYwB9kwSOTKRk56AhCOmOR9ODzXKPqtzH4Us57f7bNrFqsKNKsbPvY4X5ZOjA
rj5umShznAO+dU1Cw1m5XUJI3d7cyLB5iISEXDSMeAqlyFOc8KCOc4ANJfDzsN8l1GWB4iQEjuBl
u3r90ge55qF4RDLJ9mhDFHBj+0Y2MQpOAobOejZbHXPVc0/xBqV5BpjS2MsbPIdsbySNGgPUsx9A
vPJHesG2meySZ5dQligkWR2u2ijV2ULIzEKVOFyYzyR0x2xQBNLrEV7qsbXEpVbQJJNGS4YvIpCR
ohGcgAM3c57AmoLS61LUtS8RDToxFCrNbx3CjdHDtCk4UL95snPPJbtjmS2tbqYQRpJfizgtgGKq
kbzyuRuWOQ/MSwZtzcHI5PORRdoI/CVjYNO0NzLa/u9OsGVHTcV+XJbqeSSf9o9MkgD9QvrLTNF2
38l7qGoxRg2kKKVZZSV2IcD5m+Ykk5AwOMnmC2vo57K701717eHTlS1BlhJiI2KrttUYGQSFJ9eg
xms3T7NI/EM13bW9xcyPcHTobgztIfOkd/MkLHO0KGK7gMFt2KqDU766/wCEl+xvDZBphbbba4EZ
RIwo+QEjcxCdcDG5yMcmgCr4bha98RTahHpcNpZWe6Yyn55FjzJhAoHzORjqB90ewrUvWvEvJYrf
UrcT3am5SG5tcttQbI0wu4R4LtycAtnqDzF4dt7nw7HLFHNA9zMv2giRiUldCRIhIYEnJUYPJJ4G
cGsqDTtauNAS/NlbRrdMuZ/IIfGXfGAMncQijceflxweACe8Nlpehyafc28t9HDsRJJ4wguLgKi+
XGFwTGobPXOTkfxVo2sdzLJe3NlbRW0guXtFMrhRE5KbVJbPC5b5QP4wdvBqLSXnsZ5tbWC6uMu8
RYRlS4Z1+UlQw5K7QEBPcsMgjH07R45zYyywFJ/twVWdXhjyCxYhSBwMIWYj0HqaAN9tIt9Mlihm
gcWixsqFYZIg0y5y4C/PK/y9HIAyQOhIp35uLh7iOza1hhe2W4luJIduwsQoaNQdy4BVUHXjPTaa
2oNF8+eS0Gow3+xXhi2Mw8uNyuWQ428q3LdeHx0rmtUlsVFs0AJ01mM7ZuDI2oPC2ABHjKJtHGQA
FPGeKAI31R9Pks/7GeOK4M4Qu0DGWcmPbGVQ5UKm5gBnpii3hvdX8WiyE73OlWAjR1mBtEldIiu0
sN20thsnPTPTAqnFLcOCplupYrNheb7Q+WZZdjbNgK58tdiYPAUBjgFhi9aWc2osbe5hlgY3YaKB
hvIyBh3fAUgBWGTjHzdeAABdQu7nVrBoop7WW582SZfKlZbaztyCE+UjHRiQCA3zDr0Fi5tdNHh8
RQXk0l3MnmqqxOZ7pkAXa7kZWPaD3AIxwOak1q7+zLq2k2dndZnuRK0t6hV/vPvIOBwdyjqDlgOg
xWlplnrMcdpcWQtF+edYTcrtMxRBhsAncqgEAtkfKuOpoAwb+5t57mxskt5LC0htUDsm0h0DAE53
fMuGc5/jbAI7i7Ff6O89/s1K6tUMi29j5MUW4jazE5xtGZGzu+8Sy4IANUbLULyW+ile9i1G40tR
DDElufKMhLtlyMKQOBuYgKW4BAzTtOn1hb65u5lt2k3Nd/afLLRRqu1f3Y3BSM7OAOfk5AoAv3N5
qUnh240uw0xbqLeJ7u6nCIQFkLkNvwHOEPUAYONuRRVK60+5Fnb2ElxBdWVoEKachVFdfLyzyGNs
gBgCR0yTzgFiUAbem3f2dNQVL0xWKSgzFx5NzIdgO9nDgZLdARggL0zUsF7YahdvYJq4ZmV2nbSr
