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

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NASA NTRS2019

Quantitative Comparison of Proprietary and Open-Source Georeferencing Tools for Use with Astronaut Photography

The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA’s Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth’s surface and atmosphere. Astronauts on the International Space Station (ISS) respond to requests from CEO to acquire imagery of scientific and education targets, to include high profile targets in response to activations from the International Charter for Space & Major Disasters (also known as the International Disaster Charter, or IDC) and NASA’s Disasters Program. CEO facilitates the acquisition of astronaut photography in response to IDC events and delivers georeferenced data products to the United States Geological Survey (USGS) for distribution to the disaster community. Using GeoRef, an internal web-based tool developed in collaboration with NASA’s Ames Research Center, CEO generates data packages of georeferenced imagery, uncertainty images for assessing control and tie point accuracy, and metadata documenting raw and processed data. Operational experience with the Georef software identified vulnerabilities to internal code and server errors that can significantly increase time of data production. As such, CEO developed a backup procedure in case the GeoRef software experiences front-end or back-end errors. A system using OSGEO’s open-source QGIS software combined with a semi-automated pipeline using the object-oriented Python language and the Geospatial Abstract Library for generating metadata is quantitatively compared to GeoRef’s data package for quality and productivity. Root Mean Square Error (RMSE) provides a standard measurement of data quality as it relates to ground error. Assessing RMSE measurements generated from georeferenced astronaut photographs acquired with different obliquity and focal length offers a comprehensive accuracy assessment of the software’s transformation algorithms. This assessment will indicate the software's ability to produce data products with the least ground-error or highest data quality regarding ground accuracy. In addition, a comparison of the software’s efficiency in generating a data package that includes georeferenced images, metadata, and uncertainty images for measuring tie/ground point error was performed. Initial results, based on the comparison of three nadir-facing astronaut photographs acquired with a 95mm focal length, reveal the QGIS-based system's average RMSE is 2.36 (pixels) suggesting its georectification system produces data products that meet and perhaps improve upon Georef solution's average RMSE of 32.99 (pixels). However, the QGIS system was unable to reproduce two unique Georef data products, uncertainty images for measuring tie and control point errors and a translated unwrapped image. In addition, the Georef software is designed to accept handheld camera pose information from a hardware component (Geosens) scheduled for deployment on the ISS in late 2018; this information is intended to provide increased accuracy and auto-registration capability for astronaut photographs. Future work is expected to determine the QGIS-based georectification system’s potential as an open-source alternative (and operational backup) to Georef for georeferencing the full range of resolutions and viewing angles unique to handheld digital camera imagery in support of ISS disaster response activities.

Jagge, Amy M.↗
NASA NTRS2019

NASA Orbital Debris Engineering Model ORDEM 3.1 - Software User Guide

This National Aeronautics and Space Administration (NASA) Orbital Debris Engineering Model (ORDEM) 3.1 Software User Guide accompanies delivery of the latest upgraded version of the model, ORDEM 3.1. The user guide also provides a top-level program description and a list of capabilities. It includes descriptions of runtime error and information codes, input/output file formats, runtimes for different orbit configurations, and how to use uncertainty files. ORDEM 3.1 supersedes the previous NASA Orbital Debris Program Office (ODPO) models – ORDEM 3.0 (Stansbery, et al. 2014) and ORDEM2000 (Liou, et al. 2002). The availability of new sensor and in situ data, re-analysis of older data, and development of new analytical techniques has enabled the construction of this more comprehensive and sophisticated model. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. For example, these range from the conventional flux vs. average debris size (or altitude bin) for chosen analysis orbits (or views) to the more complex color-contoured, two-dimensional (2-D) directional flux diagrams in local spacecraft elevation and azimuth. The current model, ORDEM 3.1, supports spacecraft as well as telescope/radar project assessments. ORDEM 3.1 contains updated debris populations covering low Earth orbit (LEO, up to 2000 km altitude) to geosynchronous orbit (GEO, up to 40,000 km altitude) and can assess debris calculations up to year 2050, extending coverage past the previous limit of 2035 in ORDEM 3.0. Although populations differ from its predecessor, ORDEM 3.1 is functionally the same as ORDEM 3.0 and can support ORDEM 3.0 projects through backward compatibility.

