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At least 1,729 records · Page 96

BOREAS Level-4b AVHRR-LAC Ten-Day Composite Images: At-sensor Radiance

The BOReal Ecosystem-Atmosphere Study (BOREAS) Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. Manitoba Remote Sensing Center (MRSC) and BOREAS Information System (BORIS) personnel acquired, processed, and archived data from the Advanced Very High Resolution Radiometer (AVHRR) instruments on the National Oceanic and Atmospheric Administration (NOAA-11) and -14 satellites. The AVHRR data were acquired by CCRS and were provided to BORIS for use by BOREAS researchers. These AVHRR level-4b data are gridded, 10-day composites of at-sensor radiance values produced from sets of single-day images. Temporally, the 10- day compositing periods begin 11-Apr-1994 and end 10-Sep-1994. Spatially, the data cover the entire BOREAS region. The data are stored in binary image format files.

Cihlar, Josef↗

BOREAS Level-4c AVHRR-LAC Ten-Day Composite Images: Surface Parameters

The BOReal Ecosystem-Atmosphere Study (BOREAS) Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. Manitoba Remote Sensing Center (MRSC) and BOREAS Information System (BORIS) personnel acquired, processed, and archived data from the Advanced Very High Resolution Radiometer (AVHRR) instruments on the NOAA-11 and -14 satellites. The AVHRR data were acquired by CCRS and were provided to BORIS for use by BOREAS researchers. These AVHRR level-4c data are gridded, 10-day composites of surface parameters produced from sets of single-day images. Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994. Spatially, the data cover the entire BOREAS region. The data are stored in binary image format files. Note: Some of the data files on the BOREAS CD-ROMs have been compressed using the Gzip program.

Cihlar, Josef↗

FERMI/GLAST Integrated Trending and Plotting System Release 5.0

An Integrated Trending and Plotting System (ITPS) is a trending, analysis, and plotting system used by space missions to determine performance and status of spacecraft and its instruments. ITPS supports several NASA mission operational control centers providing engineers, ground controllers, and scientists with access to the entire spacecraft telemetry data archive for the life of the mission, and includes a secure Web component for remote access. FERMI/GLAST ITPS Release 5.0 features include the option to display dates (yyyy/ddd) instead of orbit numbers along orbital Long-Term Trend (LTT) plot axis, the ability to save statistics from daily production plots as image files, and removal of redundant edit/create Input Definition File (IDF) screens. Other features are a fix to address invalid packet lengths, a change in naming convention of image files in order to use in script, the ability to save all ITPS plot images (from Windows or the Web) as GIF or PNG format, the ability to specify ymin and ymax on plots where previously only the desired range could be specified, Web interface capability to plot IDFs that contain out-oforder page and plot numbers, and a fix to change all default file names to show yyyydddhhmmss time stamps instead of hhmmssdddyyyy. A Web interface capability sorts files based on modification date (with newest one at top), and the statistics block can be displayed via a Web interface. Via the Web, users can graphically view the volume of telemetry data from each day contained in the ITPS archive in the Web digest. The ITPS could be also used in nonspace fields that need to plot data or trend data, including financial and banking systems, aviation and transportation systems, healthcare and educational systems, sales and marketing, and housing and construction.

Ritter, Sheila↗

SYMBIOSYS: A Methodology for Performance Analysis of Composable HPC Data Services

Microservices are a powerful new way of building, customizing, and deploying distributed services owing to their flexibility and maintainability. Several large-scale distributed platforms have emerged to serve the growing needs of data-centric workloads and services in commercial computing. Concurrently, high-performance computing (HPC) systems and software are rapidly evolving to meet the demands of diversified applications and heterogeneity. The interplay of hardware factors, software configuration parameters, and the flexibility offered with a microservice architecture makes it nontrivial to estimate the optimal service instantiation for a given application workload. Further, this problem is exacerbated when considering that these services operate in a dynamic and heterogeneous HPC environment. An optimally integrated service can be vastly more performant than a haphazardly integrated one. Existing performance tools for HPC either fail to understand the request-response model of communication inherent to microservices or they operate within a narrow scope, limiting the insight that can be gleaned from employing them in isolation. We propose a methodology for integrated performance analysis of HPC microservices frameworks and applications called SYMBIOSYS. We describe its design and implementation within the context of the Mochi framework. This integration is achieved by combining distributed callpath profiling and tracing with a performance data exchange strategy that collects fine-grained, low-level metrics from the RPC communication library and network layers. The result is a portable, low-overhead performance analysis setup that provides a holistic profile of the dependencies among microservices and how they interact with the Mochi RPC software stack. Using HEPnOS, a production-quality Mochi data service, we demonstrate the low-overhead operation of SYMBIOSYS at scale and use it to identify the root causes of poorly performing service configurations.

