Computer-controlled aerospace ground equipment for the aoso communications and data- handling subsystem.
Computer controlled checkout equipment and data handling system for AOSO
GROUND SUPPORT SYSTEM↗
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Tissue Engineered Skeletal Myofibers can Directly "Sense" Gravitational Force Changes
Long-term manned space flight requires a better understanding of skeletal muscle atrophy resulting from microgravity. Atrophy most likely results from changes at both the systemic level (e.g. decreased circulating growth hormone, increased circulating glucocorticoids) and locally (e.g. decreased myofiber resting tension). Differentiated skeletal myofibers in tissue culture have provided a model system over the last decade for gaining a better understanding of the interactions of exogenous growth factors, endogenous growth factors, and muscle fiber tension in regulating protein turnover rates and muscle cell growth. Tissue engineering these cells into three dimensional bioartificial muscle (BAM) constructs has allowed us to extend their use to Space flight studies for the potential future development of countermeasures. Embryonic avian muscle cells were isolated and BAMs tissue engineered as described previously. The myoblasts proliferate and fuse into aligned postmitotic myofibers after ten to fourteen days in vitro. A cylindrical muscle-like structure containing several thousand myofibers is formed which is approximately 30 mm in length, 2-3 mm in diameter, and attached at each end. For the Space Shuttle experiments, the BAMs were transferred to 55 mL bioreactor cartridges (6 BAMs/cartridge). At Kennedy Space Center, the cartridges were mounted in two Space Tissue Loss (STL) Modules (three to four cartridges per Module) and either maintained as ground controls or loaded in a Mid-Deck locker of the Space Shuttle. The BAM cartridges were continuously perfused during the experiment at 1.5 mL/ min with tissue culture medium. Eighteen BAMs were flown for nine days on Mission STS66 while eighteen BAMs served as ground controls. The complete experiment was repeated on Mission STS77 with twenty four BAMs in each group. BAMs could be maintained in a healthy state for at least 30 days in the perfusion bioreactor cartridges. The BAM muscle fibers directly detected both the loss of gravity and the reloading effects of 1 x g. While total cellular metabolism and total protein degradation rates were not altered during 9 to 10 days in Space, protein synthesis rates were significantly reduced and resulted in significant myofiber atrophy compared to ground controls. One g reloading of the flight muscle cells post-flight significantly increased protein synthesis rates and the synthesis rates of myosin heavy chain, fibronectin, and collagen. Tissue cultured muscle cells can directly "sense" changes in gravity and provide a valid model to begin the study of countermeasures. Based on our ground based experiments, and the experiments of others, growth hormone and/or insulin-like growth factors are attractive protein therapeutics which may assisting attenuating skeletal muscle wasting in space. Our laboratory is developing a new cell-based delivery system for this and other potential therapeutic factors for attenuating muscle and bone wasting.
Vandenburgh, Herman H.↗
Human Machine Interface Programming and Testing
Human Machine Interface (HMI) Programming and Testing is about creating graphical displays to mimic mission critical ground control systems in order to provide NASA engineers with the ability to monitor the health management of these systems in real time. The Health Management System (HMS) is an online interactive human machine interface system that monitors all Kennedy Ground Control Subsystem (KGCS) hardware in the field. The Health Management System is essential to NASA engineers because it allows remote control and monitoring of the health management systems of all the Programmable Logic Controllers (PLC) and associated field devices. KGCS will have equipment installed at the launch pad, Vehicle Assembly Building, Mobile Launcher, as well as the Multi-Purpose Processing Facility. I am designing graphical displays to monitor and control new modules that will be integrated into the HMS. The design of the display screen will closely mimic the appearance and functionality of the actual modules. There are many different field devices used to monitor health management and each device has its own unique set of health management related data, therefore each display must also have its own unique way to display this data. Once the displays are created, the RSLogix5000 application is used to write software that maps all the required data read from the hardware to the graphical display. Once this data is mapped to its corresponding display item, the graphical display and hardware device will be connected through the same network in order to test all possible scenarios and types of data the graphical display was designed to receive. Test Procedures will be written to thoroughly test out the displays and ensure that they are working correctly before being deployed to the field. Additionally, the Kennedy Ground Controls Subsystem's user manual will be updated to explain to the NASA engineers how to use the new module displays.
