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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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NASA NTRS1986

The development of an intelligent user interface for NASA's scientific databases

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has as one of its components, the development of an Intelligent User Interface (IUI). The intent of the IUI effort is to develop a friendly and intelligent user interface service that is based on expert systems and natural language processing technologies. This paper presents the design concepts, development approach and evaluation of performance of a prototype Intelligent User Interface Subsystem (IUIS) supporting an operational database.

Campbell, William J.↗

NASA NTRS1992

STS-40 orbital acceleration research experiment flight results during a typical sleep period

The Orbital Acceleration Research Experiment (OARE), an electrostatic accelerometer package with complete on-orbit calibration capabilities, was flown for the first time aboard the Space Shuttle on STS-40. This is also the first time an accelerometer package with nano-g sensitivity and a calibration facility has flown aboard the Space Shuttle. The instrument is designed to measure and record the Space Shuttle aerodynamic acceleration environment from the free molecule flow regime through the rarified flow transition into the hypersonic continuum regime. Because of its sensitivity, the OARE instrument defects aerodynamic behavior of the Space Shuttle while in low-earth orbit. A 2-hour orbital time period on day seven of the mission, when the crew was asleep and other spacecraft activities were at a minimum, was examined. During the flight, a 'trimmed-mean' filter was used to produce high quality, low frequency data which was successfully stored aboard the Space Shuttle in the OARE data storage system. Initial review of the data indicated that, although the expected precision was achieved, some equipment problems occurred resulting in uncertain accuracy. An acceleration model which includes aerodynamic, gravity-gradient, and rotational effects was constructed and compared with flight data. Examination of the model with the flight data shows the instrument to be sensitive to all major expected low frequency acceleration phenomena; however, some erratic instrument bias behavior persists in two axes. In these axes, the OARE data can be made to match a comprehensive atmospheric-aerodynamic model by making bias adjustments and slight linear corrections for drift. The other axis does not exhibit these difficulties and gives good agreement with the acceleration model.

Blanchard, R. C.↗

NASA NTRS2013

Tracker Toolkit

This software can track multiple moving objects within a video stream simultaneously, use visual features to aid in the tracking, and initiate tracks based on object detection in a subregion. A simple programmatic interface allows plugging into larger image chain modeling suites. It extracts unique visual features for aid in tracking and later analysis, and includes sub-functionality for extracting visual features about an object identified within an image frame. Tracker Toolkit utilizes a feature extraction algorithm to tag each object with metadata features about its size, shape, color, and movement. Its functionality is independent of the scale of objects within a scene. The only assumption made on the tracked objects is that they move. There are no constraints on size within the scene, shape, or type of movement. The Tracker Toolkit is also capable of following an arbitrary number of objects in the same scene, identifying and propagating the track of each object from frame to frame. Target objects may be specified for tracking beforehand, or may be dynamically discovered within a tripwire region. Initialization of the Tracker Toolkit algorithm includes two steps: Initializing the data structures for tracked target objects, including targets preselected for tracking; and initializing the tripwire region. If no tripwire region is desired, this step is skipped. The tripwire region is an area within the frames that is always checked for new objects, and all new objects discovered within the region will be tracked until lost (by leaving the frame, stopping, or blending in to the background).

Lewis, Steven J.↗

NASA NTRS2019

TPSAS-NF1676L-32235-DND

This presentation will provide a summary of the Open Data Cube initiative. In addition, a training manual will be provided to support Open Data Cube training for GEO and the Senegal government.

Brian Killough↗

NASA NTRS2019

TPSAS-NF1676L-32249-DND

This presentation will provide a summary of the Open Data Cube initiative. In addition, a training manual will be provided to support Open Data Cube training for the GEO Secretariat.

Brian Killough↗

NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗

NASA NTRS1982

Correlation of flight effects on centerline velocity decay for cold-flow acoustically excited jets

Acoustic excitation can influence the large scale shear layer structure of jet flow, thereby causing the jet centerline velocity to decay more rapidly. this phenomena has numerous practical applications, such as the reduction of jet/flap impingement velocity in order to reduce flap structural loads for under-the-wing STOL aircraft concepts. Cold flow centerline velocity decay data obtained with acoustic excitation of the jet exhaust flow are correlated with and without flight effects. Initially, static data are correlated in terms of an acoustic parameter involving the threshold sound level needed to initiate acoustic excitation of the jet plume. It is shown that for a given flight speed, the same acoustic excitation parameter as that developed for the static condition is valid. Finally, an aerodynamic flight effects parameter is included to correlate the static and flight centerline velocity decay data with and without acoustic excitation.

