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NASA NTRS1972

Moonquakes and lunar tectonism results from the Apollo passive seismic experiment.

The natural seismicity of the moon appears to be very low relative to that of the earth. However, moonquakes do occur. They are detected by the stations of the Apollo seismic network at an average rate of 1800/yr at Station 14 and at lower rates at Stations 12 and 15. All of the moonquakes are small, and in the few cases for which the foci have been located, they occur at great depth (about 800 km). The frequency of occurrence of moonquakes is strongly correlated with lunar tides. The dynamic processes that generate quakes are clearly much less vigorous within the moon than they are within the earth.

Latham, G.↗

NASA NTRS1986

Goldstone solar system radar

Information is provided about physical nature planetary surfaces and their topography as well as dynamical properties such as orbits and spin states using ground based radar as a remote sensing tool. Accessible targets are the terrestrial planets: the Earth's Moon, Mercury, Venus and Mars, the outer planets rings and major moons, and many transient objects such as asteroids and comets. Data acquisition utilizes the unique facilities of the Goldstone Deep Space Network, occasionally the Arecibo radar, and proposed use of the VLA (very large array).

Jurgens, R. F.↗

NASA NTRS1994

Maja Valley and the Chryse outflow complex sites

This candidate landing site is located at 19 deg N, 53.5 deg W near the mouth of a major outflow channel. Maja Valles, and two 'valley network' channel systems, Maumee and Vedra Valles. The following objectives are to be analyzed in this region: (1) origin and paleohydrology of outflow and valley network channels; (2) fan delta complex composition (the deposit located in this area is one of the few identified at the mouth s of any channels on the planet); and (3) analysis of any paleolake sediments (carbonates, evaporites). The primary objectives of the Chryse Outflow Complex region (Ares, Tiu, Mawrth, Simud, and Shalbatana Valles) would be outflow channel dynamics (paleohydrology) of five different channel systems.

Rice, Jim W.↗

NASA NTRS2008

Impact of Surface Emissions to the Zonal Variability of Tropical Tropospheric Ozone and Carbon Monoxide for November 2004

The chemical and dynamical processes governing the zonal variability of tropical tropospheric ozone and carbon monoxide are investigated for November 2004 using satellite observations, in-situ measurements, and chemical transport models in conjunction with inverse-estimated surface emissions. Vertical ozone profile estimates from the Tropospheric Emission Spectrometer (TES) and ozone sonde measurements from the Southern Hemisphere Additional Ozonesondes (SHADOZ) network show the so called zonal 'wave-one' pattern, which is characterized by peak ozone concentrations (70-80 ppb) centered over the Atlantic, as well as elevated concentrations of ozone over Indonesia and Australia (60-70 ppb) in the lower troposphere. Observational evidence from TES CO vertical profiles and Ozone Monitoring Instrument (OMI) NO2 columns point to regional surface emissions as an important contributor to the elevated ozone over Indonesia. This contribution is investigated with the GEOS-Chem chemistry and transport model using surface emission estimates derived from an optimal inverse model, which was constrained by TES and Measurements Of Pollution In The Troposphere (MOPITT) CO profiles (Jones et al., 2007). These a posteriori estimates, which were over a factor of 2 greater than climatological emissions, reduced differences between GEOS-Chem and TES ozone observations by 30-40% and led to changes in GEOS-Chem upper tropospheric ozone of up to 40% over Indonesia. The remaining residual differences can be explained in part by upper tropospheric ozone produced from lightning NOx in the South Atlantic. Furthermore, model simulations from GEOS-Chem indicate that ozone over Indonesian/Australian is more sensitive to changes in surface emissions of NOx than ozone over the tropical Atlantic.

tropical tropospheric ozone↗

NASA NTRS2020

Distributed Spacecraft Autonomy (DSA): Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC)measurements. DSA will be demonstrated on board the NASA Ames Starling mission a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets.The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasma sphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input data set for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software,aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements,which define the range of observability for the satellites, as well as exploitative measurements,which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth's magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions.As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown.Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middle ware provides reliable delivery, routing, and topic subscription features over User Data gram Protocol (UDP).Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarms calability from 2 satellites to 100 satellites is shown.

