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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

High rate science data handling on Space Station Freedom

A study by NASA's User Information System Working Group for Space Station Freedom (SSF) has determined that the proposed onboard Data Management System, as initially configured, will be incapable of handling the data-generation rates typical of numerous scientific sensor payloads; many of these generate data at rates in excess of 10 Mbps, and there are at least four cases of rates in excess of 300 Mbps. The SSF Working Group has accordingly suggested an alternative conceptual architecture based on technology expected to achieve space-qualified status by 1995. The architecture encompasses recorders with rapid data-ingest capabilities and massive storage capabilities, optical delay lines allowing the recording of only the phenomena of interest, and data flow-compressing image processors.

Handley, Thomas H., Jr.↗
NASA NTRS1992

Gravitropic responses of plants in the absence of a complicating G-force (6-IML-1)

On the Earth it is patently impossible to measure any tropistic, physiologic, or morphogenic reactions to environmental stimuli without taking into account our planet's gravitational influence on the time course of the test subject's response. It follows that all published reports of quantitative measurements of such responses must have been contaminated by an additional gravity dependent component which probably was not trivial. Our research effort has as its principal scientific objective, the acquisition of experimental data from tests in a microgravity environment that will address a number of basic questions about plants' gravitropic responses to the perception of transversely applied g forces in the hypogravity range, from essentially zero to unit g. Comparable tests on Earth but in the same flight hardware, referred to as the Gravitational Plant Physiology Facility (GPPF), will provide 1 g data for various useful comparisons. Four specific scientific questions are addressed.

Brown, Allan H.↗
NASA NTRS1996

Queuing Models of Tertiary Storage

Large scale scientific projects generate and use large amounts of data. For example, the NASA Earth Observation System Data and Information System (EOSDIS) project is expected to archive one petabyte per year of raw satellite data. This data is made automatically available for processing into higher level data products and for dissemination to the scientific community. Such large volumes of data can only be stored in robotic storage libraries (RSL's) for near-line access. A characteristic of RSL's is the use of a robot arm that transfers media between a storage rack and the read/write drives, thus multiplying the capacity of the system. The performance of the RSL's can be a critical limiting factor for the performance of the archive system. However, the many interacting components of an RSL make a performance analysis difficult. In addition, different RSL components can have widely varying performance characteristics. This paper describes our work to develop performance models of an RSL in isolation. Next we show how the RSL model can be incorporated into a queuing network model. We use the models to make some example performance studies of archive systems. The models described in this paper, developed for the NASA EODIS project, are implemented in C with a well defined interface. The source code, accompanying documentation, and also sample JAVA applets are available at: http://www.cis.ufl.edu/~ted/

Johnson, Theodore↗
NASA NTRS2008

Safe Upper-Bounds Inference of Energy Consumption for Java Bytecode Applications

Many space applications such as sensor networks, on-board satellite-based platforms, on-board vehicle monitoring systems, etc. handle large amounts of data and analysis of such data is often critical for the scientific mission. Transmitting such large amounts of data to the remote control station for analysis is usually too expensive for time-critical applications. Instead, modern space applications are increasingly relying on autonomous on-board data analysis. All these applications face many resource constraints. A key requirement is to minimize energy consumption. Several approaches have been developed for estimating the energy consumption of such applications (e.g. [3, 1]) based on measuring actual consumption at run-time for large sets of random inputs. However, this approach has the limitation that it is in general not possible to cover all possible inputs. Using formal techniques offers the potential for inferring safe energy consumption bounds, thus being specially interesting for space exploration and safety-critical systems. We have proposed and implemented a general frame- work for resource usage analysis of Java bytecode [2]. The user defines a set of resource(s) of interest to be tracked and some annotations that describe the cost of some elementary elements of the program for those resources. These values can be constants or, more generally, functions of the input data sizes. The analysis then statically derives an upper bound on the amount of those resources that the program as a whole will consume or provide, also as functions of the input data sizes. This article develops a novel application of the analysis of [2] to inferring safe upper bounds on the energy consumption of Java bytecode applications. We first use a resource model that describes the cost of each bytecode instruction in terms of the joules it consumes. With this resource model, we then generate energy consumption cost relations, which are then used to infer safe upper bounds. How energy consumption for each bytecode instruction is measured is beyond the scope of this paper. Instead, this paper is about how to infer safe energy consumption estimations assuming that those energy consumption costs are provided. For concreteness, we use a simplified version of an existing resource model [1] in which an energy consumption cost for individual Java opcodes is defined.

