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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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NASA NTRS1972

Electrical conductivity of condensed molecular hydrogen in the giant planets

Theoretical interpretation of several phenomena concerning Jupiter and Saturn depends upon the electrical conductivity of molecular hydrogen which, according to present models, forms the outermost layer of both planets. The layer starts at the transition pressure between the metallic and the molecular form of hydrogen, that is around 1 Mbar, and extends to the outside limits of the atmosphere. Whether at the highest pressures (and temperatures) this layer is a solid or a dense fluid is not certain. In any case, the fluid is in supercritical condition so that there is only a gradual transition from dense liquid to a gaseous form. The two theories which require specific values of the conductivity of the condensed molecular hydrogen are those pertaining to the generation of a magnetic field in the liquid hydrogen rather than in the deep metallic interior (HIDE, 1967), and those concerned with the electromagnetic coupling and exchange of angular momentum between the liquid core and the solid molecular hydrogen mantle.

Smoluchowski, R.↗
NASA NTRS2003

Double Layers in Expanding Plasmas and Their Relevance to the Auroral Plasma Processes

When a dense plasma consisting of a cold and a sufficiently warm electron population expands, a rarefaction shock forms [Bezzerides et al., 1978]. In the expansion of the polar wind in the magnetosphere, it has been previously shown that when a sufficiently warm electron population also exists, in addition to the usual cold ionospheric one, a discontinuity forms in the electrostatic potential distribution along the magnetic field lines [Barakat and Schunk, 1984]. Despite the lack of spatial resolution and the assumption of quasi-neutrality in the polar wind models, such discontinuities have been called double layers (DLs). Recently similar discontinuities have been invoked to partly explain the auroral acceleration of electrons and ions in the upward current region [Ergun et al., 2000]. By means of one-dimensional Vlasov simulations of expanding plasmas, for the first time we make here the connection between (1) the rarefaction shocks, (2) the discontinuities in the potential distributions, and (3) DLs. We show that when plasmas expand from opposite directions into a deep density cavity with a potential drop across it and when the plasma on the high-potential side contains hot and cold electron populations, the temporal evolution of the potential and the plasma distribution generates evolving multiple double layers with an ,extended density cavity between them. One of the DLs is the rarefaction-shock (RFS) and it forms by the reflections of the cold electrons coming from the high-potential side; it supports a part of the potential drop approximately determined by the hot electron temperature. The other DLs evolve from charge separations arising either from reflection of ions coming from the low-potential side or stemming from plasma instabilities; they support the rest of the potential drop. The instabilities forming these additional double layers involve electron-ion (e-i) Buneman or ion-ion (i-i) two-stream interactions. The electron-electron two-stream interactions on the high-potential side of the RFS generate electron-acoustic waves, which evolve into electron phase-space holes. The ion population originating from the low-potential side and trapped by the RFS is energized by the e-i and i-i instabilities and it eventually precipitates into the high-potential plasma along with an electron beam. Applications of these findings to the auroral plasma physics are discussed.

Singh, Nagendra↗
NASA NTRSDate not supplied

Ground-Testing of One-Way Ranging from a Lunar Beacon Demonstrator Payload (Lunar Node -1)

Similar to lighthouses providing navigation aids and awareness to ships sailing along the coast, in-situ navigation beacons can be used to provide redundancy, awareness, and improved onboard position knowledge for vehicles operating in and around the lunar sphere of influence. Lunar Node - 1 is a radio frequency navigation beacon that is manifested on Intuitive Machines' NOVA-C lunar lander flight in 2022 for operation during cruise and from a mid-latitude landing location. This payload utilizes commercial components developed for Low Earth Orbit applications and packages them into an integrated package to provide a navigation reference signal as part of a large lunar-centric navigation network. As part of its operations, Deep Space Network ground stations will operate as the notional "user" of this service, allowing for performance characterization, calibration, and operation of the beacon payload. This paper provides an overview of the payload, how its fits into a large concept of operations, and ground testing results. A specific focus is on ground testing of the payload with DSN ground receivers as part of Radio Frequency (RF) Compatibility Testing. A primary goal of this testing was to characterize a pseudo-noise ranging code sequence generated by the payload. The results in this paper define the testing sequence, unique operational constraints with two-way ranging ground equipment, and applications of the ground-derived error model to predicted in-flight performance of the ranging code.