Vonglahn, U. H.↗

NASA NTRS1981

A gradient model of vegetation and climate utilizing NOAA satellite imagery. Phase 1: Texas transect

A climatological model/variable termed the sponge (a measure of moisture availability based on daily temperature maxima and minima, and precipitation) was tested for potential biogeograhic, ecological, and agro-climatological applications. Results, depicted in tabular and graphic form, suggest that, as generalized climatic index, sponge is particularly appropriate for large-area and global vegetation monitoring. The feasibility of utilizing NOAA/AVHRR data for vegetation classification was investigated and a vegetation gradient model that utilizes sponge and AVHRR data was initiated. Along an east-west Texas gradient, vegetation, sponge, and AVHRR pixel data (channels 1 and 2) were obtained for 12 locations. The normalized difference values for the AVHRR data when plotted against vegetation characteristics (biomass, net productivity, leaf area) and sponge values along the Texas gradient suggest that a multivariate gradient model incorporating AVHRR and sponge data may indeed be useful in global vegetation stratification and monitoring.

Greegor, D.↗

NASA NTRS2004

Science Data Processing for the Advanced Microwave Scanning Radiometer: Earth Observing System

The National Aeronautics and Space Administration established the framework for the Science Investigator-led Processing Systems (SIPS) to enable the Earth science data products to be produced by personnel directly associated with the instrument science team and knowledgeable of the science algorithms. One of the first instantiations implemented for NASA was the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) SIPS. The AMSR-E SIPS is a decentralized, geographically distributed ground data processing system composed of two primary components located in California and Alabama. Initial science data processing is conducted at Remote Sensing Systems (RSS) in Santa Rosa, California. RSS ingests antenna temperature orbit data sets from JAXA and converts them to calibrated, resampled, geolocated brightness temperatures. The brightness temperatures are sent to the Global Hydrology and Climate Center in Huntsville, Alabama, which generates the geophysical science data products (e.g., water vapor, sea surface temperature, sea ice extent, etc.) suitable for climate research and applications usage. These science products are subsequently sent to the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado for archival and dissemination to the at-large science community. This paper describes the organization, coordination, and production techniques employed by the AMSR-E SIPS in implementing, automating and operating the distributed data processing system.

Goodman, H. Michael↗

NASA NTRS2010

Examples L-Band Interference will be Presented and Discussed, as well as the Importance of L-Band Soil Moisture Observations

Examples of L-band interference will be presented and discussed, as well as the importance of L-band soil moisture observations, as part of this one-day GEOSS workshop XXXVII on "Data Quality and Radio Spectrum Allocation Impact on Earth Observations" will address the broad challenges of data quality and the impact of generating reliable information for decision makers who are Earth data users but not necessarily experts in the Earth observation field. GEO has initiated a data quality assessment task (DA-09-01a) and workshop users will review and debate the directions and challenges of this effort. Radio spectrum allocation is an element of data availability and data quality, and is also associated with a GEO task (AR-06-11). A recent U.S. National Research Council report on spectrum management will be addressed as part of the workshop. Key representatives from industry, academia, and government will provide invited talks on these and related issues that impact GEOSS implementation.

Kim, Edward↗

NASA NTRS2009

Progressive Classification Using Support Vector Machines

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Wagstaff, Kiri↗

NASA NTRS2010

Data Acquisition and Processing System for Airborne Wind Profiling with a Pulsed, 2-Micron, Coherent-Detection, Doppler Lidar System

A data acquisition and signal processing system is being developed for a 2-micron airborne wind profiling coherent Doppler lidar system. This lidar, called the Doppler Aerosol Wind Lidar (DAWN), is based on a Ho:Tm:LuLiF laser transmitter and 15-cm diameter telescope. It is being packaged for flights onboard the NASA DC-8, with the first flights in the summer of 2010 in support of the NASA Genesis and Rapid Intensification Processes (GRIP) campaign for the study of hurricanes. The data acquisition and processing system is housed in a compact PCI chassis and consists of four components such as a digitizer, a digital signal processing (DSP) module, a video controller, and a serial port controller. The data acquisition and processing software (DAPS) is also being developed to control the system including real-time data analysis and display. The system detects an external 10 Hz trigger pulse and initiates the data acquisition and processing process, and displays selected wind profile parameters such as Doppler shift, power distribution, wind directions and velocities. Doppler shift created by aircraft motion is measured by an inertial navigation/GPS sensor and fed to the signal processing system for real-time removal of aircraft effects from wind measurements. A general overview of the system and the DAPS as well as the coherent Doppler lidar system is presented in this paper.

