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NEW HORIZONS SDC PLUTO CRUISE RAW V2.0
data.nasa.gov | Last Updated 2023-01-26T20:54:05.000ZThis data set contains Raw data taken by the New Horizons Student Dust Counter instrument during the pluto cruise mission phase. This is VERSION 2.0 of this data set. SDC collected science data intermittently during the hibernation years following the Jupiter encounter, designated as the PLUTOCRUISE phase. There were also Annual Checkouts (ACOs), STIM calibrations, Noise calibrations, and an anomaly in November, 2007. SDC's main science data collection periods were during hibernation. During ACOs, science data are taken intermittently but the user must be careful in analyzing these data since there is usually more activity on the spacecraft during hibernation. STIM and Noise refer to scheduled calibrations and are done with a regular cadence of one per year after the Jupiter encounter; they occurred sporadically in the early years of the mission. Note that some SDC data files have the same stop and start time and a zero exposure time. The reason for this is that the start and stop time for SDC data files are the event times for the first and last events in the files, so for files that contain a single event, these two values are the same. The changes in Version 2.0 were re-running of the ancillary data in the data product, updated geometry from newer SPICE kernels, minor editing of the documentation, catalogs, etc., and resolution of liens from the December, 2014 review, plus those from the May, 2016 review of the Pluto Encounter data sets. New observations added with this version (V2.0) include ongoing cruise observations from August, 2014 through January, 2015.
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TRMM (TMPA-RT) Near Real-Time Precipitation L3 1 day 0.25 degree x 0.25 degree V7 (TRMM_3B42RT_Daily) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:59:46.000ZTMPA (3B42RT_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDE/DAY/06; 10.5067/GPM/IMERGDL/DAY/06). This daily accumulated precipitation product is generated from the Near Real-Time 3-hourly TRMM Multi-Satellite Precipitation Analysis TMPA (3B42RT). It is produced at the NASA GES DISC, as a value added product. Simple summation of valid retrievals in a grid cell is applied for the data day. The result is given in (mm). Although the grid is from 60S to 60N, the high latitudes (beyond 50S/N) near real-time retrievals are considered very unreliable and thus are screened out from the daily accumulations. The beginning and ending time for every daily granule are listed in the file global attributes, and are taken correspondingly from the first and the last 3-hourly granules participating in the aggregation. Thus the time period covered by one daily granule amounts to 24 hours, which can be inspected in the file global attributes. Counts of valid retrievals for the day are provided for every variable, making it possible to compute conditional and unconditional mean precipitation for grid cells where less than 8 retrievals for the day are available. Efforts have been made to make the format of this derived product as similar as possible to the new Global Precipitation Measurement CF-compliant file format. The latency of this derived daily product is about 7 hours after the UTC day is closed. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product. The information provided here on the TRMM mission, and on the original 3-hr 3B42 product, remain relevant for this derived product. Note, however, this product is in netCDF-4 format. The following describes the derivation in more details. The daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 3-hourly source data are in mm/hr, a factor of 3 is applied to the sum. Thus, for every grid cell we have Pdaily = 3 * SUM{Pi * 1[Pi valid]}, i=[1,Nf] Pdaily_cnt = SUM{1[Pi valid]} where: Pdaily - Daily accumulation (mm) Pi - 3-hourly input, in (mm/hr) Nf - Number of 3-hourly files per day, Nf=8 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day. Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value. On occasion, the 3-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the "true" daily total. These events are easily detectable using "counts" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which Pdaily_cnt less than Nf. There are various ways the accumulated daily error could be estimated from the source 3-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 3-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 3 is finally applied: Perr_daily = 3 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] ) where: Perr_daily - Magnitude of the daily accumulated error power, (mm) Ncnt_err - The counts for the error variable Thus computed Perr_daily represents the worst case scenario that assumes the error in the 3-hourly source data, which is given in mm/hr, is accumulating within the 3-hourly period of the source data and then during the day. These values, however, can easily be conveted to root mean square error estimate of the rainfall rate: rms_err = { (Perr_daily/3) ^2 / Ncnt
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NEW HORIZONS SDC JUPITER ENCOUNTER CALIBRATED V4.0
data.nasa.gov | Last Updated 2023-01-26T20:41:44.000ZThis data set contains Calibrated data taken by the New Horizons Student Dust Counter instrument during the Jupiter encounter mission phase. This is VERSION 4.0 of this data set. For the Jupiter encounter mission phase, SDC collected no science data during the Jupiter flyby, as the requisite spacecraft configuration prevented SDC from operating. There were some very sparse data taken from December, 2006 through April, 2007, and some of very short (or zero) duration after the Jupiter flyby from April, 2007 through June, 2007. The changes in Version 4.0 were re-running of the ancillary data in the data product, updated geometry from newer SPICE kernels, minor editing of the documentation, catalogs, etc., and resolution of liens from the December, 2014 review, plus those from the May, 2016 review of the Pluto Encounter data sets. No new observations were added with Version 4.0.
