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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Evaluation of Classifier Complexity for Delay Tolerant Network Routing

The growing popularity of small cost effective satellites (SmallSats, CubeSats, etc.) creates the potential for a variety of new science applications involving multiple nodes functioning together or independently to achieve a task, such as swarms and constellations. As this technology develops and is deployed for missions in Low Earth Orbit and beyond, the use of delay tolerant networking (DTN) techniques may improve communication capabilities within the network. In this paper, a network hierarchy is developed from heterogeneous networks of SmallSats, surface vehicles, relay satellites and ground stations which form an integrated network. There is a tradeoff between complexity, flexibility, and scalability of user defined schedules versus autonomous routing as the number of nodes in the network increases. To address these issues, this work proposes a machine learning classifier based on DTN routing metrics. A framework is developed which will allow for the use of several categories of machine learning algorithms (decision tree, random forest and deep learning) to be applied to a dataset of historical network statistics, which allows for the evaluation of algorithm complexity versus performance to be explored. We develop the emulation of a hierarchical network, consisting of tens of nodes which form a cognitive network architecture. CORE (Common Open Research Emulator) is used to emulate the network using bundle protocol and DTN IP neighbor discovery.

Dudukovich, Rachel↗
DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗
NASA NTRS2018

Differences in Pre and Post Vascular Patterning of Retinas from ISS Crew Members and HDT Subjects by VESGEN Analysis

Accelerated research by NASA [1] has investigated the significant risks for visual and ocular impairments Spaceflight Associated Neuro-Ocular Syndrome /Visual Impairment/Intracranial Pressure (SANS/VIIP) incurred by microgravity spaceflight, especially long-duration missions. Our study investigates the role of blood vessels in the incidence and etiology of SANS/VIIP within the retinas of Astronaut crewmembers pre-and post-flight to the International Space Station (ISS) by NASA's VESsel GENeration Analysis (VESGEN). The response of retinal vessels in crewmembers to microgravity was compared to that of retinal vessels to Head-Down Tilt (HDT) in subjects undergoing 70-Day Bed Rest. The study tests the proposed hypothesis that cephalad fluid shifts missions, resulting in ocular and visual impairments, are necessarily mediated in part by retinal blood vessels, and are therefore accompanied by significant remodeling of retinal vasculature.Vascular patterns in the retinas of crew members and HDTBR subjects extracted from 30° infrared (IR) Heidelberg Spectralis® images collected pre/postflight and pre/post HDTBR, respectively, were analyzed by VESGEN (patent pending). a mature, automated software developed as a research discovery tool for progressive vascular diseases in the retina and other tissues [2]. The weighted, multi-parametric VESGEN analysis generates maps of branching arterial and venous trees and quantification by parameters such as the fractal dimension (Df, a modern measure of vascular space-filling capacity), vessel diameters, and densities of vessel length and number classified into specific branching generations by vascular physiological branching rules [2,3]. The retrospective study approved by NASA’s Institutional Review Board included six HDT subjects (NASA Flight Analogs Research Unit [FARU] Campaign 11; for example, [4]) and eight ISS crewmembers monitored by routine occupational surveillance who provided their study consents to NASA’s Lifetime Surveillance of Astronaut Health (LSAH). For the initial blinded VESGEN phase, ophthalmic retinal images were masked as to subject identity and pre- and post-status. In the second unblinded phase, VESGEN results were analyzed according to the pre- and post-status of left and right retinas matched to each subject. To complete our study, vascular results will be subjected to NASA biostatistical analysis and correlated with other ophthalmic and medical findings. Preliminary results for changes in the pre- to post-status of vascular patterning in the retinas of crewmembers and HDT subjects are strikingly opposite. By Df and other vascular branching measures, the space-filling capacity of arterial and venous trees decreased in a substantial subset of crewmembers (11/16 retinas). In contrast, vascular densities increased in a substantial subset of HDT subjects by the same parameters (6/10 retinas, currently excluding one anomalous subject). To conclude the study, biostatistical and medical analyses will be of critical importance for investigating the validity of these vascular findings. Vascular densities appeared to decrease in the retinas of crewmembers following ISS Missions, and increase in subjects after HDT. The vascular increases and decreases most likely derive primarily from limits of resolution to the ophthalmic imaging that does not capture the smallest vessels, rather than from vessel growth or atrophy. Differences in arterial and venous response to cephalad fluid shifts induced by ISS and HDT may have resulted from a long-duration conditioning phenomenon (for example, 6-month ISS missions compared to 70-day HDT), or the presence of gravity in HDT compared to microgravity onboard the ISS. To conclude our study, the biostatistical and medical analyses will be of critical importance for investigating the validity and significance of the VESGEN findings.