Beyon, J. Y.↗

NASA NTRS2014

Open Source Initiative Powers Real-Time Data Streams

Under an SBIR contract with Dryden Flight Research Center, Creare Inc. developed a data collection tool called the Ring Buffered Network Bus. The technology has now been released under an open source license and is hosted by the Open Source DataTurbine Initiative. DataTurbine allows anyone to stream live data from sensors, labs, cameras, ocean buoys, cell phones, and more.

Source record↗

NASA NTRSDate not supplied

Gateway bi-propellant refueling development testing and model validation activities

The Gateway is an upcoming long term lunar exploration program to be completed by NASA in partnership with ESA and other US and international partners. The system design of the Gateway contains both a high performance Xenon based Solar Electric Propulsion system, as well as a bi-propellant attitude control system. Both propulsion systems are designed for on-orbit refueling to enable long life performance of the Gateway. The ESPRIT-RM is a module which will expand the pressurized volume of Gateway, while also providing refueling capability for both the Xenon and Bipropellant propulsion systems, therefore extending the Gateway life on orbit. As part of the Gateway bi-propellant refueling system development, a simplified fluidic breadboard system was created to evaluate system performance and response using simulant fluids. The test plan includes verification activities with simulant (water, HFE-7100) to verify joined subsystem behavior in the critical operations, including propellant transfer demonstration between modules, transient tests and venting tests. Integrated testing occurred at TASUK in collaboration with NASA to support joint verification activities to de-risk the major functions of the ESPRIT BTS and the overall CONOPS of the refueling of the Gateway chemical propulsion system. Initially collected test data from the system testing is presented, as well as initial model validation, and correlated to collected high frequency test data and system response is shown. Initially collected data has shown the architected system performance is closing initial design assumptions, but much forward work remains to characterize and develop the system.

Christopher Daniel Radke↗

NASA NTRS1974

Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation

The author has identified the following significant results. Primary emphasis during the period has been given to completing the resource and land use mask overlays for ultimate subsite MSS data extraction and analysis, refining the computer mask program, verifying resultant masks, and evaluating the initial subsite data. Standard deviations for the selected subsites were generally reduced significantly when compared to the values for the overall 7km x 7km test site areas. This indicates that the masking technique has been successful in reducing the variability of the ERTS-1 MSS data for ultimate subsite comparisons with ground data. The techniques used to locate the 7km x 7km test site areas and extract masked subsite data apparently limit the ability to reliably locate identical very small subsite areas for temporal comparisons. Current evidence indicates that areas which include less than about four pixel elements in either dimension are probably unreliable.

Rouse, J. W., Jr.↗

NASA NTRS1974

An optimized buffer controlled data compression system

The digital data compression system considered uses a buffer controlled aperture algorithm which minimizes the mean-squared error between the reconstructed receiver output and transmitter input. The data compression technique selected is based on the zero-order floating aperture prediction rule. It is assumed that the statistics of the input data are initially uniformly distributed, stationary, and first-order Markov. The problem is solved for stationary data. An approach is presented for extending the results to slowly varying uniformly distributed nonstationary Markov data.

Dosik, P. H.↗

NASA NTRS1991

Advanced techniques and technology for efficient data storage, access, and transfer

Advanced techniques for efficiently representing most forms of data are being implemented in practical hardware and software form through the joint efforts of three NASA centers. These techniques adapt to local statistical variations to continually provide near optimum code efficiency when representing data without error. Demonstrated in several earlier space applications, these techniques are the basis of initial NASA data compression standards specifications. Since the techniques clearly apply to most NASA science data, NASA invested in the development of both hardware and software implementations for general use. This investment includes high-speed single-chip very large scale integration (VLSI) coding and decoding modules as well as machine-transferrable software routines. The hardware chips were tested in the laboratory at data rates as high as 700 Mbits/s. A coding module's definition includes a predictive preprocessing stage and a powerful adaptive coding stage. The function of the preprocessor is to optimally process incoming data into a standard form data source that the second stage can handle.The built-in preprocessor of the VLSI coder chips is ideal for high-speed sampled data applications such as imaging and high-quality audio, but additionally, the second stage adaptive coder can be used separately with any source that can be externally preprocessed into the 'standard form'. This generic functionality assures that the applicability of these techniques and their recent high-speed implementations should be equally broad outside of NASA.

Rice, Robert F.↗

NASA NTRS2017

NASA GES DISC New Data Service and Data Management for the Air Quality Community

President Obama's Big Data Research and Development Initiative seeks to improve our ability to acquire knowledge and discover insights into large and complex collections of digital data. The Big Earth Data Initiative (BEDI) Invests in standardizing and optimizing the collection, management and delivery of U.S. Government's civil Earth observation data.

air pollution↗