End-to-End Data Flow on the Soil Moisture Active Passive (SMAP) Mission
No abstract available
Soil Soisture Active Passive (SMAP)↗
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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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Regional to Global Assessments of Phytoplankton Dynamics From The SeaWiFS Mission
Photosynthetic production of organic matter by microscopic oceanic phytoplankton fuels ocean ecosystems and contributes roughly half of the Earth's net primary production. For 13 years, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission provided the first consistent, synoptic observations of global ocean ecosystems. Changes in the surface chlorophyll concentration, the primary biological property retrieved from SeaWiFS, have traditionally been used as a metric for phytoplankton abundance and its distribution largely reflects patterns in vertical nutrient transport. On regional to global scales, chlorophyll concentrations covary with sea surface temperature (SST) because SST changes reflect light and nutrient conditions. However, the oceanmay be too complex to be well characterized using a single index such as the chlorophyll concentration. A semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chlorophyll a concentration, absorption by CDM and particulate backscattering. First, we show that observed long-term, global-scale trends in standard chlorophyll retrievals are likely compromised by coincident changes in CDM. Second, we partition the chlorophyll signal into a component due to phytoplankton biomass changes and a component caused by physiological adjustments in intracellular chlorophyll concentrations to changes in mixed layer light levels. We show that biomass changes dominate chlorophyll signals for the high latitude seas and where persistent vertical upwelling is known to occur, while physiological processes dominate chlorophyll variability over much of the tropical and subtropical oceans. The SeaWiFS data set demonstrates complexity in the interpretation of changes in regional to global phytoplankton distributions and illustrates limitations for the assessment of phytoplankton dynamics using chlorophyll retrievals alone.
phytoplankton↗
InSpace-3 Investigating Structure of Paramagnetic Aggregates from Colloidal Emulsions
No abstract available
Furst, Eric M.↗
BioSentinel: Mission Development of a Radiation Biosensor to Gauge DNA Damage and Repair Beyond Low Earth Orbit on a 6U Nanosatellite.
We are designing and developing a "6U" (10 x 22 x 34 cm; 14 kg) nanosatellite as a secondary payload to fly aboard NASA's Space Launch System (SLS) Exploration Mission (EM) 1, scheduled for launch in late 2017. For the first time in over forty years, direct experimental data from biological studies beyond low Earth orbit (LEO) will be obtained during BioSentinel's 12- to 18- month mission. BioSentinel will measure the damage and repair of DNA in a biological organism and allow us to compare that to information from onboard physical radiation sensors. In order to understand the relative contributions of the space environment's two dominant biological perturbations, reduced gravity and ionizing radiation, results from deep space will be directly compared to data obtained in LEO (on ISS) and on Earth. These data points will be available for validation of existing biological radiation damage and repair models, and for extrapolation to humans, to assist in mitigating risks during future long-term exploration missions beyond LEO. The BioSentinel Payload occupies 4U of the spacecraft and will utilize the monocellular eukaryotic organism Saccharomyces cerevisiae (yeast) to report DNA double-strand-break (DSB) events that result from ambient space radiation. DSB repair exhibits striking conservation of repair proteins from yeast to humans. Yeast was selected because of 1) its similarity to cells in higher organisms, 2) the well-established history of strains engineered to measure DSB repair, 3) its spaceflight heritage, and 4) the wealth of available ground and flight reference data. The S. cerevisiae flight strain will include engineered genetic defects to prevent growth and division until a radiation-induced DSB activates the yeast's DNA repair mechanisms. The triggered culture growth and metabolic activity directly indicate a DSB and its successful repair. The yeast will be carried in the dry state within the 1-atm P/L container in 18 separate fluidics cards with each card having 16 independent culture microwells, with integral microchannels and filters to supply nutrients and reagents, confine the yeast to the wells, and enable optical measurement. The measurement subsystem will monitor each subgroup of culture wells continuously for several weeks, optically tracking DSBtriggered cell growth and metabolism. BioSentinel will also include physical radiation sensors based on the TimePix sensor, as implemented by JSC's RadWorks group, which record individual radiation events including estimates of their linear-energytransfer (LET) values. Radiation-dose and LET data will be compared directly to the rate of DSB-and-repair events measured by the S. cerevisiae biosentinels. The spacecraft bus will operate in a deep space environment with functions that include command and data handling, communications, power generation (via deployable solar panels) and storage, and attitude determination-and-control system with micropropulsion. Development of the BioSentinel spacecraft will mature and prove multiple nanosatellite advances in order to function well beyond LEO: Communications from distances of ≥ 500,000 km; Autonomous attitude control, momentum management, and safe mode of nanosatellites in deep space; Shielding-, hardening-, design-, and software-derived radiation tolerance for electronics; Reliable functionality for 12 - 18 months of key subsystems for biofluidics, memory, communications, power, etc.; Close integration of living biological radiation event monitors with miniature physical radiation spectrometers; Biological measurement of solar particle events beyond Earth orbit In addition to providing the first biological results from beyond LEO in over 4 decades, BioSentinel will provide an adaptable small-satellite instrument platform to perform a range of human-exploration-relevant measurements that characterize the biological consequences of multiple outer space environments. BioSentinel is being developed under NASA's Advanced Exploration Systems program.