Navas, Jorge↗
NASA NTRS2011

Selecting Tasks for Evaluating Human Performance as a Function of Gravity

A challenge in understanding human performance as a function of gravity is determining which tasks to research. Initial studies began with treadmill walking, which was easy to quantify and control. However, with the development of pressurized rovers, it is less important to optimize human performance for ambulation as pressurized rovers will likely perform gross translation for them. Future crews are likely to spend much of their extravehicular activity (EVA) performing geology, construction,a nd maintenance type tasks. With these types of tasks, people have different performance strategies, and it is often difficult to quantify the task and measure steady-state metabolic rates or perform biomechanical analysis. For many of these types of tasks, subjective feedback may be the only data that can be collected. However, subjective data may not fully support a rigorous scientific comparison of human performance across different gravity levels and suit factors. NASA would benefit from having a wide variety of quantifiable tasks that allow human performance comparison across different conditions. In order to determine which tasks will effectively support scientific studies, many different tasks and data analysis techniques will need to be employed. Many of these tasks and techniques will not be effective, but some will produce quantifiable results that are sensitive enough to show performance differences. One of the primary concerns related to EVA performance is metabolic rate. The higher the metabolic rate, the faster the astronaut will exhaust consumables. The focus of this poster will be on how different tasks affect metabolic rate across different gravity levels.

Norcross, Jason R.↗
NASA NTRS2013

The Mission Accessible Near-Earth Objects Survey (MANOS)

Near‐Earth objects (NEOs) are essential to understanding the origin of the Solar System. Their relatively small sizes and complex dynamical histories make them excellent laboratories for studying ongoing Solar System processes. The proximity of NEOs to Earth makes them favorable targets for space missions. In addition, knowledge of their physical properties is crucial for impact hazard assessment. However, in spite of their importance to science, exploration, and planetary defense, a representative sample of physical characteristics for sub‐km NEOs does not exist. Here we present the Mission Accessible Near‐Earth Objects Survey (MANOS), a multi‐year survey of subkm NEOs that will provide a large, uniform catalog of physical properties (light curves + colors + spectra + astrometry), representing a 100‐fold increase over the current level of NEO knowledge within this size range. This survey will ultimately characterize more than 300 mission‐accessible NEOs across the visible and near‐infrared ranges using telescopes in both the northern and southern hemispheres. MANOS has been awarded 24 nights per semester for the next three years on NOAO facilities including Gemini North and South, the Kitt Peak Mayall 4m, and the SOAR 4m. Additional telescopic assets available to our team include facilities at Lowell Observatory, the University of Hawaii 2.2m, NASA's IRTF, and the Magellan 6.5m telescopes. Our focus on sub‐km sizes and mission accessibility (dv < 7 km/s) is a novel approach to physical characterization studies and is possible through a regular cadence of observations designed to access newly discovered NEOs within days or weeks of first detection before they fade beyond observational limits. The resulting comprehensive catalog will inform global properties of the NEO population, advance scientific understanding of NEOs, produce essential data for robotic and spacecraft exploration, and develop a critical knowledge base to address the risk of NEO impacts. We intend to conduct this survey with complete transparency, publicly sharing our target lists and survey progress. We invite collaborative uses for these data as a way to broaden the scientific impact of this survey.

Abell, Paul↗
NASA NTRSDate not supplied

The Radiation Biology Ontology: A New Tool Supporting FAIR Principles Across Radiation Biology Facilitating Data Discovery and Integration

Development of the Radiation Biology Ontology (RBO) was motivated by the need for a comprehensive, well-structured ontology for encoding radiation biology metadata. The primary use-cases were archiving data in the STORE database (https://www.storedb.org/), the repository for the RadoNorm Project, and in GeneLab (https://genelab.nasa.gov), NASA’s ‘omics database. The scope of radiobiology research ranges from physics to radiation oncology to socio-legal studies; no existing ontology has the necessary breadth or depth. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR radiation biology data.

ontology↗
NASA NTRS1997

Operational Issues: What Science in Available?