Foster, Thomas Garrison↗
A ground track control algorithm for the Topographic Mapping Laser Altimeter (TMLA)
The results of an analysis of an algorithm that will provide autonomous onboard orbit control using orbits determined with Global Positioning System (GPS) data. The algorithm uses the GPS data to (1) compute the ground track error relative to a fixed longitude grid, and (2) determine the altitude adjustment required to correct the longitude error. A program was written on a personal computer (PC) to test the concept for numerous altitudes and values of solar flux using a simplified orbit model including only the J sub 2 zonal harmonic and simple orbit decay computations. The algorithm was then implemented in a precision orbit propagation program having a full range of perturbations. The analysis showed that, even with all perturbations (including actual time histories of solar flux variation), the algorithm could effectively control the spacecraft ground track and yield more than 99 percent Earth coverage in the time required to complete one coverage cycle on the fixed grid (220 to 230 days depending on altitude and overlap allowance).
Blaes, V.↗
Exploring the ARNTL and RORc Genes' Impact on Lipid Accumulation in the Liver via the Circadian Rhythm
The objective of our proposed experiment is to prove the significance of the upregulation of the ARNTL gene and down regulation of the RORc gene in space, highlighting their crucial roles in causing circadian rhythm misalignment during spaceflight, ultimately resulting in liver lipid accumulation. This experiment was created by thoroughly analyzing the genetic data (UseGalaxy) in OSD-245, Transcriptional analysis of liver from mice flown on the RR-6 mission (NASA GeneLab), and making a comparison between the Flight ISS Terminal and the Ground Control ISS Terminal groups. In our study, we intend to establish experimental groups of Mus musculus (lab mice), with 4 of these groups designated for space travel, while the remaining groups will serve as ground control samples on Earth. Both the space and ground conditions are categorized into four groups according to the genetic makeup of the mice. Within each condition, there will be a control group representing the wild type genes, and the experimental groups will involve knockout mice for ARNTL and RORc. We will then compare the lipid accumulation between the ground control mice and FLT mice to understand whether or not spaceflight causes lipid accumulation, due to a misaligned circadian rhythm. The experiment is anticipated to yield two main outcomes. Firstly, the upregulation of ARNTL is expected to rectify the circadian rhythm misalignment in space, consequently heightening lipid accumulation within the liver. Secondly, the downregulation of RORc is predicted to result in a diminished immune response in the liver, thereby facilitating lipid accumulation. Our space research is expected to aid in lipid buildup countermeasures, informing scientists regarding astronaut protection and providing insights into obesity and non-alcoholic fatty liver disease.
GL4HS↗
Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.
Machine Learning↗
Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.
Machine Learning↗
Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.
Machine Learning↗
Changes in vacuolation in the root apex cells of soybean seedlings in microgravity
Changes in the vacuolation in root apex cells of soybean (Glycine max L. [Merr.]) seedlings grown in microgravity were investigated. Spaceflight and ground control seedlings were grown in the absence or presence of KMnO4 (to remove ethylene) for 6 days. After landing, in order to study of cell ultrastructure and subcellular free calcium ion distribution, seedling root apices were fixed in 2.5% (w/v) glutaraldehyde in 0.1 M cacodylate buffer and 2% (w/v) glutaraldehyde, 2.5% (w/v) formaldehyde, 2% (w/v) potassium antimonate K[Sb(OH)6] in 0.1 M K2HPO4 buffer with an osmolarity (calculated theoretically) of 0.45 and 1.26 osmol. The concentrations of ethylene in all spaceflight canisters were significantly higher than in the ground control canisters. Seedling growth was reduced in the spaceflight-exposed plants. Additionally, the spaceflight-exposed plants exhibited progressive vacuolation in the root apex cells, particularly in the columella cells, to a greater degree than the ground controls. Plasmolysis was observed in columella cells of spaceflight roots fixed in solutions with relatively high osmolarity (1.26 osmol). The appearance of plasmolysis permitted the evaluation of the water status of cells. The water potential of the spaceflight cells was higher than the surrounding fixative solution. A decrease in osmotic potential and/or an increase in turgor potential may have induced increases in cell water potential. However, the plasmolysed (i.e. non-turgid) cells implied that increases in water potential were accompanied with a decrease in osmotic potential. In such cells changes in vacuolation may have been involved to maintain turgor pressure or may have been a result of intensification of other vacuolar functions like digestion and storage. c2003 COSPAR. Published by Elsevier Ltd. All rights reserved.
