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NASA NTRS1992

Mass movements on Venus - Preliminary results from Magellan cycle 1 observations

A preliminary assessment of mass movements and their geomorphic characteristics as determined from visual inspection of Magellan cycle 1 synthetic aperture radar images is described. The primary data set was a catalog of over 200 ten-inch square photographic prints of full-resolution mosaic image data records. Venus exhibits unambiguous evidence of mass movements at a variety of scales. Mass movements appear mostly in the form of block and rock movements; there is little evidence of regolith and sediment movements. Unique Venusian conditions may play a role in the creation of some mass movement features. Dark (smooth) surfaces surrounding many rockslide avalanches are probably fine materials emplaced as part of the mass movement process, as airfall, surface-hugging density flows, or coarse-depleted debris flows. The size and efficiency of emplacement of landslide deposits on Venus are comparable to those seen on Mars, which in turn generally resemble terrestrial occurrences.

Malin, Michael C.↗

NASA NTRS1997

Passive Microwave Algorithms for Sea Ice Concentration: A Comparison of Two Techniques

The most comprehensive large-scale characterization of the global sea ice cover so far has been provided by satellite passive microwave data. Accurate retrieval of ice concentrations from these data is important because of the sensitivity of surface flux(e.g. heat, salt, and water) calculations to small change in the amount of open water (leads and polynyas) within the polar ice packs. Two algorithms that have been used for deriving ice concentrations from multichannel data are compared. One is the NASA Team algorithm and the other is the Bootstrap algorithm, both of which were developed at NASA's Goddard Space Flight Center. The two algorithms use different channel combinations, reference brightness temperatures, weather filters, and techniques. Analyses are made to evaluate the sensitivity of algorithm results to variations of emissivity and temperature with space and time. To assess the difference in the performance of the two algorithms, analyses were performed with data from both hemispheres and for all seasons. The results show only small differences in the central Arctic in but larger disagreements in the seasonal regions and in summer. In some ares in the Antarctic, the Bootstrap technique show ice concentrations higher than those of the Team algorithm by as much as 25%; whereas, in other areas, it shows ice concentrations lower by as much as 30%. The The differences in the results are caused by temperature effects, emissivity effects, and tie point differences. The Team and the Bootstrap results were compared with available Landsat, advanced very high resolution radiometer (AVHRR) and synthetic aperture radar (SAR) data. AVHRR, Landsat, and SAR data sets all yield higher concentrations than the passive microwave algorithms. Inconsistencies among results suggest the need for further validation studies.

Comiso, Josefino C.↗

NASA NTRS2001

Estimation of comprehensive forest variable sets from multiparameter SAR data over a large area with diverse species

Polarimetric and multifrequency data from the NASA/JPL airborne synthetic aperture radar (AIRSAR) have been used in a multi-tier estimation algorithm to calculate a comprehensive set of forest canopy properties including branch layer moisture and thickness, trunk density, trunk water content and diameter, trunk height, and subcanapy soil moisture. The estimation algorithm takes advantage of species-specific allometric relations, and is applied to a 100Km x 100Km area in the Canadian boreal region containing many different vegetation species types. The results show very good agreement with ground measurements taken at several focused and auxiliary study sites. This paper expands on the results reported in [1] and applies the algorithm on the regional scale.

variable estimation forestry multiparameter SAR↗

NASA NTRSDate not supplied

Aeroacoustic Computations of a Transonic Truss-Braced Wing Aircraft: Part 2 – Acoustic Signature and Noise Source Identification

