Results for “data cube” — Page 15

DOE OSTI2021

VTK-m User's' Guide (V.1.7)

High-performance computing relies on ever finer threading. Advances in processor technology include ever greater numbers of cores, hyperthreading, accelerators with integrated blocks of cores, and special vectorized instructions, all of which require more software parallelism to achieve peak performance. Traditional visualization solutions cannot support this extreme level of concurrency. Extreme scale systems require a new programming model and a fundamental change in how we design algorithms. To address these issues we created VTK-m: the visualization toolkit for multi-/many-core architectures. VTK-m supports a number of algorithms and the ability to design further algorithms through a top-down design with an emphasis on extreme parallelism. VTK-m also provides support for finding and building links across topologies, making it possible to perform operations that determine manifold surfaces, interpolate generated values, and find adjacencies. Although VTK-m provides a simplified high-level interface for programming, its template-based code removes the overhead of abstraction. VTK-m simplifies the development of parallel scientific visualization algorithms by providing a framework of supporting functionality that allows developers to focus on visualization operations. Consider the listings in Figure 1.1 that compares the size of the implementation for the Marching Cubes algorithm in VTK-m with the equivalent reference implementation in the CUDA software development kit. Because VTK-m internally manages the parallel distribution of work and data, the VTK-m implementation is shorter and easier to maintain. Additionally, VTK-m provides data abstractions not provided by other libraries that make code written in VTK-m more versatile.This book includes contributions from the VTK-m community including the VTK-m development team and the user community.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The VTK-m Users' Guide (V.1.9)

High-performance computing relies on ever finer threading. Advances in processor technology include ever greater numbers of cores, hyperthreading, accelerators with integrated blocks of cores, and special vectorized instructions, all of which require more software parallelism to achieve peak performance. Traditional visualization solutions cannot support this extreme level of concurrency. Extreme scale systems require a new programming model and a fundamental change in how we design algorithms. To address these issues we created VTK-m: the visualization toolkit for multi-/many-core architectures. VTK-m supports a number of algorithms and the ability to design further algorithms through a top-down design with an emphasis on extreme parallelism. VTK-m also provides support for finding and building links across topologies, making it possible to perform operations that determine manifold surfaces, interpolate generated values, and find adjacencies. Although VTK-m provides a simplified high-level interface for programming, its template-based code removes the overhead of abstraction. VTK-m simplifies the development of parallel scientific visualization algorithms by providing a framework of supporting functionality that allows developers to focus on visualization operations. Consider the listings in Figure 1.1 that compares the size of the implementation for the Marching Cubes algorithm in VTK-m with the equivalent reference implementation in the CUDA software development kit. Because VTK-m internally manages the parallel distribution of work and data, the VTK-m implementation is shorter and easier to maintain. Additionally, VTK-m provides data abstractions not provided by other libraries that make code written in VTK-m more versatile.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

The VTK-m Users' Guide (V.2.0)

High-performance computing relies on ever finer threading. Advances in processor technology include ever greater numbers of cores, hyperthreading, accelerators with integrated blocks of cores, and special vectorized instructions, all of which require more software parallelism to achieve peak performance. Traditional visualization solutions cannot support this extreme level of concurrency. Extreme scale systems require a new programming model and a fundamental change in how we design algorithms. To address these issues we created VTK-m: the visualization toolkit for multi-/many-core architectures. VTK-m supports a number of algorithms and the ability to design further algorithms through a top-down design with an emphasis on extreme parallelism. VTK-m also provides support for finding and building links across topologies, making it possible to perform operations that determine manifold surfaces, interpolate generated values, and find adjacencies. Although VTK-m provides a simplified high-level interface for programming, its template-based code removes the overhead of abstraction. VTK-m simplifies the development of parallel scientific visualization algorithms by providing a framework of supporting functionality that allows developers to focus on visualization operations. Consider the listings in Figure 1.1 that compares the size of the implementation for the Marching Cubes algorithm in VTK-m with the equivalent reference implementation in the CUDA software development kit. Because VTK-m internally manages the parallel distribution of work and data, the VTK-m implementation is shorter and easier to maintain. Additionally, VTK-m provides data abstractions not provided by other libraries that make code written in VTK-m more versatile.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Developing Technologies for Biological Experiments in Deep Space

