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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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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

NASA Near Earth Network (NEN) Support for Lunar and L1/L2 CubeSats

The NASA Near Earth Network (NEN) consists of globally distributed tracking stations, including NASA, commercial, and partner ground stations, that are strategically located to maximize the coverage provided to a variety of orbital and suborbital missions, including those in LEO, GEO, HEO, lunar and L1/L2 orbits. The NENs future mission set includes and will continue to include CubeSat missions. The first NEN supported CubeSat mission will be the Cubesat Proximity Operations Demonstration (CPOD) launching into low earth orbit (LEO) in early 2017. The majority of the CubeSat missions destined to fly on EM-1, launching in late 2018, many in a lunar orbit, will communicate with ground based stations via X-band and will utilize the NASA Jet Propulsion Laboratory (JPL) developed IRIS radio. The NEN recognizes the important role CubeSats are beginning to play in carrying out NASAs mission and is therefore investigating the modifications needed to provide IRIS radio compatibility. With modification, the NEN could potentially expand support to the EM-1 lunar CubeSats. The NEN could begin providing significant coverage to lunar CubeSat missions utilizing three to four of the NENs mid-latitude sites. This coverage would supplement coverage provided by the JPL Deep Space Network (DSN). The NEN, with smaller apertures than DSN, provides the benefit of a larger beamwidth that could be beneficial in the event of uncertain ephemeris data. In order to realize these benefits the NEN would need to upgrade stations targeted based on coverage ability and current configurationease of upgrade, to ensure compatibility with the IRIS radio.In addition, the NEN is working with CubeSat radio developers to ensure NEN compatibility with alternative CubeSat radios for Lunar and L1/L2 CubeSats. The NEN has provided NEN compatibility requirements to several radio developers who are developing radios that offer lower cost and, in some cases, more capabilities with fewer constraints. The NEN is ready to begin supporting CubeSat missions. The NEN is considering network upgrades to broaden the types of CubeSat missions that can be supported and is supporting both the CubeSat community and radio developers to ensure future CubeSat missions have multiple options when choosing a network for their communications support.

Global Tracking Network Ground Station↗
NASA NTRS2017

NASA Near Earth Network (NEN) Support for Lunar and L1/L2 CubeSats

The NASA Near Earth Network (NEN) consists of globally distributed tracking stations, including NASA, commercial, and partner ground stations, that are strategically located to maximize the coverage provided to a variety of orbital and suborbital missions, including those in LEO, GEO, HEO, lunar and L1/L2 orbits. The NENs future mission set includes and will continue to include CubeSat missions. The majority of the CubeSat missions destined to fly on EM-1, launching in late 2018, many in a lunar orbit, will communicate with ground based stations via X-band and will utilize the NASA Jet Propulsion Laboratory (JPL) developed IRIS radio. The NEN recognizes the important role CubeSats are beginning to play in carrying out NASAs mission and is therefore investigating the modifications needed to provide IRIS radio compatibility. With modification, the NEN could potentially expand support to the EM-1 lunar CubeSats.The NEN could begin providing significant coverage to lunar CubeSat missions utilizing three to four of the NENs mid-latitude sites. This coverage would supplement coverage provided by the JPL Deep Space Network (DSN). The NEN, with smaller apertures than DSN, provides the benefit of a larger beamwidth that could be beneficial in the event of uncertain ephemeris data. In order to realize these benefits the NEN would need to upgrade stations targeted based on coverage ability and current configuration/ease of upgrade, to ensure compatibility with the IRIS radio. In addition, the NEN is working with CubeSat radio developers to ensure NEN compatibility with alternative CubeSat radios for Lunar and L1/L2 CubeSats. The NEN has provided NEN compatibility requirements to several radio developers who are developing radios that offer lower cost and, in some cases, more capabilities with fewer constraints. The NEN is ready to begin supporting CubeSat missions. The NEN is considering network upgrades to broaden the types of CubeSat missions that can be supported and is supporting both the CubeSat community and radio developers to ensure future CubeSat missions have multiple options when choosing a network for their communications support.

