SEARCH · Engineering Papers

Results for “remote training”

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.

SourceResearch connectionFrom yearThrough year

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

At least 559 records · Page 31

All sources NASA NTRS DOE OSTI

NASA NTRS1995

Advanced Life Systems for Extreme Environments: An Arctic Application

The problems of obtaining adequate pure drinking water and disposing of liquid and solid waste in the U.S. Arctic, a region where virtually all water is frozen solid for much of the year, has led to unsanitary solutions (U.S. Arctic Research Commission). These solutions are also damaging to the environment. Sanitation and a safe water supply are particularly problems in rural villages. About one-fourth of Alaska's 86.000 Native residents live in these communities. They are without running water and use plastic buckets for toilets. The outbreak of diseases is believed to be partially attributable to exposure to human waste. Villages with the most frequent outbreaks of disease are those in which running water is difficult to obtain (Office of Technology Assessment, 1994). Waste is emptied into open lagoons, rivers, or onto the sea coast. It does not degrade rapidly and in addition to affecting human health, can be harmful to the fragile ecology of the Arctic and the indigenous wildlife and fish populations. Advanced Life Systems for Extreme Environments (ALSEE) provides a solution to sanitation and safe water problems. The system uses an advanced integrated technology developed for Antarctic and space applications. ALSEE uses the systems approach to address more than waste and water problems. By incorporating hydroponic horticulture and aquaculture into the waste treatment system, ALSEE addresses the quality and quantity of fresh foods available to Arctic residents. A temperate climate is required for year-round plant growth. ALSEE facilities can be designed to include a climate controlled area within the structure. This type of environment is a change from the long periods of darkness and cold found in the Arctic and can help alleviate stress so often associated with these extremes. While the overall concept of ALSEE projects is advanced, system facilities can be operated by village residents with appropriate training. ALSEE provides continuing training and education as a part of the project. Not only is this desirable but necessary. There is ample evidence in "gravoyard" throughout rural Alaska of technologies which failed not because they were not applic4tble, but because they were not integrated and could not be operated or repaired by village residents. Waste streams in villages will vary depending on the human diet and the non-food waste stream. ALSEE units can be adapted to different village situations once the content of the waste stream is known. Units located in population hubs can serve as research, education. and demonstration centers. Appropriate modifications can be determined at these centers to transfer technology to more remote locations where waste stream conditions may differ.

Lewis, Carol E.↗

NASA NTRSDate not supplied

Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.

quantum↗

NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗

NASA NTRS2007

Lunar International Science Coordination/Calibration Targets

A new era of international lunar exploration has begun and will expand over the next four years with data acquired from at least four sophisticated remote sensing missions: KAGUYA (SELENE) [Japan], Chang'E [China], Chandrayaan-l [India], and LRO [United States]. It is recognized that this combined activity at the Moon with modern sophisticated sensors wi II provide unprecedented new information about the Moon and will dramatically improve our understanding of Earth's nearest neighbor. It is anticipated that the blooming of scientific exploration of the Moon by nations involved in space activities will seed and foster peaceful international coordination and cooperation that will benefit all. Summarized here are eight Lunar International Science Coordination/Calibration Targets (L-ISCT) that are intended to a) allow cross-calibration of diverse multi-national instruments and b) provide a focus for training young scientists about a range of lunar science issues. The targets, discussed at several scientific forums, were selected for coordinated science and instrument calibration of orbital data. All instrument teams are encouraged to participate in a coordinated activity of early-release data that will improve calibration and validation of data across independent and diverse instruments.

Head, J. W.↗

NASA NTRS1988

Planet X probe: A fresh new look at an old familiar place

Planet X Probe utilizes a Get Away Special (GAS) payload to provide a large student population with a remote Earth sensing experimental package. To provide a cooperative as well as a competitive environment, the effort is targeted at all grade levels and at schools in different geographical regions. LANDSAT capability allows students to investigate the Earth, its physical makeup, its resources, and the impact of man. This project also serves as an educational device to get students to stand back and take a fresh look at their home planet. The key element is to treat the familiar Earth as an unknown planet with knowledge based only on what is observable and provable from the images obtained. Through participation, a whole range of experiences will include: (1) mission planning; (2) research and pilot projects to train teams; (3) identification and recruitment of scientific mentors and dialogue; (4) selection of a student advisory team to be available during the mission; (5) analysis of data and compilation of findings; (6) report preparation, constucted along sound scientific principles; and (7) presentation and defense of findings before a meeting of competitive student groups and scientist in the field.

