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NASA NTRS2015

Assessing and Promoting Functional Resilience in Flight Crews During Exploration Missions

NASA plans to send humans to Mars in about 20 years. The NASA Human Research Program supports research to mitigate the major risks to human health and performance on extended missions. However, there will undoubtedly be unforeseen events on any mission of this nature - thus mitigation of known risks alone is not sufficient to ensure optimal crew health and performance. Research should be directed not only to mitigating known risks, but also to providing crews with the tools to assess and enhance resilience, as a group and individually. We can draw on ideas from complexity theory and network theory to assess crew and individual resilience. The entire crew or the individual crewmember can be viewed as a complex system that is composed of subsystems (individual crewmembers or physiological subsystems), and the interactions between subsystems are of crucial importance for overall health and performance. An understanding of the structure of the interactions can provide important information even in the absence of complete information on the component subsystems. This is critical in human spaceflight, since insufficient flight opportunities exist to elucidate the details of each subsystem. Enabled by recent advances in noninvasive measurement of physiological and behavioral parameters, subsystem monitoring can be implemented within a mission and also during preflight training to establish baseline values and ranges. Coupled with appropriate mathematical modeling, this can provide real-time assessment of health and function, and detect early indications of imminent breakdown. Since the interconnected web of physiological systems (and crewmembers) can be interpreted as a network in mathematical terms, we can draw on recent work that relates the structure of such networks to their resilience (ability to self-organize in the face of perturbation). There are many parameters and interactions to choose from. Normal variability is an established characteristic of a healthy physiological response. Healthy coupling has been investigated less extensively, but there are cases in which too tight or too loose coupling can be problematic. This might be in inter-individual behaviors, such as sleep cycles, coordination of work and meal times, and coupled motions during communication. Less apparent are couplings of physiological systems, nevertheless examples abound of coupled systems which might be monitored: cardio-respiratory rhythms; circadian rhythms, body temperature, and sleep; stress markers and cognition, sleep, and performance; profiles of biochemical markers related to immune function and nutritional status; sensorimotor aspects such as motion sickness, ataxia, reaction time, and manual control. Tools for resilience are then the means to measure and analyze these parameters, incorporate them into appropriate models of normal variability and interconnectedness, and recognize when parameters or their couplings are outside of normal limits. What to do when a problem is identified depends on its nature. Changes can be made to crew procedures, work pacing, interpersonal interactions, sleep cycles, meal timing and content, as guided by the model. Use and continued development of these methods could not only provide tools for resilience, but also meaningful autonomous work for the crew on an extended flight.

Shelhamer, Mark↗

NASA NTRS2000

Microtubule depolymerization normalizes in vivo myocardial contractile function in dogs with pressure-overload left ventricular hypertrophy

BACKGROUND: Because initially compensatory myocardial hypertrophy in response to pressure overloading may eventually decompensate to myocardial failure, mechanisms responsible for this transition have long been sought. One such mechanism established in vitro is densification of the cellular microtubule network, which imposes a viscous load that inhibits cardiocyte contraction. METHODS AND RESULTS: In the present study, we extended this in vitro finding to the in vivo level and tested the hypothesis that this cytoskeletal abnormality is important in the in vivo contractile dysfunction that occurs in experimental aortic stenosis in the adult dog. In 8 dogs in which gradual stenosis of the ascending aorta had caused severe left ventricular (LV) pressure overloading (gradient, 152+/-16 mm Hg) with contractile dysfunction, LV function was measured at baseline and 1 hour after the intravenous administration of colchicine. Cardiocytes obtained by biopsy before and after in vivo colchicine administration were examined in tandem. Microtubule depolymerization restored LV contractile function both in vivo and in vitro. CONCLUSIONS: These and additional corroborative data show that increased cardiocyte microtubule network density is an important mechanism for the ventricular contractile dysfunction that develops in large mammals with adult-onset pressure-overload-induced cardiac hypertrophy.

