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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Learning Based Edge Computing in Air-to-Air Communication Network

This paper studies learning-based edge computing and communication in a dynamic Air-to-Air Ad-hoc Network (AAAN). Due to spectrum scarcity, we assume the number of Air-to-Air (A2A) communication links is greater than that of the available frequency channels, such that some communication links have to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the aggregate spectrum utilization efficiency under resource and fairness constraints. A distributed deep Q learning-based edge computing and communication algorithm is proposed to find the optimal solution. In particular, we design two different neural network structures and each communication link can converge to the optimal operation by exploiting only the local information from its neighbors, making it scalable to large networks. Finally, experimental results demonstrate the effectiveness of the proposed solution in various AAAN scenarios.

Zhe Wang↗
DOE OSTI2020

Grid stabilization using adjusted voltage regulator response to grid characteristics

Apparatus and methods are disclosed for control systems for improving stability of electrical grids by temporarily reducing voltage output of electrical generators responsive to transient events on an electrical grid. In one example of the disclosed technology, a controller is coupled is to an automatic voltage regulator, which in turn adjusts excitation current of an electrical generator responsive to changes in frequency detected for the electrical grid. Reducing the output voltage temporarily allows for smaller generators to provide power to the microgrid. Methods for selecting parameters determining how the controller generates a regulation signal used to adjust the excitation current are further disclosed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Surface acoustic wave sensor for refrigerant leakage detection

A SAW sensor is optimized for detection of refrigerant leakage in a refrigerant system or other gases, vapors, explosives or chemicals of interest. The SAW sensor includes a piezoelectric substrate; an interdigitated transducer deposited on the piezoelectric substrate, the interdigitated transducer having an input portion that receives input surface acoustic waves and an output portion that emits output surface acoustic waves; and a refrigerant sensor film located between the input portion and the output portion of the interdigitated transducer, the refrigerant sensor film including a sorbent material that is selected for preferential adsorption of a target refrigerant over atmospheric gases. Adsorption of the target refrigerant by the sorbent material results in a frequency shift of a frequency of the output surface acoustic waves relative to a frequency of the input surface acoustic waves. The sorbent material may be a metal organic framework (MOF) material, a covalent organic framework (COF) material, a porous organic cage or organic macrocyles such as calix [n] arene and its related derivatives.

Kunapuli, Raghuit Prasad↗
DOE OSTI2026

The Atacama Cosmology Telescope: DR6 Sunyaev-Zel'dovich Selected Galaxy Clusters Catalog

We present the results of a search for galaxy clusters in the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) microwave sky maps covering 16293 square degrees in three frequency bands, using data obtained over the lifetime of the project (2008-2022). We report redshifts and mass estimates for 10040 clusters detected via their Sunyaev-Zel'dovich (SZ) effect with signal-to-noise greater than 4 at a 2.4 arcminute filter scale. The catalog includes 1180 clusters at redshifts greater than 1, and 124 clusters at redshifts greater than 1.5. Using a relation between cluster SZ signal and mass that is consistent with recent weak-lensing measurements, we estimate that clusters detected with signal-to-noise greater than 5 form a sample which is 90% complete for clusters with masses greater than $5 \times 10^{14}$ MSun (measured within a spherical volume with mean density 500 times the critical density). El Gordo, a cluster found in an initial ACT survey of 755 square degrees, remains the most extreme cluster in mass and redshift; we find no cluster with a mass and redshift combination high enough to falsify the standard LCDM cosmology with Gaussian initial perturbations. We make public a variety of data products, including the full cluster candidate list, noise maps, and sky masks, along with our software for cluster detection and instructions for reproducing our cluster catalogs from the public ACT maps.

