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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
NASA NTRS1991

Potassium concentration effect on growth, gas exchange and mineral accumulation in potatoes

This study was conducted to evaluate the responses of potatoes to six K solution concentrations maintained with a flow-through nutrient film system. Potato plants were grown for 42 days in sloping shallow trays containing a 1 cm layer of quartz gravel with a continuous flow of 4 ml min-1 of nutrient solutions having K concentrations of 0.10, 0.55, 1.59, 3.16, 6.44, 9.77 meq L-1. Plant leaf area, total and tuber dry weights were reduced over 25% at 0.10 meq L-1 of K and over 17% at 9.77 meq L-1 of K compared to concentrations of 0.55, 1.59, 3.16 and 6.44 meq L-1 of K. Gas exchange measurements on leaflets in situ after 39 days of growth demonstrated no significant differences among different K treatments in CO2 assimilation rate, stomatal conductance, intercellular CO2 concentration, and transpiration. Further measurements made only on plants grown at 0.10, 1.59, 6.44 meq L-1 of K showed similar responses of CO2 assimilation rate to different intercellular CO2 concentrations. This suggested that the photosynthetic systems were not affected by different K nutrition. The leaves of plants accumulated about 60% less K at 0.10 meq L-1 of K than at higher K concentrations. However, Ca and Mg levels in the leaves were higher at 0.10 meq L-1 of K than at higher K concentrations. This indicates that low K nutrition not only reduced plant growth, but also affected nutrient balance between major cations.

Non-NASA Center↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Surface Hardness Testing of Mobile Launcher 1 (ML-1) Vehicle Support Posts (VSP) Following Artemis I Launch Exposure

Following the successful launch of Artemis I on November 16th, 2022 (Fig 1), a surface hardness assessment was performed on the eight primary VSPs (Figs. 2, 3) located on the Zero Deck of the ML. This assessment was performed as part of an instrument investigation for strain gauges installed on VSP interior surfaces that had a “strange reaction”. This report does not address the VSP strain gauge investigation, but rather VSP structural integrity for future service (Artemis II, III, …). Surface hardness measurements were taken to assess the strength of the ASTM A148 steel castings to confirm thermal exposure from launch had not reannealed the VSPs. However unlikely, it is possible high temperature may have heated and tempered (reannealed) the VSPs, resulting in a change of built-in stresses. This loss in built-in stress may explain the change in off-set measured by the strain gauges. Initial hardness measurements were made at two interior surfaces of VSPs near the strain gauges and the corresponding exterior surfaces (engine hole-facing). Field hardness measurements on the exterior, engine hole surface-facing surfaces for all eight VSPs ranged from 175 to 226 HB/P, well below the minimum hardness of 302 Brinell (HB). Opposing interior surface hardness measurements near strain gauges ranged from 264 to 358 HB/P. In all cases for flight VSPs, a drop in interior-to-exterior hardness ranging from 70 to 132 HB/P was measured. Surface hardness measurements were then made on the two spare VSPs not exposed to launch temperatures for baseline comparison. These two spare VSPs had a drop in Brinell hardness from interior to exterior surfaces ranging from 3 to 17 HB/P. The large interior-to-exterior delta hardness for eight flight VSPs but not for the two spare VSPs suggests launch exposure temperatures softened the exterior surfaces, raising two questions: 1) Are the VSPs compromised for future use, and 2) How deep is the exterior surface softening effect? To address concerns of low surface hardness values at non-critical exterior surfaces, additional field surface hardness measurements were taken at high-stress locations on VSP3: Both door jams, back radius, and internal left and right radius near the base. These values came in well above the required minimum hardness (317 to 365 HB/P). For a more representative pre-launch versus post-launch surface hardness comparison, VSP Acceptance Data Packages (ADPs) were reviewed, and hardness measurements were made at the same eight original locations on VSP3 where hardness measurements were made at the foundry. To determine the degree of softening, 1/8” of material was machined away at three exterior surfaces of engine hole-facing side (Fig. 4). A significant increase in surface hardness was measured ranging from 50 to 86 HB/P. Values at these three locations were still slightly below the required minimum hardness, so an additional 1/8” material was machined away, and hardness values were retaken. At the ¼” depth, hardness values increased to 321 to 324 HB/P. These values fall above the required minimum, concluding surface hardness testing. Rationale for VSP continued use was reached with no restrictions during a Chief Engineering Review based on hardness values exceeding minimum at critical locations and subsurface locations for VSP #3.