NASA Ames Research Center is the leader in developing autonomous nanosatellites or CubeSats to address strategic knowledge gaps about the effects of space travel on biological organisms, including GeneSat, PharmaSat, and EcAMSat. Now that NASA has set its sights on human exploration in deep space, such missions require significant technological and biomedical countermeasures to protect astronauts from chronic radiation exposure. CubeSats can inform these countermeasures by querying relevant space environments with model organisms and/or biosensors. BioSentinel will be the first interplanetary CubeSat to study the biological response to space radiation outside low Earth orbit in almost 50 years. BioSentinel is an autonomous platform able to support biology and to investigate the effects of space radiation on a model organism in interplanetary deep space. It will fly onboard Artemis-1, from which it will be deployed on a lunar fly-by trajectory and into a heliocentric orbit. BioSentinel, a 6U CubeSat (1U = 10-cm cube), will measure the DNA damage response to ambient space radiation in a model organism, which will be compared to information provided by an onboard radiation spectrometer and to data obtained on the ISS and on Earth. Even though the primary objective of the mission is to develop an autonomous spacecraft capable of conducting biological experiments in deep space, the 4U BioSensor science payload contained within the free-flyer is an adaptable instrument that can perform bio measurements with different microorganisms and in multiple space environments, including the ISS, lunar gateway, and on the surface of the Moon. Thus, nanosatellites like BioSentinel can be used to study the effects of both reduced gravity and space radiation and can house different organisms to answer specific science questions. In addition to their flexibility, nanosatellites also provide a low-cost alternative to more complex and larger missions, and require minimal crew support, if any

biosenses↗

NASA NTRSDate not supplied

Developing Technologies for Biological Experiments in Deep Space

NASA Ames Research Center is the leader in developing autonomous nanosatellites or CubeSats to address strategic knowledge gaps about the effects of space travel on biological organisms, including GeneSat, PharmaSat, and EcAMSat. Now that NASA has set its sights on human exploration in deep space, such missions require significant technological and biomedical countermeasures to protect astronauts from chronic radiation exposure. CubeSats can inform these countermeasures by querying relevant space environments with model organisms and/or biosensors. BioSentinel will be the first interplanetary CubeSat to study the biological response to space radiation outside low Earth orbit in almost 50 years. BioSentinel is an autonomous platform able to support biology and to investigate the effects of space radiation on a model organism in interplanetary deep space. It will fly onboard Artemis-1, from which it will be deployed on a lunar fly-by trajectory and into a heliocentric orbit. BioSentinel, a 6U CubeSat (1U = 10-cm cube), will measure the DNA damage response to ambient space radiation in a model organism, which will be compared to information provided by an onboard radiation spectrometer and to data obtained on the ISS and on Earth. Even though the primary objective of the mission is to develop an autonomous spacecraft capable of conducting biological experiments in deep space, the 4U BioSensor science payload contained within the free-flyer is an adaptable instrument that can perform bio measurements with different microorganisms and in multiple space environments, including the ISS, lunar gateway, and on the surface of the Moon. Thus, nanosatellites like BioSentinel can be used to study the effects of both reduced gravity and space radiation and can house different organisms to answer specific science questions. In addition to their flexibility, nanosatellites also provide a low-cost alternative to more complex and larger missions, and require minimal crew support, if any.

