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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Science Alert Demonstration with a Rover Traverse Science Data Analysis System

The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. OASIS is a NASA-funded research project that is currently being tested on the FIDO rover at JPL for the use on future missions.

Castano, R.↗
NASA NTRS2014

Earth Science Data Analysis in the Era of Big Data

Anyone with even a cursory interest in information technology cannot help but recognize that "Big Data" is one of the most fashionable catchphrases of late. From accurate voice and facial recognition, language translation, and airfare prediction and comparison, to monitoring the real-time spread of flu, Big Data techniques have been applied to many seemingly intractable problems with spectacular successes. They appear to be a rewarding way to approach many currently unsolved problems. Few fields of research can claim a longer history with problems involving voluminous data than Earth science. The problems we are facing today with our Earth's future are more complex and carry potentially graver consequences than the examples given above. How has our climate changed? Beside natural variations, what is causing these changes? What are the processes involved and through what mechanisms are these connected? How will they impact life as we know it? In attempts to answer these questions, we have resorted to observations and numerical simulations with ever-finer resolutions, which continue to feed the "data deluge." Plausibly, many Earth scientists are wondering: How will Big Data technologies benefit Earth science research? As an example from the global water cycle, one subdomain among many in Earth science, how would these technologies accelerate the analysis of decades of global precipitation to ascertain the changes in its characteristics, to validate these changes in predictive climate models, and to infer the implications of these changes to ecosystems, economies, and public health? Earth science researchers need a viable way to harness the power of Big Data technologies to analyze large volumes and varieties of data with velocity and veracity. Beyond providing speedy data analysis capabilities, Big Data technologies can also play a crucial, albeit indirect, role in boosting scientific productivity by facilitating effective collaboration within an analysis environment. To illustrate the effects of combining a Big Data technology with an effective means of collaboration, we relate the (fictitious) experience of an early-career Earth science researcher a few years beyond the present, interlaced and contrasted with reminiscences of its recent past (i.e., the present).

Kuo, K.-S.↗
NASA NTRS1998

Onboard Science Data Analysis: Opportunities, Benefits, and Effects on Mission Design

Much of the initial focus for spacecraft autonomy has been on developing new software and systems concepts to automate engineering functions of the spacecraft: guidance, navigation and control, fault protection, and resources management. However, the ultimate objectives of NASA missions are science objectives, which implies that we need a new framework for perfoming science data evaluation and observation planning autonomously onboard spacecraft.

spacecraft autonomy observation planning science d↗
NASA NTRS2005

Current results from a Rover Science Data Analysis System

In this paper, we provide a brief overview of the OASIS system, and then describe our recent successes in integrating with and using rover hardware. OASIS currently works in a closed loop fashion with onboard control software (e.g., navigation and vision) and has the ability to autonomously perform the following sequence of steps: analyze gray scale images to find rocks, extract the properties of the rocks, identify rocks of interest, retask the rover to take additional imagery of the identified target and then allow the rover to continue on its original mission. We also describe the early 2004 ground test validation of specific OASIS components on selected Mars Exploration Rover (MER) images. These components include the rockfinding algorithm, RockIT, and the rock size feature extraction code. Our team also developed the RockIT GUI, an interface that allows users to easily visualize and modify the rock-finder results. This interface has allowed us to conduct preliminary testing and validation of the rockfinder's performance.

Coupled Layer Architecture for Robotic Autonomy (C↗
NASA NTRS1970

Science data analysis

Computer refreshed display for processing video information with digital computer to enhance video data

Source record↗
NASA NTRS1984

Viking radio science data analysis and synthesis

The rotational motion of Mars and its geophysical ramifications were investigated. Solar system dynamics and the laws of gravitation were also studied. The planetary ephemeris program, which was the central element in data analysis for this project, is described in brief. Viking Lander data were used in the investigation.

Shapiro, I. I.↗
NASA NTRS1985

Viking radio science data analysis and synthesis

Viking radio data analysis and synthesis was used for the following: (1) Solar System Model and Data Set; (2) Rotation of Mars; and (3) Solar System Constants and Tests of Relativity.

Shapiro, I. I.↗
NASA NTRS1985

Viking Radio Science Data Analysis and Synthesis

The objectives of the analysis of the Viking radio tracking data are: (1) the study of Mars, its rotation, topography, and internal structure; (2) the development of a general dynamical model of the solar system; and (3) tests of the fundamental laws of gravitation. The central element in the data analysis is the Planetary Ephemeris Program (PEP) which embodies the mathematical models of the solar system. The asteroid model in PEP is changed to better estimate the mass of a fictitious uniform ring and the masses of eight separate asteroids. A model of the rotation of Mars include a secular rate of change of the period and both annual and semiannual variations in the phase of rotation. Other modifications to this model are discussed.