microservices↗

Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a new workflow based on the use of two neural networks to obtain high-quality accelerated reconstructions from sparse-view XCT scans of single material metal parts. The first network, implemented using fully-connected layers, helps reduce the impact of BH in the projection data without the need of any calibration or knowledge of the component material. The second network, a convolutional neural network, maps a low-quality analytic 3D reconstruction to a high-quality reconstruction. Using experimental data, we demonstrate that our method robustly generalizes across several alloys, and for a range of sparsity levels without any need for retraining the networks thereby enabling accurate and fast industrial XCT inspections.

Rahman, Obaid↗

JPSS Science Data Services for the Direct Readout Community

The Suomi National Polar-orbiting Partnership (S-NPP) and Joint Polar Satellite System (JPSS) High Rate Data (HRD) link provides Direct Broadcast data to users in real-time, utilizing their own remote field terminals. The Field Terminal Support (FTS) provides the resources needed to support the Direct Readout communities by providing software, documentation, and periodic updates to enable them to produce data products from SNPP and JPSS. The FTS distribution server will also provide the necessary ancillary and auxiliary data needed for processing the broadcasts, as well as making orbital data available to assist in locating the satellites of interest. In addition, the FTS provides development support for the algorithm and software through GSFC Direct Readout Laboratory (DRL) International Polar Orbiter Processing Package (IPOPP) and University of Wisconsin (UWISC) Community Satellite Processing Package (CSPP), to enable users to integrate the algorithms into their remote terminals. The support the JPSS Program provides to the institutions developing and maintaining these two software packages, will demonstrate the ability to produce ready-to-use products from the HRD link and provide risk reduction effort at a minimal cost. This paper discusses the key functions and system architecture of FTS.

Data Processing↗

Optimising the processing and storage of visibilities using lossy compression

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and the constraints imposed by I/O, as I/O bandwidths drop significantly on cold storage. An example of this is the data deluge expected from the SKA Telescopes of more than 60 PB per day, all to be stored on the buffer filesystem. While compressing the data is an obvious solution, the impacts on the final data products are hard to predict. In this paper, we chose an error-controlled compressor – MGARD – and applied it to simulated SKA-Mid and real pathfinder visibility data, in noise-free and noise-dominated regimes. As the data have an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. MGARD ensures the compression incurred errors adhere to the user-prescribed tolerance. To measure the degradation of images reconstructed using the lossy compressed data, we proposed a list of diagnostic measures, exploring the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images for continuum and spectral line examples. We found relative error bounds of as much as 10%, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS, whereas a 1% error bound (compression ratio of 8) introduces an increase in noise of about an order of magnitude less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a 0.1% error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds and has a host of potentially powerful additional features.

Techniques: interferometric↗

Machine learning surrogate models for Landau fluid closure

The first result of applying the machine/deep learning technique to the fluid closure problem is presented in this letter. As a start, three different types of neural networks (multilayer perceptron (MLP), convolutional neural network (CNN) and two-layer discrete Fourier transform (DFT) network) were constructed and trained to learn the well-known Hammett-Perkins Landau fluid closure in configuration space. We found that in order to train a well-preformed network, a minimum size of training data set is needed; MLP also requires a minimum number of neurons in the hidden layers equals to the degrees of freedom in Fourier space despite training data is fed in configuration space. Out of three models DFT performs the best for the clean data most likely due to the existence of nice Fourier expression for Hammett-Perkins closure but it is least robust with respect to input noise. Overall, with appropriate tuning and optimization, all three neural networks are able to accurately predict Hammett-Perkins closure and reproduce the inherit nonlocal feature, suggesting a promising path to calculate more sophisticated closures with the machine/deep learning technique.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A Python Instrument Control and Data Acquisition Suite for Reproducible Research

