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NASA NTRS2015

Identification and Evaluation of Integration and Cross Cutting Issues Across HRP Risks

The HRP Integrated Research Plan contains the research plans for the 32 risks requiring research to characterize and mitigate. These risks to human health and performance in spaceflight are identified by evidence and each one focuses on a single aspect of human physiology or performance. They are further categorized by aspects of the spaceflight environment, such as altered gravity or space radiation, that that play a major role in their likelihood and consequence. From its inception the "integrate" in the Research Plan has denoted the integrated nature of risks to human health and performance, the connectedness of physiological systems within the human body regardless of the spaceflight environment, and the integrated response of the human body to the spaceflight environment. Common characteristics of the spaceflight environment include altered gravity, atmospheres and light/dark cycles, space radiation, isolation, noise, and periods of high or low workload. Long term exposure to this unique environment produces a suite of physiological effects such as stress; vision, neurocognitive and anthropometric changes; circadian misalignment; fluid shifts, deconditioning; immune dysregulation; and altered nutritional requirements. Matrix diagraming was used to systematically identify, analyze and rate the many-to-many relationships between environmental characteristics and the suite of physiological effects. It was also to identify patterns in the relationships of common physiological effects to each other. Analyses of patterns or relationships in these diagrams help to identify issues that cut across multiple risks. Cross cutting issues benefit from a multidisciplinary approach that synthesizes concepts or data from two or more disciplines to identify and characterize risk factors or develop countermeasures relevant to multiple risks. They also help to illuminate possible problem areas that may arise when a countermeasure impacts risks other than those which it was developed to mitigate, or identify groupings of physiological changes that are likely to occur that may impact the overall risk posture.

Steinberg, S. L.↗
NASA NTRS2014

Parametric Trace Slicing

A program trace is obtained and events of the program trace are traversed. For each event identified in traversing the program trace, a trace slice of which the identified event is a part is identified based on the parameter instance of the identified event. For each trace slice of which the identified event is a part, the identified event is added to an end of a record of the trace slice. These parametric trace slices can be used in a variety of different manners, such as for monitoring, mining, and predicting.

Rosu, Grigore↗
NASA NTRS2016

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

Uncertainty↗
NASA NTRS2008

Behavior of Spinning Space Vehicles with Onboard Liquids, 2nd Edition, Technical Report B8030

Although the fundamental principles of spin stabilization are well established, uncertainty regarding the potential for rapid nutation growth caused by onboard liquids is a continuing concern. NASA and other organizations regularly encounter the issue of rapid nutation growth due to energy dissipation by liquids on spinning vehicles. Of concern is the stability of spinning upper stages and of spacecraft that spin for part or all of their missions. Several missions have required last-minute hardware or operational changes to deal with rapid nutation divergences that were identified late in the program. In some instances, major schedule slips were barely averted. In at least two cases, it was determined that a spinning upper stage was not a viable option. Historically, the "slosh" issue has been addressed by each space vehicle project individually, if it has been addressed at all. Due to budgetary and programmatic constraints, individual projects are unable to address the problem globally. Hence, there has been little effort to collect available test and flight data and use that data to make a coherent, unified picture of the "slosh" effect and how to deal with it. To some extent, each project has had to "reinvent the wheel", which can be both costly and risky. This study is a step toward correcting the situation. Specifically, the goal was to identify and collect available flight and test data for spinning vehicles with onboard liquid propellants. A total of 149 flight data points and 1,692 test points were collected as part of this study. This data was analyzed, correlated, and is presented here in a normalized form. In most cases, the normalization involves a dimensionless nutation time constant that can be used to predict performance of other vehicles with the same type of tank. For some configurations, it was also possible to identify conditions that can lead to resonance between nutational motion and liquid modes. Gaps in the knowledge base are identified and approaches to filling those gaps are outlined. The data presented here has two different but related uses. First, it can be applied directly to current and future spacecraft programs. Second, it can provide truth models for testing analytical techniques. Experience has shown that purely analytical models of the liquid "slosh" effect on spinning vehicles are unreliable unless they are validated against flight or test data. To the author's knowledge, this report contains the most extensive and varied data set available. As such, it should be a good resource for anyone seeking to develop and validate improved analytical techniques. All of the original digital data sets have been archived on disk, with copies provided to NASA/KSC. With some restrictions, many of these data sets can be made available to researchers within the United States. Whenever possible, spacecraft are identified by name in this report. However, several organizations provided access to data with the explicit proviso that their programs not be identified and that parameters be presented only in normalized form. These constraints have been respected.

