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The goal of this proposed research is to advance the development of biological in situ resource utilization for NASA's space exploration programs. We plan to build a foundation to use synthetic biology to engineer microorganisms to extract metals from naturally occurring extraterrestrial regolith. We propose to create a novel growth medium designed to mimic the lunar regolith ice discovered at the south pole of the moon by the LCROSS mission. We will develop a bioleaching column for this simulant to purify metals for consumable production in space. We will characterize known biomining organisms to leach this simulant. Finally we will study the biochemical processes taking place in the leaching of the regolith to be able to improve the metabolism of these organisms in the future. In addition, will produce a database of organisms involved in biomining on Earth and the geologies and substrates that they have been found on. This database can be used as a tool to find undersampled mine sites that may contain novel organisms suitable for biomining in space. We then plan to develop a conceptual bioreactor which is designed to extract metals from regolith in space. We will perform a trade study of the mass, productivity, cost and energy requirements of such a bioreactor. Later phases of the research will involve characterization of the important enzymes involved in biomining in key organisms, adding to the limited existing knowledge of these pathways and leading to creation of a synthetic biological system for efficiently engineering them, which we will use to optimize these organisms for extracting relevant substrates in relevant space-settlement-like conditions. This further research will also include growth on Mars-like simulant regoliths, as well as improvement of the bioreactor model in a series of increasingly durable and realistic prototypes that will undergo both physical and functional testing.

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United States Army 3d Infantry Division news and information.

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The Solar Backscattered Ultra Violet (SBUV) from NOAA-9 Level-3 monthly zonal mean (MZM) product (SBUV2N17L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 156 months of data from February 1985 through January 1998. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). The MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions: 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 deg.) 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 deg; large discrepancy between profile total and best total ozone) NOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angle where B-Pair total ozone estimate is not as reliable as profile under these conditions. This error flag may be removed in future version of algorithm. The zonal means computed for each month are screened according to the following statistical criteria: 1) number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month. 2) mean latitude of good retrievals less than or equal to 1 degree from center of latitude band. 3) mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15)

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The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. 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 DFIRM Database is derived from 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). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

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The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. 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 DFIRM Database is derived from 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).The file is georeferenced to earth's surface using the State Plane projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

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These data identify, in general, the areas of final critical habitat for the endangered Ambystoma bishopi (reticulated flatwoods salamander).

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The abbreviations used for observatory names are as follows: GEOMAGNETIC OBSERVATORIES Code Station Geomagnetic Latitude ABG Alibag AMS Martin de Vivie. These data present the principal magnetic storms for the month as reported by several observatories through cooperation with the International Association of Geomagnetism and Aeronomy (IAGA). These are the data formerly published in the Journal of Geophysical Research. They are now, however, grouped by the storm rather than by station. The geomagnetic latitude of the station is indicated. The beginning time is given to the hour and minute in UT. The tupe of sudden commencement, if any, together with its magnitude in each element D, H or Z is next in the format: sc = sudden commencement; sc* = small initial impulse followed by main impulse (in this case the amplitude is that of the main pulse only, neglecting the initial brief pulse); dots in these columns represent a stomr with gradual commencement; blanks indicate no data entries. Signs of amplitudes of D and Z are taken albegraically; D reckoned positive if toward the east and Z reckoned positive if vertically downward. In the next columns the day and the three-hour periods on that day when the K index reached its maximum are given followed by the K index value. In the next three columns the maximum ranges in D, H and Z during the storm are given. The ending time is given only to the nearest hour. This is the time of cessation of reasonably marked disturbance movements in the trace. More specifically, it is the time when the K index measure has diminished to 2 or less for a reasonable period. For each date the data are listed in north-to-south geomagnetic latitude order.

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Geostationary satellite-derived wind sets have traditionally been generated from image triplets with 30 or 60 minute intervals, and occasionally 15 minute intervals. During GOES special rapid-scan operations, co-located images are available at intervals of 7.5, 5, 3, and even 1 minute. The area covered is reduced as the interval decreases. In this experiment images at five minute intervals were used for the 0.65 micrometer VIS, 3.9 micrometer IR (darkness only), and 10.7 micrometer IR channels. GOES-11 was brought out of storage and image products were centered on the TCSP study region. Regular image processing was available beginning on 12 July. The scan schedule was maintained through the end of July.

