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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Application of digital terrain data to quantify and reduce the topographic effect on LANDSAT data

Integration of LANDSAT multispectral scanner (MSS) data with 30 m U.S. Geological Survey (USGS) digital terrain data was undertaken to quantify and reduce the topographic effect on imagery of a forested mountain ridge test site in central Pennsylvania. High Sun angle imagery revealed variation of as much as 21 pixel values in data for slopes of different angles and aspects with uniform surface cover. Large topographic effects were apparent in MSS 4 and 5 was due to a combination of high absorption by the forest cover and the MSS quantization. Four methods for reducing the topographic effect were compared. Band ratioing of MSS 6/5 and MSS 7/5 did not eliminate the topographic effect because of the lack of variation in MSS 4 and 5 radiances. The three radiance models examined to reduce the topographic effect required integration of the digital terrain data. Two Lambertian models increased the variation in the LANDSAT radiances. The nonLambertian model considerably reduced (86 per cent) the topographic effect in the LANDSAT data. The study demonstrates that high quality digital terrain data, as provided by the USGS digital elevation model data, can be used to enhance the utility of multispectral satellite data.

Justice, C. O.↗
NASA NTRS1997

Querying Semi-Structured Data

The amount of data of all kinds available electronically has increased dramatically in recent years. The data resides in different forms, ranging from unstructured data in the systems to highly structured in relational database systems. Data is accessible through a variety of interfaces including Web browsers, database query languages, application-specic interfaces, or data exchange formats. Some of this data is raw data, e.g., images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure exists but has to be extracted from the data. Sometimes also it exists but we prefer to ignore it for certain purposes such as browsing. We call here semi-structured data this data that is (from a particular viewpoint) neither raw data nor strictly typed, i.e., not table-oriented as in a relational model or sorted-graph as in object databases. As will seen later when the notion of semi-structured data is more precisely de ned, the need for semi-structured data arises naturally in the context of data integration, even when the data sources are themselves well-structured. Although data integration is an old topic, the need to integrate a wider variety of data- formats (e.g., SGML or ASN.1 data) and data found on the Web has brought the topic of semi-structured data to the forefront of research. The main purpose of the paper is to isolate the essential aspects of semi- structured data. We also survey some proposals of models and query languages for semi-structured data. In particular, we consider recent works at Stanford U. and U. Penn on semi-structured data. In both cases, the motivation is found in the integration of heterogeneous data.

DATA MANAGEMENT↗
NASA NTRS2016

GeneLab Phase 2: Integrated Search Data Federation of Space Biology Experimental Data

The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.

Space Biology↗
NASA NTRS2001

Integrated Propulsion Data System Public Web Site

The Integrated Propulsion Data System's (IPDS) focus is to provide technologically-advanced philosophies of doing business at SSC that will enhance the existing operations, engineering and management strategies and provide insight and metrics to assess their daily impacts, especially as related to the Propulsion Test Directorate testing scenarios for the 21st Century.

Hamilton, Kimberly↗
NASA NTRS1990

MEASURE: An integrated data-analysis and model identification facility

The first phase of the development of MEASURE, an integrated data analysis and model identification facility is described. The facility takes system activity data as input and produces as output representative behavioral models of the system in near real time. In addition a wide range of statistical characteristics of the measured system are also available. The usage of the system is illustrated on data collected via software instrumentation of a network of SUN workstations at the University of Illinois. Initially, statistical clustering is used to identify high density regions of resource-usage in a given environment. The identified regions form the states for building a state-transition model to evaluate system and program performance in real time. The model is then solved to obtain useful parameters such as the response-time distribution and the mean waiting time in each state. A graphical interface which displays the identified models and their characteristics (with real time updates) was also developed. The results provide an understanding of the resource-usage in the system under various workload conditions. This work is targeted for a testbed of UNIX workstations with the initial phase ported to SUN workstations on the NASA, Ames Research Center Advanced Automation Testbed.

Singh, Jaidip↗
NASA NTRS2020

Implementation of Multidomain Unified Forward Operators (UFO) Within the Joint Effort for Data Assimilation Integration (JEDI): Ocean Applications