Nicholson, James↗

NASA NTRS1998

Investigation of EM Emissions by the Electrodynamic Tether, Inclusive of an Observational Program (EMET)

Our TSS-1/R investigation, which we shall refer to as EMET in this report, was an integral part of the effort by the TSS-1/R Investigators' Working Group (IWG) to come to an understanding of the complex interaction between the tethered satellite system and the ionosphere. All of the space-borne experiments were designed to collect data relevant to the local interaction. Only the ground- based experiments, EMET and its Italian counterpart Observations on the Earth's Surface of Electromagnetic Emissions (OESEE), held out any hope of characterizing the long range effects of the interaction. This was to be done by detecting electromagnetic waves generated by the system in the ionosphere, assuming the signal reached the Earth's surface with sufficient amplitude. As the type of plasma waves excited to carry charge away from the charge-exchange regions of the system at each end of the tether is one of the theoretical points about which there is greatest disagreement, a definitive identification of tether-generated waves could mark significant progress in the so-called current closure problem of electrodynamic tethers. Dr. Mario Grossi of the Smithsonian Astrophysical Observatory (SAO) initiated the investigation, and his experience in the field of ULF-ELF waves and their detection was invaluable throughout its course. Rice University had the responsibility of setting up the EMET ULF-VLF ground stations under a subcontract from SAO. Principal Investigator (PI) for the Rice effort was Prof. William E. Gordon, who was primary observer at the Arecibo Observatory during TSS-LR. Dr. Steve Noble handled major day-to-day operations, training, and planning for the ground-based measurements. Dr. James McCoy of NASA JSC, a member of the Mona/Arecibo team, was pilot for the numerous flights ferrying personnel and equipment between Puerto Rico and Mona Island. Final responsibility for the measurements rested with SAO, and the activities of field personnel and SAO investigators were closely co-ordinated during the mission. Dr. Enrico Lorenzini of SAO served as the eyes, ears, and brain of EMET in the Science Operations Area and PI table during the mission, whenever the PI was absent during the round-the-clock mission operations. The Rice University final report to SAO, which is included as an Appendix, contains details of the remote sites, means of communication, sensors, etc., as well as the affiliations of personnel involved in the data-gathering effort.

Estes, Robert D.↗

NASA NTRS2022

A Dynamic Landslide Hazard Monitoring Framework for the Lower Mekong Region

The Lower Mekong region is one of the most landslide-prone areas of the world. Despite the need for dynamic characterization of landslide hazard zones within the region, it is largely understudied for several reasons. Dynamic and integrated understanding of landslide processes requires landslide inventories across the region, which have not been available previously. Computational limitations also hamper regional landslide hazard assessment, including accessing and processing remotely sensed information. Finally, open-source software and modelling packages are required to address regional landslide hazard analysis. Leveraging an open-source data-driven global Landslide Hazard Assessment for Situational Awareness model framework, this study develops a region-specific dynamic landslide hazard system leveraging satellite-based Earth observation data to assess landslide hazards across the lower Mekong region. A set of landslide inventories were prepared from high-resolution optical imagery using advanced image-processing techniques. Several static and dynamic explanatory variables (i.e., rainfall, soil moisture, slope, relief, distance to roads, distance to faults, distance to rivers) were considered during the model development phase. An extreme gradient boosting decision tree model was trained for the monsoon period of 2015–2019 and the model was evaluated with independent inventory information for the 2020 monsoon period. The model performance demonstrated considerable skill using receiver operating characteristic curve statistics, with Area Under the Curve values exceeding 0.95. The model architecture was designed to use near-real-time data, and it can be implemented in a cloud computing environment (i.e., Google Cloud Platform) for the routine assessment of landslide hazards in the Lower Mekong region. This work was developed in collaboration with scientists at the Asian Disaster Preparedness Center as part of the NASA SERVIR Program’s Mekong hub. The goal of this work is to develop a suite of tools and services on accessible open-source platforms that support and enable stakeholder communities to better assess landslide hazard and exposure at local to regional scales for decision making and planning.