Non-NASA Center↗

NASA NTRS1998

Design Process for High Speed Civil Transport Aircraft Improved by Neural Network and Regression Methods

A key challenge in designing the new High Speed Civil Transport (HSCT) aircraft is determining a good match between the airframe and engine. Multidisciplinary design optimization can be used to solve the problem by adjusting parameters of both the engine and the airframe. Earlier, an example problem was presented of an HSCT aircraft with four mixed-flow turbofan engines and a baseline mission to carry 305 passengers 5000 nautical miles at a cruise speed of Mach 2.4. The problem was solved by coupling NASA Lewis Research Center's design optimization testbed (COMETBOARDS) with NASA Langley Research Center's Flight Optimization System (FLOPS). The computing time expended in solving the problem was substantial, and the instability of the FLOPS analyzer at certain design points caused difficulties. In an attempt to alleviate both of these limitations, we explored the use of two approximation concepts in the design optimization process. The two concepts, which are based on neural network and linear regression approximation, provide the reanalysis capability and design sensitivity analysis information required for the optimization process. The HSCT aircraft optimization problem was solved by using three alternate approaches; that is, the original FLOPS analyzer and two approximate (derived) analyzers. The approximate analyzers were calibrated and used in three different ranges of the design variables; narrow (interpolated), standard, and wide (extrapolated).

Hopkins, Dale A.↗

NASA NTRS2013

The Deep Space Network in the Common Platform Era: A Prototype Implementation at DSS-13

To enhance NASA's Deep Space Network (DSN), an effort is underway to improve network performance and simplify its operation and maintenance. This endeavor, known as the "Common Platform," has both short- and long-term objectives. The long-term work has not begun yet; however, the activity to realize the short-term goals has started. There are three goals for the long-term objective: 1. Convert the DSN into a digital network where signals are digitized at the output of the down converters at the antennas and are distributed via a digital IF switch to the processing platforms. 2. Employ a set of common hardware for signal processing applications, e.g., telemetry, tracking, radio science and Very Long Baseline Interferometry (VLBI). 3. Minimize in-house developments in favor of purchasing commercial off-the-shelf (COTS) equipment. The short-term goal is to develop a prototype of the above at NASA's experimental station known as DSS-13. This station consists of a 34m beam waveguide antenna with cryogenically cooled amplifiers capable of handling deep space research frequencies at S-, X-, and Ka-bands. Without the effort at DSS-13, the implementation of the long-term goal can potentially be risky because embarking on the modification of an operational network without prior preparations can, among other things, result in unwanted service interruptions. Not only are there technical challenges to address, full network implementation of the Common Platform concept includes significant cost uncertainties. Therefore, a limited implementation at DSS-13 will contribute to risk reduction. The benefits of employing common platforms for the DSN are lower cost and improved operations resulting from ease of maintenance and reduced number of spare parts. Increased flexibility for the user is another potential benefit. This paper will present the plans for DSS-13 implementation. It will discuss key issues such as the Common Platform architecture, choice of COTS equipment, and the standard for radio frequency (RF) to digital interface.

Space Communications and Navigation (SCaN)↗

NASA NTRS1983

Application of VLBI and satellite laser ranging to geodynamics

The NASA Crustal Dynamics Project has developed very-long baseline interferometer (VLBI) systems and satellite laser ranging (SLR) systems for geodynamics measurements. In VLBI, a radio noise signal from a distant quasar is received by two or more radio antennas and coherently recorded. These recordings are cross-correlated to determine the relative signal delays between stations which are used to derive the vector baselines between the stations. The SLR systems accurately determine the range to a retroreflector satellite as a function of time with short laser pulses. These range measurements from several stations to the same satellite are used in orbit analysis programs to determine the position of the stations and the vector baselines between the stations. Measurements with these systems have achieved precisions of a few centimeters in length for distances of several thousand km. These systems are now operating in a global network for measuring the relative motion of the N. American, Pacific, S. American, Nazca, Eurasian and Australian tectonic plates. Highly mobile VLBI and SLR systems are being operated at many sites in the active earthquake areas in western N. America in order to determine the crustal deformation and strain accumulation.

