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At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

The Evolution of Payload Data Capabilities on the Commercial Visiting Vehicles that Service the International Space Station

For over 10 years, NASA has been working with U.S.-based commercial companies to support the design, development, and operations of new commercial space vehicles. The purpose of these vehicles is to provide cargo and crew transportation services to the International Space Station (ISS) and to stimulate the commercial space transportation industry to Low-Earth Orbit (LEO). Along with the ability to provide a couple tons of cargo to the ISS each mission, the commercial visiting vehicles also provide the ability to transfer NASA payloads to and from the ISS in an active, powered state. To take advantage of this capability, NASA requirements for the data services that payloads need while they are integrated into the visiting vehicles have grown with each set of commercial contracts. Today, NASA desires for payload data services encompass a variety of capabilities including payload health and status (H&S) telemetry monitoring, visiting vehicle environment data monitoring, and payload commanding. The intent of these capabilities is to provide payload developers with situational awareness and the ability to quickly diagnose any problems with payload operations prior to vehicle docking or post-landing to support immediate troubleshooting response. As new requirements for payload data services have emerged over the years, the ISS Payload Operations Integration Center (POIC) at the NASA Marshall Space Flight Center (MSFC) has established ground segment interfaces to the visiting vehicle control centers to enable payload developers to use these services. Many technical and programmatic challenges have been faced while establishing these interfaces. The most significant challenge faced has been in finding the right balance amongst providing interfaces to payloads that are compatible with the ISS so that payload hardware or software changes are not necessary to ensure compatibility with the visiting vehicles; keeping POIC ground system development costs low by standardizing implementation approaches across commercial vehicle partners; and fostering commercialization through supporting vendor-unique implementations that are commercially economical. This paper will first detail the data services that will be available to payload users under each commercial visiting vehicle contract. Secondly, this paper will offer discussion of the most significant technical and programmatic challenges faced to-date in offering these new data services to payload developers.

Wiggins, Lindsay M.↗
NASA NTRSDate not supplied

A Survey of Cyber Threat

Multiple companies are competing to develop human-crewed and unmanned aerial vehicles to provide flight in urban environments. With the growth of aerial systems and future airborne vehicle networks and the need to enable secure data exchange and service interactions within Urban Air Mobility (UAM) environments, cybersecurity has come to the forefront as a topic, highlighting the need to protect these networks from cyber-attacks. This paper will identify potential threats, vulnerabilities, and weaknesses of UAM environments, that could lead to system compromises and disruptions.

Cyber Security↗
NASA NTRS1987

Low cost, microcomputer-based interactive analysis system for direct-reception and archived remote sensing data

A hardware/software system has been developed for use in meteorological field experiments and by developing countries which need low-cost remote sensing data analysis capabilities. The system can directly receive satellite transmissions of the AVHRR, TOVS, Argos, GOES, GMS, and Meteosat systems. Also, the system can process computer tapes from Landsat. Received data are ingested directly into the computer's disk storage, eliminating the need for a wide-band analog tape drive. Data may be interpreted in terms of cloud imagery, cloudtop temperature, sea surface temperature, vegetation index, and temperature/humidity profiles with overlays of geographic boundaries and latitude/longitude grids. The image processing, enhancement functions, and modeling capabilities of the system are discussed.

Vermillion, Charles H.↗
NASA NTRS1989

PVDaCS - A prototype knowledge-based expert system for certification of spacecraft data

On-line data management techniques to certify spacecraft information are mandated by increasing telemetry rates. Knowledge-based expert systems offer the ability to certify data electronically without the need for time-consuming human interaction. Issues of automatic certification are explored by designing a knowledge-based expert system to certify data from a scientific instrument, the Orbiter Ultraviolet Spectrometer, on an operating NASA planetary spacecraft, Pioneer Venus. The resulting rule-based system, called PVDaCS (Pioneer Venus Data Certification System), is a functional prototype demonstrating the concepts of a larger system design. A key element of the system design is the representation of an expert's knowledge through the usage of well ordered sequences. PVDaCS produces a certification value derived from expert knowledge and an analysis of the instrument's operation. Results of system performance are presented.

