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At least 721 records · Page 40

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DOE OSTI2024

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery. Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World, based on Sentinel-2, and the NASA IMPACT model, based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1998

Geology of Lunar Landing Sites and Origin of Basin Ejecta from a Clementine Perspective

The goals of this research were to examine Clementine multispectral data covering the Apollo landing sites in order to: (1) provide ground truth for the remotely sensed observations, (2) extend our understanding of the Apollo landing sites to the surrounding regions using the empirically calibrated Clementine data, and (3) investigate the composition and distribution of impact-basin ejecta using constraints based upon the remotely sensed data and the Apollo samples. Our initial efforts (in collaboration with P. Lucey and coworkers) to use the Apollo soil compositions to "calibrate" information derived from the remotely sensed data resulted in two extremely useful algorithms for computing estimates of the concentrations of FeO and TiO2 from the UV-VIS 5-band data. In this effort, we used the average surface soil compositions from 37 individual Apollo and 3 Luna sample stations that could be resolved using the Clementine data. We followed this work with a detailed investigation of the Apollo 17 landing site, where the sampling traverses were extensive and the spectral and compositional contrast between different soils covers a wide range. We have begun to investigate the nature and composition of basin ejecta by comparing the thick deposits on the rim of Imbrium in the vicinity of the Apollo 15 site and those occurring southeast of the Serenitatis basin, in the Apollo 17 region. We continue this work under NAG5-6784, "Composition, Lithology, and Heterogeneity of the lunar crust using remote sensing of impact-basin uplift structures and ejecta as probes. The main results of our work are given in the following brief summaries of major tasks. Detailed accounts of these results are given in the attached papers, manuscripts, and extended abstracts.

Jolliff, Bradley L.↗

NASA NTRS1979

Computations of unsteady transonic flow governed by the conservative full potential equation using an alternating direction implicit algorithm

A development was the time linearization of the density function. This linearization reduces the solution process from solving just a single equation. Two sample cases were computed. First, a one dimensional traveling shock wave was computed and compared with the analytic solution. Second, a two dimensional case was calculated for a flow field which resulted from a thickening and subsequently, thinning airfoil. The resulting flow field, which included a traveling shock wave, was compared to the flow field obtained from the low frequency, small disturbance, transonic equation.

Goorjian, P. M.↗

NASA NTRS2021

Thermal Conductivity Estimation from Transient Test Data with Embedded Thermocouples using Genetic Algorithm Optimization

Inverse heat transfer methodology previously developed to estimate thermal properties of high temperature fibrous insulation from embedded thermocouples was applied to the thermoplastic polymer, polyether-ether-ketone (PEEK). A small experimental setup was utilized to test the PEEK sample between temperatures of 300 K and 525 K at atmospheric pressure. Cylindrical plugs of PEEK with three thermocouples embedded at various depths were incorporated in the test sample. The experimental PEEK thermocouple data were used as the boundary and initial conditions of a one-dimensional numerical thermal model to predict the internal temperatures of the material. The thermal conductivity of PEEK was estimated with the Continuous Genetic Algorithm optimization technique by searching for the coefficients of a functional form of thermal conductivity that minimized the difference between the experimentally measured and model predicted internal temperature values. The thermal testing, one-dimensional numerical thermal model, optimization algorithm, analysis, and results are presented.

Thermal Properties↗

NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK) LiDAR System: Overview and Applications.

