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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Moving Towards a Common Ground and Flight Data Systems Architecture for NASA's Exploration Missions

The National Aeronautics and Space Administration has embarked on an ambitious effort to return man to the moon and then on to Mars. The Exploration Vision requires development of major new space and ground assets and poses challenges well beyond those faced by many of NASA's recent programs. New crewed vehicles must be developed. Compatible supply vehicles, surface mobility modules and robotic exploration capabilities will supplement the manned exploration vehicle. New launch systems will be developed as well as a new ground communications and control infrastructure. The development must take place in a cost-constrained environment and must advance along an aggressive schedule. Common solutions and system interoperability and will be critical to the successful development of the Exploration data systems for this wide variety of flight and ground elements. To this end, NASA has assembled a team of engineers from across the agency to identify the key challenges for Exploration data systems and to establish the most beneficial strategic approach to be followed. Key challenges and the planned NASA approach for flight and ground systems will be discussed in the paper. The described approaches will capitalize on new technologies, and will result in cross-program interoperability between spacecraft and ground systems, from multiple suppliers and agencies.

Rader. Steve↗
NASA NTRS2020

Conducting Efficient Remote Science and Planning Operations for Ocean Exploration Using Exploration Ground Data Systems (xGDS)

NASA Ames’ Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade. In the last two years, we have participated in SUBSEA, a multi-institution collaborative project*. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC)**. In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule. The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team’s ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. * SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA’s Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island’s Graduate School of Oceanography (GSO).

Cohen, Tamar E.↗
NASA NTRS2019

VISAGE - A Visualization and Exploration Framework for Environmental Data

Diverse airborne and ground-based environmental observations are important technologies for disaster assessment and response, as well as for the validation of environmental satellite observations and atmospheric models which can improve forecasts. The VISAGE (Visualization for Integrated Satellite, Airborne and Ground-based data Exploration) project is working to provide three-dimensional visualization and basic analytics capabilities for such datasets in an interactive user interface. The use of cloud-native, server less technologies for analysis optimized data storage will position VISAGE for integration with other technologies into a Data Analytic Center Framework.

Conover, Helen↗
NASA NTRS1981

Dynamics Explorer Science Data Processing System

The Dynamics Explorer project has acquired the ground data processing system from the Atmosphere Explorer project to provide a central computer facility for the data processing, data management and data analysis activities of the investigators. Access to this system is via remote terminals at the investigators' facilities, which provide ready access to the data sets derived from groups of instruments on both spacecraft. The original system has been upgraded with both new hardware and enhanced software systems. These new systems include color and grey scale graphics terminals, an augmentation computer, micrographics facility, a versatile data base with a directory and data management system, and graphics display software packages.

Smith, P. H.↗
NASA NTRS1993

Initial results from the extreme ultraviolet explorer

Data obtained during the first five months of calibration and science operation of the Extreme Ultraviolet Explorer (EUVE) are presented. Spectra of an extragalactic object were obtained; the object is detectable to wavelenghts longer than 100 A, demonstrating that extragalactic EUV astronomy is possible. Spectra of a hot white dwarf, and a late-type star in quiescence and flaring are shown as examples of the type of spectrographic data obtainable with EUVE. Other objects for which broad band photometric mode data have been obtained and analyzed include an RS CVn star and several late-type stars. The backgrounds in the EUVE detectors are quite low and the character of the diffuse astronomical EUV background has been investigated using these very low rates. Evidence is presented showing that, contrary to previously published reports, EUVE is about three times more sensitive than the English Wide Field Camera in the short wavelength bandpass covered by both instruments. Only limited information has been extracted from the longer bandpasses coered only by EUVE. Nonetheless, the brightest EUV source in the sky, a B star, has been discovered and is detected only in these longer bandpasses.

Bowyer, S.↗
NASA NTRS2020

Recommended Laboratory and Field Studies ahead of Future Mercury Exploration

Data from Mariner 10, MESSENGER, and ground-based telescopic observations have facilitated great advancements towards understanding the geochemistry, geology, internal structure, and space environment (exosphere/magnetosphere) of Mercury. Future exploration efforts by BepiColombo, landed science, and sample return will revolutionize our understanding of this planet. However, to enhance the scientific return of past, present, and future data collection at Mercury, it is imperative that laboratory and field-based studies be conducted over the next decade. This white paper gives examples of laboratory and field studies, including instrument testing, which should take place over the next decade to facilitate the interpretation of data and preparation of future missions to the inner-most planet. By no means are the topics in this paper exhaustive, but rather are meant to demonstrate the immense amount of work that needs to be completed ahead of, or in conjunction with, current and future exploration efforts of Mercury. This work would amplify the benefits from additional data obtained from the planet’s surface to the fullest extent possible, and to aid in decision making of future instrument payloads. We have chosen to focus our inputs here on geochemistry, geology, and space environment related efforts, but we emphasize that extensive research in the areas of geophysics and other geosciences-related disciplines will benefit from additional studies.

