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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

The Microgravity Research Experiments (MICREX) Data Base, Volume 4

An electronic data base identifying over 800 fluids and materials processing experiments performed in a low-gravity environment has been created at NASA Marshall Space Flight Center. The compilation, called MICREX (MICrogravity Research Experiments), was designed to document all such experimental efforts performed (1) on U.S. manned space vehicles, (2) on payloads deployed from U.S. manned space vehicles, and (3) on all domestic and international sounding rockets (excluding those of China and the former U.S.S.R.). Data available on most experiments include (1) principal and co-investigators (2) low-gravity mission, (3) processing facility, (4) experimental objectives and results, (5) identifying key words, (6) sample materials, (7) applications of the processed materials/research area, (8) experiment descriptive publications, and (9) contacts for more information concerning the experiment. This technical Memorandum (1) summarizes the historical interest in reduced-gravity fluid dynamics, (2) describes the importance of a low-gravity fluids and materials processing data base, (4) describes the MICREX data base format and computational World Wide Web access procedures, and (5) documents (in hard-copy form) the descriptions of the first 600 fluids and materials processing experiments entered into MICREX.

Winter, C. A.↗
NASA NTRS2021

Application of Radon Transform to Multi-Angle Measurements Made by the Research Scanning Polarimeter: A New Approach to Cloud Tomography. Part I: Theory and Tests on Simulated Data.

The Research Scanning Polarimeter (RSP) is an airborne along-track scanner measuring the polarized and total reflectances in 9 spectral channels. The RSP was a prototype for the Aerosol Polarimetry Sensor (APS) launched on-board the NASA Glory satellite. Currently the retrieval algorithms developed for the RSP are being adopted for the measurements of the space-borne polarimeters on the upcoming NASA’s Plankton, Aerosol, Cloud Ocean Ecosystem (PACE)satellite mission. The RSP’s uniquely high angular resolution coupled with the high frequency of measurements allows for characterization of liquid water cloud droplet sizes using the polarized rainbow structure. It also provides geometric constraints on the cumulus cloud’s 2D cross section yielding the cloud’s geometric shape estimates. In this study we further build on the latter technique to develop a new tomographic approach to retrieval of cloud internal structure from remote sensing measurements. While tomography in the strict definition is a technique based on active measurements yielding a tomogram (directional optical thickness as a function of angle and offset of the view ray), we developed a “semi-tomographic” approach in which tomogram of the cloud is estimated from passive observations instead of being measured directly. This tomogram is then converted into 2D spatial distribution of the extinction coefficient using inverse Radon transform (filtered back projection) which is the standard tomographic procedure used e.g., in medical CT scans. This algorithm is computationally inexpensive compared to techniques relying on highly-multi-dimensional least-square fitting; it does not require iterative 3D RT simulations. The resulting extinction distribution is defined up to an unknown constant factor, so we discuss the ways to calibrate it using additional independent measurements. In the next step we use the profile of the droplet size distribution parameters from the cloud’s side (derived by fitting the polarized rainbows) to convert the 2D extinction distribution into that of the droplet number concentration. We illustrate and validate the proposed technique using 3D-RT-simulatedRSP observations of a LES-generated Cu cloud. Quantitative comparisons between the retrieved and the original optical and microphysical parameters are presented.

clouds↗
NASA NTRS1981

Computer implemented land cover classification using LANDSAT MSS digital data: A cooperative research project between the National Park Service and NASA. 3: Vegetation and other land cover analysis of Shenandoah National Park

Four LANDSAT frames, each corresponding to one of the four seasons were spectrally classified and processed using NASA-developed computer programs. One data set was selected or two or more data sets were marged to improve surface cover classifications. Selected areas representing each spectral class were chosen and transferred to USGS 1:62,500 topographic maps for field use. Ground truth data were gathered to verify the accuracy of the classifications. Acreages were computed for each of the land cover types. The application of elevational data to seasonal LANDSAT frames resulted in the separation of high elevation meadows (both with and without recently emergent perennial vegetation) as well as areas in oak forests which have an evergreen understory as opposed to other areas which do not.

Cibula, W. G.↗
NASA NTRS2000

NASA Historical Data Book: NASA Space Applications, Aeronautics and Space Research and Technology, Tracking and Data Acquisition/Support Operations, Commercial Programs and - Volume 6

This sixth volume of the NASA Historical Data Book is a continuation of those earlier efforts. This fundamental reference tool presents information, much of it statistical, documenting the development of several critical areas of NASA responsibility for the period between 1979 and 1988. This volume includes detailed information on the space applications effort, the development and operation of aeronautics and space research and technology programs, tracking and data acquisition/space operations, commercial programs, facilities and installations, personnel, and finances and procurement during this era. Special thanks are owed to the student research assistants who gathered and input much of the tabular material-a particularly tedious undertaking. There are numerous people at NASA associated with historical study, technical information, and the mechanics of publishing who helped in myriad ways in the preparation of this historical data book.

Rumerman, Judy A.↗
NASA NTRS1996

Advanced Video Data-Acquisition System For Flight Research

Advanced video data-acquisition system (AVDAS) developed to satisfy variety of requirements for in-flight video documentation. Requirements range from providing images for visualization of airflows around fighter airplanes at high angles of attack to obtaining safety-of-flight documentation. F/A-18 AVDAS takes advantage of very capable systems like NITE Hawk forward-looking infrared (FLIR) pod and recent video developments like miniature charge-couple-device (CCD) color video cameras and other flight-qualified video hardware.

