AndreaBozzo/ceres-open-data-index huggingface dataset:
TROPICS06 L2A Unified Resolution Brightness Temperatures V1.0

The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.

Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.

This dataset is from the TROPICS06 satellite, as the Validated Stage-1 version of the Level 2A geolocated brightness temperature with the water vapor sounding channels (Ch. 9 to 12) converted from their native G-band resolution to the temperature sounding channel (F-band) native resolution (i.e., all measurements at the same unified larger resolution). This product is used in the Atmospheric Vertical Temperature Profile (AVTP) retrievals to gain the benefit of averaging the G-band channels (i.e., noise reduction) while maintain the F-band (AVTP) spatial resolution. The conversion uses the Backus-Gilbert technique. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.

Tags: earth-science, microwave, spectral-engineering
Organization: National Aeronautics and Space Administration
SENTINEL-1A_DUAL_POL_GRD_MEDIUM_RES

Sentinel-1A Dual-pol ground projected medium resolution images

Tags: agriculture, biosphere, coastal-processes, cryosphere, earth-science, erosion-sedimentation, forest-science, frozen-ground, geomorphic-landforms-processes, glaciers-ice-sheets, land-surface, land-use-land-cover, landscape, ocean-waves, ocean-winds, oceans, sea-ice, snow-ice, solid-earth, surface-water, tectonics, terrestrial-ecosystems, terrestrial-hydrosphere, topography
Organization: National Aeronautics and Space Administration
GPM DPR Ka-band Received Power L1B 1.5 hours 5 km V07 (GPM_PRL1KA) at GES DISC

Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07.

This product contains the calibrated received power from the Ka-band of the Dual-frequency Precipitation Radar (DPR) aboard the core satellite of the Global Precipitation Measurement (GPM) mission.

One of the reasons for adding the Ka-band frequency (35.5 GHz) channel to the DPR is to provide information on the drop-size distribution that can be obtained from non-Rayleigh scattering effects at the higher frequency. Another reason for the new Ka-band channel is to provide more accurate estimates of the phase-transition height in precipitating systems. This information is very important not only in increasing the accuracy of rain rate estimation by the DPR itself, but in improving rain estimation by passive microwave radiometers.

The Ka-band Radar has a more complex scanning geometry, defined by two modes: Matched (MS) and High-sensitivity (HS). In the first type of scan (MS), the Ka Radar beams are matched to the central 25 beams of the Ku Radar, providing a swath of 120 km (within the 245km swath of the Ku Radar). In the second type of scan (HS), the Ka is operated in the high-sensitivity mode to detect light rain and snow.  In this case, its beams are interlaced within the matched scan pattern, resulting in 49 cross-track beams which, however, still cover the 120-km swath.

Tags: atmosphere, atmospheric-water-vapor, earth-science, microwave, precipitation, spectral-engineering
Organization: National Aeronautics and Space Administration
A knowledge-based system approach for sensor fault modeling, detection and mitigation

Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reli- able than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.

Tags: ames, dashlink, nasa
Organization: National Aeronautics and Space Administration
Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications

Sensor faults continue to be a major hurdle for sys- tems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally dif- ficult to seed particular sensor faults. Therefore, research is un- derway to better understand the different fault modes seen in sen- sors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper il- lustrates the work with data collected from an electromechanical actuator in an aerospace setting, equipped with temperature, vi- bration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network-based classi- fier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and dis- ambiguation efficacy with respect to severity of fault conditions.

Tags: ames, dashlink, nasa
Organization: National Aeronautics and Space Administration
Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Abstract—Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The
comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in
regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology
studies.

Tags: ames, dashlink, nasa
Organization: National Aeronautics and Space Administration
Pilot Study of a Plug Load Management System: Preparing for Sustainability Base

NASA Ames Research Center’s Sustainability Base
is a new 50,000 sq. ft. high-performance office building targeting
a LEED Platinum rating. Plug loads are expected to account for a
significant portion of overall energy consumption because building
design choices resulted in greatly reduced energy demand
from Heating, Ventilation, and Air Conditioning (HVAC) and
lighting systems, which are typically major contributors to energy
consumption in traditional buildings. This paper reports on a
pilot study where data from a variety of plug loads were collected
in a reference office building to understand usage patterns,
to make a preliminary assessment as to the effectiveness of
controlling (i.e., turning off and on) selected loads, and to evaluate
the utility of the plug load management system chosen for the
study. Findings indicate that choosing energy efficient equipment,
ensuring that power saving functionality is operating effectively,
promoting beneficial occupant energy behavior, and employing
plug load controls to turn off equipment when not in use can
lead to significant energy savings. These recommendations will
be applied to Sustainability Base and further studies of plug
load management systems and techniques to reduce plug energy
consumption will be pursued.