Vavrin, Andrew B.↗
NASA NTRSDate not supplied

Where IMERG Goes Next: Version 08 and Beyond

With the Version 07 (V07) Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm finalized and production initiated, the focus turns to enhancements for Version 08. These include innovations not included in V07 due to time constraints, plus issues revealed by the initial V07 products. One high priority is to evaluate and revise the schemes in V07 that rectify temporal artifacts caused by the time interpolation that fills the gaps between the various passive microwave (PMW) sensor overpasses. A second priority is to improve the homogeneity between the TRMM and GPM eras by characterizing differences between the two eras, determining the causes of these differences, and applying corrections as feasible, perhaps by enforcing spatial scale consistency (an overarching issue). Certainly, we must account for GPROF and the Combined Radar-Radiometer Algorithm converting to Machine Learning schemes in V08. Other priority topics include additional automated quality control for artifacts in the IR brightness temperatures and PMW precipitation fields, revisions to the specification algorithm for the probability of liquid precipitation, and accommodating new PMW sensors, which include the next generation of small-sats. We also consider the post-V08 landscape; the final GPM reprocessing will be restricted to fixing known code or algorithmic errors. Nonetheless, there are several data sources on the horizon to consider, including more small-sat PMW radiometers, AVHRR-based precipitation estimates (most useful in high latitudes), and the ISCCP-Next Generation and GEO-Ring projects that could provide easy access to multiple geosynchronous satellite channels and enable significantly improved algorithms compared to GEO-IR alone.

George J. Huffman↗
NASA NTRS2023

NASA Orbital Debris Engineering Model ORDEM 3.2 – Software User Guide

This National Aeronautics and Space Administration (NASA) Orbital Debris Engineering Model (ORDEM) 3.2 Software User Guide accompanies delivery of the latest upgraded version of the model, ORDEM 3.2. The user guide also provides a top-level program description and a list of capabilities. It includes descriptions of runtime error and information codes, input/output file formats, runtimes for different orbit configurations, and how to use uncertainty files. ORDEM 3.2 supersedes the previous NASA Orbital Debris Program Office (ODPO) models – ORDEM 3.0 (Stansbery, et al. 2014) and ORDEM2000 (Liou, et al. 2002). The availability of new sensor and in situ data, re-analysis of older data, and development of new analytical techniques has enabled the construction of this more comprehensive and sophisticated model. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. For example, these range from the conventional flux vs. average debris size (or altitude bin) for chosen analysis orbits (or views) to the more complex color-contoured, two-dimensional (2-D) directional flux diagrams in local spacecraft elevation and azimuth. The current model, ORDEM 3.2, supports spacecraft as well as telescope/radar project assessments. ORDEM 3.2 contains updated debris populations covering low Earth orbit (LEO, up to 2000 km altitude) to geosynchronous orbit (GEO, up to 40,000 km altitude) and can assess debris calculations up to year 2050, extending coverage past the previous limit of 2035 in ORDEM 3.0. Although populations differ from its predecessor, ORDEM 3.2 is functionally the same as ORDEM 3.0 and can support ORDEM 3.0 projects through backward compatibility.

Andrew Vavrin↗
NASA NTRS1990

Error control techniques for satellite and space communications

An expurgated upper bound on the event error probability of trellis coded modulation is presented. This bound is used to derive a lower bound on the minimum achievable free Euclidean distance d sub (free) of trellis codes. It is shown that the dominant parameters for both bounds, the expurgated error exponent and the asymptotic d sub (free) growth rate, respectively, can be obtained from the cutoff-rate R sub O of the transmission channel by a simple geometric construction, making R sub O the central parameter for finding good trellis codes. Several constellations are optimized with respect to the bounds.

