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At least 883 records · Page 49

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

The NASA Astrobiology Program’s Professional Advancement Workshop Series: Next Steps

Introduction: In the modern-day competitive job search, it is not enough for a candidate to have an excellent academic resume. Candidates must also present themselves well in various styles of interviews, have managerial skills, and be capable of engaging in complex interpersonal relationships and team building. The NASA Astrobiology Program’s Professional Advancement Workshop Series (PAWS) addresses the need for professional skills development that is not traditionally taught in graduate programs. PAWS is designed to supplement academic teachings by providing a space where early career scientists can learn new skills to help them explore, interview for, and be hired in the jobs and careers they want. This is especially true for jobs that are outside of the traditional academic “pipeline” (the path of student to postdoc to tenure-track professor). PAWS embraces the braided river model of career development [1], which emphasizes flexibility as a scientist’s needs change throughout their life. PAWS also presents an opportunity for other early career scientists to meet and network with each other outside of conferences and other formal events. This is especially important as the world emerges from the ongoing COVID-19 pandemic. Finally, PAWS creates an informative space full of resources available to everyone, which lowers institutional barriers regarding knowledge of opportunities and potentially provides mentorship to those in need. Both items are identified as supportive of a more diverse and inclusive future workforce [2–4]. PAWS began in August 2021 and was originally intended to last until the Astrobiology Science Conference in May 2022. The NASA Astrobiology Program initially sponsored PAWS, allowing the PAWS Leadership to bring in expert speakers from outside of NASA. These external speakers exposed the event attendees to new techniques and perspectives. PAWS has been able to continue beyond May 2022 because of many people volunteering their time for panels and a good balance between panels and expert-led workshops. Here, we share how PAWS works, what the results have been so far, and the future plans for PAWS.

Worshop↗

Chapter 8: Strategies for People, Partnerships, and Productivity

Multiple U.S. and international respondents defined leadership as "producing the next generation of scientists." BER could lose its international leadership irrevocably without a pipeline of the best and brightest talent to engage in the program's mission areas. Competition for this talent is international and increasingly intense between public and private institutions in rapidly emerging science and technology areas relevant to BER. The quickly evolving and highly competitive nature of ensuring next-generation scientific leadership raises important questions:

Biological and Environmental Research Advisory Com↗

Diversity and Inclusion in Spacecraft Science Teams: What Do We Know and What Can We Do About It?

