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A Model-Driven Visualization Tool for Use with Model-Based Systems Engineering Projects

Model-Based Systems Engineering (MBSE) promotes increased consistency between a system's design and its design documentation through the use of an object-oriented system model. The creation of this system model facilitates data presentation by providing a mechanism from which information can be extracted by automated manipulation of model content. Existing MBSE tools enable model creation, but are often too complex for the unfamiliar model viewer to easily use. These tools do not yet provide many opportunities for easing into the development and use of a system model when system design documentation already exists. This study creates a Systems Modeling Language (SysML) Document Traceability Framework (SDTF) for integrating design documentation with a system model, and develops an Interactive Visualization Engine for SysML Tools (InVEST), that exports consistent, clear, and concise views of SysML model data. These exported views are each meaningful to a variety of project stakeholders with differing subjects of concern and depth of technical involvement. InVEST allows a model user to generate multiple views and reports from a MBSE model, including wiki pages and interactive visualizations of data. System data can also be filtered to present only the information relevant to the particular stakeholder, resulting in a view that is both consistent with the larger system model and other model views. Viewing the relationships between system artifacts and documentation, and filtering through data to see specialized views improves the value of the system as a whole, as data becomes information

relational databases↗

File Specification for GEOS-5 FP (Forward Processing)

The GEOS-5 FP Atmospheric Data Assimilation System (GEOS-5 ADAS) uses an analysis developed jointly with NOAA's National Centers for Environmental Prediction (NCEP), which allows the Global Modeling and Assimilation Office (GMAO) to take advantage of the developments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). The GEOS-5 AGCM uses the finite-volume dynamics (Lin, 2004) integrated with various physics packages (e.g, Bacmeister et al., 2006), under the Earth System Modeling Framework (ESMF) including the Catchment Land Surface Model (CLSM) (e.g., Koster et al., 2000). The GSI analysis is a three-dimensional variational (3DVar) analysis applied in grid-point space to facilitate the implementation of anisotropic, inhomogeneous covariances (e.g., Wu et al., 2002; Derber et al., 2003). The GSI implementation for GEOS-5 FP incorporates a set of recursive filters that produce approximately Gaussian smoothing kernels and isotropic correlation functions. The GEOS-5 ADAS is documented in Rienecker et al. (2008). More recent updates to the model are presented in Molod et al. (2011). The GEOS-5 system actively assimilates roughly 2 × 10(exp 6) observations for each analysis, including about 7.5 × 10(exp 5) AIRS radiance data. The input stream is roughly twice this volume, but because of the large volume, the data are thinned commensurate with the analysis grid to reduce the computational burden. Data are also rejected from the analysis through quality control procedures designed to detect, for example, the presence of cloud. To minimize the spurious periodic perturbations of the analysis, GEOS-5 FP uses the Incremental Analysis Update (IAU) technique developed by Bloom et al. (1996). More details of this procedure are given in Appendix A. The assimilation is performed at a horizontal resolution of 0.3125-degree longitude by 0.25- degree latitude and at 72 levels, extending to 0.01 hPa. All products are generated at the native resolution of the horizontal grid. The majority of data products are time-averaged, but four instantaneous products are also available. Hourly data intervals are used for two-dimensional products, while 3-hourly intervals are used for three-dimensional products. These may be on the model's native 72-layer vertical grid or at 42 pressure surfaces extending to 0.1 hPa. This document describes the gridded output files produced by the GMAO near real-time operational FP, using the most recent version of the GEOS-5 assimilation system. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary. Documentation about the current access methods for products described in this document can be found on the GMAO products page: http://gmao.gsfc.nasa.gov/products/.

