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NASA NTRS2000

Atmosphere of Freedom: Sixty Years at the NASA Ames Research Center

Throughout Ames History, four themes prevail: a commitment to hiring the best people; cutting-edge research tools; project management that gets things done faster, better and cheaper; and outstanding research efforts that serve the scientific professions and the nation. More than any other NASA Center, Ames remains shaped by its origins in the NACA (National Advisory Committee for Aeronautics). Not that its missions remain the same. Sure, Ames still houses the world's greatest collection of wind tunnels and simulation facilities, its aerodynamicists remain among the best in the world, and pilots and engineers still come for advice on how to build better aircraft. But that is increasingly part of Ames' past. Ames people have embraced two other missions for its future. First, intelligent systems and information science will help NASA use new tools in supercomputing, networking, telepresence and robotics. Second, astrobiology will explore lore the prospects for life on Earth and beyond. Both new missions leverage Ames long-standing expertise in computation and in the life sciences, as well as its relations with the computing and biotechnology firms working in the Silicon Valley community that has sprung up around the Center. Rather than the NACA missions, it is the NACA culture that still permeates Ames. The Ames way of research management privileges the scientists and engineers working in the laboratories. They work in an atmosphere of freedom, laced with the expectation of integrity and responsibility. Ames researchers are free to define their research goals and define how they contribute to the national good. They are expected to keep their fingers on the pulse of their disciplines, to be ambitious yet frugal in organizing their efforts, and to always test their theories in the laboratory or in the field. Ames' leadership ranks, traditionally, are cultivated within this scientific community. Rather than manage and supervise these researchers, Ames leadership merely guided them, represents them to NASA headquarters and the world outside, then steps out of the way before they get run over.

Bugos, Glenn E.↗

NASA NTRS2004

Planning Mars Memory: Learning from the Mer Mission

Knowledge management for space exploration is part of a multi-generational effort at recognizing, preserving and transmitting learning. Each mission should be built on the learning, of both successes and failures, derived from previous missions. Knowledge management begins with learning, and the recognition that this learning has produced knowledge. The Mars Exploration Rover mission provides us with an opportunity to track how learning occurs, how it is recorded, and whether the representations of this learning will be optimally useful for subsequent missions. This paper focuses on the MER science and engineering teams during Rover operations. A NASA team conducted an observational study of the ongoing work and learning of the these teams. Learning occurred in a wide variety of areas: how to run two teams on Mars time for three months; how to use the instruments within the constraints of the martian environment, the deep space network and the mission requirements; how to plan science strategy; how best to use the available software tools. This learning is preserved in many ways. Primarily it resides in peoples memories, to be carried on to the next mission. It is also encoded in stones, in programming sequences, in published reports, and in lessons learned activities, Studying learning and knowledge development as it happens allows us to suggest proactive ways of capturing and using it across multiple missions and generations.

Linde, Charlotte↗

NASA NTRS2002

Major technological innovations introduced in the large antennas of the Deep Space Network

The NASA Deep Space Network (DSN) is the largest and most sensitive scientific, telecommunications and radio navigation network in the world. Its principal responsibilities are to provide communications, tracking, and science services to most of the world's spacecraft that travel beyond low Earth orbit. The network consists of three Deep Space Communications Complexes. Each of the three complexes consists of multiple large antennas equipped with ultra sensitive receiving systems. A centralized Signal Processing Center (SPC) remotely controls the antennas, generates and transmits spacecraft commands, and receives and processes the spacecraft telemetry.

large antennas beam waveguide dual reflector shapi↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗

NASA NTRS2021

A Dedicated Relay Network to Enable the Future of Mars Exploration

A highly successful international collaboration, the “Mars Relay Network” (MRN) leverages the combined NASA and ESA orbiter capabilities to transfer data to and from Mars surface missions. The MRN has admirably flight-demonstrated the benefits of a relay network and validated how international protocol standards may be used to ensure interoperability. However, principally designed for science missions, these orbiters addressed relay requirements as a secondary function, which introduced limitations to what can be achieved with the network. Next-decade missions are expected to have significantly greater communication needs than can be accommodated by the aging MRN. This paper reports the results of a broad study that evaluated the potential of a next-generation relay network, referencing the current MRN as a benchmark. A wide variety of orbital altitudes, surface latitudes, and mission scenarios were evaluated around a specific set of assumptions regarding the telecommunications payloads included. The study outlined how current day technologies could be applied to greatly enhance the data throughput to and from Mars on behalf of future science and reconnaissance missions. The instantiation of such a network would be enabling for a variety of missions and mission classes that have been heretofore unachievable, including both large and small orbiters, and landed vehicles representing new mission types (i.e. climbers, diggers, drones, etc.), and further argues that such a network would be instrumental in advancing human exploration interests at Mars.

Davis, Richard M.↗

NASA NTRS1997

Trends in Performance and Characteristics of Ultra-Stable Oscillators for Deep Space Radio Science Experiments

Telecommunication systems of spacecraft on deep space missions also function as instruments for Radio Science experiments. Radio scientists utilize the telecommunication links between spacecraft and Earth to examine very small changes in the phase/frequency, amplitude, and/or polarization of radio signals to investigate a host of physical phenomena in the solar system. Several missions augmented the radio communication system with an Ultra-Stable Oscillator (USO) in order to provide a highly stable reference signal for oneway downlink. This configuration is used in order to enable better investigations of the atmospheres of the planets occulting the line-of-sight to the spacecraft; one-way communication was required and the transponders' built-in auxiliary oscillators were neither sufficiently stable nor spectrally pure for the occultation experiments. Since Radio Science instrumentation is distributed between the spacecraft and the ground stations, the Deep Space Network (DSN) is also equipped to function as a world-class instrument for Radio Science research. For a detailed account of Radio Science experiments, methodology, key discoveries, and the DSN's historical contribution to the field, see Asmar and Renzetti (1993). The tools of Radio Science can be and have also been utilized in addressing several mission engineering challenges; e.g., characterization of spacecraft nutation and anomalous motion, antenna calibrations, and communications during surface landing phases. Since the first quartz USO was flown on Voyager, the technology has advanced significantly, affording future missions higher sensitivity in reconstructing the temperature pressure profiles of the atmospheres under study as well as other physical phenomena of interest to Radio Science. This paper surveys the trends in stability and spectral purity performance, design characteristics including size and mass, as well as cost and history of these clocks in space.

