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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 NTRS1985

Crustal dynamics project data analysis fixed station VLBI geodetic results

The Goddard VLBI group reports the results of analyzing the fixed observatory VLBI data available to the Crustal Dynamics Project through the end of 1984. All POLARIS/IRIS full-day data are included. The mobile site at Platteville, Colorado is also included since its occupation bears on the study of plate stability. Data from 1980 through 1984 were used to obtain the catalog of site and radio source positions labeled S284C. Using this catalog two types of one-day solutions were made: (1) to estimate site and baseline motions; and (2) to estimate Earth rotation parameters. A priori Earth rotation parameters were interpolated to the epoch of each observation from BIH Circular D.

Ryan, J. W.↗
NASA NTRS1989

Measuring Airflow With Digital Holographic Interferometry

Digital image-processing system assists in analysis of holographic interferometric images of flow about airfoil. Operating semiautomatically, identifies, counts, and labels interference fringes, then processes distances between fringes into distribution of pressure on surface of airfoil. Yields data on pressure faster than manual image-analyses techniques, and data compares favorably with those obtained by manual analysis and by probe measurements of pressure. Uses computer programs evaluating interferograms along straight or curved lines represented by polygon segments. User prompted for such inputs as reference points and fringe number. Digitizes holographic interferograms of flow and processes them into data of pressure or other properties of flow.

Torres, Francisco J.↗
NASA NTRS2009

Life Science Research in Outer Space: New Platform Technologies for Low-Cost, Autonomous Small Satellite Missions

We develop integrated instruments and platforms suitable for economical, frequent space access for autonomous life science experiments and processes in outer space. The technologies represented by three of our recent free-flyer small-satellite missions are the basis of a rapidly growing toolbox of miniaturized biologically/biochemically-oriented instrumentation now enabling a new generation of in-situ space experiments. Autonomous small satellites (~ 1 50 kg) are less expensive to develop and build than fullsize spacecraft and not subject to the comparatively high costs and scheduling challenges of human-tended experimentation on the International Space Station, Space Shuttle, and comparable platforms. A growing number of commercial, government, military, and civilian space launches now carry small secondary science payloads at far lower cost than dedicated missions; the number of opportunities is particularly large for so-called cube-sat and multicube satellites in the 1 10 kg range. The recent explosion in nano-, micro-, and miniature technologies, spanning fields from telecommunications to materials to bio/chemical analysis, enables development of remarkably capable autonomous miniaturized instruments to accomplish remote biological experimentation. High-throughput drug discovery, point-of-care medical diagnostics, and genetic analysis are applications driving rapid progress in autonomous bioanalytical technology. Three of our recent missions exemplify the development of miniaturized analytical payload instrumentation: GeneSat-1 (launched: December 2006), PharmaSat (launched: May 2009), and O/OREOS (organism/organics exposure to orbital stresses; scheduled launch: May 2010). We will highlight the overall architecture and integration of fluidic, optical, sensor, thermal, and electronic technologies and subsystems to support and monitor the growth of microorganisms in culture in these small autonomous space satellites, including real-time tracking of their culture density, gene expression, and metabolic activity while in the space environment. Flight data and results will be presented from GeneSat-1, which tracked gene expression levels of GFP-labeled E. coli and from PharmaSat, which monitored the dose dependency of an antifungal agent against S. cerevisiae. The O/OREOS SESLO instrument, which will study the effects of radiation and microgravity upon the viability and growth characteristics of B. subtilis and the halophile Halorubrum chaoviatoris for periods of 0 - 6 months in space, will be described as well. The ongoing expansion of the small satellite toolbox of biological technologies will be summarized.