GSI↗

Cleaning Genesis Sample Return Canister for Flight: Lessons for Planetary Sample Return

Sample return missions require chemical contamination to be minimized and potential sources of contamination to be documented and preserved for future use. Genesis focused on and successfully accomplished the following: - Early involvement provided input to mission design: a) cleanable materials and cleanable design; b) mission operation parameters to minimize contamination during flight. - Established contamination control authority at a high level and developed knowledge and respect for contamination control across all institutions at the working level. - Provided state-of-the-art spacecraft assembly cleanroom facilities for science canister assembly and function testing. Both particulate and airborne molecular contamination was minimized. - Using ultrapure water, cleaned spacecraft components to a very high level. Stainless steel components were cleaned to carbon monolayer levels (10 (sup 15) carbon atoms per square centimeter). - Established long-term curation facility Lessons learned and areas for improvement, include: - Bare aluminum is not a cleanable surface and should not be used for components requiring extreme levels of cleanliness. The problem is formation of oxides during rigorous cleaning. - Representative coupons of relevant spacecraft components (cut from the same block at the same time with identical surface finish and cleaning history) should be acquired, documented and preserved. Genesis experience suggests that creation of these coupons would be facilitated by specification on the engineering component drawings. - Component handling history is critical for interpretation of analytical results on returned samples. This set of relevant documents is not the same as typical documentation for one-way missions and does include data from several institutions, which need to be unified. Dedicated resources need to be provided for acquiring and archiving appropriate documents in one location with easy access for decades. - Dedicated, knowledgeable contamination control oversight should be provided at sites of fabrication and integration. Numerous excellent Genesis chemists and analytical facilities participated in the contamination oversight; however, additional oversight at fabrication sites would have been helpful.

Allton, J. H.↗

Restoration of Apollo Data by the Lunar Data Project/PDS Lunar Data Node: An Update

The Apollo 11, 12, and 14 through 17 missions orbited and landed on the Moon, carrying scientific instruments that returned data from all phases of the missions, included long-lived Apollo Lunar Surface Experiments Packages (ALSEPs) deployed by the astronauts on the lunar surface. Much of these data were never archived, and some of the archived data were on media and in formats that are outmoded, or were deposited with little or no useful documentation to aid outside users. This is particularly true of the ALSEP data returned autonomously for many years after the Apollo missions ended. The purpose of the Lunar Data Project and the Planetary Data System (PDS) Lunar Data Node is to take data collections already archived at the NASA Space Science Data Coordinated Archive (NSSDCA) and prepare them for archiving through PDS, and to locate lunar data that were never archived, bring them into NSSDCA, and then archive them through PDS. Preparing these data for archiving involves reading the data from the original media, be it magnetic tape, microfilm, microfiche, or hard-copy document, converting the outmoded, often binary, formats when necessary, putting them into a standard digital form accepted by PDS, collecting the necessary ancillary data and documentation (metadata) to ensure that the data are usable and well-described, summarizing the metadata in documentation to be included in the data set, adding other information such as references, mission and instrument descriptions, contact information, and related documentation, and packaging the results in a PDS-compliant data set. The data set is then validated and reviewed by a group of external scientists as part of the PDS final archive process. We present a status report on some of the data sets that we are processing.

Lunar↗

SLS-SPEC-159 Cross-Program Design Specification for Natural Environments (DSNE) Revision E

The DSNE completes environment-related specifications for architecture, system-level, and lower-tier documents by specifying the ranges of environmental conditions that must be accounted for by NASA ESD Programs. To assure clarity and consistency, and to prevent requirements documents from becoming cluttered with extensive amounts of technical material, natural environment specifications have been compiled into this document. The intent is to keep a unified specification for natural environments that each Program calls out for appropriate application. This document defines the natural environments parameter limits (maximum and minimum values, energy spectra, or precise model inputs, assumptions, model options, etc.), for all ESD Programs. These environments are developed by the NASA Marshall Space Flight Center (MSFC) Natural Environments Branch (MSFC organization code: EV44). Many of the parameter limits are based on experience with previous programs, such as the Space Shuttle Program. The parameter limits contain no margin and are meant to be evaluated individually to ensure they are reasonable (i.e., do not apply unrealistic extreme-on-extreme conditions). The natural environments specifications in this document should be accounted for by robust design of the flight vehicle and support systems. However, it is understood that in some cases the Programs will find it more effective to account for portions of the environment ranges by operational mitigation or acceptance of risk in accordance with an appropriate program risk management plan and/or hazard analysis process. The DSNE is not intended as a definition of operational models or operational constraints, nor is it adequate, alone, for ground facilities which may have additional requirements (for example, building codes and local environmental constraints). "Natural environments," as the term is used here, refers to the environments that are not the result of intended human activity or intervention. It consists of a variety of external environmental factors (most of natural origin and a few of human origin) which impose restrictions or otherwise impact the development or operation of flight vehicles and destination surface systems.

