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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Exploration Atmosphere Demonstration of A Multi-Functional Integrated Medical Device (Tempus Pro)

INTRODUCTION The Exploration Medical Capability (ExMC) element within the Human Research Program and the Exploration Medical Integrated Product Team (XMIPT) within the Mars Campaign Office seek to advance medical system design and risk-informed decision-making for exploration missions. This includes assessment of candidate devices and their compatibility within a variety of increasingly Earth-independent medical scenarios. The Tempus Pro (Remote Diagnostic Technologies, Ltd., Philips Corp., Farnborough, UK) is a commercial-off-the-shelf (COTS), multi-functional integrated medical device capable of vital signs monitoring, with built-in procedure support (iAssist), patient record and telemedicine communication features, and medical imaging (e.g., camera, laryngoscope, ultrasound) that meets multiple exploration medical capability needs. One of several hypobaric atmospheres being considered for exploration vehicles is 8.2 psi with 34% oxygen. To assess the useability of the Tempus Pro in such environments by individuals without formal medical training, the unit was tested in May and June of 2023 at the Johnson Space Center 20-Foot Exploration Atmosphere Chamber in conjunction with the Exploration Atmospheres-2 (EA-2) study. TECHNOLOGY DEMONSTRATION Eight subjects in the EA-2 study were assigned roles as the caregiver or patient – or both in the case of a self-exam – and caregivers were asked to place sensors for 3-Lead ECG, non-invasive blood pressure (NIBP), pulse oximetry (SpO2), and temperature using Tempus Pro’s iAssist. Depending on the procedure, additional tasks included an oropharyngeal exam of the throat with the laryngoscope and capturing ultrasound images of a cardiac subxiphoid view or of the user’s choice from a pre-specified list. Scenarios ranged from remote-guided to fully autonomous operations and surveyed the effectiveness of support, amount of support desired, and importance of support by type (e.g., written crew procedures, iAssist and device guidance, remote console guidance, etc.). Feedback from the subjects and the support personnel was also gathered to inform future designs for training and support and to determine what operations are plausible given different levels of each. An unweighted NASA task load index (TLX) was used to profile the demands of using the device, however the sample size does not support statistics. The research was exploratory in nature and qualitative information was the main goal. Calibration checks on the Tempus Pro were conducted before and after chamber testing to ensure the device was in useable condition. RESULTS All subjects performed their tasks successfully and found the Tempus Pro easy to use with the support provided. The calibration checks outside the chamber showed that the Tempus Pro remained unchanged and measurements inside the chamber were within normal values. Caregivers taking the NASA TLX reported mean scores ≤ 41/100 showing the tasks to be undemanding and less demanding with repeated use (~20/100). Novice users were able to easily connect sensors and take vital signs with the guidance from Tempus Pro’s iAssist feature. For more complex tasks, such as the oropharyngeal exam and ultrasound image acquisition, guidance beyond iAssist was needed, primarily from a supporting physician. Feedback on the importance of support types varied by person and scenario but greater than 50% of the support used by each was non-native to the device. Overall opinions were positive, but the ultrasound users expressed a lack of confidence in their results and 3 out of 4 desired more time, training, or support. Subjects found the sensors to be comfortable and comparable to prior experiences with such devices, except for the blood pressure cuff, which squeezed too tightly for uncomfortably long periods. Many observations provoked discussion, especially regarding ultrasound, that will aide decisions about future demonstrations.

R. S. Miller↗
NASA NTRS2021

Regional Air Mobility: Leveraging Our National Investments to Energize the American Travel Experience

America is home to over 5,000 airports available for public use, yet only 30 of these airports serve over 70% of all travelers.01 Despite this vast network of airports, the majority are underutilized due to air transportation services that have trended towards consolidation by putting more people into fewer, larger aircraft on well-traveled routes. Also, these bigger jet-propelled aircraft can only take off from and land on longer airstrips. Regional Air Mobility (RAM) will fundamentally change how we travel by bringing the convenience, speed, and safety of air travel to all Americans, regardless of their proximity to a travel hub or urban center. We advocate RAM technology investment as complementary to and an accelerator for Advanced Air Mobility (AAM) and other initiatives that aspire to transform the airspace. Through targeted advanced technology investments, such as aircraft automation, enhanced operational models, more efficient aircraft and propulsion systems, and expanded airport renewable energy generation, many of which are already underway, RAM will increase the safety, accessibility, and affordability of regional travel while building on the extensive and underutilized federal, state, and local investment in our nation’s local airports.