Evan J Anzalone↗
NASA NTRSDate not supplied

Deep Learning System for Efficient Processing of Geostationary Satellite Imagery

Improved capabilities of Earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), and Himawari-8/9 (JAXA), we present an interchangeable set of machine models to perform spectral adjustment among sensors, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools on the NASA Earth eXchange (NEX) to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate surface reflectance, surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Thomas Vandal↗
NASA NTRS2006

Standardizing an End-to-end Accounting Service

Currently there are no space system standards available for space agencies to accomplish end-to-end accounting. Such a standard does not exist for spacecraft operations nor for tracing the relationship between the mission planning activities, the command sequences designed to perform those activities, the commands formulated to initiate those activities and the mission data and specifically the mission data products created by those activities. In order for space agencies to cross-support one another for data accountability/data tracing and for inter agency spacecraft to interoperate with each other, an international CCSDS standard for end-to-end data accountability/tracing needs to be developed. We will first describe the end-to-end accounting service model and functionality that supports the service. This model will describe how science plans that are ultimately transformed into commands can be associated with the telemetry products generated as a result of their execution. Moreover, the interaction between end-to-end accounting and service management will be explored. Finally, we will show how the standard end-to-end accounting service can be applied to a real life flight project i.e., the Mars Reconnaissance Orbiter project.

recommendations↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary satellites↗
NASA NTRS2017

Space Launch System Spacecraft and Payload Elements: Progress Toward Crewed Launch and Beyond

While significant and substantial progress continues to be accomplished toward readying the Space Launch System (SLS) rocket for its first test flight, work is already underway on preparations for the second flight - using an upgraded version of the vehicle - and beyond. Designed to support human missions into deep space, SLS is the most powerful human-rated launch vehicle the United States has ever undertaken, and is one of three programs being managed by the National Aeronautics and Space Administration's (NASA's) Exploration Systems Development division. The Orion spacecraft program is developing a new crew vehicle that will support human missions beyond low Earth orbit (LEO), and the Ground Systems Development and Operations (GSDO) program is transforming Kennedy Space Center (KSC) into a next-generation spaceport capable of supporting not only SLS but also multiple commercial users. Together, these systems will support human exploration missions into the proving ground of cislunar space and ultimately to Mars. For its first flight, SLS will deliver a near-term heavy-lift capability for the nation with its 70-metric-ton (t) Block 1 configuration. Each element of the vehicle now has flight hardware in production in support of the initial flight of the SLS, which will propel Orion around the moon and back. Encompassing hardware qualification, structural testing to validate hardware compliance and analytical modeling, progress is on track to meet the initial targeted launch date. In Utah and Mississippi, booster and engine testing are verifying upgrades made to proven shuttle hardware. At Michoud Assembly Facility (MAF) in Louisiana, the world's largest spacecraft welding tool is producing tanks for the SLS core stage. Providing the Orion crew capsule/launch vehicle interface and in-space propulsion via a cryogenic upper stage, the Spacecraft/Payload Integration and Evolution (SPIE) element serves a key role in achieving SLS goals and objectives. The SPIE element marked a major milestone in 2014 with the first flight of original SLS hardware, the Orion Stage Adapter (OSA) which was used on Exploration Flight Test-1 with a design that will be used again on the first flight of SLS. The element has overseen production of the Interim Cryogenic Propulsion Stage (ICPS), an in-space stage derived from the Delta Cryogenic Second Stage, which was manufactured at United Launch Alliance (ULA) in Decatur, Alabama, prior to being shipped to Florida for flight preparations. Manufacture of the OSA and the Launch Vehicle Stage Adapter (LVSA) took place at the Friction Stir Facility located at Marshall Space Flight Center (MSFC) in Huntsville, Alabama. Marshall is also home to the Integrated Structural Test of the ICPS, LVSA, and OSA, subjecting the stacked components to simulated stresses of launch. The SPIE Element is also overseeing integration of 13 "CubeSat" secondary payloads that will fly on the first flight of SLS, providing access to deep space regions in a way currently not available to the science community. At the same time as this preparation work is taking place toward the first launch of SLS, however, the Space Launch System Program is actively working toward its second launch. For its second flight, SLS will be upgraded to the more-capable Block 1B configuration. While the Block 1 configuration is capable of delivering more than 70 t to LEO, the Block 1B vehicle will increase that capability to 105 t. For that flight, the new configuration introduces two major new elements to the vehicle - an Exploration Upper Stage (EUS) that will be used for both ascent and in-space propulsion, and a Universal Stage Adapter (USA) that serves as a "payload bay" for the rocket, allowing the launch of large exploration systems along with the Orion spacecraft. Already, flight hardware is being prepared for the Block 1B vehicle. Welding is taking place on the second rocket's core stage. Flight hardware production has begun on booster components. An RS-25 engine slated for that flight has been tested. Development work is taking place on the EUS, with contracts in place for both the stage and the RL10 engines which will power it. (The EUS will use four RL10 engines, an increase from one on the ICPS.) For the crew configuration of the Block 1B vehicle, the SLS SPIE element is managing the USA and accompanying Payload Adapter, which will accommodate both large payloads co-manifested with Orion and small-satellite secondary payloads. This co-manifested payload capacity will be instrumental for missions into the proving ground around the moon, where NASA will test new systems and demonstrate new capabilities needed for human exploration farther into deep space.