Ricco, Antonio J.↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Space Environment Effects on Stability of Medications Flown on Space Shuttles and the International Space Station (ISS)

The purpose is to assess physical and chemical degradation of select pharmaceutical formulations from the Shuttle and ISS medical kits. Eleven pharmaceuticals dispensed as different dosage forms were selected based on their physical / chemical characteristics and susceptibility to environmental factors such as, temperature, humidity and light sensitivity. When available, ground-controls of the study medications with matching brand and lot numbers were used for comparison. Samples retrieved from flight were stored along with their matching controls in a temperature and humidity controlled environmental chamber. Temperature, humidity, and radiation data from the Shuttle and ISS were retrieved from onboard HOBO U12 Temp/RH Data Loggers, and from passive dosimeters. Physical and chemical analyses of the pharmaceuticals were conducted using validated United States Pharmacopeia (USP) methods. Results indicated degradation of 6 of the 11 formulations returned from space flights. Four formulations, Amoxicillin / Clavulanate, promethazine, sulfamethoxazole / trimethoprim, and ciprofloxacin tablets depicted discoloration after flight. Chemical content analyses using High or Ultra Performance Liquid Chromatography (HPLC / UPLC) methods revealed that dosage forms of Amoxicillin / Clavulanate, promethazine, sulfamethoxazole / trimethoprim, lidocaine, ciprofloxacin and mupirocin contained less than 95% of manufacturer s labeled claim of active drug compound. Shuttle and ISS environments affect stability and shelf life of certain mediations flown on these missions. Data analysis is in progress to examine the effect of specific space flight environmental factors on pharmaceutical stability. The degradation profiles generated from ground studies in analog environments will be useful in establishing predictive shelf-life profiles for medications intended for use during long-term space exploration missions.

Daniels, Vernie↗
NASA NTRS1986

A fast data acquisition system for the study of transient events by high repetition rate time-of-flight mass spectrometry

Recent advances in commercially available data acquisition electronics embodying high speed A/D conversion coupled to increased memory storage have now made practical (at least within time intervals of a third of a millisecond or more) the capturing of all of the data generated by a high repetition rate time-of-flight mass spectrometer producing complete spectra every 25 to 35 microseconds. Such a system was assembled and interfaced with a personal computer for control and management of data. The applications are described for recording time-resolved spectra of individual vapor plumes induced from the pulsed-laser heating of material. Each laser pulse triggers the system to generate automatically a 3-dimensional (3-D) presentation of the time-resolved spectra with m/z labeling of the major mass peaks, plus an intensity versus time display of both the laser pulse and the resulting vapor pulse. The software also permits storing of data and its presentation in various additional forms.

Lincoln, K. A.↗
NASA NTRS2016

Implementing Connected Component Labeling as a User Defined Operator for SciDB

We have implemented a flexible User Defined Operator (UDO) for labeling connected components of a binary mask expressed as an array in SciDB, a parallel distributed database management system based on the array data model. This UDO is able to process very large multidimensional arrays by exploiting SciDB's memory management mechanism that efficiently manipulates arrays whose memory requirements far exceed available physical memory. The UDO takes as primary inputs a binary mask array and a binary stencil array that specifies the connectivity of a given cell to its neighbors. The UDO returns an array of the same shape as the input mask array with each foreground cell containing the label of the component it belongs to. By default, dimensions are treated as non-periodic, but the UDO also accepts optional input parameters to specify periodicity in any of the array dimensions. The UDO requires four stages to completely label connected components. In the first stage, labels are computed for each subarray or chunk of the mask array in parallel across SciDB instances using the weighted quick union (WQU) with half-path compression algorithm. In the second stage, labels around chunk boundaries from the first stage are stored in a temporary SciDB array that is then replicated across all SciDB instances. Equivalences are resolved by again applying the WQU algorithm to these boundary labels. In the third stage, relabeling is done for each chunk using the resolved equivalences. In the fourth stage, the resolved labels, which so far are "flattened" coordinates of the original binary mask array, are renamed with sequential integers for legibility. The UDO is demonstrated on a 3-D mask of O(1011) elements, with O(108) foreground cells and O(106) connected components. The operator completes in 19 minutes using 84 SciDB instances.