Regional Air Mobility↗
NASA NTRSDate not supplied

Uncovering Hazards Using a Multi-Objective Optimization to Explore the Faulty State-Space

Considering resilience when designing complex engineered systems is crucial to ensure the system is safe under unexpected hazardous scenarios. Traditional risk-based approaches, such as Failure Modes and Effects Analysis (FMEA) are useful for designing the system to mitigate hazardous scenarios that can be identified by the designer, but often require experience or prior knowledge of system failures to generate. More recently, researchers have developed simulation tools that enable the designer to model large sets of hazardous scenarios (driven by both internal faults and external factors) through simulation. While these tools enable a wider scope of fault modes to be evaluated (e.g., by injecting combined set of fault modes or injecting modes at different times), the resulting assessments (like FMEA) still require knowledge of the specific modes to be evaluated. However, failure to analyze a wide variety of fault scenarios can lead to an incomplete picture of the system resilience, especially to "surprise events'' which may be difficult for the designer to identify and predict beforehand. To overcome this challenge, previous work developed a fault sampling approach for resilience simulations which would procedurally-generate a wide variety of potential faults by systematically perturbing the health states of the system. While the resulting fault modes generated covered a much larger space hazards than would be otherwise considered (and identified many unique failure trajectories which would not have otherwise been identified), it also significantly increased the computational cost of the analysis and resulted in the simulation and analysis of a large set of essentially duplicate scenarios. Additionally, as the number of dimensions in the faulty state-space increases, the full elaboration of possible modes becomes computationally infeasible, justifying the use of a more targeted search. To resolve this limitation, this work proposes the use of a multiobjective optimization algorithm to search the health state space for potential fault modes that are both (1) hazardous and (2) unique. To solve this type of problem, this work proposes the use of a cooperative co-evolutionary algorithm. To demonstrate this approach, it will be applied to a model of an autonomous rover which uses line markings to navigate, focusing on potential hazards in the drive system which could cause the rover to crash. To determine the merit of the approach, it will further be compared with the previously-presented range elaboration approach and a random mode generation approach on the basis of computational efficiency and found modes.

Resilience↗
NASA NTRS2018

Lessons Learned During the Transition of SORCE Science Operations to Daylight Only Operations

In July 2013, NASA's Solar Radiation and Climate Experiment experienced a battery anomaly which placed it into safemode halting all science observations. Initial attempts to recover the spacecraft to an operational configuration failed due to the reduced capacity of the battery. As the keystone mission for measuring total solar irradiance, and the cornerstone mission for measuring the solar spectral irradiance there was a strong motivation for developing a new operations concept that would allow SORCE to resume daily measurements of the Sun. The operations team faced many challenges over the next several months. For a five-day period in late 2013 the operations team was able resume science observations to cross-calibrate SORCE data with a new instrument launched in November 2013. After the cross-calibration campaign was completed a new operations concept was deployed which allowed SORCE to perform daylight only operations. In this mode of operations all non-essential components are powered off at each eclipse entry and then turned back on at sunrise. In March 2014 SORCE resumed making daily measurements of the Sun. This paper will review the events and lessons learned from the six-month recovery effort.