Schorr, Andrew A.↗
NASA NTRS2017

Space Launch System Spacecraft and Payload Elements: Progress Toward Crewed Launch and Beyond

While significant and substantial progress continues to be accomplished toward readying the Space Launch System (SLS) rocket for its first test flight, work is already also underway on preparations for the second flight - using an upgraded version of the vehicle - and beyond. Designed to support human missions into deep space, Space Launch System (SLS), is the most powerful human-rated launch vehicle the United States has ever undertaken, and is one of three programs being managed by the National Aeronautics and Space Administration's (NASA's) Exploration Systems Development division. The Orion spacecraft program is developing a new crew vehicle that will support human missions beyond low Earth orbit (LEO), and the Ground Systems Development and Operations program is transforming Kennedy Space Center into a next-generation spaceport capable of supporting not only SLS but also multiple commercial users. Together, these systems will support human exploration missions into the proving ground of cislunar space and ultimately to Mars. For its first flight, SLS will deliver a near-term heavy-lift capability for the nation with its 70-metric-ton (t) Block 1 configuration. Each element of the vehicle now has flight hardware in production in support of the initial flight of the SLS, which will propel Orion around the moon and back. Encompassing hardware qualification, structural testing to validate hardware compliance and analytical modeling, progress in on track to meet the initial targeted launch date. In Utah and Mississippi, booster and engine testing are verifying upgrades made to proven shuttle hardware. At Michoud Assembly Facility in Louisiana, the world's largest spacecraft welding tool is producing tanks for the SLS core stage. Providing the Orion crew capsule/launch vehicle interface and in-space propulsion via a cryogenic upper stage, the Spacecraft/Payload Integration and Evolution (SPIE) element serves a key role in achieving SLS goals and objectives. The SPIE element marked a major milestone in 2014 with the first flight of original SLS hardware, the Orion Stage Adapter (OSA) which was used on Exploration Flight Test-1 with a design that will be used again on the first flight of SLS. The element has overseen production of the Interim Cryogenic Propulsion Stage (ICPS), an in-space stage derived from the Delta Cryogenic Second Stage, which was manufactured at United Launch Alliance in Decatur, Alabama, prior to being shipped to Florida for flight preparations. Manufacture of the Orion Stage Adapter and the Launch Vehicle Stage Adapter (LVSA) took place at the Friction Stir Facility located at Marshall Space Flight Center in Huntsville, Alabama. Marshall is also home to the Integrated Structural Test of the ICPS, LVSA, and OSA, subjecting the stacked components to simulated stresses of launch. The SPIE Element is also overseeing integration of 13 "CubeSat" secondary payloads that will fly on the first flight of SLS, providing access to deep space regions in a way currently not available to the science community. At the same time as this preparation work is taking place toward the first launch of SLS, however, the Space Launch System Program is actively working toward its second launch. For its second flight, SLS will be upgraded to the more-capable Block 1B configuration. While the Block 1 configuration is capable of delivering more than 70 metric tons to low Earth orbit, the Block 1B vehicle will increase that capability to 105 t. For that flight, the new configuration introduces two major new elements to the vehicle - an Exploration Upper Stage (EUS) that will be used for both ascent and in-space propulsion, and a Universal Stage Adapter (USA) that serves as a "payload bay" for the rocket, allowing the launch of large exploration systems along with the Orion spacecraft. Already, flight hardware is being prepared for the Block 1B vehicle. Welding is taking place on the second rocket's core stage. Flight hardware production has begun on booster components. An RS-25 engine slated for that flight has been tested. Development work is taking place on the Exploration Upper Stage, with contracts in place for both the stage and the RL10 engines which will power it. (The EUS will use four RL10 engines, an increase from one on the ICPS.) For the crew configuration of the Block 1B vehicle, the SLS SPIE element is managing the USA and accompanying Payload Adapter, which will accommodate both large payloads co-manifested with Orion and small-satellite secondary payloads. This co-manifested payload capacity will be instrumental for missions into the Proving Ground around the moon, where NASA will test new systems and demonstrate new capabilities needed for human exploration farther into deep space.