Schaire, Scott↗
NASA NTRS2017

SO-QT: Collaborative Tool to Project the Future Space Object Population

Earth orbit gets increasingly congested, a challenge to space operators, both in governments and industry. We present a web tool that provides: 1) data on todays and the historic space object environments, by aggregating object-specific tracking data; and 2) future trends through a collaboration platform to collect information on planed launches. The collaborative platform enables experts to pool and compare their data in order to generate future launch scenarios. The tool is intended to support decision makers and mission designers while they investigate future missions and scholars as they develop strategies for space traffic management.

space debris↗
NASA NTRS2018

Post-Flight Data Analysis Tool

A software tool that facilitates the retrieval and analysis of post-flight data. This allows our team and other teams to effectively and efficiently analyze and evaluate post-flight data in order to certify commercial providers.

George, Marina↗
NASA NTRS2019

Evolved Gas Analyses of Mudstones from the Vera Rubin Ridge

The Sample Analysis at Mars (SAM) instrument suite on the Mars Science Laboratory (MSL) rover has been essential in understanding volatile-bearing phases in Gale Crater materials. SAM’s evolved gas analysis mass spectrometry (EGA-MS) has detected H2O, CO2, O2, H2, SO2, H2S, HCl, NO, and other trace gases, including organic fragments, in many samples. The identity and evolution temperature of evolved gases can support CheMin instrument mineral detection and place constraints on trace volatile-bearing phases or phases difficult to characterize with X-ray diffraction (e.g., amorphous phases). For the past ~500 sols, MSL has been exploring the Vera Rubin Ridge (VRR), which exhibits a striking hematite signature in orbital remote sensing data, in order to understand the depositional and diagenetic history recorded in the rocks and how it relates to the underlying Murray Formation. Four rock samples were drilled, one from the Blunts Point Member (Duluth, DU), one from the Pettegrrove Point Member (Stoer, ST), and two from the Jura Member. The Jura Member displays differences in color, summarized as grey and red, and a key goal was to constrain the cause of this color difference and the associated implications for depositional or post-depositional conditions. To investigate, a grey (Highfield, HF) and a red (Rock Hall, RH) Jura sample were drilled. Here we will give an overview of results from SAM EGA-MS analyses of VRR materials, with some comparisons to analyses of samples of the underlying Murray.

McAdam, A. C.↗
NASA NTRS2019

The Search for the Gamma Lyrid Progenitor

On 5 February 2015, a previously unknown meteor shower, the γ Lyrids were observed for the first time. Because of the Sun-Earth-Moon viewing geometry, however, stream members were observed almost exclusively by the Canadian Meteor Orbit Radar (CMOR). The γ Lyrids did not appear again until 2018, and that outburst was stronger than in 2015. This study analyzed the 2015 and 2018 CMOR data in order to determine the orbital parameters of the stream in an attempt to determine the γ Lyrid parent body. Of primary importance is to determine if the γ Lyrids will recur in a predictable manner. Two bodies, with dramatically different orbital parameters and evolutionary behaviors, emerged as the leading candidates: 2003 EH1 and 1854 R1.

Grazier, Kevin R.↗
NASA NTRS2019

Utilizing Convolutional Neural Networks for Global Seagrass Habitat Mapping

Convolutional neural networks (CNNs) are becoming an increasingly prevalent machine learning algorithm due to their high accuracy and lack of reliance on heuristic processes. One of the major drawbacks of convolutional neural networks is their reliance on large amounts of training data in order to generate sensible results. This talk will cover how our team has utilized the strengths and overcome the weaknesses of convolutional neural networks as they apply to seagrass habitat mapping. We will share our technical CNN results over time, detail the requirements and challenges that our team overcame and explore how other teams can better incorporate a stronger seagrass component into their machine learning projects.

Convolutional↗
NASA NTRS2020

GPU Supported Simulation of Transition-edge Sensor Arrays

We present numerical simulations of full transition-edge sensor (TES) arrays utilizing graphical processing units (GPUs). With the support of GPUs, it is possible to perform simulations of large pixel arrays to assist detector development. Comparisons with TES small-signal and noise theory confirm the representativity of the simulated data. In order to demonstrate the capabilities of this approach, we present its implementation in xifusim, a simulator for the X-ray Integral Field Unit, a cryogenic X-ray spectrometer on board the future Athena X-ray observatory.

M Lorenz↗
NASA NTRSDate not supplied

Regional Variations in the Diurnal Cycle of Tropical Precipitation as Represented by IMERG, ERA5, and Spaceborne Ku Radar

The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing effects that can be highly localized. The diurnal cycle in high precipitation regions such as the Amazon and the Maritime Continent are of particular interest, especially due to the complex coastal effects which take place over the Maritime Continent. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset, is used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-year (2000 – 2018) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on precipitation averaged for each half-hour of the day at 0.1°x0.1° spatial resolution. We first introduce an objective method of identifying locations where the diurnal signals are robust and strong. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broad scale patterns of diurnal variability but does not capture all the fine scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.

