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NASA NTRSDate not supplied

Global 3D Data Visualization and Analysis Platform with Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera, Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗
NASA NTRS2017

NASA Advanced Supercomputing Facility Expansion

The NASA Advanced Supercomputing (NAS) Division enables advances in high-end computing technologies and in modeling and simulation methods to tackle some of the toughest science and engineering challenges facing NASA today. The name "NAS" has long been associated with leadership and innovation throughout the high-end computing (HEC) community. We play a significant role in shaping HEC standards and paradigms, and provide leadership in the areas of large-scale InfiniBand fabrics, Lustre open-source filesystems, and hyperwall technologies. We provide an integrated high-end computing environment to accelerate NASA missions and make revolutionary advances in science. Pleiades, a petaflop-scale supercomputer, is used by scientists throughout the U.S. to support NASA missions, and is ranked among the most powerful systems in the world. One of our key focus areas is in modeling and simulation to support NASA's real-world engineering applications and make fundamental advances in modeling and simulation methods.

NASA↗
NASA NTRSDate not supplied

Global 3D Data Visualization and Analysis Platform With Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks [1]. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration [2]. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera [3], Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) [5] is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools [5, 7]. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool [8]. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers [5,6]. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗
NASA NTRS2001

Code Sharing and Collaboration: Experiences from the Scientist's Expert Assistant Project and their Relevance to the Virtual Observatory

In the Virtual Observatory (VO), software tools will perform the functions that have traditionally been performed by physical observatories and their instruments. These tools will not be adjuncts to VO functionality but will make up the very core of the VO. Consequently, the tradition of observatory and system independent tools serving a small user base is not valid for the VO. For the VO to succeed, we must improve software collaboration and code sharing between projects and groups. A significant goal of the Scientist's Expert Assistant (SEA) project has been promoting effective collaboration and code sharing between groups. During the past three years, the SEA project has been developing prototypes for new observation planning software tools and strategies. Initially funded by the Next Generation Space Telescope, parts of the SEA code have since been adopted by the Space Telescope Science Institute. SEA has also supplied code for SOFIA, the SIRTF planning tools, and the JSky Open Source Java library. The potential benefits of sharing code are clear. The recipient gains functionality for considerably less cost. The provider gains additional developers working with their code. If enough users groups adopt a set of common code and tools, defacto standards can emerge (as demonstrated by the success of the FITS standard). Code sharing also raises a number of challenges related to the management of the code. In this talk, we will review our experiences with SEA - both successes and failures - and offer some lessons learned that may promote further successes in collaboration and re-use.

Jones, Jeremy↗
NASA NTRSDate not supplied

NASA Operational Simulator for SmallSats (NOS3): Design Reference Mission

The NASA Operational Simulator for Small Satellites (NOS3) has undergone significant advances including updating the framework to be “component” based and expanding the open-source code to include a generic design reference mission to enable advanced technologies. This paper details the changes to the framework as well as a number of innovative use-cases the team is currently supporting such as 1) the expansion of NOS3 to support distributed systems missions in collaboration with NASA GSFC, 2) the integration of NASA JPL’s Science Yield improvement via Onboard Prioritization and Summary of Information Systems (SYNOPSIS) for on-orbit science data prioritization, and 3) the inclusion of NASA IV&V’s software-only CCSDS encryption library (CryptoLib). NOS3 continues to serve the SmallSat community by providing an open-source digital twin that can significantly reduce costs associated with spacecraft software development, test, and operations. The NOS3 team hopes to continue to expand the resources available to the community and partner with others to resolve issues and add new features requested via the NASA GitHub.

SmallSats↗
NASA NTRSDate not supplied

WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and Monitoring

Wetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as the optical and SAR data proves high resolution (5-10 m) imagery, and SAR data is unaffected by cloud cover and light availability (day vs. night), which are common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a MODIS snow mask product to mask global snow coverage, which would affect land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd watershed located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management..

Lori Berberian↗
NASA NTRS2023

WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and Monitoring

Wetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as SAR data provides high resolution (5-10 m) imagery, unaffected by cloud cover and light availability (day vs. night), common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band (once operational and available on the GEE repository) synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow product to determine regional snow coverage, which affects land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd wetland located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management.