Hubert, Carl↗
NASA NTRS2017

Key Gaps for Enabling Plant Growth in Future Missions

Growing plants to provide food or psychological benefits to crewmembers is a common vision for the future of human spaceflight, often represented in media and in serious concept studies. The complexity of controlled environment agriculture, and plant growth in microgravity have and continue to be the subject of dedicated scientific research. However, actually implementing these systems in a way that will be cost effective, efficient, and sustainable for future space missions is a complex, multi-disciplinary problem. Key questions exist in many areas: human medical research in nutrition and psychology, horticulture, plant physiology and microbiology, multi-phase microgravity fluid physics, hardware design and technology development, and system design, operations and mission planning. This paper describes key knowledge gaps identified by a multi-disciplinary working group within the National Aeronautics and Space Administration (NASA). It also begins to identify solutions to the simpler questions identified by the group based on work initiated in 2017. Growing plants to provide food or psychological benefits to crewmembers is a common vision for the future of human spaceflight, often represented in media and in serious concept studies. The complexity of controlled environment agriculture, and plant growth in microgravity have and continue to be the subject of dedicated scientific research. However, actually implementing these systems in a way that will be cost effective, efficient, and sustainable for future space missions is a complex, multi-disciplinary problem. Key questions exist in many areas: human medical research in nutrition and psychology, horticulture, plant physiology and microbiology, multi-phase microgravity fluid physics, hardware design and technology development, and system design, operations and mission planning. This paper describes key knowledge gaps identified by a multi-disciplinary working group within the National Aeronautics and Space Administration (NASA). It also begins to identify solutions to the simpler questions identified by the group based on work initiated in 2017.

Anderson, Molly↗
NASA NTRS2019

Appreciative Methods Applied to the Assessment of Complex Systems

Complex systems have characteristics that challenge traditional systems engineering processes and methods. These characteristics have been defined in various ways. INCOSE has previously identified characteristics of complex systems and potential methods to deal with complexity in system development. The purpose of this paper is to provide definitions and describe distinguishing characteristics of complexity using example systems to illustrate approaches to assessing the extent of complexity. The paper applies Appreciative Inquiry to identify and assess complex system characteristics. The characteristics are used to examine several different examples of systems to illuminate areas of complexity. These examples range from seemingly simple systems to complicated systems to complex systems. Different tiers of complexity are identified as a result of the assessment. The paper also identified and introduces topics on managing complexity and the integrating system perspective that represent new directions for the engineering of complex systems. The Appreciative Inquiry approach provides a method for systems engineering practitioners to more readily identify complexity when they encounter it, and to deal more effectively with this complexity once it has been identified.

Watson, Michael↗
NASA NTRS2020

The Spaceport Command and Control System Security Assessor Project

This Summer, I worked as a National Aeronautics and Space Administration (NASA) Internships and Fellowships (NIF) intern under my mentor, Jill Giles within the Software Engineering Branch. Within this project, I worked alongside the Cyber Security branch to identify a list of Commercial Off the Shelf (COTS) software to analyze, research, and gain insight about potential vulnerabilities within the software that could become a threat of attack. After identifying the list of COTS software, my team and I used Microsoft Excel to create a worksheet to easily organize and design a questionnaire about the software. Security reports weregiven to us to identify the software used on the machines in the firing rooms. With these reports, we created a script that would populate the database with the software information to identify potential security weaknesses of COTS software.The goal of the project was to produce a final report, summarizing the most vulnerable launch control system servers and configurations and document vulnerabilities, residual risk, likelihood, and consequence. This project is important for the Cyber Security and Information Technology branches because it will identify security weaknesses and help to mitigate risk. From the Spaceport Command and Control System Security Assessor Project, I learned how to properly identify weaknesses and vulnerabilities within software and how to mitigate the risks within the software. This project also taught me how to create databases using scripts and input files.

Destani Satora Van Arsdalen↗
NASA NTRSDate not supplied

Microbial Monitoring of New Cleanrooms Used to Curate Astrobiologically Relevant Asteroid Samples from Bennu and Ryugu

