Search NASA⌕ Search

SEARCH · Search NASA

Results for “remote data collection”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Evaluation of a Remote Data Collection Method to Study Human-Automation Interaction and Workload

Technological advances have increased the automation of Uncrewed Aerial Vehicles, allowing human operators to manage multiple vehicles at a high-level without the need to understand low-level system behaviors. Previous laboratory studies have explored the relationship between reliability, trust, use of automation and the effects of number of vehicles under supervision on subjective workload. Due to the limitations resulting from the COVID-19 pandemic, in-person laboratory studies are not always possible. Therefore, this work aimed to investigate if remote data collection alternatives, such as Amazon’s Mechanical Turk, can provide comparative results as those obtained in laboratory settings. A study was conducted in the context of small drone operations. As expected, higher reliability led to higher trust ratings and the inclusion of more vehicles led to higher workload. In contrast, reliability unexpectedly had no effect on intention to use the automation. Though these results were encouraging, several limitations were identified.

trust↗
NASA NTRSDate not supplied

Evaluation of a Remote Data Collection Method to Study Human-Automation Interaction and Workload

Introduction - Paradigm shift from one operator supervising a single vehicle, to an operator supervising multiple highly automated vehicles (One-to-Many) - One-to-Many application - Search and Rescue - Foraging - Military ops - Etc. - Calibrated trust in automation enables human operators to effectively manage highly automated vehicles - Past studies show that trust mediates relationship between reliability and dependence (Chancey et al., 2017; Chancey et al., 2015) - Future studies needed to further understand relationship

trust↗
DOE OSTI2021

Case Study of High-Throughput Drug Screening and Remote Data Collection for SARS-CoV-2 Main Protease by Using Serial Femtosecond X-ray Crystallography

Since early 2020, COVID-19 has grown to affect the lives of billions globally. A worldwide investigation has been ongoing for characterizing the virus and also for finding an effective drug and developing vaccines. As time has been of the essence, a crucial part of this research has been drug repurposing; therefore, confirmation of in silico drug screening studies have been carried out for this purpose. Here we demonstrated the possibility of screening a variety of drugs efficiently by leveraging a high data collection rate of 120 images/second with the new low-noise, high dynamic range ePix10k2M Pixel Array Detector installed at the Macromolecular Femtosecond Crystallography (MFX) instrument at the Linac Coherent Light Source (LCLS). The X-ray Free-Electron Laser (XFEL) is used for remote high-throughput data collection for drug repurposing of the main protease (Mpro) of SARS-CoV-2 at ambient temperature with mitigated X-ray radiation damage. We obtained multiple structures soaked with nine drug candidate molecules in two crystal forms. Although our drug binding attempts failed, we successfully established a high-throughput Serial Femtosecond X-ray crystallographic (SFX) data collection protocol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Tunable White Light for Elders (TWLITE): A Protocol Demonstrating Feasibility and Acceptability for Deployment, Remote Data Collection, and Analysis of a Home-Based Lighting Intervention in Older Adults

Sleep disturbances are common in older adults and may contribute to disease progression in certain populations (e.g., Alzheimer’s disease). Light therapy is a simple and cost-effective intervention to improve sleep. Primary barriers to light therapy are: (1) poor acceptability of the use of devices, and (2) inflexibility of current devices to deliver beyond a fixed light spectrum and throughout the entirety of the day. However, dynamic, tunable lighting integrated into the native home lighting system can potentially overcome these limitations. Herein, we describe our protocol to implement a whole-home tunable lighting system installed throughout the homes of healthy older adults already enrolled in an existing study with embedded home assessment platforms (Oregon Center for Aging & Technology—ORCATECH). Within ORCATECH, continuous data on room location, activity, sleep, and general health parameters are collected at a minute-to-minute resolution over years of participation. This single-arm longitudinal protocol collected participants’ light usage in addition to ORCATECH outcome measures over a several month period before and after light installation. The protocol was implemented with four subjects living in three ORCATECH homes. Technical/usability challenges and feasibility/acceptability outcomes were explored. The successful implementation of our protocol supports the feasibility of implementing and integrating tunable whole-home lighting systems into an automated home-based assessment platform for continuous data collection of outcome variables, including long-term sleep measures. Challenges and iterative approaches are discussed. This protocol will inform the implementation of future clinical intervention trials using light therapy in patients at risk for developing Alzheimer’s disease and related conditions.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Using Remotely Piloted Aircraft and Onboard Processing to Optimize and Expand Data Collection

