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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A procedure for rule extraction from a Self-Organising plasma disruption predictor for JET

In a previous paper, a Self-Organizing Map had proven to be able to identify the regions of the plasma operative space characterizing the pre-disruptive phase at JET without relying on any a priori information. One of the strengths of this disruption predictor lies in its inherent self-organization capability. The Self-Organizing Map discovers non-trivial relationships and captures the complicated interplay of device diagnostics on the internal plasma states directly from the experimental data. Moreover, the provided model allows the visualization of high-dimensional plasma parameters and facilitates easy interrogation of the model to understand the reasons behind its correlations. In this paper, an additional step is taken towards the interpretability of models for predicting disruptions by training a Decision Tree to classify the plasma states according to the interpretation provided by the Self-Organizing Map (stable or at high risk of disruptions). The Decision tree provides a set of rules which describe the transition of the plasma towards the pre-disruptive phase as visualized in the Self-Organizing Map. The obtained rules for the database explored in the study identify four regions in the map, two of which are at risk of disruption. These regions correspond to partitions of a 3D space based on the peaking factors of the core and divertor radiation, as well as the Locked Mode. The agreement between the Self-Organizing Map answers and the rules supplied by the Decision Tree is confirmed by the comparison of the performance exhibited by the two models in the prediction of disruptions.

Setzu, Samuele [Univ. of Cagliari, Monserrato, Cag↗
DOE OSTI2019

Self-Organizing Maps and Their Applications to Data Analysis

Self-Organizing Maps(SOMs) are a form of unsupervised neural network that are used for visualization and exploratory data analysis of high dimensional datasets. Our goal was to understand how we can use a SOM to gain insights about datasets. We do this by first understanding the initialization, training, error metrics, and convergence properties of the SOM. Next we discuss the ways to interpret and visualize a Self-Organizing Map. Finally we used real datasets to understand what the Self-Organizing Map can tell us about labeled and unlabeled data. Based on experiments with our datasets we found that the Self-Organizing Map can tell us about the spacing and position of high dimensional clusters, help us find non-linear patterns, and give us insight into the shape of our data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

Application of ERTS-1 Imagery to Flood Inundation Mapping

Ground data and a variety of low-altitude multispectral imagery were acquired for the East Nishnabotna River on September 14 and 15. This successful effort concluded that a near-visible infrared sensor could map inundated areas in late summer for at least three days after flood recession. ERTS-1 multispectral scanner subsystem (MSS) imagery of the area was obtained on September 18 and 19. Analysis of MSS imagery by IGSRSL, USGS, and NASA reinforced the conclusions of the low-altitude study while increasing the time period critical for imagery acquisition to at least 7 days following flood recession. The capability of satellite imagery to map late summer flooding at a scale of 1:250,000 is exhibited by the agreement of interpreted flood boundaries obtained from ERTS-1 imagery to boundaries mapped by low-altitude imagery and ground methods.

Hallberg, G. R.↗
NASA NTRS1979

A Geophysical Atlas for Interpretation of Satellite-derived Data

A compilation of maps of global geophysical and geological data plotted on a common scale and projection is presented. The maps include satellite gravity, magnetic, seismic, volcanic, tectonic activity, and mantle velocity anomaly data. The Bibliographic references for all maps are included.

Lowman, P. D., Jr.↗
NASA NTRS1973

Interpretation of ERTS-MSS images of a Savanna area in eastern Colombia

The application of ERTS-1 imagery for extrapolating existing soil maps into unmapped areas of the Llanos Orientales of Colombia, South America is discussed. Interpretations of ERTS-1 data were made according to conventional photointerpretation techniques. Most units delineated in the existing reconnaissance soil map at a scale of 1:250,000 could be recognized and delineated in the ERTS image. The methods of interpretation are described and the results obtained for specific areas are analyzed.

Elberson, G. W. W.↗
NASA NTRS1980

Seasat SAR identification of dry climate urban land cover

Digitally processed Seasat synthetic aperture radar (SAR) imagery of the Denver, Colorado area was examined to assess its potential for mapping urban land cover and the compatibility of SAR derived classes with those described in the U.S. Geological Survey classification system. The entire scene was interpreted to generate a small-scale land cover map. In addition, six subscene enlargements representative of urban land cover categories extant in the area were used as test sites for detailed analysis of land cover types. Two distinct approaches were employed and compared in examining the imagery - a visual interpretation of black-and-white positive transparencies and an automated-machine/visual interpretation. The latter used the Image 100 interactive image analysis system to generate land cover classes by density level slicing of the image frequency histogram.