Introduction: Not only does the planetary science community lack diversity [1-3], the subset of the community that participates on spacecraft science team is even less diverse than the community as a whole [1, 4]. Results of 2020 Workforce Survey: Previous studies of the diversity of members of spacecraft science teams made incorrect assumptions about the nature of the data before collecting the data. Those analyses assumed a binary gender and ignored the existence of planetary scientists who are neither men nor women [4-6]. We present here results where demographic data was collected without assumptions; each individual surveyed supplied their own answers to demographic questions. The April 2020 survey of Planetary Scientists, which was conducted by the Statistical Research Center of the American Institute of Physics (AIP) and funded by the American Astronomical Society (AAS)’s Division of Planetary Science (DPS) asked participants their gender with 4 possible responses: Woman, Man, Another identify (please specify if you wish), and Prefer not to answer. 32% of respondents chose Woman, 67% chose Man and 1% chose Another gender identity [1]. The survey also asked demographic questions on race, ethnicity, LGBTQ+ identity, and disability. For a full list of questions, see https://dps.aas.org/sites/dps.aas.org/files/reports/2020/survey2020_questionnaire.pdf. In addition to demographic questions, the 2020 Workforce survey asked how many times respondents had been involved in Mission proposals as a Principal Investigator (PI) and, separately, as a Co-Investigator (CoI) [1]. Answers to questions about mission involvement were correlated with answers to demographic questions and the results show that members of historically underrepresented groups (non-white scientists, women, members of the LGBTQ+ community, and disabled scientists) were less likely to be involved in spacecraft mission proposals than were members of historically overrepresented groups [1]. The figures below show the correlated responses for four different axes of underrepresentation [1]. Note that while the figure on gender shows only Women and Men (due to the small percentage of folks answering “Another gender”), non-binary respondents are included in the LGBTQ+ community figure. Conclusion: Being part of a spacecraft science team is a goal for many planetary scientists. With it comes brand new data, more stable funding, and a sense of awe and exploration. It can lead to a cascade of opportunities from conference and public presentations, to membership in subsequent mission teams, and prestige in the community [4]. As a result, participation in spacecraft teams can be used a measure of success within the field. From the survey results, we see that members of historically excluded groups, even after they have overcome barriers to participating in the field, are still experiencing barriers to success within the field itself. Why?: The diminishing percentage of members of underrepresented groups as a career progresses has been referred to as a “leaky pipeline”. However, this fails to adequately capture the experiences of the members of these underrepresented groups as it implies a passive process. In order to capture the active processes (bias, discrimination, harassment, and other exclusionary behaviors) that contribute to low retention in the workforce, the term “Hostile Obstacle Course” is more useful [7,8]. It is these processes that need to be addressed in order to retain valued members of our community. Moving Forward: In order to broaden participation in planetary science, particularly mission science teams, we need to address conditions that create hostile workplace climates. What can mission teams and other groups do to address these conditions? First, each group/team needs to evaluate their own members to determine what specific barriers exist in their own interactions. One tool to accomplish this would be an anonymous survey designed to understand how team members feel about working within the group. Working with professionals who know how to create and analyze such surveys (often called “climate surveys” when applied to University students, for example) would ensure that the survey meets its goals and does not make assumptions that counter the meaningfulness of the results. Such professionals in EDIA (Equity, Diversity, Inclusion, and Accessibility) and workplace culture can make suggestions for policy changes that would eliminate hostile workplace conditions. Policy changes that are often suggested include instituting professional EDIA training for the team and/or for team leadership, instituting and following a code of conduct [9], including more interactive group activities in group meetings, etc. Training and information on EDIA is available for all members of the planetary science community. The first place to look would be in your University or Institution’s EDIA or human resources offices. Bystander Intervention is often offered as part of other meetings [10]. A newer offering is a Workshop on EDIA for Leaders in Planetary Science led by Julie Rathbun (first author of this abstract) and JA Grier (https://edialps.psi.edu/). This 3-day workshop gives participants the tools they need to enact positive change in their personal and professional spheres. The first workshop was help in November 2022 and another workshop will take place in the late spring 2023 with exact dates to be announced soon.

J. A. Rathbun↗

The Completion of a Geosynchronous Earth Orbit Survey with the Eugene Stansbery-Meter Class Autonomous Telescope

The Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) is the primary optical sensor used by the NASA Orbital Debris Program Office (ODPO) to statistically characterize the geosynchronous Earth orbit (GEO) debris environment and support future Orbital Debris Engineering Model (ORDEM) releases. The ES-MCAT completed its first optical survey of the GEO region from 2020 to 2022. The primary goal of this survey was to autonomously collect and process GEO data with calculated photometric and astrometric uncertainties. A pointing plan was developed to provide uniform sampling within the region of interest (ROI) while accounting for predicted downtime due to insufficient observing conditions. Detections are autonomously correlated to the Space Surveillance Network (SSN) catalog to determine if objects are correlated targets (CTs) or uncorrelated targets (UCTs), the latter of which are of interest for modeling the GEO orbital debris environment. To assess the size detection sensitivity over time and monitor the general performance of the telescope’s optics and software, the optical throughput of the system and limiting magnitudes are evaluated on a routine basis. While the telescope’s ability to operate autonomously and remotely allowed for the GEO survey to continue throughout the COVID-19 pandemic, travel restrictions hampered routine cleaning of the optics during this time, and the primary mirror degraded enough to require recoating. The mirror was removed in 2022, concluding the first GEO survey. The primary mirror received a new coating designed to be more robust against the harsh environment surrounding the ES-MCAT’s location on Ascension Island, accounting for experience gained during operations over the first GEO survey. In early 2023, the recoated primary mirror was reinstalled, and the second GEO survey was initiated. The primary goal of the second GEO survey is to characterize the evolving GEO debris environment with updated optics, software, and pointing strategies while allowing for the inclusion of non-GEO regimes or those that are outside of the ROI. While the pointing method implemented in the first survey allowed for adequate coverage of the ROI over two years, it has been improved to include pointings that avoid the Moon’s position and the galactic plane to reduce software processing time and maximize the detection capabilities of fainter objects. This method also accounts for the changing weather patterns throughout the year and reduces coverage gaps in the ROI. Provided the success of the first two-year GEO survey using autonomous operations, the ODPO is actively collaborating with the United States Space Force (USSF) to make the ES-MCAT a contributing sensor to the SSN. This paper presents results from the first GEO survey including magnitude distributions and orbital parameters for CTs and UCTs. Details are provided for the automated processing pipeline and the optical system throughput for the previous and current primary mirror coatings. In addition, an updated strategy for the second GEO survey to optimize coverage over the ROI is discussed, as are preliminary results from the ongoing second survey.