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The data contain records of sentenced offenders committed to the custody of the Bureau of Prisons (BOP) during fiscal year 1995. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or

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The Vaccine Adverse Event Reporting System (VAERS) online database on CDC WONDER provides counts and percentages of adverse event case reports after vaccination, by symptom, vaccine product, manufacturer, onset interval, outcome category, year and month vaccinated, year and month reported, age, sex and state / territory. The Vaccine Adverse Event Reporting System is a cooperative program for vaccine safety of the Centers for Disease Control and Prevention (CDC) and the Food and Drug Administration (FDA). VAERS is a post-marketing safety surveillance program, collecting information about adverse events (possible side effects) that occur after the administration of US licensed vaccines. Data are from the US Department of Health and Human Services (DHHS), Public Health Service (PHS), Food and Drug Administration (FDA)/ Centers for Disease Control (CDC), Vaccine Adverse Event Reporting System (VAERS).

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This Small Business Innovative Research Phase I proposal seeks to develop an ultrasensitive, multispecies sensor system for use in determining the efficacy of air revitalization systems in space vehicle environments such as the International Space Station (ISS). The proposed instrument will be capable of determining absolute concentrations of numerous target molecules in real time, including CO2, CH4, H2O, NH3, and CO. Aside from CO and NH3, these species will be monitored with a precision and accuracy of 0.1% for commonly encountered levels. The detection limits (S/N=2) for CO and NH3 will be 50 and 5 ppbv, respectively. This compact, lightweight instrument will be capable of long-term unattended operation, and require minimal power. The Phase I research will demonstrate the feasibility of the technology by performing measurements on priority targets using a bench-scale laboratory instrument that employs a single, frequency agile laser source. The results of these tests will be used to quantify detection limits for a Phase II instrument that will employ a multiplexed diode-laser configuration. The proposed system will be capable of rapidly detecting numerous species with high precision and specificity. Commercial systems based on the Phase II prototype will be developed and marketed during Phase III.

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The Airline Origin and Destination Survey (DB1B) is a 10% sample of airline tickets from reporting carriers collected by the Office of Airline Information of the Bureau of Transportation Statistics. Data includes origin, destination and other itinerary details of passengers transported. This database is used to determine air traffic patterns, air carrier market shares and passenger flows.

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This project assesses chemical contaminant status in sediment and coral tissues (Porites astroides) collected in and around Guanica Bay, Puerto Rico in 2009. Parameters monitored include sediment and coral tissue chemistry for over 100 organic and inorganic contaminants; and Clostridium perfringens (pathogen) concentrations. NOAA's National Status and Trends program regularly quantifies PAHs, PCBs, chlorinated pesticides including DDT and its metabolites, TBT and its metabolites, and trace elements nationwide.

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These data identify (in general) the areas where critical habitat for the bull trout (Salvelinus confluentus) occur.

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This is an interagency study that focuses on the exploration and investigation of deepwater hard bottom biological communities located in the northwest Atlantic Ocean offshore Virginia and Maryland. Some soft bottom work will also be included. The study area is correlated with information needs associated with future potential offshore leasing of the outer contenental shelf in the mid-Atlantic region, initially, the federal offshore area designated to the Commonwealth of Virginia.

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The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. 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 DFIRM Database is derived from 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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NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) to support individual coastal States as part of the National Tsunami Hazard Mitigation Program's (NTHMP) efforts to improve community preparedness and hazard mitigation. These integrated bathymetric-topographic DEMs are used to support tsunami and coastal inundation mapping. 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 various vertical and horizontal datums depending on the specific modeling requirements of each State. For specific datum information on each DEM, refer to the appropriate DEM documentation. Cell sizes also vary depending on the specification required by modelers in each State, but typically range from 8/15 arc-second (~16 meters) to 8 arc-seconds (~240 meters).

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SSA provides the Office of Personnel Management (OPM) with tax returns, Social Security benefits, and military retirement information for the purpose of correctly offsetting the benefits of certain Civil Service Retirement System (CSRS) annuitants and Federal Employees Retirement System (FERS) annuitants.