Flight/duty/rest considerations involve two highly complex factors: the diverse demands of aviation operations and human physiology (especially sleep and circadian rhythms). Several core operational issues related to fatigue have been identified, such as minimum rest requirements, duty length, flight time considerations, crossing multiple time zones, and night flying. Operations also can involve on-call reserve status and callout, delays due to unforeseen circumstances (e.g., weather, mechanical), and on-demand flights. Over 40 years of scientific research is now available to apply to these complex issues of flight/duty/rest requirements. This research involves controlled 'laboratory studies, simulations, and data collected during regular flight operations. When flight/duty/rest requirements are determined they are typically based on a variety of considerations, such as operational demand, safety, economic, etc. Rarely has the available, state-of-the-art science been a consideration along with these other factors when determining flight/duty/rest requirements. While the complexity of the operational demand and human physiology precludes an absolute solution, there is an opportunity to take full advantage of the current scientific data. Incorporating these data in a rational operational manner into flight/duty/rest requirements can improve flight crew performance, alertness, and ultimately, aviation safety.

Rosekind, Mark R.↗
NASA NTRSDate not supplied

NASA’s Advance Information Systems Technology (AIST) Program

NASA’s Earth Science Technology Office (ESTO) develops early-stage technologies to enhance scientific understanding. ESTO’s technologies can be reliably and confidently applied to a broad range of science measurements and missions, as well as facilitate practical applications to benefit society at large. As the lead technology office within the Earth Science Division of the NASA Science Mission Directorate, ESTO is focused on the technological challenges inherent to space-based investigations of our planet's dynamic, interrelated systems. Through flexible, science-driven technology strategies and a competitive selection process, ESTO-funded technologies have supported numerous Earth science missions as well as commercial applications. From next generation sensors and instruments to communication and information systems, ESTO technologies enable many NASA missions and data services. The Advanced Information Systems Technology (AIST) Program is one focal area of ESTO. AIST innovates information system technologies that enable the development of new observing systems as well as agile science investigations through data analytics and artificial intelligence tools and algorithms. AIST utilizes an end-to-end development approach with the goal of infusing mature technologies into future missions, measurements, and analysis. One thrust of AIST is data exploitation and analysis using an analytic center framework to make observational data and model output more accessible and usable to scientists conducting specific investigations as well as to extract higher-level science content and information from the data. The framework accelerates scientific discovery by harmonizing the data, tools, and computational resources. NASA’s upcoming Surface, Biology and Geology (SBG) mission is one such mission whose data architecture will leverage many of the technologies developed through the AIST program. This talk will highlight current and future program development goals.

Laura Rogers↗
NASA NTRS1988

Earth Observing System (EOS) advanced altimetry

In the post-TOPEX era, satellite radar altimeters will be developed with the capability of measuring the earth's surface topography over a wide swath of coverage, rather than just at the satellite's nadir. The identification of potential spacecraft flight missions in the future was studied. The best opportunity was found to be the Earth Observing System (EOS). It is felt that an instrument system that has a broad appeal to the earth sciences community stands a much better chance of being selected as an EOS instrument. Consequently, the Topography and Rain Radar Imager (TARRI) will be proposed as a system that has the capability to profile the Earth's topography regardless of the surface type. The horizontal and height resolutions of interest are obviously significantly different over land, ice, and water; but, the use of radar to provide an all-weather observation capability is applicable to the whole earth. The scientific guidance for the design and development of this instrument and the eventual scientific utilization of the data produced by the TARRI will be provided by seven science teams. The teams are formed around scientific disciplines and are titled: Geology/Geophysics, Hydrology/Rain, Oceanography, Ice/Snow, Geodesy/Orbit/Attitude, Cartography, and Surface Properties/Techniques.

