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NASA NTRS2018

TCL2 National Campaign Human Factors Brief

The Technology Capability Level-2 National Campaign (TCL2nc) was conducted at six different test-sites located across the USA, during May and June of 2017. The campaign resulted in over 240 data collection flights using 24 different aircraft and involving 23 flight crews. Flights not only varied in duration, but also in the environments and terrains over which they flew. The TCL2nc highlighted beyond visual line of sight (BVLOS) and altitude-stratified operations, and saw five partners bring their own, independently built, UAS Service Supplier (USS) for use during the flight tests. This document presents data collected during the TCL2nc that informs the 'Operator' section of the 'Requirements/Best Practices' from the UTM Technical Capability Matrix and Guidelines to Operate (Rios, version as of March 2017). A review of the data collected indicated that although teams were well qualified on paper (in terms of both completing training and having experience with flying UAS vehicles), greater consideration should be given to the unique perspectives and backgrounds of future UAS operators. Overall, teams looked at a variety of sources for information, including USS client-displays, and participants became more mindful of the need to be aware of other vehicles, highlighting the value of reporting information. Observations found that flight crews' time to respond to a UTM issue depended heavily on the team structure, communication efficiency, and crew procedures. These points are discussed in more detail below.

unmanned vehicles↗

NASA NTRS2017

NASA's UTM Research

The Technology Capability Level-2 National Campaign (TCL2nc) was conducted at six different test sites located across the USA, during May and June of 2017. The campaign resulted in over 250 data collection flights using 26 different aircraft and involving 23 flight crews. Flights not only varied in duration, but also in the environments and terrains over which they flew. The TCL2nc highlighted beyond visual line of sight (BVLOS) and altitude-stratified operations, and saw five partners bring their own, independently built, UTM Service Supplier (USS) for use during the flight tests. This document presents data collected during the TCL2nc that informs the 'Operator' section of the 'Requirements/Best Practices' from the UTM Technical Capability Matrix and Guidelines to Operate (Rios, version as of March 2017). A review of the data collected indicated that although teams were well qualified on paper (in terms of both completing training and having experience with flying UAS vehicles), greater consideration should be given to the unique perspectives and backgrounds of future UAS operators. Overall, teams looked at a variety of sources for information, including USS client-displays, and participants became more aware of the need to be aware of other vehicles, highlighting the value of reporting information. Observations found that flight crews' time to respond to a UTM issue depended heavily on the team structure, communication efficiency, and crew procedures.

Mercer, Joey↗

NASA NTRS1984

The Shuttle Mission Simulator computer generated imagery

Equipment available in the primary training facility for the Space Transportation System (STS) flight crews includes the Fixed Base Simulator, the Motion Base Simulator, the Spacelab Simulator, and the Guidance and Navigation Simulator. The Shuttle Mission Simulator (SMS) consists of the Fixed Base Simulator and the Motion Base Simulator. The SMS utilizes four visual Computer Generated Image (CGI) systems. The Motion Base Simulator has a forward crew station with six-degrees of freedom motion simulation. Operation of the Spacelab Simulator is planned for the spring of 1983. The Guidance and Navigation Simulator went into operation in 1982. Aspects of orbital visual simulation are discussed, taking into account the earth scene, payload simulation, the generation and display of 1079 stars, the simulation of sun glare, and Reaction Control System jet firing plumes. Attention is also given to landing site visual simulation, and night launch and landing simulation.

Henderson, T. H.↗

NASA NTRS1992

Supervised fully polarimetric classification of the Black Forest test site: From MAESTROI to MAC Europe