Roberts, Barry C.↗

The Value of Identifying and Recovering Lost GN&C Lessons Learned: Aeronautical, Spacecraft, and Launch Vehicle Examples

Within the broad aerospace community the importance of identifying, documenting and widely sharing lessons learned during system development, flight test, operational or research programs/projects is broadly acknowledged. Documenting and sharing lessons learned helps managers and engineers to minimize project risk and improve performance of their systems. Often significant lessons learned on a project fail to get captured even though they are well known 'tribal knowledge' amongst the project team members. The physical act of actually writing down and documenting these lessons learned for the next generation of NASA GN&C engineers fails to happen on some projects for various reasons. In this paper we will first review the importance of capturing lessons learned and then will discuss reasons why some lessons are not documented. A simple proven approach called 'Pause and Learn' will be highlighted as a proven low-impact method of organizational learning that could foster the timely capture of critical lessons learned. Lastly some examples of 'lost' GN&C lessons learned from the aeronautics, spacecraft and launch vehicle domains are briefly highlighted. In the context of this paper 'lost' refers to lessons that have not achieved broad visibility within the NASA-wide GN&C CoP because they are either undocumented, masked or poorly documented in the NASA Lessons Learned Information System (LLIS).

Dennehy, Cornelius J.↗

The GMAO Hybrid Ensemble-Variational Atmospheric Data Assimilation System: Version 2.0

This document describes the implementation and usage of the Goddard Earth Observing System (GEOS) Hybrid Ensemble-Variational Atmospheric Data Assimilation System (Hybrid EVADAS). Its aim is to provide comprehensive guidance to users of GEOS ADAS interested in experimenting with its hybrid functionalities. The document is also aimed at providing a short summary of the state-of-science in this release of the hybrid system. As explained here, the ensemble data assimilation system (EnADAS) mechanism added to GEOS ADAS to enable hybrid data assimilation applications has been introduced to the pre-existing machinery of GEOS in the most non-intrusive possible way. Only very minor changes have been made to the original scripts controlling GEOS ADAS with the objective of facilitating its usage by both researchers and the GMAO's near-real-time Forward Processing applications. In a hybrid scenario two data assimilation systems run concurrently in a two-way feedback mode such that: the ensemble provides background ensemble perturbations required by the ADAS deterministic (typically high resolution) hybrid analysis; and the deterministic ADAS provides analysis information for recentering of the EnADAS analyses and information necessary to ensure that observation bias correction procedures are consistent between both the deterministic ADAS and the EnADAS. The nonintrusive approach to introducing hybrid capability to GEOS ADAS means, in particular, that previously existing features continue to be available. Thus, not only is this upgraded version of GEOS ADAS capable of supporting new applications such as Hybrid 3D-Var, 3D-EnVar, 4D-EnVar and Hybrid 4D-EnVar, it remains possible to use GEOS ADAS in its traditional 3D-Var mode which has been used in both MERRA and MERRA-2. Furthermore, as described in this document, GEOS ADAS also supports a configuration for exercising a purely ensemble-based assimilation strategy which can be fully decoupled from its variational component. We should point out that Release 1.0 of this document was made available to GMAO in mid-2013, when we introduced Hybrid 3D-Var capability to GEOS ADAS. This initial version of the documentation included a considerably different state-of-science introductory section but many of the same detailed description of the mechanisms of GEOS EnADAS. We are glad to report that a few of the desirable Future Works listed in Release 1.0 have now been added to the present version of GEOS EnADAS. These include the ability to exercise an Ensemble Prediction System that uses the ensemble analyses of GEOS EnADAS and (a very early, but functional version of) a tool to support Ensemble Forecast Sensitivity and Observation Impact applications.