DNA damage↗
The BioSentinel Bioanalytical Microsystem: Characterizing DNA Radiation Damage in Living Organisms Beyond Earth Orbit
We will present details and initial lab test results from an integrated bioanalytical microsystem designed to conduct the first biology experiments beyond low Earth orbit (LEO) since Apollo 17 (1972). The 14-kg, 12x24x37-cm BioSentinel spacecraft (Figure 1) assays radiation-responsive yeast in its science payload by measuring DNA double-strand breaks (DSBs) repaired via homologous recombination, a mechanism common to all eukaryotes including humans. S. cerevisiae (brewer's yeast) in 288 microwells are provided with nutrient and optically assayed for growth and metabolism via 3-color absorptimetry monthly during the 18-month mission. BioSentinel is one of several secondary payloads to be deployed by NASA's Exploration Mission 1 (EM-1) launch vehicle into approximately 0.95 AU heliocentric orbit in July 2018; it will communicate with Earth from up to 100 million km.
BioSentinel↗
The SMAP Dictionary Management System
The Soil Moisture Active Passive (SMAP) Dictionary Management System is a web-based tool to develop and store a mission dictionary. A mission dictionary defines the interface between a ground system and a spacecraft. In recent years, mission dictionaries have grown in size and scope, making it difficult for engineers across multiple disciplines to coordinate the dictionary development effort. The Dictionary Management Systemaddresses these issues by placing all dictionary information in one place, taking advantage of the efficiencies inherent in co-locating what were once disparate dictionary development efforts.
systems engineering↗
Automated Commanding of the SMAP Spacecraft Enables Efficient, Reliable, and Responsive Operations
The Soil Moisture Active Passive (SMAP) mission developed and deployed a system to autonomously handle most routine commanding of the observatory. This system of ground software is able to build commands, validate them, and radiate the commands to the spacecraft, all without human interaction. In the case of an off -nominal scenario, the system will abort gracefully and notify the mission operations team of the problem. The system was phased into operations during the first three months of the SMAP mission and handles over 90% of the weekly commanding of the vehicle. The gradual introduction of the automation in flight, along with an extensive test campaign, was instrumental in the success of the software. The automation has enabled substantial efficiencies in operations team staffing and has improved reliability by removing the potential for human error. The system also allows the SMAP project to be more responsive which has shown significant benefits in areas of data latency and science accuracy.
