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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

X ray observations of late-type stars using the ROSAT all-sky survey

The ROSAT mission made the first x ray survey of the entire sky using an imaging detector. Although ROSAT is a joint NASA/German project and involves direct American participation during its second phase of pointed observations, the all-sky survey remains the sole property of the German investigators. NASA grant represented the first use of ROSAT data analysis funds to support direct American participation in the ROSAT all-sky survey. The project involved a collaborative agreement between the Joint Institute for Laboratory Astrophysics (JILA) and the Max-Planck-Institut fur Extraterrestrische Physik (MPE) where JILA supplied MPE with a post-doctoral research associate with experience in the field of stellar (coronal) x ray emission to work within their ROSAT group. In return, members of the cool star research group at JILA were given the opportunity to collaborate on projects involving ROSAT all-sky survey data. Both sides have benefitted (and still benefit) from this arrangement since MPE suffers from a shortage of researchers who are interested in x ray emission from 'normal' stars and white dwarfs. MPE has also drawn upon experience in optical identification of x ray sources from the Einstein Extended Medium Sensitivity Survey in planning their own identification strategies for the ROSAT all-sky survey. The JILA cool stars group has benefitted since access to all-sky survey data has expanded the scope of their already extensive research programs involving multiwavelength observations of late-type stars. ROSAT was successfully launched on 1 June 1990 and conducted the bulk of the survey from 30 July 1990 to 25 January 1991. Data gaps in the survey have subsequently been made up. At the time of this writing (February 1992), the survey data have been processed once with the Standard Analysis Software System (SASS). A second processing will soon begin with improvements made to the SASS to correct errors and bugs found while carrying out scientific projects with data from the first processing. We outline the major research activities of Dr. Fleming over the past year (detailed accounts of his activities during the first two years of this grant can be found in the first-year and second-year status reports on this grant). Regarding the three specific projects which were proposed in the original proposal, two of them (White Dwarfs and Late M Dwarfs) are near completion. The results are described in two conference proceedings which are appended.

Linsky, Jeffrey L.↗
NASA NTRS2012

NASA Tech Briefs, April 2012

Topics include: Computational Ghost Imaging for Remote Sensing; Digital Architecture for a Trace Gas Sensor Platform; Dispersed Fringe Sensing Analysis - DFSA; Indium Tin Oxide Resistor-Based Nitric Oxide Microsensors; Gas Composition Sensing Using Carbon Nanotube Arrays; Sensor for Boundary Shear Stress in Fluid Flow; Model-Based Method for Sensor Validation; Qualification of Engineering Camera for Long-Duration Deep Space Missions; Remotely Powered Reconfigurable Receiver for Extreme Environment Sensing Platforms; Bump Bonding Using Metal-Coated Carbon Nanotubes; In Situ Mosaic Brightness Correction; Simplex GPS and InSAR Inversion Software; Virtual Machine Language 2.1; Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction; Pandora Operation and Analysis Software; Fabrication of a Cryogenic Bias Filter for Ultrasensitive Focal Plane; Processing of Nanosensors Using a Sacrificial Template Approach; High-Temperature Shape Memory Polymers; Modular Flooring System; Non-Toxic, Low-Freezing, Drop-In Replacement Heat Transfer Fluids; Materials That Enhance Efficiency and Radiation Resistance of Solar Cells; Low-Cost, Rugged High-Vacuum System; Static Gas-Charging Plug; Floating Oil-Spill Containment Device; Stemless Ball Valve; Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs; Oxygen-Methane Thruster; Lunar Navigation Determination System - LaNDS; Launch Method for Kites in Low-Wind or No-Wind Conditions; Supercritical CO2 Cleaning System for Planetary Protection and Contamination Control Applications; Design and Performance of a Wideband Radio Telescope; Finite Element Models for Electron Beam Freeform Fabrication Process Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System; Vehicle Detection for RCTA/ANS (Autonomous Navigation System); Image Mapping and Visual Attention on the Sensory Ego-Sphere; HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis; and IMAGESEER - IMAGEs for Education and Research.

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NASA NTRS1987

Computer Sciences and Data Systems, volume 1

Topics addressed include: software engineering; university grants; institutes; concurrent processing; sparse distributed memory; distributed operating systems; intelligent data management processes; expert system for image analysis; fault tolerant software; and architecture research.