Costello, Daniel J., Jr.↗
NASA NTRS2010

Injecting Errors for Testing Built-In Test Software

Two algorithms have been conceived to enable automated, thorough testing of Built-in test (BIT) software. The first algorithm applies to BIT routines that define pass/fail criteria based on values of data read from such hardware devices as memories, input ports, or registers. This algorithm simulates effects of errors in a device under test by (1) intercepting data from the device and (2) performing AND operations between the data and the data mask specific to the device. This operation yields values not expected by the BIT routine. This algorithm entails very small, permanent instrumentation of the software under test (SUT) for performing the AND operations. The second algorithm applies to BIT programs that provide services to users application programs via commands or callable interfaces and requires a capability for test-driver software to read and write the memory used in execution of the SUT. This algorithm identifies all SUT code execution addresses where errors are to be injected, then temporarily replaces the code at those addresses with small test code sequences to inject latent severe errors, then determines whether, as desired, the SUT detects the errors and recovers

Gender, Thomas K.↗
NASA NTRS1984

Self-Checking Memory Interface

Memory-interface integrated circuit not only detects errors in data from other circuits but also detects errors within itself. Memory-interface chip encodes 16-bit words with Hamming code for single-error correction or double-error detection. Chip used in fault-tolerant computers under development by NASA.

Sievers, M. W.↗
NASA NTRSDate not supplied

Design and Performance of a Multi-mode Photon-counting Receiver for the NASA O2O Mission

The NASA Orion O2O mission was designed and tested to support high-rate bi-directional optical communications between one of multiple ground sites and a crewed capsule during a 10-day mission. This paper focuses on the design and performance of the receiver that will be based at the NASA White Sands Complex, New Mexico, USA. The main receiver components consist of two 40-cm apertures, each coupled to a specialty multi-mode optical fiber; each fiber coupled to a custom array of superconducting nanowire single-photon detectors; a high-speed, high-channel-count time-to-digital converter; and custom digitizing electronics to perform deserialization, demodulation, clock recovery, and forward error correction with a powerful serially-concatenated pulse-position-modulation turbo code. We show error-free data communication performance up to the maximum required data rate of 267 Mb/s.

optical↗
NASA NTRS1980

Adaptation and optimization of a line-by-line radiative transfer program for the STAR-100 (STARSMART)

A program to calculate upwelling infrared radiation was modified to operate efficiently on the STAR-100. The modified software processes specific test cases significantly faster than the initial STAR-100 code. For example, a midlatitude summer atmospheric model is executed in less than 2% of the time originally required on the STAR-100. Furthermore, the optimized program performs extra operations to save the calculated absorption coefficients. Some of the advantages and pitfalls of virtual memory and vector processing are discussed along with strategies used to avoid loss of accuracy and computing power. Results from the vectorized code, in terms of speed, cost, and relative error with respect to serial code solutions are encouraging.

Rarig, P. L.↗
NASA NTRS1988

The pros and cons of code validation

Computational and wind tunnel error sources are examined and quantified using specific calculations or experimental data, and a substantial comparison of theoretical and experimental results, or a code validation, is discussed. Wind tunnel error sources considered include wall interference, sting effects, Reynolds number effects, flow quality and transition, and instrumentation such as strain gage balances, electronically scanned pressure systems, hot film gages, hot wire anemometers, and laser velocimeters. Computational error sources include math model equation sets, the solution algorithm, artificial viscosity/dissipation, boundary conditions, the uniqueness of solutions, grid resolution, turbulence modeling, and Reynolds number effects. It is concluded that although improvements in theory are being made more quickly than in experiments, wind tunnel research has the advantage of the more realistic transition process of a right turbulence model in a free-transition test.

Bobbitt, Percy J.↗
NASA NTRS1988

The pros and cons of code validation

Computational and wind tunnel error sources are examined and quantified using specific calculations of experimental data, and a substantial comparison of theoretical and experimental results, or a code validation, is discussed. Wind tunnel error sources considered include wall interference, sting effects, Reynolds number effects, flow quality and transition, and instrumentation such as strain gage balances, electronically scanned pressure systems, hot film gages, hot wire anemometers, and laser velocimeters. Computational error sources include math model equation sets, the solution algorithm, artificial viscosity/dissipation, boundary conditions, the uniqueness of solutions, grid resolution, turbulence modeling, and Reynolds number effects. It is concluded that, although improvements in theory are being made more quickly than in experiments, wind tunnel research has the advantage of the more realistic transition process of a right turbulence model in a free-transition test.