Miller, Geoffrey↗
NASA NTRS1993

Current and future use of TOPSAR digital topographic data for volcanological research

In several investigations of volcanoes, high quality digital elevation models (DEM's) are required to study either the geometry of the volcano or to investigate temporal changes in relief due to eruptions. Examples include the analysis of volume changes of a volcanic dome, the prediction of flow paths for pyroclastic flows, and the quantitative investigation of the geometry of valleys carved by volcanic mudflows. Additionally, to provide input data for models of lava flow emplacement, accurate measurements are needed of the thickness of lava flows as a function of distance from the vent and local slope. Visualization of volcano morphology is also aided by the ability to view a DEM from oblique perspectives. Until recently, the generation of these DEM's has required either high resolution stereo air photographs or extensive field surveying using the Global Positioning System (GPS) and other field techniques. Through the use of data collected by the NASA/JPL TOPSAR system, it is now possible to remotely measure the topography of volcanoes using airborne radar interferometry. TOPSAR data can be collected day or night under any weather conditions, thereby avoiding the problems associated with the derivation of DEM's from air photographs that may often contain clouds. Here we describe some of our initial work on volcanoes using TOPSAR data for Mt. Hekla (Iceland) and Vesuvius (Italy). We also outline various TOPSAR topographic studies of volcanoes in the Galapagos and Hawaii that will be conducted in the near future, describe how TOPSAR complements the volcanology investigations to be conducted with orbital radars (SIR-C/X-SAR, JERS-1 and ERS-1), and place these studies into the broader context of NASA's Global Change Program.

Mouginis-Mark, Peter J.↗
NASA NTRS1983

Data acquisition and preprocessing techniques for remote sensing field research

A crops and soils data base has been developed at Purdue University's Laboratory for Applications of Remote Sensing using spectral and agronomic measurements made by several government and university researchers. The data are being used to (1) quantitatively determine the relationships of spectral and agronomic characteristics of crops and soils, (2) define future sensor systems, and (3) develop advanced data analysis techniques. Researchers follow defined data acquisition and preprocessing techniques to provide fully annotated and calibrated sets of spectral, agronomic, and meteorological data. These procedures enable the researcher to combine his data with that acquired by other researchers for remote sensing research. The key elements or requirements for developing a field research data base of spectral data that can be transported across sites and years are appropriate experiment design, accurate spectral data calibration, defined field procedures, and through experiment documentation.

Biehl, L. L.↗
NASA NTRS1992

Data management for community research projects: A JGOFS case study

Since the mid 1980s, much of the marine science research effort in the United Kingdom has been focused into large scale collaborative projects involving public sector laboratories and university departments, termed Community Research Projects. Two of these, the Biogeochemical Ocean Flux Study (BOFS) and the North Sea Project incorporated large scale data collection to underpin multidisciplinary modeling efforts. The challenge of providing project data sets to support the science was met by a small team within the British Oceanographic Data Centre (BODC) operating as a topical data center. The role of the data center was to both work up the data from the ship's sensors and to combine these data with sample measurements into online databases. The working up of the data was achieved by a unique symbiosis between data center staff and project scientists. The project management, programming and data processing skills of the data center were combined with the oceanographic experience of the project communities to develop a system which has produced quality controlled, calibrated data sets from 49 research cruises in 3.5 years of operation. The data center resources required to achieve this were modest and far outweighed by the time liberated in the scientific community by the removal of the data processing burden. Two online project databases have been assembled containing a very high proportion of the data collected. As these are under the control of BODC their long term availability as part of the UK national data archive is assured. The success of the topical data center model for UK Community Research Project data management has been founded upon the strong working relationships forged between the data center and project scientists. These can only be established by frequent personal contact and hence the relatively small size of the UK has been a critical factor. However, projects covering a larger, even international scale could be successfully supported by a network of topical data centers managing online databases which are interconnected by object oriented distributed data management systems over wide area networks.

Lowry, Roy K.↗
DOE OSTI2026

VirJenDB: a FAIR (meta)data and bioinformatics platform for all viruses

High-throughput sequencing has generated an unprecedented volume of data. However, researcher-submitted data in repositories requires extensive curation and quality control for reuse. These tasks are hindered by the multiplicity of repositories, the sheer volume of the data, and the complexity of virus (meta)data curation. To address these challenges, VirJenDB offers a user-friendly platform to facilitate versioned, community-driven curation, and ontology development. Virus sequences were ingested from 16 sources, including ~200 fields of metadata or standards, covering taxonomy, sample, and host information. Up to 85 metadata fields have undergone at least one round of curation, and are linked to 15.4 million virus sequences, with 88 % from those infecting eukaryotes and the remaining infecting prokaryotes. Subsets were created, including a novel collection of 0.91 million viral operational taxonomic unit (vOTU) sequences across all viruses, while keeping the original sequences from each vOTU to facilitate downstream analyses, e.g. sequence variation. The VirJenDB web portal (https://www.virjendb.org) provides HTTPS and Application Programming Interface (API) access to the sequence datasets and metadata, offering a search engine, filtering, download, visualizations, and documentation. VirJenDB aims to connect the phage and eukaryotic virus research communities by supporting webtool integration, meta-analyses, and metadata schema extensions.

Saghaei, Shahram↗
NASA NTRS1994

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

Djorgovski, S. George↗
NASA NTRS1994

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.

Djorgovski, S. G.↗
NASA NTRS2010

IT Challenges for Space Medicine

This viewgraph presentation reviews the various Information Technology challenges for aerospace medicine. The contents include: 1) Space Medicine Activities; 2) Private Medical Information; 3) Lifetime Surveillance of Astronaut Health; 4) Mission Medical Support; 5) Data Repositories for Research; 6) Data Input and Output; 7) Finding Data/Information; 8) Summary of Challenges; and 9) Solutions and questions.

Johnson-Throop, Kathy↗