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All sources NASA NTRS DOE OSTI

NASA NTRS2011

Graphical Language for Data Processing

A graphical language for processing data allows processing elements to be connected with virtual wires that represent data flows between processing modules. The processing of complex data, such as lidar data, requires many different algorithms to be applied. The purpose of this innovation is to automate the processing of complex data, such as LIDAR, without the need for complex scripting and programming languages. The system consists of a set of user-interface components that allow the user to drag and drop various algorithmic and processing components onto a process graph. By working graphically, the user can completely visualize the process flow and create complex diagrams. This innovation supports the nesting of graphs, such that a graph can be included in another graph as a single step for processing. In addition to the user interface components, the system includes a set of .NET classes that represent the graph internally. These classes provide the internal system representation of the graphical user interface. The system includes a graph execution component that reads the internal representation of the graph (as described above) and executes that graph. The execution of the graph follows the interpreted model of execution in that each node is traversed and executed from the original internal representation. In addition, there are components that allow external code elements, such as algorithms, to be easily integrated into the system, thus making the system infinitely expandable.

Alphonso, Keith↗

NASA NTRS2015

SmallSat Database

The SmallSat has an unrealized potential in both the private industry and in the federal government. Currently over 70 companies, 50 universities and 17 governmental agencies are involved in SmallSat research and development. In 1994, the U.S. Army Missile and Defense mapped the moon using smallSat imagery. Since then Smart Phones have introduced this imagery to the people of the world as diverse industries watched this trend. The deployment cost of smallSats is also greatly reduced compared to traditional satellites due to the fact that multiple units can be deployed in a single mission. Imaging payloads have become more sophisticated, smaller and lighter. In addition, the growth of small technology obtained from private industries has led to the more widespread use of smallSats. This includes greater revisit rates in imagery, significantly lower costs, the ability to update technology more frequently and the ability to decrease vulnerability of enemy attacks. The popularity of smallSats show a changing mentality in this fast paced world of tomorrow. What impact has this created on the NASA communication networks now and in future years? In this project, we are developing the SmallSat Relational Database which can support a simulation of smallSats within the NASA SCaN Compatability Environment for Networks and Integrated Communications (SCENIC) Modeling and Simulation Lab. The NASA Space Communications and Networks (SCaN) Program can use this modeling to project required network support needs in the next 10 to 15 years. The SmallSat Rational Database could model smallSats just as the other SCaN databases model the more traditional larger satellites, with a few exceptions. One being that the smallSat Database is designed to be built-to-order. The SmallSat database holds various hardware configurations that can be used to model a smallSat. It will require significant effort to develop as the research material can only be populated by hand to obtain the unique data required. When completed it will interface with the SCENIC environment to allow modeling of smallSats. The SmallSat Relational Database can also be integrated with the SCENIC Simulation modeling system that is currently in development. The SmallSat Relational Database simulation will be of great significance in assisting the NASA SCaN group to understand the impact the smallSats have made which have populated the lower orbit around our mother earth. What I have created and worked on this summer session 2015, is the basis for a tool that will be of value to the NASA SCaN SCENIC Simulation Environment for years to come.

Small Satellites↗

NASA NTRSDate not supplied

Overview of the Lunar Gateway External Contamination Environment

As a part of the Artemis mission, Gateway will be a long duration space station in a near rectilinear halo orbit around the Moon. Gateway will be exposed to a variety of external contamination sources, which can degrade external hardware / surfaces and impact science utilization objectives. Requirements and methodologies addressing material outgassing, chemical and electric thruster plumes, vacuum venting, and visiting vehicle interactions have been developed by the Gateway Induced Environments team to ensure vehicle performance and mission success. The external contamination requirements and integration process are described along with required data deliverables. Integrated external contamination analyses are conducted by the Gateway Environments Team to assess compliance with external contamination requirements. This paper provides a current status of integration activities and analysis results, as well as future plans to improve external contamination characterization.

Crystal A. Quiroz↗

NASA NTRS2024

Overview of the Lunar Gateway External Contamination Environment

Crystal A Quiroz↗

NASA NTRSDate not supplied

Overview of the Lunar Gateway External Contamination Environment

thruster plumes↗

NASA NTRS2011

Design Considerations for a Launch Vehicle Development Flight Instrumentation System

When embarking into the design of a new launch vehicle, engineering models of expected vehicle performance are always generated. While many models are well established and understood, some models contain design features that are only marginally known. Unfortunately, these analytical models produce uncertainties in design margins. The best way to answer these analytical issues is with vehicle level testing. The National Aeronautics and Space Administration respond to these uncertainties by using a vehicle level system called the Development Flight Instrumentation, or DFI. This DFI system can be simple to implement, with only a few measurements, or it may be a sophisticated system with hundreds of measurement and video, without a recording capability. From experience with DFI systems, DFI never goes away. The system is renamed and allowed to continue, in most cases. Proper system design can aid the transition to future data requirements. This paper will discuss design features that need to be considered when developing a DFI system for a launch vehicle. It will briefly review the data acquisition units, sensors, multiplexers and recorders, telemetry components and harnessing. It will present a reasonable set of requirements which should be implemented in the beginning of the program in order to start the design. It will discuss a simplistic DFI architecture that could be the basis for the next NASA launch vehicle. This will be followed by a discussion of the "experiences gained" from a past DFI system implementation, such as the very successful Ares I-X test flight. Application of these design considerations may not work for every situation, but they may direct a path toward success or at least make one pause and ask the right questions.