Henderson, F. M.↗
NASA NTRS2016

Interpreting The Unresolved Intensity Of Cosmologically Redshifted Line Radiation

Intensity mapping experiments survey the spectrum of diffuse line radiation rather than detect individual objects at high signal-to-noise ratio. Spectral maps of unresolved atomic and molecular line radiation contain three-dimensional information about the density and environments of emitting gas and efficiently probe cosmological volumes out to high redshift. Intensity mapping survey volumes also contain all other sources of radiation at the frequencies of interest. Continuum foregrounds are typically approximately 10(sup 2)-10(Sup 3) times brighter than the cosmological signal. The instrumental response to bright foregrounds will produce new spectral degrees of freedom that are not known in advance, nor necessarily spectrally smooth. The intrinsic spectra of fore-grounds may also not be well known in advance. We describe a general class of quadratic estimators to analyze data from single-dish intensity mapping experiments and determine contaminated spectral modes from the data themselves. The key attribute of foregrounds is not that they are spectrally smooth, but instead that they have fewer bright spectral degrees of freedom than the cosmological signal. Spurious correlations between the signal and foregrounds produce additional bias. Compensation for signal attenuation must estimate and correct this bias. A successful intensity mapping experiment will control instrumental systematics that spread variance into new modes, and it must observe a large enough volume that contaminant modes can be determined independently from the signal on scales of interest.

INTERPRETING↗
NASA NTRSDate not supplied

Assessing the feasibility of a spaceborne 3D lightning observing concept

The distribution of electrical charge in thunderclouds results from thermodynamic, microphysical, and kinematic processes, which also modulate thunderstorm evolution. It is no surprise that the connection between lightning and these physical processes is so strong that the increase and vertical growth of lightning activity closely follows the vertical growth of the thundercloud, but unraveling these connections is not trivial and requires observations of the three-dimensional (3D) structure of electrical activity in a cloud. Ground-based 3D lightning mapping networks give excellent 3D flash-level detail but are limited to regional coverage. Satellite-based optical lightning mappers give excellent global coverage but are largely limited to 2D summaries of flash rate and radiant intensity, albeit new flash products and stereographic techniques are chipping away this limitation. New observing strategies are needed to expand and diversify the corpus of 3D lightning datasets and motivate studies that unravel connections lightning has with these key physical processes and the surrounding environment. This study examines the feasibility of using a distributed network of orbing satellites with VHF-based lightning detectors to obtain global maps of 3D lightning activity and assess efficacy of this approach for use in a new, small satellite mission concept called CubeSpark. CubeSpark combines new VHF and high-resolution, bispectral optical instruments on a constellation of low-Earth orbiting (LEO) satellites to globally map the 3D electrical structure of thunderstorms and study how it relates to thunderstorm evolution, extreme weather, nitrogen oxide production and distribution, upper atmospheric electrical phenomena, and how 3D flash observations can complement existing satellite-based lightning mappers and improve decision support tools. To locate lightning discharges, CubeSpark seeks to use the VHF time-of-arrival technique, similar to ground-based total lightning mapping networks. The vertical location accuracy of these satellite retrievals will be of poorer quality compared to a ground-based network, which has non-trivial implications for lightning flash reconstruction and lightning-based interpretations of deep convection. We adapt a Lightning Mapping Array (LMA) simulation framework to an orbiting network and use it to address feasibility of 3D lightning detection from space with particular attention to location accuracy of VHF detections of lightning in the vertical. These simulations inform a constellation design study that defines a realistic orbital configuration and depicts the global coverage for CubeSpark. Results indicate that a 3D location accuracy of <1-2 km for each dimension can be achieved across 300-500 km wide swaths, which suggests that CubeSpark can resolve the charge structure of thunderclouds from the tropics to the mid- and high- latitudes.

Lightning↗
NASA NTRSDate not supplied

Field Geologic Mapping of Sample Sites From the Ground and the Air With Perseverance Rover and Ingenuity Helicopter

One of the primary mission goals for Perseverance is to determine the geologic context of sample sites. Rover-based (in situ) or field geologic context mapping (GXM) based on Perseverance rover and Ingenuity helicopter observations provides a nearly continuous record of geologic context and exposed surface structure over a 120 m-wide corridor along the traverse of Perseverance and the flight path of Ingenuity. Field geologic mapping along the traverse and flight path and outcrop-scale mapping at sample sites provides a spatial dimension to ground truth geologic, stratigraphic, and modern environmental context for samples at scales relevant to sample interpretation. Here we provide an abbreviated overview of field mapping as it relates to documenting the architecture of the Jezero fan and geologic context of several examples of sample locations.

Mars 2020↗
NASA NTRS1973

Identification of Geostructures of continental crust, particularly as they relate to mineral-resource evaluation

The author has identified the following significant results. Analysis of lineated lakes in the Umiat, Alaska area and comparison with known geology, gravity, and magnetic data in the the area suggest concealed structures exist at depth, possibly at or near basement, which may represent targets for petroleum exploration. Compilation of reconnaissance geologic data on 1:250,000 scale enlargements of ERTS-1 images near Corwin reveal structural and stratigraphic anomalies that suggest the Cretaceous sequence is less thick than supposed and is repeated in a series of plates superimposed by flat thrust faults. The structural style differs from that in coeval strata to the northeast, across the northwest-trending linear zone separating differing tectonic styles in older strata noted earlier. The regional extension of a fault known locally in the McCarthy area has been recognized; this fault appears to form the boundary of a significant terrane of mid-Paleozoic metamorphic rocks. ERTS-1 images are being used operationally, at 1:1,000,000 scale in the compilation of regional geologic maps, and at 1:250,000 scale in field mapping in the Brooks Range, in the study of faults in seismically active southern Alaska, in field-checking interpretations previously made from ERTS-1 imagery, and orthophoto base maps for geologic maps.