Fugate, Jason↗

NASA NTRS2024

Simulated Feasibility of 3D Lightning Mapping from Space

In addition to the awe it inspires, lightning can illuminate the microphysical processes hidden away within deep convection. The current generation of space-based lightning mapping uses mostly 2D optical imaging to connect overall flash characteristics to their parent storm dynamics, but are missing a dimension’s worth of information. With lightning now classified as an essential climate variable, future spaceborne mappers will need improved capabilities to take advantage of the 3D structure of lightning flashes to support meteorological and climate modeling. We report here on a study of the feasibility of implementing a radio frequency (RF)–based network of satellites for 3D lightning mapping in high resolution from low Earth orbit (LEO). Lightning sources are simulated using existing tools of the Lightning Mapping Array (LMA), modified for orbital detection, and spatially reconstructed using a Levenberg-Marquardt geolocation algorithm to assess sources of uncertainty in these solutions. We analyze the benefits and limitations of this approach compared to existing orbital and ground-based methods. Results of this study show that lightning can be mapped in 3D with a vertical location accuracy better than 2 km using as few as five satellites in LEO capable of measuring the time-of-arrival of impulsive RF signals in the very high frequency (VHF) band. The consequence of this study is that high-resolution, spaceborne 3D mapping of lightning is achievable across most of the globe, having crucial implications for our understanding of not only lightning, but also severe weather development, climate science, and more.

CubeSpark↗

NASA NTRS2008

Spiking Neurons for Analysis of Patterns

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Huntsberger, Terrance↗

NASA NTRS1990

The Interpretation of Crustal Dynamics Data in Terms of Plate Interactions and Active Tectonics of the Anatolian Plate and Surrounding Regions in the Middle East

During the past 6 months, efforts were concentrated on the following areas: (1) Continued development of realistic, finite element modeling of plate interactions and associated deformation in the Eastern Mediterranean; (2) Neotectonic field investigations of seismic faulting along the active fault systems in Turkey with emphasis on identifying seismic gaps along the North Anatolian fault; and (3) Establishment of a GPS regional monitoring network in the zone of ongoing continental collision in eastern Turkey (supported in part by NSF).

Toksoz, M. Nafi↗

NASA NTRS2005

The Dynamics of Thermospheric Composition and Ionospheric Total Electron Content During Geomagnetic Storms

The geomagnetic storms of April 17-21,2002 and May 29-30,2003 caused large decreases in the O/N2 column density ratio in the thermosphere. For these storms, O/N2 column density decreases of greater than 50% were observed to extend to mid-to-low latitudes with the FUV sensitive Earth Camera of the Visible Imaging System (VIS) on the Polar spacecraft. Simultaneously in these same regions, the ground-based GPS network observed approximately 80% reductions in the Total Electron Content (TEC) of the ionosphere. The reduction in the O/N2 column density ratio is due mainly to increases in the molecular species that have welled-up into the thermosphere from the lower levels of the atmosphere due to auroral heating. The geomagnetic-storm driven increase in molecular densities at typical ionospheric heights rapidly charge exchange with the ambient ionized atoms and subsequently dissociatively recombine with the ionospheric electrons leading to a reduction in the total charge density. The transition boundaries between high and low regions of O/N2 as well as TEC can be tracked in the images and the thermospheric winds may be inferred from the motion of the boundaries. The motion of these boundaries during the development of the geomagnetic storm will be discussed.

Sigwarth, John B.↗

NASA NTRS1995

Distributed and collaborative synthetic environments

Fast graphics workstations and increased computing power, together with improved interface technologies, have created new and diverse possibilities for developing and interacting with synthetic environments. A synthetic environment system is generally characterized by input/output devices that constitute the interface between the human senses and the synthetic environment generated by the computer; and a computation system running a real-time simulation of the environment. A basic need of a synthetic environment system is that of giving the user a plausible reproduction of the visual aspect of the objects with which he is interacting. The goal of our Shastra research project is to provide a substrate of geometric data structures and algorithms which allow the distributed construction and modification of the environment, efficient querying of objects attributes, collaborative interaction with the environment, fast computation of collision detection and visibility information for efficient dynamic simulation and real-time scene display. In particular, we address the following issues: (1) A geometric framework for modeling and visualizing synthetic environments and interacting with them. We highlight the functions required for the geometric engine of a synthetic environment system. (2) A distribution and collaboration substrate that supports construction, modification, and interaction with synthetic environments on networked desktop machines.