Tags: ames, dashlink, nasa
Organization: National Aeronautics and Space Administration
High Mountain Asia 12 km Modeled Estimates of Aerosol Transport, Chemistry, and Deposition Reanalysis, 2003-2019 V001

This data set contains a 12 km resolution, simulated reanalysis of aerosol transport, chemistry, and deposition over the High Mountain Asia (HMA) region for 1 January 2003 through 31 August 2019.

Two-dimensional surface data are provided at one hour intervals. Three-dimensional atmospheric data are provided at three-hour intervals for 35 sigma levels extending from the surface to 50 hPa.

Also known as the Model for Atmospheric Transport and Chemistry in Asia (MATCHA), the data comprise a wide range of variables intended to help assess the impacts of aerosols on the cryosphere in the HMA region, including: concentrations of black/brown carbon and other light absorbing particles (LAPs), broken out by source region; longwave/shortwave heating rates due to LAPs; wet/dry deposition of LAPs; precipitation and hydrological data; and meteorological state variables.

The simulation was generated using a fully coupled, regional chemistry-climate model (WRF-Chem-CLM-SNICAR), constrained by aerosol optical depth (AOD) and carbon monoxide (CO) satellite observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Measurements Of Pollution In The Troposphere (MOPITT) instruments, respectively.

Tags: aerosols, atmosphere, atmospheric-radiation, earth-science, national-geospatial-data-asset, ngda
Organization: National Aeronautics and Space Administration
Turbulence Models: Data from Other Experiments: Round Synthetic Jets for Separation Control on 2-D Ramp

Exp: Round Synthetic Jets for Separation Control on 2-D Ramp This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.

Tags: control, experiment, jets, models, ramp, separation, synthetic, turbulence, validation
Organization: National Aeronautics and Space Administration
OMPS-NPP L2 NP Ozone (O3) Vertical Profile swath orbital NRT

The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite in Near Real Time. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The L2 NP Ozone data are written using the Hierarchical Data Format Version 5 or HDF5.

Tags: nasa
Organization: National Aeronautics and Space Administration
Effects of Lightning Injection on Power-MOSFETs

Lightning induced damage is one of the major concerns in aircraft health monitoring. Such short-duration high voltages can cause significant damage to electronic devices. This paper presents a study on the effects of lightning injection on power metal-oxide semiconductor field effect transistors (MOSFETs). This approach consisted of pin- injecting lightning waveforms into the gate, drain and/or source of MOSFET devices while they were in the OFF-state. Analysis of the characteristic curves of the devices showed that for certain injection modes the devices can accumulate considerable damage rendering them inoperable. Early results demonstrate that a power MOSFET, even in its off-state, can incur considerable damage due to lightning pin injection, leading to significant deviation in its behavior and performance, and to possibly early device failures.

Tags: ames, dashlink, nasa
Organization: National Aeronautics and Space Administration
Vegetation Drought Response Index (VegDRI)

The National Drought Mitigation Center produces VegDRI in collaboration with the US Geological Survey's (USGS) Center for Earth Resources Observation and Science (EROS), and the High Plains Regional Climate Center (HPRCC), with sponsorship from the US Department of Agriculture's (USDA) Risk Management Agency (RMA). Main researchers working on VegDRI are Dr. Brian Wardlow and Dr. Tsegaye Tadesse at the NDMC, and Jesslyn Brown with the USGS, and Dr. Yingxin Gu with ASRC Research and Technology Solutions, contractor for the USGS at EROS.

VegDRI maps are produced every two weeks and provide regional to sub-county scale information about drought's effects on vegetation. In 2006, VegDRI covered seven states in the Northern Great Plains (CO, KS, MT, NE, ND, SD, and WY). It expanded across eight more states in 2007 to cover the rest of the Great Plains (NM, OK, MO, and TX) and parts of the Upper Midwest (IA, IL, MN, and WI). VegDRI expanded to include the western U.S. in 2008 (WA, ID, OR, UT, CA, AZ, NV). In May 2009 VegDRI began depicting the eastern states as well, covering the entire conterminous 48-state area.

Tags: atmosphere, atmospheric-phenomena, earth-science, erosion-sedimentation, geomorphic-landforms-processes, land-surface, land-use-land-cover, landscape, soils, surface-radiative-properties, topography
Organization: National Aeronautics and Space Administration
stableGP

The code in the stableGP package implements Gaussian process calculations using efficient and numerically stable algorithms. Description of the algorithms is in the paper "Stable and Efficient Gaussian Process Calculations" by L. Foster, A. Waagen, N. Aijaz, M. Hurley, A. Luis, J. Rinsky, C. Satyavolu, M. Way, P. Gazis, and A. Srivastava accepted in the Journal of Machine Learning Research, February, 2009.

