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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Daylight-driven carbon exchange through a vertically structured microbial community

Interactions between autotrophs and heterotrophs are central to carbon (C) exchange across trophic levels in essentially all ecosystems and metabolite exchange is a frequent mechanism for distributing C within spatially structured ecosystems. Yet, despite the importance of C exchange, the timescales at which fixed C is transferred in microbial communities is poorly understood. We employed a stable isotope tracer combined with spatially resolved isotope analysis to quantify photoautotrophic uptake of bicarbonate and track subsequent exchanges across a vertical depth gradient in a stratified microbial mat over a light-driven diel cycle. We observed that C mobility, both across the vertical strata and between taxa, was highest during periods of active photoautotrophy. Parallel experiments with 13 C-labeled organic substrates (acetate and glucose) showed comparably less exchange of C within the mat. Metabolite analysis showed rapid incorporation of 13 C into molecules that can both comprise a portion of the extracellular polymeric substances in the system and serve to transport C between photoautotrophs and heterotrophs. Stable isotope proteomic analysis revealed rapid C exchange between cyanobacterial and associated heterotrophic community members during the day with decreased exchange at night. We observed strong diel control on the spatial exchange of freshly fixed C within tightly interacting mat communities suggesting a rapid redistribution, both spatially and taxonomically, primarily during daylight periods.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

Quantification of waste morphology in Martian fretted terrain

Qualitative and quantitative analyses are performed on the northern fretted terrain of the Martian Isnenius Lacus quadrangle to determine the formation processes. The fretted terrain lies between 35-50 deg N latitude and 305-350 deg W longitude and was mapped with Viking orbiter instrumentation. The dominant landforms are a sequence of heavily cratered terrain, scattered flat-floored, fretted valleys, linear waste-covered valleys between flat-topped mesas, circum-mesa debris areas, a transition from mesas to knobby terrain, fewer upland forms, and low-relief, mantled, polar plains. The features spread out from a cratered terrain boundary in a succession of shapes. A principal components analysis characterizes definable spatial variations in the surface morphology and suggests that the features are youngest in the southwest direction. Relationships are also found between neighboring features. The principal components analysis technique is concluded to be a valuable tool for explaining the geomorphic evolution of the Martian surface.

Kochel, R. C.↗
DOE OSTI2024

Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

Objective analysis of sea-level winds and pressures derived from simulated observations of a satellite radar-radiometer and actual conventional data.

A computer-based procedure is developed that combines a field of simulated satellite-derived wind speeds with a limited amount of conventional surface data so as to recover the surface pressure field and the vector wind field over the North Pacific Ocean. Wind speeds are determined from an objective analysis of high spatial density ship observations in order to simulate the anticipated output of a proposed satellite-mounted radar-radiometer system. The simulated speeds are combined with the conventional network for various spatial distributions of ship data. The average rms departure of sea-level pressure fields analyzed by deleting from 75-94% of the available ship observations from the maximum data analysis is from 3.0-4.0 mb. Comparison of the wind components implied by the isobaric patterns to those of the withheld ship observations yields average rms differences of from 8.7-9.4 kt for a range of 75-94% data deletion.

Druyan, L. M.↗
NASA NTRS2001

The Statistical Consulting Center for Astronomy (SCCA)

