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NASA NTRS2020

UAS Integration in the NAS Project: Overview of Flight Test Series 6

The National Aeronautics and Space Administration (NASA) Unmanned Aircraft Systems Integration in the National Airspace System (UAS-NAS) Project has conducted a series of flight test campaigns intended to support the reduction of barriers that prevent unmanned aircraft from flying without the required waivers from the Federal Aviation Administration (FAA). The 2019 Flight Test Series 6 (FT6) campaign furthered this path and supported three test configurations: 1) Radar Characterization, 2) Scripted Encounters and 3) Full Mission. Radar Characterization assessed the performance of Honeywell’s low size, weight, and power (low SWaP) radar system; Scripted Encounters investigated the timing of Detect and Avoid (DAA) alerting thresholds using a Department of Defense (DoD) Group 3 unmanned aircraft system (UAS) equipped with low SWaP sensors and three different live intruder aircraft flown at varying encounter geometries; and Full Mission validated human-in-the-loop simulations by collecting pilot performance data from a ground control station while controlling a live unmanned aircraft on a mission in both virtual and live air traffic controlled airspace. The subject pilot observed a research display that presented DAA advisories to maintain separation from live and virtual aircraft. The test was conducted over a twenty-week period within the R-2508 special use airspace located near Edwards Air Force Base (EAFB), CA. Over 240 encounters were flown during the test series and FT6 proved to be invaluable for the purposes of planning, managing, and executing this type of integrated flight test in both live and virtual environments. Data collected from FT6 was provided to the RTCA Special Committee 228 (SC-228) to help inform the Phase 2 Minimum Operational Performance Standards (MOPS). FT6 was the final test series for the UAS-NAS project that began in 2012. This paper provides an overview of FT6 and its success can be directly attributed to the diligent work of the men and women who supported this effort.

UAS in the NAS↗

NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets such as sex or age of the model organism used. In the present study, NASA GeneLab-hosted RNAseq datasets from rodent liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC, to determine statistical differences between datasets before and after correction, Principal Component Analysis, to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the standard approach. Thus, the most robust standard correction will be implemented in the GeneLab Visualization 2.0 platform when datasets are combined.

GeneLab, RNA-seq, Batch Correction↗

NASA NTRS2020

Development of a Supervisory Tool for Fault Detection and Diagnosis of DC Electric Power Systems with the Application of Deep Space Vehicles

This dissertation formulates the problem of fault detection and diagnosis of DC electric power systems for the application of autonomous spacecraft. The ability to accurately identify and isolate failures in the electrical power system is critical to ensure the reliability of a spacecraft. This problem becomes more pronounced during deep space missions that lack the ability to monitor from ground control. The current state of electrical power system fault supervision is insufficient to guarantee highly reliable and robust operation. To solve this issue, a combination of model-based and rules-based techniques are used in a hierarchical framework to improve the diagnostic performance of the spacecraft electrical power system. Noise, disturbances, and modeling errors are considered in the design of the method. Practical considerations related to the hardware and software are discussed for the flight application. A wide array of failure types are simulated in a series of experiments to assess the functionality of the design. The experiments showed that the methods used improved the diagnostic capability of the autonomous system while taking into account the limitations attributed to flight software requirements. The significance of this study is to provide a framework capable of advanced diagnostics of an electrical power system with little to no interaction from a human operator.

Fault Detection and Diagnosis↗

NASA NTRSDate not supplied

A Qualitative Systematic Review Evaluating the Impact of Elevated Ambient Co2 in Atmosphere on Pharmaceutical Stability

Pharmaceuticals are an essential component of the medical system for crewed missions. Current research has identified decreased stability in space-flown pharmaceuticals compared to ground controls, and this presents a significant risk for future exploration-class missions where resupply will be limited. While the cause is not known, the elevated ambient carbon dioxide (CO2) in the spacecraft atmosphere has been postulated as a potential cause.