Murray, M. C.↗
NASA NTRS2021

Bhutan Agriculture: Developing a Crop Mask for Rice and Creating a Data Collection Protocol Utilizing Remotely Sensed Data in Bhutan

Rice cultivation in Bhutan has been increasingly threatened by deteriorating soil health and outbreaks of diseases and pests associated with the global change in climate patterns. Field surveys, which the national government of Bhutan has relied on to monitor remote agricultural lands, are becoming increasingly overwhelmed by growing threats to agricultural health. To address these concerns, NASA DEVELOP partnered with the Department of Agriculture of Bhutan, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER) and worked to increase the government of Bhutan’s agricultural monitoring capacity. Utilizing Earth observations including Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Shuttle Radar Topography Mission (SRTM), and Planet imagery, the DEVELOP team worked with NASA SERVIR and created a sampling protocol to identify rice plantations and supplement field surveys for more efficient agriculture monitoring. The analysis focused on districts Paro, Punakha, Samtse, Sarpang, Trongsa, Zhemgang, Wangdue Phodrang, and Samdrup Jongkhar in the year 2020 during the period of transplantation (June) to harvesting of rice (November). The team provided the partners with a sampling protocol for integrating NASA Earth observations into their crop monitoring methods, as well as a crop mask for rice identification and to aid crop management. The crop mask for rice was developed using the Random Forest (RF) classifier for the eight districts of Bhutan. Visually, the random forest model has proved to be more accurate and precise than the classification and Regression Tree model. Statistically, the Random Forest model was 91.8% accurate in identifying rice in Bhutan.

Yeshey Seldon↗
NASA NTRS1992

STARBASE: Database software for the automated plate scanner

The Automated Plate Scanner (APS) of the University of Minnesota, a unique high speed 'flying spot' laser scanner, is currently being used to scan and digitize the 936 O and E plate pairs of the first epoch Palomar Sky Survey. The resultant database will be used to produce a catalog of approximately a billion stars and several million galaxies. The authors describe the ongoing development of a dedicated APS database management system which will be made available to the astronomical community via INTERNET. A specialized DBMS called STARBASE has been written to provide fast access to the hundreds of millions of images collected by the APS. This system provides an initial reduction mode for parameterizing APS images and classifying image types using a novel set of neural network image classifiers. A second analysis mode, which will be that commonly used by the general user, provides for searches of the database which may be constrained by any combination of physical and positional parameters. Through the use of pointer hash trees, the system has been optimized for extremely fast positional searches using either right ascension and declination on the sky or linear X and Y positions on the POSS field. In addition to fast data retrieval, the system provides a graphical interface for displaying scatter plots or histograms of the collected data. In addition, a specialized image display system is being developed to allow the user to view densitometric data for all objects classified as extended by the neural network system. Finally, STARBASE has a flexible programmable interface which allows other programs to access information in the database. This allows users to write applications suited to their particular needs to process APS data.

Odewahn, S. C.↗
NASA NTRS1972

Methodology for the systems engineering process. Volume 2: Technical parameters

A scheme based on starting the logic networks from the development and mission factors that are of primary concern in an aerospace system is described. This approach required identifying the primary states (design, design verification, premission, mission, postmission), identifying the attributes within each state (performance capability, survival, evaluation, operation, etc), and then developing the generic relationships of variables for each branch. To illustrate this concept, a system was used that involved a launch vehicle and payload for an earth orbit mission. Examination showed that this example was sufficient to illustrate the concept. A more complicated mission would follow the same basic approach, but would have more extensive sets of generic trees and more correlation points between branches. It has been shown that in each system state (production, test, and use), a logic could be developed to order and classify the parameters involved in the translation from general requirements to specific requirements for system elements.

Nelson, J. H.↗
NASA NTRSDate not supplied

Assessing Alaskan boreal forest landcover affected by climate-wildfire interactions from ground truth surveys and NASA airborne remote sensing

Alaska’s boreal forest is facing unprecedented challenges under rapid climate warming (increasingly severe fires, droughts, pest/disease outbreaks) that may destabilize its function as a global carbon sink. Forests near Fairbanks may be especially vulnerable, impacting air quality and ecosystem services. We combined GT (ground truthing) with Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG) images collected by the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) program (2017-2019) to assess landcover change at five recently burned sites (2001-2019) of different fire severities and moisture regimes within 30 miles of Fairbanks. GT included tree seedling counts, understory % cover and >50% leaf canopy color assessment. 36 circular plots (1/30 ha radius) including 6 moderate to severely burned plots were selected across sites. 31 additional sites including 12 burned sites were geotagged in photos. AVIRIS images were processed from 29 spectral bands selected to identify changes in chlorophyll and water content. Images were segmented into natural boundaries (polygons) using ENVI 5.5 software. A spectral library of 8 AVIRIS bands with high between-class/low within-class variation was used in two random forest models to predict vegetation classes (model 1: 12 classes, model 2: 14 classes) in each AVIRIS scene, using 20% of the data as training data. Model 2 classified 20% more polygons overall, but only 42% of GT/geotagged polygons were correctly classified by both models. More forest sites were correctly classified (63%) than open vegetation (32%) or post-fire sites (46%). 50% of aspen forest and post-fire polygons were misclassified as shrubland. GT revealed that post-fire plots supported 134,000 (± 48,000) tree seedlings and saplings ha-1 (0.2 - 4 m height, 64% deciduous) versus 2500 (± 2100) shrubs ha-1 (1-6 m height). > 50% canopy browning was observed in conifer forest (8 plots) with no signs of insect infestation. Canopy herbivory > 50% (leaf miner, leaf beetle) and moose herbivory of tree bark was seen across aspen sites. Our study suggests: 1) low canopy vegetation presents challenges for improved landcover classification, and 2) aspen forest should be differentiated in vegetation maps which would aid in tracking herbivory.