Spacecraft Anomaly↗
NASA NTRS2012

Experimental Validation of Pulse Phase Tracking for X-Ray Pulsar Based

Pulsars are a form of variable celestial source that have shown to be usable as aids for autonomous, deep space navigation. Particularly those sources emitting in the X-ray band are ideal for navigation due to smaller detector sizes. In this paper X-ray photons arriving from a pulsar are modeled as a non-homogeneous Poisson process. The method of pulse phase tracking is then investigated as a technique to measure the radial distance traveled by a spacecraft over an observation interval. A maximum-likelihood phase estimator (MLE) is used for the case where the observed frequency signal is constant. For the varying signal frequency case, an algorithm is used in which the observation window is broken up into smaller blocks over which an MLE is used. The outputs of this phase estimation process were then looped through a digital phase-locked loop (DPLL) in order to reduce the errors and produce estimates of the doppler frequency. These phase tracking algorithms were tested both in a computer simulation environment and using the NASA Goddard Space flight Center X-ray Navigation Laboratory Testbed (GXLT). This provided an experimental validation with photons being emitted by a modulated X-ray source and detected by a silicon-drift detector. Models of the Crab pulsar and the pulsar B1821-24 were used in order to generate test scenarios. Three different simulated detector trajectories were used to be tracked by the phase tracking algorithm: a stationary case, one with constant velocity, and one with constant acceleration. All three were performed in one-dimension along the line of sight to the pulsar. The first two had a constant signal frequency and the third had a time varying frequency. All of the constant frequency cases were processed using the MLE, and it was shown that they tracked the initial phase within 0.15% for the simulations and 2.5% in the experiments, based on an average of ten runs. The MLE-DPLL cascade version of the phase tracking algorithm was used in the varying frequency case. This resulted in tracking of the phase and frequency by the DPLL outputs in both the simulation and experimental environments. The crab pulsar was experimentally tested with a trajectory with a higher acceleration. In this case the phase error tended toward zero as the observation extended to 250 seconds and the doppler frequency error tended to zero in under 100 seconds.

Experimental↗
NASA NTRSDate not supplied

BioSentinel: Mission Summary and Lessons Learned From the First Deep Space Biology CubeSat Mission

Launched on Artemis-1, BioSentinel carries a biology experiment into deep space for the first time in 50 years. A 6U CubeSat form factor was utilized for the spacecraft which included technologies newly developed or adapted for operations beyond Earth orbit. The spacecraft carries onboard budding yeast, Saccharomyces cerevisiae, as an analog to human cells to test the biological response to deep space radiation. This was the maiden deep-space voyage for many of the subsystems, and the first time to evaluate their performance in flight operation. Flying a CubeSat beyond LEO comes with unique challenges with respect to trajectory uncertainty and mission operations planning. The nominal plan was a lunar fly-by, followed by an insertion into Heliocentric orbit. However, some possible scenarios included lunar eclipses that could have severely impacted the power budget during that phase of the mission, while others could have resulted in a “Retrograde” hyperbola at swing-by resulting in the spacecraft traveling inward toward Earth or even towards a collision with the lunar surface. The commissioning phase of the mission was successful and completed a week ahead of schedule. It did not come without its exciting moments and challenges. First contact with the spacecraft uncovered that the vehicle was unexpectedly tumbling after deployment, a situation that needed to be corrected urgently. The mission operations team executed a contingency plan to stabilize the spacecraft, with just moments to spare before the battery ran out of power. The BioSensor payload onboard the spacecraft is a complex instrument that includes microfluidics, fluid systems, sensor control electronics, as well at the living yeast cells. BioSentinel also included a TimePix radiation sensor implemented by JSC’s RadWorks group. Dose and Linear Energy Transfer (LET) data is compared directly to the rate of DSB-and repair events measured by the S. cerevisiae cells. BioSentinel mature nanosatellite technologies included: deep space communications and navigation, autonomous attitude control and momentum management, and micro-propulsion systems, to provide an adaptable nanosatellite platform for deep space uses. This paper discusses the performance of the BioSentinel spacecraft through the mission phase, and includes lessons learned from challenges and anomalies. BioSentinel had many successes and will be a pathfinder for future deep space CubeSats and biology missions.