L J Hayden↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks and challenges associated with deep space missions and experiments (cis-Lunar, Mars transit/surface) require new knowledge discovery and development of novel ecosystems. Supporting distant and long-duration missions and experiments requires biological data (from yeast, microbes, fruit flies, C. elegans, plants, crops, rodents, humans) be findable, accessible, interoperable, reusable (FAIR), and maximally open-access. As data-intensive, bioinformatic, meta-analytical, and computer-assisted approaches continue to be a centerpiece of modern research, the NASA Biological and Physical Sciences division is expanding its Open Science capabilities beyond NASA GeneLab. The NASA Ames Life Sciences Data Archive (ALSDA) is a repository which is responsible for collecting and access to space biological imagery and video, alongside tabular and environmental data. In this presentation, we will discuss strategies dealing with archiving, curating, and accessibility of images from very distinct imaging modalities (e.g., micro-computed tomography, magnetic resonance imaging, photographic images of plants, fluorescence microscopy, behavioral videos, etc.). There are two main challenges: 1. Open-source data storage and 2. Metadata related to the imagery-video. Both have been solved by leveraging two existing open-source systems. For data storage, ALSDA is utilizing components through the Open Microscopy Environment (OME), which can read most imaging proprietary formats and display on a web interface complex multidimensional images (Z stack, multi-channel, temporal, spectral). Most technical metadata from imaging modalities are captured seamlessly. For metadata capturing experimental details, ALSDA (like GeneLab) uses the ISA-Tab specification which relies on the ISA data model to order and classify metadata. The ISA data model uses a tree structure with three files to capture the metadata: The top layer is the Investigations file, the second layer is the Study file(s), and the last layer is the Assay file(s). We believe such an approach may be useful for other types of image research data from other investigators in the AGU community.

imaging↗
NASA NTRSDate not supplied

Using Optical Lightning Detection Data from the 2017 GOES-R Post Launch Test Field Campaign Flights as an Evaluation of Convective Processes

The primary mechanisms for thunderstorm electrification and lightning production are widely understood and allow connections between lightning intensity and occurrence to be used to derive storm strength and forecast the probability of related weather phenomena. However, these assumed relationships between lightning and convective properties were discovered primarily using ground-based lightning sensors and measurements, meaning the true depth of insight from space or air based detections is widely unexplored. In 2017, a field campaign was launched to validate the instrumentation aboard the GOES-16 satellite. It involved flying a NASA-ER2 aircraft over multiple storms across the continental US and it included the Fly’s Eye GLM Simulator (FEGS) as a proxy for the Geostationary Lightning Mapper (GLM). Utilizing the data from this field campaign, one of the main goals of this project is to investigate how attributes of convection impact optical lightning detection and the subsequent assumptions that can be made in relation to convective dynamics and precipitation microphysics. Hydrometeor identification, 3D winds retrieval, and additional radar and LiDAR data analysis will be utilized alongside FEGS and interferometer data in order to observe these relationships. This presentation will be to lay out the initial conclusions from our analysis and to explore any new insights or contradictions within our findings.

Kaitlyn Wheeler↗
DOE OSTI2020

The Baryon Mapping Experiment (BMX), a 21cm intensity mapping pathfinder

The Baryon Mapping eXperiment (BMX) is an interferometric array designed as a pathfinder for a future post- reionization 21 cm intensity mapping survey. It consists of four 4-meter parabolic reflectors each having offset pyramidal horn feed, quad-ridge orthomode transducer, temperature-stabilized RF amplification and filtering, and pulsed noise injection diode. An undersampling readout scheme uses 8-bit digitizers running at 1.1 Gsamples/sec to provide access to signals from 1.1 - 1.55 GHz (third Nyquist zone), corresponding to HI emission from sources at redshift 0 < z < 0.3. An FX correlator is implemented in GPU and generates 28 GB/day of time-ordered visibility data. About 7,000 hours of data were collected from Jan. 2019 - May 2020, and we will present results on system performance including sensitivity, beam mapping studies, observations of bright celestial targets, and system electronics upgrades. BMX is a pathfinder for the proposed PUMA intensity mapping survey in the 2030s.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Failure prediction and estimation of failure parameters

Machine-learning methods and apparatus are disclosed to determine frictional state or other parameters in an earthquake zone or other failing medium, using acoustic emission, seismic waves, or other detectable indicators of microscopic processes. Predictions of future failures are demonstrated in different regimes. A classifier is trained using time series of acoustic emission data along with historic data of frictional state or failure events. In disclosed examples, random forests and gradient boost trees are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to testing or live data in order to assess a frictional state, assess seismic hazard, or make predictions regarding a future failure event. The technology has been developed in a double direct shear apparatus, but can be widely applied to seismic faults, other terrestrial failures, or failures in man-made structures. Variations are disclosed.