Introduction: NASA has constructed two new cleanrooms to house materials from the OSRIS-REx and Hayabusa2 missions to the asteroids Ryugu (162173) and Bennu (101955), respectively. In accordance with standard astromaterials curation practices, these cleanrooms will be monitored for particulate contamination and maintained to ISO 5 equivalent standards1. Since the samples in these collections are expected to contain prebiotic organic compounds that may help explain the origin of life on Earth, these labs will also be monitored for organic and biological contamination2. Samples from Ryugu arrived on Earth in December, 2020. After basic characterization in Japan, NASA received a subset of these samples at the astromaterials curation facility in Houston in December of 2021. OSIRIS-REx is expected to return samples in September, 2023. Here we present preliminary microbial monitoring results from monthly monitoring of these new labs and the connected microtomy and staging areas that support them, as they are being commissioned. We also compare these results to baseline values for other astromaterials curation labs. We will also briefly describe additional cleaning efforts employed to reduce the bioburden in these new cleanrooms. Methods: Microbial samples were collected from surfaces using a dry macrofoam swab (Puritan Brand 2518051PFRNDFD). Swabs were also opened in the lab but not touched to any surfaces to function as negative controls. Samples and controls were processed inside a class II biosafety cabinet to avoid inadvertent cross contamination. The swabs were suspended in 15 ml of PBS (Phosphate Buffered Saline) and vortexed for 20 seconds to remove cells from the swab surface. The PBS was used to inoculate Petri dishes filled with TSA (Tryptic Soy Agar), Blood Agar, or Reasoners 2 agar to check for microbial growth. Each plate was inoculated with 0.1 ml of PBS. The TSA and blood agar plates were incubated at 35˚C and the Reasoners 2 agar plates were incubated at 25˚C for seven days. Petri dishes filled with Potato dextrose agar, Saboraud dextrose agar, or Saboraud dextrose agar with 0.1 mg/ml of chloramphenicol, an antibiotic, were used to check for fungal growth. These plates were inoculated with 0.3 ml of PBS and incubated at 30˚C. The remaining PBS was frozen at -80 ˚C for DNA sequencing. After incubation, isolates were counted and reisolated for identification. Isolates were identified using the VITEK23 system or by sequencing a portion of the 16S rRNA gene for bacteria or the ribosomal internal transcribed spacer (ITS) for fungi. Sequencing was performed with an ABI 3500 Sanger sequencer. Results: During our initial sampling, six of the seven sites sampled (86%) displayed bacterial or fungal growth. Samples collected from the staging areas and microtomy labs are not included in this calculation since those areas are maintained at a lower ISO 7 equivalent cleanliness standard. A month later, only three of the seven sites (43%) displayed bacterial growth. No fungal growth was detected in the second sampling. Since new equipment had been introduced to the Hayabusa2 lab since the first round of sampling, an additional three sampling sites were included in the second round of sampling. None of these sites displayed microbial growth. These sites will be included in all future sampling efforts. Bacterial isolates have been identified from the following genera at multiple time points: Micrococcus, Staphylococcus, and Bacillus. Isolates from the genera: Microbacterium, Nocardioides, Methylocystis, and Microvirga were identified in the initial sampling, but were not present at later time points. Identification of fungal isolates is in progress. Results are summarized in Table 1. Discussion: The recovery rate or percentage of positive samples4 was initially 86%, which is higher than the median recovery rate for comparable ISO 5 equivalent curation labs like Stardust (33%), Hayabusa (33%), and Cosmic Dust (50%). However, after a month of operation, the recovery rate for these same sites decreased to 43%, which is similar to what we observe in comparable curation cleanrooms with no microbial control requirements. Adding in the new sampling sites further decreases the recovery rate to 30%. With the reduction in recovery rate, we also observed a decrease in microbial diversity. At the first time point, we observed at least 10 different bacterial species and at least two different fungi. This is a higher diversity than the median values for comparable ISO 5 equivalent labs (2-4 isolates per sampling event). After the second sampling, we observed at least 4 bacterial species and no fungi, which is more consistent with comparable labs. We expect the recovery rate and diversity in both labs to continue to decrease as routine operation continues. We will use ultrapure hydrogen peroxide to disinfect equipment and work areas prior to opening any sample containers. Most of the bacterial and fungal isolates were detected on samples from the cleanroom floors. This is consistent with baseline results from other curation labs. Organisms from the genera Bacillus, Staphylococcus, and Micrococcus that were repeatedly detected are common in cleanrooms and on human skin5,6. These organisms are generally thought to be introduced when people enter the cleanroom. Microbacterium, Nocardioides, and Microvirga have also previously been identified in astromaterials cleanrooms, but not as frequently as Bacillus, Staphylococcus, and Micrococcus. Methylocystis is a novel genus in the astromaterials cleanrooms, but it was identified with low accuracy (93% match in the sequenced region of the 16S rRNA gene) and further work is needed to confirm this identification. Microbacterium is a diverse genus with isolates identified from terrestrial and aquatic sediments. Some species of Microbacterium are capable of degrading complex organic compounds found in crude oil. The presence of these bacteria in the OSIRIS REx and Hayabusa2 cleanrooms should be closely monitored. Methylocystis is a genus of methanotrophic bacteria capable of oxidizing methane. If this identification proves to be correct and it is detected again, it should be closely monitored as well. Under nominal operating conditions, samples should not ever encounter the cleanroom floor or other high traffic areas. If we observe an increase in the bioburden in sensitive work areas that appears to be influenced by organism transfer from high traffic areas like the floors, we can employ additional hydrogen peroxide treatments to disinfect high traffic areas. Routine microbial monitoring of these labs will ensure that NASA’s astromaterials collections remain pristine and useful for scientific study. Table 1. Sampling Locations and Colony Counts Bacterial CFUa Fungal CFU Bacterial CFU Fungal CFU Lab - Location 11/2/2021 11/2/2021 12/13/2021 12/13/2021 H2b-Floor 4 8 1 0 H2-staging pass through 3 0 0 0 H2-microtomy pass through TNTCc 0 0 0 H2 Microscope 1 NA NA 0 0 H2 Microscope 2 NA NA 0 0 H2-Table NA NA 0 0 OREXd- microtomy pass through 0 0 6 0 OREX – Anteroom pass through 0 0 0 0 OREX – Floor 1 2 0 0 OREX Witness Foil Table 3 0 1 0 Staging-Floor 16 0 15 0 Microtomy-Floor 3 0 2 0 a: CFU = Colony Forming Unit b: H2 = Hayabusa2 Lab c: TNTC = too numerous to count d: OREX = OSIRIS-REx Lab References: 1. ISO 14644-1:2015 - Cleanrooms and associated controlled environments -- Part 1: Classification of air cleanliness by particle concentration. 37 (2015). 2. McCubbin, F. M. et al. Space Sci Rev 215, (2019). 3. Pincus, D. H. Encyclopedia of Rapid Microbiological Methods (2005). 4. The United States Pharmacopeial Convention. USP General Chapter <1116> 17, 784–794 (2013). 5. Sheraba, N. S., Yassin, A. S. & Amin, M. BMC Research Notes 3, 278 (2010). 6. Utescher, C. L. de A., Franzolin, M. R., Trabulsi, L. R. & Gambale, V. Brazilian Journal of Microbiology 38, 710–716 (2007).