WQIz9Cu9T8zEbecbQBkj1oaa1/o+rvLItlaW13cmDyElMkZjWPcoIZTkEcFs5OcAcVNZyalqGr3s
WmzWNlJCrtLJ5y7d+7bxgHoF/h4HAz8uaAJv7UXS7i6trTSGtt6bZLy4dztkY5LDIXhUz124yGAG
ecua/bUNTh02afTzp1reC1SO1cKZlWMKFEhGQOWJYsPzxS3Vx9pvn0y2vruSK9xc3M0k+0uwJDyN
Gz4CqMBQcEkA8gVoaRJptrPdXkVgkrXMRG4M8cZRD8+6R8b2bO/IA5xigBkNpdTRPFPp5MDyworw
yPN5QjByQzPztXYeABheqn71mD7fqV7M3m224xMJbfUf3StGpzCJCSWUfMW24UFu3cav2rSk8PeS
LwHR2Rw8VmfKDOSFkcnGCgJbGPvcfeJ4zNM8RaVHb3WsRm5e5dHkuJIkGFZwDywG5TkIoXdzgZxz
QBpWZlh0iG11bWTdXups0hiNqGDEA5ZVwN4OQAflGdvYGs7WopdRmtdLn1axg+0zq15aopQJDFuO
xfnI25DZGRyM1TFreedYiwu3ub0RxQrfTBjDZFyu8LvJ3udxz0I6dSAutaauXaaPT4rfVY4XkAC7
fMaRgqlmIGGUFnGXILEcdOQDWu5bd5bW7edobeKZljVxshbYuQSdpAzhk4BJLfLkDJyZNe1q3tLu
4t9HiguUik2rKXQRIZNqHBQKWDAYBOQCOByKq6quo6hG8YeyluJbdWkFrcNKc4ILMApyckAKgPQ8
HIIdq2lWuv2MkaXt1IlzcpbhFkd44gisxZlLH5vLwQoPB98igCG80nU/DNgjfb48QpjzDG0m65kK
bsPwF2ky4x90EnGSTWfpNopl1yO9mtYrO1uVnSGK1BWSQsVVAXUs/LFccEnviptblsdl3aHWWtYL
N2Zxbq8nls37ti25mBYhugxsyPU1iRFNV1bS72e3eG1uJCkTRSGURlEDDhTlmHXHGC3HTFAFm7uI
31+eUQCMKhQlpFaK2g8wk8j13D5RyenGK1Ta6jrTT67eSE2NumX85hHEqAFgqIUG3lY8kHPzkjJO
KzI7pdI15NR02wFojIqW7arKVdmb5PNCD5vvNu5ySAOmM1etCp8O6jBqGtRfZbUtNBBcxo+8DeqY
OTjpuCc9jznkALe6msrCxtpltrvVjKq2cFt8kcEYQlXLAZCksCO7FR6GqNla2thevbyrbX11YO8c
MaSSvEpUAPmQjYS5YsQfUAkAYMqRQ22IZZrl7y4iivdQvCnnOCwV8LGMf3XHTIDEkgZqXTX1DT38
+x0q105GhSFUkYJI58ssGZgB85LKTyNoA4zyACC/WGys7vT7i1na6cmSWOOSNnRHcBS82epU7cH5
fmPeqllZ6rM010bGFNRkSGVJJJUH2GBCFA2nGWbYo+jDJGakS1tyb25uNYj0yzN8jvPAfOMpVN6A
yKMLgZwMDlgMcVRvtWtNb06HTtMtr9VMcMU08sjNDGqucsEXqWIU59WI460ATSRWM1//AMTO/u5b
J4ogk5kIR4htABVFG0ELKOhOFB9zdnex1E20jXdwqzvbSLHvZYoIQzSTFt5wQNw5J5ZhjbkCpbCK
K38LQ6ncXFraRQxR+VClrsWbauATz87bt3UEkEHjdVW226BY3tvfWyXOs3NypMvlgNChBIfzAcRj
kMPUe4OAAfRbq/8AEGoKuotLNC4uHS4l3xJGshZEJZcsSNvOAPm5NW9XvNSl1OOxtdKS8uLaYfKq