Gryc, G.↗
DOE OSTI2020

Exploiting Colorimetry for Fidelity in Data Visualization

Advances in multimodal characterization methods fuel the generation of increasing immense hyper-dimensional data sets. Color mapping is employed for conveying higher dimensional data in two-dimensional (2D) representations for human consumption without relying on multiple projections. How one constructs these color maps, however, critically affects how accurately one perceives data. For simple scalar fields, perceptually uniform color maps and color selection have been shown to improve data readability and interpretation across research fields. In this work, we review core concepts underlying the design of perceptually uniform color maps and extend the concepts from scalar fields to two-dimensional vector fields and three-component composition fields frequently found in materials-chemistry research to enable high-fidelity visualization. Additionally, we develop the software tools PAPUC and CMPUC to enable researchers to utilize these colorimetry principles and employ perceptually uniform color spaces for rigorously meaningful color mapping of higher dimensional data representations. Last, we demonstrate how these approaches deliver immediate improvements in data readability and interpretation in microscopies and spectroscopies routinely used in discerning materials structure, chemistry, and properties.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Location of geologic structures from interpretation of ERTS-1 imagery, Carbon County, Wyoming

The author has identified the following significant results. Possible geologic structures in the basin sediments of Carbon County and vicinity were located by interpretation of ERTS-1 imagery. These same structures are not evident on existing conventional geologic maps of the area. Subsequent field checks confirmed much of the geologic interpretation, but revealed that two apparent closed structures identified on the ERTS-1 imagery were actually topographic pseudostructures in flat or homoclinal sediments. Stereoscopic coverage (where available) allows the interpreter to avoid such misinterpretations.

Marrs, R. W.↗
DOE OSTI2020

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

58 GEOSCIENCES↗
NASA NTRS2013

Interpretation of CHAMP Magnetic Anomaly Data over the Pannonian Basin Region Using Lower Altitude Horizontal Gradient Data

The ESA SWARM mission will have three earth orbiting magnetometer bearing satellites one in a high orbit and two side-by-side in lower orbits. These latter satellites will record a horizontal magnetic gradient. In order to determine how we can use these gradient measurements for interpretation of large geologic units we used ten years of CHAMP data to compute a horizontal gradient map over a section of southeastern Europe with our goal to interpret these data over the Pannonian Basin of Hungary.

CHAMP↗
NASA NTRS1973

Forest and range mapping in the Houston area with ERTS-1

ERTS-1 data acquired over the Houston area has been analyzed for applications to forest and range mapping. In the field of forestry the Sam Houston National Forest (Texas) was chosen as a test site, (Scene ID 1037-16244). Conventional imagery interpretation as well as computer processing methods were used to make classification maps of timber species, condition and land-use. The results were compared with timber stand maps which were obtained from aircraft imagery and checked in the field. The preliminary investigations show that conventional interpretation techniques indicated an accuracy in classification of 63 percent. The computer-aided interpretations made by a clustering technique gave 70 percent accuracy. Computer-aided and conventional multispectral analysis techniques were applied to range vegetation type mapping in the gulf coast marsh. Two species of salt marsh grasses were mapped.

Heath, G. R.↗
NASA NTRS2011

Assessing the Age of an Asteroid's Surface with Data from the International Rosetta Mission

Rosetta is an international mission led by the European Space Agency (ESA) with key support and instrumentation from the National Aeronautics and Space Administration (NASA). Rosetta is currently on a ten-year mission to catch comet 67P/Churyumov-Gerasimenko (C-G); throughout its voyage, the spacecraft has performed flybys of two main belt asteroids (MBA): Steins and Lutetia. Data on the physical, chemical, and geological properties of these asteroids are currently being processed and analyzed. Accurate interpretation of such data is fundamental in the success of Rosetta's mission and overall objectives. Post-flyby data analyses strive to correlate the size, shape, volume, and rotational rate of Lutetia, in addition to interpreting its multi-color imagining, albedo, and spectral mapping. Although advancements in science have contributed to the examination of celestial bodies, methods to analyze asteroids remain largely empirical, not semi-empirical, nor ab initio. This study aims to interpret and document the scientific methods currently utilized in the characterization of asteroid (21) Lutetia in order to render these processes and methods accessible to the public. Examples include a standardized technique for assessing the age of an asteroid surface, complete with clickable reference maps, methodology of grouping surface characteristics together, and a standardized power law equation for the age. Other examples include determining the density of an object. Context for what both density and age mean is a bi-product of this study. Results of the study will aid in the development of pedagogical material on asteroids for public use, and in creation of an academic database for selected targets that might be used as a reference.

asteriods↗