Inundation↗
NASA NTRSDate not supplied

NASA biological and physical sciences databases: who’s the FAIRest of them all?

Conceptual models are a key part of the foundation of scientific study. Scientific data discovery and retrieval are often inaccurate and incomplete because these models are not sufficiently well-incorporated into data retrieval systems. Systems often don’t provide the necessary tools to those producing scientific data to fully and unambiguously annotate them and the result is consumers of the data cannot find them efficiently. The capability of data archives to provide these tools to link data to underlying conceptual models is one of dimensions of the recently developed “FAIR” principles (https://www.go-fair.org/fair-principles/ ), and is key to many automated processes being able to operate on these data, particularly analytics involving artificial intelligence. We used an open-source web service to measure the FAIR compliance of the three data archives operated by NASA for the biological and physical sciences: the Life Sciences Data Archive, the Physical Sciences Informatics database, and GeneLab. The service ingests references to data sets in these archives, and then executes domain-non-specific examinations of these data and metadata that test compliance to the FAIR principles. Of the 22 metrics tested, GeneLab passed 11 (50%), and PSI and LSDA each passed 7 (32%). These data were gathered using only one representative data set from each archive and we anticipate variability in results as we continue to apply these metrics to other data. A preliminary study of the failure traces for each metric suggests there is a wide range of effort and complexity in the enhancements required for each system to elevate FAIR compliance, and this is the subject of continued investigation. This information has been and will likely continue to be important information in planning these enhancements, with the goal of increased readiness of the data for automated processes.

database↗
NASA NTRS2019

Multimessenger Science Opportunities with mHz Gravitational Waves

LISA will open the mHz band of gravitational waves (GWs) to the astronomy community. Thestrong gravity which powers the variety of GW sources in this band is also crucial in a numberof important astrophysical processes at the current frontiers of astronomy. These range fromthe beginning of structure formation in the early universe, through the origin and cosmic evolutionof massive black holes in concert with their galactic environments, to the evolution ofstellar remnant binaries in the Milky Way and in nearby galaxies. These processes and theirassociated populations also drive current and future observations across the electromagnetic(EM) spectrum. We review opportunities for science breakthroughs, involving either direct coincidentEM+GW observations, or indirect multimessenger studies. We argue that for the UScommunity to fully capitalize on the opportunities from the LISA mission, the US efforts shouldbe accompanied by a coordinated and sustained program of multi-disciplinary science investment,following the GW data through to its impact on broad areas of astrophysics. Supportfor LISA-related multimessenger observers and theorists should be sized appropriately for aflagship observatory and may be coordinated through a dedicated mHz GW research center.

Baker, John↗
NASA NTRS2018

How NASA is Building a Petabyte Scale Geospatial Archive in the Cloud

NASA's Earth Observing System Data and Information System (EOSDIS) is working towards a vision of a cloud-based, highly-flexible, ingest, archive, management, and distribution system for its ever-growing and evolving data holdings. This free and open source system, Cumulus, is emerging from its prototyping stages and is poised to make a huge impact on how NASA manages and disseminates its Earth science data. This talk outlines the motivation for this work, present the achievements and hurdles of the past 18 months and charts a course for the future expansion of Cumulus. We explore not just the technical, but also the socio-technical challenges that we face in evolving a system of this magnitude into the cloud. The NASA EOSDIS archive is currently at nearly 30 PBs and will grow to over 300PBs in the coming years. We've presented progress on this effort at AWS re:Invent and the American Geophysical Union (AGU) Fall Meeting in 2017 and hope to have the opportunity to share with FOSS4G attendees information on the availability of the open sourced software and how NASA intends on making its Earth Observing Geospatial data available for free to the public in the cloud.

NASA Archive↗
NASA NTRS2016

Agriculture on Mars: Soils for Plant Growth

Robotic rovers and landers have enabled the mineralogical, chemical, and physical characterization of loose, unconsolidated materials on the surface of Mars. Planetary scientists refer to the regolith material as "soil." NASA is currently planning to send humans to Mars in the mid 2030s. Early missions may rely on the use of onsite resources to enable exploration and self-sufficient outposts on Mars. The martian "soil" and surface environment contain all essential plant growth elements. The study of martian surface materials and how they might react as agricultural soils opens a new frontier for researchers in the soil science community. Other potential applications for surface "soils" include (i) sources for extraction of essential plant-growth nutrients, (ii) sources of O2, H2, CO2, and H2O, (iii) substrates for microbial populations in the degradation of wastes, and (iv) shielding materials surrounding outpost structures to protect humans, plants, and microorganisms from radiation. There are many challenges that will have to be addressed by soil scientists prior to human exploration over the next two decades.