A B Regberg↗
NASA NTRSDate not supplied

EVA Swab Kit: Tools and Techniques for Collecting Aseptic Samples from Crewed Space Missions

Introduction: When we send humans to search for life on other planets, we'll need to know what we brought with us versus what may already be there. To ensure our crewed spacecraft meet planetary protection requirements—and to protect our science from human contamination—we'll need to assess and verify whether micro-organisms may be leaking/venting from our spacesuits. This requires collecting samples under Extravehicular Activity (EVA) conditions. Detailed, systematic research on forward contamination from robotic spacecraft has been steadily progressing since the Viking missions, but systematic studies of contamination from space suits has not been conducted in many years. The modern EMU (Extravehicular Mobility Unit) suit used by NASA is designed to leak at rates as high as 100 cc/min. Before humans land on Mars there is a critical need to understand the types and quantities of microbes that could be introduced via space suits. The Human Forward Contamination Assessment team at NASA’s Johnson Space Center (JSC) has developed a prototype EVA swab tool [1,2,3,4] designed for use in space to sample cleaned and uncleaned space suits to determine the present day microbial load and eventually the rate of leakage. The ability to assess microbial leakage early in advanced space suit and life support system design cycles will help avoid costly hardware redesign later. Test Objectives: The primary objective of EMU testing was to characterize the type of micro-organisms typically found on or near selected suit pressure joints under suit differential pressure conditions. Most human-borne microbes can fit through a 0.5 to 1.0 µm gap. Knowing which joints are more likely to leak will inform hardware design decisions. Knowing which types of micro-organisms may leak from EVA suits provides a basis for subsequent studies to characterize the viability of those organisms under destination conditions, as well as how far they might spread through natural or human-influenced processes. That data, in turn, will inform exploration mission operations and hardware design. The secondary objective of testing was to evaluate the interface between a fully suited test subject and the EVA swab tool at vacuum. Bulky EVA suits can restrict movement and limit visibility through the helmet visor. Fully suited testing is important for identifying tool design issues prior to flight. At exploration destinations, such as Mars, suited crew may be required to periodically sample their suits as part of an environmental monitoring protocol. Suit Microbial Sampling Results: This report details results of microbial swabs collected from current flight suit configurations worn by crew members assigned to upcoming ISS expedition missions as well as swabs collected from prototype suits intended for use on the Orion spacecraft. These tests were intended to characterize the types of contaminants found on flight suits under current, typical handling conditions. No attempt was made to change suit handling procedures, provide additional sterilization, or to limit typical potential contaminant sources. Using culture based techniques, we cultivated 235 CFU (colony forming units) comprised of 26 bacterial species and one fungal species on the outside of the suits. The fungal species and 14 of the bacterial species were unique to the suit surfaces and were not detected in any of the background samples collected within the chambers. We sequenced 755,434 ribosomal fragments on all of the suit surfaces from swab samples. 557,016 of these sequences represent DNA that survived at least 4 hours at vacuum. These sequences formed 2,464 OTU's (Operational Taxonomic Units, 97% similarity) showing low diversity in the samples. The most abundant sequences that survived vacuum belong to the genera Staphyloccocus, Ralstona, Bacillus and Rhodobacter all of which are common to the human microbiome. [5] See Danko et al., (2021) for more complete details of these first analyses. Further analysis of EVA suit materials with respect to the efficacy of various cleaning protocols and engineered containment solutions is planned to inform suit design for NASA’s Artemis Moon to Mars program crew testing. Swab Tool Function Results: The kit was demonstrated for fit and function in suited subject vacuum tests to determine how well the tool worked as an aseptic microbial sampling device as well as to identify any design elements that could be upgraded for EVA task specific improvement. It was found that sample acquisition efficacy could be enhanced by redesign of the sample canister to end-effector interface. Several modifications of the sample caddy assemblies to optimize EVA safety and functionality were also identified. Consequently, fabrication of the redesigned sample canister to end-effector assembly interfaces and and the sample caddy assemblies are required. Fabrication of sixteen flight sample canister assemblies (8 per each of two EVA Swab Kits) and two sample caddy assemblies are in process to be followed by hardware testing and certification to produce two flight-certified EVA Swab Kits for transport to ISS no earlier than summer of 2022. Sampling Strategy: The International Space Station is an ideal testbed for systematic studies of contamination from crewed vehicles since it has been continuously occupied for 20 years and exposed to non-terrestrial conditions. We will sample the exterior of the ISS during EVA using a purpose-built swab tool capable of maintaining sterility while undergoing temperature changes from -151 to +121°C under hard vacuum. Prior to each EVA, the project team will work with ISS mission managers to identify precise sampling locations, which will vary by EVA based on the translation paths and worksites scheduled for that particular EVA. Ideally, translation path handrails and areas near ECLSS (Environmental Control and Life Support System) external vent openings on a spacecraft would be assessed. There are currently more than a dozen ECLSS external vents on the ISS. Some are connected to systems that vent waste products, while others are intended to equalize cabin pressure. As EVA opportunity allows, microbial samples from any of these external vents would provide a valuable data point, though some will be more useful than others. Four criteria have been identified to help prioritize sampling sites near vents: • EVA Accessibility: To minimize cost, it is desired to piggy-back onto a planned EVA. Therefore, the sampling location must be readily accessible by an EVA crew • Type of Vented Products: Vent products that have been in direct contact with crew, such as cabin air, are more likely to contain microorganisms than vent products associated with isolated systems, such as experiment module combustion products. • Mass of Vented Products: Higher-flow vents are more likely to contain detectible levels of microbial contaminants than lower-flow vents. • Local Environment: Sample locations with relatively benign local conditions, such as warm surfaces shielded from direct ultraviolet (UV) radiation exposure, may be more likely to support microbial growth than locations with harsher local environmental conditions. Because EVA accessibility is the most important criteria, the proposal team worked with an astronaut and flight controllers using the Dynamic Onboard Ubiquitous Graphics (DOUG) tool. The DOUG virtual environment allows an operator to “fly” around the current ISS vehicle configuration to assess EVA translation paths, attach points, and keep-out zones. While analysis on station or rapid return to Earth would be preferable, samples collected from the exterior of the ISS have already been exposed to temperature variations between -157 and +121 °C as well as hard vacuum. Therefore, they should be fairly stable and robust. We hypothesize that samples collected from the ISS exterior could be stored for up to 6 months at -80°C without degradation. Sample canisters will be returned to Earth while frozen at -80°C for analysis, and sterilized canisters can be re-flown back to ISS to support additional sampling opportunities Relevance to NASA Exploration Objectives: These data will allow us to identify new or improved methods, technologies, and procedures for spacecraft sterilization and leakage mitigation to minimize the amount of contamination introduced to the environment by human explorers. This work is funded by NASA research grant: NNH18ZDA001N-PPR References: [1] Bell, M.S. et al. (2015) LPS XLVI, Abst. #1832 [2] Rucker et al. (2018) 42nd COSPAR (PPP.3) [3] Bell, M.S. et al. (2019) Mars Extant Life Conference, Abst. #5096.[4] Bell, M.S. et al., (2020) 43rd COSPAR (BO.2).[5] Danko D, et.al.,(2021)Front.Microbiol.12:608478.