A Yuen↗

NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the respective standard approach. Of the methods tested, standard ComBat and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

GeneLab↗

NASA NTRSDate not supplied

Combining RNA-SEQ Datasets from NASA GENELAB: An Evaluation of Correction Methods

Background: Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. Methods: In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, the median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. Results: The results showed that the reference-based approach introduced several additional (and likely artificial) differentially expressed genes when compared with the respective standard approach. Conclusions: Of the methods tested, standard ComBat_seq and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

Finsam Samson↗

NASA NTRS2021

Quirky Circling Behavior in Mice Informs Research on Humans in Space

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. We previously conducted a detailed phenotypic analysis of mouse behavior during long duration (33-day) spaceflight in the NASA Rodent Habitat (Rodent Research-1; RR1 mission). Notably, we documented the emergence within the 8-10 days of launch of spontaneous ambulatory behavior in the form of circling or ‘race-tracking’ behavior in spaceflight but not in an identical ground control condition. Circling is unique to the NASA RH not having been reported to occur in other mouse habitats that provide limited opportunity for grasping, and/or are characterized by smaller habitable volumes viz., Italian Mice Drawer System (MDS) flown on ISS; Russian Block Obespecheniya Soderzhaniya (BOS) flown on Bion M-1, or JAXA Habitat Cage Unit (HCU) flown in the ISS Kibo module. Over time, circling became the primary dark cycle activity of FLT mice, occurring in individuals then as a coordinated group activity. Here we discuss possible interpretations of circling behavior including: (1) Stereotypic or abnormal repetitive behaviors (ARBs) that are unvarying, and apparently functionless behavior patterns documented numerous species in laboratory and zoo settings, possibility related to insufficient environmental enrichment and stress, (2) Rewarding effects of physical activity that are well-documented in terrestrial studies of rodents given the opportunity to run in wheels, and (3) Vestibular self-stimulation, i.e., the generation biologically-relevant amounts of vestibular sensory input to reduce the effects of microgravity. Affording mice the opportunity to grab and run in the RH resembles physical activities that the crew participate in routinely. Our approach is yielding an interesting analogue for better understanding human responses to spaceflight, and providing the opportunity to begin to address how physical movement influences responses to microgravity.

spaceflight↗

NASA NTRS1995

Demonstration of Autonomous Orbit Determination Around Small Bodies

An algorithm is described and demonstrated with a simulation that has been developed for navigation around small bodies. This is important because of the cost and complexity of using ground control for navigating around such objects. The algorithm uses a precomputed shape model of the object and images taken with a wide field-of-view (FOV) camera.

Riedel, J.E.↗

NASA NTRS1993

Demonstration of A High-Fidelity Predictive/Preview Display Technique for Telerobotic Servicing in Space

A highly effective predictive/preview display technique for telerobotic servicing in space under several seconds communication time delay has been demonstrated on a large laboratory scale in May, 1993, involving the Jet Propulsion Laboratory as the simulated ground control station and, 2500 miles away, the Goddard Space Flight Center as the simulated satellite servicing set-up. The technique isbased on a high-fidelity calibration procedure that enables a high-fidelity overlay of 3-D graphics robot arm and object models over given 2-D TV camera images of robot arm and objects. Togenerate robot arm motions, the operator can confidently interact in real time with the graphics models of the robot arm and objects overlaid on an actual camera view of the remote work site. The technique also enables the operator to generate high-fidelity synthetic TV camera views showing motion events that are hidden in a given TV camera view or for which no TV camera views are available...

Bejczy, A. K.↗

NASA NTRSDate not supplied

Reports of Resilient Performance: Investigating Operators' Descriptions of Safety-producing Behaviors in the Aviation Safety Reporting System