Aguena, M. [Trieste Observ.; LIneA, Rio de Janeiro↗
NASA NTRS1987

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system structural components

The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

Cruse, T. A.↗
NASA NTRS2001

Frequency conversion system

Laser diode pumped mid-IR wavelength sources include at least one high power, near-IR wavelength, injection and/or sources wherein one or both of such sources may be tunable providing a pump wave output beam to a quasi-phase matched (QPM) nonlinear frequency mixing (NFM) device. The NFM device may be a difference frequency mixing (DFM) device or an optical parametric oscillation (OPO) device. Wavelength tuning of at least one of the sources advantageously provides the ability for optimizing pump or injection wavelengths to match the QPM properties of the NFM device enabling a broad range of mid-IR wavelength selectivity. Also, pump powers are gain enhanced by the addition of a rare earth amplifier or oscillator, or a Raman/Brillouin amplifier or oscillator between the high power source and the NFM device. Further, polarization conversion using Raman or Brillouin wavelength shifting is provided to optimize frequency conversion efficiency in the NFM device.

Sanders, Steven↗
NASA NTRS1999

High power pumped mid-IR wavelength systems using nonlinear frequency mixing (NFM) devices

Laser diode pumped mid-IR wavelength systems include at least one high power, near-IR wavelength, injection and/or sources wherein one or both of such sources may be tunable providing a pump wave output beam to a quasi-phase matched (QPM) nonlinear frequency mixing (NFM) device. The NFM device may be a difference frequency mixing (DFM) device or an optical parametric oscillation (OPO) device. Wavelength tuning of at least one of the sources advantageously provides the ability for optimizing pump or injection wavelengths to match the QPM properties of the NFM device enabling a broad range of mid-IR wavelength selectivity. Also, pump powers are gain enhanced by the addition of a rare earth amplifier or oscillator, or a Raman/Brillouin amplifier or oscillator between the high power source and the NFM device. Further, polarization conversion using Raman or Brillouin wavelength shifting is provided to optimize frequency conversion efficiency in the NFM device.

Sanders, Steven↗
NASA NTRS2001

High power pumped MID-IR wavelength devices using nonlinear frequency mixing (NFM)

Laser diode pumped mid-IR wavelength sources include at least one high power, near-IR wavelength, injection and/or sources wherein one or both of such sources may be tunable providing a pump wave output beam to a quasi-phase matched (QPM) nonlinear frequency mixing (NFM) device. The NFM device may be a difference frequency mixing (DFM) device or an optical parametric oscillation (OPO) device. Wavelength tuning of at least one of the sources advantageously provides the ability for optimizing pump or injection wavelengths to match the QPM properties of the NFM device enabling a broad range of mid-IR wavelength selectivity. Also, pump powers are gain enhanced by the addition of a rare earth amplifier or oscillator, or a Raman/Brillouin amplifier or oscillator between the high power source and the NFM device. Further, polarization conversion using Raman or Brillouin wavelength shifting is provided to optimize frequency conversion efficiency in the NFM device.

Sanders, Steven↗
NASA NTRSDate not supplied

Toward The Development of Hailstorm Climatologies Derived From Reanalyses and Infared/Passive Microwave Satellite Imagers

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) and Meteosat series, provide both historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to United States Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are specifically designed to make hail likelihood distinctions based on satellite-indicated points of deep convection within environments favorable for storm development. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. This presentation demonstrates that statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, convective available potential energy, vertical wind shear, 500 hPa temperature, mid-level lapse rate, precipitable water, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a hail likelihood metric with a critical success index of 0.504 and Heidke skill score of 0.403, which is exceptional among recent analogous hail studies. Furthermore, applications of the DNN to select case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. Opportunities for training and applying DNN-based hailstorm predictions to recently developed GOES-8/10/12/13/16 and Meteosat Second Generation convective storm detection and characterization climatologies over South America and South Africa, respectively, will also be presented.