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NEW HORIZONS SDC PLUTO CRUISE CALIBRATED V2.0
data.nasa.gov | Last Updated 2023-01-26T20:25:34.000ZThis data set contains Calibrated data taken by the New Horizons Student Dust Counter instrument during the pluto cruise mission phase. This is VERSION 2.0 of this data set. SDC collected science data intermittently during the hibernation years following the Jupiter encounter, designated as the PLUTOCRUISE phase. There were also Annual Checkouts (ACOs), STIM calibrations, Noise calibrations, and an anomaly in November, 2007. SDC's main science data collection periods were during hibernation. During ACOs, science data are taken intermittently but the user must be careful in analyzing these data since there is usually more activity on the spacecraft during hibernation. STIM and Noise refer to scheduled calibrations and are done with a regular cadence of one per year after the Jupiter encounter; they occurred sporadically in the early years of the mission. Note that some SDC data files have the same stop and start time and a zero exposure time. The reason for this is that the start and stop time for SDC data files are the event times for the first and last events in the files, so for files that contain a single event, these two values are the same. The changes in Version 2.0 were re-running of the ancillary data in the data product, updated geometry from newer SPICE kernels, minor editing of the documentation, catalogs, etc., and resolution of liens from the December, 2014 review, plus those from the May, 2016 review of the Pluto Encounter data sets. New observations added with this version (V2.0) include ongoing cruise observations from August, 2014 through January, 2015.
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NEW HORIZONS SDC PLUTO ENCOUNTER RAW V3.0
data.nasa.gov | Last Updated 2023-01-26T20:43:40.000ZThis data set contains Raw data taken by the New Horizons Student Dust Counter instrument during the Pluto encounter mission phase. This is VERSION 3.0 of this data set. This data set contains SDC observations taken during the the Approach (Jan-Jul, 2015), Encounter, Departure, and Transition mission sub-phases, including flyby observations taken on 14 July, 2015, and departure and calibration data through late October, 2016. This data set completes the Pluto mission phase deliveries for SDC. This is version 3.0 of this data set. Changes since version 2.0 include the final batch of Pluto mission phase data, downlinked between the end of January, 2016 and late in October, 2016, including a Stim calibration in July. Also, updates were made to the documentation and catalog files, primarily to implement suggestions from the V2.0 peer review. A new table of SDC Ram (velocity) ancillary data has been provided, and the SDC on/off and Stim tables have been extended in time to cover the new data.
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Experimental and Analytical Development of a Health Management System for Electro-Mechanical Actuators
data.nasa.gov | Last Updated 2020-01-29T01:49:29.000ZExpanded deployment of Electro-Mechanical Actuators (EMAs) in critical applications has created much interest in EMA Prognostic Health Management (PHM), a key enabling technology of Condition Based Maintenance (CBM). As such, Impact Technologies, LLC is collaborating with the NASA Ames Research Center to perform a number of research efforts in support of NASA’s Integrated Vehicle Health Management (IVHM) initiatives. These efforts have combined experimental test stand development, laboratory seeded fault testing, and physical model-based health monitoring in a comprehensive PHM system development strategy. This paper discusses two closely related EMA research programs being conducted by Impact and NASA Ames. The first of these efforts resulted in the creation of an electro-mechanical actuator test stand for the Prognostics Center of Excellence at the NASA Ames Research Center. The second effort is ongoing and is utilizing physics-based modeling techniques to develop an algorithm and software package toolset for PHM of aircraft EMA systems using a hybrid (virtual sensor) approach.