Non-NASA Center↗
Typical teleoperator time delay profiles, phase 2
The results of the second phase of a study on time delays in communications systems applicable to the teleoperator program are presented. Estimates of the maximum time delays that will be encountered and presents time delay profiles are given for (1) ground control to teleoperator in low earth orbit, (2) ground control to teleoperator in geosynchronous orbit, and (3) low earth orbit control to teleoperator in low earth orbit.
Wetherington, R. D.↗
Technology for large digital mosaics of Landsat data
Procedures and software for the digital mosaicking of Landsat data are discussed. It is noted that image processing in support of JPL's planetary program has furnished the software and procedures necessary to achieve digital image mosaicking of vidicon imagery using rigid projective geometry. The incorporation of ground-control points, by either manual or automatic ground-control point file identification, has yielded rms positional accuracies exceeding the National Map Accuracy Standards. Rotation to north vertical is effected at low computational cost. It is possible to cut input data frames in any arbitrary shape to remove cloud cover and accommodate terrain offset effects. In similar fashion, the final digital mosaic can be segmented arbitrarily to conform to user requirements. Information is presented on applications in Pennsylvania and Bolivia.
Zobrist, A. L.↗
SNPP and NOAA-20 VIIRS On-Orbit Geolocation Trending and Improvements
Two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors have been in operations for more than 8.5 and 2.5 years since they were launched in October 2011 on SNPP satellite and in November 2017 on NOAA-20 satellite, respectively. These are two satellites in the Join Polar Satellite System (JPSS) constellation, of which Suomi National Polar-orbiting Partnership (SNPP) is a risk reduction satellite and NOAA-20 is the first of four JPSS satellites(JPSS-1 became NOAA-20 after launch). Accurate geolocation is a critical element in data calibration for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved geolocation accuracy in VIIRS Collection-1 (C1) and C2 re-processing. We implemented a VIIRS instrument geometric model update (VIGMU)for both sensors that correct for geolocation error oscilations in the scan direction. We borrowed code from Moderate Resolution Imaging Spectroradiometer (MODIS) geolocation software to correct for time-dependent pointing variations, that are particularly acute in NOAA-20 VIIRS, and some pointing anomalies in SNPP VIIRS. We developed a Kalman Filter using gyrodata to correct for attitude errors due to the degradation of the star trackers performance from the SNPP satellite. We also present an improved ground control point matching (CPM) tool, in which the ground control point (GCP) chips library is refreshed using recently launched Landsat-8 images.
SNPP↗
ALLY - An operator's associate for cooperative supervisory control systems
The design, implementation, and evaluation of ALLY, a computer-based associate for the human supervisor of a simulated satellite ground control system, is described. ALLY's dynamic intent inferencing abilities and system control properties are described. Experimental evaluation of ALLY compared performance of a two-person team with a person-ALLY team in the control of a simulated satellite ground control system.
Bushman, James B.↗
Configurable technology development for reusable control and monitor ground systems
The control monitor unit (CMU) uses configurable software technology for real-time mission command and control, telemetry processing, simulation, data acquisition, data archiving, and ground operations automation. The base technology is currently planned for the following control and monitor systems: portable Space Station checkout systems; ecological life support systems; Space Station logistics carrier system; and the ground system of the Delta Clipper (SX-2) in the Single-Stage Rocket Technology program. The CMU makes extensive use of commercial technology to increase capability and reduce development and life-cycle costs. The concepts and technology are being developed by McDonnell Douglas Space and Defense Systems for the Real-Time Systems Laboratory at NASA's Kennedy Space Center under the Payload Ground Operations Contract. A second function of the Real-Time Systems Laboratory is development and utilization of advanced software development practices.