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NASA NTRS2003

Patterns of software defect data on spacecraft

The identification of patterns of software defect data yields insights into improving the quality of both operational and future spacecraft. This paper describes the results of applying this technique to both post-launch and pre-launch spacecraft. It then describes four key challenges that remain to achieving fuller utilization of defect analysis in future systems.

software defect defect analysis defect data↗
NASA NTRS2011

FPA Depot - Web Application

Lines of code (LOC) analysis is one of the methods used to measure programmer productivity and estimate schedules of programming projects. The Launch Control System (LCS) had previously used this method to estimate the amount of work and to plan development efforts. The disadvantage of using LOC as a measure of effort is that one can only measure 30% to 35% of the total effort of software projects involves coding [8]. In the application, instead of using the LOC we are using function point for a better estimation of hours in each software to develop. Because of these disadvantages, Jamie Szafran of the System Software Branch of Control And Data Systems (NE-C3) at Kennedy Space Canter developed a web application called Function Point Analysis (FPA) Depot. The objective of this web application is that the LCS software architecture team can use the data to more accurately estimate the effort required to implement customer requirements. This paper describes the evolution of the domain model used for function point analysis as project managers continually strive to generate more accurate estimates.

Avila, Edwin M. Martinez↗
NASA NTRS1980

Data reduction and analysis for the graphite crystal X-ray spectrometer and polarimeter experiment flown aboard OSO-8 spacecraft

The documentation and software programs developed for the reception, initial processing (quickbook), and production analysis of data obtained by solar X-ray spectroscopy, stellar spectroscopy, and X-ray polarimetry experiments on OSO-8 are listed. The effectiveness and sensitivity of the Bragg crystal scattering instruments used are assessed. The polarization data polarimetric data obtained shows that some X-ray sources are polarized and that a larger polarimeter of this type is required to perform the measurements necessary to fully understand the physics of X-ray sources. The scanning Bragg crystal spectrometer was ideally suited for studying rapidly changing solar conditions. Observations of the Crab Nebula and pulsar, Cyg X-1, Cyg X-2, Cyg X-3, Sco X-1, Cen X-3, and Her X-1 are discussed as well as of 4U1656-53 and 4U1820-30. Evidence was obtained for iron line emission from Cyg X-3.

Novick, R.↗
NASA NTRS2010

Tropospheric Emission Spectrometer Product File Readers

TES Product File Reader software extracts data from publicly available Tropospheric Emission Spectrometer (TES) HDF (Hierarchical Data Format) product data files using publicly available format specifications for scientific analysis in IDL (interactive data language). In this innovation, the software returns data fields as simple arrays for a given file. A file name is provided, and the contents are returned as simple IDL variables.

Fisher, Brendan M.↗
NASA NTRS1994

The IUE Science Operations Ground System

The International Ultraviolet Explorer (IUE) Science Operations System provides full realtime operations capabilities and support to the operations staff and astronomer users. The components of this very diverse and extremely flexible hardware and software system have played a major role in maintaining the scientific efficiency and productivity of the IUE. The software provides the staff and user with all the tools necessary for pre-visit and real-time planning and operations analysis for any day of the year. Examples of such tools include the effects of spacecraft constraints on target availability, maneuver times between targets, availability of guide stars, target identification, coordinate transforms, e-mail transfer of Observatory forms and messages, and quick-look analysis of image data. Most of this extensive software package can also be accessed remotely by individual users for information, scheduling of shifts, pre-visit planning, and actual observing program execution. Astronomers, with a modest investment in hardware and software, may establish remote observing sites. We currently have over 20 such sites in our remote observers' network.

Pitts, Ronald E.↗
NASA NTRS1993

Science & Technology Laboratory Applications Software (ELAS)

Digital image analysis system processes Landsat multispectral data, aircraft-acquired scanner data, digitized topographical data, and such ancillary data as soil types and rainfall information. Over 230 modules aid user in performing wide range of land cover analyses and manipulations.

Junkin, B. G.↗
NASA NTRS1992

Automating sky object classification in astronomical survey images

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged data.