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CORONA Satellite Photographs from the U.S. Geological Survey
data.nasa.gov | Last Updated 2022-01-17T05:16:00.000ZThe first generation of U.S. photo intelligence satellites collected more than 860,000 images of the Earth’s surface between 1960 and 1972. The classified military satellite systems code-named CORONA, ARGON, and LANYARD acquired photographic images from space and returned the film to Earth for processing and analysis. The images were originally used for reconnaissance and to produce maps for U.S. intelligence agencies. In 1992, an Environmental Task Force evaluated the application of early satellite data for environmental studies. Since the CORONA, ARGON, and LANYARD data were no longer critical to national security and could be of historical value for global change research, the images were declassified by Executive Order 12951 in 1995. The first successful CORONA mission was launched from Vandenberg Air Force Base in 1960. The satellite acquired photographs with a telescopic camera system and loaded the exposed film into recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications. The intelligence community used Keyhole (KH) designators to describe system characteristics and accomplishments. The CORONA systems were designated KH-1, KH-2, KH-3, KH-4, KH-4A, and KH-4B. The ARGON systems used the designator KH-5 and the LANYARD systems used KH-6. Mission numbers were a means for indexing the imagery and associated collateral data. A variety of camera systems were used with the satellites. Early systems (KH-1, KH-2, KH-3, and KH-6) carried a single panoramic camera or a single frame camera (KH-5). The later systems (KH-4, KH-4A, and KH-4B) carried two panoramic cameras with a separation angle of 30° with one camera looking forward and the other looking aft. The original film and technical mission-related documents are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery. Mathematical calculations based on camera operation and satellite path were used to approximate image coordinates. Since the accuracy of the coordinates varies according to the precision of information used for the derivation, users should inspect the preview image to verify that the area of interest is contained in the selected frame. Users should also note that the images have not been georeferenced.
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Optimal Alarm Systems
data.nasa.gov | Last Updated 2020-01-29T03:25:13.000ZAn optimal alarm system is simply an optimal level-crossing predictor that can be designed to elicit the fewest false alarms for a fixed detection probability. It currently use Kalman filtering for dynamic systems to provide a layer of predictive capability for the forecasting of adverse events. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. Due to the fact that the alarm regions for an optimal level-crossing predictor cannot be expressed in closed form, one of our aims has been to investigate approximations for the design of an optimal alarm system. Approximations to this sort of alarm region are required for the most computationally efficient generation of a ROC curve or other similar alarm system design metrics. Algorithms based upon the optimal alarm system concept also require models that appeal to a variety of data mining and machine learning techniques. As such, we have investigated a serial architecture which was used to preprocess a full feature space by using SVR (Support Vector Regression), implicitly reducing it to a univariate signal while retaining salient dynamic characteristics (see AIAA attachment below). This step was required due to current technical constraints, and is performed by using the residual generated by SVR (or potentially any regression algorithm) that has properties which are favorable for use as training data to learn the parameters of a linear dynamical system. Future development will lift these restrictions so as to allow for exposure to a broader class of models such as a switched multi-input/output linear dynamical system in isolation based upon heterogeneous (both discrete and continuous) data, obviating the need for the use of a preprocessing regression algorithm in serial. However, the use of a preprocessing multi-input/output nonlinear regression algorithm in serial with a multi-input/output linear dynamical system will allow for the characterization of underlying static nonlinearities to be investigated as well. We will even investigate the use of non-parametric methods such as Gaussian process regression and particle filtering in isolation to lift the linear and Gaussian assumptions which may be invalid for many applications. Future work will also involve improvement of approximations inherent in use of the optimal alarm system of optimal level-crossing predictor. We will also perform more rigorous testing and validation of the alarm systems discussed by using standard machine learning techniques and consider more complex, yet practically meaningful critical level-crossing events. Finally, a more detailed investigation of model fidelity with respect to available data and metrics has been conducted (see attachment below). As such, future work on modeling will involve the investigation of necessary improvements in initialization techniques and data transformations for a more feasible fit to the assumed model structure. Additionally, we will explore the integration of physics-based and data-driven methods in a Bayesian context, by using a more informative prior.