The Joint Effort for Data assimilation Integration (JEDI) is a collaborative development led by the Joint Center for Satellite Data Assimilation (JCSDA) in conjunction with NASA, NOAA and the Department of Defense (NAVY and Air Force). The (Sea-Ice Ocean and Coupled Assimilation) SOCA as one of the JCSDA projects, focuses on the application of JEDI to marine data assimilation. One of the goals of SOCA is to make use of surface-sensitive radiances to constrain sea-ice and upper ocean fields (e.g., salinity, temperature, sea-ice fraction, sea-ice temperature, etc.). The first elements toward an ocean/atmosphere coupled data assimilation capability within JEDI, with a focus on supporting and developing the assimilation of radiance observations sensitive to the ocean and atmosphere has been implemented. The direct radiance assimilation of surface sensitive microwave radiances focusing on Global Precipitation Measurement (GPM) Imager (GMI) for the SST Constraint and Soil Moisture Active Passive (SMAP) for the Sea Surface Salinity (SSS) has been the main focus. Also, in UFO the capability to calculate the cool skin layer depth and skin temperature has been implemented similar to the GEOS-5. It has been tested with GMI sea surface temperature retrievals. This is important because Satellite and in-situ observations of the Sea-Surface Temperature (SST) show high variability, including a diurnal cycle and very thin, cool skin layer in contact with the atmosphere, and Incorporating a realistic skin SST is essential for atmosphere-ocean coupled data assimilation.

Unified Forward Operators (UFO)↗
NASA NTRS1974

An integrated PCM data system for full scale aeronautics testing

An integrated PCM data system is being developed at Ames Research Center to gather test data on advanced STOL propulsive lift, VTOL, rotary wing, and V/STOL control systems concepts as they pass through wind-tunnel, test-stand, flight-simulator and flight-test phases. Identical airborne signal conditioning and PCM encoding is used on test aircraft and wind tunnel models. An 80,000 word/second PCM installation will be the first all PCM-instrumented rotary wing development project. The system uses both dedicated and time-shared computers for fast data analysis with maximum use of resources. This system development shows one way to bring separate data user groups together over a common data base, while sharing computing resources for minimum cost.-

Reynolds, D. R.↗
NASA NTRS1980

An investigation for the development of an integrated optical data preprocessor

A laboratory model of a 16 channel integrated optical data preprocessor was fabricated and tested in response to a need for a device to evaluate the outputs of a set of remote sensors. It does this by accepting the outputs of these sensors, in parallel, as the components of a multidimensional vector descriptive of the data and comparing this vector to one or more reference vectors which are used to classify the data set. The comparison is performed by taking the difference between the signal and reference vectors. The preprocessor is wholly integrated upon the surface of a LiNbO3 single crystal with the exceptions of the source and the detector. He-Ne laser light is coupled in and out of the waveguide by prism couplers. The integrated optical circuit consists of a titanium infused waveguide pattern, electrode structures and grating beam splitters. The waveguide and electrode patterns, by virtue of their complexity, make the vector subtraction device the most complex integrated optical structure fabricated to date.

Verber, C. M.↗
NASA NTRS2002

The D3 Middleware Architecture

DARWIN is a NASA developed, Internet-based system for enabling aerospace researchers to securely and remotely access and collaborate on the analysis of aerospace vehicle design data, primarily the results of wind-tunnel testing and numeric (e.g., computational fluid-dynamics) model executions. DARWIN captures, stores and indexes data; manages derived knowledge (such as visualizations across multiple datasets); and provides an environment for designers to collaborate in the analysis of test results. DARWIN is an interesting application because it supports high-volumes of data. integrates multiple modalities of data display (e.g., images and data visualizations), and provides non-trivial access control mechanisms. DARWIN enables collaboration by allowing not only sharing visualizations of data, but also commentary about and views of data. Here we provide an overview of the architecture of D3, the third generation of DARWIN. Earlier versions of DARWIN were characterized by browser-based interfaces and a hodge-podge of server technologies: CGI scripts, applets, PERL, and so forth. But browsers proved difficult to control, and a proliferation of computational mechanisms proved inefficient and difficult to maintain. D3 substitutes a pure-Java approach for that medley: A Java client communicates (though RMI over HTTPS) with a Java-based application server. Code on the server accesses information from JDBC databases, distributed LDAP security services, and a collaborative information system. D3 is a three tier-architecture, but unlike 'E-commerce' applications, the data usage pattern suggests different strategies than traditional Enterprise Java Beans - we need to move volumes of related data together, considerable processing happens on the client, and the 'business logic' on the server-side is primarily data integration and collaboration. With D3, we are extending DARWIN to handle other data domains and to be a distributed system, where a single login allows a user transparent access to test results from multiple servers and authority domains.

Walton, Joan↗
NASA NTRS1993

Geologic mapping using integrated AIRSAR, AVIRIS, and TIMS data

The multi-sensor aircraft campaign called the 'Geologic Remote Sensing Field Experiment' (GRSFE), conducted during 1989 in the southwestern United States, collected multiple airborne remote sensing data sets and associated field and laboratory measurements. The GRSFE airborne data sets used in this study include the airborne Synthetic Aperture Radar (AIRSAR), the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and the Thermal Infrared Multispectral Scanner (TIMS). Each sensor's unique characteristics were used for this study in a combined analysis scheme for geologic mapping. AIRSAR was used to map structures and landforms, AVIRIS was used to map mineralogy, and TIMS was used to map lithology. Visual data integration using IHS transforms and combined numerical analysis using derived geophysical and geologic parameters with 'multispectral' techniques resulted in improved geologic mapping over that possible using each data set individually.

