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This data file was generated as part of the International Magnetospheric Study (IMS). The file consists of values of the geomagnetic components D, H, X, Y, Z, and R averaged from 10-second instantaneous measurements. The 1-minute average values are computed at the Space Environment Center (SEC) in Boulder, Colorado, on the SELDADS computer system. The 10-second values are collected digitally in real time at the ground observatories and relayed to SEC via the SMS_GOES satellite and Wallops Island, Virginia, ground station. Twenty-six geomagnetic observatories provide data.

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_Abstract:_ Monthly and annual average solar resource potential for Alaska. _Purpose:_ Provide information on the solar resource potential for Alaska. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location. _Supplemental Information:_ This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Units are in watt hours. Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME. Maxwell, E, R. George and S. Wilcox, "A Climatological Solar Radiation Model", Proceedings of the 1998 Annual Conference, American Solar Energy Society, Albuquerque NM. ### License Info DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations. DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

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This investigation was conducted to determine the nature and extent of contaminants at Maxwell National Wildlife Refuge Refuge and how they may affect fish and wildlife resources, especially migratory birds. The 3,699acre Refuge, located in northeastern New Mexico, includes several playas used for storing preuse irrigation water. Ten sediment and 42 biota samples collected from the Refuge were analyzed for 23 inorganic constituents, 6 chlorophenoxy acid herbicides, and 22 organochlorine compounds. In addition, mallard Anas platyrhynchos brains were collected and analyzed for cholinesterase activity. Organochlorine compounds and chlorophenoxy acid herbicides were below detection or were below 1 ugg ppm wet weight WW in sediment. At these levels, these compounds were considered to present little or no biological risk to biota on the Refuge. Diagnosis of brain tissue showed levels of cholinesterase activity inhibition that correlate to exposure of organophosphate or carbamate pesticides. Three out of five adult mallard brains had 28 to 52 percent inhibition. However, controls were unavailable to conclusively determine if exposure occurred within the study area. Of the 23 elements analyzed in sediment, the mean concentrations of nine exceeded geochemical baseline values for soils in the western United States These elements were beryllium, boron, chromium, iron, lead, nickel, selenium, vanadium, and zinc. Of these, selenium was also elevated in biological samples. The highest selenium concentrations in migratory birds were in killdeer Charadrius vociferus liver and kidney samples 21.6 ugg dry weight. This concentration may present biological risk teratogenesis to migratory birds that breed at the Refuge. The highest mercury concentration detected in fish from the Refuge was 0.21 ugg WW in a rainbow trout Oncorhynchus mykiss sample. This concentration is above the National Contaminant Biomonitoring Program NCBP 85th percentile mercury concentration for fish nationwide. Arsenic, beryllium, molybdenum, nickel, silver, and vanadium were detected in biological samples at such low levels, or in such a limited number of specimens, that concentrations of these elements were considered to present little or no risk to biota that inhabit the refuge. No determinations of biological risk to biota inhabiting the Refuge could be determined for lead, because the detection limits were too insensitive. Any future studies should incorporate lead analysis in tissues; however, the detection limits must be considerably lower.

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This map depicts lands owned andor administered by the U.S. Fish and Wildlife Service at Bowdoin National Wildlife Refuge.

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This project focuses on providing an infrastructure to aid the building of ontologies from existing NASA applications, in a manner that leads to long-term risk reduction. Currently numerous stove-pipe systems are in existence in the ISS (International Space Station) and Space Shuttle Program (SSP) which need to be integrated for studying the operational trade-offs through various risk-analysis tools. However, knowledge inside and across such systems has to be captured in ontologies for such systems, at appropriate abstraction levels and in a reliable manner, so that they can be analyzed holistically by existing tools. Based on our experience in using a clustering approach for analyzing knowledge bases from both NASA and non-NASA systems, we propose to build a semantic mediation toolkit that focuses on providing various types of ontological engineering aids during knowledge entry, leading to long-term quality assurance and interoperability of NASA systems. In Phase I of this project, the feasibility of applying Pragatiys MVP-CA methodology on a candidate system relevant to NASA will be demonstrated. In particular we will demonstrate feasibility of automating detection of ontological concepts, quality assurance issues and mapping and merging of concepts from NASA systems so as to lead to long-term risk reduction. The ultimate aim of the project is to reduce the risks involved in utilizing ontologies built for NASA systems.