Coates, R. J.↗

NASA NTRS1985

Water vapor radiometry research and development phase

This report describes the research and development phase for eight dual-channel water vapor radiometers constructed for the Crustal Dynamics Project at the Goddard Space Flight Center, Greenbelt, Maryland, and for the NASA Deep Space Network. These instruments were developed to demonstrate that the variable path delay imposed on microwave radio transmissions by atmospheric water vapor can be calibrated, particularly as this phenomenon affects very long baseline interferometry measurement systems. Water vapor radiometry technology can also be used in systems that involve moist air meteorology and propagation studies.

Resch, G. M.↗

NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight frameworks is paramount for establishing an effective architecture for autonomous systems. Hardware test flights are time-consuming and cost prohibitive during early system design and development. Simulation environments can be useful tools to accelerate algorithm development and testing. However, transitions from simulation to flight (sim-to-flight) can be challenging, unless systems are designed with this transition in mind and with the necessary capabilities built into the architecture and framework. One of the objectives of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. ATTRACTOR’s objective was to construct computational concepts of trustworthiness and justifiable trust in multi-agent autonomous teams, to inform future certification of safety-critical and time-critical autonomous systems in aviation. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation (ModSim) environment for test and evaluation of autonomous systems. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single-and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. The Autonomous Entity Operational Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications, enabling sim-to-flight with minimal configuration changes. Using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗

NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗

NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

Benjamin N Kelley↗

NASA NTRS2021

Scheduling the NASA Deep Space Network with Deep Reinforcement Learning

With three complexes spread evenly across the Earth, NASA’s Deep Space Network (DSN) is the primary means of communications as well as a significant scientific instrument for dozens of active missions around the world. A rapidly rising number of spacecraft and increasingly complex scientific instruments with higher bandwidth requirements have resulted in demand that exceeds the network’s capacity across its 12 antennae. The existing DSN scheduling process operates on a rolling weekly basis and is time-consuming; for a given week, generation of the final baseline schedule of spacecraft tracking passes takes roughly 5 months from the initial requirements submission deadline, with several weeks of peer-to-peer negotiations in between. This paper proposes a deep reinforcement learning (RL) approach to generate candidate DSN schedules from mission requests and spacecraft ephemeris data with demonstrated capability to address real-world operational constraints. A deep RL agent is developed that takes mission requests for a given week as input, and interacts with a DSN scheduling environment to allocate tracks such that its reward signal is maximized. A comparison is made between an agent trained using Proximal Policy Optimization and its random, untrained counterpart. The results represent a proof-of-concept that, given a well-shaped reward signal, a deep RL agent can learn the complex heuristics used by experts to schedule the DSN. A trained agent can potentially be used to generate candidate schedules to bootstrap the scheduling process and thus reduce the turnaround cycle for DSN scheduling.

Wilson, Brian↗

NASA NTRS1979

Solar power satellite system definition study. Volume 1: Executive summary

Configuration concepts, option sizes, and systems definitions study design evolutions are reviewed. The main features of the present reference design silicon solar cell solar power satellite are described, as well as the provisions for space construction and support systems. The principal study accomplishments and conclusions are summarized according to the following tasks: (1) baseline critique; (2) construction and maintenance; (3) industrial complex needs, cost estimates, and production capacity; (4) launch complex requirements at KSC or at an offshore facility; (5) integration of the SPS/ground power network; (6) technology advancement and development; (7) costs and schedules; and (8) exploratory technology: laser annealing of solar cells degraded by proton irradiation, and a fiber-optic phase distribution link at 980 MHz.