Kruse, Fred A.↗
NASA NTRSDate not supplied

Py MILab: Capturing, Analyzing and Storing Test Data

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which is heavily dependent on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results. Populating searchable information management systems with such rich data sets is often burdensome for data producers, resulting in a lack of findable data for modelers to validate and verify their models. To overcome these cultural barriers to ICME, NASA has developed of various database-integration toolsets that perform both data management activities within the organization’s best practices with additional functionality that relieves the effort of the data producer and promotes adoption of information management system. One such tool currently under development is Py MILab, an automatic framework for automatic capturing, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. TMAnalysis is a Python-based tool that performs automatic data reduction and analysis of uniaxial thermomechanical test data. The TMAnalysis toolset can be implemented within the Analysis module of Py MILab, and thus requires a populated neutral file form the Raw Data Module of Py MILab and outputs a Analysis neutral file compatible with the Database Module of Py MILab. TMAnalysis is able to perform automatic segmentation of multistage tests and perform data analysis and reduction, including determination of point-wise properties in tension, compression, and shear, analysis of stress relaxation tests, creep analysis and zone identification, and combination of these stage types for tests with complex loading histories. The TMAnalysis code is accompanied with a graphical user interface (GUI) that allows users to easily analyze test data in bulk, verify the automatic, consistent analysis performed by the backend code, and edit stage segmentation if necessary before producing the output neutral files, ensuring data is properly analyzed and maintained with full traceability.

Data management↗
NASA NTRS1994

The VIS-AD data model: Integrating metadata and polymorphic display with a scientific programming language

The VIS-AD data model integrates metadata about the precision of values, including missing data indicators and the way that arrays sample continuous functions, with the data objects of a scientific programming language. The data objects of this data model form a lattice, ordered by the precision with which they approximate mathematical objects. We define a similar lattice of displays and study visualization processes as functions from data lattices to display lattices. Such functions can be applied to visualize data objects of all data types and are thus polymorphic.

Hibbard, William L.↗
NASA NTRS2010

Integration of Apollo Lunar Sample Data into Google Moon

The Google Moon Apollo Lunar Sample Data Integration project is a continuation of the Apollo 15 Google Moon Add-On project, which provides a scientific and educational tool for the study of the Moon and its geologic features. The main goal of this project is to provide a user-friendly interface for an interactive and educational outreach and learning tool for the Apollo missions. Specifically, this project?s focus is the dissemination of information about the lunar samples collected during the Apollo missions by providing any additional information needed to enhance the Apollo mission data on Google Moon. Apollo missions 15 and 16 were chosen to be completed first due to the availability of digitized lunar sample photographs and the amount of media associated with these missions. The user will be able to learn about the lunar samples collected in these Apollo missions, as well as see videos, pictures, and 360 degree panoramas of the lunar surface depicting the lunar samples in their natural state, following collection and during processing at NASA. Once completed, these interactive data layers will be submitted for inclusion into the Apollo 15 and 16 missions on Google Moon.

Dawson, Melissa D.↗
NASA NTRS2012

Integrating Engineering Data Systems for NASA Spaceflight Projects

NASA has a large range of custom-built and commercial data systems to support spaceflight programs. Some of the systems are re-used by many programs and projects over time. Management and systems engineering processes require integration of data across many of these systems, a difficult problem given the widely diverse nature of system interfaces and data models. This paper describes an ongoing project to use a central data model with a web services architecture to support the integration and access of linked data across engineering functions for multiple NASA programs. The work involves the implementation of a web service-based middleware system called Data Aggregator to bring together data from a variety of systems to support space exploration. Data Aggregator includes a central data model registry for storing and managing links between the data in disparate systems. Initially developed for NASA's Constellation Program needs, Data Aggregator is currently being repurposed to support the International Space Station Program and new NASA projects with processes that involve significant aggregating and linking of data. This change in user needs led to development of a more streamlined data model registry for Data Aggregator in order to simplify adding new project application data as well as standardization of the Data Aggregator query syntax to facilitate cross-application querying by client applications. This paper documents the approach from a set of stand-alone engineering systems from which data are manually retrieved and integrated, to a web of engineering data systems from which the latest data are automatically retrieved and more quickly and accurately integrated. This paper includes the lessons learned through these efforts, including the design and development of a service-oriented architecture and the evolution of the data model registry approaches as the effort continues to evolve and adapt to support multiple NASA programs and priorities.

Carvalho, Robert E.↗