Remotely piloted aircraft (RPA) have the potential to revolutionize local to regional data collection for geophysicists as platform and payload size decrease while aircraft capabilities increase. In particular, data from RPAs combine high-resolution imagery available from low flight elevations with comprehensive areal coverage, unattainable from ground investigations and difficult to acquire from manned aircraft due to budgetary and logistical costs. Low flight elevations are particularly important for detecting signals that decay exponentially with distance, such as electromagnetic fields. Onboard data processing coupled with high-bandwidth telemetry open up opportunities for real-time and near real-time data processing, producing more efficient flight plans through the use of payload-directed flight, machine learning and autonomous systems. Such applications not only strive to enhance data collection, but also enable novel sensing modalities and temporal resolution. NASA’s Airborne Science Program has been refining the capabilities and applications of RPA in support of satellite calibration and data product validation for several decades. In this paper, we describe current platforms, payloads, and onboard data systems available to the research community. Case studies include Fluid Lensing for littoral zone 3D mapping, structure from motion for terrestrial 3D multispectral imaging, and airborne magnetometry on medium and small RPAs.

Fladeland, Matthew M.↗
NASA NTRS2021

Bhutan Agriculture: Developing a Crop Mask for Rice and Creating a Data Collection Protocol Utilizing Remotely Sensed Data in Bhutan

Rice cultivation in Bhutan has been increasingly threatened by deteriorating soil health and outbreaks of diseases and pests associated with the global change in climate patterns. Field surveys, which the national government of Bhutan has relied on to monitor remote agricultural lands, are becoming increasingly overwhelmed by growing threats to agricultural health. To address these concerns, NASA DEVELOP partnered with the Department of Agriculture of Bhutan, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER) and worked to increase the government of Bhutan’s agricultural monitoring capacity. Utilizing Earth observations including Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Shuttle Radar Topography Mission (SRTM), and Planet imagery, the DEVELOP team worked with NASA SERVIR and created a sampling protocol to identify rice plantations and supplement field surveys for more efficient agriculture monitoring. The analysis focused on districts Paro, Punakha, Samtse, Sarpang, Trongsa, Zhemgang, Wangdue Phodrang, and Samdrup Jongkhar in the year 2020 during the period of transplantation (June) to harvesting of rice (November). The team provided the partners with a sampling protocol for integrating NASA Earth observations into their crop monitoring methods, as well as a crop mask for rice identification and to aid crop management. The crop mask for rice was developed using the Random Forest (RF) classifier for the eight districts of Bhutan. Visually, the random forest model has proved to be more accurate and precise than the classification and Regression Tree model. Statistically, the Random Forest model was 91.8% accurate in identifying rice in Bhutan.

Yeshey Seldon↗
NASA NTRS1982

Analysis of remote sensing data collected for detection and mapping of oil spills: Reduction and analysis of multi-sensor airborne data of the NASA Wallops oil spill exercise of November 1978

Airborne, remotely sensed data of the NASA Wallops controlled oil spill were corrected, reduced and analysed. Sensor performance comparisons were made by registering data sets from different sensors, which were near-coincident in time and location. Multispectral scanner images were, in turn, overlayed with profiles of correlation between airborne and laboratory-acquired fluorosensor spectra of oil; oil-thickness contours derived (by NASA) from a scanning fluorosensor and also from a two-channel scanning microwave radiometer; and synthetic aperture radar X-HH images. Microwave scatterometer data were correlated with dual-channel (UV and TIR) line scanner images of the oil slick.