A study on the performance of a supervised fully polarimetric maximum likelihood classifier for synthetic aperture radar (SAR) data when applied to a specific classification context: forest classification based on age classes and in the presence of a sloping terrain is presented. For the experimental part, the polarimetric AIRSAR data at P, L, and C-band, acquired over the German Black Forest near Freiburg in the frame of the 1989 MAESTRO-1 campaign and the 1991 MAC Europe campaign was used, MAESTRO-1 with an ESA/JRC sponsored campaign, and MAC Europe (Multi-sensor Aircraft Campaign); in both cases the multi-frequency polarimetric JPL Airborne Synthetic Aperture Radar (AIRSAR) radar was flown over a number of European test sites. The study is structured as follows. At first, the general characteristics of the classifier and the dependencies from some parameters, like frequency bands, feature vector, calibration, using test areas lying on a flat terrain are investigated. Once it is determined the optimal conditions for the classifier performance, we then move on to the study of the slope effect. The bulk of this work is performed using the Maestrol data set. Next the classifier performance with the MAC Europe data is considered. The study is divided into two stages: first some of the tests done on the Maestro data are repeated, to highlight the improvements due to the new processing scheme that delivers 16 look data. Second we experiment with multi images classification with two goals: to assess the possibility of using a training set measured from one image to classify areas in different images; and to classify areas on critical slopes using different viewing angles. The main points of the study are listed and some of the results obtained so far are highlighted.

Degrandi, G.↗

NASA NTRSDate not supplied

A Crew and Logistics Lander for the Common Habitat Architecture

The Common Habitat Architecture is a conceptual study that explores the use of a large habitat derived from the Space Launch System (SLS) liquid oxygen tank as a core habitation element that can be used for crew missions or training in 0g, 1/6g, 3/8g, and 1g. This is not part of current NASA Artemis mission planning, but instead represents an architecture that could potentially follow after the Artemis missions and initial human Mars landings. This architecture leverages Starship-derived vehicles for crew landing on and ascent from the Moon and Mars in support of long-duration surface missions with pressurized crew transfer baselined as a nominal capability. This is not the Human Landing System variant of Starship but represents a number of modifications to enable long-duration surface missions with an eight-person crew size. A rapid brainstorming study was conducted in February of 2021, using only public data, to identify options to deliver the 90-ton Common Habitat to the surface and emplace it at the intended habitation site. This study compared three lunar lander concepts and three Mars lander concepts before ultimately selecting the SpaceX Starship as the most viable lander. The Common Habitat Architecture assumes that this Starship variant can further be modified for crew and cargo delivery, assuming that the use of a common system will lead to cost benefits. It is assumed that the Starship will expend too much propellant in landing crew and logistics to be able to launch, given the assumed absence of surface propellant production. Consequently, a separable ascent stage is used for crew ascent. The pressurized elements of this modified Starship are discussed: Starship Ascent Module, Airlock, Transfer Tunnel, Pressurized Crew Transfer Module, and Logistics Modules. For each element, a description, dimensions, rough mass estimates, core capabilities, and design features are presented. Key mechanisms and internal structures of the starship are also discussed. This will include flame diverters for the ascend module, the orbital docking hatch, Pressurized Crew Transfer Module Garage door, Starship Ascent Module fairing, flame diverter blow-out panels, Pressurized Crew Transfer Module lift system, logistics module lift system, contingency crew ascent via the logistics module lift system, internal catwalks, and structural interfaces. Concept of Operations will be discussed for both Moon and Mars. This will include in-space crew rendezvous and transfer, crewed landing, shirtsleeve crew transfer, crew departure, contingency surface operations, and final element disposition. Key differences for Mars will be discussed, such as the Martian atmosphere, dust storms, the absence of crew handover, and Deep Space Exploration Vehicle rendezvous. Contingency microgravity maintenance access will also be discussed. This work will demonstrate viable pressurized crew transfer with a Starship-based lander architecture. Forward work includes Garage lighting and camera systems, Starship Ascent Module propulsion system and docking port trades, contingency habitation trades, and mass/power equipment estimation. Finally, forward work includes developing a heavy cargo return system derived from the Crew and Logistics Starship. The goal of this system is the return of at least 10,000 kg payloads from the surfaces of the Moon and Mars.