Fayyad, Usama M.↗
NASA NTRSDate not supplied

Predicting the Seawater Chemistry of an Ocean World Using Machine Learning on Isotopic Measurements of Volatile CO2

Introduction: Given the long time intervals required for data transmission to and from ocean worlds targets, low bandwidth for data transmission, time required for data processing and analysis, and potentially extreme radiation environments (e.g., Europa), it is clear that ocean worlds missions will need more autonomous flight instruments and software in order to achieve established science goals. Protracted time intervals for data analysis (e.g., Europa Lander) strongly motivates the development of rapid, consistent and streamlined methods for interpreting data from flight mass spectrometers to e.g., determine how mass spectra from a plume or surface liquid/ice relates to the surface/subsurface. Since mass spectrometry also has the potential to correctly identify biosignatures[1], it is imperative that such methods for interpreting data are consistent and accurate. We used 848 isotope ratio mass spectra from laboratory analyses of CO2 that interacted with ocean worlds-relevant seawaters as a ‘training’ dataset for ‘unsupervised’ machine learning. In unsupervised learning, characteristics of the data are not labeled or linked, and any similarities found only result from the neural network. CO2 isotopologues analyzed for this dataset mimic the remote measurements of CO2 by a flight mass spectrometer, and are detailed in Theiling [2]. From this dataset, we used measured features of the spectra, such as retention time, intensity, and (isotopologue) mass ratios as inputs for our autoencoder neural network. Our neural network was trained to find similarities in these and other spectral features for seawaters of a particular composition and amount of initial CO2. Successful training then created an output of these similarities for various seawaters, which included MgSO4, Na2SO4, NaCl, MgCl2, KCl, and NaHCO3, and combinations of these salts. We then applied dimensionality reduction techniques such as Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (TSNE), and Uniform Manifold Approximation and Projection (UMAP) to demonstrate latent data features as a two-dimensional projection in a unitless, high-dimensional space. In this projection, a data point represents the combined effect of spectral features such as intensity, retention time, and isotope ratio. Our initial UMAP demonstrates data clustering (organization of the data by the neural network) based on the amount of CO2 that had initially interacted with each seawater. Further training using more ‘supervised’ learning techniques demonstrate strong clustering of preliminary data based on initial CO2 concentration, seawater chemical composition, and ionic strength (salinity). Our preliminary work therefore suggests that machine learning has the potential to identify compositional variants of an ocean world seawater based on mass spectra from volatile CO2 measurements. Acknowledgments: This work was funded through a Strategic Task Group at NASA Goddard Space Flight Center. The training dataset was collected through funding from the Oklahoma Space Grant Consortium. References: [1] Pappalardo, R. et al. (2013) Astrobiology, 13, 740–773. [2] Theiling (2020) Icarus, 114216.

Europa↗
NASA NTRS2020

On Development of Three-Dimensional Visualization Capabilities in Glenn Research Center Communication Analysis Suite

With NASA’s upcoming mission to return to the Moon sustainably by 2024 and using that success as a means to step onto the barren world of Mars, it remains more important than ever to conduct research and planning as thoroughly and efficiently as possible. In a mission as complex as landing humans onto another celestial body, a network of orbiting satellites and ground stations must accurately and reliably communicate with each other, enabling crucial data communications throughout the mission. Visualizing this important data communication increases the understanding of the data and can accelerate analyses efforts. The purpose of this software development is to create an interactive visualization with data taken MATLAB® scripts in the GRC Communication Analysis Suite that is easy to understand, can show all necessary data, and display the data accurately. The main types of data to visualize are from the State Propagation, Line of Sight and Dynamic Link Margin scripts. These all show positions and orbits of satellites and ground stations, while the Line of Sight data shows when they have the ability to communicate with each other based on their respective antenna positions and fields of view. Additionally, the Dynamic Link Margin mode color-codes the communication link performance onto the Line of Sight access lines. Visualization requires a graphics language that is easily accessible, has the needed features, and able to easily read data produced by the GRC Communication Analysis Suite MATLAB® scripts. ThreeJS, a graphics library for Web Graphics Library, coded in JavaScript was selected for the visualization. The next part of the software development was to move the data from MATLAB® to the JavaScript. The best way to accomplish this was to implement a MATLAB® function converting the output data of the scripts to a JavaScript Object Notation file. A key part of the development was creating the visualization within JavaScript and ThreeJS to visualize any combination of planets, moons, orbits, satellites, ground stations, line of sight links, and handle future features without changing major parts of the code. The current visualization capability runs directly from MATLAB®, and can dynamically create any scene. This software development currently supports the lunar communications analysis underway by NASA, and can be easily expanded upon in the future to aid any analysis requirements to help plan current and future space missions.

