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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 NTRS2020

Using X-Ray Computed Tomography to Image Apollo Drive Tube 73002

The Apollo missions collected 382 kg of rock, regolith, and core samples from six locations on the nearside of the Moon. Today, just over 84% by mass of the Apollo collection remains in pristine condition within the curation facility at Johnson Space Center. Most Apollo samples have been well characterized, however there are several types of samples that have remained wholly or largely unstudied since their return, and/or that have been curated under special conditions. These sample types are: (1) unopened samples sealed under vacuum on the Moon; (2) unopened (but unsealed) drive tubes; (3) Apollo 17 samples frozen shortly after their return; and (4) Apollo 15 samples opened and stored in a helium atmosphere since their return. Last summer, NASA solicited proposals for the Apollo Next Generation Sample Analysis Program (ANGSA), and 9 teams were selected to study: (1) unsealed, unopened drive tube 73002; (2) sealed, unopened drive tube 73001 (paired with 73002); and (3) a subset of the frozen and He-purged samples [1]. The first sample opened as part of the ANGSA program was drive tube 73002. This is a 30 cm long, 4 cm diameter drive tube collected on a landslide deposit near Lara Crater at the Apollo 17 landing site. It was part of a 60 cm long double drive tube collected, and the bottom half of the tube (73001) was sealed under vacuum on the Moon [2]. Prior to opening sample 73002, the sample was imaged with a high resolution Xray Computed Tomography (XCT) scan of the entire tube. Additional XCT scans have been made of “large” clasts removed from the core as part of the dissection process [3]. Here we present a first look at the XCT data from 73002, and talk about the utility of the scans as part of the curation process, including the potential for future science returns from the high resolutions scans.

Zeigler, R. A.↗
NASA NTRSDate not supplied

Using X-Ray Computed Tomography to Image Apollo Drive Tube 73002

The Apollo missions collected 382 kg of rock, regolith, and core samples from six locations on the nearside of the Moon. Today, just over 84% by mass of the Apollo collection remains in pristine condition within the curation facility at Johnson Space Center. Most Apollo samples have been well characterized, however there are several types of samples that have remained wholly or largely unstudied since their return, and/or that have been curated under special conditions. These sample types are: (1) unopened samples sealed under vacuum on the Moon; (2) unopened (but unsealed) drive tubes; (3) Apollo 17 samples frozen shortly after their return; and (4) Apollo 15 samples opened and stored in a helium atmosphere since their return. NASA solicited proposals for the Apollo Next Generation Sample Analysis Program (ANGSA), and 9 teams were selected to study: (1) unsealed, unopened drive tube 73002; (2) sealed, unopened drive tube 73001 (paired with 73002); and (3) a subset of the frozen and He-purged samples [1]. The first sample opened as part of the ANGSA program was drive tube 73002. This was originally a ~30 cm long, 4 cm diameter drive tube collected on a landslide deposit near Lara Crater at the Apollo 17 landing site. It was part of a ~60 cm long double drive tube collected, and the bottom half of the tube (73001) was sealed under vacuum on the Moon [2]. Prior to opening sample 73002, the sample was imaged with a high resolution X-ray Computed Tomography (XCT) scan of the entire tube. Additional XCT scans have been made of “large” clasts removed from the core as part of the dissection process [3]. Here we present the whole tube and close-up XCT data from 73002, and talk about the utility of the scans as part of the curation process, including the potential for future science returns from the high resolutions scans.

R A Zeigler↗
DOE OSTI2022

Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1987

Gravity field fine structure estimation techniques for a spaceborne gravity gradiometer

Use of standard estimation techniques to recover geopotential fine structure from gradiometer data requires the adjustment of small subsets of parameters while constraining others to their a priori values in order to minimize the computational load. Here, gravitational anomalies are selected as a parametrization of the gravity field which permits such an approach. Techniques coupled with numerical results for a spaceborne gravity gradiometer mission simulation are described which demonstrate that if a satellite is in a polar/circular orbit at an altitude of 160 km, 1 deg mean free air gravity anomalies can be recovered to an accuracy of 0.4 mgal, where 1 mgal = 0.001 cm/sq s.