Asmar, Sami↗

NASA NTRS1990

The new space and earth science information systems at NASA's archive

The on-line interactive systems of the National Space Science Data Center (NSSDC) are examined. The worldwide computer network connections that allow access to NSSDC users are outlined. The services offered by the NSSDC new technology on-line systems are presented, including the IUE request system, ozone TOMS data, and data sets on astrophysics, atmospheric science, land sciences, and space plasma physics. Plans for future increases in the NSSDC data holdings are considered.

Green, James L.↗

NASA NTRS2021

Draft LunaNet Interoperability Specification

Under the guidance of NASA’s Space Communication and Navigation (SCaN) program, this document, along with its companion documents, LunaNet Services Requirements Document (TBD) and the LunaNet Concept of Operations and Architecture (TBD), provides the basis for a comprehensive set of requirements for operation of a lunar communications and navigation network capable of interoperating with other networks compliant with the Lunar Network (LunaNet) for services to the human exploration, lunar science, and space technology missions. LunaNet will start with a simple architecture of a few nodes to meet the needs of the early missions and evolve to meet the growing needs of a sustained lunar presence. All relay network services are not expected to be met by a single spacecraft platform, or node. The expectation is that the needs of NASA, its partners and other users will be met through a combination of interoperable systems provided by NASA, international partners, and commercial providers. Interoperability across this network-of-networks can be achieved through negotiation of mutually-agreed-upon standards that will be reflected in this document and in the specifications defined by other participants in the combined, cooperative lunar network.

LunaNet↗

NASA NTRS2023

Distributed Quantum Processing via Integrated Regional Quantum Networks Using Free-Space Optical Links

The landscape for quantum computing is rapidly evolving with the development of quantum computers and the emergence of future science enabling applications being pursued in many areas, such as distributed quantum computing, hybrid computing scenarios that require cooperatively networked quantum and classical computing assets and more. Quantum networking is also advancing with multiple regional quantum networks and testbeds maturing across the nation. These quantum networking testbeds typically include multiple access points with a heterogeneous mixture of capabilities interconnected by fiber spanning anywhere from tens to hundreds of kilometers; many such networks incorporate a classical networking framework. Alongside these connectivity advancements in the infrastructure, there is quantum sensing device and component technology maturation that is serving to expand the body of quantum networking scenarios that are presently possible and redefining the quantum- enabled future. Bridging the present collective progress in quantum computing and quantum networking will enable the next era of purposeful advancements for national priority use-cases for the benefit of science. One such use-case is enabling future quantum computing applications via integrated regional quantum networking infrastructure in neighboring locations like New York and Maryland, both states are home to robust classical and quantum networking capabilities. To enable near-term distributed quantum computing applications via the practical integration of regional quantum networking infrastructure, free-space optical links via satellite communications will be essential to avoid the exponential loss commonly observed with fiber implementations. There is both an urgent need and a practical justification for pursuing such space-based integration scope now.

Harry Shaw↗

NASA NTRS1990

The new space and Earth science information systems at NASA's archive

The on-line interactive systems of the National Space Science Data Center (NSSDC) are examined. The worldwide computer network connections that allow access to NSSDC users are outlined. The services offered by the NSSDC new technology on-line systems are presented, including the IUE request system, Total Ozone Mapping Spectrometer (TOMS) data, and data sets on astrophysics, atmospheric science, land sciences, and space plasma physics. Plans for future increases in the NSSDC data holdings are considered.

Green, James L.↗

NASA NTRS2006

GPS

Geodetic networks support the TRF requirements of NASA ESE missions. Each of SLR, VLBI, GPS substantially and uniquely contributes to TRF determination. NASA's SLR, VLBI, and GPS groups collaborate toward wide-ranging improvements in the next 5 years. NASA leverages considerable resources through its significant activity in international services. NASA faces certain challenges in continuing and advancing these activities. The Terrestrial Reference Frame (TRF) is an accurate, stable set of positions and velocities. The TRF provides the stable coordinate system that allows us to link measurements over space and time. The geodetic networks provide data for determination of the TRF as well as direct science observations.

Global Positioning System (GPS)↗

NASA NTRS2021

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet [1], outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Cognitive Networking↗

NASA NTRSDate not supplied

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Rachel Dudukovich↗

NASA NTRS1999

Global Observation Information Networking: Using the Distributed Image Spreadsheet (DISS)

The DISS and many other tools will be used to present visualizations which span the period from the original Suomi/Hasler animations of the first ATS-1 GEO weather satellite images in 1966 ....... to the latest 1999 NASA Earth Science Vision for the next 25 years. Hot off the SGI Onyx Graphics-Supercomputers are NASA's visualizations of Hurricanes Mitch, Georges, Fran and Linda. These storms have been recently featured on the covers of National Geographic, Time, Newsweek and Popular Science and used repeatedly this season on National and International network TV. Results will be presented from a new paper on automatic wind measurements in Hurricane Luis from 1-min GOES images that appeared in the November BAMS.

Hasler, Fritz↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

space biology↗

NASA NTRS2022

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

cognitive networking↗