UDO↗
NASA NTRS1992

Adaptation of Cancellous Bone to Aging and Immobilization in Growing Rats

Two-and-a half month-old female rats were subjected to right hindlimb immobilization or served as controls for 0, 1, 2, 8, 14, and 20 weeks. The right hindlimb was immobilized by bandaging it against the abdomen, thus unloading it. Cancellous bone histomorphometry was performed on microradiographs and double-fluorescent labeled 20 micron sections of the distal femoral metaphyses. Primary spongiosa bone loss occurred rapidly by 2 weeks, and secondary spongiosa bone loss occurred rapidly by 8 weeks of immobilization, and then equilibrated at 60% less bone mass than age-related controls. The negative bone balance induced by immobilization was caused by transient increase in bone resorption, decrease in bone formation, and longitudinal bone growth. The dynamic data of secondary spongiosa cancellous bone showed that percent eroded perimeter was transiently elevated by 55% to 82% between 1 and 8 weeks, percent labeled perimeter was transiently depressed by 32% to 50% between 1 and 14 weeks, mineral apposition rate was depressed by 23% and 19% at 1 and 2 weeks, and bone formation rate-bone area referent was transiently depressed by 35% and 59%c at 1 and 2 weeks. All the above parameters were at age-related control levels by 20 weeks of immobilization. However, bone formation rate-tissue area referent was depressed (-65%) throughout the study. Immobilization depressed completely longitudinal bone growth by 2 weeks and remained so. Only 0.65 mm of new metaphysis was generated in the immobilized versus 2.1 mm in controls during the study period. The immobilization induced an early cancellous bone loss which equilibrated at a new steady state with less bone and a normal (age-related control) bone turnover rate. When these findings were compared to an earlier study of 9 month-old virgin females subjected to right hindlimb immobilization up to 26 weeks, we found the adaptive responses of the cancellous bone were identical except that they occurred earlier and equilibrated sooner in younger rats.

Chen, Meng Meng↗
NASA NTRS1992

Adaptation of Cancellous Bone to Aging and Immobilization in Growing Rats

Two-and-half-month-old female rats were subjected to right hindlimb immobilization or served as controls for 0, 1, 2, 8, 14, and 20 weeks. The right hindlimb was immobilized by bandaging it against the abdomen, thus unloading it. Cancellous bone histomorphometry was performed on microradiographs and double-fluorescent labeled 20 tLm sections of the distal femoral metaphyses. Primary spongiosa bone loss occurred rapidly by 2 weeks, and secondary spongiosa bone loss occurred rapidly by 8 weeks of immobilization, and then equilibrated at 60% less bone mass than age-related controls. The negative bone balance induced by immobilization was caused by transient increase in bone resorption, decrease in bone formation, and longitudinal bone growth. The dynamic data of secondary spongiosa cancellous bone showed that percent eroded perimeter was transiently elevated by 55 to 82% between 1 and 8 weeks, percent labeled perimeter was transiently depressed by 32% to 50% between 1 and 14 weeks, mineral apposition rate was depressed by 23% and 19% at I and 2 weeks, and bone formation rate-bone area referent was transiently depressed by 35% and 59% at 1 and 2 weeks. All the above parameters were at age-related control levels by 20 weeks of immobiliza- tion. However, bone formation rate-tissue area referent was depressed (-65%) throughout the study. Immobilization depressed completely longitudinal bone growth by 2 weeks and remained so. Only 0.65 mm of new metaphysis was generated in the immobilized versus 2.1 mm in controls during the study period. The immobilization induced an early cancellous bone loss which equilibrated at a new steady state with less bone and a normal (age-related control) bone turnover rate. When these findings were compared to an earlier study of 9-month-old virgin females subjected to right hindlimb immobilization up to 26 weeks, we found the adaptive responses of the cancellous bone were identical except that they occurred earlier and equilibrated sooner in younger rats.