Schorr, Andrew A.↗
NASA NTRS1987

A coupled dynamic-thermodynamic model of an ice-ocean system in the marginal ice zone

Thermodynamics are incorporated into a coupled ice-ocean model in order to investigate wind-driven ice-ocean processes in the marginal zone. Upswelling at the ice edge which is generated by the difference in the ice-air and air-water surface stresses is found to give rise to a strong entrainment by drawing the pycnocline closer to the surface. Entrainment is shown to be negligible outside the areas affected by the ice edge upswelling. If cooling at the top is included in the model, the heat and salt exchanges are further enhanced in the upswelling areas. It is noted that new ice formation occurs in the region not affected by ice edge upswelling, and it is suggested that the high-salinity mixed layer regions (with a scale of a few Rossby radii of deformation) will overturn due to cooling, possibly contributing to the formation of deep water.

Hakkinen, Sirpa↗
NASA NTRS1998

Mission Operations with an Autonomous Agent

The Remote Agent (RA) is an Artificial Intelligence (AI) system which automates some of the tasks normally reserved for human mission operators and performs these tasks autonomously on-board the spacecraft. These tasks include activity generation, sequencing, spacecraft analysis, and failure recovery. The RA will be demonstrated as a flight experiment on Deep Space One (DSI), the first deep space mission of the NASA's New Millennium Program (NMP). As we moved from prototyping into actual flight code development and teamed with ground operators, we made several major extensions to the RA architecture to address the broader operational context in which PA would be used. These extensions support ground operators and the RA sharing a long-range mission profile with facilities for asynchronous ground updates; support ground operators monitoring and commanding the spacecraft at multiple levels of detail simultaneously; and enable ground operators to provide additional knowledge to the RA, such as parameter updates, model updates, and diagnostic information, without interfering with the activities of the RA or leaving the system in an inconsistent state. The resulting architecture supports incremental autonomy, in which a basic agent can be delivered early and then used in an increasingly autonomous manner over the lifetime of the mission. It also supports variable autonomy, as it enables ground operators to benefit from autonomy when L'@ey want it, but does not inhibit them from obtaining a detailed understanding and exercising tighter control when necessary. These issues are critical to the successful development and operation of autonomous spacecraft.

Pell, Barney↗
NASA NTRS2024

A Thermally-Conductive Martian Core and Implications for Its Dynamo Cessation

Mars experienced a dynamo process that generated a global magnetic field ~4.3 (or earlier) to 3.6 billion years ago (Ga). The cessation of this dynamo strongly affected Mars’ history and is expected to be linked to thermochemical evolution of Mars’ iron-rich liquid core, which is strongly influenced by its thermal conductivity. Here, we directly measured thermal conductivities of solid iron-sulfur alloys to pressures relevant to the Martian core and temperatures to 1023 Kelvin. Our results show that a Martian core with 16 weight % sulfur has a thermal conductivity of ~19 to 32 Watt meter −1 Kelvin −1 from its top to the center, much higher than previously inferred from electrical resistivity measurements. Our modeled thermal conductivity profile throughout the Martian deep-mantle and core indicates a ~4- to 6-fold discontinuity across the core-mantle boundary. The core’s efficient cooling resulting from the depth-dependent, high conductivity diminishes thermal convection and forms thermal stratification, substantially contributing to cessation of Martian dynamo.