Improved terrain characterization and navigation sensors and methods are needed to enhance crew safety, ISRU return, and scientific understanding of future landing sites. Specific to the Artemis Program and sustained exploration at the lunar South Pole, extreme low-angle solar illumination conditions pose significant challenges to existing photogrammetry-based robotic navigation. Additionally, a major challenge for navigation on the Moon and other planetary surfaces is the lack of Global Positioning and Navigation Systems (GPS or GNSS). Thus, there is a need for an alternative to image-based navigation that allow for precise and accurate mapping in GPS-denied environments on any planetary body. Here, we describe the Kinematic Navigation and Cartography Knapsack (KNaCK) LiDAR system; a backpack-mounted, mobile navigation and terrain mapping system that uses a velocity-sensing coherent light detection and ranging (LiDAR) system based on a frequency modulated continuous wave (FMCW) technique, contains minimal moving parts, and employs sophisticated positioning algorithms. During a traverse, this instrument emits light pulses to continually scan a scene to build a three-dimensional point cloud representation of topography. A measure of the Doppler-velocity at each of millions of range points sampled per second allows for a 6 degree of freedom (6- DoF) estimate of the sensor’s position and the development of novel position-from-velocity mapping and positioning algorithms for loop-closure in GPS denied environments. Included with paper is the video presentation for the Figure 2: FMCW-LiDAR sensor on Kinematic Navigation and Cartography Knapsack (KNaCK) (Aeva Aeries 1)

M. Zanetti↗

NASA NTRS1986

Quantitative thermal characterization of thin plates

The principles of quantitative thermal testing of the mechanical integrity of platelike structures are discussed. In this technique, a scanned laser pulse is used to inject a thermal pattern in a sample, and a thermal IR imager is used to document the evolution of the pattern subsequent to injection. The measurements are remote, noncontacting, and one-sided, and the algorithms used to obtain diffusivity are all emissivity-independent. The results yielded by application of the three analytical techniques, used to obtain diffusivity values from the image data on stainless steel, a graphite-epoxy composite, brass, and aluminum 2024 samples, are presented.

Welch, C. S.↗

DOE OSTI2025

PhaseT3M: 3D imaging at 1.6 Å resolution via electron cryo-tomography with nonlinear phase retrieval

Electron cryo-tomography (cryo-ET) enables 3D imaging of complex, radiation-sensitive structures with molecular detail. However, image contrast from the interference of scattered electrons is nonlinear with atomic density and multiple scattering further complicates interpretation. These effects degrade resolution, particularly in conventional reconstruction algorithms, which assume linearity. Particle averaging can reduce such issues but is unsuitable for heterogeneous or dynamic samples ubiquitous in biology, chemistry, and materials sciences. Here, we develop a phase retrieval-based cryo-ET method, PhaseT3M. We experimentally demonstrate its application to an approximately 7 nm Co3O4 nanoparticle on an approximately 30 nm carbon substrate, achieving a maximum resolution of 1.6 Å, surpassing conventional limits using standard cryo-TEM equipment. PhaseT3M uses a multislice model for multiple scattering and Bayesian optimization for alignment and computational aberration correction, with a positivity constraint to recover ‘missing wedge’ information. Applied directly to biological particles, it enhances reconstruction quality and reduces artifacts, establishing a standard for routine 3D imaging with phase contrast.

Biophysics↗

DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Sparse optimization of two-dimensional terahertz spectroscopy

Two-dimensional terahertz spectroscopy (2DTS) is a low-frequency analog of two-dimensional optical spectroscopy that is rapidly maturing as a probe of a wide variety of condensed matter systems. However, a persistent problem with 2DTS is the long experimental acquisition times, which prevent its broader adoption. A potential solution, requiring no increase in experimental complexity, is signal reconstruction via compressive sensing. In this work, we apply the sparse exponential mode analysis (SEMA) technique to 2DTS of a cuprate superconductor. We benchmark the performance of the algorithm in reconstructing terahertz nonlinearities and find that SEMA reproduces the asymmetric photon echo line shapes at sampling rates as low as 10%, reaching the reconstruction noise floor at sampling rates beyond 20%–30%. The success of SEMA in reproducing such subtle, asymmetric line shapes confirms compressive sensing as a general method to accelerate 2DTS and multidimensional spectroscopies more broadly.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Unsupervised Image-Based Classification of Corrosion Severity in Automobile Engine Connecting Rods

Corrosion in engine connecting rods is a critical issue in the automotive industry, potentially leading to catastrophic engine failure, monetary losses, and safety hazards. The labor shortage in the industry further emphasizes the need for fast, accurate, and automated corrosion detection methods to ensure appropriate surface treatments can be applied to restore component integrity. We present an unsupervised image-based framework for classifying corrosion severity in automobile engine connecting rods using short-wave infrared (SWIR) and telecentric grayscale imaging. We employ the structural similarity index measure (SSIM) as a dissimilarity metric and the k-medians clustering algorithm for classification. Our algorithm achieves an overall accuracy of 80.64% for SWIR images, with 100% accuracy in classifying highly corroded samples. For grayscale images, the method attains an overall accuracy of 77.42%, with 90.91% accuracy for highly corroded samples. The method’s ability to work with different imaging modalities and its high accuracy in identifying severe corrosion cases make it a promising tool for automated corrosion assessment in the automotive industry, potentially improving efficiency and safety in engine component maintenance.

