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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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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Protecting Neural Structures and Cognitive Function During Prolonged Space Flight by Targeting the Brain Derived Neurotrophic Factor Molecular Network

Brain derived neurotrophic factor (BDNF) is the main activity-dependent neurotrophin in the human nervous system. BDNF is implicated in production of new neurons from dentate gyrus stem cells (hippocampal neurogenesis), synapse formation, sprouting of new axons, growth of new axons, sprouting of new dendrites, and neuron survival. Alterations in the amount or activity of BDNF can produce significant detrimental changes to cortical function and synaptic transmission in the human brain. This can result in glial and neuronal dysfunction, which may contribute to a range of clinical conditions, spanning a number of learning, behavioral, and neurological disorders. There is an extensive body of work surrounding the BDNF molecular network, including BDNF gene polymorphisms, methylated BDNF gene promoters, multiple gene transcripts, varied BDNF functional proteins, and different BDNF receptors (whose activation differentially drive the neuron to neurogenesis or apoptosis). BDNF is also closely linked to mitochondrial biogenesis through PGC-1alpha, which can influence brain and muscle metabolic efficiency. BDNF AS A HUMAN SPACE FLIGHT COUNTERMEASURE TARGET Earth-based studies reveal that BDNF is negatively impacted by many of the conditions encountered in the space environment, including oxidative stress, radiation, psychological stressors, sleep deprivation, and many others. A growing body of work suggests that the BDNF network is responsive to a range of diet, nutrition, exercise, drug, and other types of influences. This section explores the BDNF network in the context of 1) protecting the brain and nervous system in the space environment, 2) optimizing neurobehavioral performance in space, and 3) reducing the residual effects of space flight on the nervous system on return to Earth

Schmidt, M. A.↗
NASA NTRS2016

The Cognitive Challenges of Flying a Remotely Piloted Aircraft

A large variety of Remotely Piloted Aircraft (RPA) designs are currently in production or in development. These aircraft range from small electric quadcopters that are flown close to the ground within visual range of the operator, to larger systems capable of extended flight in airspace shared with conventional aircraft. Before RPA can operate routinely and safely in civilian airspace, we need to understand the unique human factors associated with these aircraft. The task of flying an RPA in civilian airspace involves challenges common to the operation of other highly-automated systems, but also introduces new considerations for pilot perception, decision-making, and action execution. RPA pilots participated in focus groups where they were asked to recall critical incidents that either presented a threat to safety, or highlighted a case where the pilot contributed to system resilience or mission success. Ninety incidents were gathered from focus-groups. Human factor issues included the impact of reduced sensory cues, traffic separation in the absence of an out-the-window view, control latencies, vigilance during monotonous and ultra-long endurance flights, control station design considerations, transfer of control between control stations, the management of lost link procedures, and decision-making during emergencies. Some of these concerns have received significant attention in the literature, or are analogous to human factors of manned aircraft. The presentation will focus on issues that are poorly understood, and have not yet been the subject of extensive human factors study. Although many of the reported incidents were related to pilot error, the participants also provided examples of the positive contribution that humans make to the operation of highly-automated systems.

remote pilot station↗
NASA NTRS2019

Reinforcement Learning Applied to Cognitive Space Communications

The future of space exploration depends on robust, reliable communication systems. As the number of such communication systems increase, automation is fast becoming a requirement to achieve this goal. A reinforcement learning solution can be employed as a possible automation method for such systems. The goal of this study is to build a reinforcement learning algorithm which optimizes data throughput of a single actor. A training environment was created to simulate a link within the NASA Space Communication and Navigation (SCaN) infrastructure, using state of the art simulation tools developed by the SCaN Center for Engineering, Networks, Integration, and Communications (SCENIC) laboratory at NASA Glenn Research Center to obtain the closest possible representation of the real operating environment. Reinforcement learning was then used to train an agent inside this environment to maximize data throughput. The simulation environment contained a single actor in low earth orbit capable of communicating with twenty-five ground stations that compose the Near-Earth Network (NEN). Initial experiments showed promising training results, so additional complexity was added by augmenting simulation data with link fading profiles obtained from real communication events with the International Space Station. A grid search was performed to find the optimal hyperparameters and model architecture for the agent. Using the results of the grid search, an agent was trained on the augmented training data. Testing shows that the agent performs well inside the training environment and can be used as a foundation for future studies with added complexity and eventually tested in the real space environment.

Schubert, Carson D.↗
NASA NTRS2019

State Predictor of Classification Cognitive Engine Applied to Channel Fading

This study presents the application of machine learning (ML) to a space-to-ground communication link, showing how ML can be used to detect the presence of detrimental channel fading. Using this channel state information, the communication link can be used more efficiently by reducing the amount of lost data during fading. The motivation for this work is based on channel fading observed during on-orbit operations with NASA's Space Communication and Navigation (SCaN) testbed on the International Space Station (ISS). This paper presents the process to extract a target concept (fading and not-fading) from the raw data. The pre-processing and data exploration effort is explained in detail, with a list of assumptions made for parsing and labelling the dataset. The model selection process is explained, specifically emphasizing the benefits of using an ensemble of algorithms with majority voting for binary classification of the channel state. Experimental results are shown, highlighting how an end-to-end communication system can utilize knowledge of the channel fading status to identity fading and take appropriate action. With a laboratory testbed to emulate channel fading, the overall performance is compared to standard adaptive methods without fading knowledge, such as adaptive coding and modulation.

Fading↗
NASA NTRS2019

SCREAM: Space-based Cognitive Reliability Error Analysis Method

Given both ground-based performance shaping factors (PSFs) collected by the U.S. Nuclear Regulatory Commission (USNRC) and potential space-based PSFs collected by JSC's HH&P, this year's task is to determine how to combine them for future human reliability analysis (HRA) of space missions. All HRA to date for space missions have been based on ground data (i.e. 1-G environment). There is no space-based HRA approach. The target of this project to produce a "first of its kind" HRA approach for space missions. As humans go beyond Earth orbit and missions become longer, human performance and reliability is expected to degrade. The questions become by how much, when, and what, leading to defining the overall effect on crew risk. Knowing this information earlier rather than later will help us mitigate this risk by vehicle design, crew training, and operational workarounds.

Boyer, Roger L.↗
NASA NTRS2021

Topical: Reverse Translation Strategies to Support Cognitive and Behavioral Risk Characterization

A coordinated suite of measurements to assess humans and animals needs to be established to support the translation and harmonization of animal research data to the astronaut corps due to the necessity of using animal subjects for radiation testing. The identification of POLs and PELs for spaceflight stressors (i.e. space radiation, altered gravity, isolation and confinement, sleep disruption) individually and combined will depend on defining scaling factors or transfer functions that can be used to relate human and animal outcomes.

Gregory Nelson↗