While many existing taxonomies and frameworks provide a common vocabulary for describing how human operators fail in the context of sociotechnical systems, at present, there is no common vocabulary to describe how humans succeed. Such a framework would facilitate systematically collecting and analyzing data on how human performance can produce safety, not just how it can reduce safety. One potentially rich source of currently available information for exploring desired performance is the reports submitted to NASA’s Aviation Safety Reporting System (ASRS). These de-identified, confidential, and voluntary narrative reports are submitted by pilots, controllers, ground operators, and others within aviation operations. While these reports are primarily submitted to describe safety risks, incidents, and problems, they also often describe how those risks were mitigated, and provide a window into aspects of everyday work in aviation. This paper describes an analysis of ASRS narratives to understand how operators talk about their own resilient behaviors during adverse safety conditions and events. Guided by Erik Hollnagel’s Resilience Assessment Grid framework (i.e., anticipate, monitor, respond, learn), we illustrate our approach and methodology with examples from reports. We also highlight some of the challenges and how further research is needed in developing a taxonomy of operators’ descriptions of resilient performance.

resilient behaviors↗

NASA NTRSDate not supplied

Reports of Resilient Performance: Investigating Operators' Descriptions of Safety-producing Behaviors in the Aviation Safety Reporting System

While many existing taxonomies and frameworks provide a common vocabulary for describing how human operators fail in the context of sociotechnical systems, at present, there is no common vocabulary to describe how humans succeed. Such a framework would facilitate systematically collecting and analyzing data on how human performance can produce safety, not just how it can reduce safety. One potentially rich source of currently available information for exploring desired performance is the reports submitted to NASA’s Aviation Safety Reporting System (ASRS). These de-identified, confidential, and voluntary narrative reports are submitted by pilots, controllers, ground operators, and others within aviation operations. While these reports are primarily submitted to describe safety risks, incidents, and problems, they also often describe how those risks were mitigated, and provide a window into aspects of everyday work in aviation. These reports can be searched in a variety of ways. This paper describes methods for systematically examining ASRS narratives to understand how operators talk about their own resilient behaviors during adverse safety conditions and events. Guided by Erik Hollnagel’s Resilience Assessment Grid framework (i.e., anticipate, monitor, respond, learn), various approaches and tools for such inquiries are described. The approach, process, challenges, and suggestions to building an operator-based description of resilient performance are discussed, and tools to facilitate data analysis to maximize learning from these reports are described.

ASRS narratives↗

NASA NTRSDate not supplied

BioNutrients: Microbial on-demand production of short shelf-life micronutrients in space

Long duration deep space missions pose a significant challenge when considering adequate nutrition for human spaceflight. Many micronutrients supplied in pre-packaged foods for low Earth orbit flights have a short shelf life and will degrade over multi-year missions. One strategy to mitigate the risk of micronutrient malnutrition is to grow fresh foods to supplement a pre-packaged diet. The BioNutrients project aims to develop systems for growing edible microorganisms that are genetically engineered to produce short shelf life micronutrients. BioNutrients-1 (BN-1) is a five-year spaceflight project currently underway on the International Space Station (ISS) that tests long-term storage and production of two carotenoids, β-carotene and zeaxanthin, in genetically engineered yeast strains. BN-1 on-orbit and ground control activities indicate that the nutrient production system will be stable for at least two years in ambient storage conditions. BN-1 is also testing the effects of long-term ISS storage on viability and gene expression using “stasis packs” containing a series of microorganisms useful in food and biomanufacturing processes. Stasis pack analysis is distinguishing microorganisms with full viability after two years of storage from other species that lose viability rapidly. In parallel, BioNutrients-2 (BN-2) is being developed as a six-month flight experiment that will expand the range of foods produced on ISS to include yogurt, kefir, and probiotic yeast. BN-2 will improve upon hardware design by transitioning from a hard-shell polycarbonate bioreactor to a flexible fluorinated ethylene propylene (FEP) bag. This change reduces the mass and volume required to store production packs. The long-term goal of BioNutrients is to develop the capability for rapid and safe delivery of multiple micronutrients in single-use production packs. We expect synthetic biology approaches similar to BioNutrients will play an important role for astronaut health in future NASA missions to the Moon and Mars.