42 ENGINEERING↗

DOE OSTI2025

An Advanced Machine Learning and Artificial Intelligence System for Demonstrating Radiation Regulatory Compliance in DOE Accelerator Facilities

In this Phase II proposal, Applied Research LLC (ARLLC), Thomas Jefferson National Accelerator Facility (Jefferson Lab), and Old Dominion University (ODU) propose the combination of domain knowledge (beam characteristics, fixed structural shielding, earthen burden (the soil and foliage added to the dome of the experimental halls as additional shielding), etc.), machine learning (ML) and/or artificial intelligence (AI) to correlate a variety of multi-modal onsite signals and the radiation fields seen in accessible areas of the accelerator site and the site boundary. The ML/AI will consider the complex influence of environmental parameters affecting the radon contribution of the measurements, focusing on actual data obtained from Jefferson Lab. In Phase I, the coded beam and location data were fed into a deep learning model to predict doses at several designated locations in Jefferson Lab’s facility. Moreover, a dense radiation map was generated using only a sparse collection of the samples in a facility. In Phase II, we will develop a software prototype containing a radiation prediction algorithm, dense radiation map algorithms, and background noise prediction algorithms, with actual data used to evaluate the prototype. This work will provide a framework for evaluation of radiation measurement results around the site based on learned responses. In addition, the proposed approach allows more granular mapping of radiation levels. Better understanding and communication of these levels is related to the overall approach in keeping doses to personnel ALARA.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Data-Driven Tailoring Optimization of Thermoset Polymers Using Ultrasonics and Machine Learning

Thermoset polymers are highly demanded for their structural robustness, thermal stability, and chemical resistance. Tailoring the properties of these polymers for high-performance applications is often preferred to designing brand-new polymers. However, the traditional destructive techniques used to characterize their properties as a function of manufacturing parameters are expensive and time-consuming. A novel non-destructive, data-driven method leveraging ultrasonics and machine learning techniques to tailor the properties of thermosets as a function of the manufacturing parameters is demonstrated. Thermoset epoxy samples with varying curing temperatures (15–40 °C) and curing agent amounts (±40%) were manufactured and tested. Their curing kinetics were monitored by determining the sound speed in the material in real time, while the longitudinal modulus of the samples was determined post-cure. Machine learning models were developed using a k-nearest neighbors algorithm. These models were implemented to predict the curing and final elastic properties using the manufacturing parameters, i.e., stoichiometry and curing temperature, and vice versa. Understanding and modeling how these parameters affect the cure kinetics and final properties will allow for efficient and reliable optimization of thermoset tailoring and manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2026

The Compilation and Validation of the Spectroscopic Redshift Catalogs for the DESI-COSMOS and DESI-XMM-LSS Fields

Over several dedicated programs that include targets beyond the main cosmological samples, the Dark Energy Spectroscopic Instrument collected spectra for 304,970 unique objects in two fields centered on the COSMOS and XMM-LSS fields. In this work, we develop spectroscopic redshift robustness criteria for those spectra, validate these criteria using visual inspection, and provide two custom value-added catalogs with our redshift characterizations. With these criteria, we reliably classify 212,935 galaxies below z < 1.6, 9713 quasars, and 35,222 stars. The resulting catalogs achieve a redshift purity exceeding 99.4% across all galaxy samples. As a critical element in characterizing the selection function, we provide the description of 70 different algorithms that were used to select these targets from imaging data. To facilitate joint imaging/spectroscopic analyses, we provide row-matched photometry from the Dark Energy Camera, Hyper-Suprime Cam, and public COSMOS2020 photometric catalogs. Finally, we demonstrate example applications of these large catalogs to photometric redshift estimation, cluster finding, and completeness studies.