Wen-Pin Hsieh↗
NASA NTRS1993

A new technology for manufacturing scheduling derived from space system operations

A new technology for producing finite capacity schedules has been developed in response to complex requirements for operating space systems such as the Space Shuttle, the Space Station, and the Deep Space Network for telecommunications. This technology has proven its effectiveness in manufacturing environments where popular scheduling techniques associated with Materials Resources Planning (MRPII) and with factory simulation are not adequate for shop-floor work planning and control. The technology has three components. The first is a set of data structures that accommodate an extremely general description of a factory's resources, its manufacturing activities, and the constraints imposed by the environment. The second component is a language and set of software utilities that enable a rapid synthesis of functional capabilities. The third component is an algorithmic architecture called the Five Ruleset Model which accommodates the unique needs of each factory. Using the new technology, systems can model activities that generate, consume, and/or obligate resources. This allows work-in-process (WIP) to be generated and used; it permits constraints to be imposed or intermediate as well as finished goods inventories. It is also possible to match as closely as possible both the current factory state and future conditions such as promise dates. Schedule revisions can be accommodated without impacting the entire production schedule. Applications have been successful in both discrete and process manufacturing environments. The availability of a high-quality finite capacity production planning capability enhances the data management capabilities of MRP II systems. These schedules can be integrated with shop-floor data collection systems and accounting systems. Using the new technology, semi-custom systems can be developed at costs that are comparable to products that do not have equivalent functional capabilities and/or extensibility.

Hornstein, R. S.↗
NASA NTRS2007

Using High Frequency Passive Microwave, A-train, and TRMM Data to Evaluate Hydrometer Structure in the NASA GEOS-5 Data Assimilation System

Validating water vapor and prognostic condensate in global models remains a challenging research task. Model parameterizations are still subject to a large number of tunable parameters; furthermore, accurate and representative in situ observations are very sparse, and satellite observations historically have significant quantitative uncertainties. Progress on improving cloud / hydrometeor fields in models stands to benefit greatly from the growing inventory ofA-Train data sets. ill the present study we are using a variety of complementary satellite retrievals of hydrometeors to examine condensate produced by the emerging NASA Modem Era Retrospective Analysis for Research and Applications, MERRA, and its associated atmospheric general circulation model GEOS5. Cloud and precipitation are generated by both grid-scale prognostic equations and by the Relaxed Arakawa-Schubert (RAS) diagnostic convective parameterization. The high frequency channels (89 to 183.3 GHz) from AMSU-B and MRS on NOAA polar orbiting satellites are being used to evaluate the climatology and variability of precipitating ice from tropical convective anvils. Vertical hydrometeor structure from the Tropical Rainfall Measuring Mission (TRMM) and CloudSat radars are used to develop statistics on vertical hydrometeor structure in order to better interpret the extensive high frequency passive microwave climatology. Cloud liquid and ice water path data retrieved from the Moderate Resolution Imaging Spectroradiometer, MODIS, are used to investigate relationships between upper level cloudiness and tropical deep convective anvils. Together these data are used to evaluate cloud / ice water path, gross aspects of vertical hydrometeor structure, and the relationship between cloud extent and surface precipitation that the MERRA reanalysis must capture.

Robertson, Franklin↗
NASA NTRS2023

Predicting Two-Dimensional Airfoil Performance Using Graph Neural Networks

Computer simulations require the use of meshes to simulate geometries. These meshes capture important geometric features of the design and can be used in machine learning modeling. This report explores the use of graph neural networks (GNNs) to learn features from two-dimensional (2D) airfoil designs represented as a set of nodes connected using edges. This type of network is common in aerospace applications: most geometries are represented as a mesh in order to perform analysis. The objective of this work is to use GNNs to predict the performance of 2D airfoils generated using the program XFOIL. The predicted performance parameters include bulk quantities such as coefficients of lift (C L ), drag (C d , C dp ), moment (C m ), and node-specific quantities such as coefficient of pressure (C p ). In this report, a spline convolutional graph-based neural network is compared with deep learning neural networks to predict both bulk and node-specific quantities. The findings indicate the GNNs are able to predict bulk quantities quite well; however, when the number of outputs is increased, the deep neural network (DNN) proves to be better in its prediction capability. Two different normalization strategies were compared in the training of both GNNs and DNNs: minmax and standard deviation. In both types of networks, standard deviation scaling proved to be the best.