Corbin Cruz↗

MLtool Python Code

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine Learning↗

Robust Vision-based Multi-spacecraft Guidance Navigation and Control using CNN-based Pose Estimation

In this paper, we present an end-to-end simulation framework for tracking an uncooperative Target spacecraft in Low Earth Orbit using a CubeSat-class Ego spacecraft outfitted with a camera. Currently, capturing high-fidelity realistic images in space for this scenario is difficult and exorbitantly expensive. Therefore, we developed a framework to simulate the spacecraft orbits in Basilisk software and generate high-fidelity realistic images of spacecraft in Unreal Engine, including the effects from Sun, Earth, Moon and stars. The Ego spacecraft uses cameras to capture images of the uncooperative Target and estimates its position and attitude using a CNN based 6DOF pose estimation pipeline, eliminating need for large SWAP-C(Size, Weight, Power and Cost) sensors like LIDAR or reliance on inter-spacecraft communication, This CNN, which is motivated by ESA’s Pose Estimation challenge of 2019, is trained using simulated data from our end-to-end simulation framework. We compare the performance of two distinct CNNbased algorithms for pose estimation along a nominal trajectory. In presence of non-Gaussian modeling uncertainties, the statedependent estimation error is characterized with a quadratic upper-bound. The quadratically-bounded error can be used by a robust controller to maneuver

Rahmani, Amir↗

A Modular Framework for Integrating and Visualizing Telemetry for Mars 2020 Rover Mechanism Operations

The analysis of mechanism telemetry requires a wide variety of tools to quickly and effectively assess spacecraft state, capture long-term trends in system performance, and identify and track anomalous events. Such analysis often requires spacecraft telemetry to first be transformed into derived fields and aggregated statistics before operators can begin their analysis. In past missions, aspects of this process have been automated, but operators were expected to use their own tools and procedures to understand and visualize the data, which led to redundant and inconsistent tools and processes. The Mech Data Tools Python library (MDT) was developed to provide a flexible, unified tool set for operators to extract and analyze mechanism telemetry over the life of the Mars 2020 surface mission. MDT consists of a set of configurable components that implement standard interfaces for ingesting input and producing output. Components can be chained together to form a data processing pipeline. Data are ingested from several sources within the greater Mars 2020 cloud infrastructure and stored in pandas DataFrames, which allows users to leverage the data manipulation capabilities present within the widely-used pandas library. Visualization capabilities are provided through the Plotly library, which generates interactive plots for users to interpret. Following the beginning of Mars 2020 surface operations, usage of MDT has spread to all mechanism-focused subsystems and has demonstrated great utility in analyzing early surface activities. This paper describes MDT’s evolution from heritage mechanism telemetry tools, the critical architecture decisions and challenges faced over MDT’s two years of development, and current applications of MDT in support of mechanism operations.