Uhrlaub, David R.↗
Intelligent Change Detection System: Autonomous Intelligent Machine Agent Model Development
NASA’s significant role in facilitating the harmonious integration of unmanned aircraft systems (UAS), with other aerial vehicles operating in the National Airspace System (NAS), has revealed a need for more advanced technological tools than are being utilized currently. This technology would lend itself to significantly assisting Direct-Action Aviation Personnel (DAAP) with the ingress and egress of UAS operations within the NAS. Providing research findings that would reduce technical barriers, associated with UAS-NAS integration, has been a persistent effort by both NASA and the FAA. One such research effort, pursued by NASA’s Transformative Tools and Technologies – Revolutionary Aviation Mobility (T3-RAM) project, is the development of an autonomous intelligent machine (AIM) agent that would aid DAAP functioning as implemented in remote ground control stations (RGCS). This evolution of the “human-machine” symbiosis, within the aviation environment, is necessary for many reasons. For example, there are projections of large increases to the 864,000 registered UAS and 45,000 aviation operations taking place in the NAS each day. With a data output range from 1 to 20 terabytes per flight or each aerial vehicle, which is projected to have proportional rate increase to that of registered UASs. It is evident, that due to the projected increase of UASs and their generated data, the human-agent’s data managing capabilities will be quickly overwhelmed by the enormous amounts of data emanating in the NAS. The research efforts presented in this paper puts forward results from the development, assessment, and verification of a previously conceptualized AIM-Agent that combats actionable-data (information) errors resulting from the visual perception phenomenon known as “change blindness” (CB). CB has been identified as one of the main culprits of information erroring encountered within the ground control station operator (GCSO) community.
Change Blindness↗
Towards an Open, Distributed Software Architecture for UxS Operations
To address the growing need to evaluate, test, and certify an ever expanding ecosystem of UxS platforms in preparation of cultural integration, NASA Langley Research Center's Autonomy Incubator (AI) has taken on the challenge of developing a software framework in which UxS platforms developed by third parties can be integrated into a single system which provides evaluation and testing, mission planning and operation, and out-of-the-box autonomy and data fusion capabilities. This software framework, named AEON (Autonomous Entity Operations Network), has two main goals. The first goal is the development of a cross-platform, extensible, onboard software system that provides autonomy at the mission execution and course-planning level, a highly configurable data fusion framework sensitive to the platform's available sensor hardware, and plug-and-play compatibility with a wide array of computer systems, sensors, software, and controls hardware. The second goal is the development of a ground control system that acts as a test-bed for integration of the proposed heterogeneous fleet, and allows for complex mission planning, tracking, and debugging capabilities. The ground control system should also be highly extensible and allow plug-and-play interoperability with third party software systems. In order to achieve these goals, this paper proposes an open, distributed software architecture which utilizes at its core the Data Distribution Service (DDS) standards, established by the Object Management Group (OMG), for inter-process communication and data flow. The design decisions proposed herein leverage the advantages of existing robotics software architectures and the DDS standards to develop software that is scalable, high-performance, fault tolerant, modular, and readily interoperable with external platforms and software.
Cross, Charles D.↗
Balancing Training Techniques for Flight Controller Certification
Training of ground control teams has been a difficult task in space operations. There are several intangible skills that must be learned to become the steely eyed men and women of mission control who respond to spacecraft failures that can lead to loss of vehicle or crew if handled improperly. And as difficult as training is, it can also be costly. Every day, month or year an operator is in training, is a day that not only they are being trained without direct benefit to the organization, but potentially an instructor or mentor is also being paid for hours spent assisting them. Therefore, optimization of the training flow is highly desired. Recently the Expedition Division (DI) at Johnson Space Flight Center has recreated their training flows for the purpose of both moving to an operator/specialist/instructor hierarchy and to address past inefficiencies in the training flow. This paper will discuss the types of training DI is utilizing in their new flows, and the balance that has been struck between the ideal learning environments and realistic constraints. Specifically, the past training flow for the ISS Attitude Determination and Control Officer will be presented, including drawbacks that were encountered. Then the new training flow will be discussed and how a new approach utilizes more training methods and teaching techniques. We will look at how DI has integrated classes, workshops, checkouts, module reviews, scenarios, OJT, paper sims, Mini Sims, and finally Integrated Sims to balance the cost and timing of training a new flight controller.
Gosling, Christina↗
Mapping experiment with space station
Mapping of the Earth from space stations can be approached in two areas. One is to collect gravity data for defining topographic datum using Earth's gravity field in terms of spherical harmonics. The other is to search and explore techniques of mapping topography using either optical or radar images with or without reference to ground central points. Without ground control points, an integrated camera system can be designed. With ground control points, the position of the space station (camera station) can be precisely determined at any instant. Therefore, terrestrial topography can be precisely mapped either by conventional photogrammetric methods or by current digital technology of image correlation. For the mapping experiment, it is proposed to establish four ground points either in North America or Africa (including the Sahara desert). If this experiment should be successfully accomplished, it may also be applied to the defense charting systems.
Wu, S. S. C.↗