Alaska↗
DOE OSTI2025

Distinguishing Orbiting and Infalling Dark Matter Particles with Machine Learning

Dark matter halos are typically defined as spheres that enclose some overdensity, but these sharp, somewhat arbitrary boundaries introduce nonphysical artifacts such as backsplash halos, pseudo-volution, and an incomplete accounting of halo mass. A more physically motivated alternative is to define halos as the collection of particles that are physically orbiting within their potential well. However, existing methods to classify particles as orbiting or infalling suffer from trade-offs between accuracy, computational cost, and generalizability across cosmologies. We present an efficient, yet accurate, supervised machine learning approach using decision trees. The classification is based on only the particle radii and velocities at two epochs. Compared to detailed analysis of particle trajectories, we find that our model matches the classification of 97% of particles. Consequently, we are able to quickly and accurately reproduce the density profiles of the orbiting and infalling components out to many virial radii. We demonstrate that our model generalizes to a significantly different cosmology that lies outside the training data set. We make publicly available both our final model and the code to train similar models.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2003

Subauditory Speech Recognition based on EMG/EPG Signals

Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

Jorgensen, Charles↗
DOE OSTI2026

New avenues for |∆ B | = 2 processes beyond neutron-antineutron oscillations

We explore baryon-number-violating (|∆ B | = 2) processes beyond the well-known neutron-antineutron ($n - \bar{n}$) oscillations, focusing on the $Λ - \bar{Λ}$ system. The presence of a strange quark in the Λ baryon introduces a new set of six-quark operators roughly of the form (uds) 2 , which are different from the (udd) 2 operators responsible for oscillations. Using the Standard Model Effective Field Theory (SMEFT), we classify all dimension-9 operators that cause |∆ B | = 2 transitions and study their UV completions mediated by exotic scalar fields with trilinear interactions. We demonstrate that in these models, oscillations can occur at tree level, with $n - \bar{n}$ mixing potentially appearing at higher loop levels. We employ a chiral effective theory to constrain the effective mass mixing δm Λ , deriving bounds from current experimental limits on $n - \bar{n}$ oscillations and dinucleon decays such as pp → K + K + . These bounds indicate that $Λ - \bar{Λ}$ oscillations probe a complementary parameter space, sensitive to baryon-number violation at scales up to 10 2 − 10 3 TeV. We show that the existing indirect bounds make it challenging to provide a competitive bound on δm Λ at BESIII.

Baryon/Lepton Number Violation↗
NASA NTRS1974

A technique for correcting ERTS data for solar and atmospheric effects

The author has identified the following significant results. Based on processing ERTS CCTs and ground truth measurements collected on Michigan test site for January through June 1973 the following results are reported: (1) atmospheric transmittance varies from: 70 to 85% in band 4, 77 to 90% in band 5, 80 to 94% in band 6, and 84 to 97% in band 7 for one air mass; (2) a simple technique was established to determine atmospheric scattering seen by ERTS-1 from ground-based measurements of sky radiance. For March this scattering was found to be equivalent to that produced by a target having a reflectance of 11% in band 4, 5% in band 5, 3% in band 6, and 1% in band 7; (3) computer ability to classify targets under various atmospheric conditions was determined. Classification accuracy on some targets (i.e. bare soil, tended grass, etc.) hold up even under the most severe atmospheres encountered, while performance on other targets (trees, urban, rangeland, etc.) degrades rapidly when atmospheric conditions change by the smallest amount.