Nishan Kumar Biswas↗

NASA NTRS1981

The influence of autocorrelation in signature extraction: An example from a geobotanical investigation of Cotter Basin, Montana

The presence of positive serial correlation (autocorrelation) in remotely sensed data results in an underestimate of the variance-covariance matrix when calculated using contiguous pixels. This underestimate produces an inflation in F statistics. For a set of Thematic Mapper Simulator data (TMS), used to test the ability to discriminate a known geobotanical anomaly from its background, the inflation in F statistics related to serial correlation is between 7 and 70 times. This means that significance tests of means of the spectral bands initially appear to suggest that the anomalous site is very different in spectral reflectance and emittance from its background sites. However, this difference often disappears and is always dramatically reduced when compared to frequency distributions of test statistics produced by the comparison of simulated training sets possessing equal means, but which are composed of autocorrelated observations.

Labovitz, M. L.↗

NASA NTRS1982

The influence of autocorrelation in signature extraction - An example from a geobotanical investigation of Cotter Basin, MT

Labovitz, M. L.↗

NASA NTRS1984

The influence of autocorrelation in signature extraction - An example from a geobotanical investigation of Cotter Basin, Montana

The presence of positive serial correlation (autocorrelation) in remotely sensed data results in an underestimate of the variance-covariance matrix when calculated using contiguous pixels. This underestimate produces an inflation in F statistics. For a set of Thematic Mapper Simulator data (TMS), used to test the ability to discriminate a known geobotanical anomaly from its background, the inflation in F statistics related to serial correlation is between 7 and 70 times. This means that significance tests of means of the spectal bands initially appear to suggest that the anomalous site is very different in spectral reflectance and emittance from its background sites. However, this difference often disappears and is always dramatically reduced when compared to frequency distributions of test statistics produced by the comparison of simulated training sets possessing equal means, but which are composed of autocorrelated observations.

Labovitz, M. L.↗

NASA NTRS2022

A Novel Machine Learning-Based Gap-Filling of Fine-Resolution Remotely Sensed Snow Cover Fraction Data By Combining Downscaling and Regression

Satellite-based remotely sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud covered. This study’s prototype predicts a 1-km version of the 500-m MOD10A1 SCF target. Due to noncollocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a two-dimensional masking. To overcome reduced usable data from noncollocated spatial gaps across inputs, we innovate a fully generalized three-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus, our gap-agnostic technique can use significantly more examples for training (∼67%) and prediction (∼100%), instead of only less than 10% for the previous partial convolution. We train an example simple three-layer legacy super-resolution convolutional neural network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple Earth science applications like downscaling, regression, classification, and segmentation that were hindered by data gaps.

Soni Yatheendradas↗

NASA NTRS1988

Wave/current interaction model

The wave-current interaction for the application to remote sensing data via numerical simulations and data comparison is modelled. Using the field data of surface current shear, wind condition and ambient wave spectrum, the numerical simulations of directional wave spectrum evolution were used to interpret and to compare with the aircraft data from Radar Ocean Wave Spectrometer (ROWS) and Surface Contour Radar (SCR) across the front during Frontal Air Sea Interaction Experiment (FASINEX). The wave-ice interaction was inspired by the observation of large amplitude waves hundreds of kms inside the ice pack in the Weddell Sea, resulting in breakup of the ice pack. The developed analysis of processes includes the refraction of waves at the pack edge, the effects of pack compression on wave propagation, wave train stability and buckling stability in the ice pack. Sources of pack compression and interaction between wave momentum and pack compression are investigated. Viscous camping of propagating waves in the marginal ice zone are also studied. The analysis suggests an explanation for the change in wave dispersion observed from the ship and the sequence of processes that cause ice pack breakup, pressure ridge formation and the formation of open bands of water.