Wolsieffer, Ben↗

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

Find My Astronaut Photo

An automated localization and georectification pipeline for astronaut photography

Machine Learning↗

Optimizing Single Nuclei Sequencing of Brain Samples From Space Flown Mice Across Age and Strain

The NASA GeneLab Sample Processing Laboratory offers high-throughput sequencing services to NASA-funded space biology researchers. Space biology studies have specific challenges such as low sample numbers, introducing susceptibility to batch effects from sample handling. These issues are compounded by complex protocols such as single-nuclei isolation and sequencing, which has recently become an attractive methodology for assessing the cellular diversity within spaceflight samples. High quality single-nuclei sequencing requires reproducible protocols to dissociate tissue and generate clean suspension of intact single nuclei. Producing single-nuclei suspension from brain tissue is particularly challenging due to cell type heterogeneity and the myelin sheath that carries over into the nuclei suspension as debris. Current procedures tend to be time consuming and sometimes include steps that can alter gene expression and create cell-type bias. Commercially available nuclei isolation kits, such as the 10X Genomics nuclei isolation kit, offers a streamlined way to process samples for nuclei isolation, thereby minimizing batch effects and enabling reproducibility. In this study, we report on the performance of the 10X Genomics nuclei isolation kit and Chromium Next GEM Single Cell Multiome ATAC + Gene Expression kit to generate sequencing libraries from space-flown mouse brain samples. Single nuclei sequencing was performed on frozen mouse brain tissue from two spaceflight missions, Rodent Research-10 (RR-10) and RR Reference Mission-2 (RRRM-2). RR-10 mice were female B6129SF2/J, euthanized at 18-19 weeks whereas RRRM-2 mice were female C57BL/6NTac, euthanized at 20 or 37 weeks. Sequencing data was processed using standard GeneLab data processing pipelines. We report evaluation of the performance of the 10X Genomics nuclei isolation kit for spaceflight samples from mouse brain, and evaluation of reproducibility across different mouse strains and age groups. We also report preliminary scientific results including cell type inference, cell clustering, and differentially expressed genes and pathways between spaceflight and ground control samples.

RR-10↗

RadLab and the Environmental Data Application Dashboard: Graphical and Programming Interfaces for Interrogation of Space Telemetry Data

Sensors on the International Space Station (ISS) and multiple spacecraft elsewhere in Earth orbit and in deep space continuously monitor and collect environmental data, transmitting this information back to Earth. These data include ionizing radiation and, on the ISS, CO2, relative humidity levels, and temperature, and are of great importance to space biology research. Ionizing radiation in particular has been established in ground-based experiments as being correlated with increased risk of carcinogenesis and cardiovascular and neurological effects. Looking ahead to future long duration crewed missions beyond low Earth orbit, the ability to study how factors including CO2 levels, light cycle, temperature modulate the response to ionizing radiation and microgravity is essential. To date, access to these data has been fragmented across space agencies, spacecraft, and databases. To address this issue, NASA’s Open Science Data Repository (osdr.nasa.gov) has developed two Web applications: the Environmental Data Application (EDA) and a radiation-specific RadLab. Each consists of an API (application programming interface) and an associated GUI (graphical user interface) that provide single points of access to the data. To date, OSDR has focused on the sensors from payloads and radiation detectors located on the ISS. The Web applications process telemetry information and associated data, such as spacecraft location and orientation, from multiple international databases. The applications’ request syntax enables users to interrogate these data by craft, sensor type, time range, radiation type (galactic cosmic rays, solar particle events, the contribution of the South Atlantic Anomaly), facilitating arbitrary comparisons of original source data at varying time resolutions. The applications provide programmatic access for use in computational pipelines and GUIs for data visualization and exploration, making these data FAIR (Findable, Accessible, Interoperable, and Reusable), complementing the biological data contained in OSDR, and providing the space science community with a valuable resource for scientific analyses.