Mary Suzanne Bell↗
NASA NTRSDate not supplied

Monitoring Airspace Complexity and Determining Contributing Factors

The national airspace has evolved over many years to accommodate increased traffic demand [1] while simultaneously maintaining one of the safest forms of transportation [2], [3]. One of the reasons for this success is the ability of the system and the operators to adapt and accommodate to situations that routinely disrupt optimal operations. These situations may include: adverse weather, delays, early arrivals, equipment outages, and other factors that are outside the operators ability to control. These factors can lead to states where automation is unable to properly handle these issues and therefore air traffic controllers and pilots have to intervene, ultimately increasing communication between operators resulting in higher workload. As controller workload increases to handle sub-optimal operating conditions this can be viewed as an increase in complexity. The reasoning for this is because humans are now required to make tactical decisions in response to external factors, resulting in a departure from the strategic plan where operations would be more efficiently managed. Human operators control airspace complexity under rigid regulations that are constantly changing. The airspace is divided into sectors and the number of aircraft assigned to each controller is limited for safe handling. There has been past work that devised airspace complexity metrics in commercial aviation and related these metrics to controller workload (e.g., [4],[5]). The upper bounds on the system load are pre-determined. Such bounds on complexity make for a safe system, but the system cannot scale and adapt to autonomous, dense, and heterogeneous traffic, including the many types of Unmanned Aerial Vehicles (UAVs) envisioned to be added to the operations. We hypothesize that, as traffic density and heterogeneity grow, and other key metrics change, there will be phase transitions at which the way traffic should be managed changes significantly [6]. We offer a method for in-time detection of contributing factors that lead to phase transitions, characterized by increased complexity. To the best of our knowledge, there is no tool similar to our proposed effort that identifies such contributing factors or precursor patterns. To define the scope we are proposing to measure complexity from the viewpoint of the Terminal Radar Approach Control Facilities (TRACON) controller’s perspective. In particular we are analyzing arrivals into KSFO. With safety as the top concern for airspace operators, it is important to recognize that as density and heterogeneity grow, the focus of the system will change. Times of the day when the airspace has low density and heterogeneity, the flights will follow more efficient paths where the aircraft move on established routes that are more or less directly to the destination. However, when density and heterogeneity increases, the system will begin changing focus to avoiding conflicts and collisions and route the flights in a more flexible way. Higher flexibility requires more communication and coordination between controllers and pilots which the current automation is unable to handle. This paper proposes a novel approach that monitors airspace complexity at multiple scales, uses a Machine Learning-based tool that predicts when operations will transition to a regime of greater complexity, and identifies actions that can reduce the complexity while still maintaining efficient and safe operations. We demonstrate our proposed approach using data from multiple complementary sources. This includes, but is not limited to: historical aircraft surveillance data from NASA’s Sherlock Data Warehouse [7], METAR weather data, and airport configuration data from Aviation System Performance Metrics (ASPM). The surveillance data flight paths are sampled at a variable sample rate — increasing as the aircraft approaches the airport. This is due to how Sherlock manages flight track stitching between different radar facilities which have different sampling rates. The weather and performance data are logged at defined intervals throughout the day at a courser refresh rate. In addition to the logged data and metrics, we leverage pre-defined Standard Terminal Arrival Routes (STARs) procedures to characterize the path of each flight. Each flight files for one of these routes in the flight plan well before entering the terminal airspace, and approximately follows the route until it leaves the STAR, typically on the final fix of a runway transition. However, most flights do not always fly the full STAR procedure to completion [8], but the majority do adhere to the fixes within the common route of the procedure. Our approach leverages fixes in the common route of each of the STARs to build a reference path to the airport. This allows us to characterize the flight paths in what we are defining as the “maneuvering area” (the airspace between the STAR and before the flight is lined up on the runway’s final approach) to determine how off nominal the flights are to calculate its complexity score. Determining airspace complexity is a concept that does not have a concrete answer. In