Ming, D. W.↗
NASA NTRS2019

Highly Charged Ions in a New Era of High Resolution X-Ray Astrophysics

X‐ray astronomy and ground‐based atomic physics have a long history of fruitful collaboration: Sound understanding of the underlying atomic physics is the key to reliable interpretation of the spectra from celestial sources; conversely, astronomical spectra have been used to benchmark and advance atomic physics. This interplay is about to become even more important as we enter a new era of high‐resolution X‐ray astrophysics with large effective collection area. Although high‐resolution observations with the gratings on the Chandra and XMM‐Newton observatories continue to drive new science, upcoming planned and proposed missions will open up new discovery space in the near future that is currently challenging to access: high‐resolution spectroscopy on extended sources, in the Fe K band, and on short time scales. This review summarizes open questions in these areas and the design parameters for the Hitomi, XRISM, Athena, and Arcus observatories. The expected high quality of spectra taken with these observatories puts new constraints on the accuracy of atomic reference data required to take full advantage of the diagnostic potential of these spectra.

Natalie Hell↗
NASA NTRS1980

Limitless Horizons. Careers in Aerospace

A manual is presented for use by counselors in career guidance programs. Pertinent information is provided on choices open in aerospace sciences, engineering, and technology. Accredited institutions awarding degrees in pertinent areas are listed as well as additional sources of aerospace career information. NASA's role and fields of interest are emphasized.

Lewis, M. H.↗
NASA NTRS2001

Code Sharing and Collaboration: Experiences From the Scientist's Expert Assistant Project and Their Relevance to the Virtual Observatory

In the Virtual Observatory (VO), software tools will perform the functions that have traditionally been performed by physical observatories and their instruments. These tools will not be adjuncts to VO functionality but will make up the very core of the VO. Consequently, the tradition of observatory and system independent tools serving a small user base is not valid for the VO. For the VO to succeed, we must improve software collaboration and code sharing between projects and groups. A significant goal of the Scientist's Expert Assistant (SEA) project has been promoting effective collaboration and code sharing among groups. During the past three years, the SEA project has been developing prototypes for new observation planning software tools and strategies. Initially funded by the Next Generation Space Telescope, parts of the SEA code have since been adopted by the Space Telescope Science Institute. SEA has also supplied code for the SIRTF (Space Infrared Telescope Facility) planning tools, and the JSky Open Source Java library. The potential benefits of sharing code are clear. The recipient gains functionality for considerably less cost. The provider gains additional developers working with their code. If enough users groups adopt a set of common code and tools, de facto standards can emerge (as demonstrated by the success of the FITS standard). Code sharing also raises a number of challenges related to the management of the code. In this talk, we will review our experiences with SEA--both successes and failures, and offer some lessons learned that might promote further successes in collaboration and re-use.

Korathkar, Anuradha↗
NASA NTRS2011

Open Collaboration: A Problem Solving Strategy That Is Redefining NASA's Innovative Spirit

In 2010, NASA?s Space Life Sciences Directorate announced the successful results from pilot experiments with open innovation methodologies. Specifically, utilization of internet based external crowd sourcing platforms to solve challenging problems in human health and performance related to the future of spaceflight. The follow-up to this success was an internal crowd sourcing pilot program entitled NASA@work, which was supported by the InnoCentive@work software platform. The objective of the NASA@work pilot was to connect the collective knowledge of individuals from all areas within the NASA organization via a private web based environment. The platform provided a venue for NASA Challenge Owners, those looking for solutions or new ideas, to pose challenges to internal solvers, those within NASA with the skill and desire to create solutions. The pilot was launched in 57 days, a record for InnoCentive and NASA, and ran for three months with a total of 20 challenges posted Agency wide. The NASA@work pilot attracted over 6000 participants throughout NASA with a total of 183 contributing solvers for the 20 challenges posted. At the time of the pilot?s closure, solvers provided viable solutions and ideas for 17 of the 20 posted challenges. The solver community provided feedback on the pilot describing it as a barrier breaking activity, conveying that there was a satisfaction associated with helping co-workers, that it was "fun" to think about problems outside normal work boundaries, and it was nice to learn what challenges others were facing across the agency. The results and the feedback from the solver community have demonstrated the power and utility of an internal collaboration tool, such as NASA@work.