Todling, Ricardo↗

Category B Plastic Pane Testing for JSC 66320 Rev A Requirements Verifiable Through Acceptance Testing

JSC 66320, Revision A, Optical Property Requirements for Glasses, Ceramics, and Plastics in Spacecraft Window Systems, lists several quantitative requirements that spacecraft windowpanes must meet. Recently, we were asked to establish a capability at the Kennedy Space Center to perform these measurements on category B plastic panes, i.e., plastic panes that could be used on a spacecraft for long-focal-length photography and piloting. Two of the criteria, normal wavefront and 30-degree wavefront attributes, can be measured with existing equipment and processes (see NASA TM NESC-RP-14-00951, April 2016) and are not discussed in this document. However, the other six criteria-haze, wedge angle, birefringence, reflectance, transmittance, and color balance-required substantial development and are the subject of this document. In this document, we do not discuss the rationale behind the requirements, but we did engage in discussions with the authors of JSC 66320 in order to better understand the requirements and the verifications being imposed on windows and their testing. Accordingly, this document presents our best understanding of the requested requirements and verifications. We also present our methodology for performing each of the six measurements, along with applicable mathematics and a description, with photos, of the hardware used. In addition, we supply the results of a test on a low-quality in-house plastic window as an example of the system operation. Only requirements that can be met by acceptance test and analysis, as opposed to optical inspection, are considered in this document.

spaceflight windows↗

Human Factors Guidelines for Remotely Piloted Aircraft System (RPAS) Remote Pilot Stations (RPS): Guidelines - Version 1.1

This document contains a list of human factors guidelines for remote pilot stations (RPS) arranged within an organizing structure. The guidelines are intended for the remote pilot stations (RPSs) of remotely piloted aircraft (RPA) that are capable of operating beyond visual line-of-sight in all airspace classes of the United States National Airspace System (NAS). Numerous human factors guidelines and standards for technological systems have been published by standards agencies and regulatory authorities. In compiling this document, the intent was not to reproduce or re-state existing human factors material. Instead, this document focuses on the unique issues of civilian RPAS, and contains guidelines specific to this sector. As a result, it should be seen as a supplement to existing aviation human factors standards and guidance material. Two constraints were used to focus the scope of this document. First, the assumptions contained in the FAA (2013a) UAS roadmap were used to define the responsibilities that will be assigned to the pilot of a RPAS operating beyond visual line-of-sight in the NAS. This in turn, helped to define the tasks that the remote pilot must perform via the RPS, and thereby the required features and characteristics of the RPS. Second, the points of difference between RPAS and conventional aviation were used to further focus the guidelines on the considerations that make piloting a RPA significantly different to piloting a conventional aircraft. Five broad categories of guidelines are identified. These are (1) performance-based descriptions of pilot tasks that must be accomplished via the RPS, (2) information content of displays, (3) descriptions of control inputs, (4) properties of the interface, and (5) high-level design considerations. Some of the guidelines in this document have been adapted from existing RPAS human factors material from several sources, including RTCA publications and Standardization Agreements (STANAGs) published by the North Atlantic Treaty Organization (NATO).