Chen, Meng-Meng↗
DOE OSTI2026

An improved dataset for predicting mammal infecting viruses from genetic sequence information

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A structural equation modeling approach to leveraging the power of extant sentiment analysis tools

Machine-derived sentiment analysis has become a pervasive and useful tool to address a wide array of issues in natural language processing. Leading technology companies such as Google now provide sentiment analysis tools (SATs) as readily accessible online products. Academic researchers develop and make available SATs to support the research enterprise. One of the major challenges with SATs is the inconsistencies in results among the various SATs. Consequently, the selection of a SAT for a specific purpose may significantly impact the application. This study addresses the foregoing problem by utilizing structural equation modeling to merge the outputs of SATs to develop a combined sentiment metric without the need for a labeled training dataset. This method is applicable to a wide range of text-based problems, is data-driven, and replicable. It was tested using three publicly available datasets and compared against seven different SATs. The results indicate that as a continous measure, the proposed method outperformed other SATs in the movie reviews and SemEval datasets, and achieved a tie for first place with IBM Watson on the Sentiment 140 dataset. Also, compared to the published major alternatives, the arithmetic mean solution, this approach performed better across these three datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

XRF-XFS-XAS-Auto v1.0 - Beta release

This software allows to analyze XRF maps, XFS spectra and XAS spectra collected at the Advanced Light Source's Beamline 10.3.2. Features include: 1) XRF maps: - process XRF maps, all elemental maps are saved as bmp automatically and labeled with the incident energy used, the scale bar is also labeled and can be controlled. - XRF elemental correlation plots, save the correlation plots automatically - Extract single or multiple transects in XRF maps on one or several regions of interest, each transect profile is numbered and saved in a corresponding folder, along with the corresponding maps showing transect location. 2) XFS spectra - save in log10 scale the XFS spectra, either a single or multiple files all at once. The files are saved as .bmp. - XFS spectra are labeled according to tabulated fluorescence emission lines. 3) XAS spectra - allows to plot individual scalers in the raw data. - allows calibration of the spectra using an Io internal glitch present in all spectra and performing 1st derivative. - Least-square linear combination fitting of XANES or extended XANES spectra using a database of standards using 1, 2 or 3 components maximum. It also provides the 5 top combinations and provide the user for the possibility of saving the 2nd, 3rd, 4th and 5th best combinations in addition to the best one. The processed spectra (pre-edge background substracted, post-edge normalized), the fits and residuals are automatically saved. A table of the component, with fit% and SSN is provided and saved automatically as well.

Fakra, Sirine↗
DOE OSTI2026

Lattice dynamics of alpha-uranium measured on ARCS.

The combined inelastic neutron scattering data from ARCS for alpha-Uranium single crystal measured at 300K, 200K, 70K and 20K. The incident neutron energy was 30 meV. To obtain a significant four-dimensional Q-E volume, the crystal was measured in two scattering geometries, with the [001] and [011] directions oriented along the vertical rotations axis. For each geometry, the crystal was rotated in 1 degree steps with respect to the incident beam. All the individual angles data files at each temperature were merged and analyzed using SHIVER software package (https://neutrons.github.io/Shiver/). The files labeled a_U_HKL_20K_new.nxs, a_U_HKL_70K.nxs, a_U_HKL_200K.nxs and a_U_HKL_300K.nxs are for [011] oriented scattering at 20K, 70K, 200K, and 300K respectively. The files labeled a_U_HK0_70K.nxs, a_U_HK0_200K.nxs and a_U_HK0_300K.nxs are for [001] oriented scattering at temperatures 70K, 200K and 300K respectively.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Computer simulation of the mathematical modeling involved in constitutive equation development: Via symbolic computations