Parsons, C. L.↗
NASA NTRS1996

Multimode Data-Compression System

Data-compression system developed to satisfy need for high-speed, high-performance compression of data from sources as diverse as medical images, high-definition television images, audio signals, readouts from scientific instruments, and binary data files. Maximum data-transmission capability of communication channel or storage capacity of storage device multiplied by approximately compression ratio. Various combinations of lossless and lossy compression chosen to suit various data streams.

Fang, Wai-Chi↗
NASA NTRS1998

Natural thermoluminescence of Antarctic meteorites and related studies

The natural thermoluminescence (TL) laboratory's primary purpose is to provide data on newly recovered Antarctic meteorites that can be included in discovery announcements and to investigate the scientific implications of the data. Natural TL levels of meteorites are indicators of recent thermal history and terrestrial history, and the data can be used to study the orbital/radiation history of groups of meteorites (e.g., H chondrites) or to study the processes leading to the concentration of meteorites at certain sites in Antarctica. An important application of these data is the identification of fragments, or "pairs" of meteorites produced during atmospheric passage or during terrestrial weathering. Thermoluminescence data are particularly useful for pairing within the most common meteorite classes, which typically exhibit very limited petrographic and chemical diversity. Although not originally part of the laboratory's objectives, TL data are also useful in the identification and classification of petrographically or mineralogically unusual meteorites, including unequilibrated ordinary chondrites and some basaltic achondrites. In support of its primary mission, the laboratory also engages in TL studies of modern falls, finds from hot deserts, and terrestrial analogs and conducts detailed studies of the TL properties of certain classes of meteorites. These studies include the measurement of TL profiles in meteorites, the determination of TL levels of finds from the Sahara and the Nullarbor region of Australia, and comparison of TL data to other indicators of irradiation or terrestrial history, such as cosmogenic noble gas and radionuclide abundances. Our current work can be divided into five subcategories, (a) TL survey of Antarctic meteorites, (b) pairing and field relations of Antarctic meteorites, (c) characterization of TL systematics of meteorites, (d) comparison of natural TL and other terrestrial age indicators for Antarctic meteorites, and for meteorites from hot deserts, and (e) characterization of the TL properties of fusion crust of meteorites.

Benoit, Paul H.↗
NASA NTRS1975

Atmosphere Explorer set for launch

The Atmosphere Explorer-D (Explorer-54) is described which will explore in detail an area of the earth's outer atmosphere where important energy transfer, atomic and molecular processes, and chemical reactions occur that are critical to the heat balance of the atmosphere. Data are presented on the mission facts, launch vehicle operations, AE-D/Delta flight events, spacecraft description, scientific instruments, tracking, and data acquisition.

Source record↗
NASA NTRS1987

SAM 2 and SAGE data management and processing

The data management and processing supplied by ST Systems Corporation (STX) for the Stratospheric Aerosol Measurement 2 (SAM 2) and Stratospheric Aerosol and Gas Experiment (SAGE) experiments for the years 1983 to 1986 are described. Included are discussions of data validation, documentation, and scientific analysis, as well as the archival schedule met by the operational reduction of SAM 2 and SAGE data. Work under this contract resulted in the archiving of the first seven years of SAM 2 data and all three years of SAGE data. A list of publications and presentations supported was also included.

Osborn, M. T.↗
NASA NTRS2005

Algorithm Science to Operations for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Visible/Infrared Imager/Radiometer Suite (VIIRS)

The VIIRS sensor provides measurements for 22 Environmental Data Records (EDRs) addressing the atmosphere, ocean surface temperature, ocean color, land parameters, aerosols, imaging for clouds and ice, and more. That is, the VIIRS collects visible and infrared radiometric data of the Earth's atmosphere, ocean, and land surfaces. Data types include atmospheric, clouds, Earth radiation budget, land/water and sea surface temperature, ocean color, and low light imagery. This wide scope of measurements calls for the preparation of a multiplicity of Algorithm Theoretical Basis Documents (ATBDs), and, additionally, for intermediate products such as cloud mask, et al. Furthermore, the VIIRS interacts with three or more other sensors. This paper addresses selected and crucial elements of the process being used to convert and test an immense volume of a maturing and changing science code to the initial operational source code in preparation for launch of NPP. The integrity of the original science code is maintained and enhanced via baseline comparisons when re-hosted, in addition to multiple planned code performance reviews.

