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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14

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NASA NTRS1990

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data sources. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Kim, H.↗

NASA NTRS1990

Sphere quadtrees - A new data structure to support the visualization of spherically distributed data

The concept of the sphere quadtree (SQT) is introduced to enable the structuring of spherically distributed data to be consistent with its geometry and facilitate mapping of the data onto a flat file system. The SQT is based on the recursive subdivision of the spherical triangles that result from the projection of the faces of an icosahedron onto a sphere. The SQT concept is insensitive to the distortions that occur far from the equator in spherically distributed data sets. Geographic data can be shown at several levels and at any resolution, allowing a system of referencing between data sets of different resolutions as well as data that are not geographically registered. SQTs are found to facilitate the search for particular spherically distributed data sets and improve the efficiency of surface rendering algorithms.

Fekete, Gyorgy↗

NASA NTRS1993

Space Station Freedom (SSF) Data Management System (DMS) performance model data base

The purpose of this document was originally to be a working document summarizing Space Station Freedom (SSF) Data Management System (DMS) hardware and software design, configuration, performance and estimated loading data from a myriad of source documents such that the parameters provided could be used to build a dynamic performance model of the DMS. The document is published at this time as a close-out of the DMS performance modeling effort resulting from the Clinton Administration mandated Space Station Redesign. The DMS as documented in this report is no longer a part of the redesigned Space Station. The performance modeling effort was a joint undertaking between the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) Flight Data Systems Division (FDSD) and the NASA Ames Research Center (ARC) Spacecraft Data Systems Research Branch. The scope of this document is limited to the DMS core network through the Man Tended Configuration (MTC) as it existed prior to the 1993 Clinton Administration mandated Space Station Redesign. Data is provided for the Standard Data Processors (SDP's), Multiplexer/Demultiplexers (MDM's) and Mass Storage Units (MSU's). Planned future releases would have added the additional hardware and software descriptions needed to describe the complete DMS. Performance and loading data through the Permanent Manned Configuration (PMC) was to have been included as it became available. No future releases of this document are presently planned pending completion of the present Space Station Redesign activities and task reassessment.

Stovall, John R.↗

NASA NTRS1993

Mars Observer data production, transfer, and archival: The data production assembly line

This paper describes the data production, transfer, and archival process designed for the Mars Observer Flight Project. It addresses the developmental and operational aspects of the archive collection production process. The developmental aspects cover the design and packaging of data products for archival and distribution to the planetary community. Also discussed is the design and development of a data transfer and volume production process capable of handling the large throughput and complexity of the Mars Observer data products. The operational aspects cover the main functions of the process: creating data and engineering products, collecting the data products and ancillary products in a central repository, producing archive volumes, validating volumes, archiving, and distributing the data to the planetary community.

Childs, David B.↗

NASA NTRS2000

On Fast Post-Processing of Global Positioning System Simulator Truth Data and Receiver Measurements and Solutions Data

Post-Processing of data related to a Global Positioning System (GPS) simulation is an important activity in qualification of a GPS receiver for space flight. Because a GPS simulator is a critical resource it is desirable to move off the pertinent simulation data from the simulator as soon as a test is completed. The simulator data files are usually moved to a Personal Computer (PC), where the post-processing of the receiver logged measurements and solutions data and simulated data is performed. Typically post-processing is accomplished using PC-based commercial software languages and tools. Because of commercial software systems generality their general-purpose functions are notoriously slow and more than often are the bottleneck problem even for short duration experiments. For example, it may take 8 hours to post-process data from a 6-hour simulation. There is a need to do post-processing faster, especially in order to use the previous test results as feedback for a next simulation setup. This paper demonstrates that a fast software linear interpolation algorithm is applicable to a large class of engineering problems, like GPS simulation data post-processing, where computational time is a critical resource and is one of the most important considerations. An approach is developed that allows to speed-up post-processing by an order of magnitude. It is based on improving the post-processing bottleneck interpolation algorithm using apriori information that is specific to the GPS simulation application. The presented post-processing scheme was used in support of a few successful space flight missions carrying GPS receivers. A future approach to solving the post-processing performance problem using Field Programmable Gate Array (FPGA) technology is described.