unmanned↗

The Global Exploration Roadmap: Opportunities for Lunar Science

The Global Exploration Roadmap (GER) has been developed by the International Space Exploration Coordination Group (ISECG comprised of 14 space agencies) to define various pathways to getting humans beyond low Earth orbit and eventually to Mars. Such pathways include visiting asteroids or the Moon before going on to Mars. This document has been written at a very high level and many details are still to be determined. However, a number of important papers regarding international space exploration can form a basis for this document.This poster will focus on developing the Lunar Vicinity scenario by adding detail via mapping a number of recent reportsdocuments into the GER. The documents highlighted here are in no way meant to be all encompassing and other documents can and should be added, (e.g., the JAXA Space Exploration Roadmap). This exercise is intended to demonstrate that existing documents can be mapped into the GER despite the major differences in granularity, and that this mapping is a way to promote broader national and international buy-in to the Lunar Vicinity scenario.The documents used here are: the Committee on Space Research (COSPAR) Panel on Exploration report on developing a global space exploration program, the Strategic Knowledge Gaps (SKGs) report from the Lunar Exploration Analysis Group (LEAG), the Lunar Exploration Roadmap developed by LEAG, the National Research Council report Scientific Context for the Exploration of the Moon (SCEM), and two journal articles, the scientific rationale for resuming lunar surface exploration, and the astrobiological benefits of human space exploration.In addition, the ISECG is in the process of developing a Science White Paper (SWP) to accompany the next edition of the GER, due in late 2016. The SWP will be an important tool to communicate science which will be able to be accomplished at human exploration destinations to policymakers. This abstract will discuss the process of developing this SWP and ways in which the global science community can become engaged in its development.

Schmidt, Gregory↗

UAS Service Supplier Framework for Authentication and Authorization

The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) Service Supplier (USS) Framework for Authentication and Authorization (UFAA) is the basis for secure and confident data exchanges between the Flight Information Management System (FIMS) and the USS Network and within the USS Network itself. UFAA is built upon the OAuth 2.0 approach to federated authorization, with details supplied by various Internet Engineering Task Force (IETF) Request for Comment documents (RFCs). Whenever possible, a specific standard is used to support a design decision within UFAA, and when an applicable standard is not found/used, the technical decision is explained as much as possible. This document describes UFAA in detail. It is intended to serve as a reference for other key UTM documents and should be useful to many UTM stakeholders. This is an informative document. The requirements stated or implied in this document will need to be fully vetted and reviewed by appropriate stakeholders before becoming operational. It is highly encouraged that in any design decisions for future applicable standards that are counter to or missing from the decisions presented herein be justified in that future standard.

UTM↗

Systems Development, Datamining and Knowledge Discovery Abstract

This summer, 2020, during my NASA internship I worked with my mentor, Ali Shaykhian, as well as a group of four other interns: Javel Gramling, Janelisse Morales, Tristian Running Crane, and Zulmarie Jiménez. Our research and projects all differ but work together to solve datamining unstructured data into an easy to read format. Turning unstructured data into something easier to follow is important for helping quickly pull data out of larger documents, so that one doesn’t have to go through multiple pages to find certain data points. By being able to structure data pulled from a document, it can be usedto collect data from mass amounts of forms and arrange it in an easy to glance at table instead of multiple forms. I chose to focus mainly on creating form templates with both Microsoft Word and Excel and getting used to the types of data that can be collected; as well as learning where both programs differed. After I was familiar with what could be gathered, I worked towards taking data collected by a Word form and importing it into an Excel spreadsheet. By being able to transfer data from a Word document to an Excel document, there is an added layer of functionality to the datamining. Moving data around between Excel sheets isn’t that complex of a process, but when you try to import from a Word document a lot of formatting and readability can be lost. The purpose of my research is to reduce that loss by using Visual Basic scripts to clean and arrange imported data.

Makayla Amber Renfro↗

ISS Payload Operations Training During the COVID-19 Pandemic: Impacts and Solutions