designing this metric, we consider what increases the workload for the air traffic controllers. Consequently more specialized vectoring maneuvers results in higher workload. Accordingly, we start with a theory: each flight has a direct path it takes from the STAR’s common route to the final approach’s outer marker fix for the flight’s landing runway. It is important to note that the direct path is only used as a reference. If the majority of the flights have a large consistent offset as compared to other routes it does not necessarily mean that those flights have higher complexity. We are merely building a distribution based on this direct path for that particular STAR and runway pair to determine the normal mode of operations for that route. Flights that are in the upper tail of these distributions will result in higher complexity scores and flights that fly in the median will represent the normal mode of operations and therefore will have lower complexity scores. Since flights following each STAR route take different paths to the airport, we have a different distribution for each STAR route and therefore can model these distributions to compute a complexity score from their respective normalized distributions. To evaluate the effectiveness of our proposed airspace complexity metric we will compare against an established approach based on trajectory clustering [9]. This unsupervised learning technique consists of the following steps: (1) identify the general maneuvering areas (waypoints) by performing $\kappa$-means or DBSCAN clustering on locations where aircraft frequently turn based on the surveillance radar track data, (2) map flight trajectories onto sequences of waypoints, and (3) cluster the sequences based on their common subsequences. From a high-level perspective, this baseline model learns nominal operations in the airspace through the sequence of waypoints that are representative of where aircraft change direction and defines deviations from the nominal operations as “complex.” Therefore, more deviations from the nominal operations correspond to higher complexity values. For our validation, we re-implemented this technique and tune model hyper-parameters to correctly detect waypoints for the arrival traffic into the San Francisco bay area. We will compute the complexity measure over a one-year period using our proposed technique as well as the baseline. Our validation will be based on each technique’s ability to detect a set of undesirable outcomes (e.g., go-arounds, holding patterns, average time in the airspace, etc.). Since our current complexity metric is derived from the offset from the direct reference path, it’s important to understand what causes these offsets. In many of the flights with high offset distance, flights performing holding patterns and S turns can be observed. These maneuvering tactics are utilized to add distance between the aircraft and the destination runway to prevent multiple flights from having conflicting arrival times. In order to predict a rise in complexity (or the precursor to complexity), it’s necessary to be able to identify these potential conflicts (which in turn, result in higher offsets). To do this, we define a “representative flight” for each STAR route and runway pair. This flight is approximately the path the flight would take if there was a clear path with no other flights in the airspace — including the time remaining to the airport. We first identify the flights for a given STAR runway pair using the offset to the reference path distributions that fall between the 44-55 percentiles. This yields the flights that conform to the most normal mode of operation. Each of these flights is partitioned based on the percent complete from the entry point into the maneuvering areas from 0\% – 100\% complete. Then for each percent “bin”, we take the median value of the flight’s latitude/longitude coordinates, airspeed, and (non causal) time remaining to the airport to construct a lookup table for each percent complete bin on a given route. As a flight enters the maneuvering area, we can find the estimated arrival time of a flight to the airport by finding the closest point to the representative path’s percent complete bin (relative to the flight’s current position at any snapshot in the airspace) and therefore retrieve the corresponding remaining time left on the “representative path”. We assume that the flight will follow the representative path to completion when deriving these estimates. We can then compare these estimated arrival times against other flights for the same snapshot in time to identify potential conflicts. If more flights are estimated to arrive within a tolerance window than there are runways available, then we have a potential conflict. We can use this derived measure along with other factors expected to add disruption to the operation such as weather and runway configuration changes as an input to machine learning tools to detect precursors that increases in our complexity measure. This novel method will assist in uncovering insights into the contributing factors that lead to increased complexity that may allow for in-time responses to avoid reaching a high complexity state in the airspace.