The easiest way to get started using the code is to download and unzip the zip file, start Matlab (7.0 or higher), move to the appropriate folder and type either "demo_bootstrap" or "demo_history" to run one of the demonstration files.

Code in the zip file is based on the code used in the text Gaussian Processes or Machine Learning by Rasmussen and Williams (www.gaussianprocess.org/gpml/).  So that the demonstrations are self contained and do not require that the user download additional code the zip file contains a few functions that are copied directly from Rasmussen and Williams' code.

Tags: ames, dashlink, nasa
Organization: National Aeronautics and Space Administration
IceBridge Accumulation Radar L1B Geolocated Radar Echo Strength Profiles V002

This data set contains radar echograms taken over Greenland and Antarctica using the Center for Remote Sensing of Ice Sheets (CReSIS) Accumulation Radar instrument. The data were collected as part of Operation IceBridge funded campaigns.

Tags: cryosphere, earth-science, glaciers-ice-sheets, oceans, radar, sea-ice, snow-ice, spectral-engineering, terrestrial-hydrosphere
Organization: National Aeronautics and Space Administration
A Model-based Avionic Prognostic Reasoner (MAPR)

The Model-based Avionic Prognostic Reasoner (MAPR) presented in this paper is an
innovative solution for non-intrusively monitoring the state of health (SoH) and predicting the remaining useful life (RUL) of electronic and electromechanical assets by accessing and processing data obtained from a standard avionics data bus. To support Integrated Vehicle Health Monitoring (IVHM) initiatives, the solution being described here has been designed to be as non-intrusive as possible. An innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems was developed to mitigate potentially catastrophic faults. EMA systems are used in a wide variety of aircraft applications to control critical components such as control surfaces, landing gear and thrust vector control. Failure in any one of these systems can compromise passenger safety, as well as mission success. A MIL-STD-1553 bus interface and monitor were designed to extract environmental (e.g., altitude, air speed, air density) and operational (i.e., response of system to a commanded change) data of a representative EMA system and to determine whether an anomaly is detected, and the corresponding severity. The MIL-STD-1553 bus was chosen as the test bed to develop this approach, due to its large installed base and availability of compatible development tools. Advanced and unique reasoning methodologies are applied to the extracted data sets to provide anomaly detection and fault classification on various fault modes and eventually yield SoH and RUL. In this paper we describe a data monitoring unit that will, in real time, identify, isolate, and characterize faults and establish their severity so that major performance problems can be alleviated. When built, this system will consist of a laptop with a Peripheral Component Interconnect (PCI) card slot that can accept multiple interfaces to the MAPR software package. The MAPR package will be designed to be adaptable for a large number of different platforms, for portability and for maximum input data type flexibility. This paper describes a ground-based prototype of the technology to show the efficacy of the method.

Tags: ames, dashlink, nasa
Organization: National Aeronautics and Space Administration
ALOS_PALSAR_LEVEL2.2

ALOS PALSAR Level 2.2

Tags: agriculture, biosphere, coastal-processes, cryosphere, earth-science, erosion-sedimentation, forest-science, frozen-ground, geomorphic-landforms-processes, glaciers-ice-sheets, land-surface, land-use-land-cover, landscape, ocean-waves, ocean-winds, oceans, sea-ice, snow-ice, solid-earth, surface-water, tectonics, terrestrial-ecosystems, terrestrial-hydrosphere, topography
Organization: National Aeronautics and Space Administration
Entropy-based Probabilistic Fatigue Damage Prognosis and Algorithmic Performance Comparison

In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison. It is shown that the proposed maximum relative entropy methodology has narrower confidence bounds of the remaining life prediction than classical Bayesian updating algorithm.

Tags: ames, dashlink, nasa
Organization: National Aeronautics and Space Administration
A Diagnostic Approach for Electro-Mechanical Actuators in Aerospace Systems

Electro-mechanical actuators (EMA) are finding increasing use in aerospace applications, especially with the trend towards all all-electric aircraft and spacecraft designs. However, electro-mechanical actuators still lack the knowledge base accumulated for other fielded actuator types, particularly with regard to fault detection and characterization. This paper presents a thorough analysis of some of the critical failure modes documented for EMAs and describes experiments conducted on detecting and isolating a subset of them. The list of failures has been prepared through an extensive Failure Modes and Criticality Analysis (FMECA) reference, literature review, and accessible industry experience. Methods for data acquisition and validation of algorithms on EMA test stands are described. A variety of condition indicators were developed that enabled detection, identification, and isolation among the various fault modes. A diagnostic algorithm based on an artificial neural network is shown to operate successfully using these condition indicators and furthermore, robustness of these diagnostic routines to sensor faults is demonstrated by showing their ability to distinguish between them and component failures. The paper concludes with a roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators.

Tags: ames, dashlink, nasa
Organization: National Aeronautics and Space Administration