Lunar Lander↗

NASA NTRS2001

Virtual reality robotic telesurgery simulations using MEMICA haptic system

The authors conceived a haptic mechanism called MEMICA (Remote Mechanical Mirroring using Controlled stiffness and Actuators) that can enable the design of high dexterity, rapid response, and large workspace haptic system. The development of a novel MEMICA gloves and virtual reality models are being explored to allow simulation of telesurgery and other applications. The MEMICA gloves are being designed to provide intuitive mirroring of the conditions at a virtual site where a robot simulates the presence of a human operator. The key components of MEMICA are miniature electrically controlled stiffness (ECS) elements and electrically controlled force and stiffness (ECFS) actuators that are based on the use of Electro-Rheological Fluids (ERF. In this paper the design of the MEMICA system and initial experimental results are presented.

Haptic interfaces↗

NASA NTRS1988

Antarctic Planetary Testbed (APT): A facility in the Antarctic for research, planning and simulation of manned planetary missions and to provide a testbed for technological development

The notion of using Antarctica as a planetary analog is not new. Ever since the manned space program gained serious respect in the 1950's, futurists have envisioned manned exploration and ultimate colonization of the moon and other extraterrestrial bodies. In recent years, much attention has been focused on a permanently manned U.S. space station, a manned Lunar outpost and a manned mission to Mars and its vicinity. When such lofty goals are set, it is only prudent to research, plan and rehearse as many aspects of such a mission as possible. The concept of the Antarctic Planetary Testbed (APT) project is intended to be a facility that will provide a location to train and observe potential mission crews under conditions of isolation and severity, attempting to simulate an extraterrestrial environment. Antarctica has been considered as an analog by NASA for Lunar missions and has also been considered by many experts to be an excellent Mars analog. Antarctica contains areas where the environment and terrain are more similar to regions on the Moon and Mars than any other place on Earth. These features offer opportunities for simulations to determine performance capabilities of people and machines in harsh, isolated environments. The initial APT facility, conceived to be operational by the year 1991, will be constructed during the summer months by a crew of approximately twelve. Between six and eight of these people will remain through the winter. As in space, structures and equipment systems will be modular to facilitate efficient transport to the site, assembly, and evolutionary expansion. State of the art waste recovery/recycling systems are also emphasized due to their importance in space.

Ahmadi, Mashid↗

NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗

NASA NTRSDate not supplied

Ground Validation Activities for the INCUS Satellite Mission

Over the next decade, there will be several satellite-based radar missions designed to estimate vertical motions and mass flux within deep convection. One of these is the upcoming Investigation of Cumulus Updrafts (INCUS) mission. INCUS will do this using a low-Earth orbit train of three small satellites carrying Ka-band radars to observe the 3-D evolution of radar reflectivity within deep convective cells at time intervals of 30-, 90-, and 120-sec. One approach to validating this novel time-difference (delta-t) approach would include a pair of Doppler radars flown on two different high-altitude aircraft (i.e., satellite simulator), but that solution is not practical within the scope of a cost-capped Earth Venture mission like INCUS. Instead, the ground-based validation (GV) approach for INCUS will consist of using vertical profiling radars, including analysis of existing convective cases, and multiple polarimetric, Doppler radars performing coordinated rapid sampling of the same convective cell. These new observations will be collected in the coming years through local deployments of existing research radar facilities at 1 - 3 sites over multiple seasons to capture a statistically significant sample of storm types (both pre- and post-launch) or as a single post-launch field campaign in a region where convection is very predictable. This presentation will include an overview of INCUS GV, both pre- and post-launch activities, as well as some of our collaborations with the radar community to address some of the challenges in sampling the vertical evolution of convective cells on 120-sec or less time scales.

