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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 NTRS2013

Relay Support for the Mars Science Laboratory Mission

The Mars Science Laboratory (MSL) mission landed the Curiosity Rover on the surface of Mars on August 6, 2012, beginning a one-Martian-year primary science mission. An international network of Mars relay orbiters, including NASA's 2001 Mars Odyssey Orbiter (ODY) and Mars Reconnaissance Orbiter (MRO), and ESA's Mars Express Orbiter (MEX), were positioned to provide critical event coverage of MSL's Entry, Descent, and Landing (EDL). The EDL communication plan took advantage of unique and complementary capabilities of each orbiter to provide robust information capture during this critical event while also providing low-latency information during the landing. Once on the surface, ODY and MRO have provided effectively all of Curiosity's data return from the Martian surface. The link from Curiosity to MRO incorporates a number of new features enabled by the Electra and Electra-Lite software-defined radios on MRO and Curiosity, respectively. Specifically, the Curiosity-MRO link has for the first time on Mars relay links utilized frequency-agile operations, data rates up to 2.048 Mb/s, suppressed carrier modulation, and a new Adaptive Data Rate algorithm in which the return link data rate is optimally varied throughout the relay pass based on the actual observed link channel characteristics. In addition to the baseline surface relay support by ODY and MRO, the MEX relay service has been verified in several successful surface relay passes, and MEX now stands ready to provide backup relay support should NASA's orbiters become unavailable for some period of time.

Telecommunications↗
NASA NTRS2017

Participatory Technology Assessment for Mars Mission Planning: Public Values and Rationales

Public support and interest are needed to design an ambitious human spaceflight program. However, it is difficult to understand what the public values and would support. And it is even more challenging and rare to consider public views prior to actually developing a mission. Participatory technology assessment (pTA) is a method that aims to understand public preferences and values in order to inform upstream government decision-making. We assess a recently completed experiment in pTA, the "Informing NASA's Asteroid Initiative" project. Through a cooperative agreement with NASA, the Expert and Citizen Assessment of Science and Technology (ECAST) network conducted a pTA-based forum on NASA's Asteroid Initiative and the Journey to Mars. ECAST organized two citizen forums in Phoenix, Arizona and Boston, Massachusetts in November 2014, with a total of 183 citizens selected so as to minimize self-selection biases. This paper focuses on the "Journey to Mars" session, which had the primary goal of soliciting citizen perceptions about different Mars exploration scenarios and mission planning approaches. Citizens were given background information about three potential Mars exploration scenarios that NASA could carry out: 1) Crewed orbital mission to direct robots on the surface of Mars; 2) Short exploratory crewed mission to the surface of Mars; and 3) establishing a permanent settlement. Citizens then engaged in structured facilitated discussions about their preferences among the scenarios and NASA's mission planning approach. Using a grounded theory coding approach, we analyzed participants' written rationales and dialogue about Mars exploration. In general, participants did not show a strong preference for any particular mission profile, but there was a slight preference for the crewed orbital robotics scenario. Participants who supported this approach saw it as the quickest, safest, and least costly road to a successful mission. However, many participants were interested in seeing "boots on the ground," as they believed this would propel scientific advancement, increase excitement about space exploration, and make humans a "two-planet species."

Bertrand, Pierre↗
NASA NTRS1977

DSN radio science system description and requirements

The data system created to collect the functions performed by the Deep Space Network in support of spacecraft radio science experiments is described. Some of the major functional requirements presently being considered for the system are delineated.

Mulhall, B. D. L.↗
NASA NTRS2003

Sensor Calibration and Ocean Products for TRMM Microwave Radiometer

During the three years of finding, we have carefully corrected for two sensor/platform problems, developed a physically based retrieval algorithm to calculate SST, wind speed, water vapor, cloud liquid water and rain rates, validated these variables, and demonstrated that satellite microwave radiometers can provide very accurate SST retrievals through clouds. Prior to this, there was doubt by some scientists that the technique of microwave SST retrieval from satellites is a viable option. We think we have put these concerns to rest, and look forward to making microwave SSTs a standard component of the Earth science data sets. Our TMI SSTs were featured on several network news broadcasts and were reported in Science magazine. Additionally, we have developed a SST algorithm for VIRS to facilitate IR/MW inter-comparisons and completed research into diurnal cycles and air-sea interactions.