complexity↗
NASA NTRS2024

Automated Detection of Spurious Signals in VLBI Phase Calibration Data

In this memorandum, a set of processing strategies for automatic masking of phase calibration tones is outlined as implemented in the software package PIMA in the task Generate Phase Calibration Mask, or GEPM. The task relies on a robust procedure of cleaning phase calibration data before employing several mathematical strategies designed to selectively identify spurious signals from phase calibration tones. These strategies were derived as more rigorous implementations of heuristics traditionally used by analysts in manually identifying problematic phase calibration data. The task is intended to automate the process of generating a phase calibration mask and in so doing increase the speed and regularity of VLBI analysis. At the outset of the project, a series of goals were identified to evaluate the success of this mask generation. This included the development of an algorithm to identify and mask short-term (defined as less than 10 seconds in length) spurious signals affecting phase calibration data, the development of an algorithm to identify and mask phase calibration tones affected by constant radio-frequency interference, the implementation of a detection scheme for identifying large jumps in phase calibration phase caused by clock breaks, and finally a method of reporting phase calibration health metrics to the user. Processing of over 10 experiments has demonstrated that the final form of the task GEPM as detailed in this technical memorandum satisfies each of these conditions and satisfactorily performs the task of automatic phase calibration data masking, although the wide variety in quality and characteristics of phase calibration data makes a single solution to the problem quite difficult. A series of user inputs have therefore been defined to assist analysts in tailoring automatic masking to specific stations and phase calibration generators. The vast majority of applicable code was written in FORTRAN to increase execution speed and ease of interfacing with the existing code base in PIMA, but a wrapper function in Python was also written to allow for a simpler method of interacting with GEPM and inputting relevant parameters. In addition to this report, thorough documentation was added to the already existing repositories associated with PIMA as a whole.

Geodesy↗
NASA NTRSDate not supplied

Anomaly Detection for the Roman Space Telescope Wide Field Instrument’s Science Data Processing Pipeline

The Roman Space Telescope (RST) Wide Field Instrument (WFI) will be utilizing a preliminary Science Data Processing (SDP) pipeline during its Integration and Test, and to some extent during Operations, to track basic statistics and identify known features such as cosmic rays, snowballs as well as possible anomalies in raw detector data. In our detectors, these anomalies appear as jumps in the ramp of a readout and are classified as cosmic rays if they appear as a streak or snowballs if they’re more circular. The WFI employs an array of 18 H4RG-10 detectors that collect image samples. Each set of raw frames within a non-destructive exposure is packaged by the SDP pipeline into image cubes for each detector. Each cube is a time series of 4096 × 4096 accumulating pixel frames. The preliminary analysis pipeline is used to locate anomalies in these time-series accumulation frames and identify the type of anomaly, either natural phenomena or detector characteristic. To compare different methods, we’ve implemented both heuristic-based and data-driven methods to identify anomalies. For the heuristic-based approach, we identify snowballs and cosmic rays by the size and shape of outlier pixel clusters between consecutive frames. For data driven methods, we evaluated a Convolutional Neural Network (CNN) model, and more traditional methods like Principal Component Analysis (PCA). CNN is a supervised learning/classification method. Thus, we used a labeled dataset of anomalies to perform segmentation of the image and identify anomalies. We used previously identified cosmic rays and snowballs to measure the accuracy and efficiency of the mentioned approaches. In evaluating these methods, we aim to pick the best fit for the SDP pipeline’s anomaly detection in terms of both performance and runtime.