remote sensing↗

NASA NTRS2006

Ikhana: A NASA UAS Supporting Long Duration Earth Science Missions

NASA's Ikhana unmanned aerial vehicle (UAV) is a General Atomics MQ-9 Predator-B modified to support the conduct of Earth science missions for the NASA Science Mission Directorate through partnerships, other government agencies and universities. Ikhana, a Native American word meaning 'intelligence', can carry over 2000 lbs of atmospheric and remote sensing instruments in the payload bay and external pods. The aircraft is capable of mission durations in excess of 24 hours at altitudes above 40,000 ft. Redundant flight control, avionics, power, and network systems increase the system reliability and allow easier access to public airspace. The aircraft is remotely piloted from a mobile ground control station (GCS) using both C-band line-of-sight and Ku-band over-the-horizon satellite datalinks. NASA's GCS has been modified to support on-site science monitoring, or the downlink data can be networked to remote sites. All ground support systems are designed to be deployable to support global Eart science investigations. On-board support capabilities include an instrumentation system and an Airborne Research Test System (ARTS). The ARTS can host research algorithms that will autonomously command and control on-board sensors, perform sensor health monitoring, conduct data analysis, and request changes to the flight plan to maximize data collection. The ARTS also has the ability to host algorithms that will autonomously control the aircraft trajectory based on sensor needs, (e.g. precision trajectory for repeat pass interferometry) or to optimize mission objectives (e.g. search for specific atmospheric conditions). Standard on-board networks will collect science data for recording and for inclusion in the aircraft's high bandwidth downlink. The Ikhana project will complete GCS development, science support systems integration, external pod integration and flight clearance, and operations crew training in early 2007. A large-area remote sensing mission is currently scheduled for the Summer 2007.

Cobleigh, Brent R.↗

NASA NTRS2007

Robotics

Lunar robotic functions include: 1. Transport of crew and payloads on the surface of the moon; 2. Offloading payloads from a lunar lander; 3. Handling the deployment of surface systems; with 4. Human commanding of these functions from inside a lunar vehicle, habitat, or extravehicular (space walk), with Earth-based supervision. The systems that will perform these functions may not look like robots from science fiction. In fact, robotic functions may be automated trucks, cranes and winches. Use of this equipment prior to the crew s arrival or in the potentially long periods without crews on the surface, will require that these systems be computer controlled machines. The public release of NASA's Exploration plans at the 2nd Space Exploration Conference (Houston, December 2006) included a lunar outpost with as many as four unique mobility chassis designs. The sequence of lander offloading tasks involved as many as ten payloads, each with a unique set of geometry, mass and interface requirements. This plan was refined during a second phase study concluded in August 2007. Among the many improvements to the exploration plan were a reduction in the number of unique mobility chassis designs and a reduction in unique payload specifications. As the lunar surface system payloads have matured, so have the mobility and offloading functional requirements. While the architecture work continues, the community can expect to see functional requirements in the areas of surface mobility, surface handling, and human-systems interaction as follows: Surface Mobility 1. Transport crew on the lunar surface, accelerating construction tasks, expanding the crew s sphere of influence for scientific exploration, and providing a rapid return to an ascent module in an emergency. The crew transport can be with an un-pressurized rover, a small pressurized rover, or a larger mobile habitat. 2. Transport Extra-Vehicular Activity (EVA) equipment and construction payloads. 3. Transport habitats and power modules over long distances, pre-positioning them for the arrival of crew on a subsequent lander. Surface Handling 1. Offload surface system payloads from the lander, breaking launch restraints and power/data connections. Payloads may be offloaded to a wheeled vehicle for transport. 2. Deploy payloads from a wheeled vehicle at a field site, placing the payloads in their final use site on the ground or mating them with existing surface systems. 3. Support regolith collection, site preparation, berm construction, or other civil engineering tasks using tools and implements attached to rovers. Human-Systems Interaction 1. Provide a safe command and control interface for suited EVA to ride on and drive the vehicles, making sure that the systems are also safe for working near dismounted crew. 2. Provide an effective control system for IV crew to tele-operate vehicles, cranes and other equipment from inside the surface habitats with evolving independence from Earth. .. Provide a supervisory system that allows machines to be commanded from the ground, working across the Earth-Lunar time delays on the order of 5-10 seconds (round trip) to support operations when crew are not resident on the surface. Technology Development Needs 1. Surface vehicles that can dock, align and mate with outpost equipment such as landers, habitats and fluid/power interfaces. 2. Long life motors, drive trains, seals, motor electronics, sensors, processors, cable harnesses, and dash board displays. 3. Active suspension control, localization, high speed obstacle avoidance, and safety systems for operating near dismounted crew. 4. High specific energy and specific power batteries that are safe, rechargeable, and long lived.