Kathleen E. Vander Kaaden↗
NASA NTRS1990

Gaussian windows: A tool for exploring multivariate data

Presented here is a method for interactively exploring a large set of quantitative multivariate data, in order to estimate the shape of the underlying density function. It is assumed that the density function is more or less smooth, but no other specific assumptions are made concerning its structure. The local structure of the data in a given region may be examined by viewing the data through a Gaussian window, whose location and shape are chosen by the user. A Gaussian window is defined by giving each data point a weight based on a multivariate Gaussian function. The weighted sample mean and sample covariance matrix are then computed, using the weights attached to the data points. These quantities are used to compute an estimate of the shape of the density function in the window region. The local structure of the data is described by a method similar to the method of principal components. By taking many such local views of the data, we can form an idea of the structure of the data set. The method is applicable in any number of dimensions. The method can be used to find and describe simple structural features such as peaks, valleys, and saddle points in the density function, and also extended structures in higher dimensions. With some practice, we can apply our geometrical intuition to these structural features in any number of dimensions, so that we can think about and describe the structure of the data. Since the computations involved are relatively simple, the method can easily be implemented on a small computer.

Jaeckel, Louis A.↗
NASA NTRS1988

Empirical global model of upper thermosphere winds based on atmosphere and dynamics explorer satellite data

Thermospheric wind data obtained from the Atmosphere Explorer E and Dynamics Explorer 2 satellites have been used to generate an empirical wind model for the upper thermosphere, analogous to the MSIS model for temperature and density, using a limited set of vector spherical harmonics. The model is limited to above approximately 220 km where the data coverage is best and wind variations with height are reduced by viscosity. The data base is not adequate to detect solar cycle (F10.7) effects at this time but does include magnetic activity effects. Mid- and low-latitude data are reproduced quite well by the model and compare favorably with published ground-based results. The polar vortices are present, but not to full detail.

Hedin, A. E.↗
NASA NTRS2007

Exploration Blueprint: Data Book

The material contained in this report was compiled to capture the work performed by the National Aeronautics and Space Administration's (NASA's) Exploration study team in the late 2002 timeframe. The "Exploration Blueprint Data Book" documents the analyses and findings of the 90-day Agency-wide study conducted from September - November 2002. During the summer of 2002, the NASA Deputy Administrator requested that a study be performed with the following objectives: - Develop the rationale for exploration beyond low-Earth orbit - Develop roadmaps for how to accomplish the first steps through humans to Mars - Develop design reference missions as a basis for the roadmaps - Make recommendations on what can be done now to effect this future This planning team, termed the Exploration Blueprint, performed architecture analyses to develop roadmaps for how to accomplish the first steps beyond LEO through the human exploration of Mars. The previous NASA Exploration Team activities laid the foundation and framework for development of NASA's Integrated Space Plan. The reference missions resulting from the analysis performed by the Exploration Blueprint team formed the basis for requirement definition, systems development, technology roadmapping, and risk assessments for future human exploration beyond low-Earth orbit. Emphasis was placed on developing recommendations on what could be done now to effect future exploration activities. The Exploration Blueprint team embraced the "Stepping Stone" approach to exploration where human and robotic activities are conducted through progressive expansion outward beyond low-Earth orbit. Results from this study produced a long-term strategy for exploration with near-term implementation plans, program recommendations, and technology investments. Specific results included the development of a common exploration crew vehicle concept, a unified space nuclear strategy, focused bioastronautics research objectives, and an integrated human and robotic exploration strategy. Recommendations from the Exploration Blueprint included the endorsement of the Nuclear Systems Initiative, augmentation of the bioastronautics research, a focused space transportation program including heavy-lift launch and a common exploration vehicle design for ISS and exploration missions, as well as an integrated human and robotic exploration strategy for Mars.

Bret G Drake↗
NASA NTRS2017

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