High-fidelity, time-dependent simulations of a Boeing-designed, transonic, truss-braced-wing aircraft in cruise (clean) and landing configurations are leveraged to generate synthetic microphone-phased-array data for airframe noise prediction and assessment. These data sets are used to compute source localization (beamform) maps to determine the location and strength of primary and secondary airframe noise sources associated with this unique configuration. The synthetic phased-array implementation mimics the setup of a flight test. As this study is ongoing, preliminary integrated far-field spectra for the cruise configuration obtained at multiple spatial resolutions revealed significant tonal content that lacked convergence with increased resolution. The origin of several of these tones and their unusual convergence behavior was traced to the larger-than-normal trailing-edge thickness of the “as-tested” cruise model being simulated. Reducing the trailing-edge thickness to more realistic values eliminated most of the tones at low to moderate frequencies and improved spectrum convergence significantly. Applying lessons learned from the cruise simulations, several modifications to the geometry of the landing configuration were made and are described in this work. Results from permeable and solid Ffowcs-Williams and Hawkings surfaces at two different spatial resolutions (coarse and medium) are used to illustrate the major noise sources and determine convergence of the CLEAN integrated noise levels for the entire aircraft as well as major subcomponents. We demonstrate that the low-frequency content of the far-field spectrum is dominated by noise generated from the main landing gear, while the medium- and high-frequency content is dominated by the wing-leading-edge Krueger flaps. Since analysis of the acoustic maps for the landing configuration revealed several clusters of multiple sources along the wing leading edge, “high resolution” processing of the array data was used to distinguish more accurately the locations of sources.

Transonic Truss-Braced Wing↗

NASA NTRS2013

Height Error Correction for the New SRTM Elevation Product

The Shuttle Radar Topography Mission (SRTM), carrying a single-pass interferometric synthetic aperture radar(SAR) instrument, collected a global elevation data set, which has been widely used in scientific, military and commercial communities. In the new proposed NASA SRTM reprocessing task, the SRTM elevation data is going to be processed at higher spatial resolution and with improved height accuracy. Upon completion, the improved SRTM product will be freely available. This paper describes the calibration approaches for reduction of elevation ripple effects and height accuracy improvements.

Synthetic Aperture Radar Interferometry (InSAR)↗

DOE OSTI2021

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-ray Probed Laser Anneal Mapping (XPLAM) Time-Temperature Quench Maps

Many material systems have known or predicted functional phases that are metastable at standard temperature and pressure. While substantial advances have been made in the high-throughput and combinatorial synthesis of materials with a range of stoichiometries, investigation of thermal processing remains largely the domain of iterative uniform anneals or static gradients. Here we develop X-ray probed laser anneal mapping (XPLAM), a high throughput technique coupling spatially resolved X-ray diffraction with microsecond to millisecond laser gradient anneals to produce temperature–dwell–transformation (TDT) diagrams of the phase as a function of quench time and temperature. In addition to showing regimes where specific metastable phases form preferentially, TDT diagrams provide insight into the submillisecond kinetics of solid–solid phase transitions. This is a unique tool for mapping reaction pathways for metastable phases. As a first demonstration of XPLAM, we study Bi2O3, which has a rich set of polytypes, including the d-phase with an exceptionally high oxygen ion conductivity. We demonstrate the first annealing-driven synthesis of room temperature d-Bi2O3. We expect XPLAM to prove a powerful technique for rapid identification of synthetic routes to metastable phases and to generate the exhaustive data sets required for machine learning-guided exploration of materials processing.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗

DOE OSTI2022

Deep Realistic Extragalactic Model (DREaM) Galaxy Catalogs: Predictions for a Roman Ultra-deep Field

In the next decade, deep galaxy surveys from telescopes such as the James Webb Space Telescope and Roman Space Telescope will provide transformational data sets that will greatly enhance the understanding of galaxy formation during the epoch of reionization (EoR). In this work, we present the Deep Realistic Extragalactic Model (DREaM) for creating synthetic galaxy catalogs. Our model combines dark matter simulations, subhalo abundance matching and empirical models, and includes galaxy positions, morphologies, and spectral energy distributions. The resulting synthetic catalog extends to redshifts z ~ 12, and galaxy masses log 10 (M/M ⊙ ) = 5 covering an area of 1 deg 2 on the sky. We use DREaM to explore the science returns of a 1 deg 2 Roman ultra-deep field (UDF), and to provide a resource for optimizing ultra-deep survey designs. We find that a Roman UDF to ~30 m AB will potentially detect more than 10 6 M UV < -17 galaxies, with more than 10 4 at redshifts z > 7, offering an unparalleled data set for constraining galaxy properties during the EoR. Our synthetic catalogs and simulated images are made publicly available to provide the community with a tool to prepare for upcoming data.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2007