Wentz, Frank J.↗
NASA NTRS2003

Sensor Calibration and Ocean Products for TRMM Microwave Radiometer

During the three years of fundin& we have carefully corrected for two sensor/platform problems, developed a physically based retrieval algorithm to calculate SST, wind speed, water vapor, cloud liquid water and rain rates, validated these variables, and demonstrated that satellite microwave radiometers can provide very accurate SST retrievals through clouds. Prior to this, there was doubt by some scientists that the technique of microwave SST retrieval from satellites is a viable option. We think we have put these concerns to rest, and look forward to making microwave SSTs a standard component of the Earth science data sets. Our TMI SSTs were featured on several network news broadcasts and were reported in Science magazine. Additionally, we have developed a SST algorithm for VIRS to facilitate IR/MW inter-comparisons and completed research into diurnal cycles and air-sea interactions.

Lawrence, Richard J.↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗
NASA NTRS2022

Machine learning for Deep Space Network antenna motions detection

Highly stable frequency and timing standards are essential for deep-space missions and radio science. At the NASA Deep Space Network (DSN), these standards are distributed through a network of underground fiber cables to support several Goldstone antennas. Independently developed frequency-measuring instruments generate tremendous quantities of data to monitor and validate the antennas’ stringent frequency requirements. In this paper, we propose a lightweight processing tool capable of detecting disturbances on the frequency signal caused by DSN antenna motions. Our training data is sampled from the movement log of the antenna of interest and the generated data from the fiber optic metrology instrument linked to the antenna. We demonstrate that a convolutional neural network (CNN) model can achieve high accuracies on classifying instances of antenna movements and is an effective predictor when used iteratively on longer, variable stretches of metrology data. The simplicity, low training cost, and high accuracies of our model strongly suggest its efficacy in identifying and troubleshooting frequency disturbances caused by the antenna.

Yi, Lin↗
NASA NTRS1972

The deep space network, Volume 11

Deep Space Network progress in flight project support, Tracking and Data Acquisition research and technology, network engineering, hardware and software implementation, and operations are presented. Material is presented in each of the following categories: description of DSN; mission support; radio science; support research and technology; network engineering and implementation; and operations and facilities.

Source record↗
DOE OSTI2022

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Science ecosystems are being built by federating computing systems and instruments located at geographically distributed sites over wide-area networks. These computing-instrument ecosystems are expected to support complex workflows that incorporate remote, automated AI-driven science experiments. Their realization, however, requires various designs to be explored and software components to be developed, in order to support the orchestration of distributed computations and experiments. It is often too expensive, infeasible, or disruptive for the entire ecosystem to be available during the typically long software development and testing periods. We propose a Virtual Infrastructure Twin (VIT) of the ecosystem that emulates its network and computing components, and incorporates its instrument software simulators. It provides a software environment nearly identical to the ecosystem to support early development and testing, and design space exploration. We present a brief overview of previous digital infrastructure twins that culminated in the VIT concept, including (i) the virtual science network environment for developing software-defined networking solutions, and (ii) the virtual federated science instrument environment for testing the federation software stack and remote instrument control software. We briefly describe VITs for Nion microscope steering and access to GPU systems.

Rao, Nageswara↗
DOE OSTI2023

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Science ecosystems are being built by federating computing systems and instruments located at geographically distributed sites over wide-area networks. These computing-instrument ecosystems are expected to support complex workflows that incorporate remote, automated AI-driven science experiments. Their realization, however, requires various designs to be explored and software components to be developed, in order to support the orchestration of distributed computations and experiments. It is often too expensive, infeasible, or disruptive for the entire ecosystem to be available during the typically long software development and testing periods. We propose a Virtual Infrastructure Twin (VIT) of the ecosystem that emulates its network and computing components, and incorporates its instrument software simulators. It provides a software environment nearly identical to the ecosystem to support early development and testing, and design space exploration. We present a brief overview of previous digital infrastructure twins that culminated in the VIT concept, including (i) the virtual science network environment for developing software-defined networking solutions, and (ii) the virtual federated science instrument environment for testing the federation software stack and remote instrument control software. We briefly describe VITs for Nion microscope steering and access to GPU systems.

