Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Remote Tooling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Variabilities in Pm2.5 and Black Carbon Surface Concentrations Reproduced by Aerosol Optical Properties Estimated by In-Situ Data, Ground Based Remote Sensing and Modeling

Because of the increased temporal and spatial resolutions of the sensors onboard recently launched satellites, satellite-based surface aerosol concentration, which is usually estimated from the aerosol optical depth (AOD), is expected to become a strategic tool for air quality studies in the future. By further exploring the relationships of aerosol concentrations and their optical properties using ground observations, the accuracies of these products can be improved. Here, we analyzed collocated observations of surface mass concentrations of fine particulate matter (PM2.5) and black carbon (BC), as well as columnar aerosol optical properties from a sky radiometer and aerosol extinction profiles obtained by multi-axis differential optical absorption spectroscopy (MAX-DOAS), during the 2019–2020 period. We focused the analyses on a daily scale, emphasizing the role of the ultraviolet (UV) spectral region. Generally, the correlation between the AOD of the fine fraction (i.e., fAOD) and the PM2.5 surface concentration was moderately strong, regardless of considerations of boundary layer humidity and altitude. In contrast, the fAOD of the partial column below 1 km, which was obtained by combining sky radiometer and MAX-DOAS retrievals, better reproduced the variability of the PM2.5 and resulted in a linear relationship. In the same manner, we demonstrated that the absorption AOD of the fine fraction (fAAOD) of the partial column was related to the variability of the BC concentration. Analogous analyses based on aerosol products from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) confirmed these findings and highlighted the importance of the shape of the aerosol profile. Overall, our results indicated a remarkable consistency among the retrieved datasets, and between the datasets and MERRA-2 products. These results confirmed the well-known sensitivity to aerosol absorption in the UV spectral region; they also highlighted the efficacy of combined MAX-DOAS and sky radiometer observations.

Alessandro Damiani↗
NASA NTRSDate not supplied

Foundational Human-Autonomy Teaming Research and Development in Scalable Remotely Operated Advanced Air Mobility Operations: Research Model and Initial Work

To achieve the scalability envisioned for many Advanced Air Mobility (AAM) applications, uncrewed aerial system (UAS) concepts are being pursued with the goal of enabling fewer human operators to manage more increasingly autonomous vehicles. NASA’s Transformational Tools and Technologies – Revolutionary Aviation Mobility (T3-RAM) subproject has identified human-autonomy teaming (HAT) as a critical area of research required to support these operations. Under T3-RAM, the HAT Foundational Research Activity has been tasked with providing basic research to identify HAT and human-automation interaction (HAI) principles that can be used to achieve scalable multi-vehicle UAS operations. This paper first outlines a research model to produce ecologically relevant basic research, then contextualizes completed and planned research and development activities within this model. Proposed research threads are presented, along with their practical and theoretical implications.

Human-Autonomy Teaming↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

58 GEOSCIENCES↗
NASA NTRS1980

Introducing remote sensing to county-level agencies in Michigan through the Cooperative Extension Service

State and regional planning agencies in Michigan are aware of remote sensing technology, however, many agencies at the county and township level have not been introduced to its potential. This paper describes an educational effort undertaken by the MSU Remote Sensing Project and the Michigan Cooperative Extension Service aimed at reaching local decision makers. Remote sensing materials and a questionnaire were used to elicit interest and aid in planning the program. A series of seven workshops were held throughout Michigan for governmental officials and the staff of public agencies from 38 counties. Emphasis was placed on the characteristics, availability and usefulness of remotely-sensed data through a series of successful applications conducted in the state. The workshops were highly successful and many local decision makers are not aware of a new tool to call upon in their responses to land use and natural resource concerns.

Enslin, W. R.↗
NASA NTRS2005

Lessons Learned in the Livingstone 2 on Earth Observing One Flight Experiment

The Livingstone 2 (L2) model-based diagnosis software is a reusable diagnostic tool for monitoring complex systems. In 2004, L2 was integrated with the JPL Autonomous Sciencecraft Experiment (ASE) and deployed on-board Goddard's Earth Observing One (EO-1) remote sensing satellite, to monitor and diagnose the EO-1 space science instruments and imaging sequence. This paper reports on lessons learned from this flight experiment. The goals for this experiment, including validation of minimum success criteria and of a series of diagnostic scenarios, have all been successfully net. Long-term operations in space are on-going, as a test of the maturity of the system, with L2 performance remaining flawless. L2 has demonstrated the ability to track the state of the system during nominal operations, detect simulated abnormalities in operations and isolate failures to their root cause fault. Specific advances demonstrated include diagnosis of ambiguity groups rather than a single fault candidate; hypothesis revision given new sensor evidence about the state of the system; and the capability to check for faults in a dynamic system without having to wait until the system is quiescent. The major benefits of this advanced health management technology are to increase mission duration and reliability through intelligent fault protection, and robust autonomous operations with reduced dependency on supervisory operations from Earth. The work-load for operators will be reduced by telemetry of processed state-of-health information rather than raw data. The long-term vision is that of making diagnosis available to the onboard planner or executive, allowing autonomy software to re-plan in order to work around known component failures. For a system that is expected to evolve substantially over its lifetime, as for the International Space Station, the model-based approach has definite advantages over rule-based expert systems and limit-checking fault protection systems, as these do not scale well. The model-based approach facilitates reuse of the L2 diagnostic software; only the model of the system to be diagnosed and telemetry monitoring software has to be rebuilt for a new system or expanded for a growing system. The hierarchical L2 model supports modularity and expendability, and as such is suitable solution for integrated system health management as envisioned for systems-of-systems.