Rando, Cynthia M.↗
NASA NTRS2012

Coastal Changes in Temperature and Salinity Observed during Hurricane Isaac Recorded and Downloaded by NASA DRIFTERs Moored in Heron Bay and at Half Moon Island, Louisiana

Major changes in salinity (approx.14 ppt.) and temperature (approx.40C) were continuously registered by two prototype NASA DRIFTERs, surface moored floaters, that NASA's Applied Science and Technology Project Office (ASTPO) has developed. The DRIFTER floating sensor module is equipped with an Arduino open-source electronics prototyping platform and programming language (http://www.arduino.cc), a GPS (Global Positioning System) module with antenna, a cell phone SIM (Subscriber Identity Module) card and a cellular antenna which is used to transmit data, and a probe to measure temperature and conductivity (from which salinity can be derived). The DRIFTER is powered by a solar cell panel and all the electronic components are mounted and sealed in [ waterproof encasement. Position and measurement data are transmitted via short message service (SMS) messaging to a Twitter site (DRIFTER 002@NASADRIFTER_002 and DRIFTER 004@NASADRIFTER_004), which provides a live feed. These data are the imported into a Google spreadsheet where conductivity is converted to salinity, and graphed in real-time. The spreadsheet data will be imported into a webpage maintained by ASTPO, where it will be displayed available for dO\\1lload.

Kalcic, Maria↗
NASA NTRS2001

PiVoT GPS Receiver

NASA Goddard Space Flight Center has built an open architecture, 24 channel spaceflight Global Positioning System (GPS) receiver. The compact PCI PiVoT GPS receiver card is based on the Mitel/GEC Plessey Builder 2 board. PiVoT uses two Plessey 2021 correlators to allow tracking of up to 24 separate GPS SV's on unique channels. Its four front ends can support four independent antennas, making it a useful card for hosting GPS attitude determination algorithms. It has been built using space quality, radiation tolerant parts. The PiVoT card works at a lower signal to noise ratio than the original Builder 2 board. It also hosts an improved clock oscillator. The PiVoT software is based on the original Piessey Builder 2 software ported to the Linux operating system. The software is posix compliant and can be easily converted to other posix operating systems. The software is open source to anyone with a licensing agreement with Plessey. Additional tasks can be added to the software to support GPS science experiments or attitude determination algorithms. The next generation PiVoT receiver will be a single radiation hardened compact PCI card containing the microprocessor and the GPS receiver optimized for use above the GPS constellation.

Wennersten, Miriam↗
NASA NTRS2001

PiVoT GPS Receiver

NASA Goddard Space Flight Center has built an open architecture, 24 channel space flight GPS receiver. The CompactPCI PiVoT GPS receiver card is based on the Mitel/GEC Plessey Builder-2 board. PiVoT uses two Plessey 2021 correlators to allow tracking of up to 24 separate GPS SV's on unique channels. Its four front ends can support four independent antennas, making it a useful card for hosting GPS attitude determination algorithms. It has been built using space quality, radiation tolerant parts. The PiVoT card will track a weaker signal than the original Builder 2 board. It also hosts an improved clock oscillator. The PiVoT software is based on the original Plessey Builder 2 software ported to the Linux operating system. The software is POSIX complaint and can easily be converted to other POSIX operating systems. The software is open source to anyone with a licensing agreement with Plessey. Additional tasks can be added to the software to support GPS science experiments or attitude determination algorithms. The next generation PiVoT receiver will be a single radiation hardened CompactPCI card containing the microprocessor and the GPS receiver optimized for use above the GPS constellation. PiVoT was flown successfully on a balloon in July, 2001, for its first non-simulated flight.

Wennersten, Miriam Dvorak↗