Program Merges SAR Data on Terrain and Vegetation Heights

X/P Merge is a computer program that estimates ground-surface elevations and vegetation heights from multiple sets of data acquired by the GeoSAR instrument [a terrain-mapping synthetic-aperture radar (SAR) system that operates in the X and bands]. X/P Merge software combines data from X- and P-band digital elevation models, SAR backscatter magnitudes, and interferometric correlation magnitudes into a simplified set of output topographical maps of ground-surface elevation and tree height.

Siqueira, Paul↗

DOE OSTI2024

PlantSegNet: 3D point cloud instance segmentation of nearby plant organs with identical semantics

In this study, we introduce PlantSegNet, a novel neural network model for instance segmentation of nearby objects with similar geometric structures. Our work addresses the challenges of instance segmentation of plant point clouds, including the difficulty of annotating and labeling point clouds, the loss of local structural information in neural network components, and the generation of large numbers of incorrect small clusters due to poor choices of the loss function. One of the key contributions of our approach is a digital twin of sorghum, i.e., a procedural sorghum model, which was used to generate point clouds of sorghum fields. This allowed us to create a large-scale, annotated, synthetic dataset of sorghum plants that we used to train our PlantSegNet model. We demonstrated the effectiveness of our method in segmenting instances of sorghum leaves grown in outdoor field settings. To the best of our knowledge, this is the first study to address this specific instance segmentation problem for plants grown in such a setting. We compared our proposed method with other state-of-the-art methods for indoor settings, including SGPN and TreePartNet, on both synthetic and real data. Furthermore, our results show that PlantSegNet outperforms these methods regarding accuracy, robustness, and efficiency.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Photometric calibration in u -band using blue halo stars

ABSTRACT We develop a method to calibrate u-band photometry based on the observed colour of blue Galactic halo stars. The Galactic halo stars belong to an old stellar population of the Milky Way and have relatively low metallicity. The ‘blue tip’ of the halo population – the main sequence turn-off (MSTO) stars – is known to have a relatively uniform intrinsic edge u-g colour with only slow spatial variation. In SDSS data, the observed variation is correlated with Galactic Latitude, which we attribute to contamination by higher metallicity disc stars and fit with an empirical curve. This curve can then be used to calibrate u-band imaging if g-band imaging of matching depth is available. Our approach can be applied to single-field observations at |b| > 30°, and removes the need for standard star observations or overlap with calibrated u-band imaging. We include in our method the calibration of g-band data with ATLAS-Refcat2. We test our approach on stars in KiDS DR 4, ATLAS DR 4, and DECam imaging from the NOIRLab Source Catalog (NSC DR2), and compare our calibration with SDSS. For this process, we use synthetic magnitudes to derive the colour equations between these data sets, in order to improve zero-point accuracy. We find an improvement for all data sets, reaching a zero-point precision of 0.016 mag for KiDS (compared to the original 0.033 mag), 0.020 mag for ATLAS (originally 0.027 mag), and 0.016 mag for DECam (originally 0.041 mag). Thus, this method alone reaches the goal of 0.02 mag photometric precision in u-band for the Rubin Observatory’s Legacy Survey of Space and Time (LSST).