Synthetic Biology↗

NASA NTRSDate not supplied

Single-Cell mRNA Sequencing of Bone Marrow Mesenchymal Stem Cell Lineages Shows that Gravity Mechanical Loading Regulates Regenerative Osteogenesis in a CDKN1A-Dependent Manner

Weight-bearing at 1g is increasingly recognized as an important stimulus driving stem cell-based tissue regenerative homeostasis and health. In the bone marrow compartment, mechanical stimuli from gravity can drive osteogenesis from mesenchymal progenitors and hematopoiesis from hematopoietic progenitors. Conversely, hematopoietic monocyte/macrophage progenitors can also be driven into osteoclastogenesis by mechanical unloading in microgravity. Although the cell cycle inhibitor gene Cdkn1a is known to be upregulated in microgravity, the great diversity of stem cell lineages and stages of differentiation in bone marrow, make it difficult to attribute gene expression changes to specific cell types. To overcome this limitation, we used single-cell mRNAseq analyses with both in-vitro and in-vivo strategies, including in-vitro stretch of marrow osteogenic cultures, and in-vivo hindlimb unloading, running wheels, treadmills, and exposure to real microgravity. For in-vitro studies marrow osteoprogenitors from WT and Cdkn1a-null mice were subjected to substrate stretch during osteogenesis, with static cultures simulating unloading. From those experiments we determined Cdkn1a-null cells transition more readily from progenitors to early osteoblasts and further to mineralizing osteoblasts. Additionally, single-cell mRNAseq resolution identified Cdkn1a suppression in response to mechanical loading predominantly in early proliferative osteoblasts rather than progenitor or mineralizing osteoblasts. These findings support the hypothesis that CDKN1A plays a mechano-reversible, anti-proliferative role during bone regenerative osteogenesis. To investigate in-vivo mechanoregulation of osteogenesis, mice were flown in microgravity during the Rodent Research-10 experiment comparing wildtype and Cdkn1a-null mice kept on ISS for 30 days. The mice were euthanized and dissected on-orbit in microgravity and femur bone marrow was collected using a custom sample isolation and live-cell cryo-preservation technique allowing for single cell transcriptomics post-flight processing. Ground control experiments with hindlimb-unloading, running wheels, and treadmills, were also conducted for a comprehensive analysis of mechanical loading effects on the regenerative health of bone tissue and its marrow stem cell progenitor lineages.

single cell sequencing↗

NASA NTRSDate not supplied

Biological Insights at the Interface of Multiple Arabidopsis Legacy Datasets

The NASA GeneLab database includes an open-access collection of datasets yielded by space biology experiments. Six Gene Lab Data Sets (GLDS’s) performed in Arabidopsis were selected for analysis (7/17/44/121/205/213), all of which included transcriptome data from spaceflight and ground control environments. Hardware, ecotype, environmental conditions, and other experimental conditions varied, allowing the observations of overarching gene expression impacts of microgravity on plant life without focusing on effects of specific experimental conditions. Using GeneLab pre-processed datasets as the basis for the study, RNA microarray data were analyzed to identify genes that showed altered expression in microgravity when compared to control samples for each individual GLDS. All differentially expressed genes were compared to locate differentially expressed genes common between spaceflight experiments. The most noteworthy result is that not one gene shared differential expression among the six GLDS’s. However, gene expression was not influenced randomly by the microgravity environment, as there were several gene ontology terms that were significantly enriched across all experiments. These included 20 significantly enriched biological processes, and although the genes which enriched each term varied, there were many cases of specific genes common to clusters of multiple GLDS’s. Gene expression such as NAC92 and ERF011 or membrane structural element FFP6 provide insight and direction toward understanding the plant response to spaceflight. Characterizing these common processes and the shared differentially expressed genes has demonstrated potential targets for further study to understand and modulate the biological response of plants in microgravity. Life on Earth has never been subjected to the absence of gravity as a selective pressure, so observing how life forms react to a microgravity environment could provide insight to our shared fundamental biological processes. It is also feasible that the genetic modification of specific genes linked to the microgravity response could improve health and yield of space crops.