BioSentinel↗
NASA NTRSDate not supplied

Biosentinel: Mission Summary and Lessons Learned From the First Deep Space Biology CubeSat Mission

Launched on Artemis-1, BioSentinel carries a biology experiment into deep space for the first time in 50 years. A 6U CubeSat form factor was utilized for the spacecraft which included technologies newly developed or adapted for operations beyond Earth orbit. The spacecraft carries onboard budding yeast, Saccharomyces cerevisiae, as an analog to human cells to test the biological response to deep space radiation. This was the maiden deep-space voyage for many of the subsystems, and the first time to evaluate their performance in flight operation. Flying a CubeSat beyond LEO comes with unique challenges with respect to trajectory uncertainty and mission operations planning. The nominal plan was a lunar fly-by, followed by an insertion into Heliocentric orbit. However, some possible scenarios included lunar eclipses that could have severely impacted the power budget during that phase of the mission, while others could have resulted in a “Retrograde” hyperbola at swing-by resulting in the spacecraft traveling inward toward Earth or even towards a collision with the lunar surface. The commissioning phase of the mission was successful and completed a week ahead of schedule. It did not come without its exciting moments and challenges. First contact with the spacecraft uncovered that the vehicle was unexpectedly tumbling after deployment, a situation that needed to be corrected urgently. The mission operations team executed a contingency plan to stabilize the spacecraft, with just moments to spare before the battery ran out of power. The BioSensor payload onboard the spacecraft is a complex instrument that includes microfluidics, fluid systems, sensor control electronics, as well at the living yeast cells. BioSentinel also included a TimePix radiation sensor implemented by JSC’s RadWorks group. Dose and Linear Energy Transfer (LET) data is compared directly to the rate of DSB-and repair events measured by the S. cerevisiae cells. BioSentinel mature nanosatellite technologies included: deep space communications and navigation, autonomous attitude control and momentum management, and micro-propulsion systems, to provide an adaptable nanosatellite platform for deep space uses. This paper discusses the performance of the BioSentinel spacecraft through the mission phase, and includes lessons learned from challenges and anomalies. BioSentinel had many successes and will be a pathfinder for future deep space CubeSats and biology missions.

BioSentinel↗
NASA NTRSDate not supplied

Biosentinel: The First Deep Space Biology CubeSat Mission- Mission Summary and Lessons Learned

Launched on Artemis-1, BioSentinel carries a biology experiment into deep space for the first time in 50 years. A 6U CubeSat form factor was utilized for the spacecraft which included technologies newly developed or adapted for operations beyond Earth orbit. The spacecraft carries onboard budding yeast, Saccharomyces cerevisiae, as an analog to human cells to test the biological response to deep space radiation. This was the maiden deep-space voyage for many of the subsystems, and the first time to evaluate their performance in flight operation. Flying a CubeSat beyond LEO comes with unique challenges with respect to trajectory uncertainty and mission operations planning. The nominal plan was a lunar fly-by, followed by an insertion into Heliocentric orbit. However, some possible scenarios included lunar eclipses that could have severely impacted the power budget during that phase of the mission, while others could have resulted in a “Retrograde” hyperbola at swing-by resulting in the spacecraft traveling inward toward Earth or even towards a collision with the lunar surface. The commissioning phase of the mission was successful and completed a week ahead of schedule. It did not come without its exciting moments and challenges. First contact with the spacecraft uncovered that the vehicle was unexpectedly tumbling after deployment, a situation that needed to be corrected urgently. The mission operations team executed a contingency plan to stabilize the spacecraft, with just moments to spare before the battery ran out of power. The BioSensor payload onboard the spacecraft is a complex instrument that includes microfluidics, fluid systems, sensor control electronics, as well at the living yeast cells. BioSentinel also included a TimePix radiation sensor implemented by JSC’s RadWorks group. Dose and Linear Energy Transfer (LET) data is compared directly to the rate of DSB-and repair events measured by the S. cerevisiae cells. BioSentinel mature nanosatellite technologies included: deep space communications and navigation, autonomous attitude control and momentum management, and micro-propulsion systems, to provide an adaptable nanosatellite platform for deep space uses. This paper discusses the performance of the BioSentinel spacecraft through the mission phase, and includes lessons learned from challenges and anomalies. BioSentinel had many successes and will be a pathfinder for future deep space CubeSats and biology missions.