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This dataset contains directory structure for use with the Shoreline Management Tool as described in Open File Report 2012-1247 entitled "The Shoreline Management Tool: An ArcMap Tool for Analyzing Water Depth, Inundated Area, Volume, and Selected Habitats, with an Example for the Lower Wood River Valley, Oregon". The ShorelineDirectory contains folder in which the Shoreline Management Tool will operate and create output files. The geodatabase in the Processed_Data folder contains the parcel layer for the Wood River Valley, the inundation areas organized by parcel for stages 4135.30 to 4176.30 at every tenth of a foot, and stage-area-volume tables at every hundredth of a foot for each parcel. The naming convention for the inundation area feature datasets is InundationAreasParcel_*, where * is the parcel number. In each feature dataset, the naming convention for the inundation polygons is p#******_polygon, where # is the parcel number and ****** is the stage to the nearest tenth of a foot (no decimal point). The naming convention of the stage-area-volume tables is PARC_*-88 for NAVD88, where * is the parcel number. The parcel number of each parcel can be found in the parcels layer polygon attribute table in the field named PARC_NUMBER.

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The Joint Quantum Institute (JQI) is pursuing that goal through the work of leading quantum scientists from the Department of Physics of the University of Maryland (UMD), the National Institute of Standards and Technology (NIST) and the Laboratory for Physical Sciences (LPS). Each institution brings to JQI major experimental and theoretical research programs that are dedicated to the goals of controlling and exploiting quantum systems. JQI's Objectives serve as a world-class research institute, conducting fundamental investigations of coherent quantum phenomena and thereby laying the foundation for engineering and controlling complex quantum systems capable of using the coherence and entanglement of quantum mechanics; maintain and enhance the nation's leading role in high technology through a powerful collaboration among NIST, UMD and LPS; and provide a unique, interdisciplinary center for the interchange of ideas among atomic physics, condensed matter and quantum information scientists. In pursuing these objectives, the JQI is expected to train scientists and engineers for future industrial opportunities and provide U.S. industry with cutting-edge research results. - See more at: http://jqi.umd.edu/about#sthash.5OiPmqxA.dpuf

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ArcSight, Firewalls, and Intrusion Detection Systems (to protect MSS resources).

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The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe navigation and to provide background data for engineers, scientific, and other commercial and industrial activities. Hydrographic survey data primarily consist of water depths, but may also include features (e.g. rocks, wrecks), navigation aids, shoreline identification, and bottom type information. NOAA is responsible for archiving and distributing the source data as described in this metadata record.

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Description: HMDS (hexamethyldisilzane) promotes photoresist adhesion by creating a bond between the resist and silicon. The silizanes bond to the silicon in the wafer while the methyls bond with the photoresist. Vapor priming allows for HMDS application in a monolayer and reduces the chance of contamination. Specifications / Capabilities: Quality of Coating Surface dehydration and subsequent vapor priming results in chemical bonding to surface hydroxyl ions, not weakly bound surface moisture Reesulting coatings are repeatable and stable for extended periods Efficiency of Chemical Usage Uses less than 5 ml to coat up to 200 wafers System uses pure HMDS so there is no possibility of variation due to changes in carrier concentration Waste Disposal Virtually no waste - no liquid solvents or residues to dispose of Flexibility of Substrate Size and Shape Any size and shape substrate can be treated with equal efficiency up to 16" There are no issues with flow effects or substrate topology due to use of vapor Flexibility of Type of Substrate Delicate or difficult substrates can be accommodated with simple temperature or recipie changes With no substrate movement during priming, there is no risk of breakage or other damage HMDS is stored in the system under vacuum Chemical does not degrade from exposure to moisture in the air Operators are not exposed to fumes from the HMDS or carrier solvent

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A complete set of water column profile data and CTD Summary Report (if generated) generated by the Okeanos Explorer during EX1402L1: Gulf of Mexico Mapping and Exploration between 20140224 and 20140315

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This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer.

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<p>The LEARN Project explores the creation of novel concepts and processes with the potential to create new capabilities in aeronautics research through awards to the external community including university and industry teams. The LEARN Project incorporates a competitive review process of the external teams’ proposals to develop integrated solutions for complex technical problems captured in the ARMD strategic thrusts, followed by short duration activities for feasibility assessment. Follow-on phases of the most promising ideas are also funded. LEARN also utilizes challenges and prizes to the external community.  With these processes, NASA funds also help catalyze investments from the aerospace and non-aerospace communities toward solving problems aligned with NASA interests.</p><p>The NASA Aeronautics Research Institute (NARI) has been established to achieve the LEARN Project’s goals.  NARI will complement other ARMD efforts in seeking early-stage innovative concepts applicable to a broad spectrum of aeronautical challenges in the nation’s air transportation system by sponsoring research solicitations and by hosting future competitive challenges. The Institute will coordinate these efforts and communicate the outcome of the research conducted to interested parties both internal and external to NASA. ARMD’s goal is to mature the new concepts in order to infuse them into current ARMD research programs, to enable new avenues of aeronautics research that are not currently supported by ARMD program and project funds, or to achieve practical application by the aeronautics community.</p>

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Charts of Note from ERS is distributed Monday-Friday by the Economic Research Service to highlight charts of interest from our current and past research.