Paul Horton↗
NASA NTRS1973

Urban and regional land use analysis: CARETS and Census Cities experiment package

The author has identified the following significant results. Areas of post 1970 and 1972 land use changes were identified solely from the Skylab imagery from comparisons with 1970 land use maps. Most land use changes identified involved transition from agriculture to single family residential land use. The second most prominent changes identified from the Skylab imagery were areas presently under construction. Post 1970 changes from Skylab were compared with the 1972 changes noted from the high altitude photographs. A good correlation existed between the change polygons mapped from Skylab and those mapped from the 1972 high altitude aerial photos. In addition, there were a number of instances where additional built-up land use not noted in the 1972 aerial photo as being developed were identified on the Skylab imagery. While these cases have not been documented by field observation, by correlating these areas with the appearance of similar land use areas whose identity has been determined, we can safely say that we have been able to map further occurrences of land use change beyond existing high altitude photo coverage from the Skylab imagery. It was concluded that Skylab data can be used to detect areas of land use change within an urban setting.

Alexander, R. H.↗
NASA NTRS1974

Helicopter derivative identification from analytic models and flight test data

Recent results of stability derivative identification from helicopter analytic models and flight test data are presented. Six and nine degree-of-freedom (DOF) linear models are identified from an analytic nonlinear helicopter simulation using a least square technique. The identified models are compared with the convectional partial differentiation method for obtaining derivatives to form the basis for interpretation of derivatives identified from flight data. Six degree-of-freedom models are identified from CH-53A and CH-54B flight data, using an extended Kalman filter modified to process several maneuvers simultaneously. The a priori derivative estimate is obtained by optimal filtering of the data and then using a least square method. The results demonstrate that a six DOF identified model is sufficient to determine the low frequency modes of motion, but a nine DOF rotor/body model is necessary for proper representation of short-term response.

Molusis, J. H.↗
NASA NTRS1974

Land use mapping in Erie County, Pennsylvania: A pilot study

The author has identified the following significant results. A pilot study was conducted to determine the feasibility of mapping land use in the Great Lakes Basin area utilizing ERTS-1 data. Small streams were clearly defined by the presence of trees along their length in predominantly agricultural country. Field patterns were easily differentiated from forested areas; dairy and beef farms were differentiated from other farmlands, but no attempt was made to identify crops. Large railroad lines and major highway systems were identified. The city of Erie and several smaller towns were identified, as well as residential areas between these towns, and docks along the shoreline in Erie. Marshes, forests, and beaches within Presque Isle State Park were correctly identified, using the DCLUS program. Bay water was differentiated from lake water, with a small amount of misclassification.

Mcmurtry, G. J.↗
NASA NTRS1978

26.3 MHz radio source survey. III - Correlation with extragalactic X-ray sources

The correlation between extragalactic X-ray sources and radio sources in a 26.3-MHz catalog is studied. A list of reliably identified sources is developed by examining X-ray, optical, and radio data for those candidate objects that are in or near the X-ray error boxes. The source 3UR 0432+05 is identified with 3C 120, 3UR 1555+27 (identified with A2142) is shown to be a steep-spectrum radio source, and it is found that 86% of the high-galactic-latitude sources can be reliably identified when the X-ray source error areas are no more than 1 sq deg. The results also indicate that: (1) X-ray sources identified with clusters of galaxies as a whole, with individual galaxies in clusters, and with separate isolated galaxies have similar decametric properties in that their spectral indices and radio luminosities fall in the same range; (2) there is a Bautz-Morgan dependence of both the X-ray and the decametric luminosities of clusters and of individual objects in clusters; and (3) the X-ray luminosity of all except compact sources appears to be approximately proportional to the decametric luminosity.

Erickson, W. C.↗
NASA NTRS1980

First Scientific Working Group Meeting of Airborne Doppler Lidar Wind Velocity Measurement Program

The purpose of the first scientific working group meeting was fourfold: (1) to identify flight test options for engineering verification of the MSFC Doppler Lidar; (2) to identify flight test options for gathering data for scientific/technology applications; (3) to identify additional support equipment needed on the CV 990 aircraft for the flight tests; and (4) to identify postflight data processing and data sets requirements. The working group identified approximately ten flight options for gathering data on atmospheric dynamics processes, including turbulence, valley breezes, and thunderstorm cloud anvil and cold air outflow dynamics. These test options will be used as a basis for planning the fiscal year 1981 tests of the Doppler Lidar system.

Kaufman, J. W.↗
NASA NTRS1989

X-ray bright points and He I lambda 10830 dark points

Using near-simultaneous full disk Solar X-ray images and He I 10830 lambda, spectroheliograms from three recent rocket flights, dark points identified on the He I maps were compared with X-ray bright points identified on the X-ray images. It was found that for the largest and most obvious features there is a strong correlation: most He I dark points correspond to X-ray bright points. However, about 2/3 of the X-ray bright points were not identified on the basis of the helium data alone. Once an X-ray feature is identified it is almost always possible to find an underlying dark patch of enhanced He I absorption which, however, would not a priori have been selected as a dark point. Therefore, the He I dark points, using current selection criteria, cannot be used as a one-to-one proxy for the X-ray data. He I dark points do, however, identify the locations of the stronger X-ray bright points.

Golub, L.↗