automation↗
Overview of the Exploration Exercise Device Validation Study Plans
The NASA has determined that a multi-functional exercise device will be developed for use as an exercise device during exploration missions. The device will allow for full body resistance and metabolic exercise necessary to minimize physiological losses during space flight and to maintain fitness necessary to perform critical mission tasks. Prior to implementation as an exercise device on an Exploration vehicle, there will be verification and validation testing completed to determine device efficacy at providing the necessary training stimuli to achieve desired goals. Because the exploration device will be new device that has yet be specified, specific Verification and Validation (V&V) protocols have yet to be developed. Upon delivery of an exploration exercise device training unit, stakeholders throughout NASA will develop V&V plans that include ground-based testing and testing on the International Space Station (ISS). Stakeholders will develop test protocols that include success criterion for the device. Ground tests will occur at NASA Johnson Space Station prior to flight testing. The intents of the ground tests are to allow crew, spaceflight medicine, science, engineering, Astronaut Strength, Conditioning, and Reconditioning staff, and others to gain experience in the best utilization of the device. The goal is to obtain an evidence base for recommending use of the device on the ISS. The developed protocol will be created to achieve multiple objectives, including determining if the device provides an adequate training stimulus for 5th - 95th percentile males and females, allows for exercise modalities that protect functional capability, and is robust and can withstand extensive human use. Although protocols are yet to be determined, current expectations include use of the device by test subjects and current crew in order to obtain quantitative and qualitative feedback. Information obtained during the ground tests may be used to influence device modifications during design iterations. Assuming successful ground tests, the device will be installed on the ISS for testing during space flight. Spaceflight testing is envisioned to include an activation and checkout (ACO) phase and a V&V phase. During the ACO phase, 1-2 crewmembers will exercise with the device to ensure proper function. ACO is expected to last multiple months because of the many modes and methods of exercise that need to be assessed. However, the goal is to complete the ACO as quickly as possible. Once successful ACO occurs, the crew will be free to use the device for normal exercise pending concurrence from stakeholders. V&V tests on the ISS will ideally consist of crew using the device for all of their exercise for an entire mission. Exercise prescriptions will be supplied that replicate expected prescriptions during exploration missions. Crew that are not enrolled in the V&V studies would be also free to use the device as their schedule permits. As experience is gained by users, exercise protocols could change. The intent of all V&V testing is to ensure that all have thorough understanding of experience at optimizing device capability
DeWitt, J. K.↗
Assessment of Exploration Exercise Concepts and Operations Methodology in a BAA Habitat Test Environment
No abstract available
Fincke, Renita S.↗
Assessing the Ground Data System Automation that Enables Lights-Out Operation of SMAP
No abstract available
Sanders, Antonio↗
AI4MARS: A Dataset for Terrain-Aware Autonomy on Mars
Deep learning has quickly become a necessity for selfdriving vehicles on Earth. In contrast, the self-driving vehicles on Mars, including NASA’s latest rover, Perseverance, which is planned to land on Mars in February 2021, are still driven by classical machine vision systems. Deep learning capabilities, such as semantic segmentation and object recognition, would substantially benefit the safety and productivity of ongoing and future missions to the red planet. To this end, we created the first large-scale dataset, AI4Mars, for training and validating terrain classification models for Mars, consisting of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by ~10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers. We trained a DeepLabv3 model on the AI4Mars training dataset and achieved over 96% overall classification accuracy on the test set. The dataset is made publicly available.1
Ono, Hiro↗
Benchmarking Machine Learning on the Myriad X Processor Onboard the ISS
No abstract provided
Knox, Carrie↗
Multi-Mission Terrain Classifier for Safe Rover Navigation and Automated Science
We previously presented Soil Property and Object Classification (SPOC), a machine learning-based terrain classifier for Mars rovers, for automatically segmenting rover images by its surface type such as sand and bedrock. This paper presents a number of practical improvements to pave the way for potential future onboard deployment. First, we achieved 97.0% overall pixel accuracy, evaluated against the classification generated by human experts on images from Mars Science Laboratory (MSL) missions. The substantial increase in accuracy was primarily enabled by the sheer volume of data used for training; we created a new large-scale dataset of Martian terrain labels, namely AI4Mars, which contains more than 400k labels contributed by citizen scientists for 50k images taken by the Mars Exploration Rovers (MER) and Mars Science Laboratory (MSL) rover. Second, we demonstrated that SPOC can quickly adapt to a new mission landed on a previously unseen site. Specifically, we pretrained a model with MER and MSL data from the AI4Mars dataset and then adapted to the Mars 2020 Rover (M2020) by feeding a small volume of data between Sol 0 and 157; the adapted model was tested on Sol 200-203 and resulted in 84.2% overall pixel accuracy and 93.4% reliability (recall) for detecting sand, the most concerning class for rover’s traversability. Third, we found that pretraining can substantially mitigate the decline of accuracy over time. We showed that the performance of a SPOC model pretrained with the ImageNet dataset and then trained by MSL images only up to Sol 390 remains comparable to a model trained by images up to Sol 1689 on the test data after Sol 1689. Fourth, we reimplemented SPOC with a light-weight convolutional neural network (CNN), MobileNetV2, which typically runs within tens of milliseconds (ms) on mobile processors such as Qualcomm’s Snapdragon. Finally, we released the AI4Mars dataset to the public to encourage open innovation.
Ono, Masahiro↗