biosenses↗

NASA NTRSDate not supplied

BioSentinel - ISS mission

BioSentinel will be the first interplanetary CubeSat to study the biological response to space radiation outside low Earth orbit in almost 50 years. BioSentinel is an autonomous platform able to support biology and to investigate the effects of space radiation on a model organism in interplanetary deep space. It will fly onboard Artemis-1, from which it will be deployed on a lunar fly-by trajectory and into a heliocentric orbit. BioSentinel, a 6U CubeSat (1U = 10-cm cube), will measure the DNA damage response to ambient space radiation in a model organism, which will be compared to information provided by an onboard radiation spectrometer and to data obtained on the ISS and on Earth. Even though the primary objective of the mission is to develop an autonomous spacecraft capable of conducting biological experiments in deep space, the 4U BioSensor science payload contained within the free-flyer is an adaptable instrument that can perform bio measurements with different microorganisms and in multiple space environments, including the ISS. Thus, nanosatellites like BioSentinel can be used to study the effects of both reduced gravity and space radiation and can house different organisms to answer specific science questions. In addition to their flexibility, nanosatellites also provide a low-cost alternative to more complex and larger missions, and require minimal crew support, if any

space biosensors↗

DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗

NASA NTRSDate not supplied

Anomaly Detection for the Roman Space Telescope Wide Field Instrument’s Science Data Processing Pipeline

The Roman Space Telescope (RST) Wide Field Instrument (WFI) will be utilizing a preliminary Science Data Processing (SDP) pipeline during its Integration and Test, and to some extent during Operations, to track basic statistics and identify known features such as cosmic rays, snowballs as well as possible anomalies in raw detector data. In our detectors, these anomalies appear as jumps in the ramp of a readout and are classified as cosmic rays if they appear as a streak or snowballs if they’re more circular. The WFI employs an array of 18 H4RG-10 detectors that collect image samples. Each set of raw frames within a non-destructive exposure is packaged by the SDP pipeline into image cubes for each detector. Each cube is a time series of 4096 × 4096 accumulating pixel frames. The preliminary analysis pipeline is used to locate anomalies in these time-series accumulation frames and identify the type of anomaly, either natural phenomena or detector characteristic. To compare different methods, we’ve implemented both heuristic-based and data-driven methods to identify anomalies. For the heuristic-based approach, we identify snowballs and cosmic rays by the size and shape of outlier pixel clusters between consecutive frames. For data driven methods, we evaluated a Convolutional Neural Network (CNN) model, and more traditional methods like Principal Component Analysis (PCA). CNN is a supervised learning/classification method. Thus, we used a labeled dataset of anomalies to perform segmentation of the image and identify anomalies. We used previously identified cosmic rays and snowballs to measure the accuracy and efficiency of the mentioned approaches. In evaluating these methods, we aim to pick the best fit for the SDP pipeline’s anomaly detection in terms of both performance and runtime.

Paul Horton↗

NASA NTRS2015

Nominal SARAL Transfer Function

This paper gives a calculation of the range correction and cross section of the SARAL (Satellite with Argos and ALtiKa) Indian/French ocean radar satellite retroreflector array assuming the cube corners are coated and have a dihedral angle offset of about 1.5 arcseconds to account for velocity aberration. The cubes are assumed to all have the same orientation within the mounting. The derived range correction may be applied in precise orbit determination analyses that use Satellite Laser Ranging (SLR) data to SARAL.

Precise orbit determination↗

NASA NTRS2015

Nominal SARAL Transfer Function

This paper gives a calculation of the range correction and cross section of the SARAL (Satellite with Argos and ALtiKa) Indian/French ocean radar satellite retroreflector array assuming the cube corners are coated and have a dihedral angle offset of about 1.5 arcseconds to account for velocity aberration. The cubes are assumed to all have the same orientation within the mounting. The derived range correction may be applied in precise orbit determination analyses that use Satellite Laser Ranging (SLR) data to SARAL.

