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NASA NTRS2023

Robotic and Crewed Mars Missions Increasing the Demand for Planetary Protection Technology Needs

Planetary protection (PP) policy seeks to avoid harmful contamination by limiting biological and relevant organic contamination from spacecraft as well as preventing adverse changes to Earth’s biosphere when extraterrestial samples are brought back to Earth. The PP policy at NASA was updated in 2021 (NPR 8715.24) and 2022 (NASA-STD-8719.27) to enable missions by expanding the decades old prescriptive requirements to allow for an option of adopting performance-based requirements that are objectivesdriven, risk-informed and case-assured. In parallel, the final PP knowledge gap workshop was completed representing the international consensus on the key areas to be considered in developing crew PP policy. These knowledge gaps focused on key technology development areas in 1) microbial and human health monitoring, 2) technical and operations needed for contamination control and 3) natural transport of contamination on Mars. As robotic missions start to implement performance-based approaches and research and technology efforts commence to inform crew policy the demand for data quality driven verification and validation in relevant space environments. Examples of the types of testing that is envisioned includes test as you fly validation and verification of decontamination systems in a relevant on-orbit and Mars environment, developing lethality curves of terrestrial organisms to further our understanding of the biocidal impacts of Mars and the space environment, and particle transport model validation and verification. Thus, the PP discipline has identified the need for groundbased space environments to perform preliminary testing as validation and verification of flight systems and to advance the technology readiness level prior to further testing on-orbit or lunar environments to prepare for Mars.

J. Nick Benardini↗
NASA NTRSDate not supplied

Robotic and Crewed Mars Missions Increasing the Demand for Planetary Protection Technology Needs

Planetary protection (PP) policy seeks to avoid harmful contamination by limiting biological and relevant organic contamination from spacecraft as well as preventing adverse changes to Earth’s biosphere when extraterrestrial samples are brought back to Earth. The PP policy at NASA was updated in 2021 (NPR 8715.24) and 2022 (NASA-STD-8719.27) to enable missions by expanding the decades old prescriptive requirements to allow for an option of adopting performance-based requirements that are objectives-driven, risk-informed and case-assured. In parallel, the final PP knowledge gap workshop was completed representing the international consensus on the key areas to be considered in developing crew PP policy. These knowledge gaps focused on key technology development areas in 1) microbial and human health monitoring, 2) technical and operations needed for contamination control and 3) natural transport of contamination on Mars. As robotic missions start to implement performance-based approaches and research and technology efforts commence to inform crew policy the demand for data quality driven verification and validation in relevant space environments. Examples of the types of testing that is envisioned includes test as you fly validation and verification of decontamination systems in a relevant on-orbit and Mars environment, developing lethality curves of terrestrial organisms to further our understanding of the biocidal impacts of Mars and the space environment, and particle transport model validation and verification. Thus, the PP discipline has identified the need for ground-based space environments to perform preliminary testing as validation and verification of flight systems and to advance the technology readiness level prior to further testing on-orbit or lunar environments to prepare for Mars.

J Nick Benardini↗
NASA NTRS2023

Human Landing System (HLS) Program Non-Design Driver Lunar Environments

The purpose of this white paper is to provide a source of information on lunar environments which, according to scientific and engineering consensus, should not impact human-class spacecraft design. Additionally, comparisons are shown between the Apollo missions and Artemis mission parameters for solar lighting angles at the landing sites and plasma region exposure.

HLS↗
NASA NTRSDate not supplied

Optimizing a Small RNAseq Analysis Pipeline for NASA GeneLab Using Open-Source Tools and Libraries