Ambrose, Robert O.↗

NASA NTRS2004

Real-Time Adaptive Color Segmentation by Neural Networks

Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural network and algorithm is that each update of synaptic weights takes place in conjunction with the addition of another hidden unit, which then remains in place as still other hidden units are added on subsequent iterations. For a given training pattern, the synaptic weight between (1) the inputs and the previously added hidden units and (2) the newly added hidden unit is updated by an amount proportional to the partial derivative of a quadratic error function with respect to the synaptic weight. The synaptic weight between the newly added hidden unit and each output unit is given by a more complex function that involves the errors between the outputs and their target values, the transfer functions (hyperbolic tangents) of the neural units, and the derivatives of the transfer functions.

Duong, Tuan A.↗

NASA NTRS2002

National Directory of NASA Space Grant Contacts

Congress enacted the National Space Grant College and Fellowship Program (also known as Space Grant). NASA's Space Grant Program funds education, research, and public service programs in all 50 States, the District of Columbia, and the Commonwealth of Puerto Rico through 52 university-based Space Grant consortia. These consortia form a network of colleges and universities, industry partners, State and local Government agencies, other Federal agencies, museum and science centers, and nonprofit organizations, all with interests in aerospace education, research, and training. Space Grant programs emphasize the diversity of human resources, the participation of students in research, and the communication of the benefits of science and technology to the general public. Each year approximately one-third of the NASA Space Grant funds support scholarships and fellowships for United States students at the undergraduate and graduate levels. Typically, at least 20 percent of these awards go to students from underrepresented groups, and at least 40 percent go to women. Most Space Grant student awards include a mentored research experience with university faculty or NASA scientists or engineers. Space Grant consortia also fund curriculum enhancement and faculty development programs. Consortia members administer precollege and public service education programs in their States. The 52 consortia typically leverage NASA funds with matching contributions from State, local, and other university sources, which more than double the NASA funding. For more information, consult the Space Grant Web site at http://education.nasa.gov/spacegrant/

Source record↗

NASA NTRS2009

A Summary of NASA and USAF Hypergolic Propellant Related Spills and Fires

Several unintentional hypergolic fluid related spills, fires, and explosions from the Apollo Program, the Space Shuttle Program, the Titan Program, and a few others have occurred over the past several decades. Spill sites include the following government facilities: Kennedy Space Center (KSC), Johnson Space Center (JSC), White Sands Test Facility (WSTF), Vandenberg Air Force Base (VAFB), Cape Canaveral Air Force Station (CCAFS), Edwards Air Force Base (EAFB), Little Rock AFB, and McConnell AFB. Until now, the only method of capturing the lessons learned from these incidents has been "word of mouth" or by studying each individual incident report. The root causes and consequences of the incidents vary drastically; however, certain "themes" can be deduced and utilized for future hypergolic propellant handling. Some of those common "themes" are summarized below: (1) Improper configuration control and internal or external human performance shaping factors can lead to being falsely comfortable with a system (2) Communication breakdown can escalate an incident to a level where injuries occur and/or hardware is damaged (3) Improper propulsion system and ground support system designs can destine a system for failure (4) Improper training of technicians, engineers, and safety personnel can put lives in danger (5) Improper PPE, spill protection, and staging of fire extinguishing equipment can result in unnecessary injuries or hardware damage if an incident occurs (6) Improper procedural oversight, development, and adherence to the procedure can be detrimental and quickly lead to an undesirable incident (7) Improper materials cleanliness or compatibility and chemical reactivity can result in fires or explosions (8) Improper established "back-out" and/or emergency safing procedures can escalate an event The items listed above are only a short list of the issues that should be recognized prior to handling hypergolic fluids or processing vehicles containing hypergolic propellants. The summary of incidents in this report is intended to cover many more issues than those listed above.