The process by which raw astronomical data acquisition is transformed into scientifically meaningful results and interpretation typically involves many statistical steps. Traditional astronomy limits itself to a narrow range of old and familiar statistical methods: means and standard deviations; least-squares methods like chi(sup 2) minimization; and simple nonparametric procedures such as the Kolmogorov-Smirnov tests. These tools are often inadequate for the complex problems and datasets under investigations, and recent years have witnessed an increased usage of maximum-likelihood, survival analysis, multivariate analysis, wavelet and advanced time-series methods. The Statistical Consulting Center for Astronomy (SCCA) assisted astronomers with the use of sophisticated tools, and to match these tools with specific problems. The SCCA operated with two professors of statistics and a professor of astronomy working together. Questions were received by e-mail, and were discussed in detail with the questioner. Summaries of those questions and answers leading to new approaches were posted on the Web (www.state.psu.edu/ mga/SCCA). In addition to serving individual astronomers, the SCCA established a Web site for general use that provides hypertext links to selected on-line public-domain statistical software and services. The StatCodes site (www.astro.psu.edu/statcodes) provides over 200 links in the areas of: Bayesian statistics; censored and truncated data; correlation and regression, density estimation and smoothing, general statistics packages and information; image analysis; interactive Web tools; multivariate analysis; multivariate clustering and classification; nonparametric analysis; software written by astronomers; spatial statistics; statistical distributions; time series analysis; and visualization tools. StatCodes has received a remarkable high and constant hit rate of 250 hits/week (over 10,000/year) since its inception in mid-1997. It is of interest to scientists both within and outside of astronomy. The most popular sections are multivariate techniques, image analysis, and time series analysis. Hundreds of copies of the ASURV, SLOPES and CENS-TAU codes developed by SCCA scientists were also downloaded from the StatCodes site. In addition to formal SCCA duties, SCCA scientists continued a variety of related activities in astrostatistics, including refereeing of statistically oriented papers submitted to the Astrophysical Journal, talks in meetings including Feigelson's talk to science journalists entitled "The reemergence of astrostatistics" at the American Association for the Advancement of Science meeting, and published papers of astrostatistical content.

Akritas, Michael↗
DOE OSTI2020

Three-Dimensional Imaging Lidar for Characterizing Particle Fields and Organisms in the Mesopelagic Zone

The ocean’s mesopelagic zone is largely uncharacterized despite its vital role in sustaining ocean ecosystems. The composition, cycling, and fate of particle fields in the mesopelagic lacks an integrative multi-scale understanding of organism migration patterns, distribution, and diversity. This problem is addressed by combining complementary technologies with overlapping size spectra, including profiler mounted optical scattering sensors, profiler, and ship mounted acoustic devices, and a custom Unobtrusive Multi-Static Lidar Imager (UMSLI). This unique sensor suite can observe distributions of particles including organisms over a six order of magnitude dynamic size range, from microns to meters. Overlapping size ranges between different methods allows for cross-validation. This work focuses on the lidar imaging measurements and optical backscattering and attenuation, covering a combined particle size range of 0.1 mm to several cm. Particles at the small end of this range are sized using an existing backscattering time series inversion method after Briggs et al. (2013). Larger particles are resolved with UMSLI over an expanding volume using three-dimensional photo-realistic laser serial imaging. UMSLI’s image rectifying ability over time allows for derivation of particle concentration, size, and spatial distribution. Technical details on the development and post-processing methods for the novel UMSLI system are provided. Image resolved particle size distributions (PSDs) revealed a size shift from smaller to larger particles (>0.5 mm) as indicated by flatter slopes from dawn (slope = 2.6) to dusk (slope = 3.0). PSD trends are supported by an optical backscatter and transmissometer time series inversion analysis. Size shifts in the particle field are largely attributed to aggregation effects. Images support evidence of temporal variation between dusk and dawn stations through statistical analysis of particle concentrations for particle sizes 0.50–5.41 mm. Spatial analysis of the particle field revealed a dominantly uniform distributed marine snow background. The importance and potential of integrated approaches to studying particle and organism dynamics in ocean environments are discussed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

Spatial Link Coverage Projections for the Glenn Research Center Communication Analysis Suite (GCAS)

Space communications are a fundamental part of every NASA mission, and reliable space communications are essential. Precise planning, simulation, and analysis of communications abilities are needed to know exactly what data rates, visibility times, and signal strength can be expected during the real missions. The goal of this work was to take a 3D icosphere, where each face was a point to be evaluated, and display the link performance onto a 2D projection of celestial bodies for links to a node location that could be spatially located at any of the icosahedral faces. 2D projections were produced for a wide range of spatial link grid sizes and compressed into low-size MAT files for ease of access and rapid implementation. The final icosahedral projections can be displayed onto various celestial body maps, using ultra-high-resolution maps for stitching. The results have proven satisfactory and are planned to support various simulation efforts.

communication link analysis↗
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1972