Small RNA sequencing (small RNAseq) is a powerful tool for studying the regulation of gene expression in various organisms. Small RNAseq has been leveraged in space biology research to study how expression of small RNAs, e.g. micro RNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs), change upon exposure to the space environment. NASA GeneLab currently hosts small RNAseq raw data derived from space-relevant experiments on the Open Science Data Repository (OSDR). To maximize the accessibility of these data to the scientific community, in addition to hosting raw data, which is only interpretable by bioinformaticians, GeneLab plans to process all small RNAseq datasets and make those processed data available to the scientific community via the OSDR. In this study, we present the development of the GeneLab standardized pipeline for processing small RNAseq datasets. Using human, plant, and synthetic small RNAseq datasets, we interrogate various open-source software and publicly available databases to evaluate their accuracy and reproducibility in each step of the pipeline. For quality control and adapter detection and trimming, we evaluated TrimGalore!, FASTX, SeqKit, and DNApi methods to optimize alignment to reference genomes. We compared BWA, Bowtie, and Bowtie2 to determine the optimal alignment tool. For each alignment tool we also assessed various reference databases, including Ensembl reference genomes and different types of small RNA reference databases, including genome, hairpin, and miRNA references from the miRbase and MirGeneDB databases. To quantify the aligned data, we compared SAMtools, HTSeq, and RSEM for counting alignment events from each alignment tool used. Finally, we evaluated various tools, including DESeq2 and EdgeR, for data normalization and subsequent differential expression analysis. We will present the results from our comparative analyses for each pipeline step and propose a consensus pipeline for processing small RNAseq data derived from various organisms exposed to the space environment.

SmallRNAseq, NASA GeneLab, quality control, adapte↗
NASA NTRSDate not supplied

Viper Science Operations: Lunar Dynamic Science Table and ‘Tracker’ Tool.

Introduction: The NASA VIPER lunar rover mission [1] presents a unique operational paradigm within the history of robotic spaceflight. The proximity of the Moon to the Earth and the terrain elements (surface characteristics, light/shadow dynamics, communication links) of the Lunar South Polar landing site create unprecedented operational conditions between these two planetary bodies. Apollo era lunar science and exploration included humans in situ to operate instruments and assimilate observational inputs in real-time. Previous lunar orbital missions have worked to operational timescales, e.g., decisional timelines and communication exchanges, that were weeks in duration. Mars rover missions have worked to operational timescales, e.g., decisional timelines and communication exchanges between Mars and Earth, that were hours, days, and weeks in length. In the case of the VIPER mission, our operational decisioning for rover driving and instrument commanding will be compressed to minute-scale timeframes. These operational conditions will directly impact the workflow and speed with which the VIPER Science Team (VST) will be required to synthesize and analyze data and produce timely science-driven decisions throughout surface mission operations [2]. The VST in the VIPER Mission Science Center (MSC) and the Mission Operations Center (MOC) shall provide mission-enhancing scientific input to guide traverse planning and drill site confirmation/selection throughout surface operations. Further, the VST input will be of vital importance to the mission’s ability to maximize science return and to meet broader NASA objectives for future lunar ISRU and exploration activities. Specifically, the VST in the MSC and MOC will provide science-driven, consensus-based, timely input and decision-making to enhance mission operations and align mission science return with broader Agency goals. They will enable the characterization of the distribution (lateral and vertical extent, concentration, variability), form (chemical/physical state of these reservoirs of lunar water and key isotopes), and context (e.g., accessibility/overburden, environment, soil mechanics, trafficability, and temperatures) of lunar polar volatiles and water content for the VIPER mission. Additionally, the MSC will be selecting or reconfirming the location and path towards and from the third drill site (Drill Site Charlie) within each Science Station [6]. To enable scientific decision-making within the operational paradigm of the VIPER lunar rover mission requires detailed articulation of the VST’s scientific objectives and goals, and the operationalization of these objectives and goals through their association with specific data products, tasks, and decisional procedures. Further, defining and tracking scientific success metrics throughout surface operations will enable the VST to have a quantified understanding of the mission’s evolving ability to accomplish the stated scientific objectives and goals both during and after the mission. This abstract provides an overview of the methods and development activities towards defining, operationalizing, and tracking scientific objectives and goals throughout VIPER surface operations. Specifically, we focus on the VIPER Lunar Dynamic Science Table (LDST) and the VIPER “Tracker” tool.

Darlene Sze Shien Lim↗
NASA NTRS2024

Non-interference of Lunar Activities

As dozens of countries and private sector companies have expressed interest in establishing lunar operations by the end of the decade, including many in the South Pole region, it will be critical to determine how to minimize interference in lunar activities. Although lunar interference concerns have been broadly identified, and deconfliction has been identified as an area of further work in Section 11 of the Artemis Accords, there is not broad consensus in the lunar scientific or technical community on key questions such as general principles for non-interference or mechanisms for mitigation. This paper presents a draft framework for review and feedback from the scientific and technical community to determine the breadth of interference concerns and clarify community usage of the terms “interference”, “contamination”, and “deconfliction”.