Kahn, W. D.↗
NASA NTRS2007

Emission-Line Galaxies from the PEARS Hubble Ultra Deep Field: A 2-D Detection Method and First Results

The Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) grism PEARS (Probing Evolution And Reionization Spectroscopically) survey provides a large dataset of low-resolution spectra from thousands of galaxies in the GOODS North and South fields. One important subset of objects in these data are emission-line galaxies (ELGs), and we have investigated several different methods aimed at systematically selecting these galaxies. Here we present a new methodology and results of a search for these ELGs in the PEARS observations of the Hubble Ultra Deep Field (HUDF) using a 2D detection method that utilizes the observation that many emission lines originate from clumpy knots within galaxies. This 2D line-finding method proves to be useful in detecting emission lines from compact knots within galaxies that might not otherwise be detected using more traditional 1D line-finding techniques. We find in total 96 emission lines in the HUDF, originating from 81 distinct "knots" within 63 individual galaxies. We find in general that [0 1111 emitters are the most common, comprising 44% of the sample, and on average have high equivalent widths (70% of [0 1111 emitters having rest-frame EW> 100A). There are 12 galaxies with multiple emitting knots; several show evidence of variations in H-alpha flux in the knots, suggesting that the differing star formation properties across a single galaxy can in general be probed at redshifts approximately greater than 0.2 - 0.4. The most prevalent morphologies are large face-on spirals and clumpy interacting systems, many being unique detections owing to the 2D method described here, thus highlighting the strength of this technique.

Gardner, J. P.↗
NASA NTRS2012

The Effects of an Absorbing Smoke Layer on MODIS Marine Boundary Layer Cloud Optical Property Retrievals and Radiative Forcing

Clouds, aerosols, and their interactions are widely considered to be key uncertainty components in our current understanding of the Earth's atmosphere and radiation budget. The work presented here is focused on the quasi-permanent marine boundary layer . (MBL) clouds off the southern Atlantic coast of Africa and the effects on MODIS cloud optical property retrievals (MOD06) of an overlying absorbing smoke layer. During much of August and September, a persistent smoke layer resides over this region, produced from extensive biomass burning throughout the southern African savanna. The resulting absorption, which increases with decreasing wavelength, potentially introduces biases into the MODIS cloud optical property retrievals of the underlying MBL clouds. This effect is more pronounced in the cloud optical thickness retrievals, which over ocean are derived from the wavelength channel centered near 0.86 micron (effective particle size retrievals are derived from the longer-wavelength near-IR channels at 1.6, 2.1, and 3.7 microns). Here, the spatial distributions of the scalar statistics of both the cloud and aerosol layers are first determined from the CALIOP 5 km layer products. Next, the MOD06 look-up tables (LUTs) are adjusted by inserting an absorbing smoke layer of varying optical thickness over the cloud. Retrievals are subsequently performed for a subset of MODIS pixels collocated with the CALIOP ground track, using smoke optical thickness from the CALIOP 5km aerosol layer product to select the appropriate LUT. The resulting differences in cloud optical property retrievals due to the inclusion of the smoke layer in the LUTs will be examined. In addition, the direct radiative forcing of this smoke layer will be investigated from the perspective of the cloud optical property retrieval differences.

Meyer, Kerry↗
NASA NTRS2018

Preparation for Pick-and-Eat Food Production on the International Space Station: Flight Definition for the VEG-04 and VEG-05 Missions