Ratajczak, J. [Univ. of Utah, Salt Lake City, UT (↗

NASA NTRS1975

Mars sample return by orbital rendezvous

The return of Mars surface samples by the orbital rendezvous mode would require smaller, lighter vehicles than the direct return mode and would limit the exposure of the earth return systems to possible contamination by Martian biota. The reference mission considered would use a single Titan IIIE/Centaur launch vehicle, carry a 1 kg sample, and be launched in late 1981. The spacecraft would consist of an orbiter, a Mars lander, a Mars ascent vehicle, an earth return vehicle, and an earth entry capsule. The Mars ascent vehicle is the only all-new vehicle required, the others being derived from Viking and other programs. A key consideration is the feasibility of automatic rendezvous and sample transfer between the ascent vehicle and orbiter in Mars orbit. The maneuvers required and the maneuver algorithm developed in a NASA feasibility study are described in detail. The possibility of international cooperation on a Mars orbital rendezvous mission is discussed.

Moore, J. W.↗

NASA NTRS1983

Design and implementation of a multigrid code for the Euler equations

The steady-state equations of inviscid fluid flow, the Euler equations, are a nonlinear nonelliptic system of equations admitting solutions with discontinuities (for example, shocks). The efficient numerical solution of these equations poses a strenuous challenge to multigrid methods. A multigrid code has been developed for the numerical solution of the Euler equations. In this paper some of the factors that had to be taken into account in the design and development of the code are reviewed. These factors include the importance of choosing an appropriate difference scheme, the usefulness of local mode analysis as a design tool, and the crucial question of how to treat the nonlinearity. Sample calculations of transonic flow about airfoils will be presented. No claim is made that the particular algorithm presented is optimal.

Jespersen, D. C.↗

NASA NTRS1989

Multispectral retrieval of rainfall rates using the Special Sensor Microwave/Imager (SSM/I)

Collocated SSM/I and radar measurements are compared to model simulated radiances upwelling from precipitating clouds. A methodology maximizing the correlation between the radar rain rate estimates and surface rainfall amounts is presented, along with data sources and samples. Geolocation of both the satellite and radar data is assessed, and statistical rainfall-rate retrieval algorithms are analyzed. Regression analysis of collocated SSM/I brightness-temperature measurements and radar-derived rainfall rates show the potential of the SSM/I to provide 25-km resolution rain-rate estimates over land and ocean surfaces.

Olson, William S.↗

NASA NTRS1992

Analog hardware for delta-backpropagation neural networks

This is a fully parallel analog backpropagation learning processor which comprises a plurality of programmable resistive memory elements serving as synapse connections whose values can be weighted during learning with buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in a plurality of neuron layers in accordance with delta-backpropagation algorithms modified so as to control weight changes due to circuit drift.

Eberhardt, Silvio P.↗

NASA NTRS1995

A first determination of the surface density of galaxy clusters at very low x-ray fluxes

We present the first results of a serendipitous search for clusters of galaxies in deep ROSAT position sensitive proportional counter (PSPC) pointed observations at high Galactic latitude. The survey is being carried out using a wavelet-based detection algorithm which is not biased against extended, low surface brightness sources. A new flux-diameter limited sample of 10 cluster candidates has been created from approximately 3 deg(exp 2) surveyed area. Preliminary CCD observations have revealed that a large fraction of these candidates correspond to a visible enhancement in the galaxy surface density, and several others have been identified from other surveys. We believe these sources to be either low- to moderate-redshift groups or intermediate- to high-redshift clusters. We show X-ray and optical images of some of the clusters identified to date. We present, for the first time, the derived number density of the galaxy clusters to a flux limit of 1 x 10(exp -14) ergs cm(exp -2) s(exp -1) (0.5-2.0 keV). This extends the log N-log S of previous cluster surveys by more than one decade in flux. Results are compared to theoretical predictions for cluster number counts.

Rosati, Piero↗