radiation↗

Data-constrained Solar Modeling with GX Simulator

To facilitate the study of solarflares and active regions, we have created a modeling framework, the freelydistributed GX Simulator IDL package, that combines 3D magnetic and plasma structures with thermal andnonthermal models of the chromosphere, transition region, and corona. Its object-based modular architecture,which runs on Windows, Mac, and Unix/Linux platforms, offers the ability to either import 3D density andtemperature distribution models, or to assign numerically defined coronal or chromospheric temperatures anddensities, or their distributions, to each individual voxel. GX Simulator can apply parametric heating modelsinvolving average properties of the magneticfield lines crossing a given voxel, as well as compute and investigatethe spatial and spectral properties of radio,(sub)millimeter, EUV, and X-ray emissions calculated from the model,and quantitatively compare them with observations. The package includes a fully automatic model productionpipeline that, based on minimal users input, downloads the required SDO/HMI vector magneticfield data,performs potential or nonlinear force-freefield extrapolations, populates the magneticfield skeleton withparameterized heated plasma coronal models that assume either steady-state or impulsive plasma heating, andgenerates non-LTE density and temperature distribution models of the chromosphere that are constrained byphotospheric measurements. The standardized models produced by this pipeline may be further customizedthrough specialized IDL scripts, or a set of interactive tools provided by the graphical user interface. Here, wedescribe the GX Simulator framework and its applications.

Solar active regions↗

Automated sUAS Inspection Capability for NASA’s Mission Critical Testing Facilities

The wind tunnels at Ames are crucial to NASA and industry but pose unique inspection challenges. Current inspection processes are highly manual, and as such are labor and schedule intensive. These needs can be better met with emerging technology such as sUAS (drones), computer vision, and machine learning. This work seeks to bring the drone based inspection workflow into production-ready status and integrate into existing facility inspections. This includes operation of a drone platform, establishing a photogrammetry pipeline, development of procedures and streamlining flight approval processes, and establishing a base of experience at Ames for this work. We have worked with the NFAC, unitary, and Arcjet facilities to identify use cases and have performed test flights at Ames (both indoors and outdoors). The system is being readied for incorporation into routine inspection operations.

David Daisuke Murakami↗

Prediction of Aircraft Estimated Time of Arrival Using A Supervised Learning Approach

We present a novel data-driven approach for prediction of the estimated time of arrival (ETA) of aircraft in the terminal area via the implementation of a Random Forest regression model. The model uses data fused from a number of sources (flight track, weather, flight plan information, etc.) and provides predictions for the remaining flight time for aircraft landing at Dallas/Fort Worth (DFW) International Airport. The predictions are made when the aircraft is at a distance of 200-miles from the airport. The results show that the model is able to predict estimated time of arrival to within ± 5 min for 90% of the flights in the test data with the mean absolute error being lower at 145 seconds. This paper covers the entire pipeline of data collection, preprocessing, setup and training of the ML model, and the results obtained for DFW.

Machine learning↗

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

Uncovering Unique Molecular Adaptations in the Arabidopsis Thaliana Cvi-0 Ecotype

This research proposal aims to investigate the unique molecular adaptations exhibited by Arabidopsis Thaliana, specifically focusing on the Cape Verde Islands (Cvi-0) ecotype, in response to microgravity conditions. The study examines data from NASA’s Open Science Data Repository and applies a multifaceted RNAseq analysis pipeline using tools in the UseGalaxy.org open platform. Through transcriptomic analysis, differential gene expression patterns were identified in Cvi-0, revealing an absence of heat shock protein (HSP) upregulation and an upregulation of Rubisco Activase (RCA) and chloroplast-related pathways. To test the hypothesis that these adaptations may contribute to Cvi-0’s increased adaptability in microgravity, a three-fold experimental design is proposed. Four experimental groups will be cultivated under simulated microgravity and ground control conditions, including Cvi-0, Col-0, and genetically modified Col-0 with silenced HSP genes, and genetically modified Col-0 with upregulated RCA gene. Growth parameters will be measured to assess plant resilience, and RNA sequencing will provide transcriptomic data for pathway analysis. Anticipated outcomes include improved markers of plant health (mass, growth, etc.) of Cvi-0 in simulated microgravity and enhanced resilience in genetically altered Col-0 variants, providing insights into potential mechanisms of adaptation. This research would bear significance for space agriculture, nutrition for extended space missions, and sustainable terrestrial crop enhancement. Moreover, the insights gained could reshape crop engineering on Earth, enhancing robustness to climate induced stresses and bolstering global food security. The proposal’s trajectory blends scientific curiosity with practical applicability, forging a path towards sustainable food production and improving human exploration beyond our planet.

GL4HS↗