SARAL↗

DOE OSTI2025

ILAMBv2.7 benchmarking results comparing E3SMv2.1 land-atmosphere coupled (BGCv2LNDATM) and stand alone land (ELM) simulations with CMIP6 emission driven historical simulations

This dataset contains land model benchmarking results for the Energy Exascale Earth System Model version 2.1 (E3SMv2.1), including outputs from both coupled biogeochemistry simulations and stand-alone land model simulations. These results are compared against several emission-driven historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Benchmarking was conducted using the International Land Model Benchmarking (ILAMB) package, version 2.7 (ILAMBv2.7). CMIP6 model outputs were sourced from the Earth System Grid Federation (ESGF), while the E3SMv2.1 results were derived from raw model outputs. These outputs underwent processing steps such as time serialization, conservative regridding, and data standardization to ensure comparability. For spatial interpolation, the Earth System Modeling Framework (ESMF) tool, ESMF_RegridWeightGen, was employed to generate regridding weights, enabling the transformation of E3SM’s native cubed-sphere grid to a regular latitude-longitude grid.

Feng, Sha [PNNL]↗

DOE OSTI2024

Reaction Rate Ratios for Recent Fast Metal Experiments with Large Plutonium Masses

Reaction rate ratios are integral responses that are used within the criticality experiments field because they contain spectral information. While these types of measurements have been utilized for nuclear data validation with historic experiments, few experiments of this type have been utilized for recent experiments, as few exist. This work focuses on measured reaction rate ratios for two nearly bare plutonium critical assemblies with different geometries: one that is cube like (with a Pu mass of 40 kg) and one that is slab like (with a Pu mass of 109 kg). Irradiations were performed with both configurations in which foils were placed near the center of the assembly. Plutonium, highly enriched uranium, depleted uranium, and Au foils were included in the irradiation and counted via high-purity germanium detectors. From these measurements, reaction rate ratios were calculated. Measured and simulated values and uncertainties are presented for the reaction rate ratios. Ratios utilizing the following reactions are given in this work: 197 Au(n, γ), 197 Au(n,2n), 235 U(n,fission), 238 U(n, fission), 238 U(n,2n), 238 U(n,γ), and 239 Pu(n,fission). Uncertainties for the measured reaction rate ratios ranged from 4% to 7%, and the contribution of various parameters to this uncertainty was investigated. The results are compared to historical experiments and should be used for nuclear data validation for future nuclear data library releases. These measurements are part of the EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project, which utilizes measurement responses in addition to k eff (such as these reaction rate ratios) to help reduce uncertainties in 239 Pu nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1977

Submillimeter photometry of extragalactic objects

Submillimeter flux densities from five extragalactic objects have been measured, and upper limits set for three others. The galaxies NGC 253 and NGC 1068 have submillimeter spectra steeper than the cube of frequency. Of several source mechanisms considered for the far-infrared and submillimeter flux from NGC 1068, only thermal emission from dust at about 30 K is consistent with the data. The source of that flux, if thermal, must have a diameter of at least 5 arcsec. If one assumes radiation from dust, the masses of interstellar material derived for the central 80-arcsec diameter regions of NGC 253, NGC 1068, and NGC 5236 are, respectively, 800 million, 10 billion, and 3 billion solar masses. Measurements of submillimeter flux densities for 3C 273, BL Lac, NGC 1275, and NGC 5128 show no excess above values obtained by interpolation between radio and mid-infrared data.

Hildebrand, R. H.↗

NASA NTRS1991

Multifrequency radio observations of Saturn at ring inclination angles between 5 and 26 degrees

In order to investigate hypothesized latitudinal variations in the thermal emission from Saturn, the planet has been imaged at different wavelengths by the VLA and BIMA arrays. A bright band is found at 6 and 20 cm at midnorthern latitudes. The latitudinal brightness variation is suggested to be due to variations in the location of the NH4SH cloud. The extant data base on ring brightness temperatures and optical depths has been expanded by the present efforts. The data obtained are congruent with ice particles of the order of millimeters-meters in size; the number of particles has a power-law distribution that is proportionate to the radius cubed.