Source record↗

NASA NTRSDate not supplied

Sub-City-Scale Air Quality Forecasts Combining Models, Satellites, and Surface Measures

Poor air quality is a global major concern, especially in cities with their higher emissions and large numbers of exposed people. Air quality monitoring has traditionally relied on ground-based measurements from a few accurate but expensive regulatory-grade monitors, leading to limited spatial data coverage. More recently, these have been supplemented with other data sources, including satellite observations of pollutants, atmospheric chemistry model simulations, and low-cost monitors allowing for denser spatial data collection at the expense of lower accuracy compared to regulatory-grade monitors. Each of these air quality data sources have their own benefits and drawbacks, and so there is an opportunity to combine these data together while respecting their relative strengths and weaknesses in order to generate a more comprehensive and detailed picture of local air quality. I will present our current work towards such a combination, with a focus on producing higher spatial resolution estimates and near-term forecasts of Nitrogen Dioxide in urban areas in the United States. Our method combines data from the GEOS Composition Forecasting (GEOS-CF) model system, TROPOMI tropospheric NO2 satellite data products, and ground measurements from the EPA regulatory monitoring network using a combination of simple intuitive relationships and machine learning techniques. I will show the performance of this proposed method in several urban areas in the United States, comparing it with baseline approaches using single data sources separately. Overall, we find that combining these disparate datasets together leads to more accurate air quality forecasts in the target areas than is possible using each data source separately.

Air quality↗

NASA NTRS2020

Experimental Validation of Swarm Array Results Using Mars Cube One (MarCO) A,B Downlink Signals

In this paper, we investigate the feasibility of calibrating an orbiting phaased array composed of CubeSat class spacecraft together with a larger reference (or “chief”) spacecraft, comprising an overall Swarm Array. Considered as an analog to the Deep Space Network (DSN) Uplink Arraying problem [1], a spaceborne Swarm Array that can potentially deliver comparable or even greater operational performance than large monolithic spacecraft (such as increased datarates for telecommunications, or greater baselines for improved spatial resolution), thus increasing future mission capability but with significantly enhanced flexibility, evolvability and robustness.

Vilnrotter, Victor↗

NASA NTRSDate not supplied

Analysis of Improved Navigation Data for NASA Near Space Network (NSN) Direct-to-Earth (DTE) Ground Stations

Spacecraft navigation is a complex concept that requires a collaboration between network entities and constant corrective action to be successful in the highly variable environment. NASA’s Near Space Network (NSN), Direct-to-Earth (DTE) ground station team is constantly exploring methods to provide better service to NASA spacecraft, including providing radiometric tracking data, subject to cost constraints. Navigation teams, such as the Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF), use the tracking data to perform orbit determination to assess where a spacecraft has been, identify where a spacecraft is now, and predict where a spacecraft will be in the future. As NASA plans for more missions beyond low Earth orbit (LEO), there is a need for improved navigation performance which can be directly achieved through improvements in the radiometric tracking data. NASA’s new DTE Ka-band antennas have finer autotrack angle resolution, and the availability of low-cost improved off-the-shelf ground station components, which are involved with producing navigation data. NASA formed a team to provide an assessment of the current NSN, DTE spacecraft navigation service (radiometric tracking) performance baseline with the end goal of defining needed future enhancements to meet the needs for spacecraft above LEO. The communications and navigation community is deriving new techniques in an effort to better service the navigation needs of spacecraft using ground station networks. This paper provides an overview Earth-orbit, direct-to-ground navigation techniques and performance, the development of specifications for new antennas, accuracy of data, spacecraft requirements, accuracy of orbit determination, history of challenges, and considerations for improvements. The discussions in this paper may assist ground station, spacecraft, and mission design teams advance navigation capabilities and more effectively meet requirements.

networks↗

NASA NTRS1999

Design of a Channel Error Simulator using Virtual Instrument Techniques for the Initial Testing of TCP/IP and SCPS Protocols

