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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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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Software Supports Distributed Operations via the Internet

Multi-mission Encrypted Communication System (MECS) is a computer program that enables authorized, geographically dispersed users to gain secure access to a common set of data files via the Internet. MECS is compatible with legacy application programs and a variety of operating systems. The MECS architecture is centered around maintaining consistent replicas of data files cached on remote computers. MECS monitors these files and, whenever one is changed, the changed file is committed to a master database as soon as network connectivity makes it possible to do so. MECS provides subscriptions for remote users to automatically receive new data as they are generated. Remote users can be producers as well as consumers of data. Whereas a prior program that provides some of the same services treats disconnection of a user from the network of users as an error from which recovery must be effected, MECS treats disconnection as a nominal state of the network: This leads to a different design that is more efficient for serving many users, each of whom typically connects and disconnects frequently and wants only a small fraction of the data at any given time.

Norris, Jeffrey↗
NASA NTRS2014

Smart Grid Development Issues for Terrestrial and Space Applications

The development of the so called Smart Grid has as many definitions as individuals working in the area. Based on the technology or technologies that are of interest, be it high speed communication, renewable generation, smart meters, energy storage, advanced sensors, etc. they can become the individual defining characteristic of the Smart Grid. In reality the smart grid encompasses all of these items and quite at bit more. This discussion attempts to look at what the needs are for the grid of the future, such as the issues of increased power flow capability, use of renewable energy, increased security and efficiency and common power and data standards. It also shows how many of these issues are common with the needs of NASA for future exploration programs. A common theme to address both terrestrial and space exploration issues is to develop micro-grids that advertise the ability to enable the load leveling of large power generation facilities. However, for microgrids to realize their promise there needs to a holistic systems approach to their development and integration. The overall system integration issues are presented along with potential solution methodologies.

Power Systems↗
NASA NTRSDate not supplied

NNREPAIR: Constraint-based Repair of Neural Network Classifiers

We present NNREPAIR, a constraint-based technique for repairing neural network classifiers by i) fixing the logic of the network at an intermediate layer, and ii) fixing the decision conditions of the network at the last layer. NNREPAIR first uses fault localization to find potentially faulty network parameters (specifically weights) and then performs repair using constraint solving to apply small modifications to remedy the defects. We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts wrt each class. We demonstrate the technique in the context of three different scenarios: (1) Improving the over all accuracy of a model, (2) Fixing security vulnerabilities caused by poisoning of training data and (3) Improving the robustness of the network against adversarial attacks. Our evaluation on MNIST and CIFAR-10 models shows that NNREPAIR can improve the accuracy by 45.56% on poisoned data and 11.11% on adversarial data. NNREPAIR also provides improvement, albeit modest, in the overall accuracy of models, without requiring new data or re-training.

Repair↗
NASA NTRS2023

Nonrepudiation for Drone Related Data

In order for UTM to support safe multiple UAS operations within and beyond visual line of sight, data related to weather, 3D structures, other aircraft, etc. must be made available. To support safe operations data must be collected and maintained considering security and resilience.

Security↗
DOE OSTI2024

Secure hierarchical processing using a secure ledger

Disclosed is a system and method for processing data using blockchain technology. The system includes a memory having programmable instructions stored thereon that, when executed by a processor, cause the system to: authenticate one or more sensors in anticipation of receiving component data; receive component data, upon successful authentication; store the component data locally or to a cloud-based server and/or calculate a root value for the component data; store or embed the root value with the stored component data; condense the component data and link the condensed component data to the stored component data via the root value. The system further includes instructions to log the condensed data, including the root value, to a ledger, and to identify a tag or transaction id corresponding to the logging event for subsequent retrieval of the condensed data using the tag or transaction id.

Zhao, Wenbing↗
NASA NTRS1991

Aero/fluids database system

The AFAS Database System was developed to provide the basic structure of a comprehensive database system for the Marshall Space Flight Center (MSFC) Structures and Dynamics Laboratory Aerophysics Division. The system is intended to handle all of the Aerophysics Division Test Facilities as well as data from other sources. The system was written for the DEC VAX family of computers in FORTRAN-77 and utilizes the VMS indexed file system and screen management routines. Various aspects of the system are covered, including a description of the user interface, lists of all code structure elements, descriptions of the file structures, a description of the security system operation, a detailed description of the data retrieval tasks, a description of the session log, and a description of the archival system.

Reardon, John E.↗
NASA NTRS1999

Technology Assessment of High Capacity Data Storage Systems: Can We Avoid a Data Survivability Crisis?