Source record↗
NASA NTRS2012

Comparisons of a Constrained Least Squares Model Versus Human-in-the-Loop for Spectral Unmixing to Determine Material Type of GEO Debris

Spectral reflectance data through the visible regime was collected at Las Campanas Observatory in Chile using an imaging spectrograph on one of the twin 6.5-m Magellan telescopes. The data were obtained on 1-2 May 2012 on the 'Landon Clay' telescope with the LDSS3 (Low Dispersion Survey Spectrograph 3). Five pieces of Geosynchronous Orbit (GEO) or near-GEO debris were identified and observed with an exposure time of 30 seconds on average. In addition, laboratory spectral reflectance data was collected using an Analytical Spectral Device (ASD) field spectrometer at California Polytechnic State University in San Luis Obispo on several typical common spacecraft materials including solar cells, circuit boards, various Kapton materials used for multi-layer insulation, and various paints. The remotely collected data and the laboratory-acquired data were then incorporated in a newly developed model that uses a constrained least squares method to unmix the spectrum in specific material components. The results of this model are compared to the previous method of a human-in-the-loop (considered here the traditional method) that identifies possible material components by varying the materials and percentages until a spectral match is obtained. The traditional model was found to match the remotely collected spectral data after it had been divided by the continuum to remove the space weathering effects, or a "reddening" of the materials. The constrained least-squares model also used the de-reddened spectra as inputs and the results were consistent with those obtained through the traditional method. For comparison, a first-order examination of including reddening effects into the constrained least-squares model will be explored and comparisons to the remotely collected data will be examined. The identification of each object's suspected material component will be discussed herein.

Rapp, Jason↗
NASA NTRS2013

Comparisons of a Constrained Least Squares Model versus Human-in-the-Loop for Spectral Unmixing to Determine Material Type of GEO Debris

Spectral reflectance data through the visible regime was collected at Las Campanas Observatory in Chile using an imaging spectrograph on one of the twin 6.5-m Magellan telescopes. The data were obtained on 1-2 May 2012 on the 'Landon Clay' telescope with the LDSS3 (Low Dispersion Survey Spectrograph 3). Five pieces of Geosynchronous Orbit (GEO) or near-GEO debris were identified and observed with an exposure time of 30 seconds on average. In addition, laboratory spectral reflectance data was collected using an Analytical Spectral Device (ASD) field spectrometer at California Polytechnic State University (Cal Poly) in San Luis Obispo on several typical common spacecraft materials including solar cells, circuit boards, various Kapton materials used for multi-layer insulation, and various paints. The remotely collected data and the laboratory-acquired data were then incorporated in a newly developed model that uses a constrained least squares method to unmix the spectrum in specific material components. The results of this model are compared to the previous method of a human-in-the-loop (considered here the traditional method) that identifies possible material components by varying the materials and percentages until a spectral match is obtained. The traditional model was found to match the remotely collected spectral data after it had been divided by the continuum to remove the space weathering effects, or a reddening of the materials. The constrained least-squares model also used the de-reddened spectra as inputs and the results were consistent with those obtained through the traditional method. For comparison, a first-order examination of including reddening effects into the constrained least-squares model will be explored and comparisons to the remotely collected data will be examined. The identification of each object s suspected material component will be discussed herein.

Abercromby, Kira J.↗
NASA NTRS1982

Analysis of high altitude remotely sensed data collected in the Nantucket Shoals experiment 4-15 May, 1981

High altitude ocean color scanner ratios of band 2 (456 to 476 nanometers) to band 4 (539 to 559 nanometers) and band 1 (418 to 438 nanometers) to band 3 (498 to 518 nanometers) had high correlation coefficient values (-0.928 and 0.891 respectively) with seven boat sampled chlorophyll a measurements. The range of chlorophyll a concentrations was small (1.7-2.58 mg/cu m.). Each ratio was used to calculate chlorophyll a values for the center pixel of each scan line on flight lines 5 and 6. The two ratios produced dissimilar chlorophyll a trends. Due to the high noise level in the scanner data, no reliable synoptic chlorophyll a map could be generated with either ratio algorithm.