Visualization↗
NASA NTRS1995

Data management, archiving, visualization and analysis of space physics data

A series of programs for the visualization and analysis of space physics data has been developed at UCLA. In the course of those developments, a number of lessons have been learned regarding data management and data archiving, as well as data analysis. The issues now facing those wishing to develop such software, as well as the lessons learned, are reviewed. Modern media have eased many of the earlier problems of the physical volume required to store data, the speed of access, and the permanence of the records. However, the ultimate longevity of these media is still a question of debate. Finally, while software development has become easier, cost is still a limiting factor in developing visualization and analysis software.

Russell, C. T.↗
NASA NTRS2014

Clearance Analysis of Node 3 Aft CBM to the Stowed FGB Solar Array

In early 2011, the ISS Vehicle Configuration Office began considering the relocation of the Permanent Multipurpose Module (PMM) to the aft facing Common Berthing Mechanism (CBM) on Node 3 to open a berthing location for visiting vehicles on the Node 1 nadir CBM. In this position, computer-aided design (CAD) models indicated that the aft end of the PMM would be only a few inches from the stowed Functional Cargo Block (FGB) port solar array. To validate the CAD model clearance analysis, in the late summer of 2011 the Image Science and Analysis Group (ISAG) was asked to determine the true geometric relationship between the on-orbit aft facing Node 3 CBM and the FGB port solar array. The desired measurements could be computed easily by photogrammetric analysis if current imagery of the ISS hardware were obtained. Beginning in the fall of 2011, ISAG used the Dynamic Onboard Ubiquitous Graphics (DOUG) program to design a way to acquire imagery of the aft face of Node 3, the aft end-cone of Node 1, the port side of pressurized mating adapter 1 (PMA1), and the port side of the FGB out to the tip of the port solar array using cameras on the Space Station Remote Manipulator System (SSRMS). This was complicated by the need to thread the SSRMS under the truss, past Node 3 and the Cupola, and into the space between the aft side of Node 3 and the FGB solar array to acquire more than 100 images from multiple positions. To minimize the number of SSRMS movements, the Special Purpose Dexterous Manipulator (SPDM) would be attached to the SSRMS. This would make it possible to park the SPDM in one position and acquire multiple images by changing the viewing orientation of the SPDM body cameras using the pan/tilt units on which the cameras are mounted. Using this implementation concept, ISAG identified four SSRMS/SPDM positions from which all of the needed imagery could be acquired. Based on a photogrammetric simulation, it was estimated that the location of the FGB solar array could be measured within an accuracy of about 1 in. in each axis relative to the ISS Analysis Coordinate System (ISSACS). In October 2011, a proposed image-acquisition plan was drafted by ISAG and released for review. The ISS Robotics flight control team (ROBO) proposed minor changes to SPDM positions 1 and 4 to meet ISS proximity requirements. The updated image acquisition plan and draft chit were presented to and approved by the Systems Working Group (SWG) November 18 and were sent to the Vehicle Configuration Board (VCB) in early December 2011. Working with ROBO on 3 successive days (February 21, 22, and 23), ISAG collected 161 images of the ISS. Approximately 40 images were collected from each of the four different SSRMS/SPDM positions, with each set mapping the region from the Node 3 end cone, across Node 1, along the forward port side portion of the FGB, and out the port side FGB solar arrays. From this imagery, the best 80 images were selected for use in the analysis. The images were radiometrically enhanced to improve color and contrast and loaded into the FotoG analysis software along with the camera parameters and control data, which consisted of the coordinates for 54 handrail attachment bolts on the aft face of Node 3, in the ISSACS coordinate system. The results of this analysis produced the measured coordinates of 116 points distributed across the face of the FGB solar array panels (see figure 3) along with propagated uncertainty estimates in each coordinate axis. These results were sent to the ISS Vehicle Configuration Office, which sent them to the Configuration Analysis Modeling and Mass Properties (CAMMP) team for comparison with the Russian-provided CAD model for the retracted FGB solar arrays. The CAMMP analysis unexpectedly showed that the measured location of the port FGB solar array was up to 41-in. further outboard than the design and was slightly twisted about its rotational axis. The unexpected comparison results produced some initial concern regarding the accuracy of the photogrammetric measurements. To verify the measured results, ISAG personnel conducted a second analysis using just the imagery of the solar arrays in an arbitrary coordinate system defined by the three corner points of the inboard-most panel, with the design distance between points A1 and A10 as the only scale. The new measurements agreed with the original results to within less than 1 in. RMS in each axis, confirming the original solar array measurements. ISAG produced a final report for the ISS Vehicle Configuration Office documenting an apparent anomaly in the retracted configuration of the port FGB solar arrays. A copy of the measurement report was translated and sent to the Russian Space Agency. During a Vehicle Integrated Performance and Resources (VIPeR) teleconference September 24, 2012, the Russians acknowledged receipt of a translated copy of the ISAG report. The Russian representative stated that the head of the solar array design team claimed that the measured configuration was impossible unless the structure was physically broken. The Russians acknowledged that they had no expertise in photogrammetry, so the analysis technique employed was a "black box" to them, and they did not know how to use the ISAG results. They asked for a single image in which the overextension of the port solar array could be obviously seen. On November 10, 2012, during a face-to-face meeting with their Russian counterparts at JSC, ISAG presented nadir-view imagery of the FGB acquired during Space Shuttle rendezvous. Using the known width of the pressurized portion of the FGB as a scale, this analysis clearly showed that the port FGB solar array was extended outboard further than the Russian design for the retracted solar array.

