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Aviation System Monitoring and Modeling Project

Air transportation, one of the most important modes of transportation, is also one of the safest. Nevertheless, the public demands that safety levels continuously improve and that the absolute number of aviation accidents continue to decline, even as air-traffic levels increase. On February 12, 1997, after the tragedy of TWA 800, President William J. Clinton declared, “We will achieve a national goal of reducing the fatal aircraft accident rate by 80% within 10 years.” In response to this presidential declaration, the administrator of NASA announced that NASA would undertake a new program in aviation safety in support of this objective. NASA quickly formed the Aviation Safety Investment Strategy Team (ASIST) in collaboration with the Federal Aviation Administration (FAA) and the National Transportation Safety Board (NTSB), which organized a series of five workshops to examine the options and recommend an approach for NASA to develop the enabling technologies that address the president's goal. The exceptional and dedicated participation from all sectors of the aviation industry in the development of NASA’s strategy was remarkable.

SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS

SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS VARUN CHANDOLA* AND RANGA RAJU VATSAVAI* Abstract. Biomass monitoring, specifically, detecting changes in the biomass or vegetation of a geographical region, is vital for studying the carbon cycle of the system and has significant implications in the context of understanding climate change and its impacts. Recently, several time series change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) has been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. In our previous work we proposed an efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we apply these solutions to a GP based change detection algorithm. The proposed change detection algorithm requires a memory footprint which is linear in the length of the input time series and runs in time which is quadratic to the length of the input time series. Experimental results show that both serial and parallel implementations of our proposed method achieve significant speedups over the serial implementation. Finally, we demonstrate the effectiveness of the proposed change detection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.

ACE Electron Proton Alpha Monitor (EPAM) Hourly Count Rate Data

This descriptor identifies an ensemble of 8 data sets of EPAM count rates averaged over 1-hour intervals in each of several energy bins. One data set (PG65) has spin-averaged rates while the other 7 have rates in each of 4 or 8 spin sectors. Each of the 7 sectored data sets is specific to a given one of the several EPAM instruments (apertures). Each of the 8 data sets is individually described in the VHO/VEPO interface at https://vho.gsfc.nasa.gov.

ACE Electron Proton Alpha Monitor (EPAM) 12-sec Level 2 Data

ACE EPAM Electron Proton Alpha Monitor (EPAM) Level 2 intensities at 12-sec time resolution. Calibrated science quality data. The data are accessible via ftp in HDF and CDF from the ACE Science Center and CDAWeb, respectively, and in ASCII format from the value-added interfaces at those sites. The "parameter keys" given below are as used by CDAWeb.

BeXRB Monitor Data API

A fully queryable REST API with JSON, XML, and CSV output as well as inline, runable examples. This is a monitor using data from Swift/BAT, MAXI and Fermi/GBM instruments.

Ariel V All-Sky Monitor

The All Sky Monitor was one of six X-ray instruments on the Ariel 5 satellite. The satellite was launched into a low inclination (2.8 degrees), nearly circular orbit (altitude ~520 km) on 15 October 1974. Ariel 5 was actively pointed so that objects of interest could be observed by the four instruments aligned along its spin axis. The ASM was mounted 90 degrees from the spin axis; the satellite had a spin period of 6 seconds. The ASM operated from October 18, 1974 to March 10, 1980. The ASM instrument, built by the Lab for High Energy Astrophysics at NASA- Goddard Space Flight Center, provided continuous coverage of the entire sky, except for a 20 degree band straddling the satellite's equator. The ASM was intended to act as an early detection system for transients, and to monitor the variability of bright ( > 0.2 Crab) galactic sources. The instrument consisted of a pair of X-ray pinhole cameras, each covering opposite halves of the sky, with gas-filled imaging proportional counters. Position determination of sources was accomplished through position-sensitive anode wires and satellite rotation. Each camera had a 1-cm2 aperture. Overall telemetry constraints limited the duty cycle for any given source to 1 percent. With the low telemetry rate provided for this instrument (1 bit/s), temporal and spectral information were sacrificed for the sake of all-sky coverage. Hence, spectral information was limited to a single 3 - 6 keV bandpass, and temporal resolution was limited to the satellite orbital period, ~100 minutes. This is a service provided by NASA HEASARC .