Kizhner, Semion↗

NASA NTRS2005

Data Assimilation and Data Fusion for Planetary Atmospheres

The overarching goal of this Cooperative Agreement was to develop a model and procedures for the data assimilation of planetary spacecraft atmospheric observations. Data assimilation - in its application to weather analysis and prediction - is the process of finding an initial state of the meteorological variables (winds, temperatures, pressures, etc.) of an atmosphere, which, when propagated forward in time using a deterministic general circulation model, reproduces all of the available observations over that time to within the measurement and computational errors. With this definition, data assimilation is seen to be a natural extension of well-known least-squares minimization techniques. The primary complication arises from the scale of the problem: For the Martian atmosphere with the available nadir-viewing Thermal Emission Spectrometer data from Mars Global Surveyor, approximately 1,000,000 individual measurements of channel radiances (in the 15-micrometer region, where these radiances relate directly to the surface and atmospheric temperature) were made per day. A suitable general circulation model for dealing with this data set has on the order of 20,000 independent variables. After some spatial and temporal averaging of the data - which provides a necessary statistical estimate of the representativeness of the measurements, a crucial issue in data assimilation - the problem reduces in scale to the solution of approximately 50,000 equations for the 20,000 variables.

Houben, Howard↗

NASA NTRS2006

Linking Asthma Exacerbation and Air Pollution Data: A Step Toward Public Health and Environmental Data Integration

Studies have shown that reducing exposure to triggers such as air pollutants can reduce symptoms and the need for medication in asthma patients. However, systems that track asthma are generally not integrated with those that track environmental hazards related to asthma. Tlvs lack of integration hinders public health awareness and responsiveness to these environmental triggers. The current study is a collaboration between health and environmental professionals to utilize NASA-derived environmental data to develop a decision support system (DSS) for asthma prediction, surveillance, and intervention. The investigators link asthma morbidity data from the University of Mississippi Medical Center (UMMC) and Mississippi Department of Health (MDH) with air quality data from the Mississippi Department of Environmental Quality (MDEQ) and remote sensing data from NASA. Daily ambient environmental hazard data for PM2.5 and ozone are obtained from the MDEQ air quality monitoring locations and are combined with remotely sensed data from NASA to develop a state-wide spatial and time series profile of environmental air quality. These data are then used to study the correlation of these measures of air quality variation with the asthma exacerbation incidence throughout the state over time. The goal is to utilize these readily available measures to allow real-time risk assessment for asthma exacerbations. GeoMedStat, a DSS previously developed for biosurveillance, will integrate these measures to monitor, analyze and report the real-time risk assessment for asthma exacerbation throughout the state.

Faruque, Fazlay↗

NASA NTRS2010

Common Data Format (CDF) and Coordinated Data Analysis Web (CDAWeb)

The Coordinated Data Analysis Web (CDAWeb) data browsing system provides plotting, listing and open access v ia FTP, HTTP, and web services (REST, SOAP, OPeNDAP) for data from mo st NASA Heliophysics missions and is heavily used by the community. C ombining data from many instruments and missions enables broad resear ch analysis and correlation and coordination with other experiments a nd missions. Crucial to its effectiveness is the use of a standard se lf-describing data format, in this case, the Common Data Format (CDF) , also developed at the Space Physics Data facility , and the use of metadata standa rds (easily edited with SKTeditor ). CDAweb is based on a set of IDL routines, CDAWlib . . The CDF project also maintains soft ware and services for translating between many standard formats (CDF. netCDF, HDF, FITS, XML) <! .

Candey, Robert M.↗

NASA NTRS2012

Restoration of APOLLO Data by the NSSDC and PDS Lunar Data Node

The Apollo Lunar Surface Experiment Packages (ALSEPs), suites of instruments deployed by the Apollo 12. 14, 15, 16 and 17 astronauts on the lunar surface, still represent the only in-situ measurements of the Moon's environment taken over long time periods, Much of these data are housed at the National Space Science Data Center (NSSDC) at Goddard Space Flight Center but are in forms that are not readily usable, such as microfilm, hardcopy, and magnetic tapes with older, obsolete formats. The Lunar Data Node (LDN) has been formed under the auspices of the Planetary Data System (PDS) Geosciences Node to put relevant, scientifically important Apollo data into accessible digital form for use by researchers and mission planners. The LDN has prioritized the restoration of these data based on their scientific and engineering value and the level of effort required. We will report on progress made and plans for future data restorations.

Williams, David R.↗

NASA NTRS2014

Research Data Alliance: Understanding Big Data Analytics Applications in Earth Science

The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.