Nufer, Brian M.↗

NASA NTRS2017

Osiris-REx Spacecraft Current Status and Forward Plans

The NASA New Frontiers OSIRIS-REx spacecraft executed a flawless launch on September 8, 2016 to begin its 23-month journey to near-Earth asteroid (101955). The primary objective of the OSIRIS-REx mission is to collect and return to Earth a pristine sample of regolith from the asteroid surface. The sampling event will occur after a two-year period of remote sensing that will ensure a high probability of successful sampling of a region on the asteroid surface having high science value and within well-defined geological context. The OSIRIS-REx instrument payload includes three high-resolution cameras (OCAMS), a visible and near-infrared spectrometer (OVIRS), a thermal imaging spectrometer (OTES), an X-ray imaging spectrometer (REXIS), and a laser altimeter (OLA). As the spacecraft follows its nominal outbound-cruise trajectory, the propulsion, power, communications, and science instruments have undergone basic functional tests, with no major issues. Outbound cruise science investigations include a search for Earth Trojan asteroids as the spacecraft approaches the Sun-Earth L4 Lagrangian point in February 2017. Additional instrument checkouts and calibrations will be carried out during the Earth gravity assist maneuver in September 2017. During the Earth-moon flyby, visual and spectral images will be acquired to validate instrument command sequences planned for Bennu remote sensing. The asteroid Bennu remote sensing campaign will yield high resolution maps of the temperature and thermal inertia, distributions of major minerals and concentrations of organic matter across the asteroid surface. A high resolution 3d shape model including local surface slopes and a high-resolution gravity field will also be determined. Together, these data will be used to generate four separate maps that will be used to select the sampling site(s). The Safety map will identify hazardous and safe operational regions on the asteroid surface. The Deliverability map will quantify the accuracy with which the navigation team can deliver the spacecraft to and from specific sites on the asteroid surface. The Sampleability map quantifies the regolith properties, providing an estimation of how much material would be sampled at different points on the surface. The final Science Value map synthesizes the chemical, mineralogical, and geological, observations to identify the areas of the asteroid surface with the highest science value. Here, priority is given to organic, water-rich regions that have been minimally altered by surface processes. Asteroid surface samples will be acquired with a touch-and-go sample acquisition system (TAGSAM) that uses high purity pressurized N2 gas to mobilize regolith into a stainless steel canister. Although the mission requirement is to collect at least 60 g of material, tests of the TAGSAM routinely exceeded 300 g of simulant in micro-gravity tests. After acquiring the sample, the spacecraft will depart Bennu in 2021 to begin its return journey, with the sample return capsule landing at the Utah Test and Training Range on September 23, 2023. The OSIRIS-REx science team will carry out a series of detailed chemical, mineralogical, isotopic, and spectral studies that will be used to determine the origin and history of Bennu and to relate high spatial resolution sample studies to the global geological context from remote sensing. The outline of the sample analysis plan is described in a companion abstract.

Messenger, Scott↗

NASA NTRS1992

Neural network classification of questionable EGRET events

High energy gamma rays (greater than 20 MeV) pair producing in the spark chamber of the Energetic Gamma Ray Telescope Experiment (EGRET) give rise to a characteristic but highly variable 3-D locus of spark sites, which must be processed to decide whether the event is to be included in the database. A significant fraction (about 15 percent or 10(exp 4) events/day) of the candidate events cannot be categorized (accept/reject) by an automated rule-based procedure; they are therefore tagged, and must be examined and classified manually by a team of expert analysts. We describe a feedforward, back-propagation neural network approach to the classification of the questionable events. The algorithm computes a set of coefficients using representative exemplars drawn from the preclassified set of questionable events. These coefficients map a given input event into a decision vector that, ideally, describes the correct disposition of the event. The net's accuracy is then tested using a different subset of preclassified events. Preliminary results demonstrate the net's ability to correctly classify a large proportion of the events for some categories of questionables. Current work includes the use of much larger training sets to improve the accuracy of the net.