Bajaj, Chandrajit L.↗

NASA NTRS1997

Carbon Nanotubes: Molecular Electronic Components

The carbon Nanotube junctions have recently emerged as excellent candidates for use as the building blocks in the formation of nanoscale molecular electronic networks. While the simple joint of two dissimilar tubes can be generated by the introduction of a pair of heptagon-pentagon defects in an otherwise perfect hexagonal graphene sheet, more complex joints require other mechanisms. In this work we explore structural characteristics of complex 3-point junctions of carbon nanotubes using a generalized tight-binding molecular-dynamics scheme. The study of pi-electron local densities of states (LDOS) of these junctions reveal many interesting features, most prominent among them being the defect-induced states in the gap.

Srivastava, Deepak↗

NASA NTRS2008

Tools, Techniques, and Applications: Normalizing the VR Paradigm

Oshynee's precision Learning Objective performance factor rubrics with associated behavioral anchors integrates with Thinking Worlds(TradeMark), to provide event data recording and dynamic prescriptive feedback. Thinking Worlds(TradeMark) provides SCORM parametric data for reporting within the game and within overarching curricula or workplace evaluation strategy. - Open-sourced, browser-based digital dashboard reporting tools collect data from TW, LMS, LCMS, HR, and workplace metrics or control systems The games may be delivered across the internet or in a range of networked and stand-alone methods using the delivery model (s) required by the host organization.

Duncan, Gaeme↗

NASA NTRS2010

Current Trends in Satellite Laser Ranging

Satellite Laser Ranging (SLR) techniques are used to accurately measure the distance from ground stations to retroreflectors on satellites and the moon. SLR is one of the fundamental techniques that define the international Terrestrial Reference Frame (iTRF), which is the basis upon which we measure many aspects of global change over space, time, and evolving technology. It is one of the fundamental techniques that define at a level of precision of a few mm the origin and scale of the ITRF. Laser Ranging provides precision orbit determination and instrument calibration/validation for satellite-borne altimeters for the better understanding of sea level change, ocean dynamics, ice budget, and terrestrial topography. Laser ranging is also a tool to study the dynamics of the Moon and fundamental constants. Many of the GNSS satellites now carry retro-reflectors for improved orbit determination, harmonization of reference frames, and in-orbit co-location and system performance validation. The GNSS Constellations will be the means of making the reference frame available to worldwide users. Data and products from these measurements support key aspects of the GEOSS 10-Year implementation Plan adopted on February 16, 2005, The ITRF has been identified as a key contribution of the JAG to GEOSS and the ILRS makes a major contribution for its development since its foundation. The ILRS delivers weekly additional realizations that are accumulated sequentially to extend the ITRF and the Earth Orientation Parameter (EOP) series with a daily resolution. Additional products are currently under development such as precise orbits of satellites, EOP with daily availability, low-degree gravitational harmonics for studies of Earth dynamics and kinematics, etc. SLR technology continues to evolve toward the next generation laser ranging systems as programmatic requirements become more stringent. Ranging accuracy is improving as higher repetition rate, narrower pulse lasers and faster detectors are implemented. Automation and pass interleaving at some stations is already expanding temporal coverage. Web-based safety keys are allowing the SLR network stations to range to optically vulnerable satellites. Some stations are experimenting with two-wavelength operation as a means of better understanding the atmospheric refraction and with very low power laser to improve eye-safety conditions. New retroreflector designs are improving the signal link and enable daylight ranging. Dramatic improvements have also been made with lunar ranging with the new APOLLO Site in New ?Mexico, USA and the upgraded lunar station "MEO" in Grasse,

Pearlman, M. R.↗

NASA NTRSDate not supplied

Distributed Spacecraft Autonomy - Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC) measurements. DSA will be demonstrated onboard the NASA Ames Starling mission – a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets. The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasmasphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input dataset for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software, aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements, which define the range of observability for the satellites, as well as exploitative measurements, which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth’s magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions. As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown. Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middleware provides reliable delivery, routing, and topic subscription features over User Datagram Protocol (UDP). Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarm scalability from 2 satellites to 100 satellites is shown.