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1993

Unusual radar echoes from the Greenland ice sheet

In June 1991, the NASA/Jet Propulsion Laboratory airborne synthetic-aperture radar (AIRSAR) instrument collected the first calibrated data set of multifrequency, polarimetric, radar observations of the Greenland ice sheet. At the time of the AIRSAR overflight, ground teams recorded the snow and firn (old snow) stratigraphy, grain size, density, and temperature at ice camps in three of the four snow zones identified by glaciologists to characterize four different degrees of summer melting of the Greenland ice sheet. The four snow zones are: (1) the dry-snow zone, at high elevation, where melting rarely occurs; (2) the percolation zone, where summer melting generates water that percolates down through the cold, porous, dry snow and then refreezes in place to form massive layers and pipes of solid ice; (3) the soaked-snow zone where melting saturates the snow with liquid water and forms standing lakes; and (4) the ablation zone, at the lowest elevations, where melting is vigorous enough to remove the seasonal snow cover and ablate the glacier ice. There is interest in mapping the spatial extent and temporal variability of these different snow zones repeatedly by using remote sensing techniques. The objectives of the 1991 experiment were to study changes in radar scattering properties across the different melting zones of the Greenland ice sheet, and relate the radar properties of the ice sheet to the snow and firn physical properties via relevant scattering mechanisms. Here, we present an analysis of the unusual radar echoes measured from the percolation zone.

Rignot, E. J.↗

DOE OSTI2023

High-fidelity model-driven deception platform for cyber-physical systems

Methods are described for protecting a cyber-physical system against a potential attacker of the system. The methods include a method of generating a plurality of examples for a training data set and training a system model using the training data set to generate a decoy configured to generate a synthetic output that mimics historical outputs generated by the system for a given historical system context. Also described is a method including receiving a system context of a cyber-physical system; receiving an inquiry into the system by a potential attacker; applying a system model to the system context and the inquiry; obtaining from the system model a synthetic output that mimics how a component of the system would respond to the inquiry given the system context; and providing the synthetic output to the potential attacker.

Edgar, Thomas W.↗

DOE OSTI2020

Building Large-Scale U.S. Synthetic Electric Distribution System Models

Rapid increases in distributed energy resources on distribution systems are prompting research efforts to improve and evaluate electric power distribution algorithms; however, there is a shortage of realistic, large-scale, U.S.-style test systems for the evaluation of such advanced algorithms. Some available tools to build large-scale test systems are of European style, and their application to studies focused on the United States might not be desirable given very different characteristics between the European and U.S. distribution designs. Motivated by this need, this paper develops detailed algorithms to build large-scale U.S. distribution systems and incorporates them in a new Reference Network Model, RNM-US. The approach starts with information from street maps and a catalog with electric equipment that includes power lines, transformers, voltage regulators, capacitors, and switching devices. The paper presents the algorithms through an illustrative case study of the different steps that comprise the process of building a synthetic distribution grid. Finally, the paper presents a medium- and a large-scale data set covering 10 million electrical nodes and 120,000 km of power lines, demonstrating the applicability of the proposed method to build very large-scale synthetic distribution systems.

27 ARPA - Advanced Research Projects Agency-Energy↗

NASA NTRS1991

Geologic mapping in Death Valley, California/Nevada using NASA/JPL airborne systems (AVIRIS, TIMS, and AIRSAR)

A multi-sensor aircraft campaign called the Geologic Remote Sensing Field Experiment (GRSFE) conducted during 1989 resulted in acquisition of high quality multispectral images in the visible, near infrared, shortwave infrared, thermal infrared, and microwave regions of the electromagnetic spectrum. The airborne data sets include the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the Thermal Infrared Multispectral Scanner (TIMS), and the Airborne Synthetic Aperture Radar (SAR). Ancillary data include Landsat Thematic Mapper, laboratory and field spectral measurements, and traditional geologic mapping. The GRSFE data for a site in the northern Death Valley, (California and Nevada) region were calibrated to physical units and geometrically registered to a map base. Various aspects of this experiment are briefly discussed.