Wharton, Cathleen↗
NASA NTRS1985

Crop Characteristics Research: Growth and Reflectance Analysis

Much of the early research in remote sensing follows along developing spectral signatures of cover types. It was found, however, that a signature from an unknown cover class could not always be matched to a catalog value of known cover class. This approach was abandoned and supervised classification schemes followed. These were not efficient and required extensive training. It was obvious that data acquired at a single time could not separate cover types. A large portion of the proposed research has concentrated on modeling the temporal behavior of agricultural crops and on removing the need for any training data in remote sensing surveys; the key to which is the solution of the so-called 'signature extension' problem. A clear need to develop spectral estimaters of crop ontogenic stages and yield has existed even though various correlations have been developed. Considerable effort in developing techniques to estimate these variables was devoted to this work. The need to accurately evaluate existing canopy reflectance model(s), improve these models, use them to understand the crop signatures, and estimate leaf area index was the third objective of the proposed work. A synopsis of this research effort is discussed.

Badhwar, G. D.↗
NASA NTRS1978

A manual for inexpensive methods of analyzing and utilizing remote sensor data

Instructions are provided for inexpensive methods of using remote sensor data to assist in the completion of the need to observe the earth's surface. When possible, relative costs were included. Equipment need for analysis of remote sensor data is described, and methods of use of these equipment items are included, as well as advantages and disadvantages of the use of individual items. Interpretation and analysis of stereo photos and the interpretation of typical patterns such as tone and texture, landcover, drainage, and erosional form are described. Similar treatment is given to monoscopic image interpretation, including LANDSAT MSS data. Enhancement techniques are detailed with respect to their application and simple techniques of creating an enhanced data item. Techniques described include additive and subtractive (Diazo processes) color techniques and enlargement of photos or images. Applications of these processes, including mappings of land resources, engineering soils, geology, water resources, environmental conditions, and crops and/or vegetation, are outlined.

Elifrits, C. D.↗
NASA NTRS1978

Information systems

Space-derived information is the principal product of our civilian space program. Associated data from earth-orbital and planetary spacecraft will exceed the equivalent of some 500 television pictures per second during the 1980's. Cost-effective management of this data flood requires a new look at NASA's overall information systems. Advanced technologies being pursued by industry and NASA could provide order-of-magnitude reductions in data-handling costs and times to permit a massive increase in the use of space-acquired data for both individual and national needs. This paper discusses attendant thrusts in efficient data acquisition, instrument pointing, real-time data management, and low-cost data distribution. Current and planned technology activities in these areas and their anticipated impact on future space missions are described.

Kurzhals, P. R.↗
NASA NTRS1983

A versatile ground data display system for Spacelab experiments

A microprocessor-based data capture and display system suited to the needs of many Spacelab experimenters is described. Although developed specifically for the needs of the FAUST UV astronomy telescope, several features of the system may make it attractive to other groups who need to interact with their Spacelab data in real or near-real time. The display system captures data from the serial data stream originating in the Spacelab experiment computer input/output (ECIO) data channel, and can reformat and display several hundred variables per second in real time. The system offers a combination of CRT display and analog paper strip-chart output. A facility is also provided for simultaneously creating floppy-disk records of short segments of data according to prearranged criteria, to allow detailed examination of critical events in the operation of the experiment. The simplicity, economy, reliability, and flexibility of the display system suggest its use for monitoring a variety of low- and medium-data-rate Spacelab experiments.

Werthimer, D.↗
NASA NTRS1995

Computing Thickness Of Sea Ice From Polarimetric SAR Data

Artificial neural network developed for computing thickness of sea ice from polarimetric synthetic-aperture-radar (SAR) data. Once neural network trained, no further intervention needed: when stimulated with SAR scattering data, quickly responds with estimate of thickness of sea ice.

Huynh, Dave D.↗
NASA NTRS2018

Introduction to Big Earth Data Applications

Climate and weather modeling generate enormous volumes that make iterative analysis challenging, spurring the development of new ways to work with the data. A theme going across applications is the need to identify and highlight "interesting" data for the scientist to focus on. Operational applications often scale up from small, local studies to larger spatial scales with more analysis targets.

parallel processing (computers)↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
NASA NTRSDate not supplied