De Pater, Imke↗

NASA NTRS2012

Analysis of Temperature Maps of Selected Dawn Data Over the Surface of Vesta

The thermal behavior of areas of unusual albedo at the surface of Vesta can be related to physical properties that may provide some information about the origin of those materials. Dawn s Visible and Infrared Mapping Spectrometer (VIR) [1] hyperspectral cubes can be used to retrieve surface temperatures. Due to instrumental constraints, high accuracy is obtained only if temperatures are greater than 180 K. Bright and dark surface materials on Vesta are currently investigated by the Dawn team [e.g., 2 and 3 respectively]. Here we present temperature maps of several local-scale features that were observed by Dawn under different illumination conditions and different local solar times.

Tosi, F.↗

NASA NTRS2001

Low-Altitude AVIRIS Data for Mapping Land Cover in Yellowstone National Park: Use of Isodata Clustering Techniques

Northeast Yellowstone National Park (YNP) has a diversity of forest, range, and wetland cover types. Several remote sensing studies have recently been done in this area, including the NASA Earth Observations Commercial Applications Program (EOCAP) hyperspectral project conducted by Yellowstone Ecosystems Studies (YES) on the use of hyperspectral imaging for assessing riparian and in-stream habitats. In 1999, YES and NASA's Commercial Remote Sensing Program Office began collaborative study of this area, assessing the potential of synergistic use of hyperspectral, synthetic aperture radar (SAR), and multiband thermal data for mapping forest, range, and wetland land cover. Since the beginning, a quality 'reference' land cover map has been desired as a tool for developing and validating other land cover maps produced during the project. This paper recounts an effort to produce such a reference land cover map using low-altitude Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and unsupervised classification techniques. The main objective of this study is to assess ISODATA classification for mapping land cover in Northeast YNP using select bands of low-altitude AVIRIS data. A secondary, more long-term objective is to assess the potential for improving ISODATA-based classification of land cover through use of principal components analysis and minimum noise fraction (MNF) techniques. This paper will primarily report on work regarding the primary research objective. This study focuses on an AVIRIS cube acquired on July 23, 1999, by the confluence of Soda Butte Creek with the Lamar River. Range and wetland habitats dominate the image with forested habitats being a comparatively minor component of the scene. The scene generally tracks from southwest to northeast. Most of the scene is valley bottom with some lower side slopes occurring on the western portion. Elevations within the AVIRIS scene range from approximately 1998 to 2165 m above sea level, based on US Geological Survey (USGS) 30-m digital elevation model (DEM) data. Despain and the National Park Service (NPS) provide additional description of the study area.

Spruce, Joseph P.↗

NASA NTRS1989

Concurrent hypercube system with improved message passing

A network of microprocessors, or nodes, are interconnected in an n-dimensional cube having bidirectional communication links along the edges of the n-dimensional cube. Each node's processor network includes an I/O subprocessor dedicated to controlling communication of message packets along a bidirectional communication link with each end thereof terminating at an I/O controlled transceiver. Transmit data lines are directly connected from a local FIFO through each node's communication link transceiver. Status and control signals from the neighboring nodes are delivered over supervisory lines to inform the local node that the neighbor node's FIFO is empty and the bidirectional link between the two nodes is idle for data communication. A clocking line between neighbors, clocks a message into an empty FIFO at a neighbor's node and vica versa. Either neighbor may acquire control over the bidirectional communication link at any time, and thus each node has circuitry for checking whether or not the communication link is busy or idle, and whether or not the receive FIFO is empty. Likewise, each node can empty its own FIFO and in turn deliver a status signal to a neighboring node indicating that the local FIFO is empty. The system includes features of automatic message rerouting, block message transfer and automatic parity checking and generation.

Peterson, John C.↗

NASA NTRS2019

Attitude control system for the Mars Cube One spacecraft

The paper begins with a discussion of the hardware that was selected for the two systems as well as descriptions of the interface between the attitude control and propulsion systems and the interface between these systems and the rest of the spacecraft’s command and data handling system. Next, the paper summarizes the different types of tests that were performed at the system and spacecraft levels. Test data is included for some of these tests which helped define the methods by which the spacecraft is operated in space. Lastly, the paper lists a series of lessons-learned for developing attitude control and propulsion systems for interplanetary CubeSats.

Krajewski, Joel↗