Growth of fresh, nutritious, palatable produce for crew consumption during spaceflight may provide health-promoting, bioavailable nutrients and enhance the dietary experience as we move toward longer-duration missions. Tending plants also may serve as a countermeasure for crew psychological stresses associated with long duration spaceflight. However, requirements to support consistent growth of a variety of high quality, nutritious crops under spaceflight environmental conditions is unknown. This study is exploring the potential to grow plants for food production on the International Space Station (ISS) using the Veggie vegetable production system. Ground testing is underway to compare the impacts of several fertilizer and lighting treatments on growth, quality, and nutritional composition of the leafy green crop mizuna, and the dwarf tomato crop Red Robin when subjected to Veggie ISS environmental conditions. Early testing focused on the leafy crop Tokyo Bekana Chinese cabbage, but ground tests indicated that this plant suffered from stress responses when grown under LEDs and the chronically elevated CO2 levels found on the ISS. Mizuna, a related leafy variety that grows well in the presence of high CO2, and has excellent organoleptic characteristics, was selected as an alternate crop. Tomato crops have been grown using two fertilizer formulations and two pollination techniques, and growth tests using different red:blue lighting environments are underway. Chemical analysis is also being conducted and these data, when coupled with the growth results, will be used to down-select to the two best lighting treatments and best fertilizer treatment for future testing of each crop on the ISS. Additionally, seed-source testing has become important, with mizuna seeds from two different vendors growing very differently. A seed source has been selected, and seed-surface-sanitizing methods have been confirmed for mizuna, but these remain under development for tomato. A crop-handling protocol is also being evaluated to support food safety. All harvests reserve a subset of samples for microbial analysis to determine baseline microbial levels and help establish critical control points for food safety. Testing was initially conducted in hardware analogs of the standard Veggie plant pillows. However, a new Veggie watering system, the Passive Orbital Nutrient Delivery System or PONDS, has been designed and is being prepared for future flight experiments. With the selection of this growth system, ground tests have shifted to analog PONDS systems. Crop tests on ISS, designated VEG-04 for mizuna and VEG-05 for tomato, are planned in 2018 to evaluate any additional impacts of spaceflight on the light and fertilizer conditions down-selected from ground tests. A set of Veggie-specific questions has been developed to characterize the psychological impacts of plant growth and plant-care activities during spaceflight. Organoleptic questionnaires have been developed to assess produce attributes in microgravity taste sessions. These tests for plants growing in the Veggie hardware on ISS will help to mitigate the risk of an inadequate food supply for long duration missions by developing methods and determining hardware requirements to integrate fresh vegetables as a dietary supplement. This research was co-funded by the Human Research Program and Space Biology (MTL1075) in the ILSRA 2015 NRA call.

Veggie↗
NASA NTRS2018

Preparation for Pick-and-Eat Food Production on the International Space Station: Flight Definition for the VEG-04 and VEG-05 Missions

Growth of fresh, nutritious, palatable produce for crew consumption during spaceflight may provide health-promoting, bioavailable nutrients and enhance the dietary experience as we move toward longer-duration missions. Tending plants also may serve as a countermeasure for crew psychological stresses associated with long duration spaceflight. However, requirements to support consistent growth of a variety of high quality, nutritious crops under spaceflight environmental conditions is unknown. This study is exploring the potential to grow plants for food production on the International Space Station (ISS) using the Veggie vegetable production system. Ground testing is underway to compare the impacts of several fertilizer and lighting treatments on growth, quality, and nutritional composition of the leafy green crop mizuna, and the dwarf tomato crop "Red Robin" when subjected to Veggie ISS environmental conditions. Early testing focused on the leafy crop "Tokyo Bekana" Chinese cabbage, but ground tests indicated that this plant suffered from stress responses when grown under LEDs and the chronically elevated CO2 levels found on the ISS. Mizuna, a related leafy variety that grows well in the presence of high CO2, and has excellent organoleptic characteristics, was selected as an alternate crop. Tomato crops have been grown using two fertilizer formulations and two pollination techniques, and growth tests using different red:blue lighting environments are underway. Chemical analysis is also being conducted and these data, when coupled with the growth results, will be used to down-select to the two best lighting treatments and best fertilizer treatment for future testing of each crop on the ISS. Additionally, seed-source testing has become important, with mizuna seeds from two different vendors growing very differently. A seed source has been selected, and seed-surface-sanitizing methods have been confirmed for mizuna, but these remain under development for tomato. A crop-handling protocol is also being evaluated to support food safety. All harvests reserve a subset of samples for microbial analysis to determine baseline microbial levels and help establish critical control points for food safety. Testing was initially conducted in hardware analogs of the standard Veggie plant pillows. However, a new Veggie watering system, the Passive Orbital Nutrient Delivery System or PONDS, has been designed and is being prepared for future flight experiments. With the selection of this growth system, ground tests have shifted to analog PONDS systems. Crop tests on ISS, designated VEG-04 for mizuna and VEG-05 for tomato, are planned in 2018 to evaluate any additional impacts of spaceflight on the light and fertilizer conditions down-selected from ground tests. A set of Veggie-specific questions has been developed to characterize the psychological impacts of plant growth and plant-care activities during spaceflight. Organoleptic questionnaires have been developed to assess produce attributes in microgravity taste sessions. These tests for plants growing in the Veggie hardware on ISS will help to mitigate the risk of an inadequate food supply for long duration missions by developing methods and determining hardware requirements to integrate fresh vegetables as a dietary supplement. This research was co-funded by the Human Research Program and Space Biology (MTL#1075) in the ILSRA 2015 NRA call.