BioSentinel↗
NASA NTRS2014

SpaceCube v2.0 Space Flight Hybrid Reconfigurable Data Processing System

This paper details the design architecture, design methodology, and the advantages of the SpaceCube v2.0 high performance data processing system for space applications. The purpose in building the SpaceCube v2.0 system is to create a superior high performance, reconfigurable, hybrid data processing system that can be used in a multitude of applications including those that require a radiation hardened and reliable solution. The SpaceCube v2.0 system leverages seven years of board design, avionics systems design, and space flight application experiences. This paper shows how SpaceCube v2.0 solves the increasing computing demands of space data processing applications that cannot be attained with a standalone processor approach.The main objective during the design stage is to find a good system balance between power, size, reliability, cost, and data processing capability. These design variables directly impact each other, and it is important to understand how to achieve a suitable balance. This paper will detail how these critical design factors were managed including the construction of an Engineering Model for an experiment on the International Space Station to test out design concepts. We will describe the designs for the processor card, power card, backplane, and a mission unique interface card. The mechanical design for the box will also be detailed since it is critical in meeting the stringent thermal and structural requirements imposed by the processing system. In addition, the mechanical design uses advanced thermal conduction techniques to solve the internal thermal challenges.The SpaceCube v2.0 processing system is based on an extended version of the 3U cPCI standard form factor where each card is 190mm x 100mm in size The typical power draw of the processor card is 8 to 10W and scales with application complexity. The SpaceCube v2.0 data processing card features two Xilinx Virtex-5 QV Field Programmable Gate Arrays (FPGA), eight memory modules, a monitor FPGA with analog monitoring, Ethernet, configurable interconnect to the Xilinx FPGAs including gigabit transceivers, and the necessary voltage regulation. The processor board uses a back-to-back design methodology for common parts that maximizes the board real estate available. This paper will show how to meet the IPC 6012B Class 3A standard with a 22-layer board that has two column grid array devices with 1.0mm pitch. All layout trades such as stack-up options, via selection, and FPGA signal breakout will be discussed with feature size results. The overall board design process will be discussed including parts selection, circuit design, proper signal termination, layout placement and route planning, signal integrity design and verification, and power integrity results. The radiation mitigation techniques will also be detailed including configuration scrubbing options, Xilinx circuit mitigation and FPGA functional monitoring, and memory protection.Finally, this paper will describe how this system is being used to solve the extreme challenges of a robotic satellite servicing mission where typical space-rated processors are not sufficient enough to meet the intensive data processing requirements. The SpaceCube v2.0 is the main payload control computer and is required to control critical subsystems such as autonomous rendezvous and docking using a suite of vision sensors and object avoidance when controlling two robotic arms.

Xilinx FPGA↗
NASA NTRS2018

BioSentinel: An Adaptable Platform for Studying the Biological Effects of Deep Space Radiation