Marissa S Herron↗
NASA NTRS2024

Non-interference of Lunar Activities (Town Hall)

As dozens of countries and private sector companies have expressed interest in establishing lunar operations by the end of the decade, including many in the South Pole region, it will be critical to determine how to minimize interference in lunar activities. Although lunar interference concerns have been broadly identified, and deconfliction has been identified as an area of further work in Section 11 of the Artemis Accords, there is not broad consensus in the lunar scientific or technical community on key questions such as general principles for non-interference or mechanisms for mitigation. This session seeks feedback from the scientific and technical community to determine the breadth of interference concerns and clarify community usage of the terms “interference”, “contamination”, and “deconfliction”. The session proposes a framework for further deconfliction activity.

Marissa S Herron↗
NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗
NASA NTRS2024

Behavioral Makers to Assess During Selection for Active and Non-Active Astronauts

INTRODUCTION: NASA and our international partners seek to stay ahead of the evolving nature of the astronaut role. Spaceflight comes with various risks to team and individual. NASA has worked with its international partners in developing selection guidelines for minimal behavioral competencies needed upon selection. Training should be provided on the remaining behavioral competencies required for spaceflight. We will provide an overview of the process of identifying competencies important to assess as part of a multi-method selection program for active and non-active astronauts. TOPIC: NASA, ESA, CSA, and JAXA agency representatives used their vast combined knowledge and experience to develop competency requirement standards that future selection programs should assess when selecting astronauts. The astronaut role is expanding to include astronauts whose purposes are quite varied. NASA and its international partners identified the need to identify what competencies are required for anyone who will be experiencing the incredible demands, risks, and unique circumstances associated with spaceflight. Competencies include those required for individual and team functioning within the isolated, confined, and extreme environment. This need grew out of greater commercialization of spaceflight and other budding spaceflight prospects. 1. Each agency rated previously defined spaceflight required competencies (not important 0, somewhat important .5, or extremely important 1) to assess during selection. We did this for short duration (<14 days) and for long duration (<14 days) missions as some competencies appear to become of greater importance with longer duration missions. 2. We rated competencies for non-active crew and revisited previous ratings for active crew. 3. We compared agency ratings for non-active/active and short-duration/long-duration requirements and discussed in ratings until consensus was reached. We will use ratings to compile selection standards for active and non-active astronauts. APPLICATION: Our working group has vast operational experience and knowledge of the risk factors present during spaceflight that can disrupt individual and team functioning. Selection is one of the most important countermeasures for mitigating risk to individual and team health and performance. Our work should inform future programs for selecting crewmembers for short- and long-duration spaceflight among active and non-active crews.

Alexa Baxley↗
NASA NTRS2024

Components Refurbishment and Chemical Analysis Facility, Hot Spot 1 Solid Waste Management Unit #041 Year 4 Annual Performance Monitoring Report Kennedy Space Center, Florida

