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At least 1,099 records · Page 61

TPSAS-NF1676L-32467-DND

With the launch of a new generation of Geostationary satellites (GEO) such as Himawari and GOES-16 & 17, cloud detection using satellite imager data has been greatly enhanced with increased spectral bands, and higher temporal and spatial resolutions. A concern for all geostationary sensors, however, are changes in instrument sensitivity and algorithm performance at different viewing for daytime and nighttime. CALIPSO lidar observations provide a valuable reference for assessing these impacts as the satellite flies in a sun-synchronous orbit and crosses a wide range of GEO viewing angles each day. This paper will present the cloud mask results using the imager data from Himawari (AHI) and GOES-16&17 (ABI). The detection algorithms have been adapted from the Cloud and Earth’s Radiant Energy System (CERES) MODIS Edition 4 cloud mask, and adjusted and tuned to geo-satellites. They are used operationally for the CERES Time and Space Averaging (TISA) gridded cloud products and for near-real-time retrievals for weather and nowcasting applications.

Qing Z. Trepte↗

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗

Fail-over file transfer process

The present invention provides a fail-over file transfer process to handle data file transfer when the transfer is unsuccessful in order to avoid unnecessary network congestion and enhance reliability in an automated data file transfer system. If a file cannot be delivered after attempting to send the file to a receiver up to a preset number of times, and the receiver has indicated the availability of other backup receiving locations, then the file delivery is automatically attempted to one of the backup receiving locations up to the preset number of times. Failure of the file transfer to one of the backup receiving locations results in a failure notification being sent to the receiver, and the receiver may retrieve the file from the location indicated in the failure notification when ready.

Semancik, Susan K.↗

Data On Flow About A Compressor Blade In Cascade

Development and testing of software to compute values of flow-field characteristics greatly enhanced by using recorded experimental data to test results of computations. Report "The Measurement Of Boundary Layers On Compressor Blade In Cascade" is data base providing detailed measurements of boundary layer and wake in flow field about double-circular-arc compressor blades in cascade of such blades.

Deutsch, S.↗

Integration of Apollo Lunar Sample Data into Google Moon

The Google Moon Apollo Lunar Sample Data Integration project is a continuation of the Apollo 15 Google Moon Add-On project, which provides a scientific and educational tool for the study of the Moon and its geologic features. The main goal of this project is to provide a user-friendly interface for an interactive and educational outreach and learning tool for the Apollo missions. Specifically, this project?s focus is the dissemination of information about the lunar samples collected during the Apollo missions by providing any additional information needed to enhance the Apollo mission data on Google Moon. Apollo missions 15 and 16 were chosen to be completed first due to the availability of digitized lunar sample photographs and the amount of media associated with these missions. The user will be able to learn about the lunar samples collected in these Apollo missions, as well as see videos, pictures, and 360 degree panoramas of the lunar surface depicting the lunar samples in their natural state, following collection and during processing at NASA. Once completed, these interactive data layers will be submitted for inclusion into the Apollo 15 and 16 missions on Google Moon.

Dawson, Melissa D.↗

Advances in imaging, scattering, spectroscopy, and machine learning-aided approaches for multiscale characterization of cementitious systems

Recent progress in methods used in the multiscale characterization of cementitious systems is reviewed, focusing on advances in imaging, scattering, and spectroscopy. The review includes relevant applications and developments in machine learning and other data analytics approaches to enhance characterization. Developments in imaging using light and electron microscopy as well as x-ray (i.e., from synchrotron) methods are summarized. Updates include scanning electron microscopy (SEM), transmission electron microscopy (TEM), tomography, and holography. A critical overview of spectroscopy (e.g., MAS NMR, Raman) and scattering (e.g., neutron, x-ray, synchrotron x-ray) methods is provided, and the intersection of these with imaging is developed (e.g., Raman imaging). Additionally, the paper summarizes recent developments in and implementations of state-of-the-art machine-learning algorithms and data analytics methods for automated, systematic, and/or quantitative analyses of image data sets. The review considers but is not limited to the application of these methods for investigating the hydration and microstructure development of cement phases, low-carbon-footprint cements (e.g., limestone calcined clay cements, LC3), environmental interactions (e.g., ASR) and model systems. Finally, the present work provides a critical presentation of advances in characterization methods that link together the composition and multiscale structure of cementitious materials.