Joseph Emhof↗

NASA NTRSDate not supplied

A Cognitive Walkthrough of Multiple Drone Delivery Operations

Advances of early twenty-first century aviation and transportation technologies provide opportunities for enhanced aerial projects, and the overall integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) has applications across a wide range of operations. Through these, remote operators have learned to manage several UAS at the same time in a variety of operational environments. The present work details a component piece of an ongoing body of research into multi-UAS operations. Beginning in early 2020, NASA has collaborated with Uber Technologies to design and develop concepts of operations, roles and responsibilities, and ground control station (GCS) concepts to enable food delivery operations via multiple, small UAS (sUAS). A cognitive walkthrough was chosen as the method for data collection. This allowed information to be gathered from UAS subject matter experts (SMEs) that could further mature designs for future human-in-the-loop (HITL) simulations; in addition, it allowed information to be collected remotely during the stringent restrictions of the COVID-19 pandemic. Consequently, the described cognitive walkthrough activity utilized remote data collection protocols mediated through the usage of programs designed for presentation and telecommunications. Scenarios were designed, complete with airspace, contingencies, and remedial actions, to be presented to the SMEs. Information was collected using a combination of rating scales and open-ended questions. Results received from the SMEs revealed expected hazards, workloads, and information concerns inherent in the contingency scenarios. SMEs also provided insight into the design of GCS tools and displays as well as the duties and relationships of human operators (i.e., monitors) and automation (i.e., informers and flight managers). Implications of these findings are discussed.

unmanned aircraft systems↗

NASA NTRS2021

A Cognitive Walkthrough of Multiple Drone Delivery Operations

unmanned aircraft systems↗

NASA NTRSDate not supplied

RadBREAD: Radiation Biology Research at an Elevated Altitude through Dosimetry – A student-designed payload

NASA uses extreme environment platforms (ground testing facilities, high-altitude balloons and aircraft, and CubeSats) to provide greater understanding of the conditions and limitations of extra-terrestrial environments. As part of a two-week flight planned for summer 2021, RadBREAD (Radiation Biology Research at an Elevated Altitude through Dosimetry) will fly as a secondary payload consisting of a M-42C (German Aerospace Center, DLR) ionizing radiation dosimeter, UV micro-logger, and multiple desiccated yeast samples. The platform is a novel high-altitude solar-powered aircraft: the Swift Engineering High-Altitude samples. The platform is a novel high-altitude solar-powered aircraft: the Swift Engineering High-Altitude Long-Endurance Unmanned Aircraft System (HALE UAS), which offers significantly longer flight durations than other high-altitude platforms. The yeast Saccharomyces cerevisiae will provide meaningful biological correlation for the sensor readings, due to its resistance to extremely low temperature and pressure when desiccated, ease of genetic manipulation, and homology to human genes. The RadBREAD team comprises the 2020 cohort of NASA’s Space Life Sciences Training Program (SLSTP) research associates as well as NASA scientists, engineers and radiation experts from NASA and the DLR. Yeast survival, metabolic, and transcriptomic changes will be correlated with environmental data collected during long-term exposure to the upper atmosphere. Additionally, the team will evaluate the upper atmospheric environment (radiation, pressure, and temperature) provided by the HALE UAS platform as a Mars surface analog for biological payloads. We hypothesize that exposure to upper atmospheric conditions during the HALE UAS flight will alter the survival, metabolism, and transcriptome of desiccated wild-type S. cerevisiae upon rehydration compared to sensitive and tolerant yeast strains exposed to the same conditions, and between the flight samples compared to asynchronous ground controls.

radiation exposure↗

NASA NTRSDate not supplied

ISAAC: An Integrated System for Autonomous and Adaptive Caretaking

The Integrated System for Autonomous and Adaptive Caretaking (ISAAC) project is developing technology for autonomous caretaking of spacecraft, primarily during uncrewed mission phases. ISAAC aims to integrate autonomous intra-vehicular robots (IVR) with spacecraft infrastructure (power, life support, etc.) and ground control. It focuses on capabilities required for NASA’s Gateway cis-lunar outpost that also apply to human missions to Mars and beyond. Its development strategy is to test using existing IVR on the ISS (the Astrobee free-flyer and Robonaut dextrous manipulator) as an analog for future IVR on Gateway.

Trey Smith↗