LED light↗
DOE OSTI2022

Community Structure and Microbial Associations in Sediment-Free Methanotrophic Enrichment Cultures from a Marine Methane Seep

We report Syntrophic consortia of anaerobic methanotrophic archaea (ANME) and sulfate-reducing bacteria (SRB) consume large amounts of methane and serve as the foundational microorganisms in marine methane seeps. Despite their importance in the carbon cycle, research on the physiology of ANME-SRB consortia has been hampered by the slow growth and complex physicochemical environment the consortia inhabit. Here, we report successful sediment-free enrichment of ANME-SRB consortia from deep-sea methane seep sediments in the Santa Monica Basin, California. Anoxic Percoll density gradients and size-selective filtration were used to separate ANME-SRB consortia from sediment particles and single cells to accelerate the cultivation process. Over a 3-year period, a subset of the sediment-associated ANME and SRB lineages, predominantly comprised of ANME-2a/2b (“Candidatus Methanocomedenaceae”) and their syntrophic bacterial partners, SEEP-SRB1/2, adapted and grew under defined laboratory conditions. Metagenome-assembled genomes from several enrichments revealed that ANME-2a, SEEP-SRB1, and Methanococcoides in different enrichments from the same inoculum represented distinct species, whereas other coenriched microorganisms were closely related at the species level. This suggests that ANME, SRB, and Methanococcoides are more genetically diverse than other members in methane seeps. Flow cytometry sorting and sequencing of cell aggregates revealed that Methanococcoides, Anaerolineales, and SEEP-SRB1 were overrepresented in multiple ANME-2a cell aggregates relative to the bulk metagenomes, suggesting they were physically associated and possibly interacting. Overall, this study represents a successful case of selective cultivation of anaerobic slow-growing microorganisms from sediments based on their physical characteristics, introducing new opportunities for detailed genomic, physiological, biochemical, and ecological analyses. Biological anaerobic oxidation of methane (AOM) coupled with sulfate reduction represents a large methane sink in global ocean sediments. Methane consumption is carried out by syntrophic archaeal-bacterial consortia and fuels a unique ecosystem, yet the interactions in these slow-growing syntrophic consortia and with other associated community members remain poorly understood. The significance of this study is the establishment of sediment-free enrichment cultures of anaerobic methanotrophic archaea and sulfate-reducing bacteria performing AOM with sulfate using selective cultivation approaches based on size, density, and metabolism. By reconstructing microbial genomes and analyzing community composition of the enrichment cultures and cell aggregates, we shed light on the diversity of microorganisms physically associated with AOM consortia beyond the core syntrophic partners. These enrichment cultures offer simplified model systems to extend our understanding of the diversity of microbial interactions within marine methane seeps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Functional Design of Peptide Materials Based on Supramolecular Cohesion

Peptide materials offer a broad platform to design biomimetic soft matter, and filamentous networks that emulate those in extracellular matrices and the cytoskeleton are among the important targets. Given the vast sequence space, a combination of computational approaches and readily accessible experimental techniques is required to design peptide materials efficiently. Here, we report here on a strategy that utilizes this combination to predict supramolecular cohesion within filaments of peptide amphiphiles, a property recently linked to supramolecular dynamics and consequently bioactivity. Using established coarse-grained simulations on 10,000 randomly generated peptide sequences, we identified 3500 likely to self-assemble in water into nanoscale filaments. Atomistic simulations of small clusters were used to further analyze this subset of sequences and identify mathematical descriptors that are predictive of intermolecular cohesion, which was the main purpose of this work. We arbitrarily selected a small cohort of these sequences for chemical synthesis and verified their fiber morphology. With further characterization, we were able to link the latent heat associated with fiber to micelle transitions, an indicator of cohesion and potential supramolecular dynamicity within the filaments, to calculated hydrogen bond densities in the simulation clusters. Based on validation from in situ synchrotron X-ray scattering and differential scanning calorimetry, we conclude that the phase transitions can be easily observed by very simple polarized light microscopy experiments. We are encouraged by the methodology explored here as a relatively low-cost and fast way to design potential functions of peptide materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Robust clustering of the local Milky Way stellar kinematic substructures with Gaia eDR3

