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PHOENIX MARS ROBOTIC ARM CAMERA 5 NORMAL OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:52:37.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains normal data from the Robotic Arm Camera (RAC).
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India Annual Winter Cropped Area, 2001-2016
data.nasa.gov | Last Updated 2022-01-17T05:29:43.000ZThe India Annual Winter Cropped Area, 2001 - 2016 consists of annual winter cropped areas for most of India (except the Northeastern states) from 2000-2001 to 2015-2016. This data set utilizes the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI; spatial resolution: 250m) for the winter growing season (October-March). The methodology uses an automated algorithm identifying the EVI peak in each pixel for each year and linearly scales the EVI value between 0% and 100% cropped area within that particular pixel. Maps were then resampled to 1 km and were validated using high-resolution QuickBird, RapidEye, SkySat, and WorldView-2 images spanning 2008 to 2016 across 11 different agricultural regions of India. The spatial resolution of the data set is 1 km, resampled from 250m. The data are distributed as GeoTIFF and NetCDF files and are in WGS 84 projection.
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PHOENIX MARS ROBOTIC ARM CAMERA 5 XYZ OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:09:16.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains xyz data from the Robotic Arm Camera (RAC).
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GPM Ground Validation SEA FLUX ICE POP V1
data.nasa.gov | Last Updated 2022-06-07T06:12:15.000ZThe GPM Ground Validation SEA FLUX ICE POP dataset includes estimates of ocean surface latent and sensible heat fluxes, 10m wind speed, 10m air temperature, 10m air humidity, and skin sea surface temperature in support of the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP) field campaign in South Korea. The two major objectives of ICE-POP were to study severe winter weather events in regions of complex terrain and improve the short-term forecasting of such events. These data contributed to the Global Precipitation Measurement mission Ground Validation (GPM GV) campaign efforts to improve satellite estimates of orographic winter precipitation. This data file is available in netCDF-4 format from September 1, 2017 through April 30, 2018.
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Metrics for Evaluating Performance of Prognostic Techniques
data.nasa.gov | Last Updated 2020-01-29T03:23:28.000ZPrognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenancehave been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.
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PHOENIX MARS ROBOTIC ARM CAMERA 4 LINEARIZED OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:23:42.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains linearized data from the Robotic Arm Camera (RAC).
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PHOENIX MARS ROBOTIC ARM CAMERA 5 DISPARITY OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:39:06.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains disparity data from the Robotic Arm Camera (RAC).
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PHOENIX MARS ROBOTIC ARM CAMERA 5 ROUGHNESS OPS V1.0
data.nasa.gov | Last Updated 2023-01-26T20:30:16.000ZThe Robotic Arm Camera (RAC) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This RAC Imaging Operations RDR data set contains roughness data from the Robotic Arm Camera (RAC).
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Metrics for Offline Evaluation of Prognostic Performance
data.nasa.gov | Last Updated 2020-01-29T01:57:42.000ZPrognostic performance evaluation has gained significant attention in the past few years.*Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end- user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments.
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OWLETS-1 Ozonesonde Data
data.nasa.gov | Last Updated 2022-07-18T13:04:47.000ZOWLETS1_Sondes_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) ozone data collected via synchronous ozonesonde launches at the NASA Langley Research Center ground site and Chesapeake Bay Bridge Tunnel site during the OWLETS field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete. Coastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA’s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 – August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 – July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA’s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.