Hayden, Sandra C.↗
NASA NTRS2009

An Operational Assessment of the MODIS False Color Composite with the Great Falls, Montana National Weather Service

The close and productive collaborations between the NWS Warning and Forecast Office (WFO) in Great Falls, MT and the Short Term Prediction and Research Transition (SPORT) Center at NASA/Marshall Space Flight Center have provided a unique opportunity for science sharing and technology transfer. In particular, SPoRT has provided a false color composite product derived from MODIS data, which is part of NASA's Earth Observing System. This product is designed to delineate snow and ice covered ground, bare ground and clouds. The Great Falls WFO has been a test bed of the MODIS false color composite as a tool in operations to monitor the development and dissipation of snow cover In particular, preliminary applications have shown that the product can be used to monitor snow cover in remote locations as well as ice in rivers. This information can lead to improved assessments of flooding potential during post event conditions where rapid melting and runoff are anticipated. The potential of this product on future geostationary satellites may substantially contribute to the NWS mission by providing enhanced situational awareness. The operational use of this product has been transitioned at WFO Great Falls through a process of product implementation, discussions with the service hydrologist and forecasters, and post event analysis. A concentrated assessment period from January to March, 2008 was initiated to investigate the impact of the MODIS false color product on WFO Great Falls' operations. This presentation will emphasize the impact the MODIS false color product had in the WFO's situational awareness and how best this information can be used to influence operational decisions.

Loss, GIna↗
NASA NTRS2016

Measuring Small Debris - What You Can't See Can Hurt You

While modeling gives us a tool to better understand the Earth orbit debris environment, it is measurements that give us "ground truth" about what is happening in space. Assets that can detect orbital debris remotely from the surface of the Earth, such as radars and telescopes, give us a statistical view of how debris are distributed in space, how they are being created, and how they are evolving over time. In addition, in situ detectors in space are giving us a better picture of how the small particle environment is actually damaging spacecraft today. IN addition, simulation experiments on the ground help us to understand what we are seeing in orbit. This talk will summarize the history of space debris measurements, how it has changed our view of the Earth orbit environment, and how we are designing the experiments of tomorrow.

Matney, Mark↗
NASA NTRS1995

Marketing Remote Sensing Data for North Pacific Fisheries Development and Management

Fish poaching, drug trafficking, ocean dumping, and other illegal activities are important problems on the high seas and in national economic zones. The primary thrust of the EOCAP II project, "Marketing Remote Sensing Data for North Pacific Fisheries Development and Management", was to use space-based sensors to improve the effectiveness of marine monitoring, control, and surveillance (MCS). Our initial objectives were to concentrate on the development of MCS tools using Advanced Very High Resolution Radiometry (AVHRR) and Synthetic Aperture Radar (SAR) data. Although we have successfully completed development of an initial version of our SAR-based monitoring tool (OmniVision), project activity has resulted in a much broader application of space-based assets to marine applications. Based in part on work commenced within EOCAP II, a new company, Ocean and Coastal Environmental Sensing, Inc. (OCENS), has been launched and the development of several new software products outside of the MCS arena initiated. One of those products, SeaStation, is near completion with a Fall, 1995 release date. Equity investment in OCENS now totals $70,000-with an additional amount being sought in the first round of financing. One of the pre-eminent objectives of EOCAP II is to make contributions to the US economy and job growth through the expansion of commercial uses of remotely sensed data. OCENS and the software products it is introducing into marine and coastal zone markets responds to this primary object*e. EOCAP II funding leveraged the market and technical know-how of OCENS founders into smart products that benefit marine and coastal zone users. Although technical difficulties and geopolitical shifts damaged the commercial feasibility of initial project objectives, the flexibility of the EOCAP II program now permits long-term business success. This in no small part stems from the fact that the EOCAP program recognizes the realities of small and start-up businesses and does not attempt to force these conditions to fit the apparent needs of big government. Instead, EOCAP works with those who know their market best in order to produce successful products and expanding businesses.