Introduction: The onset and the protracted duration of the COVID-19 pandemic brought a dramatic and rapid transformation to almost every aspect of humanity in 2020. The world’s space agencies and their missions were not immune to the unpredictable and wide-sweeping changes. One discipline principally affected was mission operations and the various groups supporting that critical function. Mission support teams, especially for complex and crewed missions like the International Space Station (ISS) were forced to rapidly rethink how and where control center staff performed their vital work. Operations training – an essential element to mission ops, had unique hurdles to overcome. Operations training is responsible for preparing astronaut crews for their scientific missions, providing recurring proficiency and currency material to flight controllers, as well as ensuring new team members are ready to join their colleagues on console. Every element of training was impacted by COVID restrictions. From orientation and introductory classes for new controllers, training for scientists and payload developers, to simulations and advanced lessons, critical On the Job Training (OJT) and final evaluations; all aspects faced new challenges. Operations trainers at NASA’s Marshall Space Flight Center (MSFC) in Huntsville, Alabama, were forced to rethink, retool, and become more efficient with trainees and training resources in order to continue supporting payload operations on the ISS. Impacts and Barriers to Operations Training: The pandemic started in the United States in early March 2020. Immediately, NASA mandated that the support for ISS real-time operations was critical. As a result, physical access to the ISS Payload Operations Center was restricted to only those people supporting console operations. Thus, use of on-site NASA facilities and any trainer to trainee face-to-face interaction was not authorized. Trainers and trainees had to quickly shift to 100% remote learning. In the short term, this was not a problem since NASA had already prepared Information Technology (IT) resources that facilitated remote access to mission resources like classes, documents and teleconferences. However, instructors quickly discovered the classes that they were accustomed to delivering in a large classroom environment did not translate well to remote teaching. The lack of non-verbal communication with the class became a major barrier to instruction. Another significant hurdle to operations training was the mandate that all simulations could only be held remotely. The logistics of planning and executing even small simulations proved to be challenging, almost impossible, due to IT technology and public internet limitations. With simulations essentially stopped, as well as the restrictions on most OJT training, trainees were essentially stopped in their advancement towards certification. An additional barrier that had to be overcome was the formal documentation of the curriculum objectives. Even though NASA had been deliberately transitioning to electronic documents for years, some organizations were still relying on “pen and ink” documents to formalize training completion. The logistics of implementing secure and controlled e-signature documents in a standardized, NASA approved format had to be addressed. Lesson Learned and Efficiencies: Once the various limitations were identified, ISS trainers at MSFC discussed, proposed, and prioritized new options and techniques. Transitioning to all electronic learning materials and certifications was a relevantly easy fix. Teaching courses to large and diverse groups took additional shifts in the NASA training paradigm. Likewise, methods and procedures for preparing astronauts for their missions were forced to be revised. Simulation supervisors and their teams were also forced to find more efficient techniques to provide realistic training experience. Communication and approvals from management was essential. In every case, the ISS payload operations instructors found novel and efficient solutions to all functions listed above. This paper will discuss the factors and solutions payloads operations trainers found to keep scientific research on the ISS flying forward to mission success.

ISS Operations↗

Logistics Transfer Methods – Offloading SPLCs from CLPS

Presentation is accompanied by "Lunar Logistics Frequently Asked Questions" document. This document contains an evolving set of assumptions and ideas pertaining to the provision, delivery, transportation and accommodation of logistics for the lunar surface operations at Artemis Base Camp. It does not represent requirements, but a starting point from which partners can iterate. Given that the systems and the location for Artemis Base Camp are still being determined, the attached document represents a snapshot in time of ideas and some bounding constraints with respect to logistics operations. Partners are free to challenge operational constructs and assumptions to achieve better solutions. The concepts and assumptions in this document serve as a starting point for innovation to help identify factors that need to be considered for logistics operations and system design. It will be updated periodically as more information is developed and refined, and eventually serve as a basis for documenting ground rules and assumptions, leading eventually to requirements in the future.

Douglas A Craig↗

Low-Speed Performance Enhancement using Localized Active Flow Control: Program Overview and Summary (1/4)