Johnson, Martin L.↗

NASA NTRSDate not supplied

Datascope to Enable Earth Independent Medical Operations (EIMO)

BACKGROUND: NASA has amassed sixty years of knowledge and experience relevant to maintenance of crew health and performance in low earth orbit. The Apollo Program introduced the importance of ensuring progressively autonomous operational capability. Earth Independent Medical Operations (EIMO) will require a gradual shift in the balance of medical responsibility, management, and authority from terrestrial to space-based assets. Terrestrial assets will continue to be essential for pre-mission screening and planning in addition to maintenance of crew health and performance. However, new capabilities are needed to enable EIMO and the amount of data required to support these systems, and mitigate the impacts of data transmission delays and reduced bandwidth coupled with lack of cloud-like resources and on-board computing capacity that is currently unclear or operationally insufficient. OVERVIEW: The overall goal of EIMO is to develop artificial intelligence (AI)-based solutions to analyze crew health and performance data utilizing a clinical decision support system (CDSS) to provide crew medical officers (CMO) with the equivalent of real-time, on-board medical consults. The EIMO ecosystem is envisioned as a “system of systems” where embedded reference databases and real-time data streams from multiple input vectors continuously and seamlessly assess crew health and performance. EIMO will be designed to make recommendations to the CMO using multi-modal AI-based natural language processing and machine learning methods with interoperability to push/pull data within and between multiple vehicle and habitat architectures. DISCUSSION: Data flows and storage/retrieval capacity are severely constrained during space missions and the challenges will become even greater during exploration missions. Just as each past program from Mercury to the International Space Station (ISS) required rethinking the interaction between ground-based controllers and space-based crew, so too will future missions to the Moon and Mars. While the NASA High-Performance Spaceflight Computing Processor project aims to increase computational capacity by 100 times over current spaceflight computers, the projected deliverable still lags considerably behind what will be needed to enable an AI-driven CDSS. Restrictions in processing speed and data storage capacity, coupled with transmission bottlenecks and delays, necessitate definition and optimization of an integrated data architecture to enable a progressively autonomous medical capability.

Medical operations↗

NASA NTRS1974

Engineering studies related to the GEOS-C radar altimeter

Experiment requirements, technical characteristics, and GEOS-C radar altimeter related analyses are discussed along with results of a study on engineering test data requirements. Statistical analyses related to determination of wave height resolution achievable as a function of system characteristics and averaging period are described, in addition to a discussion on the desirability of using computer procedures to compensate for altitude tracker time-jitter. Data processing considerations for the GEOS-C system are examined. An extensive analysis of the spatial filter effect is given and results of a computation of geoidal power spectral density, based on Skylab altimeter data, is displayed and interpreted in terms of projected GEOS-C random errors. This information is then used in deriving minimum-mean-square filter procedures for both geoid undulation and slope data. The characteristics of mean received waveforms as a function of off-nadir angle are used to obtain tracker bias as a function of sea state and pointing angle. The angle estimation process proposed by the GEOS-C hardware contractor is also investigated from a standpoint of achievable angular resolution.

Miller, L. S.↗

NASA NTRS1978

Sensor needs for agricultural applications

The peculiarities of agricultural remotely sensed data requirements evoke special sensor requirements. Vegetative species do not possess significantly different spectral signature at given phases of their development cycle. Hence, the key to their discriminability is the phasing of the phenologic cycle of the subject species. Significant improvements in classification can be obtained by consistently employing multi-temporal observations taken at specific times during the year. The present approach to agricultural data processing results in extracted data equal to approximately .05% of the acquired data. This paper discusses the derivation of agricultural peculiar requirements and the benefits to the end-to-end processing system by judicial utilization and placement of key editing functions such as sample segment extraction, cloudy image removal, sample registration and the elimination of redundant data.

Golden, H.↗

NASA NTRS1973

Mission requirements for a manned earth observatory. Task 2: Reference mission definition and analyiss, volume 2

The mission requirements and conceptual design of manned earth observatory payloads for the 1980 time period are discussed. Projections of 1980 sensor technology and user data requirements were used to formulate typical basic criteria pertaining to experiments, sensor complements, and reference missions. The subjects discussed are: (1) mission selection and prioritization, (2) baseline mission analysis, (3) earth observation data handling and contingency plans, and (4) analysis of low cost mission definition and rationale.