Riedel, Morris↗

NASA NTRS2014

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-user Resources, Answers to Faqs, and Common Mistakes to Avoid

Satellite data of atmospheric pollutants are becoming more widely used in the decision-making and environmental management activities of public, private sector and non-profit organizations. They are employed for estimating emissions, tracking pollutant plumes, supporting air quality forecasting activities, providing evidence for "exceptional event" declarations, monitoring regional long-term trends, and evaluating air quality model output. However, many air quality managers are not taking full advantage of the data for these applications nor has the full potential of satellite data for air quality applications been realized. A key barrier is the inherent difficulties associated with accessing, processing, and properly interpreting observational data. A degree of technical skill is required on the part of the data end-user, which is often problematic for air quality agencies with limited resources. Therefore, we 1) review the primary uses of satellite data for air quality applications, 2) provide some background information on satellite capabilities for measuring pollutants, 3) discuss the many resources available to the end-user for accessing, processing, and visualizing the data, and 4) provide answers to common questions in plain language.

remote sensing↗

NASA NTRS2014

Challenges in Evaluating Relationships Between Quantitative Data (Carbon Dioxide) and Qualitative Data (Self-Reported Visual Changes)

Understanding the nuances in clinical data is critical in developing a successful data analysis plan. Carbon dioxide (CO2) data are collected on board the International Space Station (ISS) in a continuous stream. Clinical data on ISS are primarily collected via conversations between individual crewmembers and NASA Flight Surgeons during weekly Private Medical Conferences (PMC). Law, et.al, 20141 demonstrated a statistically significant association between weekly average CO2 levels on ISS and self-reported headaches over the reporting period from March 14, 2001 to May 31, 2012. The purpose of this analysis is to describe the evaluation of a possible association between visual changes and CO2 levels on ISS and to discuss challenges in developing an appropriate analysis plan. METHODS & PRELIMINARY RESULTS: A first analysis was conducted following the same study design as the published work on CO2 and self-reported headaches1; substituting self-reported changes in visual acuity in place of self-reported headaches. The analysis demonstrated no statistically significant association between visual impairment characterized by vision symptoms self-reported during PMCs and ISS average CO2 levels over ISS missions. Closer review of the PMC records showed that vision outcomes are not well-documented in terms of clinical severity, timing of onset, or timing of resolution, perhaps due to the incipient nature of vision changes. Vision has been monitored in ISS crewmembers, pre- and post-flight, using standard optometry evaluations. In-flight visual assessments were limited early in the ISS program, primarily consisting of self-perceived changes reported by crewmembers. Recently, on-orbit capabilities have greatly improved. Vision data ranges from self-reported post-flight changes in visual acuity, pre- to postflight changes identified during fundoscopic examination, and in-flight progression measured by advanced on-orbit clinical imaging capabilities at predetermined testing intervals. In contrast, CO2 data are recorded in a continuous stream over time; however, for the initial analysis this data was categorized into weekly averages.

Mendez, C. M.↗

NASA NTRS2014

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-User Resources, Answers to FAQs, and Common Mistakes to Avoid

Duncan, Bryan↗

NASA NTRS2008

Onboard Data Compression of Synthetic Aperture Radar Data: Status and Prospects

Synthetic aperture radar (SAR) instruments on spacecraft are capable of producing huge quantities of data. Onboard lossy data compression is commonly used to reduce the burden on the communication link. In this paper an overview is given of various SAR data compression techniques, along with an assessment of how much improvement is possible (and practical) and how to approach the problem of obtaining it. Synthetic aperture radar (SAR) instruments on spacecraft are capable of acquiring huge quantities of data. As a result, the available downlink rate and onboard storage capacity can be limiting factors in mission design for spacecraft with SAR instruments. This is true both for Earth-orbiting missions and missions to more distant targets such as Venus, Titan, and Europa. (Of course for missions beyond Earth orbit downlink rates are much lower and thus potentially much more limiting.) Typically spacecraft with SAR instruments use some form of data compression in order to reduce the storage size and/or downlink rate necessary to accommodate the SAR data. Our aim here is to give an overview of SAR data compression strategies that have been considered, and to assess the prospects for additional improvements.