Liu, A. K.↗

NASA NTRS2011

A Panchromatic Imaging Fourier Transform Spectrometer for the NASA Geostationary Coastal and Air Pollution Events Mission

This paper summarizes the design and development of the Panchromatic Imaging Fourier Transform Spectrometer (PanFTS) for the NASA Geostationary Coastal and Air Pollution Events (GEO-CAPE) Mission. The PanFTS instrument will advance the understanding of the global climate and atmospheric chemistry by measuring spectrally resolved outgoing thermal and reflected solar radiation. With continuous spectral coverage from the near-ultraviolet through the thermal infrared, this instrument is designed to measure pollutants, greenhouse gases, and aerosols as called for by the U.S. National Research Council Decadal Survey; Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond1. The PanFTS instrument is a hybrid instrument based on spectrometers like the Tropospheric Emissions Spectrometer (TES) that measures thermal emission, and those like the Orbiting Carbon Observatory (OCO), and the Ozone Monitoring Instrument (OMI) that measure scattered solar radiation. Simultaneous measurements over the broad spectral range from IR to UV is accomplished by a two sided interferometer with separate optical trains and detectors for the ultraviolet-visible and infrared spectral domains. This allows each side of the instrument to be independently optimized for its respective spectral domain. The overall interferometer design is compact because the two sides share a single high precision cryogenic optical path difference mechanism (OPDM) and metrology laser as well as a number of other instrument systems including the line-of-sight pointing mirror, the data management system, thermal control system, electrical system, and the mechanical structure. The PanFTS breadboard instrument has been tested in the laboratory and demonstrated the basic functionality for simultaneous measurements in the visible and infrared. It is set to begin operations in the field at the California Laboratory for Atmospheric Remote Sensing (CLARS) observatory on Mt. Wilson measuring the atmospheric chemistry across the Los Angeles basin. Development has begun on a flight size PanFTS engineering model (EM) that addresses all critical scaling issues and demonstrates operation over the full spectral range of the flight instrument which will show the PanFTS instrument design is mature.

Imaging spectrometer(↗

NASA NTRS2017

The 2015-2016 SEPMAP Program at NASA JSC: Science, Engineering, and Program Management Training

The Systems Engineering Project Management Advancement Program (SEPMAP) at NASA Johnson Space Center (JSC) is an employee development program designed to provide graduate level training in project management and systems engineering. The program includes an applied learning project with engineering and integrated science goals requirements. The teams were presented with a task: Collect a representative sample set from a field site using a hexacopter platform, as if performing a scientific reconnaissance to assess whether the site is of sufficient scientific interest to justify exploration by astronauts. Four teams worked through the eighteen-month course to design customized sampling payloads integrated with the hexacopter, and then operate the aircraft to meet sampling requirements of number (≥ 5) and mass (≥ 5g each). The "Mars Yard" at JSC was utilized for this purpose. This project activity closely parallels NASA plans for the future exploration of Mars, where remote sites will be reconnoitered ahead of crewed exploration.

Graham, L.↗

NASA NTRS2023

Marin County Wildland Fires: Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wildfire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry (a Marin-based fire service workforce development program) and the Marin County Fire Department to develop models to assist with fire management. Using data from Sentinel-2A, PlanetScope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), our team developed a number of input data layers for three different models to evaluate wildfire severity. One model performed a suitability analysis with weights based on scientific literature; another model utilized a U-Net Convolutional Neural Network trained on previous fires in Marin and neighboring Sonoma County to predict the difference normalized burn severity; and the third inputted data layers into the FlamMap tool that outputs risk categories. We compared model outputs and performed a weighted overlay analysis to identify specific locations where a fireline could be constructed to interrupt the progress of an active fire. These tools will assist partners in preparing for and managing active wildfire situations.