iSIqUAJJBAVQSvG0ADGCN1LY6HMLeB/LhsIdZtLm4Z7S4MR8raoSNi55AGGbqTk596oudP0jUmS0
a6axhuA8l1xI0wCDcsQBAx1Xcd3LDpk0AQaZbXaafLDbam0NpcQ7LlrYhI1T958uW5+bBAOBk7+o
XJ2xpptLGzsrT7Mqz/ZrYyu7K04mXzMZPSMMy5UZLbT05qnfzRai9houti5tbOwAcQWarNIihFVB
8pI4O/qM4x0zxLcRSGXTrWa1RI2bmDzGzL5u/Ekj5cs2EbovyhsZBzQAx9dvUbVdNsG023+2LKpu
HUZIG0c4X77sxOOcZwACAKKlj1C8+3adYXF7aafFazvFGkiEmJF2gAjgjnPJxnkDHSigCnqd5qaa
d9nFlFMYLVbV7mY+WwkIVnaQZxhQcAZ53ZGcHG5bJaxWnkQX9x/aD2KNczKglcqxwseFVfnZmYbS
c/d5xmmaz4O1d7ebR9O1GVdLuZEJt2KgsBksTsALc4A3AngZzjNQoq2/iASwzPHqckccoNxdkiAg
tuyz8Eqj88AZ4HU0AWrexh02Sa9nt7y2vHDu+beKTzIhzhowTtAVckY+9wSSOc7xZMLnSYdNGlmf
U5dkgma4LCEYy0eDkAgYBUc8qeSaY8NpeC8nS6W5jm/cTX9xO7G43FtsUSh1JyRknGPl4HUHQ0+6
luNaubjTbI2UcDS77poWlAbOxQrAFixDbiQQG4465AKN1eS2uprYQi/lv4rf7Na2skylEnBAQhiS
pCkMcEA84OMDMUolD2enXUElnEZNss0V000EFvHGRjHTeVI+Yqcb1OMkVe03T7zUW8QRW+l2ccN6
+6OW/tpIZGG0EsqjOB3Azklh6mrth4Pns1tTLJ9mlW2SDMcRBgyRvRQA37w8EttJwQTjuAY0msWX
lXCPJG2nMQyWkCLALnJZim8KuMKE3FR3wASTV2K/1K5covhdk0qwcyeYyNaSM45+U44AztHTseuR
WlHY2mmvZ2kdlMJV8y4vTbs8gVEDIirno7OeCQCSueOo6FJ0g0Kcu2nGzZSjtcS+VwV+bzGBZSWx
wM5AYc9aAOI0rS737NDA1x5Gqz24KRwyR7LaJGRAzvzn5SWDL6nB54t2dxFJbapvuZLq3id7TTTb
t5MkpCEuwk3l3XcSOCck/lrW/wBjnthfaqQ1vN5dzdPKMIIky6qiuB8hOMDGTubkn7tbWZb+GYWK
6vFaxFXhS2gk8nyYlOZZAFwCSAyj+FQSBljQBzF0ZtUsJ9PsIYbhXkaRzJlxGH8qPOAcEqIz8oB2
4HJ4zJ9juZb7wxfXd7I+WkjjzIFKYI5AGFU7fTHQckYNWmuLWJRCku+2M4iuDBIEhFvEY1WFg+CB
l8tu5Y9eK5udZZtWtJZbqT+zvtgkEjTgKGkTJIUErkEMWIJA3AEKeKAL+twWtlqN3bLqV1PDb2pN
3MkbNLMfOKCPzJM7FUEDI4Jz14q3p8T2+uC9v5IbR309Zraa6i3iEMd4I3bhwDhSFAO0DA7w3X26
y2NNqItbee8EkuHzKghU4Zg+TkpnauPm+U4XPNC0gjkvbfUZzDJEgMjXctw0bvGGbdgDoSoYKOOF
JwRzQBasnikWOa91S2hime1eZCQ9zd7yxLMRyq5IOzk4Of8Aeu3upSSa7dzaXot3JbNch5HvVZpJ
3+YBVVh8oxvA5yAG78VUupxEkaWj6bDeXN+I7eJlaWQHcArPIeCkYAAHTI781HFY3Oo32ojSbi5h
0jT2ea2YTsFdsbd4+XqBz0ySevOQAWGsZZ55NOaC2jewkkvdTu/OdoXlXdIoUAdUDNlTgdsjg1D4