NASA's BioSentinel mission is a 6U nanosatellite with autonomous life support that will utilize the budding yeast Saccharomyces cerevisiae to study the DNA damage response to the deep space radiation environment. BioSentinel is planned to launch in 2019 as a secondary payload on the Space Launch System's first Exploration Mission (EM-1), and will undergo a lunar fly-by and enter heliocentric orbit after deployment. As the first biological mission beyond Low Earth Orbit (LEO) in nearly half a century, this mission will help fill critical gaps in knowledge about the effects of uniquely composed, chronic, low-flux deep space radiation on biological systems. Yeast is well-suited for this mission due to its desiccation tolerance and space-flight heritage. As a eukaryotic model organism, it also serves as a robust analog for human cells. Data gathered on this mission will thus inform us of the hazards involved in long-duration human exploration in deep space, and the protections necessary to mitigate them. Due to its low-cost, flexible and advanced technology, the 4U BioSensor payload contained within the nanosatellite is adaptable to other model microorganisms, exploration platforms and environments relevant to human exploration, such as the ISS, the Lunar Orbital Platform - Gateway and future lunar landers. In order to query the DNA damage response to deep space radiation, BioSentinel contains a wild type yeast strain as a positive control, and a radiation sensitive rad51 mutant strain that is defective for DNA repair. Yeast cells are desiccated in microfluidic cards, and rehydrated with growth medium and metabolic indicator dye at the desired time points during the mission. A thermal control system supports these stasis and growth states, and an optical system continuously measures cell growth and metabolism. An onboard radiation spectrometer and dosimeter allows us to correlate the dose, energy and particle-type of deep space radiation to the biological response. Data received from the deep space biosensor will be compared to control payloads on Earth and the ISS. Ongoing science testing for the BioSentinel project includes optimization for cell viability, desiccation tolerance, and long-term biocompatibility, as well as radiation experiments to understand the sensitivity and responsiveness of cells to varying radiation doses and particle types.

BioSentinel↗
NASA NTRSDate not supplied

Scalar Atomic Defect-Based Solid-State Self-calibrating Magnetometer (3SM) for Space Plasma Analysis

The Earth’s magnetosphere is a system of multiple, co-located particle populations interacting via plasma waves. Things to understand are driving processes, radiation belt and ring current issues, auroral physics, internal plasma processes, and magnetosphere-ionosphere mapping issues. These plasma-physics processes enable the Earth’s magnetosphere to evolve in response to temporal changes in the solar wind and they underlie the phenomena of space weather, which impacts spacecraft systems, astronauts, radio communications, and ground based electric-power grids. The objective of 3SM is to measure magnetic field strength and to calibrate the Vector Magnetometer (VM) device to maintain absolute accuracy during a mission. The 3SM features make the instrument preferably suited not only for the traditional role of scalar magnetometers as absolute references for the calibration of the on-board vector instruments, but also for extended operational capacities, such as higher frequency scalar measurements (of potential interest for magnetosphere studies for the low frequency part of the spectrum) or autonomous scalar / vector operations. Diamond has been the solid-state platform of choice for quantum device technologies for some time, however, it suffers from difficulties such as scalability, integration, and cost. While the diamond platform is very useful for quantum technologies, further development is needed to make it viable. The material platform of choice for NASA Glenn’s Quantum Sensing And Spin Physics (Q-SASP) is silicon carbide (SiC). This is due to the much higher industry development of the SiC material platform for high-power and high-temperature electronics. It leverages both the decades-long SiC development expertise and infrastructure at NASA Glenn and its growing capabilities in quantum metrology. To make SiC devices usable for quantum technologies such as quantum sources, a much deeper understanding of defects is needed. Q-SASP is developing quantum metrology capabilities to evaluate the energy structure, defect formation energy, band structure augmentation, generation/recombination rates, and limits of dipole-dipole coupling in non-metal implanted SiC devices. This can be achieved by analysis of zero-field splitting, low-field resonance, and singlet-triplet mixing through various forms of Electrically Detectable Magnetic Resonance (EDMR) and Near-Zero Field Magnetic Resonance (NZFMR) spectroscopy. This work will discuss recent system developments, device developments, computational modeling, and spectroscopy results and analysis of defects created by non-metal implantations in SiC devices. The defect formation energies of V Si ,V C , V C V Si , N C V Si , N Si , N C in 4H-SiC are previously reported values in other research [2]-[3]. The defect formation energies of PSi and PC were calculated in GPAW [fig 1A]. The basic underlying mechanism of the zero-field phenomenon is the mixing of singlet and triplet states [4]-[5]. In most spin-dependent transport, two electron spins are involved, and thus one must consider each of their interactions with the field. We investigated the electronic and magnetic properties of 4H-SiC and 6H-SiC. The defect formation energy helps us determine what types of defects we are observing in the SiC EDMR experiment. They have very low formation energy (it is negative). The phosphorus substitution in 4H-SiC is a very stable defect. The band diagrams provide us with vital information about how the electronic properties of SiC (such as band gap) change as we add non-metal defects. The zero-field splitting parameters allow us to study the inflection point in the NZFMR [fig 1B]. We clearly observed zero-field splitting. We also noted that the zero-field splitting remained constant with changing bias. We aspect it zero-field splitting to remain constant while the hyperfine and exchange interaction perturbations shift under the influence of an external magnetic field. This is the essence of quantum magnetometry and self-calibration.