Source record↗
NASA NTRS2010

The Use of Remote Sensing Data for Advancing America's Energy Policy

After briefly reviewing America's Energy Policy laid out by the Obama Administration, we outline how a Global Carbon Observing System designed to monitor Carbon from space can provide the necessary data and tools to equip decision makers with the knowledge necessary to formulate effective energy use and practices policy. To stabilize greenhouse gas emissions in the atmosphere in a manner that it does not interfere with the Earth's climate system (which is one of the goals of United Nations Framework for Convention on Climate Change) requires vastly improved prediction of the atmospheric carbon dioxide (CO2) concentrations. This in torn requires a robust understanding of the carbon exchange mechanisms between atmosphere, land, and oceans and a clear understanding of the sources and sinks (i.e. uptake and storage) of CO2. We discuss how the Carbon Observing System from space aids in better understanding of the connection between the carbon cycle and climate change and provides more accurate predictions of atmospheric CO2 concentration. It also enables implementation of greenhouse gas (GHG) mitigation policies such as cap and trade programs, international climate treaties, as well as formulation of effective energy use policies.

Valinia, Azita↗
NASA NTRS2011

Recent Developments in the Design, Capabilities and Autonomous Operations of a Lightweight Surface Manipulation System and Test-bed

The first generation of a versatile high performance device for performing payload handling and assembly operations on planetary surfaces, the Lightweight Surface Manipulation System (LSMS), has been designed and built. Over the course of its development, conventional crane type payload handling configurations and operations have been successfully demonstrated and the range of motion, types of operations and the versatility greatly expanded. This enhanced set of 1st generation LSMS hardware is now serving as a laboratory test-bed allowing the continuing development of end effectors, operational techniques and remotely controlled and automated operations. This paper describes the most recent LSMS and test-bed development activities, that have focused on two major efforts. The first effort was to complete a preliminary design of the 2nd generation LSMS that has the capability for limited mobility and can reposition itself between lander decks, mobility chassis, and fixed base locations. A major portion of this effort involved conducting a study to establish the feasibility of, and define, the specifications for a lightweight cable-drive waist joint. The second effort was to continue expanding the versatility and autonomy of large planetary surface manipulators using the 1st generation LSMS as a test-bed. This has been accomplished by increasing manipulator capabilities and efficiencies through both design changes and tool and end effector development. A software development effort has expanded the operational capabilities of the LSMS test-bed to include; autonomous operations based on stored paths, use of a vision system for target acquisition and tracking, and remote command and control over a communications bridge.

Dorsey, John T.↗
DOE OSTI2024

Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants

Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, light doses of stress result in the production of positive attributes in crops, like tolerance to stress and biosynthesis of metabolites, called hormesis. Advancement in artificial intelligence (AI) has enabled the development of high-throughput gadgets such as high-resolution imagery sensors and robotic aerial vehicles, i.e., satellites and unmanned aerial vehicles (UAV), to overcome biotic and abiotic stresses. These High throughput (HTP) gadgets produce accurate but big amounts of data. Significant datasets such as transportable array for remotely sensed agriculture and phenotyping reference platform (TERRA-REF) have been developed to forecast abiotic stresses and early detection of biotic stresses. For accurately measuring the model plant stress, tools like Deep Learning (DL) and Machine Learning (ML) have enabled early detection of desirable traits in a large population of breeding material and mitigate plant stresses. In this review, advanced applications of ML and DL in plant biotic and abiotic stress management have been summarized.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1975

A manipulator arm for zero-g simulations

A 12-ft counterbalanced Slave Manipulator Arm (SMA) was designed and fabricated to be used for resolving the questions of operational applications, capabilities, and limitations for such remote manned systems as the Payload Deployment and Retrieval Mechanism (PDRM) for the shuttle, the Free-Flying Teleoperator System, the Advanced Space Tug, and Planetary Rovers. As a developmental tool for the shuttle manipulator system (or PDRM), the SMA represents an approximate one-quarter scale working model for simulating and demonstrating payload handling, docking assistance, and satellite servicing. For the Free-Flying Teleoperator System and the Advanced Tug, the SMA provides a near full-scale developmental tool for satellite servicing, docking, and deployment/retrieval procedures, techniques, and support equipment requirements. For the Planetary Rovers, it provides an oversize developmental tool for sample handling and soil mechanics investigations. The design of the SMA was based on concepts developed for a 40-ft NASA technology arm to be used for zero-g shuttle manipulator simulations.

Brodie, S. B.↗
NASA NTRS1986

A multidisciplinary study of planetary, solar and astrophysical radio emissions

Combination of the related fields of planetary, solar, and astrophysical radio emissions was attempted in order to more fully understand the radio emission processes. Topics addressed include: remote sensing of astrophysical plasma turbulence; Alfven waves; astrophysical shock waves; surface waves; very long base interferometry results; very large array observations; solar magnetic flux; and magnetohydrodynamic waves as a tool for solar corona diagnostics.