This Year 4 Annual Performance Monitoring Report (PMR) presents the operations, maintenance, and monitoring activities for the Hydraulic Containment System (HCS) Interim Measure (IM) at the Components Refurbishment and Chemical Analysis (CRCA) facility located at John F. Kennedy Space Center (KSC), Florida. The primary objective of the HCS is to attain hydraulic control of the dissolved-phase chlorinated volatile organic compound (CVOC) plume, with the secondary objective to reduce concentrations of CVOCs in the high-concentration plume to support transition to monitored natural attenuation (MNA). CRCA has been designated Solid Waste Management Unit 041 under the KSC Resource Conservation and Recovery Act Corrective Action Program. The timeframe for activities documented in this Year 4 PMR extends from November 2022 through September 2023. Baseline sampling activities were completed in June 2019, and full-scale startup of the HCS IM was completed in July-August 2019. The operational runtime of the HCS for the Year 4 reporting period was approximately 94%, with the majority of downtime attributed to associated groundwater sampling events, maintenance, and Hurricane Nicole. Almost five million gallons of groundwater were treated during Year 4 of HCS operations, and concentrations of the site’s contaminants of concern (trans-1,2-dichloroethene and vinyl chloride) have been reduced by over 99%. This PMR describes the activities that were performed during Year 4 to operate and monitor the HCS IM, which includes three extraction wells, seven injection wells, and conveyance piping to a modular structure containing the control panel and an air stripper. Influent and effluent sampling results from the air stripper show that the system is operating as designed and is reducing concentrations of contaminants of concern to below detection limits. In addition to HCS operation, this PMR also discusses performance monitoring that has been implemented to assess progress of the HCS IM and overall plume conditions through scheduled groundwater (quarterly and semi-annual) and sub-slab soil gas (quarterly) sampling and analysis. Two ambient air samples were also collected on a quarterly basis in the vicinity of the modular structure and the paved driveway east of the Solvent Reclamation Area during routine operation and maintenance (O&M) activities to ensure safe breathing zone air quality for on-site personnel. All sub-slab soil gas and ambient air sampling conducted during the Year 4 operational period showed results below applicable regulatory air screening limits. Predictions made during the Year 2 groundwater model updates were in close correlation to post Year 4 plume conditions. A supplemental DPT study conducted in 2022 and 2023. This study indicated that low-concentration plume conditions, where concentrations exceed State of Florida Groundwater Cleanup Target Levels, expanded westward to Kennedy Parkway North and northward to the vicinity of the railroad tracks. Based on these results, recommendations were made to install 14 wells to monitor the downgradient and boundary conditions of the expanded LCP. The contents of this Year 4 PMR were presented during the November 2023 KSC Remediation Team meeting, where Team consensus was reached on several items including continued O&M of the HCS, and continued monitoring of groundwater, ambient air, and sub-slab soil gas. Sampling for per- and polyfluoroalkyl substances at CRCA is ongoing and will be submitted under separate cover.

K. Alex Murphy↗
NASA NTRS2024

Recommendations on Evidence and Process for Certification of Learning-enabled Components in Aerospace Systems

This report primarily identifies a collection of relevant and necessary evidence for assurance of machine learnt components (MLCs)—also known as learning-enabled components—integrated into aircraft systems, and gives preliminary suggestions on the elements of a certification process that invoke the identified evidence. The main focus is on feedforward neural networks that are static and trained offline through supervised learning. A brief background on the generic elements of the lifecycle of an MLC is given to contextualize the assurance considerations and, consequently, the evidence that is relevant and necessary to support certification. At the level of an MLC, those considerations relate to: (i) the consistency and correctness of MLC contributions to system functions in the context of a validated functional intent; and (ii) the absence of MLC contributions to aircraft-level failure conditions. At an ML model level, confidence in model and data properties contribute to assurance of the containing MLC, in particular: (a) generalizability and robustness of models, in the presence of inputs not previously seen during training, disturbances to inputs, and unexpected inputs; and (b) valid data, i.e., data that are at least representative, relevant, complete, and accurate. Evidence for the above span the elements of the ML lifecycle, and includes, at a minimum, lifecycle artifacts that pertain to: (1) properties of requirements capturing functional intent, safety constraints, and aspects of the intended use and operating environment; (2) model performance, model complexity and design, and algorithm choice; (3) achievement of required performance at the levels of a trained model during model development, a trained model after model development is complete, and a trained model that is transformed into an executable equivalent; (4) model implementation aspects necessary for transforming a trained model into the executable equivalent; (5) integration of the executable trained model into the containing MLC, and eventually the larger system; and, (6) lastly, the verification and validation (V&V) of each of the above. Such V&V lifecycle artifacts themselves include: aspects of coverage, e.g., of various levels of requirements by the input space of the model and the data; traceability (where applicable); application of formal methods for property specification, analysis, and checking. Examples of evidence generation methods and tools further ground the discussion on what constitutes evidence, and the contribution to assurance during certification. The identified assurance considerations and supporting evidence is not a comprehensive set. Additionally, neither what should be considered as sufficient evidence relative to the assigned criticality of an MLC, nor how criticality ought to be determined and adjusted, have been considered in this report. However, suggestions are made for potential activities of the ML lifecycle that are aimed at providing confidence that an MLC can be relied upon when integrated into its containing (aircraft) system. Those activities are proposed as candidate elements of a certification process for MLCs. The main purpose of this report to inform regulatory guidance and consensus standards that may be used to meet the safety intent of the applicable regulations.