space plasma↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRS2016

CubeSub

This presentation introduces and discusses the development of the CubeSub submersible concept, an Autonomous Underwater Vehicle (AUV) designed around the CubeSat satellite form factor. The presented work is part of the author's MSc thesis in Aerospace Engineering at the Royal Institute of Technology, Stockholm, Sweden, and was performed during an internship at the Mission Design Division of the NASA Ames Research Center, Moffett Field, CA. Still in the early stages of its development, the CubeSub is to become a submersible test-bed for technology qualified for underwater and space environments. With the long-term goal of exploring the underwater environments in outer space, such as the alleged subsurface ocean of Jupiter's moon Europa, a number of technology and operational procedures must be developed and matured. To assist in this, the CubeSub platform is introduced as a tool to allow engineers and scientists to easily test qualified technology underwater. A CubeSat is a class of miniaturized satellite built to a standardized size. The base size is 1U (U for unit), corresponding to a 100 x 100 x 113.5 cu mm cube. A 1U CubeSat can in other words easily be held in one hand. Stacking units give larger satellite sizes such as the also commonly used 1.5U, 2U and 3U. The CubeSat standard is in itself already well established and hundreds of CubeSats have to date been launched into space. Compatible technology is readily available and the know-how exists in the space industry, all of which makes it a firm ground to stand on for the CubeSub. The rationale behind using the CubeSat form factor is to make use of this pre-existing foundation, making the CubeSub easy to develop, modular and readily available. It will thereby aid in the process of maturing the concept of a fully space qualified submersible headed for outer space. As a further clarification, the CubeSub is itself not meant for outer space, but to facilitate development of such a vessel. Along with its uses as a testbed, the CubeSub also holds the potential to become a useful tool for exploration and experimentation here on Earth. A highly standardized system utilizing well-known hardware can reduce the cost and required work load for researchers wishing to perform experiments and exploration. Users could design sensors and experiments to comply with the already well established CubeSat standard, which are then carried by the CubeSub to the region of interest. This in turn means that the end users can focus more on formulating the experiment itself and less about how to get it where they want it. The CubeSub is designed to be built up by modules, which can be assembled in different configurations to fulfill different needs. Each module will be powered individually and intermodular communication will be wireless, removing the need for wiring. The inside of the cylindrical hull will be flooded with ambient water to enhance the interaction between payloads and surrounding environment. The overall torpedo-like shape is similar to that of a conventional AUV, slender and smooth. This is to make for a low drag, reduce the risk of snagging on surrounding objects and make it possible to deploy through an ice sheet via a narrow borehole or navigate in tight areas. To keep costs low and further accelerate development, rapid prototyping is utilized wherever possible. Full-scale prototypes are being constructed through 3D-printing and using COTS (Commercial Off-The-Shelf) components. 3D-printing is used both for the largest hull components and the relatively small and delicate propellers. Arduino boards are used for control and internal communication

AUV↗
NASA NTRS1999

Integration of a Decentralized Linear-Quadratic-Gaussian Control into GSFC's Universal 3-D Autonomous Formation Flying Algorithm