Ohlhorst, C. W.↗
NASA NTRS1981

Real-time test of MOCS algorithm during Superflux 1980

A remote sensing experiment was conducted in which success depended upon the real-time use of an algorithm, generated from MOCS (multichannel ocean color sensor) data onboard the NASA P-3 aircraft, to direct the NOAA ship Kelez to oceanic stations where vitally needed sea truth could be collected. Remote data sets collected on two consecutive days of the mission were consistent with the sea truth for low concentrations of chlorophyll a. Two oceanic regions of special interest were located. The algorithm and the collected data are described.

Grew, G. W.↗
NASA NTRS1971

Field Data Collection: an Essential Element in Remote Sensing Applications

Field data collected in support of remote sensing projects are generally used for the following purposes: (1) calibration of remote sensing systems, (2) evaluation of experimental applications of remote sensing imagery on small test sites, and (3) designing and evaluating operational regional resource studies and inventories which are conducted using the remote sensing imagery obtained. Field data may be used to help develop a technique for a particular application, or to aid in the application of that technique to a resource evaluation or inventory problem for a large area. Scientists at the Forestry Remote Sensing Laboratory have utilized field data for both purposes. How meaningful field data has been collected in each case is discussed.

Pettinger, L. R.↗
NASA NTRS1976

Stratospheric measurement requirements and satellite-borne remote sensing capabilities

The capabilities of specific NASA remote sensing systems to provide appropriate measurements of stratospheric parameters for potential user needs were assessed. This was used to evaluate the capabilities of the remote sensing systems to perform global monitoring of the stratosphere. The following conclusions were reached: (1) The performance of current remote stratospheric sensors, in some cases, compares quite well with identified measurement requirements. Their ability to measure other species has not been demonstrated. (2) None of the current, in-situ methods have the capability to satisfy the requirements for global monitoring and the temporal constraints derived from the users needs portion of the study. (3) Existing, non-remote techniques will continue to play an important role in stratospheric investigations for both corroboration of remotely collected data and in the evolutionary development of future remote sensors.

Carmichael, J. J.↗
NASA NTRS1991

Apollo 14 Mission to Fra Mauro

The 1971 Apollo 14 Mission to Fra Mauro, a lunar highland area, is highlighted in this video. The mission's primary goal was the collection of lunar rocks and soil samples and lunar exploration. The soil and rock sampling was for the geochronological determination of the Moon's evolution and its comparison with that of Earth. A remote data collection station was assembled on the Moon and left for continuous data collection and surface monitoring experiments. The Apollo 14 astronauts were Alan B. Shepard, Edgar D. Mitchell, and Stuart A. Rossa. Astronauts Shepard and Mitchell landed on the Moon (February 5, 1971) and performed the sampling, the EVA, and deployment of the lunar experiments. There is film-footage of the lunar surface, of the command module's approach to both the Moon and the Earth, Moon and Earth spacecraft launching and landing, in-orbit command- and lunar-module docking, and of Mission Control.

Beasley, Brian D.↗
NASA NTRS2017

United Kingdom Infrared Telescope's Spectrograph Observations of Human-Made Space Objects

Presented here are the results of the United Kingdom Infrared Telescope (UKIRT) spectral observations of human-made space objects taken from 2014 to 2015. The data collected using the UIST infrared spectrograph cover the wavelength range 0.7-2.5 micrometers. Overall, data were collected on 18 different orbiting objects at or near the geosynchronous (GEO) regime. Thirteen of the objects are spacecraft, one is a rocket body, and four are cataloged as debris pieces. The remotely collected data are compared to the laboratory-collected reflectance data on typical spacecraft materials; thereby general materials are identified but not specific types. These results highlight the usefulness of observations in the infrared by focusing on features from hydrocarbons and silicon. The spacecraft show distinct features due to the presence of solar panels. Signature variations between rocket bodies, due to the presence of various metals and paints on their surfaces, show a clear distinction from those objects with solar panels, demonstrating that one can distinguish most spacecraft from rocket bodies through infrared spectrum analysis. Finally, the debris pieces tend to show featureless, dark spectra. These results show that the laboratory data in its current state give excellent indications as to the nature of the surface materials on the objects. Further telescopic data collection and model updates to include more materials, noise, surface roughness, and material degradation are necessary to make better assessments of orbital object material types. A comparison conducted between objects observed previously with the NASA Infrared Telescope Facility (IRTF) shows similar materials and trends from the two telescopes and from the two distinct data sets. However, based on the current state of the model, infrared spectroscopic data are adequate to classify objects in GEO as spacecraft, rocket bodies, or debris.