Aviation safety↗
NASA NTRS2024

Smallsat 2024 - Starling Cubesat Swarm Technology Demonstration Flight Results

The Starling swarm of four 6U CubeSats launched in July 2023 to test four key technologies to enable future swarm missions: 1) Mobile Ad-Hoc Networking (MANET) over a crosslink radio network 2) Autonomous onboard decision-making for operations 3) Optical-based absolute and relative navigation 4) Autonomous maneuver planning and execution The Starling team implemented the Better Approach to Mobile Ad-hoc Networking (B.A.T.M.A.N.) protocol to automatically manage the crosslink network of four satellites. The B.A.T.M.A.N. protocol uses a decentralized approach to managing a multi-hop mesh network of devices, in this case, a satellite swarm. The four satellites were able to successfully establish a network at multiple data rates and demonstrate file transfer and command issuance between spacecraft over the network. Starling incorporated Distributed Spacecraft Autonomy's (DSA) software to demonstrate onboard decision-making. The DSA software takes L1/L2 band GPS measurements and uses them to estimate the relative Total Electron Count (TEC) in the ionosphere. The onboard software then determines if there are any features of interest and provides that information to the other satellites over the crosslink network. The swarm of satellites then reaches a consensus on the optimal TEC observation strategy and adjusts its measurement collection tactics autonomously. The Starling Formation-Flying Optical Experiment (StarFOX), produced by Stanford's Space Rendezvous Laboratory, uses the onboard star trackers to collect images of the other swarm spacecraft and produce angles-only navigation estimates. This system is envisioned to be valuable in applications in which Global Navigation Satellite Systems (GNSS) are not available, such as in cis-lunar or deep space. StarFOX successfully applied its algorithms to multiple simultaneous spacecraft targets using the star tracker imagery. Finally, Starling used Emergent Space's Cluster Flight Application (CFA) software suite for the Reconfiguration and Orbit Maintenance Experiments Onboard (ROMEO) demonstration of autonomously planning and executing propulsive maneuvers. Large swarms will need to be able to maintain formation requirements with minimal operator involvement, especially as the size of the swarm scales up. Results from the ROMEO experiment are presented. Starling is funded by the Small Spacecraft Technology (SST) program out of NASA's Space Technology Mission Directorate (STMD).

distributed systems↗
NASA NTRSDate not supplied

NASA AREAL Testbed and UE Power Quality Testing

A large number of electrified aircraft propulsion systems currently in development consist of high-voltage direct current (HVDC) power systems and utilization equipment (UE). There is a need for industry consensus standards and guidelines in order to support the development and verification of these rapidly maturing systems, user equipment, and associated technologies. To support the aforementioned need, the National Aeronautics and Space Administration (NASA) Revolutionary Vertical Lift Technology (RVLT) Project is performing ongoing power quality testing on HVDC power systems and associated UE. The testing described in this paper describes the UE testing completed to date in the NASA Advanced Reconfigurable Electrified Aircraft Lab (AREAL).

electrified aircraft↗
NASA NTRSDate not supplied

Multi-Party Flight Trajectory Negotiation for Upper Class E Traffic Management

A new operational concept has been proposed in Upper Class E airspace at or above 60,000 feet (Flight Level / FL600), which will allow operators of diverse vehicle characteristics to cooperatively manage and share their operational intents with neighboring operators to avoid conflict. There is a consensus in the community that negotiation for strategic deconfliction is needed, but there are no specific guidelines for how the negotiation should be conducted. There is a need for a structured and cooperative way to resolve the conflict between a wide variety of aircraft projected to be operating in Upper Class E for the negotiation to be carried out routinely. The use of negotiation models are a promising solution that can resolve conflict risks during flight in real-time while taking into account the uncertainty of future vehicle positions and dynamic business considerations. A two-party negotiation model has been researched, but as the traffic demand grows, there is a higher likelihood of conflict involving multiple aircraft that would require a method to handle multi-party conflict. This paper proposes a cooperative multi-party negotiation model inspired by game theory concepts for application to flight trajectory negotiation in Upper Class E traffic management. This model can be applied in flight with operators communicating directly after a potential conflict is detected. Some of the model’s benefits include allowing business costs to be private to operators, allowing operators to collaborate together to find conflict-free flight trajectories, and being compatible with different aircraft and operation types. This model provides a structured procedure for conflict resolution that can handle conflict involving multiple parties, assuming each operator is willing to take on a small cost to themselves in order to reduce the total cost to the group.