Understanding local stellar kinematic substructures in the solar neighbourhood helps build a complete picture of the formation of the Milky Way, as well as an empirical phase space distribution of dark matter that would inform detection experiments. We apply the clustering algorithm HDBSCAN on the Gaia early third data release to identify a list of stable clusters in velocity space and action-angle space by taking into account the measurement uncertainties and studying the stability of the clustering results. We find 1405 (497) stars in 23 (6) robust clusters in velocity space (action-angle space) that are consistently not associated with noise. We discuss the kinematic properties of these structures and study whether many of the small clusters belong to a similar larger cluster based on their chemical abundances. They are attributed to the known structures: the Gaia Sausage-Enceladus, the Helmi Stream, and globular cluster NGC 3201 are found in both spaces, while NGC 104 and the thick disc (Sequoia) are identified in velocity space (action-angle space). Although we do not identify any new structures, we find that the HDBSCAN member selection of already known structures is unstable to input kinematics of the stars when resampled within their uncertainties. We therefore present the stable subset of local kinematic structures, which are consistently identified by the clustering algorithm, and emphasize the need to take into account error propagation during both the manual and automated identification of stellar structures, both for existing ones as well as future discoveries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging

The Datasets folder contains 3 datasets, corresponding to the datasets mentioned in the article with 670, 2691 and 586 images. The Runs folder has all the runs used. Besides the images itself it also contains tags for each image under `/entry_1/image_1/detector_and_photon_corrected/tags`. The `headings` attribute gives the name of each tag. The `Jonas Strict` tag corresponds to the 670 images, the `Redflamingo` tag to the 2691 images and the `Redflamingo_Ida_subset` tag to the 586 images. The final dataset with 260 images can be obtained from `Datasets/amoc6914-Redflamingo-manual-586.h5` and use the `/model/diameterNM` group to select the 260 images with size closest to 214 nm.

AMO↗
DOE OSTI2020

Weak-lensing Analysis of X-Ray-selected XXL Galaxy Groups and Clusters with Subaru HSC Data

We present a weak-lensing analysis of X-ray galaxy groups and clusters selected from the XMM-XXL survey using the first-year data from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. Our joint weaklensing and X-ray analysis focuses on 136 spectroscopically confirmed X-ray-selected systems at 0.031 6 z 6 1.033 detected in the 25 deg2 XXL-N region, which largely overlaps with the HSC-XMM field. With high-quality HSC weak-lensing data, we characterize the mass distributions of individual clusters and establish the concentration–mass (c–M) relation for the XXL sample, by accounting for selection bias and statistical effects and marginalizing over the remaining mass calibration uncertainty. We find the mass-trend parameter of the c–M relation to be β = -0.07 ± 0.28 and the normalization to be c200 = 4.8 ± 1.0 (stat) ± 0.8 (syst) at M 200 = 1014h -1M and z = 0.3. We find no statistical evidence for redshift evolution. Our weaklensing results are in excellent agreement with dark-matter-only c–M relations calibrated for recent ΛCDM cosmologies. The level of intrinsic scatter in c200 is constrained as σ(ln c 200 ) < 24% (99.7% CL), which is smaller than predicted for the full population of ΛCDM halos. This is likely caused in part by the X-ray selection bias in terms of the cool-core or relaxation state. We determine the temperature–mass (TX–M 500 ) relation for a subset of 105 XXL clusters that have both measured HSC lensing masses and X-ray temperatures. The resulting TX–M 500 relation is consistent with the self-similar prediction. Our TX–M 500 relation agrees with the XXL DR1 results at group scales but has a slightly steeper mass trend, implying a smaller mass scale in the cluster regime. The overall offset in the TX–M 500 relation is at the ~ 1.5σ level, corresponding to a mean mass offset of (34 ± 20)%. We also provide bias-corrected, weak-lensing-calibrated M 200 and M 500 mass estimates of individual XXL clusters based on their measured X-ray temperatures.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1999

High-Resolution Spectroscopy of Auroras on Jupiter and Saturn/Earth Dayglow

The purpose of the grant was to allow the researcher to: (1) reduce and analyze Orbiting and Retrievable Far and Extreme Ultraviolet Spectrometers (ORFEUS) II observations of Jupiter (200s) and Saturn (1200s); (2) Reduce and analyze selected ORFEUS-II Earth Far Ultraviolet (FUV) airglow data; (3) Modify existing scripts for simulating Earth FUV airglow emissions to model a subset of the ORFEUS data.