Bobbitt, Percy J.↗
NASA NTRS1981

On the error statistics of Viterbi decoding and the performance of concatenated codes

Computer simulation results are presented on the performance of convolutional codes of constraint lengths 7 and 10 concatenated with the (255, 223) Reed-Solomon code (a proposed NASA standard). These results indicate that as much as 0.8 dB can be gained by concatenating this Reed-Solomon code with a (10, 1/3) convolutional code, instead of the (7, 1/2) code currently used by the DSN. A mathematical model of Viterbi decoder burst-error statistics is developed and is validated through additional computer simulations.

Miller, R. L.↗
NASA NTRS1992

Least reliable bits coding (LRBC) for high data rate satellite communications

LRBC, a bandwidth efficient multilevel/multistage block-coded modulation technique, is analyzed. LRBC uses simple multilevel component codes that provide increased error protection on increasingly unreliable modulated bits in order to maintain an overall high code rate that increases spectral efficiency. Soft-decision multistage decoding is used to make decisions on unprotected bits through corrections made on more protected bits. Analytical expressions and tight performance bounds are used to show that LRBC can achieve increased spectral efficiency and maintain equivalent or better power efficiency compared to that of BPSK. The relative simplicity of Galois field algebra vs the Viterbi algorithm and the availability of high-speed commercial VLSI for block codes indicates that LRBC using block codes is a desirable method for high data rate implementations.

Vanderaar, Mark↗
NASA NTRS1980

Synchronization of Reed-Solomon codes

The synchronization capabilities of Reed-Solomon codes when an appropriate coset of the code is used instead of the code itself are examined. In this case an E-error correcting Reed-Solomon code is transformed into a code capable of determining that there are m symbols out of sync, if e symbol errors occurred, whenever m + e E. In the event that m = 0, i.e., the word is in sync, then decoder will correct any pattern of E - 1 on fewer symbol errors.

Miller, R. L.↗
NASA NTRS1993

Real-time transmission of digital video using variable-length coding

Huffman coding is a variable-length lossless compression technique where data with a high probability of occurrence is represented with short codewords, while 'not-so-likely' data is assigned longer codewords. Compression is achieved when the high-probability levels occur so frequently that their benefit outweighs any penalty paid when a less likely input occurs. One instance where Huffman coding is extremely effective occurs when data is highly predictable and differential coding can be applied (as with a digital video signal). For that reason, it is desirable to apply this compression technique to digital video transmission; however, special care must be taken in order to implement a communication protocol utilizing Huffman coding. This paper addresses several of the issues relating to the real-time transmission of Huffman-coded digital video over a constant-rate serial channel. Topics discussed include data rate conversion (from variable to a fixed rate), efficient data buffering, channel coding, recovery from communication errors, decoder synchronization, and decoder architectures. A description of the hardware developed to execute Huffman coding and serial transmission is also included. Although this paper focuses on matters relating to Huffman-coded digital video, the techniques discussed can easily be generalized for a variety of applications which require transmission of variable-length data.

Bizon, Thomas P.↗
NASA NTRS1990

Synchronization Technique For Reception Of Coded Data

Shortest sequence of bits likely to be filled with error bursts examined. Algorithm improves synchronization of frames of noisy binary-coded data signals after Viterbi decoding (recovery from "inner" convolutional code used in transmission channel) and before Reed-Solomon or other decoding (recovery from "outer" error-correcting block code). Based on comparisons of sequences of correct and erroneous Viterbi-decoded received bits with known marker sequence denoting beginning of frame of data. Does not require count of number of bits in received sequence disagreeing with corresponding bits in marker sequence.

Shahshahani, Mehrdad M.↗
NASA NTRS2009

From Verified Models to Verifiable Code

Declarative specifications of digital systems often contain parts that can be automatically translated into executable code. Automated code generation may reduce or eliminate the kinds of errors typically introduced through manual code writing. For this approach to be effective, the generated code should be reasonably efficient and, more importantly, verifiable. This paper presents a prototype code generator for the Prototype Verification System (PVS) that translates a subset of PVS functional specifications into an intermediate language and subsequently to multiple target programming languages. Several case studies are presented to illustrate the tool's functionality. The generated code can be analyzed by software verification tools such as verification condition generators, static analyzers, and software model-checkers to increase the confidence that the generated code is correct.

Lensink, Leonard↗