Suzaku Wide-Band All-Sky Monitor (WAM) Catalog of Event Lightcurves

This database table contains information of the events detected by the Wide-band All-sky Monitor (WAM) on Suzaku during its ~10 years of operation. Each event has associated data products. The lightcurves are derived from the on-board operating modes that collected the WAM data from each of the sub-detectors. The lightcurves are rates as a function of time in different energy bands, and they can be used with the general FITS tools within HEAsoft or XRONOS. Version 3 of the lightcurves were generated at HEASARC from the version 2 files, and they are now the final versions hosted at both HEASARC and DARTS. This database table reflects version 3 of the data products. Version 2 of the FITS lightcurves and their plots were generated at Saitama University in Japan and a copy hosted at the DARTS archive, located at ISAS (<a href="http://www.darts.isas.ac.jp/pub/suzaku/wam-2.0/">http://www.darts.isas.ac.jp/pub/suzaku/wam-2.0/</a>). This is a service provided by NASA HEASARC .

ACE Electron Proton Alpha Monitor (EPAM) 5-min Count Rate Data

This descriptor identifies an ensemble of 8 data sets of EPAM count rates averaged over 5-min intervals in each of several energy bins. One data set (PG65) has spin-averaged rates while the other 7 have rates in each of 4 or 8 spin sectors. Each of the 7 sectored data sets is specific to a given one of the several EPAM instruments (apertures). Each of the 8 data sets is individually described in the VHO/VEPO interface at https://vho.gsfc.nasa.gov.

Integrated Diagnostic/Prognostic Experimental Setup for Capacitor Degradation and Health Monitoring

This paper proposes the experiments and setups for studying diagnosis and prognosis of electrolytic capacitors in DC-DC power converters. Electrolytic capacitors and power MOSFET’s have higher failure rates than other components in DC-DC converter systems. Currently, our work focuses on experimental analysis and modeling electrolytic capacitors degradation and its effects on the output of DC-DC converter systems. The output degradation is typically measured by the increase in Equivalent series resistance and decrease in capacitance leading to output ripple currents.Typically, the ripple current effects dominate, and they can have adverse effects on downstream components. A model based approach to studying degradation phenomena enables us to combine the physics based modeling of the DC-DC converter with physics of failure models of capacitor degradation, and predict using stochastic simulation methods how system performance deteriorates with time. Degradation experiments were conducted where electrolytic capacitors were subjected to electrical and thermal stress to accelerate the aging of the system. This more systematic analysis may provide a more general and accurate method for computing the remaining useful life (RUL) of the component and the converter system.

ACE Electron, Proton, and Alpha Monitor (EPAM) Solar Wind Plasma Energetic Particle Fluxes, Key Parameter (K1), 1 h Data

ACE Browse Data are designed for monitoring large-scale Particle and Field Behavior and for selecting interesting Time Periods. The Data are automatically generated from the Spacecraft Data Stream using simple Algorithms provided by the Instrument Teams. It is not routinely checked for Accuracy and is subject to Revision. Use this Data at your own Risk, and consult with the appropriate Instrument Teams about citing it. EPAM Browse Data are not validated by the Experimenters and should not be used except for Preliminary Examination prior to detailed Studies. ACE Electron, Proton, and Alpha Monitor (EPAM) References: http://www.srl.caltech.edu/ACE/.

DIAGNOSTIC/PROGNOSTIC EXPERIMENTS FOR CAPACITOR DEGRADATION AND HEALTH MONITORING IN DC-DC CONVERTERS

Studying and analyzing the ageing mechanisms of electronic components avionics in systems such as the GPS and INAV are of critical importance. In DC-DC power converter systems electrolytic capacitors and MOSFET’s have higher failure rates among the components. Degradation in the capacitors under varying operating conditions leads to high ripples output voltages and currents affecting downstream components leading to cascading faults. For example, in avionics systems where the power supply drives a GPS unit, ripple currents can cause glitches in the GPS position and velocity output, and this may cause errors in the Inertial Navigation (INAV) system, causing the aircraft to fly off course The work in this paper proposes a detail experimental and systematic study on analyzing the degradation phenomenon is electrolytic capacitors under high stress operating conditions. The output degradation is typically measured by an increase in ESR (Equivalent Series Resistance) and decrease in the capacitance value. We present the details of our accelerated ageing methodology along with analysis and comparison of the results.

Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and esti- mation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochem- ical processes in the form of equivalent electric circuit parame- ters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal frame- work. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.1 Index Terms—Battery health, Bayesian learning, particle filter, prognostics, relevance vector machine, remaining useful life.

XMM-Newton Optical Monitor SUSS Catalog, v6.1: Observation IDs

The 2023 release of the XMM OM Serendipitous Ultraviolet Source Survey (XMM-SUSS6.1) Catalog, a catalog of optical/UV sources detected by the Optical Monitor (OM) on-board the European Space Agency's (ESA's) XMM-Newton observatory, spans the period of observations from 2000 to November 2022. The data processing was performed at the European Space Astronomy Centre (ESAC, Madrid, Spain) using the XMM Science Analysis Software system (SAS) versions 18 and 19. In addition to covering a larger observation period, this sixth release reflects a change in philosophy with regard to the origin of the incorporated data. In previous releases, the data were generated via a bespoke processing of the OM Observation Data Files (ODFs) while in this new release, the catalog has been guided by the XMM user community and the authors have sought to harmonize the contents of the catalog with those of the OM data in the XMM-Newton Science Archive (XSA), which derive from the standard XMM-Newton pipeline processing system. While the bespoke processing and pipeline systems are fundamentally very similar, they are not identical and the differences lead to some differences in the output. The number of observations (OBSIDs) included in the catalog is 12,057. This table (XMMOMSUOB) contains the list of these observations and their characteristics, giving for each observation the filters used, the exposure time for each filter, the number of sources detected in each filter and the detection magnitude limit for each filter. The total number of entries in this release is 9,920,390. They correspond to 6,659,554 unique sources, of which 1,225,117 have multiple entries in the source table, corresponding to different observations. This list of sources is available at the HEASARC as the <a href="/W3Browse/xmm-newton/xmmomsuss.html">XMMOMSUSS table</a>. The documentation on the first release of this catalog is available at <a href="http://www.mssl.ucl.ac.uk/www_astro/XMM-OM-SUSS/Summary.shtml">http://www.mssl.ucl.ac.uk/www_astro/XMM-OM-SUSS/Summary.shtml</a>. This HEASARC database table contains the sixth release of the XMM-OM SUSS catalog, XMM-SUSS6.1, released by ESA in October 2023, obtained from the XMM-Newton Science Archive (<a href="http://xmm.esac.esa.int/xsa">http://xmm.esac.esa.int/xsa</a>), and ingested into the HEASARC database in October 2023. It is also available at the HEASARC as the gzipped FITS file <a href="https://heasarc.gsfc.nasa.gov/FTP/xmm/data/catalogues/XMM-OM-SUSS6-1.1.fits.gz">https://heasarc.gsfc.nasa.gov/FTP/xmm/data/catalogues/XMM-OM-SUSS6-1.1.fits.gz</a>. This is a service provided by NASA HEASARC .

XTE All-Sky Monitor Long-Term Observed Sources

The XTEASMLONG table was created for the purpose of providing a complete listing of RXTE's All-Sky Monitor (ASM) observations of sources. It is based on information culled from the definitive 1-dwell long-term ASM light curve data products. These data products are available for downloading at <a href="https://heasarc.gsfc.nasa.gov/FTP/xte/data/archive/ASMProducts/definitive_1dwell/lightcurves/">https://heasarc.gsfc.nasa.gov/FTP/xte/data/archive/ASMProducts/definitive_1dwell/lightcurves/</a> The XTEASMLONG table is updated usually every week or two whenever new definitive ASM data products are delivered to the RXTE GOF from MIT where the definitive ASM processing is done. This is a service provided by NASA HEASARC .

ACE Electron Proton Alpha Monitor (EPAM) 12-sec Count Rate Data

This descriptor identifies an ensemble of 8 data sets of EPAM count rates averaged over 12-sec intervals in each of several energy bins. One data set (PG65) has spin-averaged rates while the other 7 have rates in each of 4 or 8 spin sectors. Each of the 7 sectored data sets is specific to a given one of the several EPAM instruments (apertures). Each of the 8 data sets is individually described in the VHO/VEPO interface at https://vho.gsfc.nasa.gov.