42 ENGINEERING↗

Some practical universal noiseless coding techniques, part 2

This report is an extension of earlier work (Part 1) which provided practical adaptive techniques for the efficient noiseless coding of a broad class of data sources characterized by only partially known and varying statistics (JPL Publication 79-22). The results here, while still claiming such general applicability, focus primarily on the noiseless coding of image data. A fairly complete and self-contained treatment is provided. Particular emphasis is given to the requirements of the forthcoming Voyager II encounters of Uranus and Neptune. Performance evaluations are supported both graphically and pictorially. Expanded definitions of the algorithms in Part 1 yield a computationally improved set of options for applications requiring efficient performance at entropies above 4 bits/sample. These expanded definitions include as an important subset, a somewhat less efficient but extremely simple "FAST' compressor which will be used at the Voyager Uranus encounter. Additionally, options are provided which enhance performance when atypical data spikes may be present.

Rice, R. F.↗

Calcium ion dependency of ethylene production in segments of primary roots of Zea mays

We investigated the effect of Ca2+ on ethylene production in 2-cm long apical segments from primary roots of corn (Zea mays L., B73 x Missouri 17) seedlings. The seedlings were raised under different conditions of Ca2+ availability. Low-Ca and high-Ca seedlings were raised by soaking the grains and watering the seedlings with distilled water or 10 mM CaCl2, respectively. Segments from high-Ca roots produced more than twice as much ethylene as segments from low-Ca roots. Indoleacetic acid (IAA; 1 micromole) enhanced ethylene production in segments from both low-Ca and high-Ca roots but auxin-induced promotion of ethylene production was consistently higher in segments from high-Ca roots. Addition of 1-aminocyclopropane-1-carboxylic acid (ACC) to root segments from low-Ca seedlings doubled total ethylene production and the rate of production remained fairly constant during a 24 h period of monitoring. In segments from high-Ca seedlings ACC also increased total ethylene production but most of the ethylene was produced within the first 6 h. The data suggest that Ca2+ enhances the conversion of ACC to ethylene. The terminal 2 mm of the root tip were found to be especially important to ethylene biosynthesis by apical segments and, experiments using 45Ca2+ as tracer indicated that the apical 2 mm of the root is the region of strongest Ca2+ accumulation. Other cations such as Mn2+, Mg2+, and K+ could largely substitute for Ca2+. The significance of these findings is discussed with respect to recent evidence for gravity-induced Ca2+ redistribution and its relationship to the establishment of asymmetric growth during gravitropic curvature.