Acquisition of and Access to Research Omics Data

Omics data are essential for understanding the myriad and complex effects of space environments on humans. To assure maximum benefit from these kinds of data, the NASA Human Research Program Data Management Plan stipulates that human omics data should be archived within and accessed through the NASA Life Sciences Portal (NLSP). The NLSP has the capability to acquire and provision access to omics (and other kinds of) research results for individual and ad-hoc groups of subjects at the direction of institutional review boards, or other authorizing bodies or individuals, per institutional, program and investigation-specific policies and procedures. However, because some single-subject omics data, like CT scans and other kinds of large, complex biomedical data, could be used to identify heretofore unknown risks to the subject’s health, or, in certain cases, be used to identify a subject, NASA Policy Directive 7170.1 describes various policies regarding the management of and access to “research genetic testing” data, which includes many kinds of omics data. For example, NPD 7170.1 prohibits access to human research genetic data by NASA personnel who make employment decisions for the subjects from whom the data were obtained. To meet the objective of acquiring research omics data for NLSP in compliance with the policies in NPD 7170.1 and other applicable NASA policies, we designed NOMADS (the NLSP Omics Multimodal Acquisition of Data System), a new component that supports the transfer of large research data files, including research genetic testing data, using one of several different transfer mechanisms. The choice of mechanism is made by the submitter of the data, with guiding information from the system, and is likely to often be determined in large part by the nature and source location of the data. For example, for small files where the source data files are not already stored in a cloud storage system, users are likely to prefer to transfer their data to the NLSP via a web browser. Conversely, for large sets of files already organized and stored in a cloud storage system, users may opt for NOMAD’s cloud-to-cloud transfer method. All omics datasets targeted for the NASA Life Sciences Data Archive must pass a variety of quality checks to ensure data integrity and adherence to the standards defined by the LSDA Data Submission Guidelines (DSG) (see https://nlsp.nasa.gov/explore/lsdahome/datasubmit). These include requirements that data are consistent with open standards established by the omics community. Non-compliant data will not be accepted however archivists are available to advise submitters on how to revise data submissions and re-submit until compliance is achieved. Following compliance with the LSDA DSG, omics data next undergo a variety of additional quality checks to ensure the data meet omics community standards. Domain specific Omics data quality control tools and techniques are continually evolving and linked to the advancements in omics assays utilized and thus, the tools and techniques utilized by the LSDA for data quality control and validation will need to be sustained accordingly. All human omics data will be access controlled according to the policies described above, and requiring IRB approval for any additional access grants once the data are acquired (including access for analysis using the NLSP workspace tools).

Omics↗
NASA NTRS2024

Acquisition of and Access to Research Omics Data

Omics data are essential for understanding the myriad and complex effects of space environments on humans. To assure maximum benefit from these kinds of data, the NASA Human Research Program Data Management Plan stipulates that human omics data should be archived within and accessed through the NASA Life Sciences Portal (NLSP). The NLSP has the capability to acquire and provision access to omics (and other kinds of) research results for individual and ad-hoc groups of subjects at the direction of institutional review boards, or other authorizing bodies or individuals, per institutional, program and investigation-specific policies and procedures. However, because some single-subject omics data, like CT scans and other kinds of large, complex biomedical data, could be used to identify heretofore unknown risks to the subject’s health, or, in certain cases, be used to identify a subject, NASA Policy Directive 7170.1 describes various policies regarding the management of and access to “research genetic testing” data, which includes many kinds of omics data. For example, NPD 7170.1 prohibits access to human research genetic data by NASA personnel who make employment decisions for the subjects from whom the data were obtained. To meet the objective of acquiring research omics data for NLSP in compliance with the policies in NPD 7170.1 and other applicable NASA policies, we designed NOMADS (the NLSP Omics Multimodal Acquisition of Data System), a new component that supports the transfer of large research data files, including research genetic testing data, using one of several different transfer mechanisms. The choice of mechanism is made by the submitter of the data, with guiding information from the system, and is likely to often be determined in large part by the nature and source location of the data. For example, for small files where the source data files are not already stored in a cloud storage system, users are likely to prefer to transfer their data to the NLSP via a web browser. Conversely, for large sets of files already organized and stored in a cloud storage system, users may opt for NOMAD’s cloud-to-cloud transfer method. All omics datasets targeted for the NASA Life Sciences Data Archive must pass a variety of quality checks to ensure data integrity and adherence to the standards defined by the LSDA Data Submission Guidelines (DSG) (see https://nlsp.nasa.gov/explore/lsdahome/datasubmit). These include requirements that data are consistent with open standards established by the omics community. Non-compliant data will not be accepted however archivists are available to advise submitters on how to revise data submissions and re-submit until compliance is achieved. Following compliance with the LSDA DSG, omics data next undergo a variety of additional quality checks to ensure the data meet omics community standards. Domain specific Omics data quality control tools and techniques are continually evolving and linked to the advancements in omics assays utilized and thus, the tools and techniques utilized by the LSDA for data quality control and validation will need to be sustained accordingly. All human omics data will be access controlled according to the policies described above, and requiring IRB approval for any additional access grants once the data are acquired (including access for analysis using the NLSP workspace tools).