Rao, Nageswara↗
DOE OSTI2020

Softwarized Federations of Science Instruments with Edge-Continuum Containers

Significant expansion of capabilities of DOE science complex of supercomputers, instruments and networks is expected as powerful experimental facilities, exascale computers and terabit networks are added. Combined with the advances in edge and cloud computing technologies, DOE science users now have the promise of unprecedented execution of complex, continuum workflows, namely, small and latency-sensitive computations at the edge and site, massive computations at remote HPC systems, and everything in between on the cloud. But, bringing this capability to the science user requires overcoming the overwhelming complexity of forming the federations of systems, and efficiently and effectively orchestrating the workflows while ensuring high utilization of the expensive facilities. Current manual configuration of the federated systems simply will not scale, since the coordination across sites may take weeks to months, often leading to under-utilized and hard-to-diagnose compositions. A powerful, composable software stack will be developed to (i) wrap the systems so that federations can be composed fast in software, and (ii) containerize computations to be orchestrated across the edge, site, cloud and HPC resources.

Rao, Nageswara S.↗
NASA NTRS2009

The International Lunar Network (ILN) Anchor Nodes Mission Update

NASA s Science Mission Directorate (SMD) established the Lunar Quest Program (LQP) to accomplish lunar science objectives embodied in the National Academies report The Scientific Context for Exploration of the Moon (2007) and the NASA Advisory Council-sponsored Workshop on Science Associated with the Lunar Exploration Architecture (2007). A major element of LQP's lunar flight projects is the International Lunar Network (ILN), a network of small geophysical nodes on the lunar surface. NASA plans to provide the first two stations around 2014 and a second pair in the 2016-2017 timeframe. International involvement to provide additional stations will build up the network so that 8-10 nodes could be simultaneously operating. This flight project complements SMD's initiatives to build a robust lunar science community through R&A lines and increases international participation in NASA's robotic exploration of the moon.

Cohen, B. A.↗
NASA NTRSDate not supplied

The TOLNet 2.0 Website: How an API Can Promote Open Science and FAIR Principles

The Tropospheric Ozone Lidar Network (TOLNet) has generated over a decade of ozone vertical profile data products over North America. The science value of the TOLNet data has been demonstrated in numerous peer-reviewed publications on air quality and other ozone relevant research. To support the broad spectrum of data use, the TOLNet team launched a major effort to upgrade the web-based data repository aiming to enhance the data discoverability and to enable machine-to-machine data upload and download processes. Specifically, the TOLNet website included an application programming interface (API), which supports machine-to-machine data search and data download. The API also extracts selected variables from the files, which can be retrieved as JSON objects and used to create data displays without having to download or open the underlying files. The TOLNet science team members can also use the API for automated data upload, including a data file scanning feature to ensure data product integrity. To be presented will include a summary of key features of data repositories, an actual use case of machine-to-machine data access/use, as well as our journey to make TOLNet data more FAIR, i.e., more findable, accessible, interoperable, and (re)usable.

Crystal Gummo↗
DOE OSTI2022

Quantum Networking and Communications at Oak Ridge National Laboratory

In this article we review recent and ongoing research in quantum communications and networking within the Quantum Information Science Section at Oak Ridge National Laboratory. Our research spans applications such as quantum key distribution as well as more fundamental aspects needed to develop scalable quantum networks including quantum repeaters and space-based quantum communications platforms.

Peters, Nicholas↗
NASA NTRS1996

Earth-Mars Telecommunications and Information Management System (TIMS): Antenna Visibility Determination, Network Simulation, and Management Models

This report presents the results of our research on Earth-Mars Telecommunications and Information Management System (TIMS) network modeling and unattended network operations. The primary focus of our research is to investigate the feasibility of the TIMS architecture, which links the Earth-based Mars Operations Control Center, Science Data Processing Facility, Mars Network Management Center, and the Deep Space Network of antennae to the relay satellites and other communication network elements based in the Mars region. The investigation was enhanced by developing Build 3 of the TIMS network modeling and simulation model. The results of several 'what-if' scenarios are reported along with reports on upgraded antenna visibility determination software and unattended network management prototype.

Odubiyi, Jide↗
DOE OSTI2023

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2008

International Lunar Network (ILN) Anchor Nodes

This slide presentation reviews what we know about the interior and surface of the moon and the need to establish a robotic set of geophysical monitoring stations on the surface of the Moon for the purpose of providing significant scientific value to the exploration of the Moon. The ILN Anchor Nodes will provide the backbone of the network in a way that accomplishes new science and allows other nodes to be flexible contributors to the network.

Cohen, Barbara A.↗