The Boeing team executed a NASA task order, titled “Low Speed Performance Enhancement using Localized Active Flow Control”, under the BAART framework NNL16AA04B, contract number 80LARC20F0082. The project was executed from Sept. 2020 through April 2022 with the objective to explore localized active flow control (AFC) concepts on a representative commercial aircraft. A detailed literature review solidified the choice of AFC concepts to be explored, including AFC over a deflected aileron, in the leading edge region of the wing, and in the nacelle/pylon/wing region. All three concepts were investigated with numerical tools. The results were used in an integration study to assess the net benefits over the lifetime of a commercial aircraft. The aerodynamic studies concluded that AFC applied over a deflected aileron yields significant net L/D improvements (incl. the penalty of AFC system requirements) of up to potentially 5% L/D. Depending on the configuration, AFC applied in the leading edge region showed improvements in L/D of up to ~1.5%, as well as opportunities for maximum lift and increased lift at fixed angle of attack. AFC applied to the nacelle/pylon/wing region delivered 1.5% increase in L/D during take-off and ~4% increase in CLmax during landing. After considering assumed onboard sources, aspects of system integration and weight penalties, these aerodynamic improvements translate to: ~ 0.5% block fuel reduction potential for AFC over the aileron ~ 0.1% block fuel reduction potential for AFC in the LE slat region ~ 0.2% block fuel reduction potential for AFC in the nacelle/pylon/wing region considering take-off scenarios (potentially larger benefit may be realized considering the landing scenarios which haven’t been fully analyzed yet) The final report is comprised of four separate documents. The current document provides an overview and technical background. The second document details the numerical studies and the third document summarizes the integration and aircraft performance assessment. The fourth document focuses on the Common Research Model, specifically how AFC performs over a deflected aileron and how the NASA 10% scale wind tunnel model may be modified to implement an AFC equipped aileron. Each report offers significantly more detailed summaries and suggestions for future work.

Active flow control↗

Directed Acyclic Graphs: A Tool for Understanding the NASA Human Spaceflight System Risks - Human System Risk Board

For over a decade, the National Aeronautics and Space Administration (NASA) has tracked and configuration-managed approximately 30 risks to astronaut health and performance that occur before, during and after spaceflight. The Human System Risk Board (HSRB), a Health and Medical Technical Authority (HMTA) Board at NASA Johnson Space Center, is the entity responsible for identifying, assessing, analyzing, and monitoring the official understanding of the risk or risk posture for each of the Human System Risks and determining – based on evaluation of the available evidence – when that risk posture changes. The ultimate purpose of tracking and researching these risks is to find ways to reduce the risk that astronaut crews face during spaceflight. Historically, research, development and operations relevant to one risk have been conducted in isolation from other risks; these individual risk ‘silos’ enabled initial characterization of each specific risk. In spaceflight however, the impact of exposure to risk for astronaut crews is cumulative, and not independent of exposures or other risks, as all the adverse effects of the spaceflight environment begin at launch, continue throughout the duration of the mission and in some cases across the lifetime of the crews. In January of 2020, the HSRB at NASA embarked on a pilot project designed to assess the potential value of causal diagramming as a tool to facilitate understanding of these cumulative and interdependent effects as applied within Human System Risk management. This process uses directed acyclic graphs as a means of formalizing a shared mental model of the causal flow of risk among Risk Board stakeholders. Initially this model was to improve communication among those stakeholders, but the potential value exceeds communication alone. The causal diagrams are formulated as directed acyclic graphs (DAGs) to function as a type of knowledge graph for reference for the board and its stakeholders. This document is a sister document to NASA/TM 20220006812 Directed Acyclic Graph Guidance Documentation (1). In that document, the basic guidance for creating and standardizing directed acyclic graphs as tools for cross-risk analysis is provided. This document contains the initial configuration managed DAGs that were created as a result of applying those principles. These initial versions were accepted by the HSRB in January of 2022. Each of the Human System Risks are represented by a DAG that has been reviewed by the larger Human Health and Performance community at NASA including life scientists, physical scientists, physicians, nurses, pharmacists, exercise specialists and more. These results show the starting point for Human System Risk DAGs as shared mental models and communication aids across the boundaries of the various expertise needed to understand and mitigate the human risks in spaceflight. Because they are a starting point, each of these DAGs can be expected to change over time as new or refined evidence becomes available. The process for updating these DAGs can be found in the JSC-66705 Human System Risk Management Plan (2) that is publicly available on the NASA Technical Reports Server.

Erik L. Antonsen↗

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