Artemis I↗
NASA NTRS2013

Using a Blender to Assess the Microbial Density of Encapsulated Organisms

There are specific NASA requirements for source-specific encapsulated microbial density for encapsulated organisms in non-metallic materials. Projects such as the Mars Science Laboratory (MSL) that use large volumes of non-metallic materials of planetary protection concern pose a challenge to their bioburden budget. An optimized and adapted destructive hardware technology employing a commercial blender was developed to assess the embedded bioburden of thermal paint for the MSL project. The main objective of this optimization was to blend the painted foil pieces in the smallest sizes possible without excessive heating. The small size increased the surface area of the paint and enabled the release of the maximum number of encapsulated microbes. During a trial run, a piece of foil was placed into a blender for 10 minutes. The outside of the blender was very hot to the touch. Thus, the grinding was reduced to five 2-minute periods with 2-minute cooling periods between cycles. However, almost 20% of the foil fraction was larger (>2 mm). Thus, the largest fractions were then put into the blender and reground, resulting in a 71% increase in particles less than 1 mm in size, and a 76% decrease in particles greater than 2 mm in size. Because a repeatable process had been developed, a painted sample was processed with over 80% of the particles being <2 mm. It was not perceived that the properties (i.e. weight and rubber-like nature) of the painted/foil pieces would allow for a finer size distribution. With these constraints, each section would be ground for a total of 10 minutes with five cycles of a 2-minute pulse followed by a 2-minute pause. It was observed on several occasions that a larger blade affected the recovery of seeded spores by approximately half an order of magnitude. In the standard approach, each piece of painted foil was aseptically removed from the bag and placed onto a sterile tray where they were sized, cut, and cleaned. Each section was then weighed and placed into a sterile Waring Laboratory Blender. Samples were processed on low speed. The ground-up samples were then transferred to a 500-mL bottle using a sterile 1-in. (.2.5-cm) trim brush. To each of the bottles sterile planetary protection rinse solution was added and a modified NASA Standard Assay (NASA HBK 6022) was performed. Both vegetative and spore plates were analyzed.