Upper Class E Traffic Management (ETM)↗
NASA NTRSDate not supplied

Non-Interference of Lunar Activities

As dozens of countries and private sector companies have expressed interest in establishing lunar operations by the end of the decade, including many in the South Pole region, it will be critical to determine how to deconflict lunar activities. Although lunar interference concerns have been broadly identified, and deconfliction has been identified as an area of further work in Section 11 of the Artemis Accords, there is not broad consensus in the lunar scientific or technical community on key questions such as how to identify relevant sites, the extent of protection needed, or mechanisms for mitigation. This paper seeks feedback from the scientific and technical community to determine the breadth of interference concerns and clarify community usage of the terms “interference”, “contamination”, and “deconfliction”. The paper proposes a framework for further deconfliction activity.

Lunar↗
NASA NTRSDate not supplied

Beyond Fair: Engagement, Data Usability, and Open Community Productivity through the NASA Open Science Data Repository

The FAIR principle (findable, accessible, interoperable, and reusable) governs the storage and sharing of NASA space biology and health data[1]. These guiding principles maximize reuse of data and the reproducibility of scientific findings. The NASA Open Science Data Repository (OSDR; an expansion of NASA GeneLab) was built on the FAIR principles and houses over 500 studies and close to 1000 datasets from decades of space life sciences experiments. OSDR embodies the FAIR principles through data governance that includes mediated, embargoed, and fully open access data. The FAIR data governance principles were recently proposed to be expanded to encompass a FAIREST framework for assessing research data repositories (FAIR + Engagement, Social connections, and Trust)[2]. FAIREST emphasizes the importance of data repositories engaging with the scientific community and gaining the trust of researchers regarding data quality. Trust also refers to the TRUST principles developed for assessment of digital repositories: Transparency, Responsibility, User Focus, Sustainability, Technology[3]. We present the “Open Science for Life in Space” Analysis Working Groups (AWGs) as evidence regarding the power of engagement, social connections, and trust which has enhanced OSDR’s capabilities and productivity. AWG members engage in two main activities. One, members provide feedback on OSDR scientific standards for data ingestion, curation, and reuse (study, subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability). Two, AWG members collaborate to mine-reuse OSDR data to conduct scientific analysis. With nearly 800 active members, the AWGs have resulted in 32 publications re-using OSDR data and contributed many papers in two major special issues in Cell (2020) and Nature (2024). AWGs also serve as networking groups, facilitate social connections between researchers at all levels of experience, and also have a social online ‘Forum’ used to keep members informed on projects and opportunities. This community-centric, productive, and trustworthy data culture has resulted in a broader effect with international space agencies, academics, and the commercial space sector wanting to submit their data to OSDR. Ten studies of Inspiration 4 data were recently publicly released by OSDR, as were some JAXA human data. Coming up soon in OSDR are data submissions from the European Space Agency, Virgin Galactic PIs, and SpaceX Polaris Dawn. A major benefit of OSDR is the array of standardized and uniformly formatted data (which was developed through AWG member consensus), from which visualization tools, analysis tools, and machine learning models can be built or trained. This talk will cover the Multi-Study Visualization Tool, the Environmental Data Application, RadLab, and a UCSF-NSF funded knowledge graph biomedical health discovery tool ‘SPOKE’ currently being integrated with OSDR. OSDR also provides training programs in bioinformatics and machine learning to improve the scientific community’s awareness of data availability and to boost their ability to perform data analysis. The increasing engagement of the scientific community and the public with technologies powered by artificial intelligence (AI) heightens the need for data analysis to be transparent. The AI for Life in Space initiative leverages the data products provided in OSDR to train AI models, with an emphasis on explainable and trustworthy AI, which would not be possible without FAIR data and metadata. Overall, here we will demonstrate the importance for NASA life sciences data repositories to adhere to the FAIREST framework, by providing examples and success stories from different aspects of OSDR.