NPOESS (NATIONAL POLAR-ORBITING OPERATIONAL ENVIRO↗
NASA NTRS1993

DataHub: Science data management in support of interactive exploratory analysis

The DataHub addresses four areas of significant needs: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactives nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc), in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.

Handley, Thomas H., Jr.↗
NASA NTRS2011

Fast Plasma Investigation for MMS: Simulation of the Burst Triggering System

The Magnetospheric Multiscale (MMS) mission will study small-scale reconnection structures and their rapid motions from closely spaced platforms using instruments capable of high angular, energy, and time resolution measurements. To meet these requirements, the Fast Plasma Instrument (FPI) consists of eight (8) identical half top-hat electron sensors and eight (8) identical ion sensors and an Instrument Data Processing Unit (IDPU). The sensors (electron or ion) are grouped into pairs whose 6 degree x 180 degree fields-of-view (FOV) are set 90 degrees apart. Each sensor is equipped with electrostatic aperture steering to allow the sensor to scan a 45 degree x 180 degree fan about the its nominal viewing (0 deflection) direction. Each pair of sensors, known as the Dual Electron Spectrometer (DES) and the Dual Ion Spectrometer (DIS), occupies a quadrant on the MMS spacecraft and the combination of the eight electron/ion sensors, employing aperture steering, image the full-sky every 30-ms (electrons) and 150-ms (ions), respectively. To probe the diffusion regions of reconnection, the highest temporal/spatial resolution mode of FPI results in the DES complement of a given spacecraft generating 6.5-Mb (raised dot) per second of electron data while the DIS generates 1.1-Mb (raised dot) per second of ion data yielding an FPI total data rate of 6.6-Mb (raised dot) per second. The FPI electron/ion data is collected by the IDPU then transmitted to the Central Data Instrument Processor (CIDP) on the spacecraft for science interest ranking. Only data sequences that contain the greatest amount of temporal/spatial structure will be intelligently down-linked by the spacecraft. This requires a data ranking process known as the burst trigger system. The burst trigger system uses pseudo physical quantities to approximate the local plasma environments. As each pseudo quantity will have a different value, a set of two scaling factors is employed for each pseudo term. These pseudo quantities are then combined at the instrument, spacecraft, and observatory level leading to a final ranking of data based on expected scientific interest. Here, we present simulations of the fixed point burst trigger system for the FPI. A variety of data sets based on previous mission data as well as analytical formulations are tested. Comparisons of floating point calculations versus the fixed point hardware simulation are shown. Analysis of the potential sources of error from overflows, quantization, etc. are examined and mitigation methods are presented. Finally a series of calibration curves are presented, showing the expected error in pseudo quantities based solely on the scale parameters chosen and the expected data range. We conclude with a presentation of the current base-lined FPI burst trigger approach.

Barrie, A. C.↗
NASA NTRS2011

An Investigation Into HPLC Data Quality Problems

This report summarizes the analyses and results produced by a five-member investigative team of Government, university, and industry experts, established by NASA HQ. The team examined data quality problems associated with high performance liquid chromatography (HPLC) analyses of pigment concentrations in seawater samples produced by the San Diego State University (SDSU) Center for Hydro-Optics and Remote Sensing (CHORS). This report shows CHORS did not validate the methods used before placing them into service to analyze field samples for NASA principal investigators (PIs), even though the HPLC literature contained easily accessible method validation procedures, and the importance of implementing them, more than a decade ago. In addition, there were so many sources of significant variance in the CHORS methodologies, that the HPLC system rarely operated within performance criteria capable of producing the requisite data quality. It is the recommendation of the investigative team to a) not correct the data, b) make all the data that was temporarily sequestered available for scientific use, and c) label the affected data with an appropriate warning, e.g., "These data are not validated and should not be used as the sole basis for a scientific result, conclusion, or hypothesis--independent corroborating evidence is required."

Hooker, Stanford B.↗