Kristopher Bedka↗
NASA NTRS2021

Satellite Remote Sensing to Assess Cyanobacterial Bloom Frequency Across the United States at Multiple Spatial Scales

Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria. Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Imager for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.

remote sensing↗
DOE OSTI2016

Angular dependence of Raman scattering selection rules for long-wavelength optical phonons in short-period GaAs/AlAs superlattices

The angular dependence of Raman scattering selection rules for optical phonons in short-period (001) GaAs/AlAs superlattices is calculated and experimentally studied. Experiments are performed using a micro-Raman setup, in the scattering geometry with the wavevectors of the incident and scattered light lying in the plane of superlattices (so-called in-plane geometry). Phonon frequencies are calculated using the Born model taking the Coulomb interaction into account in the rigid-ion approximation. Raman scattering spectra are calculated in the framework of the deformation potential and electro-optical mechanisms. Calculations show an angular dependence of the selection rules for optical phonons with different directions of the wavevectors. Drastic differences in the selection rules are found for experimental and calculated spectra. Presumably, these differences are due to the Fröhlich mechanism in Raman scattering for short-period superlattices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1988

Probabilistic Structural Analysis Methods for select space propulsion system structural components (PSAM)

The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

Cruse, T. A.↗
DOE OSTI2014

Genome-wide Selective Sweeps in Natural Bacterial Populations Revealed by Time-series Metagenomics

Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ‘ecotype model’ of diversification, but not previously observed in natural populations.

Chan, Leong-Keat↗
DOE OSTI2014

Genome-wide Selective Sweeps in Natural Bacterial Populations Revealed by Time-series Metagenomics

Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ecotype model? of diversification, but not previously observed in natural populations.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1986

Temporal frequency of radio emissions for the April 25, 1984 flare

The National Geophysical Data Center archives data of the solar-terrestrial environment. The USAF Radio Solar Telescope Network (RSTN) data allow performance of time series analysis to determine temporal oscillations as low as three seconds. The X13/3B flare which erupted in region 4474 (S12E43) on the 24 to 25 of April 1984, was selected. The soft X-rays, 1 to 8 A, remained above X-levels for 50 minutes and the radio emissions measured at Learmonth Solar Observatory reached a maximum of 3.15 x 10 to the 5th power SFUs at 410 MHz at 0000UT. A power spectral analysis of the fixed frequency RSTN data from Learmonth shows possible quasi-periodic fluctuations in the range two to ten seconds. Repetition rates or quasi-periodicities, in the case of the power spectral analysis, generally showed the same trends as the average solar radio flux at 245 and 8800 MHz. The quasi-periodicities at 1415 MHz showed no such trends.

Wells, G. D.↗
DOE OSTI2019

Ancient polymorphisms contribute to genome-wide variation by long-term balancing selection and divergent sorting in Boechera stricta

Genomic variation is widespread, and both neutral and selective processes can generate similar patterns in the genome. These processes are not mutually exclusive, so it is difficult to infer the evolutionary mechanisms that govern population and species divergence. Boechera stricta is a perennial relative of Arabidopsis thaliana native to largely undisturbed habitats with two geographic and ecologically divergent subspecies. Here, we delineate the evolutionary processes driving the genetic diversity and population differentiation in this species. Using whole-genome re-sequencing data from 517 B. stricta accessions, we identify four genetic groups that diverged around 30-180 thousand years ago, with long-term small effective population sizes and recent population expansion after the Last Glacial Maximum. We find three genomic regions with elevated nucleotide diversity, totaling about 10% of the genome. These three regions of elevated nucleotide diversity show excess of intermediate-frequency alleles, higher absolute divergence ( d XY ), and lower relative divergence ( F ST ) than genomic background, and significant enrichment in immune-related genes, reflecting long-term balancing selection. Scattered across the genome, we also find regions with both high F ST and d XY among the groups, termed F ST -islands. Population genetic signatures indicate that F ST -islands with elevated divergence, which have experienced directional selection, are derived from divergent sorting of ancient polymorphisms. Our results suggest that long-term balancing selection on disease resistance genes may have maintained ancestral haplotypes across different geographical lineages, and unequal sorting of balanced polymorphisms may have generated genomic regions with elevated divergence. This study highlights the importance of ancestral balanced polymorphisms as crucial components of genome-wide variation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

42 ENGINEERING↗
DOE OSTI2023

Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

47 OTHER INSTRUMENTATION↗