Buckalew, Brent↗
NASA NTRS2017

United Kingdom Infrared Telescope's Spectrograph Observations of Human-Made Space Objects

Presented here are the results of the United Kingdom Infrared Telescope (UKIRT) spectral observations of human-made space objects taken from 2014 to 2015. The data collected using the UKIRT 1-5 micron Imager Spectrometer (UIST) cover the wavelength range 0.7-2.5 micrometers. Overall, data were collected on 18 different orbiting objects at or near geosynchronous orbit (GEO). Two of the objects are controlled spacecraft, twelve are non-controlled spacecraft, one is a rocket body, and three are cataloged as debris. The remotely collected data are compared to the laboratory-collected reflectance data on typical spacecraft materials; thereby general materials are identified but not specific types. These results highlight the usefulness of observations in the infrared by focusing on features from hydrocarbons and silicon. The spacecraft, both the controlled and non-controlled, show distinct features due to the presence of solar panels whereas the rocket bodies do not. Signature variations between rocket bodies, due to the presence of various metals and paints on their surfaces, show a clear distinction from those objects with solar panels, demonstrating that one can distinguish most spacecraft from rocket bodies through infrared spectrum analysis. Finally, the debris pieces tend to show featureless, dark spectra. These results show that the laboratory data in its current state give well-correlated indications as to the nature of the surface materials on the objects. Further telescopic data collection and model updates to include noise, surface roughness, and material degradation are necessary to make better assessments of orbital object material types. A comparison conducted between objects observed previously with the NASA Infrared Telescope Facility (IRTF) shows similar materials and trends from the two telescopes and different times. However, based on the current state of the model, infrared spectroscopic data are adequate to classify objects in GEO as spacecraft, rocket bodies, or debris.

Buckalew, Brent↗
NASA NTRS2015

Infrared Telescope Facility's Spectrograph Observations of Human-Made Space Objects

Presented here are the results of the Infrared Telescope Facility (IRTF) spectral observations of human-made space objects taken from 2006 to 2008. The data collected using the SpeX infrared spectrograph cover the wavelength range 0.7-2.5 micrometers. Overall, data were collected on 20 different orbiting objects at or near the geosynchronous (GEO) regime. Four of the objects were controlled spacecraft, seven were non-controlled spacecraft, five were rocket bodies, and the final four were cataloged as debris pieces. The remotely collected data are compared to the laboratory-collected reflectance data on typical spacecraft materials, thereby general materials are identified but not specific types. These results highlight the usefulness of observations in the infrared by focusing on features from hydrocarbons, silicon, and thermal emission. The spacecraft, both the controlled and non-controlled, show distinct features due to the presence of solar panels, whereas the rocket bodies do not. Signature variations between rocket bodies, due to the presence of various metals and paints on their surfaces, show a clear distinction from those objects with solar panels, demonstrating that one can distinguish most spacecraft from rocket bodies through infrared spectrum analysis. Finally, the debris pieces tend to show featureless, dark spectra. These results show that the laboratory data in its current state give excellent indications as to the nature of the surface materials on the objects. Further telescopic data collection and model updates to include noise, surface roughness, and material degradation are necessary to make better assessments of orbital object material types. However, based on the current state of the comparison between the observations and the laboratory data, infrared spectroscopic data are adequate to classify objects in GEO as spacecraft, rocket bodies, or debris.

Abercromby, K.↗