data↗
NASA NTRSDate not supplied

Non-Interference of Lunar Activities

As dozens of countries and private sector companies have expressed interest in establishing lunar operations by the end of the decade, including many in the South Pole region, it will be critical to determine how to deconflict lunar activities. Although lunar interference concerns have been broadly identified, and deconfliction has been identified as an area of further work in Section 11 of the Artemis Accords, there is not broad consensus in the lunar scientific or technical community on key questions such as how to identify relevant sites, the extent of protection needed, or mechanisms for mitigation. This paper seeks feedback from the scientific and technical community to determine the breadth of interference concerns and clarify community usage of the terms “interference”, “contamination”, and “deconfliction”. The paper proposes a framework for further deconfliction activity.

Lunar↗
NASA NTRS2024

Launch Complex 39B, SWMU 009, 2023 Performance Monitoring and Air Sparge Expansion Construction Completion Report, Kennedy Space Center, Florida

The 2023 Performance Monitoring and Construction Completion Report (PM-CCR) presents the findings, observations, and results for Air Sparging (AS) operations and expansion activities, as well as sitewide groundwater monitoring for Launch Complex 39B (LC39B), Solid Waste Management Unit (SWMU) 009, at Kennedy Space Center (KSC), Florida. The reporting period for activities covered under this PM-CCR is from January 1, 2023, to December 31, 2023. At LC39B, AS operations began in 2017 in the area west of the launch pad, in the liquid oxygen (LOX) tank area located northwest of the launch pad, and in an area outside of the perimeter fence to protect nearby Outstanding Florida Waters (OFW). The LC39B AS system was installed with 279 AS wells to depths ranging from 23 to 60 feet below land surface (bls), including the sump, correlating to top of screen depths ranging from 20 to 57 feet bls. In December 2022, a total of 22 AS wells were abandoned to support launch pad crane operations, and in November 2023, the system was expanded with five additional AS wells installed to 13 or 17 feet bls near the LOX tank. The remedial objective of the LC39B AS Interim Measure (IM) is to actively decrease concentrations of contaminants of concern (COCs) in groundwater, specifically trichloroethene (TCE), cis-1,2-Dichloroethene (cDCE), and vinyl chloride (VC), to less than their respective Natural Attenuation Default Concentrations (NADCs), so LC39B can transition into a Long-Term Monitoring (LTM) program. This PM-CCR presents the following information for LC39B: • AS system operations and maintenance (O&M) (Year 7 of operation) from January 2023 to December 2023, to include AS trailer relocation in March 2023 and subsequent replacement and re-start in June 2023. • Construction completion details for AS system expansion, which included installation of five new AS wells and one new monitoring well in November 2023. As part of expansion activities, soil samples were also collected for petroleum analysis; no exceedances were identified, and no further investigation for petroleum is warranted. • Performance monitoring results for groundwater sampling events conducted in May/June 2023 (30 wells) and November 2023 (31 wells) in the AS IM area and in the Low Concentration Plume (LCP) areas located north and east of the launch pad for volatile organic compound (VOC) analysis. • Sampling results for one monitoring well, LOX-IW0012S, which is sampled for aluminum on an annual basis (May/June 2023). This well was resampled in November 2023 for both total and dissolved aluminum. Due to a communication error with the laboratory, the May/June 2023 sample was analyzed for total aluminum only. • Groundwater sampling results for per- and polyfluoroalkyl substances (PFAS) collected from seven monitoring wells during the May/June 2023 event to further investigate the Former Sewage Treatment Plant #6 and Percolation Pond area, west of the launch pad. O&M and performance monitoring results show that the AS system at LC39B is operating as designed and meeting performance criteria. Overall runtime was 45 percent (%) during the reporting period (January to December), but the operational runtime was 78% during the timeframe when the system could run (June to December). The most significant downtime contributor was post-launch crane operations following the Artemis launch on November 16, 2022, which lasted until June 2023. During that timeframe, the AS trailer at LC39B was relocated to another KSC remediation site (Wilson Corners) and was subsequently replaced with the AS trailer from the