Liddle, Donn↗
NASA NTRS2008

Extraction and Analysis of Display Data

The Display Audit Suite is an integrated package of software tools that partly automates the detection of Portable Computer System (PCS) Display errors. [PCS is a lap top computer used onboard the International Space Station (ISS).] The need for automation stems from the large quantity of PCS displays (6,000+, with 1,000,000+ lines of command and telemetry data). The Display Audit Suite includes data-extraction tools, automatic error detection tools, and database tools for generating analysis spread sheets. These spread sheets allow engineers to more easily identify many different kinds of possible errors. The Suite supports over 40 independent analyses, 16 NASA Tech Briefs, November 2008 and complements formal testing by being comprehensive (all displays can be checked) and by revealing errors that are difficult to detect via test. In addition, the Suite can be run early in the development cycle to find and correct errors in advance of testing.

Land, Chris↗
NASA NTRSDate not supplied

A Workflow to Rapidly Interrogate Multiscale Model Simulation Results Across Multiple Length Scales

Many tools can be used to visualize field and state variables for a single scale analysis so that the influence of relevant mechanisms can be evaluated. Finite element software is often utilized to simulate a unit cell of a material and visualize results at that scale. Material properties can be homogenized from individual constituents and local deformation, damage, and failure mechanisms can be evaluated within the unit cell due to globally applied boundary conditions. Such solutions can produce satisfactory results if a user is only interested in analyzing a single scale. But materials in general contain features across multiple length scales, and assumptions must be made when attempting to account for lower length scale phenomena within a higher length scale model. Multiscale modeling is an attractive means to model materials because detailed material responses can be tracked across multiple disparate length scales while reducing the amount of required assumptions. However, as the complexity of these models increases, a large amount of data can be produced, and data traceability can become increasingly more difficult. Field and state variables, which are naturally dependent on spatial position, may themselves be calculated from one or more lower length scale unit cell models each with their own appropriate field and state variables. The NASA Multiscale Analysis Tool (NASMAT) is one software that can be used to perform a multiscale analysis efficiently and output requested data at all length scales in the analysis. A companion open-source Python software, NASMAT PrePost, can be used to visualize NASMAT model results and rapidly interrogate multiscale data across multiple length scales. This presentation will demonstrate some of the key features of NASMAT PrePost on two multiscale problems by quickly displaying and demonstrating connectivity among multiscale results from large datasets.

Python↗
NASA NTRS1976

Spacelab user implementation assessment study. (Software requirements analysis). Volume 2: Technical report

The engineering analyses and evaluation studies conducted for the Software Requirements Analysis are discussed. Included are the development of the study data base, synthesis of implementation approaches for software required by both mandatory onboard computer services and command/control functions, and identification and implementation of software for ground processing activities.

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