A decentralized control is investigated for applicability to the autonomous formation flying control algorithm developed by GSFC for the New Millenium Program Earth Observer-1 (EO-1) mission. This decentralized framework has the following characteristics: The approach is non-hierarchical, and coordination by a central supervisor is not required; Detected failures degrade the system performance gracefully; Each node in the decentralized network processes only its own measurement data, in parallel with the other nodes; Although the total computational burden over the entire network is greater than it would be for a single, centralized controller, fewer computations are required locally at each node; Requirements for data transmission between nodes are limited to only the dimension of the control vector, at the cost of maintaining a local additional data vector. The data vector compresses all past measurement history from all the nodes into a single vector of the dimension of the state; and The approach is optimal with respect to standard cost functions. The current approach is valid for linear time-invariant systems only. Similar to the GSFC formation flying algorithm, the extension to linear LQG time-varying systems requires that each node propagate its filter covariance forward (navigation) and controller Riccati matrix backward (guidance) at each time step. Extension of the GSFC algorithm to non-linear systems can also be accomplished via linearization about a reference trajectory in the standard fashion, or linearization about the current state estimate as with the extended Kalman filter. To investigate the feasibility of the decentralized integration with the GSFC algorithm, an existing centralized LQG design for a single spacecraft orbit control problem is adapted to the decentralized framework while using the GSFC algorithm's state transition matrices and framework. The existing GSFC design uses both reference trajectories of each spacecraft in formation and by appropriate choice of coordinates and simplified measurement modeling is formulated as a linear time-invariant system. Results for improvements to the GSFC algorithm and a multiple satellite formation will be addressed. The goal of this investigation is to progressively relax the assumptions that result in linear time-invariance, ultimately to the point of linearization of the non-linear dynamics about the current state estimate as in the extended Kalman filter. An assessment will then be made about the feasibility of the decentralized approach to the realistic formation flying application of the EO-1/Landsat 7 formation flying experiment.

Folta, David C.↗
NASA NTRS2018

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.

Pasolli, Edoardo↗
NASA NTRS2002

Cardiovascular and sympathetic neural responses to handgrip and cold pressor stimuli in humans before, during and after spaceflight

Astronauts returning to Earth have reduced orthostatic tolerance and exercise capacity. Alterations in autonomic nervous system and neuromuscular function after spaceflight might contribute to this problem. In this study, we tested the hypothesis that exposure to microgravity impairs autonomic neural control of sympathetic outflow in response to peripheral afferent stimulation produced by handgrip and a cold pressor test in humans. We studied five astronauts approximately 72 and 23 days before, and on landing day after the 16 day Neurolab (STS-90) space shuttle mission, and four of the astronauts during flight (day 12 or 13). Heart rate, arterial pressure and peroneal muscle sympathetic nerve activity (MSNA) were recorded before and during static handgrip sustained to fatigue at 40 % of maximum voluntary contraction, followed by 2 min of circulatory arrest pre-, in- and post-flight. The cold pressor test was applied only before (five astronauts) and during flight (day 12 or 13, four astronauts). Mean (+/- S.E.M.) baseline heart rates and arterial pressures were similar among pre-, in- and post-flight measurements. At the same relative fatiguing force, the peak systolic pressure and mean arterial pressure during static handgrip were not different before, during and after spaceflight. The peak diastolic pressure tended to be higher post- than pre-flight (112 +/- 6 vs. 99 +/- 5 mmHg, P = 0.088). Contraction-induced rises in heart rate were similar pre-, in- and post-flight. MSNA was higher post-flight in all subjects before static handgrip (26 +/- 4 post- vs. 15 +/- 4 bursts min(-1) pre-flight, P = 0.017). Contraction-evoked peak MSNA responses were not different before, during, and after spaceflight (41 +/- 4, 38 +/- 5 and 46 +/- 6 bursts min(-1), all P > 0.05). MSNA during post-handgrip circulatory arrest was higher post- than pre- or in-flight (41 +/- 1 vs. 33 +/- 3 and 30 +/- 5 bursts min(-1), P = 0.038 and 0.036). Similarly, responses of MSNA and blood pressure to the cold pressor test were well maintained in-flight. We conclude that modulation of muscle sympathetic neural outflow by muscle metaboreceptors and skin nociceptors is preserved during short duration spaceflight.

NASA Discipline Neuroscience↗