Rogers, R. H.↗
NASA NTRS2024

A Multi‐Probe Automated Classification of Ice Crystal Habits During the IMPACTS Campaign

Although all ice crystals are unique, many can be grouped together by shape or habit, with members of a habit class sharing similar representations of properties such as fall velocity and growth rate. A decision tree algorithm designed to be adaptable to any particle imaging probe, thus enabling the creation of habit size distributions over a size range larger than that of any probe on its own, is used to classify ice crystals imaged by three airborne cloud probes in mid-latitude winter cyclones during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Crystals are sorted into seven habit classes based on their morphological properties: sphere, column/needle, plate, graupel, dendrite, aggregate, and irregular. Although adaptability was its primary goal, the algorithm was found to be moderately skillful for identifying idealized habit images. Quantitative tests of the algorithm’s adaptability displayed mixed results, as Two-Dimensional Stereo Probe (2DS) classifications showed moderate correlation with Particle Habit Imaging and Polar Scattering Probe (PHIPS) classifications, but only weak correlation with High Volume Precipitation Spectrometer (HVPS) classifications. The algorithm was applied to random sets of images from each probe in a case study of a mesoscale snow band sampled on 7 February 2020. In the case study, qualitative analysis of particle images revealed general agreement on classifications among the probes, supporting the algorithm’s applicability to multiple cloud probes. Most classifications appeared correct upon manual inspection, suggesting that in practical use, the algorithm is reasonably able to classify non-idealized images.

Julian Schima↗
NASA NTRS1982

Geobotanical techniques for discriminating serpentine rock types in Western United States

The effectiveness of airborne multispectral imagery for identifying vegetation indicative of serpentine soils, which usually contain deposits of Ni, Cr, and Co, was examined with ground truth data. Analyses were carried out in SW Oregon, the Jasper Ridge of the Santa Cruz Mountains, and the Sierra Nevada foothills. Airborne studies were performed with 10-channel scanner, a IR scanner, and with false color composites, while ground studies classified the vegetation and mineral contents of the soil. The airborne studies were successfully used to discern serpentine soils from nonultramafic rock types, although difficulties were encountered in separating serpentine from nonserpentinized peridotite. A lack of deciduous trees on serpentized land was noted.

Mouat, D.↗
NASA NTRS1993

A new clustering algorithm applicable to multispectral and polarimetric SAR images

We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

Wong, Yiu-Fai↗
NASA NTRS2011

Feature Acquisition with Imbalanced Training Data

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

Thompson, David R.↗
DOE OSTI2026

Entire four-graviton EFT from the duality between color and kinematics

The Bern-Carrasco-Johansson (BCJ) double-copy construction reveals a fundamental structural connection between gauge and gravity theories. At its core, the BCJ double copy is directly due to a duality between the algebraic relations of a color root and those of a kinematic root. We generalize this principle beyond the conventional Lie algebra structure of tree-level Yang-Mills theory. By demanding color-kinematics duality for the complete basis of four-point color structures—including those involving the symmetric 𝑑 𝑎⁢𝑏⁢𝑐 constants—we define the universal double copy. We systematically classify the bases of all such parity-even generalized gauge-theory numerators and, independently, the space of all parity-even four-graviton higher-derivative operators. We demonstrate that our universal double-copy construction precisely spans the entire tower of parity-even four-graviton amplitudes in any dimension, except for the Lovelock 𝑅 3 contribution in 𝐷 > 6 which we can express in terms of a particularly simple universal triple-copy involving gauge theories coupled to scalars. Explicit machine-readable expressions for the complete basis of gauge-theory numerators and fundamental gravitational building blocks are provided in the Supplemental Material. This establishes that all possible four-point gravitational interactions can be factorized into products of gauge-theory building blocks governed by this universal notion of color-kinematics duality.

Carrasco, John Joseph M. [Northwestern Univ., Evan↗
NASA NTRSDate not supplied

Black Hills Wildfires: Mapping Post-fire Conifer Regeneration using Snow-on Imagery

The 2000 Jasper Fire in the Black Hills of South Dakota was the largest wildfire to date in the region, burning over 83,000 acres of ponderosa pine forest. In collaboration with partners from the United States Forest Service (USFS) Black Hills Experimental Forest, USFS Rocky Mountain Research Station, and United States Geological Survey Geosciences and Environmental Change Science Center, we characterized post-fire forest regeneration within high-severity burn patches. We accomplished this by implementing novel conifer detection techniques using a snow index mask to create a winter, snow-on image composite from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data. We utilized 2015 USFS stem maps of field-observed regeneration plots and ocularly sampled additional reforestation sites planted in 2001–2013. In Google Earth Engine (GEE), the field data and imagery were used to train a Random Forest (RF) model. The RF model classified 2021 conifer regeneration density as low, medium, or high across the high-severity burn area with an overall accuracy of 81.3%. Approximately 45.9% of the high-severity burn had low or no regeneration (0-40 trees per acre) 20 years post-fire. Given our partners' desire to find easily accessible low conifer regeneration zones, we identified 4,079 acres of priority planting sites that were within 1,500 feet of roads, had not been planted previously, and were larger than 50 acres. This method supports the use of snow-on imagery as a successful technique to identify conifer regeneration.

Casey Menick​↗