evvNv1v1S4ttP2GSN2O1SEYBcqvJO9ixJPzFeo5xq6To9s/hJbueCeGzuPlP2iZ2DmUqd5YMBkgK
NxCqGOcZGKtaVImpXEejvLNcaZHCJZjHEIobUBNiRAsRnflhnIzuzjkigDEl8RXp06Pw34ahhv4I
oHhu5jb/ACM/J3A9woRmGf1Aq9rEqeG9M/se2tzPqWoRRi4nN2xXCIwG6R+gDqemBhecdBCZbaK6
jstK0oQ2KXazwXMjOhaJd5kcfdHRCedvXABqPTktkGmLdxtLKl47S29tgr8u8ojueB1JC7sYyzGg
CC20SS6uoHu97GezZbS1jaSFIogOGdmA4J2sckDt14BpTx2trHdjUbSOK3dIknltVmCmNcLsBcA4
3FsnjP3dxApLW8trthq03mM3mCKGwhYMrbdhVAAhKxcbF689Byc1k027m1cXut3BMd1JOpQKrToQ
FyoUHrtyPQYBPpQBqabp8unRvd3V5sQI5huniMiyOrtvIUH/AFhwOSDgL64qjo1hLPqE2qNsCxKw
lkbfM5GN5JH3Rtj+UgkHcSO5qKb+zbqZbOaG4urkRSuLNFeSOCQk5C4PLZC5bOME8Z+Y609slpqe
mLrEdpZQyyNHK9vJG7MmcxqAF5UBduW+XkAABVFAGfqds+kWVzGljI10mJGlmARVO0tkgdW3IzYy
w+TqORRVmWKfV9Q1Gb7Zc39nOytdXciFRGkbFTGgDBW+RlJJ6Ajj5qKAPRJr1P7BGriACaa3TGG+
7vHGT/EBu6HiuHsZYNQ8dX9headYyjPzv5PVWIQKBkgcszE85J7UUUAdNaeCtLtdSgLedKsUJmhU
yugi4T5RtIOPnOO/uaytJ1K01PxFrGnzaVALXSXmZYkkdVlZJm2swBAPA757HsKKKALXhrVr7UdU
u54nit4UhVhbCMtGSw3AkZ5woCD0Hp0qHxd4zuNDvpdLsLOKOSe6jDXBZvvEqd2B36DGcY4IoooA
19Blgure3urm33T3TTsHVtpjEDhEA656lvmz8xzjpXJavqavoEclnZw2q2ix3EUYy6gOwiK4Jx0L
EnHJPbnJRQBo63HeaTfQWLahNcnUPssVzJIzKTHkZRApART82QOu4+1ZkcciadpyiZjd37hPtDkk
xo0kzEYzzkx9T/eNFFAFKyu5bm1MrSSIbG5ZIxEEjwZXcAqVUbQpGQBx6Y607SZEvrWTUgJkGlXf
2e3jeZpNu92VSu7hdodf4ckpnIJoooATQxa6vLHutzE1oXaEq/IQR7imcbvQZBBA9zup406d9MOr
y3rMmmWk8lvarGqx4jdYwG/vZ3kkn2FFFAElhZrbX+nSARnUb5jIt7sw0JmLdEB2NjHcc5xxVS6g
uLfw1Bf32p316bpZITF53lptjckAgfeBG4fRjRRQBo6/Yz209rplxquo3Sslzdyb5QIy8UbOoEYG
Au5RxzWNFdTy6zo+kCeYPPDbZnLnIZmEm7Axkgg4yT1zRRQBcj0aSTxLodlb6hcQyX2leeJWZpPK
jMO8RAMeR1B5wcngZxXNPrMabbWayWZZUESOz/NHv2iV8kHLsBgE8KCcDFFFAHZ6Ja22peENT8Xz
wKzJdPcQ2pJwhX5QCwxkYYY4GMcdaZc6DJpel31xDqEome6iUOkaqV3qJDz15LnoR91fTFFFAD9e
uLfQJ0srayjb7FZNO8jnm4/ehNr+vLs344xin398vhLwRYa1b2ltNqV8EBuHiXdEskRl2qSDgKSA
B0wKKKAKujzySeFdLtEZxLJDPKZHkcg/vxjcqsu4gsDycHbyDxgoooA//9k=