Improvement of synoptic scale moisture and wind field analyses using the Nimbus 4 THIR 6.7 micron observations

The Nimbus 4 temperature-humidity infrared radiometer (THIR) monitors radiation in the 6.5 to 7.2 micron water vapor absorption with a 23 kilometer spatial resolution at the sub-satellite point. Radiation monitored in this spectral region results primarily from emission in the 250 to 500 millibar region of the upper troposphere. The THIR 6.7 micron observations are readily available in photofacsimile imagery form which shows very distinctive patterns associated with spatial variations in atmospheric water vapor. These radiometric observations were combined in several instances with moisture values measured in the upper troposphere by the standard radiosonde network. In each instance, the result is a much more consistent analysis showing increased spatial detail that agrees with the radiometric observations and does not compromise the conventional data. The improved moisture analyses show relatively dry and moist tongues that are very difficult or impossible to infer from the conventional data alone. The patterns in the moisture fields can be tracked over 12 and 24 hour periods. In addition, by keeping in mind the advective properties of the moisture field, success was achieved in improving streamline analyses at the 400 mb level over data sparse regions on a global scale.

Steranko, J.↗
NASA NTRS1994

An X-ray study of five supernova remnants in the Carina spiral arm

The ROSAT Position Sensitive Proportional Counter (PSPC) is used to perform an exploratory study of four fields in the Carina spiral arm containing five radio supernova remnants, only one of which has previously been studied in X-rays. We present upper limits for the detection in X-rays of G298.5 - 0.3, G298.6 - 0.0, and G299.0+0.2, and report a 4 sigma detection of G296.8-0.3. In addition, we present detailed spatial and spectral analysis of the bright X-ray remnant G296.1-0.7, which has previously been studied by both the Einstein IPC and EXOSAT LE/CMA. We detect relatively slight, but statistically significant, variations in the spectrum across the remnant via spatially resolved spectral fits and a study of the spatial variation of hardness ratios. In general, the spectrum is characteristic of a thermal plasma with kT about 0.2 keV and N(sub H) about 1.5 x 10(exp 21/sq. cm). The total X-ray emitting mass is estimated to be about 250 solar mass for an optically estimated distance of 4 kpc to the remnant. At this distance, the linear dimensions of the remnant are roughly 35 - 50 pc, implying an age on the order of 20,000 yr. Assuming that X-ray and radio brightnesses are related by SIGMA(sub R) proportional to SIGMA(exp 0.69)(sub X) and that the four radio remnants have X-ray spectral characteristics similar to G296.1-0.7, we find that the column densities to these sources must be several times 10(exp 22)/sq cm in order to explain their low X-ray count rates. This column density is considerably in excess of the X-ray fitted column density to G296.1-0.7, but is comparable to the total column densities in H I measured via the 21 cm line in the directions to all five remnants. This implies that G296.1 - 0.7 is at a significantly smaller distance than the other remnants.

Hwang, Una↗
NASA NTRS2015

Composite Characterization Using Laser Doppler Vibrometry and Multi-Frequency Wavenumber Analysis

NASA has recognized the need for better characterization of composite materials to support advances in aeronautics and the next generation of space exploration vehicles. An area of related research is the evaluation of impact induced delaminations. Presented is a non-contact method of measuring the ply depth of impact delamination damage in a composite through use of a Scanning Laser Doppler Vibrometer (SLDV), multi-frequency wavenumber analysis, and a wavenumber-ply correlation algorithm. A single acquisition of a chirp excited lamb wavefield in an impacted composite is post-processed into a numerous single frequency excitation wavefields through a deconvolution process. A spatially windowed wavenumber analysis then extracts local wavenumbers from the wavefield, which are then correlated to theoretical dispersion curves for ply depth determination. SLDV based methods to characterize as-manufactured composite variation using wavefield analysis will also be discussed.

Juarez, Peter↗
DOE OSTI2021

How much information on precipitation is contained in satellite infrared imagery?