Gurnett, D. A.↗
NASA NTRS1993

Preprocessing: Geocoding of AVIRIS data using navigation, engineering, DEM, and radar tracking system data

Remotely sensed data have geometric characteristics and representation which depend on the type of the acquisition system used. To correlate such data over large regions with other real world representation tools like conventional maps or Geographic Information System (GIS) for verification purposes, or for further treatment within different data sets, a coregistration has to be performed. In addition to the geometric characteristics of the sensor there are two other dominating factors which affect the geometry: the stability of the platform and the topography. There are two basic approaches for a geometric correction on a pixel-by-pixel basis: (1) A parametric approach using the location of the airplane and inertial navigation system data to simulate the observation geometry; and (2) a non-parametric approach using tie points or ground control points. It is well known that the non-parametric approach is not reliable enough for the unstable flight conditions of airborne systems, and is not satisfying in areas with significant topography, e.g. mountains and hills. The present work describes a parametric preprocessing procedure which corrects effects of flight line and attitude variation as well as topographic influences and is described in more detail by Meyer.

Meyer, Peter↗
NASA NTRS1995

Electronic collection system for spacelab mission timeline requirements

This paper describes the Functional Objective Requirements Collection System (FORCS) software tool that has been developed for use by Principal Investigators (PI's) and Payload Element Developers (PED's) on their own personal computers to develop on-orbit timelining requirements for their payloads. The FORCS tool can be used either in a totally stand-alone mode, storing the information in a local file on the user's personal computer hard disk or in a remote mode where the user's computer is linked to a host computer containing the integrated database of the timeline requirements for all of the payloads on a mission. There are a number of features incorporated in the FORCS software to assist the user. The user may move freely back and forth between the various forms for inputting the data. Several methods are used to input the information, depending on the type of the information. These methods range from filling in text boxes, using check boxes and radio buttons, to inputting information into a spreadsheet format. There are automated features provided to assist in developing the proper format for the data, ranging from limit checking on some of the parameters to automatic conversion of different formats of time data inputs to the one standard format used for the timeline scheduling software.

Lindberg, James P.↗
NASA NTRS1995

SAVS: A Space and Atmospheric Visualization Science system

The research environment faced by space and atmospheric scientists in the 1990s is characterized by unprecedented volumes of new data, by ever-increasing repositories of unexploited mission files, and by the widespread use of empirical and large-scale computational models needed for the synthesis of understanding across data sets and discipline boundaries. The effective analysis and interpretation of such massive amounts of information have become the subjects of legitimate concern. With SAVS (a Space and Atmospheric Visualization Science System), we address these issues by creating a 'push-button' software environment that mimics the logical scientific processes in data acquisition, reduction, and analysis without requiring a detailed understanding of the methods, networks, and modules that link the tools and effectively execute the functions. SAVS provides (1) a customizable framework for accessing a powerful set of visualization tools based on the popular AVS visualization software with hooks to PV-Wave and access to Khoros modules, (2) a set of mathematical and statistical tools, (3) an extensible library of discipline-specific functions and models (e.g., MSIS, IRI, Feldstein Oval, IGRF, satellite tracking with CADRE-3, etc.), and (4) capabilities for local and remote data base access. The system treats scalar, vector, and image data, and runs on most common Unix workstations. We present a description of SAVS and its components, followed by several applications based on generic research interests in interplanetary and magnetospheric physics (IMP/ISTP), active experiments in space (CRRES), and mission planning focused on the Earth's thermospheric, ionospheric, and mesospheric domains (TIMED).

Szuszczewicz, E. P.↗
NASA NTRS1999

Hyperspectral Thermal Fabry-Perot Modeling

Fabry-Perot interferometer are simple elegant, tunable filters that can be used to make compact hyperspectral thermal imaging system. To foster the development of these sensors, software tools for the design and simulation of tunable Fabry-Perot infrared imagers have been developed. The tools are provided at three levels: basic, design, and system. Basic tools describe a nearly ideal Fabry-Perot filter with perfectly flat and parallel mirrors in collimated space. Design tools that take into account non-ideal behavior such as mirror and collimation defects calculate free spectral range, finesse, and spectral width of the interferometer. System tools help analyze an integration of the Fabry-Perot filter into a camera system. They include spectral convolution, first-order optical layout, and an estimation of signal-to-noise ratio. The complete set of tools allows for simulations of system operation and performance with various illumination sources. Spectral images generated in such simulations were used to examine applicability of Fabry-Perot system in remote sensing of atmospheric gases including detection of environmental pollutants and hazardous gases. Different operating conditions and system configurations are presented.

Ryan, Robert↗