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The volume of data that NASA collects has grown dramatically as new missions come on line with hundreds of terabytes of data of all types. Often, this involves semi-autonomous exploration to collect a representative set of different scenes and objects so that research scientists and mission controllers can decide what regions are of particular interest and should be explored further. The lack of bandwidth to transmit all this data back from deep space has prompted the need to determine what data is relevant, important, and interesting enough to be sent back immediately. A common understanding among potential consumers (including the general public) of what signifies as an image or sensor reading of value is needed. To make these decisions quickly, the data needs to be presented intuitively using a common semantic vocabulary that describes interesting or anomalous features or events (e.g., composition of a certain mineral above some threshold, visual evidence of terrain features created by moving water, or a volcanic eruption in real-time during an orbital pass) so that opportunities for further inspection and analysis are not lost to long transmission times. We propose a framework for onboard intelligent data understanding algorithms to mitigate this opportunity risk and improve collection efficiency and effectiveness by summarizing the data and enabling the download of a subset containing the most valuable portions called Discovery of Interesting Patterns and Semantic Analysis in Remote Space (DIPSARS). DIPSARS is a framework for semantically characterizing, detecting, and fusing interesting and anomalous 1D sensor measurements and 2D imagery onboard remote spacecraft and planetary rovers to conserve transmission bandwidth. We do this by summarizing data events so that mission controllers and research scientists can analyze representative feature data and provide instructive feedback to seize emergent opportunities for data collection.

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<p> Develop and demonstrate a next-generation digitally calibrated, highly scalable, L-band Transmit/Receive (TR) module to enable a precision beamforming SweepSAR architecture for interferometric radar applications and phase-stable electronically steered arrays.<br /> 0.01 dB amplitude, 0.06 degrees phase.<br /> Develop technologies to enable real-time on-board beamforming capability for use in phase-stable array antennas:<br /> High efficiency L-band TR module.<br /> Closed-loop transmit and receive calibration circuitry.<br /> On-board real-time digital calibration and beamforming.<br />  </p>

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<p>This project will develop the capability to identify anomalous conditions (indications to potential impending system failure) in ground system operations before such failures occur. These indicators are not presently detectable by traditional command and control and fault detection systems.  This project enables the delivery of system's health advisories to ground system operators so they can take action prior to experiencing systems failures. Inductive Monitoring System (IMS) detected anomalies can be sent to a diagnostic software module for diagnosis.</p><p>Anomaly Detection provides the 21st Century Launch Complex Program with the ability to identify/recognize systems' anomalies before they become faults in the system; it supports the resolution of such anomalies to assure system availability and mission success. This capability also allows reduction in systems' maintenance costs by dictating when maintenance is needed (Maintenance-on-Demand) versus performing maintenance on schedule. </p>

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The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. 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 DFIRM Database is derived from 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).The file is georeferenced to earth's surface using the State Plane projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

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The NOAA NOS SOS server is part of the IOOS DIF SOS Project. The stations in this dataset have wind data. ****These services are for testing and evaluation use only**** Because of the nature of SOS requests, requests for data MUST include constraints for the longitude, latitude, time, and/or station_id variables.

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The United States Geological Survey (USGS) has a long history of involvement in multi-scale, and multi-temporal land cover characterization and mapping of the world. During the 1970's, the Anderson System for land use and land cover classification system was developed and the conterminous United States (US) was mapped using aerial photographs. During 1980's, 75% of state of Alaska was mapped using Landsat satellite data. During the 1990's, (i) land cover characteristics database concept was demonstrated, (ii) Multi-Resolution Land Characteristics (MRLC) consortium was formed with Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), and US Forest Service (USFS), (iii) Global Land Cover Characteristics database was completed, and (iv) land cover and vegetation databases of the U.S. using Landsat TM data were completed. During 2000-2002, a forest canopy density map was produced as a part of Forest Resources Assessment 2000 for the Food and Agriculture Organization of the United Nations (FAO), and MRLC-2001 dataset was released. In 2003, land cover mapping of North America was carried out as a contribution to the Global Land Cover 2000 project being implemented by the Joint Research Center (JRC) of European Commission (EC).