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<p>The task goal is to develop and demonstrate an innovative software architecture, the “Resilient Spacecraft Executive”, that will enable highly-resilient spacecraft and reduce future mission risk and cost. We will develop resilient execution capability that is easily deployable and can handle multiple novel situations. The task will also mature and integrate technologies such as risk-aware execution, correct-by-construction control policy synthesis, and model-based systems engineering.</p>

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The Floodplain Mapping/Redelineation study deliverables depict and quantify the flood risks for the study area. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The Floodplain Mapping/Redelineation flood risk boundaries are derived from the engineering information Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).

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The Intergovernmental Panel on Climate Change (IPCC) IS92 Emissions Scenarios (A, B, C, D, E, F) Dataset Version 1.1 consists of six global and regional greenhouse gases (GHGs) emissions scenarios projected from 1990 through 2100. The six alternative IPCC scenarios (IS92 A to F) were published in the 1992 Supplementary Report to the IPCC Assessment. These scenarios embodied a wide array of assumptions affecting how future greenhouse gas emissions might evolve in the absence of climate policies beyond those already adopted. The dataset was originally produced by IPCC in 1992, and the digital version was re-edited in 2005 to resolve the discrepancies among versions of the data over years. The definitive version of this dataset is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

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Chemistry technology experts at NCATS engage in a variety of innovative translational research activities, including: Design of bioactive small molecules. Development of small molecule libraries. Design and implementation of novel phenotypic screening platforms, which seek to identify substances that can alter the observable characteristics of a target of interest. The aim of the Chemistry Technology program is to provide cutting-edge resources for drug development that benefit all NCATS scientists and their many research partners. The program is highly collaborative and supports projects and platforms that are currently underdeveloped or brand new. Learn more about these projects. The program's ultimate goal is to advance technology development to enable the broader biomedical research community to pursue basic and translational studies in a faster and more in-depth manner.

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The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe navigation and to provide background data for engineers, scientific, and other commercial and industrial activities. Hydrographic survey data primarily consist of water depths, but may also include features (e.g. rocks, wrecks), navigation aids, shoreline identification, and bottom type information. NOAA is responsible for archiving and distributing the source data as described in this metadata record.

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Terrain data, as defined in FEMA Guidelines and Specifications, Appendix M: Data Capture Standards, describe the digital topographic data that were used to create the elevation data representing the terrain environment of a watershed and/or floodplain. Terrain data requirements allow for flexibility in the types of information provided as sources used to produce final terrain deliverables. Once this type of data is provided, FEMA will be able to account for the origins of the flood study elevation data. (Source: FEMA Guidelines and Specifications, Appendix M, Section N.1.2)

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NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and modeling efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of Mean High Water (MHW) and horizontal datum of World Geodetic System 1984 (WGS84). Grid spacings for the DEMs range from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).

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This 30-meter resolution data set represents the imperviousness layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition. The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004) and http://www.mrlc.gov/mrlc2k.asp.. The NLCD 2001 was created by partitioning the United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

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This dataset represents weekly statistics of applications submitted, compensations paid and medical bills paid.

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Map (letter-size) showing BLM-administered lands available for solar energy development as identified in the Solar PEIS Record of Decision, including maps of the solar energy zones (SEZs) and the variance areas.

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<p>This task will enable future planetary mapping missions through a technique called interferometric synthetic aperture radar, using microwave and triangulation to create very high precision maps of extra- terrestrial bodies by orbiting spacecraft. One of the key challenges of any mapping mission is the amount of data generated by the spacecraft, and the difficulty of returning that data to earth. This task will  develop new methods to reduce data processing onboard the spacecraft by using self focusing and fully processing the images  within the instrument itself, thereby reducing the burden on the spacecraft and the down link network by orders of magnitude. This can significantly reduce  mission duration and thus mission cost, enabling these mapping techniques for planetary bodies.</p>