Omics↗
NASA NTRS1995

Geophysical phenomena classification by artificial neural networks

Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

Gough, M. P.↗
NASA NTRS2004

Initial Validation of NDVI time seriesfrom AVHRR, VEGETATION, and MODIS

The paper will address Theme 7: Multi-sensor opportunities for VEGETATION. We present analysis of a long-term vegetation record derived from three moderate resolution sensors: AVHRR, VEGETATION, and MODIS. While empirically based manipulation can ensure agreement between the three data sets, there is a need to validate the series. This paper uses atmospherically corrected ETM+ data available over the EOS Land Validation Core Sites as an independent data set with which to compare the time series. We use ETM+ data from 15 globally distributed sites, 7 of which contain repeat coverage in time. These high-resolution data are compared to the values of each sensor by spatially aggregating the ETM+ to each specific sensors' spatial coverage. The aggregated ETM+ value provides a point estimate for a specific site on a specific date. The standard deviation of that point estimate is used to construct a confidence interval for that point estimate. The values from each moderate resolution sensor are then evaluated with respect to that confident interval. Result show that AVHRR, VEGETATION, and MODIS data can be combined to assess temporal uncertainties and address data continuity issues and that the atmospherically corrected ETM+ data provide an independent source with which to compare that record. The final product is a consistent time series climate record that links historical observations to current and future measurements.

Morisette, Jeffrey T.↗
NASA NTRS2007

General-Purpose Front End for Real-Time Data Processing

FRONTIER is a computer program that functions as a front end for any of a variety of other software of both the artificial intelligence (AI) and conventional data-processing types. As used here, front end signifies interface software needed for acquiring and preprocessing data and making the data available for analysis by the other software. FRONTIER is reusable in that it can be rapidly tailored to any such other software with minimum effort. Each component of FRONTIER is programmable and is executed in an embedded virtual machine. Each component can be reconfigured during execution. The virtual-machine implementation making FRONTIER independent of the type of computing hardware on which it is executed.

James, Mark↗
NASA NTRS2012

Satellite Observation Systems for Polar Climate Change Studies

The key observational tools for detecting large scale changes of various parameters in the polar regions have been satellite sensors. The sensors include passive and active satellite systems in the visible, infrared and microwave frequencies. The monitoring started with Tiros and Nimbus research satellites series in the 1970s but during the period, not much data was stored digitally because of limitations and cost of the needed storage systems. Continuous global data came about starting with the launch of ocean color, passive microwave, and thermal infrared sensors on board Nimbus-7 and Synthetic Aperture Radar, Radar Altimeter and Scatterometer on board SeaSat satellite both launched in 1978. The Nimbus-7 lasted longer than expected and provided about 9 years of useful data while SeaSat quit working after 3 months but provided very useful data that became the baseline for follow-up systems with similar capabilities. Over the years, many new sensors were launched, some from Japan Aeronautics and Space Agency (JAXA), some from the European Space Agency (ESA) and more recently, from RuSSia, China, Korea, Canada and India. For polar studies, among the most useful sensors has been the passive microwave sensor which provides day/night and almost all weather observation of the surface. The sensor provide sea surface temperature, precipitation, wind, water vapor and sea ice concentration data that have been very useful in monitoring the climate of the region. More than 30 years of such data are now available, starting with the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7, the Special Scanning Microwave/Imager (SSM/I) on board a Defense Meteorological Satellite Program (DMSP) and the Advanced Microwave Scanning Radiometer on board the EOS/ Aqua satellite. The techniques that have been developed to derive geophysical parameters from data provided by these and other sensors and associated instrumental and algorithm errors and validation techniques will be discussed. An important issue is the organization and storage of hundreds of terabytes of data collected by even just a few of these satellite sensors. Advances in mass storage and computer technology have made it possible to overcome many of the collection and archival problems and the availability of comprehensive satellite data sets put together by NASA's Earth Observing System project will be discussed.

Comiso, Josefino C.↗