Kruse, Fred A.↗

NASA NTRS2017

Pathways to Meteoritic Glycine and Methylamine

Glycine and methylamine are meteoritic water-soluble organic compounds that provide insights into the processes that occurred before, during, and after the formation of the Solar System. Both glycine and methylamine and many of their potential synthetic precursors have been studied in astrophysical environments via observations, laboratory experiments, and modeling. Despite these studies, the synthetic mechanisms for their formation leading to their occurrence in meteorites remain poorly understood. Typical 13C-isotopic values ((delta)13C) of meteoritic glycine and methylamine are 13C-enriched relative to their terrestrial counterparts; thus, analyses of their stable carbon isotopic compositions (13C/12C) may be used not only to assess terrestrial contamination in meteorites but also to provide information about their synthetic routes inside the parent body. Here, we examine potential synthetic routes of glycine and methylamine from a common set of precursors present in carbonaceous chondrite meteorites, using data from laboratory analyses of the well-studied CM2 Murchison meteorite. Several synthetic mechanisms for the origins of glycine and methylamine found in carbonaceous chondrites may be possible, and the prevalence of these mechanisms will largely depend on (a) the molecular abundance of the precursor molecules and (b) the levels of processing (aqueous and thermal) that occurred inside the parent body. In this work, we also aim to contextualize the current knowledge about gas-phase reactions and irradiated ice grain chemistry for the synthesis of these species through parent body processes. Our evaluation of various mechanisms for the origins of meteoritic glycine and methylamine from simple species shows what work is still needed to evaluate both the abundances and isotopic compositions of simpler precursor molecules from carbonaceous chondrites as well as the effects of parent body processes on those abundances and isotopic compositions. The analyses presented here combined with the indicated measurements will aid a better interpretation of quantitative analysis of reaction rates, molecular stability, and distribution of organic products from laboratory simulations of interstellar ices, astronomical observations, and theoretical modeling.

interstellar ice↗

DOE OSTI2022

Realistic galaxy image simulation via score-based generative models

ABSTRACT We show that a denoising diffusion probabilistic model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic Instrument (DESI) grz imaging of galaxies from the Photometry and Rotation curve OBservations from Extragalactic Surveys (PROBES) sample and galaxies selected from the Sloan Digital Sky Survey. Subjectively, the generated galaxies are highly realistic when compared with samples from the real data set. We quantify the similarity by borrowing from the deep generative learning literature, using the ‘Fréchet inception distance’ to test for subjective and morphological similarity. We also introduce the ‘synthetic galaxy distance’ metric to compare the emergent physical properties (such as total magnitude, colour, and half-light radius) of a ground truth parent and synthesized child data set. We argue that the DDPM approach produces sharper and more realistic images than other generative methods such as adversarial networks (with the downside of more costly inference), and could be used to produce large samples of synthetic observations tailored to a specific imaging survey. We demonstrate two potential uses of the DDPM: (1) accurate inpainting of occluded data, such as satellite trails, and (2) domain transfer, where new input images can be processed to mimic the properties of the DDPM training set. Here we ‘DESI-fy’ cartoon images as a proof of concept for domain transfer. Finally, we suggest potential applications for score-based approaches that could motivate further research on this topic within the astronomical community.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Conformal Hierarchical Simulation-Based Inference with Local Validity

Trustworthy and interpretable uncertainty quantification is a long-standing challenge in artificial intelligence. Simulation-based inference (SBI) comprises a broad swath of approaches for estimating latent parameters with uncertainties. Although flexible neural density estimators in SBI can be remark- ably expressive capturing highly structured, high-dimensional posteriors their credible regions can be badly mis-calibrated and are often only accompanied by heuristic coverage checks. We present the first SBI framework that delivers finite-sample local valid coverage guarantees that hold in the neighborhood of each observation. Our framework can couple any off-the-shelf hierarchical SBI engine with a confor- mal Bayesian post-processing step that operates on the posterior predictive density. A kernel-weighted conformity score adapts the conformal quantile to the local geometry of the data, yielding prediction sets that are simultaneously (i) marginally calibrated, (ii) locally valid, and (iii) hierarchical, handling global and observation-specific parameters in a single pass. Through experiments on synthetic data and benchmarks from neuroscience and physics, we show that our approach attains 1 − α coverage, where prior SBI methods under- or over-cover. Our approach also maintains a competitive, credible set size with minimal computational overhead. Finally, our approach can be used to make predictions on real data and give valid credible regions modulo weight-initialization-based model mis-specification.

Trivedi, Shubhendu [Fermilab]↗