Meetre, C. A.↗

NASA NTRS2000

Web Exploration Tools for a Fast Federated Optical Survey Database

We implemented several new web-based tools to improve the efficiency and versatility of access to the APS Catalog of the POSS I (Palomar Observatory-National Geographic Sky Survey) and its associated image database. The most important addition was a federated database system to link the APS Catalog and image database into one Internet-accessible database. With the FDBS, the queries and transactions on the integrated database are performed as if it were a single database. We installed Myriad the FDBS developed by Professor Jaideep Srivastava and members of his group in the University of Minnesota Computer Science Department. It is the first system to provide schema integration, query processing and optimization, and transaction management capabilities in a single framework. The attached figure illustrates the Myriad architecture. The FDBS permits horizontal access to the data, not just vertical. For example, for the APS, queries can be made not only by sky position, but also by any parameter present in either of the databases. APS users will be able to produce an image of all the blue galaxies and stellar sources for comparison with x-ray source error ellipses from AXAF (X Ray Astrophysics Facility) (Chandra) for example. The FDBS is now available as a beta release with the appropriate query forms at our web site. While much of our time was occupied with adapting Myriad to the APS environment, we also made major changes in Star Base, our DBMS for the Catalog, at the web interface to improve its efficiency for issuing and processing queries. Star Base is now three times faster for large queries. Improvements were also made at the web end of the image database for faster access; although work still needs to be done to the image database itself for more efficient return with the FDBS. During the past few years, we made several improvements to the database pipeline that creates the individual plate databases queries by StarBase. The changes include improved positions especially for galaxies, using a new median centroider and integrated magnitudes for galaxies with an improved density-to-intensity calibration with a "sky" background subtraction. In the original version of StarBase the object classification fainter than 19.5-20.0 mag., was an extrapolation of the networks trained on brighter objects. We have used a new catalog of galaxies at the NGP to train a neural network on objects fainter than 20th mag. This improved classification is used in the new version of StarBase. We have also added a FITS table option for the returned data from queries on the object catalog. The APS image database includes images in both colors so we have added a tool for querying the image database in both colors simultaneously. The images can be displayed in parallel or blinked for comparison.

Humphreys, Roberta M.↗

NASA NTRS2012

Virtual Ultrasound Guidance for Inexperienced Operators

Medical ultrasound or echocardiographic studies are highly operator-dependent and generally require lengthy training and internship to perfect. To obtain quality echocardiographic images in remote environments, such as on-orbit, remote guidance of studies has been employed. This technique involves minimal training for the user, coupled with remote guidance from an expert. When real-time communication or expert guidance is not available, a more autonomous system of guiding an inexperienced operator through an ultrasound study is needed. One example would be missions beyond low Earth orbit in which the time delay inherent with communication will make remote guidance impractical. The Virtual Ultrasound Guidance system is a combination of hardware and software. The hardware portion includes, but is not limited to, video glasses that allow hands-free, full-screen viewing. The glasses also allow the operator a substantial field of view below the glasses to view and operate the ultrasound system. The software is a comprehensive video program designed to guide an inexperienced operator through a detailed ultrasound or echocardiographic study without extensive training or guidance from the ground. The program contains a detailed description using video and audio to demonstrate equipment controls, ergonomics of scanning, study protocol, and scanning guidance, including recovery from sub-optimal images. The components used in the initial validation of the system include an Apple iPod Classic third-generation as the video source, and Myvue video glasses. Initially, the program prompts the operator to power-up the ultrasound and position the patient. The operator would put on the video glasses and attach them to the video source. After turning on both devices and the ultrasound system, the audio-video guidance would then instruct on patient positioning and scanning techniques. A detailed scanning protocol follows with descriptions and reference video of each view along with advice on technique. The program also instructs the operator regarding the types of images to store and how to overcome pitfalls in scanning. Images can be forwarded to the ground or other site when convenient. Following study completion, the video glasses, video source, and ultrasound system are powered down and stored. Virtually any equipment that can play back video can be used to play back the program. This includes a DVD player, personal computer, and some MP3 players.

Caine, Timothy↗