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Rapid Electrochemical Detection and Identification of Microbiological and Chemical Contaminants for Manned Spaceflight Project
data.nasa.gov | Last Updated 2020-01-29T03:33:53.000Z<p>A great deal of effort has gone into the development of point-of-use methods to meet the challenge of rapid bacterial identification for both environmental monitoring and clinical applications.&nbsp; Unfortunately, most of the methods developed rely on Preliminary Chain Reaction (PCR) and face inherent limitations because of the requirement for enzymatic components and thermal control.&nbsp; Other methods based on surface plasmon resonance, quartz crystal microbalance, and fluorescence has been reported with good detection limits, but, these methods are immunological and cannot provide genetic-level information.&nbsp; Further, they require labeled markers, complicated fluid handling systems, and sensitive optics that drive up cost and complexity and preclude them from outside the laboratory.&nbsp; Recent work by a group at the University of Toronto has focused on developing an electrochemical platform that combines ultrasensitive detection, straightforward sample processing, and inexpensive components to create a cost-effective, user-friendly device for detection and identification of microorganisms.&nbsp; The platform combines an electrical cell lysis chamber, and electrochemical reporter system, and nanostructured microelectrodes (NMEs) to detect specific nucleic acid sequences.&nbsp; The nucleic acid sequences are unique to a given type of microorganism and can be used to identify the microorganisms present in a sample.</p><p>From the perspective of the anticipated prototype device &nbsp;(Lam, et al. 2012. <em>Polymerase Chain Reaction-Free, Sample-to-Answer Bacterial Detection in 30 Minutes with Integrated Cell Lysis</em>. Anal. Chem. <strong>84(1)</strong>: 21-5), detection of microbial contaminants will begin with a lysis chamber designed to release DNA and RNA from microorganisms present in the sample using ultrasonic or electrochemical technology.&nbsp; The DNA and RNA mixture is then passed into an analysis chamber containing an array of nanostructured microelectrodes (NMEs).&nbsp; The surface of the NMEs will be functionalized with probe molecules for DNA or RNA sequences specific to the bacteria being targeted.&nbsp; Binding of the DNA or RNA to the appropriate detection probe on the NME surface in the presence of an electrochemical reporter system will change the electrochemical properties of the NMEs.&nbsp; A potentiostat is used to measure the current at each individual electrode before and after addition of the DNA and RNA mixture.&nbsp; The difference in current before and after addition of the mixture to the NMEs is compared against a pre-determined threshold to check for the presence of target bacteria in the sample.&nbsp; The process for detection of chemical contaminants is very similar.&nbsp; The lysis chamber would be bypassed and the sample would flow directly into the analysis chamber.&nbsp; The NMEs will be functionalized with molecules to selectively bind the desired targets (analytes) and the change in the electrochemical response of each NME can again be used to detect and quantify the contaminants.&nbsp; Depending on the analyte of interest, it may be possible to directly measure analyte binding on the surface of the NMEs without the use of an electrochemical reporter system. The overall project will focus on optimization of the individual aspects of the detection platform in preparation for construction of a prototype for a flight experiment.&nbsp; The scope of the work in this proposal is limited to characterization and optimization of the lysis step/sample preparation, probe selection, and NME structure.&nbsp; Lysis conditions will be optimized by evaluating parameters associated with the oscillation frequency and lysis time for ultrasonic techniques and applied voltage for the electrochemical techniques.&nbsp; Cell viability, as determined by fluorescent detection of DNA or R
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Airborne Electro-Mechanical Actuator Test Stand for Development of Prognostic Health Management Systems
data.nasa.gov | Last Updated 2020-01-29T04:05:01.000ZWith the advent of the next generation of aerospace systems equipped with fly-by-wire controls, electro- mechanical actuators (EMA) are quickly becoming components critical to safety of aerospace vehicles. Being relatively new to the field, however, EMA lack the knowledge base compared to what is accumulated for the more traditional actuator types, especially when it comes to fault detection and prognosis. Scarcity of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests create the need to build high-fidelity system models and design affordable yet realistic experimental setups. The objective of this work is to build an EMA test stand that, unlike current laboratory stands typically weighing in excess of one metric ton, is portable enough to be easily placed aboard a wide variety of aircraft. This stand, named the FLEA (for Flyable Electro- mechanical Actuator test stand), allows testing EMA fault detection and prognosis technologies in flight environment, thus substantially increasing their technology readiness level – all without the expense of dedicated flights, as the stand is designed to function as a non-intrusive secondary payload. No aircraft modifications are required and data can be collected during any available flight opportunity: pilot currency flights, ferry flights, or flights dedicated to other experiments. The stand is currently equipped with a prototype version of NASA Ames developed prognostic health management system with models aimed at detecting and tracking several fault types. At this point the team has completed test flights of the stand on US Air Force C-17 aircraft and US Army UH-60 helicopters and more experiments, both laboratory and airborne, are planned for the coming months.