Tools that standardize and automate experimental data collection are needed for greater confidence in research results. The National Synchrotron Light Source-II (NSLS-II) has generated an open-source Python data acquisition, management, and analysis software suite that automates X-ray experiments and collects an experimental record that facilitates complete reproducibility. Here, we show that the NSLS-II tools are not only useful for X-ray science at large-scale facilities by presenting an add-on package that adapts these tools for use in a small laboratory with common physics and electrical engineering instruments. The composite software suite eases and automates the execution of experiments, records extensive metadata, stores data in portable containers, and speeds up the analysis through tools for comprehensive searches. In total, this software suite increases the reproducibility of laboratory experiments. We demonstrate the software via the evaluation of two lock-in amplifiers-the miniature ADA2200 and the ubiquitous Stanford Research Systems (SRS) SR810. The frequency resolution, signal-to-noise ratio, and dynamic reserve of the lock-in amplifiers are measured and presented. The usage of the software suite is described throughout these measurements so that the reader can implement the tools in their lab.

97 MATHEMATICS AND COMPUTING↗

The nature of multiple solutions for surface wind speed over the oceans from scatterometer measurements

The satellite SEASAT-A will carry a radar scatterometer in order to measure microwave backscatter from the sea surface. From pairs of radar measurements at angles separated by 90 deg in azimuth the surface wind speed and direction may be inferred, though not uniquely. The character of the solutions for wind speed and direction is displayed, as well as the nature of the ambiguities of these solutions. An economical procedure for handling such data is described, plus a criterion for the need for conventional (surface) data in order to resolve the ambiguities of solutions.

Price, J. C.↗

The nature of multiple solutions for surface wind speed over the oceans from scatterometer measurements

The satellite SEASAT-A will carry a radar scatterometer in order to measure microwave backscatter from the sea surface. From pairs of radar measurements at angles separated by 90 deg in azimuth the surface wind speed and direction may be inferred, though not uniquely. In this paper the character of the solutions for wind speed and direction is displayed, as well as the nature of the ambiguities of these solutions. An economical procedure for handling such data is described, plus a criterion for the need for conventional (surface) data in order to resolve the ambiguities of solutions.

Price, J. C.↗

United Nations principles on remote sensing - Report on developments, 1970-1980

The paper deals with the efforts of the United Nations Committee on the Peaceful Uses of Outer Space (COPUOS) to reach a consensus on legal principles governing the international use of remote sensing of the earth from space. The proceedings of the Working Group from 1971 to 1974 and the Legal Subcommittee from 1973 to 1980 are presented. Though the Committee has reached agreement on certain points, some key questions remain unresolved relative to dissemination of data and to the need for or method of classifying remote sensing data based on spatial resolution. An appendix of texts and draft principles worked out by COPUOS is also presented.

Mossinghoff, G. J.↗

The crush of new earth science data knocking at our door

The reasons for collecting massive amounts of earth science data in the Earth Observing System (EOS) Project are discussed. A processing hierarchy for handling the data is described, and the prospects for adequate throughput and storage for operational data analysis in the EOS era are addressed. Needs for successful exploratory data analysis are examined, and the policy issues implicated by the large stream of EOS data are considered.

Kahn, Ralph↗

Development of a naming convention and data ma nagement system

This contract covered work in data management, acquisition, and analysis for the Earth Science and Applications Division (ESAD) of the Space Science Laboratory of MSFC. Under this contract, a naming convention and data management system were developed for ESAD data sets, data visualization, and standard data format issues were investigated, and specific data analysis and management needs were addressed for several ESAD projects and data sets. In the following sections, details of the research performed over the duration of this contract are summarized.

Mcnider, Richard T.↗

Science-based Region-of-Interest Image Compression

As the number of currently active space missions increases, so does competition for Deep Space Network (DSN) resources. Even given unbounded DSN time, power and weight constraints onboard the spacecraft limit the maximum possible data transmission rate. These factors highlight a critical need for very effective data compression schemes. Images tend to be the most bandwidth-intensive data, so image compression methods are particularly valuable. In this paper, we describe a method for prioritizing regions in an image based on their scientific value. Using a wavelet compression method that can incorporate priority information, we ensure that the highest priority regions are transmitted with the highest fidelity.