Development of new material models for describing the high temperature constitutive behavior of real materials represents an important area of research in engineering disciplines. Derivation of mathematical expressions (constitutive equations) which describe this high temperature material behavior can be quite time consuming, involved and error prone; thus intelligent application of symbolic systems to facilitate this tedious process can be of significant benefit. A computerized procedure (SDICE) capable of efficiently deriving potential based constitutive models, in analytical form is presented. This package, running under MACSYMA, has the following features: partial differentiation, tensor computations, automatic grouping and labeling of common factors, expression substitution and simplification, back substitution of invariant and tensorial relations and a relational data base. Also limited aspects of invariant theory were incorporated into SDICE due to the utilization of potentials as a starting point and the desire for these potentials to be frame invariant (objective). Finally not only calculation of flow and/or evolutionary laws were accomplished but also the determination of history independent nonphysical coefficients in terms of physically measurable parameters, e.g., Young's modulus, was achieved. The uniqueness of SDICE resides in its ability to manipulate expressions in a general yet predefined order and simplify expressions so as to limit expression growth. Results are displayed when applicable utilizing index notation.

Arnold, S. M.↗
NASA NTRS1999

Fe(VI) as a Possible Oxidant on the Martian Surface

The essential findings of the three biological experiments (Gas Exchange, Labeled Released, and Pyrolitic Release) aboard the Viking Mars landers were the discovery of the presence of one or more strong oxidants on the Martian surface. The Gas Exchange experiments showed that wetting Martian soil leads to the evolution of oxygen, while in the Labeled Release experiment addition of a nutrient solution containing C-14-labeled formate, glycine, lactate, alanine, and glycolic acid induced CO2 evolution. A general consensus was reached that all data taken together pointed to the presence on Martian surface of a strong oxidant, or most probably several different types of oxidants. Several candidates have been proposed as oxidants, including superoxides, hydrogen peroxide, and iron oxides (possibly gamma-Fe2O3). Additional information is contained in the original extended abstract.

Tsapin, A. I.↗
NASA NTRS2016

Resources for Radiation Test Data

The performance of electronic devices in a space radiation environment is often limited by susceptibility to single-event effects (SEE), total ionizing dose (TID), and displacement damage (DD). Interpreting the results of SEE, TID, and DD testing of complex devices is quite difficult given the rapidly changing nature of both technology and the related radiation issues. Radiation testing is performed to establish the sensitivities of candidate spacecraft electronics to single-event upset (SEU), single-event latchup (SEL), single-event gate rupture (SEGR), single-event burnout (SEB), single-event transients (SETs), TID, and DD effects. Knowing where to search for these test results is a valuable resource for the aerospace engineer or spacecraft design engineer. This poster is intended to be a resource tool for finding radiation test data.

Single Event Effects (SEE)↗
NASA NTRSDate not supplied

Natural Language Processing Methods for Air Traffic Management Text and Speech Data

This presentation discusses two efforts of the NARI AI/ML Intern team during the Fall 2021 OSTEM Internship term. For Letters of Agreement (LoA), we have studied how LoAs are structured and explored the question ‘What is an LoA constraint?’ To do this, our approach is data-driven, iterative, and assisted by machine learning when available. In this presentation, we will walk through our tasks of manually scanning through documents, performing a preliminary entity labelling task, and our unsupervised analysis on LoA procedures sections. After this research phase, we define the smallest constraint unit in an LoA, and start to perform entity extraction. Looking towards constraint extraction, we are also exploring the use of a one-class support vector machine (OneClassSVM) model to identify patterns within the data. The second effort of our team this term is focused on Air Traffic Control System Command Center (ATCSCC) advisory meetings, and the subsequent advisory documents that get published from their content. These advisory documents are important to give readily accessible summaries of daily operations, so that data centers, airline officials, and other stakeholders can easily understand the context of these meetings in real time. In applying machine learning to this scenario, two natural language processing tasks are used. First is developing machine learning models to convert the meeting speech data into text. With this text, use of extractive and abstractive text summarization models are used to automatically generate preliminary versions of the advisory documents.

Natural Language Processing↗