machine learning↗
NASA NTRS2011

MOS 2.0: Modeling the Next Revolutionary Mission Operations System

Designed and implemented in the 1980's, the Advanced Multi-Mission Operations System (AMMOS) was a breakthrough for deep-space NASA missions, enabling significant reductions in the cost and risk of implementing ground systems. By designing a framework for use across multiple missions and adaptability to specific mission needs, AMMOS developers created a set of applications that have operated dozens of deep-space robotic missions over the past 30 years. We seek to leverage advances in technology and practice of architecting and systems engineering, using model-based approaches to update the AMMOS. We therefore revisit fundamental aspects of the AMMOS, resulting in a major update to the Mission Operations System (MOS): MOS 2.0. This update will ensure that the MOS can support an increasing range of mission types, (such as orbiters, landers, rovers, penetrators and balloons), and that the operations systems for deep-space robotic missions can reap the benefits of an iterative multi-mission framework.12 This paper reports on the first phase of this major update. Here we describe the methods and formal semantics used to address MOS 2.0 architecture and some early results. Early benefits of this approach include improved stakeholder input and buy-in, the ability to articulate and focus effort on key, system-wide principles, and efficiency gains obtained by use of well-architected design patterns and the use of models to improve the quality of documentation and decrease the effort required to produce and maintain it. We find that such methods facilitate reasoning, simulation, analysis on the system design in terms of design impacts, generation of products (e.g., project-review and software-delivery products), and use of formal process descriptions to enable goal-based operations. This initial phase yields a forward-looking and principled MOS 2.0 architectural vision, which considers both the mission-specific context and long-term system sustainability.

ground support systems↗
NASA NTRS2021

Spectral Synthesis for Geostationary Satellite-to-Satellite Translation

Earth-observing satellites carrying multispectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have different vantage points above the Earth and different spectral imaging bands resulting in inconsistent imagery from one to another. This presents challenges in building downstream applications. What if we could generate synthetic bands for existing satellites from the union of all domains? We tackle the problem of generating synthetic spectral imagery for multispectral sensors as an unsupervised image-to-image translation problem modeled with a variational autoencoder (VAE) and generative adversarial network (GAN) architecture. Our approach introduces a novel shared spectral reconstruction loss to constrain the high-dimensional feature space of multispectral images. Simulated experiments performed by dropping one or more spectral bands show that cross-domain reconstruction outperforms measurements obtained from a second vantage point. Our proposed approach enables the synchronization of multispectral data and provides a basis for more homogeneous remote sensing datasets.

Geostationary satellites↗
NASA NTRS1997

A Generative Control Capability for a Model-based Executive

This paper describes Burton, a core element of a new generation of goal-directed model-based autonomous executives. This executive makes extensive use of component-based declarative models to analyze novel situations and generate novel control actions both at the goal and hardware levels. It uses an extremely efficient online propositional inference engine to efficiently determine likely states consistent with current observations and optimal target states that achieve high level goals. It incorporates a flexible generative control sequencing algorithm within the reactive loop to bridge the gap between current and target states. The system is able to detect and avoid damaging and irreversible situations, After every control action it uses its model and sensors to detect anomalous situations and immediately take corrective action. Efficiency is achieved through a series of model compilation and online policy construction methods, and by exploiting general conventions of hardware design that permit a divide and conquer approach to planning. The paper presents a formal characterization of Burton's capability, develops efficient algorithms, and reports on experience with the implementation in the domain of spacecraft autonomy. Burton is being incorporated as one of the key elements of the Remote Agent core autonomy architecture for Deep Space One, the first spacecraft for NASA's New Millenium program.

Williams, Brian C.↗
NASA NTRS2008

DSN Array Simulator

The DSN Array Simulator (wherein 'DSN' signifies NASA's Deep Space Network) is an updated version of software previously denoted the DSN Receive Array Technology Assessment Simulation. This software (see figure) is used for computational modeling of a proposed DSN facility comprising user-defined arrays of antennas and transmitting and receiving equipment for microwave communication with spacecraft on interplanetary missions. The simulation includes variations in spacecraft tracked and communication demand changes for up to several decades of future operation. Such modeling is performed to estimate facility performance, evaluate requirements that govern facility design, and evaluate proposed improvements in hardware and/or software. The updated version of this software affords enhanced capability for characterizing facility performance against user-defined mission sets. The software includes a Monte Carlo simulation component that enables rapid generation of key mission-set metrics (e.g., numbers of links, data rates, and date volumes), and statistical distributions thereof as functions of time. The updated version also offers expanded capability for mixed-asset network modeling--for example, for running scenarios that involve user-definable mixtures of antennas having different diameters (in contradistinction to a fixed number of antennas having the same fixed diameter). The improved version also affords greater simulation fidelity, sufficient for validation by comparison with actual DSN operations and analytically predictable performance metrics.

Tikidjian, Raffi↗