NASA Discipline Plant Biology↗

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

Deep Blue↗

Case for Deploying Complex Systems Utilizing Commodity Components

When the International Space Station (ISS) finally reached an operational state, many of the Payload Operations and Integration Facility (POIF) hardware components were reaching end of life, COTS product costs were soaring, and the ISS budget was becoming severely constrained. However, most requirement development was complete. In addition, the ISS program is a fully functioning program with at least fifteen years of operational life remaining. Therefore it is critical that any upgrades, refurbishments, or enhancements be accomplished in realtime with minimal disruptions to service. For these and other reasons, it was necessary to ensure the viability of the POIF. Due to the to the breadth of capability of the POIF (a NASA ground station), it is believed that the lessons to be learned by other complex systems are applicable and any solutions garnered by the POIF are applicable to other complex systems as well. With that in mind, a number of new approaches have been investigated to increase the portability of the POIF and reduce the cost of refurbishment, operations, and maintenance. These new approaches were directed at the Total Cost of Ownership (TCO); not only the refurbishment but also current operational difficulties, licensing, and anticipation of the next refurbishment. Our basic premise is that technology had evolved dramatically since the concept of the POIF ground system and we should leverage our experience on this new technological landscape. Fortunately, Moore's law and market forces have changed the landscape considerably. These changes are manifest in five (5) ways that are particularly relevant to POIF: 1. Complex Instruction Set Computing (CISC) processors have advanced to unprecedented levels of compute capacity with a dramatic cost break, 2. Linux has become a major operating system supported by most vendors on a broad range of platforms, 3. Windows(TradeMark) based desktops are pervasive in the office environment, 4. Stable and affordable WindowsTM development environments and tools are available and offer a rich set of capabilities, 5. The WindowsTM 2000 provides a stable client platform, Therefore, five studies were proposed, developed, and are in the current process of deployment which dramatically reduces the cost of operations, maintenance, refurbishment, and deployment of a ground system. Restating and refining the basic premise stated earlier, it is possible to enhance operations through the replacement of hardware and software components with commodity based items wherever applicable. This will dramatically reduce the overall lifecycle cost of the project. The first study leveraged the POIF S secure, three-tier, web architecture to replace the client workstations with lower cost PC platforms. A second study initiated a review of COTS products to examine the level of added value of each product. This study included replacement of some COTS products with custom code, deletions, substitutions, and consolidation of COTS products. Studies three and four reviewed the server architectures of the data distribution systems and Enhanced HOSC System (EHS) command and telemetry system to propose migration to new platforms, both software and hardware. The final study reviewed current IP communication technologies, developed an operational model for flight operations, and demonstrated that voice over IP was practical and could be integrated into operations.

Bryant, Barry S.↗

Design-controlled synthesis of IrO 2 sub-monolayers on Au nanoflowers: marrying plasmonic and electrocatalytic properties

We develop herein plasmonic–catalytic Au–IrO 2 nanostructures with a morphology optimized for efficient light harvesting and catalytic surface area; the nanoparticles have a nanoflower morphology, with closely spaced Au branches all partially covered by an ultrathin (1 nm) IrO 2 shell. This nanoparticle architecture optimizes optical features due to the interactions of closely spaced plasmonic branches forming electromagnetic hot spots, and the ultra-thin IrO 2 layer maximizes efficient use of this expensive catalyst. This concept was evaluated towards the enhancement of the electrocatalytic performances towards the oxygen evolution reaction (OER) as a model transformation. The OER can play a central role in meeting future energy demands but the performance of conventional electrocatalysts in this reaction is limited by the sluggish OER kinetics. We demonstrate an improvement of the OER performance for one of the most active OER catalysts, IrO 2 , by harvesting plasmonic effects from visible light illumination in multimetallic nanoparticles. We find that the OER activity for the Au–IrO 2 nanoflowers can be improved under LSPR excitation, matching best properties reported in the literature. Our simulations and electrocatalytic data demonstrate that the enhancement in OER activities can be attributed to an electronic interaction between Au and IrO 2 and to the activation of Ir–O bonds by LSPR excited hot holes, leading to a change in the reaction mechanism (rate-determinant step) under visible light illumination.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Combining Space-Based and In-Situ Measurements to Track Flooding in Thailand

We describe efforts to integrate in-situ sensing, space-borne sensing, hydrological modeling, active control of sensing, and automatic data product generation to enhance monitoring and management of flooding. In our approach, broad coverage sensors and missions such as MODIS, TRMM, and weather satellite information and in-situ weather and river gauging information are all inputs to track flooding via river basin and sub-basin hydrological models. While these inputs can provide significant information as to the major flooding, targetable space measurements can provide better spatial resolution measurements of flooding extent. In order to leverage such assets we automatically task observations in response to automated analysis indications of major flooding. These new measurements are automatically processed and assimilated with the other flooding data. We describe our ongoing efforts to deploy this system to track major flooding events in Thailand.