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring System data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how Inductive Monitoring System can be used to complement a suite of other Integrated System Health Management tools, providing initial fault detection support for diagnosis and recovery.

Vela 5B All-Sky Monitor Lightcurves

Data for these sources were obtained from the Vela 5B all-sky XC detector. The Vela 5B nuclear test detection satellite was part of a program run jointly by the Advanced Research Projects of the U.S. Department of Defense and the U.S.Atomic Energy Commission, managed by the U.S. Air Force. It was placed in a nearly circular orbit at a geocentric distance of ~118,000 km on 23 May 1969; the orbital period was ~112 hours. The satellite rotated about its spin axis with a ~64-sec period. The X-ray detector was located ~90 degrees from the spin axis, and so covered the celestial sphere twice per satellite orbit. Data were telemetered in 1-sec count accumulations. Vela 5B operated until 19 June 1979, although telemetry tracking was poor after mid-1976. The scintillation X-ray detector (XC) aboard Vela 5B consisted of two 1-mm-thick NaI(Tl) crystals mounted on photomultiplier tubes and covered by a 5-mil-thick beryllium window. Electronic thresholds provided two energy channels, 3-12 keV and 6-12 keV. In front of each crystal was a slat collimator providing a FWHM aperture of ~6.1x6.1 degrees. The effective detector area was ~26 sq-cm. Sensitivity to celestial sources was severely limited by the intrinsic detector background of ~36 cts/sec. The Vela 5B X-ray detector yielded ~40 cts/sec for the Crab, so 1 Vela ct/sec ~25 UFU~4.5E-10 ergs/sq-cm/sec in the 3-12 keV response band. This is a service provided by NASA HEASARC .

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MSL MARS ROVER ENV MONITORING STATION 6 ADR V1.0

Ancillary data used in the processing of the data taken by the sensors of the Rover Environmental Monitoring Station (REMS) aboard the Mars Science Laboratory.

XMM-Newton Optical Monitor Chandra Deep Field-South UV Catalog

The XMM-Newton X-ray observatory has performed repeated observations of the Chandra Deep Field-South (CDFS) in 33 epochs (2001 - 2010) through the XMM-CDFS Deep Survey (Comastri et al. 2011, A&A, 526, L9). During the X-ray observations, the XMM-Newton Optical Monitor (XMM-OM) targeted the central 17 x 17 arcmin2 region of the X-ray field of view, providing simultaneous optical/UV coverage of the CDFS. The resulting set of data can be taken into account to build an XMM-OM catalog of the CDFS, filling the UV spectral coverage between the optical surveys and GALEX observations. This table contains the UV catalog of the XMM-CDFS Deep Survey. Its main purpose is to provide complementary UV average photometric measurements of known optical/UV sources in the CDFS, taking advantage of the unique characteristics of the survey. The data reduction is intended also to improve the standard source detection on individual observations, by cataloguing faint sources through the stacking of their exposure images. The authors reprocessed the XMM-OM data of the survey and stacked the exposures from consecutive observations using the standard SAS tools to process the data obtained during single observations. Average measurements of detections with SAS good quality flags from individual observations and from stacked images have been joined to compile the catalogue. Sources have been validated through the cross-identification within the ESO Imaging Survey (EIS: Arnouts et al. 2001, A&A, 379, 740) and COMBO-17 (Wolf et al. 2004, A&A, 421, 913; 2008, A&A, 492, 933) surveys. Photometric data of 1129 CDFS sources are provided in the main catalog, and optical/UV/X-ray photometric and spectroscopic information from other surveys are also included. The stacking extends the detection limits by ~1 mag in the three UV bands, contributing 30% of the catalogued UV sources. The comparison with the available measurements in similar spectral bands confirms the validity of the XMM-OM calibration. The combined COMBO-17/X-ray classification of the "intermediate" sources (e.g. optically diluted and/or X-ray absorbed AGN) is also discussed in the reference paper. This table was created by the HEASARC in June 2015 based on the union of <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/ApJS/217/4/">CDS Catalog J/ApJS/217/4/</a> files omcdfst7.dat (the 1,129 sources in the main catalog) and omcdfst8.dat (the 44 sources in the supplementary catalog). This is a service provided by NASA HEASARC .