Paint & Oil Locker (POL) remediation site at KSC to resume AS system operations. Performance monitoring results in the AS IM and LCP areas continue to show reduction in COC concentrations over time when compared to baseline levels. In 2023, only one monitoring well (MW0048) detected a COC exceeding its NADC (VC at 740 micrograms per liter [µg/L]), which marks the baseline result for this new well installed during system expansion. Across the rest of the site, VC concentrations have declined or remained stable during the 2023 sampling events. Excluding MW0048, the highest VC result in 2023 was during the May/June sampling event with a concentration of 63 µg/L at MW0032, which is located near MW0048 and the AS expansion area by the LOX tank. TCE was detected in select monitoring wells in the IM area in 2023, but only two locations exceeded the State of Florida Groundwater Cleanup Target Level (GCTL): MW0032 (21 µg/L in May/June 2023 and 9.1 µg/L in November 2023) and MW0036 (5.0 µg/L in November 2023). MW0036 is also located near the LOX tank, on the north side, where the AS system is still operational (Zone Z4). cDCE and trans-1,2-dichloroethene concentrations were less than laboratory method detection limits or their respective GCTLs in all wells sampled in 2023. Near the OFW located northwest of the launch complex, all COC concentrations were less than laboratory method detection limits from monitoring wells (MW0039, MW0040, and LOXTA0002S) sampled in 2023. Aluminum results from LOX-IW0012S, which has been sampled routinely since 2006, detected a total aluminum concentration of 3,900 µg/L during the May/June 2023 sampling event. Results from the November 2023 event detected 5,700 µg/L for total aluminum and 5,500 µg/L for dissolved aluminum. These concentrations slightly decreased from the previous year but remain relatively consistent with historical detections. Aluminum will continue to be sampled on an annual basis at this well as results still exceed the GCTL of 200 µg/L and the Upper Limit of the KSC Background Concentration of 280 µg/L. PFAS results detected nine different PFAS compounds (out of 32 analyzed) from seven wells sampled. Two PFAS compounds, perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), currently have FDEP Provisional GCTLs of 70 nanograms per liter (ng/L). All seven samples collected resulted in concentrations less than the FDEP Provisional GCTLs for both PFAS compounds; no exceedances were observed. PFOS and PFOA also have assigned United States Environmental Protection Agency (USEPA) Maximum Contaminant Levels (MCLs) of 4 nanograms per liter (ng/L). None of the PFOS results exceeded the USEPA MCLs. PFOA was detected in two samples above the USEPA MCL at concentrations of 5.8 ng/L (ECS-IW0009I) and 5.5 ng/L (ECS-IW0009S). Three other PFAS compounds, perfluorohexanesulfonic acid (PFHxS), perfluoro-n-nonanoic acid (PFNA), and hexafluoropropylene oxide dimer acid (GenX), currently have USEPA MCLs of 10 ng/L. PFHxS, PFNA, and GenX were not detected at concentrations greater than their respective USEPA MCLs in any of the seven wells. PFAS compounds without FDEP Provisional GCTLs or USEPA MCLs were screened against USEPA RSLs. No other detections exceeded their respective USEPA RSLs. Additional PFAS sampling will be conducted as part of a future PFAS Site Assessment. Based on O&M activities and performance monitoring, the following is recommended for LC39B: • Continue with Year 8 AS system operation within Zone Z4, which includes the AS expansion area. Zone Z3, which has been off since 2018, should remain off as no rebound has been observed. Zones Z1 and Z2, which were turned off at the end of 2022, will remain shut down as monitoring well results have consistently been below GCTLs or have low-level detections with stable or decreasing trends (Meeting Minute 2402-M10, Decision 2402-D30). • Continue with performance monitoring in 2024 with the same monitoring well network as 2023, except with the addition of MW0048 in both semi-annual events. Baseline concentrations for this well were collected during the November 2023 performance monitoring event. Semi-annual sampling should be planned for the May 2024 and November 2024 timeframes (Meeting Minute 2402-M10, Decision 2402-D31). • Continue sampling monitoring well, LOX-IW0012S, for aluminum (total and dissolved) on an annual basis in May 2024. It is also recommended to re-develop this well prior to the next sampling event (Meeting Minute 2402-M10, Decision 2402-D32). The above recommendations for LC39B were presented at the February 2024 KSCRT Meeting, with Team consensus reached on the path forward. The contents of this PM-CCR were also presented at this meeting.

Deborah M Wilson↗