In a recent address at the California Science Center in Los Angeles, Vice President Al Gore articulated a Digital Earth Vision. That vision spoke to developing a multi-resolution, three-dimensional visual representation of the planet into which we can roam and zoom into vast quantities of embedded geo-referenced data. The vision was not limited to moving through space, but also allowing travel over a time-line, which can be set for days, years, centuries, or even geological epochs. A working group of Federal Agencies, developing a coordinated program to implement the Vice President's vision, developed the definition of the Digital Earth as a visual representation of our planet that enables a person to explore and interact with the vast amounts of natural and cultural geo-referenced information gathered about the Earth. One of the challenges identified by the agencies was whether the technology existed that would be available to permanently store and deliver all the digital data that enterprises might want to save for decades and centuries. Satellite digital data is growing by Moore's Law as is the growth of computer generated data. Similarly, the density of digital storage media in our information-intensive society is also increasing by a factor of four every three years. The technological bottleneck is that the bandwidth for transferring data is only growing at a factor of four every nine years. This implies that the migration of data to viable long-term storage is growing more slowly. The implication is that older data stored on increasingly obsolete media are at considerable risk if they cannot be continuously migrated to media with longer life times. Another problem occurs when the software and hardware systems for which the media were designed are no longer serviced by their manufacturers. Many instances exist where support for these systems are phased out after mergers or even in going out of business. In addition, survivability of older media can suffer from physical breakdown of components (e.g. tapes simply lose their magnetic properties after a long time in storage). As a result, a potential data survivability crisis is emerging. The scale of the crisis is comparable to that facing the Social Security System. Sometime in one or two decades, the exponential growth of data will become so great that many enterprises will not be able to migrate through their data to more permanent media during the lifetime of the media on which it resides. This will result in significant losses of data and their resultant impacts. To avoid this crisis, we need to plan and devote greater financial and intellectual resources are needed for the development and refinement of new storage media and migration technologies in order to preserve all data any organization determines worth saving permanently. This talk will explore technological solutions and suggested recommendations to address this technological data crisis.

Halem, Milton↗
NASA NTRSDate not supplied

Gateway Implementation of Cybersecurity Requirements

Cyber threats are a constant present-day reality for any type of business -- Space exploration is not excluded from these threats either. The Gateway Program is one of NASA’s latest initiatives that extend space exploration beyond low earth orbit. Gateway allows for NASA to prove technologies and mature systems necessary to live and work on another celestial body before embarking on multi-year missions to Mars. The Gateway is a small, human-tended space station in orbit around the Moon. With the increased autonomy, distance and criticality of systems, cybersecurity is one of the critical subsystems that touches and integrates with most if not all subsystems of the Gateway. Building a gateway to the lunar orbit is no simple task. In this presentation, we outline an approach that the Gateway team adopted in creating a cyber safe and robust vehicle to support operations and assure protection of the critical functions. Gateway Program is required to implement National Institute of Standards and Technology (NIST) guidelines to adhere to the Federal Information Security Modernization Act (FISMA). NIST provides a framework for managing and controlling cybersecurity risks by defining cybersecurity controls and methodologies for implementation. The NIST framework is based upon the system, data within the system, integrations with external systems, and risk assessments to determine impacts for each of those systems. The goals and objectives are to identify appropriate security controls that fulfill and map to the NIST 800-53 framework. The implementation process involves developing an organizational understanding to manage cybersecurity risk to systems, people, assets, data, and capabilities. NIST Security controls are interpreted and defined within the Gateway vehicle requirements subsystems specifications. This paper details the approach, implementation, and challenges faced during the development and design phases to address cyber threats during the Gateway vehicle operations.

Svetlana Hanson↗
NASA NTRSDate not supplied

Gateway Implementation of Cybersecurity Requirements

Cybersecurity threats are a constant present-day reality for any type of business -- Space exploration is not excluded from these threats either. The Gateway Program is one of NASA’s latest initiatives that extend space exploration beyond low earth orbit. Gateway allows for NASA to prove technologies and mature systems necessary to live and work on another celestial body before embarking on multi-year missions to Mars. The Gateway is a small, human-tended space station in orbit around the Moon. With the increased autonomy, distance and criticality of systems, cybersecurity is a critical discipline that touches and integrates with most if not all subsystems of the Gateway. Building a gateway to the lunar orbit is no simple task. In this presentation, we outline an approach that the Gateway team adopted in creating a cyber safe and robust vehicle to support operations and assure protection of the critical functions. Gateway Program is required to implement National Institute of Standards and Technology (NIST) guidelines to adhere to the Federal Information Security Modernization Act (FISMA). NIST provides a framework for managing and controlling cybersecurity risks by defining cybersecurity controls and methodologies for implementation. The NIST framework is based upon the system, data within the system, integrations with external systems, and risk assessments to determine impacts for each of those systems. The goals and objectives are to identify appropriate security controls that fulfil and map to the NIST 800-53 framework. The implementation process involves developing an organizational understanding to manage cybersecurity risk to systems, people, assets, data, and capabilities. NIST Security controls are interpreted and defined within the Gateway vehicle requirements subsystems specifications. This paper details the approach, implementation, and challenges faced during the development and design phases to address cyber threats during the Gateway vehicle operations.