Gladstone, G. Randall↗
NASA NTRS2012

CHARM: A CubeSat Water Vapor Radiometer for Earth Science

The Jet Propulsion Laboratory (JPL) and Ames Research Center (ARC) are partnering in the CubeSat Hydrometric Atmospheric Radiometer Mission (CHARM), a water vapor radiometer integrated on a 3U CubeSat platform, selected for implementation under NASA Hands-On Project Experience (HOPE-3). CHARM will measure 4 channels at 183 GHz water vapor line, subsets of measurements currently performed by larger and more costly spacecraft (e.g. ATMS, AMSU-B and SSMI/S). While flying a payload that supports SMD science objectives, CHARM provides a hands-on opportunity to develop technical, leadership, and project skills. CHARM will furthermore advance the technology readiness level (TRL) of the 183 GHz receiver subsystem from TRL 4 to TRL 6 and the CubeSat 183 GHz radiometer system from TRL 4 to TRL 7.

Monolithic microwave integrated circuits (MMICs)↗
NASA NTRSDate not supplied

Laboratory Radar Measurements in Support of the NASA Orbital Debris Program Office’s Size Estimation Model

The NASA Orbital Debris Program Office (ODPO) relies on ground-based radar measurements from both the Haystack Ultrawideband Satellite Imaging Radar (HUSIR) and the Goldstone Solar System Radar (Goldstone) to characterize mm to cm debris population in low Earth orbit (LEO). Radar measurements help characterize the size of orbital debris objects, particularly fragmentation debris. However, debris size is not directly measured by radar but inferred from the measured radar cross section (RCS) which depends on several parameters in addition to physical size including electrical conductivity and polarization. To interpret the observed RCS of orbital debris objects detected by radar measurements as physical sizes, NASA uses an empirical size estimation model (SEM) based on laboratory RCS measurements of breakup fragments generated during hypervelocity impact tests as well as some pieces of non-impact-generated “artificial” debris-like objects expected to be representative of the debris population. Since the development of the ODPO SEM, many new materials have been introduced to spacecraft construction. Consequently, ODPO plans to update the radar SEM based on planned laboratory RCS measurements of debris fragments from DebriSat, a ground-based hypervelocity impact experiment conducted in 2014 that consisted of a high-fidelity spacecraft model characteristic of a modern LEO spacecraft. Prior to measuring DebriSat fragments, a set of calibration targets with well-defined geometries and material compositions were measured at The Ohio State University’s ElectroScience Laboratory (OSU-ESL) compact radar range. These calibration measurements help to validate, and understand any limitations of, laboratory measurements of RCS. Calibration targets include idealizations of typical shape categories seen in DebriSat fragments such as nuggets, flat plates, and cylinders. As with DebriSat, calibration target materials were chosen to represent typical modern-day spacecraft components and include stainless steel, aluminum, printed circuit board (PCB) substrate, and carbon fiber-reinforced polymer (CFRP). These materials also represent a wide range of electrical conductivities, which strongly influences measured RCS and inferred target size. The RCS calibration measurements were collected over a frequency sweep from 2 to 18 GHz and stepping through different azimuth angles from 0 to 360 degrees at an elevation of 0 degrees. A second set of calibration measurements is in work consisting of more complex shapes such as bent rods and plates as well as different mounting options including epoxy and a 3D printed holder. These further measurements along with our initial calibration set will inform selection of representative DebriSat fragments for laboratory RCS measurements that will contribute to the planned update to the ODPO radar SEM. An appropriate subset of both the radar calibration and DebriSat samples will also be measured in the ODPO Optical Measurements Center to cross-calibrate size estimates over these different wavelength regimes.

Radar↗
DOE OSTI2023

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2010

Fast Solution in Sparse LDA for Binary Classification

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic form along with the inherent sequential nature of greedy search itself. Together this enables the use of highly-efficient partitioned-matrix-inverse techniques that result in large speedups of computation in both the forward-selection and backward-elimination stages of greedy algorithms in general.

Moghaddam, Baback↗