Distributed Autonomy↗

NASA NTRS2021

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

Dense time-series remote sensing data with detailed spatial information are highly desired for the monitoring of dynamic earth systems. Due to the sensor tradeoff, most remote sensing systems cannot provide images with both high spatial and temporal resolutions. Spatiotemporal image fusion models provide a feasible solution to generate such a type of satellite imagery, yet existing fusion methods are limited in predicting rapid and/or transient phenological changes. Additionally, a systematic approach to assessing and understanding how varying levels of temporal phenological changes affect fusion results is lacking in spatiotemporal fusion research. The objective of this study is to develop an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images. To systematically assess the effects of varying levels of phenological changes, we identify image phenological transition dates and design three temporal phenological change scenarios representing rapid, moderate, and minimal phenological changes. The hybrid deep learning model, alongside three benchmark fusion models, is assessed in different scenarios of phenological changes. Results indicate the hybrid deep learning model yields significantly better results when rapid or moderate phenological changes are present. It holds great potential in generating high-quality time-series datasets of both high spatial and temporal resolutions, which can further benefit terrestrial system dynamic studies. The innovative approach to understanding phenological changes’ effect will help us better comprehend the strengths and weaknesses of current and future fusion models.

spatiotemporal fusion↗

NASA NTRS1989

Interset: A natural language interface for teleoperated robotic assembly of the EASE space structure

A teleoperated robot was used to assemble the Experimental Assembly of Structures in Extra-vehicular activity (EASE) space structure under neutral buoyancy conditions, simulating a telerobot performing structural assembly in the zero gravity of space. This previous work used a manually controlled teleoperator as a test bed for system performance evaluations. From these results several Artificial Intelligence options were proposed. One of these was further developed into a real time assembly planner. The interface for this system is effective in assembling EASE structures using windowed graphics and a set of networked menus. As the problem space becomes more complex and hence the set of control options increases, a natural language interface may prove to be beneficial to supplement the menu based control strategy. This strategy can be beneficial in situations such as: describing the local environment, maintaining a data base of task event histories, modifying a plan or a heuristic dynamically, summarizing a task in English, or operating in a novel situation.

Boorsma, Daniel K.↗

NASA NTRS1993

Cognitive engineering models in space systems

NASA space systems, including mission operations on the ground and in space, are complex, dynamic, predominantly automated systems in which the human operator is a supervisory controller. Models of cognitive functions in complex systems are needed to describe human performance and form the theoretical basis of operator workstation design, including displays, controls, and decision aids. Currently, there several candidate modeling methodologies. They include the Rasmussen abstraction/aggregation hierarchy and decision ladder, the goal-means network, the problem behavior graph, and the operator function model. The research conducted under the sponsorship of this grant focuses on the extension of the theoretical structure of the operator function model and its application to NASA Johnson mission operations and space station applications. The initial portion of this research consists of two parts. The first is a series of technical exchanges between NASA Johnson and Georgia Tech researchers. The purpose is to identify candidate applications for the current operator function model; prospects include mission operations and the Data Management System Testbed. The second portion will address extensions of the operator function model to tailor it to the specific needs of Johnson applications. At this point, we have accomplished two things. During a series of conversations with JSC researchers, we have defined the technical goal of the research supported by this grant to be the structural definition of the operator function model and its computer implementation, OFMspert. Both the OFM and OFMspert have matured to the point that they require infrastructure to facilitate use by researchers not involved in the evolution of the tools. The second accomplishment this year was the identification of the Payload Deployment and Retrieval System (PDRS) as a candidate system for the case study. In conjunction with government and contractor personnel in the Human-Computer Interaction Lab, the PDRS was identified as the most accessible system for the demonstration. Pursuant to this a PDRS simulation was obtained from the HCIL and an initial knowledge engineering effort was conducted to understand the operator's tasks in the PDRS application. The preliminary results of the knowledge engineering effort and an initial formulation of an operator function model (OFM) are contained in the appendices.

Mitchell, Christine M.↗

NASA NTRS1998

Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems

The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.

Esogbue, Augustine O.↗