Wagstaff, K. L.↗

NASA’s Global Precipitation Measurement Mission: Leveraging Stakeholder Engagement & Applications Activities to Inform Decision-making

The application of satellite precipitation estimates from NASA’s Global Precipitation Measurement (GPM) Mission for decision-making has been a focus for the mission since launch. As a result, GPM data have enabled a range of applications that address societal needs, including water resource management, crop forecasting, ecological monitoring, disaster response, public health, aviation, weather forecasting, and climate modeling, among others. GPM applications activities have continued to focus on user engagement through in person trainings and interviews, workshops, webinars, and educational outreach activities. The goals of these efforts are to synthesize community data needs in order to effectively support and enable decision-making across agencies, academia and the global community. While these efforts have helped the GPM mission establish a large stakeholder community that encompasses federal and state partners, academic institutions, nd private and nonprofit companies, there remains difficulties associated with accessing, processing, and applying the data to support or enable applications. In this article, we present GPM applications strategies and approaches used to enhance the applications value of GPM data, and most importantly, demonstrate how these efforts have and can inform different decision-making contexts. This work also provides a discussion on key lessons learned from the user community and how this information can be utilized to help better support and shape applications approaches for future NASA Earth Science missions.

Satellite precipitation↗

Automation of the CFD Process on Distributed Computing Systems

A script system was developed to automate and streamline portions of the CFD process. The system was designed to facilitate the use of CFD flow solvers on supercomputer and workstation platforms within a parametric design event. Integrating solver pre- and postprocessing phases, the fully automated ADTT script system marshalled the required input data, submitted the jobs to available computational resources, and processed the resulting output data. A number of codes were incorporated into the script system, which itself was part of a larger integrated design environment software package. The IDE and scripts were used in a design event involving a wind tunnel test. This experience highlighted the need for efficient data and resource management in all parts of the CFD process. To facilitate the use of CFD methods to perform parametric design studies, the script system was developed using UNIX shell and Perl languages. The goal of the work was to minimize the user interaction required to generate the data necessary to fill a parametric design space. The scripts wrote out the required input files for the user-specified flow solver, transferred all necessary input files to the computational resource, submitted and tracked the jobs using the resource queuing structure, and retrieved and post-processed the resulting dataset. For computational resources that did not run queueing software, the script system established its own simple first-in-first-out queueing structure to manage the workload. A variety of flow solvers were incorporated in the script system, including INS2D, PMARC, TIGER and GASP. Adapting the script system to a new flow solver was made easier through the use of object-oriented programming methods. The script system was incorporated into an ADTT integrated design environment and evaluated as part of a wind tunnel experiment. The system successfully generated the data required to fill the desired parametric design space. This stressed the computational resources required to compute and store the information. The scripts were continually modified to improve the utilization of the computational resources and reduce the likelihood of data loss due to failures. An ad-hoc file server was created to manage the large amount of data being generated as part of the design event. Files were stored and retrieved as needed to create new jobs and analyze the results. Additional information is contained in the original.

Tejnil, Ed↗

Potential Applications of Active Antenna Technologies for Emerging NASA Space Communications Scenarios

Abstract—The National Aeronautics and Space Administration (NASA) is presently embarking on the implementation of far-reaching changes within the framework of both space and aeronautics communications architectures. For example, near earth relays are looking to transition from the traditional few large geostationary satellites to satellite constellations consisting of thousands of small low earth orbiting satellites while lunar space communications will require the need to relay data from many assets distributed on the lunar surface back to earth. Furthermore, within the aeronautics realm, satellite communications for beyond line of sight (BLOS) links are being investigated in tandem with the proliferation of unmanned aerial systems (UAS) within the urban air mobility (UAM) environment. In all of these scenarios, future communications architectures will demand the need to connect and quickly transition between many nodes for large data volume transport. As such, NASA Glenn Research Center (GRC) has been heavily investigating the development of low cost phased array technologies that can readily address these various scenario conditions. In particular, GRC is presently exploring 5G-based beamformer technologies to leverage commercial timescale and volume production cycles which have heretofore not existed within the frequency allocations utilized for NASA applications. In this paper, an overview of the potential future applications of phased arrays being envisioned by NASA are discussed, along with technology feasibility demonstrations being conducted by GRC implementing low cost, 5G based beamformer technologies.

Nessel, James A.↗