Cybersecurity↗
DOE OSTI2026

Data-Driven Atomic Physics: Harnessing Machine Learning and High-Repetition-Rate Experiments for Laser-driven HED

High-energy-density plasma experiments are central to progress in atomic physics, fusion energy, and national security science, but they have traditionally been constrained by slow data collection and manual, time-intensive analysis. This project targeted that bottleneck by enabling high-repetition-rate experiments to produce and interpret much larger volumes of data quickly enough to guide experiments while they run.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Raw_data_Batch_I: Argonne to Shorewood via I-55

Date of collection: May 12, 2023 Location: Interstate 55, DuPage County, IL This data set contains lidar and vision data collected along a round trip between I-55 Exit 273A and Exit 253. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![argonne shorewood image](argone-shorewood.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Ashland Avenue

Date of collection: May 26, 2023 Location: Ashland Avenue, Chicago, IL This data set contains lidar and vision data collected along Ashland Avenue. A south-to-north run starts from the intersection of Irving Park and Ashland and ends at Andersonville Garden. A north-to-south run starts from Andersonville Garden and ends around the intersection of Irving Park and Ashland. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![ashland avenue image](ashland-avenue.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Downers Grove to Darien

Date of collection: May 11, 2023 Location: Downers Grove to Darien, IL This dataset contains lidar and vision data collected in Downers Grove and Darien, IL. The vehicle started in Downers Grove at the intersection of Main and Ogden, headed east. At the intersection of Odgen and IL 83, it then headed south until IL 33 and then west along IL 33 until the intersection of IL 33 and Lemont Road. It then headed north along Lemont Road/Main Street until the intersection of Main and Ogden. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![downers grove image](downers-grove-darien.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Garfield Ridge

Date of collection: May 4, 2023 Location: Garfield Ridge, Chicago, IL This data set contains lidar and vision data collected in Garfield Ridge, Chicago. The vehicle started from the intersection of Garfield Ridge and S. Harlem, headed east until S. Central Ave. The vehicle headed south along S. Central Ave. until West 60th Street, headed west, and turned north along S. Austin Ave. until it turned west onto W. 59th Street. The vehicle then headed north along S. Harlem Ave. and returned to the intersection of Garfield Ridge and S. Harlem. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![garfield ridge image](garfield-ridge.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Lakeshore Drive

Date of collection: May 18, 2023 Location: Lakeshore Drive, Chicago, IL Content: “North to South” “South to North” This data set contains lidar and vision data collected along Lakeshore Drive. The “South to North” folder starts from the intersection of Lakeshore Drive and 31st Street and ends at Hollywood Towers Chicago. The “North to South” folder starts from the intersection of Lakeshore Drive and Sheridan Avenue and ends at the 31st Street intersection. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![lakeshore drive image](lakeshore-drive.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Lisle to Waterfall Glen

Date of collection: May 11, 2023 Location: DuPage County, IL This dataset contains lidar and vision data collected between Lisle, IL, and the Waterfall Glen parking lot. The vehicle started near Cass School District 63, headed east along IL 34. The vehicle then turned south along IL 83 until Interstate 55. Finally, the vehicle turned southwest along I 55 until Exit 273A and headed toward the Waterfall Glen parking lot. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![lisle waterfall image](lisle-waterfall.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Randall Road

Date of collection: June 3, 2022 Location: Randall Road, DuPage County, IL Content: “North to South” “South to North” This data set contains lidar and vision data collected along Randall Road in DuPage County, Illinois. The “South to North” folder starts at 1480 N. Orchard Road, Aurora, IL 60506, headed north along Randall Road until 238 N. Randall Road, St. Charles, IL 60174. The “North to South” folder starts from 238 N. Randall Road, St. Charles, IL 60174, headed south along Randall Road until 1480 N. Orchard Road, Aurora, IL. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![randall road image](randall-road.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: State Street

Date of collection: May 18, 2023 Location: State Street, Chicago, IL This data set contains lidar and vision data collected along State Street. The vehicle started from outside of the McCormick Tribune Campus Center at the Illinois Institute of Technology’s Mies Campus and headed north along State Street, until the north end of State Street in the Gold Coast. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![state street image](state-street.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