Johnson, Paul Allan↗
DOE OSTI2024

Adaptive control of wave energy converters

A wave energy capture system deployed in water converts mechanical motion induced by waves in the water to electrical energy. A controller of the wave energy capture system receives input regarding real-time wave conditions in a vicinity of the wave energy capture system. The controller applies a control model to the received input to select a value of a control parameter for the wave energy capture system, where the control model includes a model that has been trained using machine learning to take wave condition data as input and to output control parameter values selected based on the wave condition data in order to increase an amount of energy captured by the wave energy capture system. The controller implements the selected value of the control parameter on the wave energy capture system.

Orona, Alexander J.↗
DOE OSTI2025

Automation of Vulnerability and Patch Management: Information Extraction, Association, and Optimization

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout the vulnerability management cycle. Security advisories provide key information about mitigation strategies, or actions that can be taken when a patch is unavailable or cannot be installed. Details of these strategies are not shared in public vulnerability databases and must be found manually. We approach this problem by designing a solution to automatically identify that information within vendor security advisories and retrieve it for operator use. We start with an approach that requires domain-specific knowledge of certain frequently-seen reference websites. Next, an approach that can work on an arbitrary website but relies on certain keywords. Finally, an approach that uses Natural Language Processing (NLP) methods and does not require specific knowledge or keywords. Each of these approaches is more general than its predecessor; we demonstrate high accuracy for all approaches Advisories also often contain details of affected products in non-standard or natural language formats. While this information can be easily understood when read by an operator, the non-standard format acts as a barrier to effective automation. We provide an approach for the first step in this process: identifying vendors in security advisories and mapping them to a standard framework for representing digital assets and software products. We evaluate five established string similarity algorithms, plus one of our own design that combines string similarity and information theory, on the task of mapping vendors to their corresponding entries in the Common Platform Enumeration (CPE) repository. Our results show that our proposed metric outperforms all others. Due to the constraints on time, finances, and personnel for organizations, Large Language Models (LLMs) may seem like attractive opportunities for security operators to speed up information gathering; however, it is still not clear whether LLMs can handle vulnerability management tasks well. To answer this question, we perform an empirical study of LLMs’ ability to provide consistent, accurate information about vulnerabilities in order to guide organizations in their adoption of LLMs. We observe poor performance for all models tested, suggesting that these models are not well-suited to the consistent retrieval of accurate vulnerability information. Finally, once vulnerabilities have been identified and any additional information has been obtained, operators must decide which remediation actions to implement based on their available resources. This already-complex problem becomes even more so when we consider that a vulnerability may have multiple avenues for remediation. We formulate this scenario as two knapsack problems and provide solutions, which we then compare against several existing strategies for vulnerability prioritization seen in real operational environments.

McClanahan, Kylie↗
NASA NTRS1987

Edge detection - Image-plane versus digital processing

To optimize edge detection with the familiar Laplacian-of-Gaussian operator, it has become common to implement this operator with a large digital convolution mask followed by some interpolation of the processed data to determine the zero crossings that locate edges. It is generally recognized that this large mask causes substantial blurring of fine detail. It is shown that the spatial detail can be improved by a factor of about four with either the Wiener-Laplacian-of-Gaussian filter or an image-plane processor. The Wiener-Laplacian-of-Gaussian filter minimizes the image-gathering degradations if the scene statistics are at least approximately known and also serves as an interpolator to determine the desired zero crossings directly. The image-plane processor forms the Laplacian-of-Gaussian response by properly combining the optical design of the image-gathering system with a minimal three-by-three lateral-inhibitory processing mask. This approach, which is suggested by Marr's model of early processing in human vision, also reduces data processing by about two orders of magnitude and data transmission by up to an order of magnitude.

Huck, Friedrich O.↗