Shapiro, I. I.↗
NASA NTRS1994

FOC SV support and science data analysis

The main activity of NASA grant NAG5-1733 has been the study of Planetary Nebulae in the Magellanic Clouds through observations using the ESA Faint Object Camera conducted during Hubble Space Telescope (HST) Observing Cycles 1 and 2 in the pre-Costar era and in Cycle 4 with Costar and the FOC. A table of the observations obtained during these observing periods is attached along with figures showing the final images. Both the table and the figures are intended for publication later this year in the Astrophysical journal. Work that had been carried out under this grant included: astrometric measurement of ground-based images and preparation of detailed observing proposals; re-calibration of FOC images; deconvolution; and data analysis and measurement. The work has resulted in two papers, the first already published and the second in preparation by Blades, Osmer and Barlow. A reprint of the first paper is attached. The second paper will enlarge upon the results described in the first paper, bearing in mind the much larger sample of objects. Finally, the recently acquired images in Cycle 4 with the corrected optics will allow us to make direct and quantitative comparison between the pre- and post-correction for FOC. This work will be carried out under a separate grant. All the observations concerning Planetary Nebulae as described in an earlier status report have been collected and measured. In addition, a preliminary study of the photometric response of the FOC was begun under this grant but not completed because the analysis of the Cycle 2 Planetary Nebulae took much longer and was much more difficult than originally envisaged (due to the spherical aberration problem). However, because our data covers a long time period, we shall be able to deduce the photometric properties now using the actual PN themselves. This work will be carried out under a separate grant.

Blades, J. Chris↗
NASA NTRS2012

Autonomous Onboard Science Data Analysis for Comet Missions

Coming years will bring several comet rendezvous missions. The Rosetta spacecraft arrives at Comet 67P/Churyumov-Gerasimenko in 2014. Subsequent rendezvous might include a mission such as the proposed Comet Hopper with multiple surface landings, as well as Comet Nucleus Sample Return (CNSR) and Coma Rendezvous and Sample Return (CRSR). These encounters will begin to shed light on a population that, despite several previous flybys, remains mysterious and poorly understood. Scientists still have little direct knowledge of interactions between the nucleus and coma, their variation across different comets or their evolution over time. Activity may change on short timescales so it is challenging to characterize with scripted data acquisition. Here we investigate automatic onboard image analysis that could act faster than round-trip light time to capture unexpected outbursts and plume activity. We describe one edge-based method for detect comet nuclei and plumes, and test the approach on an existing catalog of comet images. Finally, we quantify benefits to specific measurement objectives by simulating a basic plume monitoring campaign.

comets↗
NASA NTRS2021

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗
NASA NTRS2018

PYSAT: Python Satellite Data Analysis Toolkit

A common problem in space science data analysis is combining complementary data sources that are provided and analyzed in different formats and programming languages. The Python Satellite Data Analysis Toolkit (pysat) addresses this issue by providing an open source toolkit that implements the general process of space science data analysis, from beginning to end, in an instrumentindependent manner. This toolkit uses an Instrument object that enables systematic analysis of science data from a variety of platforms within a single interface. Basic functions such as downloading, loading, and cleaning are included for all supported instruments. Common analysis routines are also included, which are instrument and data source independent. A nanokernel is used to provide instrument independence, it is attached to the Instrument object and mediates the systematic and arbitrary modification of loaded data. Pysat uses the nanokernel to improve the rigor of time series analysis, support onthefly orbit determination, and cleanly span file breaks. Pysat's functions and higherlevel scientific analysis features are validated through the use of unit testing. Further adoption by the community provides a set of scientific results produced by a common core, constituting a distributed heritage that supports the validity of the underlying processing and scientific output. These features are used to demonstrate consistency between derived electron density profiles and measured ion drifts, particularly downward ion drifts in the afternoon hours during extreme solar minimum. Pysat builds upon open source Python software that is freely available and encourages communitydriven development.

Stoneback, R.A.↗
NASA NTRS1994

Scientific and Technical Support for the Galileo Net Flux Radiometer Experiment

Tasks required during the post launch period are briefly as follows: attend Project Science Group meetings; support in-flight checkouts; maintain and keep safe the spare instrument and ground support equipment (GSE); organize and maintain documentation; finish calibration measurements, documentation, and analysis; characterize and diagnose instrument anomalies; develop descent data analysis tools; and science data analysis and publication. This report provides background information on the net flux radiometer (NFR) instrument as well as being a complete progress report on the support for the Galileo NFR. experiment.

Sromovsky, Lawrence A.↗