Klimesh, Matthew A.↗

NASA NTRS2016

Availability of Previously Unprocessed ALSEP Raw Instrument Data, Derivative Data, and Metadata Products

In year 2010, 440 original data archival tapes for the Apollo Lunar Science Experiment Package (ALSEP) experiments were found at the Washington National Records Center. These tapes hold raw instrument data received from the Moon for all the ALSEP instruments for the period of April through June 1975. We have recently completed extraction of binary files from these tapes, and we have delivered them to the NASA Space Science Data Cordinated Archive (NSSDCA). We are currently processing the raw data into higher order data products in file formats more readily usable by contemporary researchers. These data products will fill a number of gaps in the current ALSEP data collection at NSSDCA. In addition, we have estabilished a digital, searcheable archive of ALSEP document and metadata as part of the web portal of the Lunar and Planetary Institute. It currently holds approx. 700 documents totaling approx. 40,000 pages

ALSEP↗

NASA NTRS2016

Global Positioning System Energetic Particle Data: The Next Space Weather Data Revolution

The Global Positioning System (GPS) has revolutionized the process of getting from point A to point Band so much more. A large fraction of the worlds population relies on GPS (and its counterparts from other nations) for precision timing, location, and navigation. Most GPS users are unaware that the spacecraft providing the signals they rely on are operating in a very harsh space environment the radiation belts where energetic particles trapped in Earths magnetic field dash about at nearly the speed of light. These subatomic particles relentlessly pummel GPS satellites. So by design, every GPS satellite and its sensors are radiation hardened. Each spacecraft carries particle detectors that provide health and status data to system operators. Although these data reveal much about the state of the space radiation environment, heretofore they have been available only to system operators and supporting scientists. Research scientists have long sought a policy shift to allow more general access. With the release of the National Space Weather Strategy and Action Plan organized by the White House Office of Science Technology Policy (OSTP) a sample of these data have been made available to space weather researchers. Los Alamos National Laboratory (LANL) and the National Center for Environmental Information released a months worth of GPS energetic particle data from an interval of heightened space weather activity in early 2014 with the hope of stimulating integration of these data sets into the research arena. Even before the public data release GPS support scientists from LANL showed the extraordinary promise of these data.

Knipp, Delores J.↗

NASA NTRS2017

Characterizing Non-Resolved Debris Through Spectral and Photometric Ground-Based Telescopic Data: What Can Laboratory Ground-truth Data Do for You?

NASA's ODPO has recently collected data of unresolved objects at GEO with the 3.8m UKIRT infrared telescope on Mauna Kea and the 1.3m MCAT visible telescope on Ascension Island. Analyses of SWIR data of rocket bodies and HS-376 solar-panel covered buses demonstrate the uniqueness of spectral signatures. Data of 3 classes of rocket bodies show similarities amongst a given class, but distinct differences from one class to another, suggesting that infrared reflectance spectra could effectively be used toward characterizing and constraining potential parent bodies of uncorrelated targets (UCTs). The Optical Measurements Center (OMC) at NASA JSC is designed to collect photometric signatures in the laboratory that can be used for comparison with telescopic data. NASA also has a spectral database of spacecraft materials for use with spectral unmixing models. Spectral unmixing of the HS-376 bus data demonstrates how absorption features and slopes can be used to constrain material characteristics of debris. Broadband photometry likewise can be compared with MCAT data of non-resolved debris images. Similar studies have been applied to IDCSP satellites to demonstrate how color-color photometry can be compared with lab data to constrain bulk materials signatures of spacecraft and debris.

Lederer, Susan↗

NASA NTRS2017

Data Reduction Pipeline for the CHARIS Integral-Field Spectrograph I: Detector Readout Calibration and Data Cube Extraction

We present the data reduction pipeline for CHARIS, a high-contrast integral-field spectrograph for the Subaru Telescope. The pipeline constructs a ramp from the raw reads using the measured nonlinear pixel response and reconstructs the data cube using one of three extraction algorithms: aperture photometry, optimal extraction, or chi-squared fitting. We measure and apply both a detector flatfield and a lenslet flatfield and reconstruct the wavelength- and position-dependent lenslet point-spread function (PSF) from images taken with a tunable laser. We use these measured PSFs to implement a chi-squared-based extraction of the data cube, with typical residuals of approximately 5 percent due to imperfect models of the under-sampled lenslet PSFs. The full two-dimensional residual of the chi-squared extraction allows us to model and remove correlated read noise, dramatically improving CHARIS's performance. The chi-squared extraction produces a data cube that has been deconvolved with the line-spread function and never performs any interpolations of either the data or the individual lenslet spectra. The extracted data cube also includes uncertainties for each spatial and spectral measurement. CHARIS's software is parallelized, written in Python and Cython, and freely available on github with a separate documentation page. Astrometric and spectrophotometric calibrations of the data cubes and PSF subtraction will be treated in a forthcoming paper.

Brandt, Timothy D.↗