There exists a need for designers and developers to have a method to conveniently test a variety of communications parameters for an overall system design. This is no different when testing network protocols as when testing modulation formats. In this report, we discuss a means of providing a networking test device specifically designed to be used for space communications. This test device is a PC-based Virtual Instrument (VI) programmed using the LabVIEW(TM) version 5 software suite developed by National Instruments(TM)TM. This instrument was designed to be portable and usable by others without special, additional equipment. The programming was designed to replicate a VME-based hardware module developed earlier at New Mexico State University (NMSU) and to provide expanded capabilities exceeding the baseline configuration existing in that module. This report describes the design goals for the VI module in the next section and follows that with a description of the design of the VI instrument. This is followed with a description of the validation tests run on the VI. An application of the error-generating VI to networking protocols is then given.

Horan, Stephen↗

NASA NTRS1987

TAVERNS and the space station software support environment

The Space Station Information System (SSIS) provides the data processing capability for the Space Station Program (SSP). The Software Support Environment (SSE) System for the SSP is the collection of software, procedures, standards, hardware specification, documentation, policy, and training materials. The Ada programming language was baselined by the Space Station Program Office as the language for development and maintenance of all space station software including the software of the SSE itself. The Test And Validation Environment for Remote Networked Systems (TAVERNS) is a distributed philosophy for development and validation of Ada applications software for the space station and as such is closely related to the SSE. An overview of the system is provided.

Howes, Norman R.↗

NASA NTRS2018

MoonBEAM: A Beyond Earth-Orbit Gamma-Ray Burst Detector for Multi-Messenger Astronomy

Moon Burst Energetics All-sky Monitor (MoonBEAM) is a CubeSat concept of deploying gamma-ray detectors in cislunar space to increase gamma-ray burst detections and improve localization precision with the timing triangulation technique. A gamma-ray instrument in cislunar orbit will have greatly reduced sky blockage compared to instruments in low Earth orbit. Working in conjunction with another instrument in low Earth orbit, MoonBEAM can also help constrain the arrival direction of the wavefront to an annulus on the sky by utilizing the light arrival times between the different orbits. This method has been demonstrated by the Interplanetary Gamma- Ray Burst Timing Network. However, delays in data downlink for instruments outside the Tracking and Data Relay Satellite network prevent rapid follow-up observations. We present here a gamma-ray CubeSat concept in Earth-Moon L3 halo orbit that is capable of faster response and provide a timing baseline for localization improvement. Such an instrument would aid in the gravitational wave follow-up observations in other wavelengths to identify the gamma-ray burst afterglow and kilonova emission. Reducing the region of interest makes identifying afterglows much faster, allowing for rapid on-source observations and monitoring of the rise and decay times. It will also prevent source confusion between two transients and enable robust association. A gamma-ray detection could also increase the confidence of a simultaneous but marginal gravitational wave signal, extending the detection horizon. MoonBEAM is a 12U CubeSat concept of deploying gamma-ray detectors in cislunar space to increase gamma-ray burst detections and improve localization precision with the timing triangulation technique. Such an instrument would probe the extreme processes in cosmic collision of compact objects and facilitate multi-messenger time-domain astronomy to explore the end of stellar life cycles and black hole formations.

gamma-ray Cubesat↗

NASA NTRS2020

Experimental Validation of Swarm Array Results Using Mars Cube One (Marco) A,B Downlink Signals

In this paper, we investigate the feasibility of calibrating an orbiting phased array composed of CubeSat class spacecraft together with a larger reference (or “chief”) spacecraft, comprising an overall Swarm Array. Considered as an analog to the Deep Space Network (DSN) Uplink Arraying problem [1], a spaceborne Swarm Array that can potentially deliver comparable or even greater operational performance than large monolithic spacecraft (such as increased data rates for telecommunications, or greater baselines for improved spatial resolution), thus increasing future mission capability but with significantly enhanced flexibility, evolvability and robustness.

Kahan, Daniel↗