Source record↗

NASA NTRS1993

Supporting multidisciplinary science within NASA's discipline data systems

Many current and future NASA and non-NASA missions are focusing on multidisciplinary science. The current paradigm for data identification and effective use by the NASA science community is based on the CODMAC model proposed in 1986. As multidisciplinary investigations become more prevalent, many existing systems and the newly developing systems will have to augment the current data identification and access strategies and tools and form alliances with other data systems to provide the broad range of data required. This paper describes the current paradigm, surveys and characterizes, within that paradigm, efforts to develop new access methods and data analysis and visualization tools, identifies additional areas of research not adequately addressed and recommends a further plan of action.

Preheim, Larry E.↗

NASA NTRS2007

Estimating the Inertia Matrix of a Spacecraft

A paper presents a method of utilizing some flight data, aboard a spacecraft that includes reaction wheels for attitude control, to estimate the inertia matrix of the spacecraft. The required data are digitized samples of (1) the spacecraft attitude in an inertial reference frame as measured, for example, by use of a star tracker and (2) speeds of rotation of the reaction wheels, the moments of inertia of which are deemed to be known. Starting from the classical equations for conservation of angular momentum of a rigid body, the inertia-matrix-estimation problem is formulated as a constrained least-squares minimization problem with explicit bounds on the inertia matrix incorporated as linear matrix inequalities. The explicit bounds reflect physical bounds on the inertia matrix and reduce the volume of data that must be processed to obtain a solution. The resulting minimization problem is a semidefinite optimization problem that can be solved efficiently, with guaranteed convergence to the global optimum, by use of readily available algorithms. In a test case involving a model attitude platform rotating on an air bearing, it is shown that, relative to a prior method, the present method produces better estimates from few data.

Acikmese, Behcet↗

DOE OSTI2022

A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning

Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this paper, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: 1) pre-hoc experiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and 2) post-hoc dataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. Here, we validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of effects of dataset size on IRL.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Carbon Storage Technical Viability Approach (CS TVA) Matrix

The Carbon Storage Technical Viability Approach (CS TVA) Matrix is a knowledge framework developed to outline the information needed for geologic carbon storage. The CS TVA Matrix contains 5 categories, 14 sub-categories, and 47 components. This framework can be leveraged to assess the availability of data and information needed for a carbon storage project. The information categories of the matrix are tied to a list of required data using weighted mapping, published herein.

carbon storage↗

NASA NTRS1981

Multispectral scanner flight model (F-1) radiometric calibration and alignment handbook

This handbook on the calibration of the MSS-D flight model (F-1) provides both the relevant data and a summary description of how the data were obtained for the system radiometric calibration, system relative spectral response, and the filter response characteristics for all 24 channels of the four band MSS-D F-1 scanner. The calibration test procedure and resulting test data required to establish the reference light levels of the MSS-D internal calibration system are discussed. The final set of data ("nominal" calibration wedges for all 24 channels) for the internal calibration system is given. The system relative spectral response measurements for all 24 channels of MSS-D F-1 are included. These data are the spectral response of the complete scanner, which are the composite of the spectral responses of the scan mirror primary and secondary telescope mirrors, fiber optics, optical filters, and detectors. Unit level test data on the measurements of the individual channel optical transmission filters are provided. Measured performance is compared to specification values.

Source record↗

DOE OSTI2025

Data for "Implications of Zoning Ordinances for Rural Utility-Scale Solar Deployment and Power System Decarbonization in the Great Lakes Region".

This dataset includes the Energy Zoning Database and geographic shapefiles used to identify suitable rural areas for utility-scale solar development. Additionally, it contains the input data required for the capacity expansion model, including information on existing electric generators and their associated costs, solar resource potential, and transmission infrastructure data. These datasets collectively support the analysis presented in the paper, ensuring a comprehensive assessment of zoning regulations, land suitability, and the economic and technical feasibility of solar deployment in rural areas.

Owusu-Obeng, Papa Yaw (ORCID:0000000334385183)↗

NASA NTRS1978

DSN radio science system, Mark III-78

Mark III-78, is one of eight DSN Data Systems which provide major data types and functional capabilities to flight projects. It includes the equipment, software, personnel, documentation, procedures, and resources necessary to deliver the required data to flight projects. A system definition is given along with key characteristics, functional description, system configuration, testing, implementation status, and system schedule.

Berman, A. L.↗

NASA NTRS1986

A database management capability for Ada

The data requirements of mission critical defense systems have been increasing dramatically. Command and control, intelligence, logistics, and even weapons systems are being required to integrate, process, and share ever increasing volumes of information. To meet this need, systems are now being specified that incorporate data base management subsystems for handling storage and retrieval of information. It is expected that a large number of the next generation of mission critical systems will contain embedded data base management systems. Since the use of Ada has been mandated for most of these systems, it is important to address the issues of providing data base management capabilities that can be closely coupled with Ada. A comprehensive distributed data base management project has been investigated. The key deliverables of this project are three closely related prototype systems implemented in Ada. These three systems are discussed.

Chan, Arvola↗