Satellite infrared (IR) brightness temperature providing information on the characteristics of cloud tops is one of the primary input data used for estimating precipitation from satellites. Thus, the natural questions to ask are: Does IR imagery contain more information about precipitation in some regions more than others? What are the factors that lead to such differences? Should we expect the amount of information provided by IR imagery to change with respect to temporal or spatial aggregation? Comprehensive analysis of the accuracy of satellite-based precipitation estimates obtained from any given algorithm may provide qualitative answers to the above questions. However, such an approach only offers conclusions within the limited context of the algorithm used to obtain precipitation estimates; that is, patterns caused by the inherent information content of IR imagery are inextricable from those induced by assumptions embedded in the precipitation estimation algorithm. Here, we address the abovementioned questions from a different perspective using an information-theoretic measure, free of structural assumptions and general to a wide range of relationships, to characterize the average and seasonal dependence between IR imagery provided by satellite sensors in the spectral range (10.7–11.5 μm) and precipitation rates in the contiguous United States (CONUS) across distinct temporal and spatial scales. We analyze a total of more than 1.3 billion pairs of IR and precipitation observations over CONUS, and we observe interesting patterns. First, we show that there is a strong inverse relationship (ρ = - 0.73) between the information content of IR data and the number of no-rain observations; however, its robustness varies regionally and seasonally with less significant correlation during the warm season. Second, we demonstrate that the intuitive relationship of increasing dependence between IR and precipitation as a result of temporal or spatial aggregation exhibits a diminishing returns behavior. For instance, temporal aggregation from 1 to 3 h increases the dependence approximately 7 times as much as temporal aggregation from 12 to 24 h. Furthermore, we quantitatively examine and re-confirm several statements previously reported in the literature such as the strong association between IR and precipitation in convective storms and the low association in orographic rainfall. Finally, we conclude by illustrating the potential of the analysis in diagnosis of operational algorithms for estimating precipitation from IR brightness temperature. The findings of this study pinpoint spatial domains and time scales at which IR is not an adequate proxy for estimating precipitation. Thus, they can potentially guide the development of operational algorithms that utilize satellite Infrared imagery for estimating, downscaling and data fusion of precipitation. Finally, the analysis presented here opens up the possibility of developing new methodologies for diagnosis of satellite-based precipitation estimation algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1973

Application of Nimbus 4 THIR 6.7-micron observations to regional and global moisture and wind field analyses.

The Nimbus 4 temperature-humidity infrared radiometer (THIR) monitors radiation in the 6.5 to 7.2-micron water vapor absorption region with a 23-km spatial resolution at the subsatellite point. Radiation monitored in this spectral region results primarily from emission in the 250 to 500-mb region of the upper troposphere. The THIR 6.7-micron observations are readily available in photofacsimile imagery form which shows very distinctive patterns associated with spatial variations in atmospheric water vapor. These radiometric observations have been combined in several instances with moisture values measured in the upper troposphere by the standard radiosonde network. In each instance, the result is a much more consistent analysis showing increased spatial detail that agrees with the radiometric observations and does not compromise the conventional data. The improved moisture analyses show relatively dry and moist tongues that are very difficult or impossible to infer from the conventional data alone.

Steranka, J.↗
DOE OSTI2024

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. A systematic evaluation of the interaction between coastal catchment properties and carbon dioxide (CO2) emission by soil respiration is significant for assessing carbon dynamics and predicting the future trajectory of atmospheric CO2 concentrations in coastal TAIs. The soil CO2 efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation index (kNDVI), climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, there is no one-size-fits-all approach to modeling carbon fluxes in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Frame of reference for electronic maps - The relevance of spatial cognition, mental rotation, and componential task analysis

Three experiments are reported that examine the difference between north-up and track-up maps for airborne navigation. The results of the first two experiments, conducted in a basic laboratory setting, identified the cost associated with mental rotation, when a north-up map is used. However, the data suggest that these costs are neither large nor consistent. The third experiment examined a range of tasks in a higher fidelity helicopter flight simulation, and associated the costs of north-up maps with a cognitive component related to orientation, and the costs of track-up maps with a cognitive component related to inconsistent landmark location. Different tasks are associated with different dependence on these components. The results are discussed in terms of their implications for map design, and for cognitive models of navigational processes.

Wickens, Christopher D.↗