Remote sensing↗

NASA NTRS1992

Meeting human needs

Manned space flight can be viewed as an interaction of three general elements: the human crewmember, spacecraft systems, and the environment. While the human crewmember is a crucial element in the system, certain physiological, psychological, environ- mental and spacecraft systems factors can compromise human performance in space. These factors include atmospheric pressure, physiology, uncertainties associated with space radiation, the potential for exposure to toxic materials in the closed environment, and spacecraft habitability. Health protection in space, for current and future missions, relies on a philosophy of risk reduction, which in the space program is achieved in four ways-through health maintenance, health care, design criteria, an selection and training. Emphasis is place upon prevention, through selection criteria and careful screening. Spacecraft health care systems must be absolutely reliable, and they will be automated and computerized to the maximum extent possible, but still designed with the human crewmember's capabilities in mind. The autonomy and technological sophistication of future missions will require a greater emphasis on high-level interaction between the human operator and automated systems, with effective allocation of tasks between humans and machines. Performance in space will include complex tasks during extravehicular activity (EVA) and on planetary surfaces, and knowledge of crewmembers' capability and limitations during such operations will be critical to mission success. Psychological support will become increasingly important on space missions, as crews spend long periods in remote and potentially hazardous environments. The success of future missions will depend on both individual psychological health and group cohesion and productivity, particularly as crew profiles become more heterogeneous. Thus, further human factors are needed in the area of small-group dynamics and performance.

Nicogossian, Arnauld E.↗

NASA NTRS2011

Telemedicine in Space Flight - Summary of a NASA Workshop

The Exploration Medical Capability Element of the Human Research Program at NASA Johnson Space Center hosted the Telemedicine Workshop in January 2011 to discuss the medical operational concept for a crewed mission to a near-Earth asteroid (NEA) and to identify areas for future work and collaboration. With the increased likelihood of a medical incident on a long duration exploration mission to a near-Earth asteroid, as well as the fact that there will likely be limited medical capabilities and resources available to diagnose and treat medical conditions, it is anticipated that a more structured use of telemedicine will become highly desirable. The workshop was convened to solicit expert opinion on current telemedicine practices and on medical care in remote environments. Workshop Objectives: The workshop brought together leaders in telemedicine and remote medicine from The University of Texas Medical Branch, Henry Ford Hospital, Ontario Telemedicine Network, U.S. Army Institute of Surgical Research, University of Miami, American Telemedicine Association, Doctors Without Borders, and the Pan American Health Organization. The primary objectives of the workshop were to document the medical operations concept for a crewed mission to a NEA, to determine gaps between current capabilities and the capabilities outlined in the operations concept, to identify research required to close these gaps, and to discuss potential collaborations with external-to-NASA organizations with similar challenges. Summary of Discussions and Conclusions: The discussions held during the workshop and the conclusions reached by the workshop participants were grouped into seven categories: Crew Medical Officers, Patient Area in Spacecraft, Training, Electronic Medical Records, Intelligent Care Systems, Consultation Protocols, Prophylactic Surgical Procedures, and Data Prioritization. The key points discussed under each category will be presented.

Barsten, K. N.↗

NASA NTRSDate not supplied

Eye Glance Behaviors of Ground Control Station Operators in a Simulated Urban Air Mobility Environment

Research into concepts such as advanced air mobility (AAM) and urban air mobility (UAM) offers an opportunity for successfully and safely adding new classes of vehicles into the National Airspace System. However, a need exists for research into the human factors associated with these concepts. In this paper, we evaluate the gaze behaviors of three remote ground control station operators (GCSOs) conducting simulated UAM operations. The participants monitored and controlled an unmanned aircraft system (UAS) from pre-flight to landing using ground control station (GCS) software across nine scenarios within a remote UAS operations center at NASA Langley Research Center (LaRC). As this work was exploratory, descriptive statistics were calculated to provide some initial insight into GCSO gaze patterns associated with the GCS display. Scenarios that required the operator to directly interact with an airspace scheduling system off-screen resulted in fewer on-screen glances than scenarios that did not include direct interactions with the off-screen system. After investigating several areas of interest (AOIs) within the GCS display, participants primarily viewed three AOIs: the map, vehicle status, and operations checklist. The results yielded several GCS design and operational improvement recommendations to include: (a) adding altitude information to the vehicle icon, (b) adding additional traffic information, and (c) including additional GCS training, to reduce the need to scan an operations checklist, which would allow allocation of visual attention towards other AOIs.

Urban Air Mobility↗