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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 sources NASA NTRS DOE OSTI

DOE OSTI2022

Graph link prediction in computer networks using Poisson matrix factorisation

Graph link prediction is an important task in cybersecurity: relationships between entities within a computer network, such as users interacting with computers or system libraries and the corresponding processes that use them, can provide key insights into adversary behaviour. Poisson matrix factorisation (PMF) is a popular model for link prediction in large networks, particularly useful for its scalability. In this article PMF is extended to include scenarios that are commonly encountered in cybersecurity applications. Specifically, an extension is proposed to explicitly handle binary adjacency matrices and include known categorical covariates associated with the graph nodes. A seasonal PMF model is also presented to handle seasonal networks. To allow the methods to scale to large graphs, variational methods are discussed for performing fast inference. The results show an improved performance over the standard PMF model and other statistical network models.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1973

Measuring terrestrial radio frequency interference at orbit altitudes

An experiment has been designed to measure and characterize the effect of man-made interference on satellite receivers at orbital altitudes. The experiment, as designed, requires dedicated hardware on a spacecraft of specified orbit and will measure (within the frequency range from 400 MHz to 12.4 GHz) peak levels of interference in cells dimensioned in space, time, and frequency. The experiment will provide data indicative of some of the statistical characteristics of interference levels at satellite orbital altitudes and will provide designers of satellite communications links with new facilities for the prediction and prevention of interference problems.

Bayol, M. E.↗

NASA NTRSDate not supplied

Enhancing Dataset Discovery With Knowledge Graph Link Prediction Techniques

● In the evolving landscape of open science, the ability to navigate and discover pertinent datasets is increasingly significant. This primarily hinges on the presence of detailed metadata, delineating the dataset’s content, and potential spheres of application. ● The GES DISC datasets are characterized by science keywords to enable dataset discovery in web search interfaces. ● A problem may arise where a dataset lacks a science keyword that it otherwise should have. ● Machine learning techniques such as link prediction can be used to detect these missing science keywords by estimating the probability of new links forming between dataset and keyword nodes.

machine learning↗

DOE OSTI2023

Conserved unique peptide patterns (CUPP) online platform 2.0: implementation of +1000 JGI fungal genomes

Carbohydrate-processing enzymes, CAZymes, are classified into families based on sequence and three-dimensional fold. Because many CAZyme families contain members of diverse molecular function (different EC-numbers), sophisticated tools are required to further delineate these enzymes. Such delineation is provided by the peptide-based clustering method CUPP, Conserved Unique Peptide Patterns. CUPP operates synergistically with the CAZy family/subfamily categorizations to allow systematic exploration of CAZymes by defining small protein groups with shared sequence motifs. The updated CUPP library contains 21,930 of such motif groups including 3,842,628 proteins. The new implementation of the CUPP-webserver, https://cupp.info/, now includes all published fungal and algal genomes from the Joint Genome Institute (JGI), genome resources MycoCosm and PhycoCosm, dynamically subdivided into motif groups of CAZymes. This allows users to browse the JGI portals for specific predicted functions or specific protein families from genome sequences. Thus, a genome can be searched for proteins having specific characteristics. All JGI proteins have a hyperlink to a summary page which links to the predicted gene splicing including which regions have RNA support. The new CUPP implementation also includes an update of the annotation algorithm that uses only a fourth of the RAM while enabling multi-threading, providing an annotation speed below 1 ms/protein.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Feedstock to Function (F2F) v1

The Feedstock to Function (F2F) tool was designed to help scientists and companies explore viable biofuels and bioproducts early in the R&D cycle to support more productive experimentation, while reducing early-stage exploration from months/years to days/weeks (feedstock-to-function.lbl.gov). The tool focuses on using machine learning to predict biomass-derived molecule properties, while evaluating the cost, benefits, and risks of promising molecules for sustainable aviation fuels. The tool successfully predicts (within 15% of experimental values) high-throughput aviation properties for over 10,000 molecules while enabling users to explore new possibilities and opportunities rapidly and effortlessly. It also links to lightweight life-cycle analysis and techno-economic tools for cost and emissions analyses. Predicted molecule properties include melting point, boiling point, flash point, yield sooting index, and heat of combustion. To date, F2F is more expansive and outperforms several other molecule property prediction models while enabling users (scientists, companies, and policy makers) to explore new possibilities and opportunities rapidly and effortlessly. F2F provides the foundation for developing an adaptive computational tool that predicts properties, cost, benefits, and risk of promising new and uncertified alternative jet fuel pathways and their blending effects.