Benardini, James N.↗
NASA NTRSDate not supplied

A METHOD TO REDUCE BIOBURDEN IN ASTROMATERIALS CURATION FACILITIES WITHOUT INTRODUCING UNWANTED CONTAMINATION

Introduction: NASA curates its Astromaterials collections in cleanrooms that are carefully monitored for particulate, inorganic and trace metal contamination. Current sample collections are not particularly susceptible to organic contamination or biological alteration. However, new collections like those from the OSIRIS-REx and Hayabusa2 missions will have organic contamination requirements and are susceptible to biodegradation. It will be necessary sterilize or at least disinfect curation labs, as well as tools and equipment in a manner that does not introduce additional contamination and does not affect the samples 1. Current curation cleaning procedures utilize isopropyl alcohol which offers some bioburden reduction, but is not effective against spore-forming bacteria or fungal spores 2. We present a modified disinfection method that uses ultrapure hydrogen peroxide to reduce bioburden inside curation labs and glove boxes without introducing contamination or damaging curation equipment. We tested this method in the meteorite processing lab as well as on a glovebox being cleaned for use in processing ANGSA (Apollo Next Generation Sample Analysis) samples and present the results of those tests. We discuss the limitations of this method and describe potential situations in which it will not be applicable. The CDC guidelines for disinfection andsterilization in healthcare facilities discusses over 15different methods for reducing bioburden in hospitalsettings 3. The most common method, steamsterilization, is well suited to sterilizing curationprocessing tools but cannot easily be used to sterilizecleanroom surfaces or large equipment likegloveboxes. Chemical sterilization with bleach(NaOCl) is also a common strategy in healthcare andpharmaceutical settings that presents materialcompatibility issues as well as serious inorganiccontamination concerns for curation facilities.Introducing a new source of Na and Cl into curationlabs is not acceptable. Other chemical methods likeethylene oxide, formaldehyde, iodophors andquaternary ammonium compounds could introduceorganic and inorganic contamination. We chose tofocus on hydrogen peroxide because it is generallycompatible with commonly used curation materialslike stainless steel, aluminum and Teflon and becauseit decomposes to oxygen and water. The CDCguidelines for hydrogen peroxide specify using a 7.5wt% solution at 25 ̊C with a contact time of 30 minutesfor high level disinfection and 6 hours for sterilization.High level disinfection is defined as a technique thatwill kill all microorganisms except large numbers ofbacterial spores 3. Methods: We prepared a solution of 7.5 wt%hydrogen peroxide from a stock solution of ultrapure30 wt% peroxide (JT Baker) and curation gradeultrapure water. This ultrapure water is already used incuration cleaning procedures and thus is not consideredand additional source of contamination. We conducteda materials compatibility test by exposing unanodizedand anodized 6061 T6 Al alloy to the peroxide solutionfor up to six hours and periodically inspecting thesurfaces for visible defects. We used this peroxide todisinfect the floor of the meteorite processing lab andthe interior of a curation glovebox by exposing thesesurfaces to the peroxide solution for 30 min. Thesurfaces were swabbed with a dry macrofoam swabbefore (Puritan Brand 2518051PFRNDFD) and afterperoxide treatment to collect microbes present on thesurfaces. Microbes were extracted by sonication fromthe swab into 15 ml of PBS (phosphate buffered saline)and inoculated onto the following media: TSA (trypticsoy agar) BA (blood agar), R2A (Reasoners 2 agar),Potato Dextrose Agar, Saboraud Dextrose Agar andSaboraud Dextrose Agar with 0.1 mg/ mlchloramphenicol. Four TSA plates and two BA plateswere inoculated with 0.1 ml of PBS each andincubated at 35 and 37 for 48 hours. Two R2A°C°Cplates (0.1 ml of PBS each) were incubated at 25 .°CThe remaining plates were inoculated with 0.2ml ofPBS and incubated at 30 ̊C for seven days. Afterincubation bacterial and fungal isolates were countedand transferred to new plates for identification usingthe VITEK24 automated system or by sequencing aportion of the barcode gene (16S rRNA for bacteria,small subunit gene for fungi) on an ABI 3500 Sangersequencer. Negative controls consisted of swabs thatwere opened in the sampling environment andanalyzed alongside the experimental samples.Results: A 6 hour exposure to hydrogen peroxideresulted in visible pitting on un-anodized 6061 Al, butnot on anodized surfaces. No visible pitting occurredafter a 30 minute exposure. Therefore, we decided tolimit our experimental tests to 30 min. exposures. 17bacterial CFU (colony forming units) representing 4distinct organisms were isolated from the meteorite processing lab floor prior to hydrogen peroxidetreatment. We were unable culture any organisms afterperoxide treatment. In the glovebox we were able toculture three bacterial CFU representing three distinctspecies, including a spore forming bacterium prior todisinfection with peroxide. After the peroxidetreatment we were unable to culture any organisms.Routine monitoring of the meteorite processing lab andthe glovebox did not indicate any increase in unwantedinorganic contamination after these peroxidetreatments. Discussion: A 30 minute treatment with 7.5 wt%peroxide appears to be an effective method forreducing bioburden on typical cleanroom surfaces. Themethod does not introduce unwanted organic orinorganic contamination and is compatible withcommonly used curation materials like stainless steel,Teflon and anodized aluminum alloys. Special careshould be taken with un-anodized aluminum.Prolonged exposure to hydrogen peroxide can causepitting on this material. We recommend using thismethod to disinfect curation labs and equipment whenbiological alteration is a concern. This method iseffective at room temperature and cannot be used todisinfect labs and equipment where the ambienttemperature is < 0 ̊C. Astromaterials samples shouldbe removed from the area where disinfection is tooccur. Hydrogen peroxide is a powerful oxidizingagent and will react with any organic carbon present inthe sample. References: [1.] Mccubbin, F. M. et al.Sp. Sci Rev(2019) doi:10.1007/s11214-019-0615-9. [2.] Mogul, R.et al.Astrobiology 18, ast.2017.1814 (2018). [3.]Rutala, W. A. & Weber, D. J. Guideline for Disinfection and Sterilization in Healthcare Facilities, 2008. [4.] Pincus, D. H. in Encyclopedia of Rapid Microbiological Methods (2005).