Flooding↗

Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data

Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-vision transformer (ViT)-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a ViT architecture. Applied to 531 basins across the Contiguous United States, our method demonstrated superior prediction accuracy in both temporal and spatiotemporal extrapolation scenarios. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for better understanding the environment responses to climate change.

Tayal, Kshitij↗

Evaluation of Thematic Mapper Performance as Applied to Hydrocarbon Exploration

The role data from the first three LANDSAT satellites have in geologic exploration and their current level of acceptance is reviewed and the advantages of LANDSAT 4 TM data over MSS data are discussed. Specially enhanced Thematic Mapper imager can make a very significant contribution to the oil and gas and mineral exploration industries. The TM's increased spatial resolution enables the production of larger scale imagery, which greatly increases the amount of geomorphic and structural information interpretable. TM's greater spectral resolution, combined with the smaller, more homogeneous pixels, should enable a far greater confidence in mapping lithologies and detecting geobotanical anomalies from space. The results from its applications to hydrocarbon and mineral exploration promise to bring the majority of the geologic exploration community into that final stage of acceptance and routine application of the satellite data.

Everett, J. R.↗

Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification" Willard et al. (2025).

This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items:Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025).Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code:- data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repositoryData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2025streamensembles,author = {Jared Willard and Charuleka Varadharajan},title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"},year = {2024},doi = {10.15485/2527393},publisher = {ESS-DIVE Repository},url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2527393}}MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.

54 ENVIRONMENTAL SCIENCES↗

Accomplishments of Thermal Neutron Scattering Research at North Carolina State University [Slides]

Thermal neutron scattering law (TSL) data evaluations have been completed and contributed to ENDF. New evaluations are underway. FLASSH is developing as a modern platform for thermal neutron data analysis featuring an enhanced user experience and low learning overhead. Advanced TSL techniques in combination with Doppler analysis have been developed, which were incorporated into FLASSH. ML/DL NeTS method development has been initiated. Experimental capabilities for validation have been developed at the PULSTAR reactor.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Voyager image data compression and block encoding

Telemetry enhancement techniques used by Voyager-2 to reduce telemetry transmission rates by over 50 percent compared to those used at Saturn, with negligible loss in information return, are described. The use of the Reed-Solomon encoder is discussed, and the principles and implementation of an Image Data Compressor algorithm for noiseless coding techniques are addressed. Parallel operation of the redundant Flight Data Subsystem processors is discussed.

Urban, Michael G.↗

Benchmark Data for Evaluation of Aeroacoustic Propagation Codes With Grazing Flow

Increased understanding of the effects of acoustic treatment on the propagation of sound through commercial aircraft engine nacelles is a requirement for more efficient liner design. To this end, one of NASA s goals is to further the development of duct propagation and impedance reduction codes. A number of these codes have been developed over the last three decades. These codes are typically divided into two categories: (1) codes that use the measured complex acoustic pressure field to reduce the acoustic impedance of treatment that is positioned along the wall of the duct, and (2) codes that use the acoustic impedance of the treatment as input and compute the sound field throughout the duct. Clearly, the value of these codes is dependent upon the quality of the data used for their validation. Over the past two decades, data acquired in the NASA Langley Research Center Grazing Incidence Tube have been used by a number of researchers for comparison with their propagation codes. Many of these comparisons have been based upon Grazing Incidence Tube tests that were conducted to study specific liner technology components, and were incomplete for general propagation code validation. Thus, the objective of the current investigation is to provide a quality data set that can be used as a benchmark for evaluation of duct propagation and impedance reduction codes. In order to achieve this objective, two parallel efforts have been undertaken. The first of these is the development of an enhanced impedance eduction code that uses data acquired in the Grazing Incidence Tube. This enhancement is intended to place the benchmark data on as firm a foundation as possible. The second key effort is the acquisition of a comprehensive set of data selected to allow propagation code evaluations over a range of test conditions.

Jones, Michael G.↗