Rapp, Vi↗

NASA NTRSDate not supplied

Flight and Direct to Earth/Space Relay Communication System Architecture for GSFC CubeSat Missions

The CubeSat platform is finding increasing use in space science applications due to its low cost and comparative ease of launch. It is becoming a key scientific discovery tool in low Earth orbit (LEO) and beyond, including geosynchronous equatorial orbit (GEO), the Lagrange Points, Lunar missions, and more. The increasing complexity of these missions and their scientific goals must be supported by equal advancements in communications technology. Higher data rates and greater reliability are required every year. However, the reduced Size, Weight, and Power (SWaP) constraints of CubeSat platforms introduce unique challenges in the area of satellite communications. There is currently a lack of communication equipment tailored specifically to the CubeSat platform. This lack of standardized, tested equipment extends development time and reduces mission confidence. Furthermore, missions utilizing the CubeSat platform are often subject to more difficult design constraints. Antenna placement, size, and pointing are often subordinate to the requirements of the payload instruments and mission goals. Traditional link margin estimation techniques are insufficient in these cases, as they emphasize worst case scenarios. In reality the actual link parameters may vary widely even during a single pass. This presents new challenges in predicting communications performance and scheduling ground station contacts, but also new opportunities for improving efficiency. This paper presents the integration, testing, and validation process for a new software defined radio (SDR) designed for the CubeSat platform in conjunction with Vulcan Wireless, Inc. The SDR is planned for use on 5 upcoming CubeSat missions at NASAs Goddard Space Flight Center (GSFC) including a Geosynchronous Transfer Orbit (GTO) mission and it may also serve as a standard and well-tested option for future missions by enabling a standardized, rapid and low cost CubeSat communication system network integration process. Detailed simulations have been developed to estimate the communication performance of these missions, taking the unique antenna placements and attitude behavior of each satellite into account. These simulations allow a much more accurate analysis of the expected link margin, which varies considerably during each pass for the NASA Space Relay (SR) and Direct to Earth (DTE) network. The modelling procedures are outlined, and the results are used to predict communications performance of the missions.

Space Networks↗

NASA NTRSDate not supplied

Real-Time Dose Prediction for Artemis Missions

As large solar energetic particle (SEP) events can add significant radiation dose to astronauts in a short period of time and even induce acute clinical responses during missions, they present a concern for manned space flight operation. To assist the operations team in modeling and monitoring organ doses and any possible acute radiation-induced risks to astronauts during SEP events in real time, ARRT (Acute Radiation Risks Tool) 1.0 has been developed and successfully tested for Artemis I mission. The ARRT 2.0 described in this work integrates an established SEP forecasting model – UMASEP-100, further enabling real-time dose prediction for the upcoming Artemis II and following missions. With the new module linking with UMASEP-100 outputs in real time, the total BFO doses of most significant events can be communicated at the time of onset and hours before the peak. This is based on a flux-dose formula identified from comparing UMASEP-100 results with transport calculation for the events during 1994-2013 and validated with events outside that period. ARRT 2.0 also shows capability to distinguish minor events from significant ones to screen false alarms that will cause disruptions for space activities. This improvement provides additional information for operational teams to make timely decisions in contingent scenarios of severe SEP events to mitigate radiation exposure.

S Hu↗

NASA NTRS2024

Real-Time Dose Prediction for Artemis Missions

Shaowen Hu↗

NASA NTRS2018

Quantifying the Uncertainties in an Ensemble of Decadal Climate Predictions

Meaningful climate predictions should be accompanied by the corresponding uncertainty range. Common methods for estimating the uncertainty range are based on the spread of ensemble predictions. However, a simulation ensemble is not necessarily a proper sample of the real distribution of the climate, and therefore, the ensemble spread cannot be interpreted as the actual uncertainty. We propose a new method that links between the ensemble spread and the uncertainty without relying on any assumptions regarding the distribution of the ensemble predictions. The method is tested using CMIP5 1981-2010 decadal predictions and is shown to outperform other common methods.

Strobach, Ehud↗

DOE OSTI2022

Perspective—Emergent Phases in Rare Earth Nickelate Heterostructure

The prediction of high T c superconductivity in layers of LaNiO 3 through orbital engineering has led to extensive research efforts over the last fifteen years. During this period, a plethora of thin films and heterostructures based rare-earth nickelate family with perovskite structure has been synthesized and explored. Here, in this short perspective, we briefly review the complexity of bulk RENiO 3 , spotlighting several recent findings of emergent phenomena in heterostructures containing the interface between RENiO 3 and another transition metal oxide. Finally, we outline potentially interesting future directions linked to time-domain dynamics to harness new Mott and topological phases in artificial structures of RENiO 3 .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1986

ICE encounter operations

The operations encompassing the International Cometary Explorer's (ICE) encounter with the Comet Giacobini-Zinner on September 11, 1985 are documented. The ICE mission presented new challenges for the Deep Space Network (DSN) 64 meter subnetwork. Because of poor telemetry link margin predicted for Giacobini-Zinner (GZ) encounter, supplemental support by the Japanese Institute for Space and Astronautical Sciences 64-meter antenna at Usuda, Japan and the 305-meter Arecibo Radio Observatory in Puerto Rico was required. To improve the 64 meter subnetwork telemetry performance the following were also implemented: (1) Real time antenna array of 64 meter and 34 meter at a single complex and the required performance testing; and (2) Nonreal time antenna array of two complexes was implemented as a backup in the event of ground or spacecraft failure.