A. B. Regberg↗
NASA NTRSDate not supplied

Utilization of Machine Learning Techniques for Managing the Tracking and Data Relay Satellite Constellation

National Aeronautics and Space Administration’s (NASA) Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The TDRS constellation consists of multiple geosynchronous communication relay satellites located around the equator so they can provide continual coverage of any mission in low earth orbit. The TDRS are located primarily in three oceanic regions around the earth. NASA’s White Sands Complex provides the ground communication support for TDRS located over the Atlantic and Pacific Oceans. Another TDRS ground station in Guam supports the TDRS over the Indian Ocean. With these satellites the TDRS network can provide continuous coverage of satellites in low-earth orbit. The NASA Space Network (SN) project office at GSFC manages the constellation of spacecraft. Major customers of the TDRS constellation include, but are not limited to, the International Space Station and the Hubble Space Telescope. The TDRS constellation has three generations of satellites and has been active for over 30 years providing reliable communication links between customer satellites and corresponding ground stations. However, one of the major concerns for TDRS, and in any space mission, is to ensure the health and safety of the spacecraft. Generally, engineers use telemetry data to monitor and analyze the performance and state of health of the spacecraft. Telemetry data contains hundreds of parameters that monitor each important component in the spacecraft, which can be utilized to recognize and characterize the behavior of the spacecraft. Each parameter contains considerable information to represent time-dependent properties of each spacecraft subsystem and component. During the entire life of a TDRS spacecraft, thousands of gigabytes of telemetry data are transmitted in real-time from the spacecraft to the ground station at the White Sands Complex in Las Cruces, New Mexico, and recorded as historical data sets for engineers to process and analyze the events that occurred on-orbit. These parameters contain the function of multiple spacecraft subsystems, such as the attitude control system (ACS), Thermal, Electrical Power Subsystem (EPS), etc. . The first and second generations have exceeded their required lifetime and NASA is keen to manage these spacecrafts carefully in order to maximize the remaining life using the spacecraft telemetry. The challenge is to know when the risk of losing a spacecraft in geosynchronous orbit exceeds the benefit of continued operations for customer support. In the TDRS fleet, the EPS is the most critical subsystem related to spacecraft operations. Failure of the EPS would strand a spacecraft in geosynchronous orbit. Since EPS provides power to the spacecraft, component failures ultimately lead to the inability to support the spacecraft loads and the communications payload. For instance, TDRS-8 has several anomalies in EPS including the Bus Voltage Limiter (BVL) shunt current, solar array loss of circuits, and failed battery cells. Any of these anomalies can cause critical issues to the spacecraft. Therefore, developing a system to analyze and perform early detection of a potential anomaly is an important issue in telemetry data analysis. In recent years, Telemetry Mining (TM) has been proposed to process telemetry data by using Data Mining (DM) techniques such as classification, clustering, regression and anomaly detection. Anomaly detection, also known as outlier detection, has been widely used in many data mining areas such as remote sensing, medical data processing and digital image processing. The goal of anomaly detection is to detect abnormal data, which contains a relatively low probability of occurrence among the entire data set. Early detection of anomalies is one of the most significant issues in managing the spacecraft configuration. If anomalies can be detected early enough, then the redundant resources can be used to extend the life of the operational spacecraft. We present an unsupervised anomaly detection method to process the EPS data extracted from TDRS-8. This is different from traditional analytical methods, which use telemetry data to illustrate behavior and physical meaning of each spacecraft component. TM connects multiple parameters as a vector and then conducts data analysis on this high dimension telemetry vector. This method is looking at the properties of a high dimensional vector that is able to consider the relationship between different parameters in the anomaly detection problem. This kind of method performs much better than the traditional limit checking method. In addition, we propose a new approach of real-time anomaly detection to process telemetry data in real-time, which can then be applied to spacecraft monitoring with high reliability, low cost and high accuracy.

Machine Learning (ML)↗