Fanelli, N.↗

DOE OSTI2023

Dislocation-density evolution and pileups in bicrystalline systems

Here, a dislocation-density crystalline plasticity (DCP) framework based on total and partial dislocation densities interactions was used to investigate the behavior of Cu/Pb bicrystals with a focus on GB effects. The modeling predictions were validated with bicrystal compression micropillar experiments. A key new aspect of the modeling approach is to account for partial dislocation-densities. A GB formulation that is directly linked to GB energies was used to monitor GB transmission and blockages, such that pileups can be monitored and predicted at the GB interfaces for misorientations. The predictions indicate that pileups can form due to fully and partially blocked slip-rates and perfect and partial dislocation-densities. As the nominal strain increases from five to fifteen percent, dislocation-densities and pileups significantly increase by almost an order of magnitude. The proposed validated approach provides a microstructural scale predictive framework that accounts for a myriad of defects related to the interactions of partial and perfect dislocation densities that interact at highly misoriented GBs; it is these interactions that are critical to the formation and evolution of dislocation-density pileups that can lead to physically limiting stress accumulations in bicrystals.

36 MATERIALS SCIENCE↗

NASA NTRS1984

Origin of the Moon: In search of the holy grail

The Moon's origin could be deduced with certainty if its bulk chemistry were known. However, determination of this chemistry is difficult because of the profound and complex redistribution of elements that occurred in the outer portions of the Moon during crystallization of the magma ocean. The compositions of 23 varieties of volcanic glass, erupted from depths approaching 300 miles, were used to predict the chemistry of a special glass (genesis glass) having a direct link to primordial lunar matter. A sample of glass with the predicted composition was discovered. This allows a new estimate to be made of the Moon's bulk composition. The data indicate that the Moon shares some intriquing chemical similarities with the Earth's mantle. Both genesis glass and lunar gas are furnishing definitive data on the Moon's composition and origin.

Delano, J. W.↗

NASA NTRS1983

The System View

Analysis of spacecraft to ground communications link performance and a description of the Deep Space Network system is provided. Due to the tremendous distances involved in communicating between the spacecraft at the edge of our solar system and Earth, communications link performance is stretched to the limit of theoretical predictions. This is required in order to return the maximum amount of data possible during critical events such as planetary flybys. The link analysis provides a basis for the initial link design before spacecraft launch and performance prediction and monitoring during spacecraft flight. Additionally, it indicates what performance upgrades are required for mission extensions and new missions. Performance is improved through the use of a larger antenna collecting area, greater transmitter power, lower receiving system noise temperature and more sophisticated data coding schemes. The performance of the Deep Space Station configuration which serves as the ground portion of the communications link is assessed.

Noreen, G. K.↗

NASA NTRS1993

The mass-luminosity relation for stars of mass 1.0 to 0.08 solar mass

Mass-luminosity relations determined at IR wavelengths are presented for stars with masses 1.0 to 0.08 solar mass. Using IR speckle imaging techniques on a sample of nearby binaries, we have been able to concentrate on the lower main sequence, for which an accurate mass-luminosity calibration has remained problematic. In addition, the mass-visual luminosity relation for stars with 2.0-0.08 solar mass is produced by implementing new photometric relations linking V to JHK wavelengths for the nearby stars, supplemented with eclipsing binary information. These relations predict that objects with masses of about 0.08 solar mass have M(K) of about 10 and M(V) of about 18.

Henry, Todd J.↗

NASA NTRSDate not supplied

Quantifying radiation quality for space relevant radiation types: Fitting excess risk models to outbred mice data

Accurately quantifying the differences in radiation quality between space and terrestrial environments is important for predicting health risks for astronauts.Recently, Edmundson et al. 2020[1] provided valuable new results from out-bred mice linking tumor induction and genetic background aer exposure to low and high-LET radiation. The goal of the current study is to more rigorously estimate a relative biological effectiveness (RBE) factor by leveraging the solid tumor data from Edmundson et al. 2020. Excess relative risk (ERR) models and excess absolute risk (EAR) models were fit using Poisson regression similar to the models that the Radiation Effects Research Foundation uses to fit atomic bomb survivor data. Linear ERR and EAR slopes were simulated usingBayesian analyses, and RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope using the full posterior distribution.

Lori J. Chappell↗

DOE OSTI2025

Predicting cutoff L-shells of solar protons using the GPPSn particle dataset

Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for > 30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones.

58 GEOSCIENCES↗