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Land/Ocean Biogeochemical Observatory (LOBO) water quality monitoring system timeseries

Measurements made near Dartmouth, Nova Scotia in 2009.

ACE Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) Plasma Moments, Preliminary Values, Key Parameter (K0), 5 min Data

The 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. These are SWEPAM Browse Data. These Data have not validated by the Experimenters and should not be used except for preliminary Examination prior to detailed Studies. SWEPAM - Solar Wind Electron Proton Alpha Monitor References: https://izw1.caltech.edu/ACE/.

XMM-Newton Optical Monitor Serendipitous UV Source Survey Catalog, v6.1

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. 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. For each entry, positional and photometric data (count rate, magnitude and flux) and quality flags for each measurement are provided. The description of the previous release of the catalog can be found in Page M.J. et al. (2012, MNRAS, 426, 903). U, B, V, UVW2, UVM2 and UVW1 refer to the filter bandpasses defined in the Source Properties: Filter Set section of the MSSL documentation for this catalog: <a href="http://www.mssl.ucl.ac.uk/www_astro/XMM-OM-SUSS/SourcePropertiesFilters.shtml">http://www.mssl.ucl.ac.uk/www_astro/XMM-OM-SUSS/SourcePropertiesFilters.shtml</a>. There is a second, related table which gives a summary of the observations from which the XMM-SUSS6.1 sources listed in this table have been detected and measured. That summary table is available at the HEASARC as the <a href="/W3Browse/xmm-newton/xmmomsuob.html">XMMOMSUOB table</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 .

restrictedus-pdMar 2025View details →

MSL MARS ROVER ENVIRONMENTAL MONITORING STATION 2 EDR V1.0

Raw, unprocessed scientific and housekeeping engineering data taken from the Rover Environmental Monitoring Station (REMS) aboard the Mars Science Laboratory.

ACE Electron Proton Alpha Monitor (EPAM) Daily-Averaged Flux Data

This descriptor identifies an ensemble of 26 data sets of daily-averaged, background-corrected energetic particle fluxes. Various data sets have fluxes that are: spin-averaged or spin-sectored; if sectored then from one of four telescope apertures (LEMS30, LEFS60, LEMS120, LEFS150); of ~63-4430 keV protons or ~40-360 keV electrons or 100-6500 keV/n Z>1 ions where any one of these energy ranges is partitioned into 10-25 energy bins; in the spacecraft rest frame or solar wind rest frame. Each of the 26 data sets is individually described in the VHO/VEPO interface at https://vho.gsfc.nasa.gov.

ACE Electron Proton Alpha Monitor (EPAM) 1-Hour Level 2 Data

ACE Electron Proton Alpha Monitor (EPAM) Level 2 intensities at 1-hour 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. Daily and 27-day averages are also available via the ASC paths given below.

Active Cavity Radiometer Irradiance Monitor (ACRIM) II Total Solar Irradiance (TSI) aboard UARS in Native format

ACRIMII_TSI_UARS_NAT data are Active Cavity Radiometer Irradiance Monitor II (ACRIM II) Total Solar Irradiance (TSI) aboard the Upper Atmosphere Research Satellite (UARS) Data in Native (NAT) format. The ACRIMII_TSI_UARS_NAT data product consists of the Level 2 total solar irradiance in the form of daily means gathered by the ACRIM II instrument on the UARS satellite. The daily means are constructed from the shutter cycle results for each day. This data set is considered Version 2.

Microbial monitoring in the ISS-Kibo

Continuous monitoring of bacterial community structure in the ISS-Kibo. This data contains numerous sequence reads of 16S rRNA gene fragments.

ACE Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) Plasma Moments, Level 2 (H2), 1 h Data

This Data Product contains Measurements from the ACE Solar Wind Electron Proton Alpha Monitor (SWEPAM) Instrument. The Quality of the ACE Level 2 Data are such that it is suitable for serious Scientific Study. However, to avoid Confusion and Misunderstanding, it is recommended that Users consult with the appropriate ACE Team Members before publishing Work derived from the Data. The ACE Team has worked hard to ensure that the Level 2 Data are free from Errors, but the Team cannot accept Responsibility for erroneous Data, or for Misunderstandings about how the Data may be used. This is especially true if the appropriate ACE Team Members are not consulted before Publication. At the very least, Preprints should be forwarded to the ACE Team before Publication. SWEPAM References: https://izw1.caltech.edu/ACE/

SOHO Charge, Element, and Isotope Analysis System (CELIAS) Proton Monitor (PM) 5-minute Solar Wind Data in CDF Format

The data include 5-minute proton speed, density, most probable thermal speed, and arrival direction, as well as predicted alpha-particle speed. Also included are the SOHO spacecraft predicted positon in GSE coordinates, Heliocentric Range, Heliographic Latitude and Longitude, and Earth Carrington Rotation Number. This CDF dataset was converted from the original ASCII dataset at the SOHO Archive https://soho.nascom.nasa.gov/data/archive.html. Description of the CELIAS-PM instrument and scientific scope can be found on the CELIAS instrument home page http://www2.physik.uni-kiel.de/SOHO-CELIAS/ and on the SOHO mission home page https://sohowww.nascom.nasa.gov/. CELIAS Instrument Publication: Hovestadt, D., Hilchenbach, M., Bürgi, A. et al. CELIAS - Charge, Element and Isotope Analysis System for SOHO, Sol Phys 162, 441–481 (1995), https://doi.org/10.1007/BF00733436.

restrictedus-pdMar 2025View details →

STARDUST C/E/L DUST FLUX MONITOR INSTRUMENT-2-EDR-V1.0

Data collected by the Dust Flux Monitor Instrument (DFMI) of the Stardust satellite, taken during the early cruise phase of the mission. These data were determined by peer review to be of no scientific use.

restrictedus-pdApr 2025View details →

MSL MARS ROVER ENV MONITORING STATION 3 TELRDR V1.0

Data taken by the sensors of the Rover Environmental Monitoring Station (REMS) aboard the Mars Science Laboratory, in electrical and thermal units.

ACE Electron, Proton, and Alpha Monitor (EPAM) Solar Wind Plasma Energetic Particle Fluxes, Key Parameter (K0), 5 min 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/.

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

This descriptor identifies an ensemble of 8 data sets of EPAM count rates averaged over 1-day 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.

Distributed Monitoring of the R2 Statistic for Linear Regression

The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more dependent target variables. This problem becomes challenging for large scale data in a distributed computing environment when only a subset of instances is available at individual nodes and the local data changes frequently. Data centralization and periodic model recomputation can add high overhead to tasks like anomaly detection in such dynamic settings. Therefore, the goal is to develop techniques for monitoring and updating the model over the union of all nodes' data in a communication-efficient fashion. Correctness guarantees on such techniques are also often highly desirable, especially in safety-critical application scenarios. In this paper we develop DReMo --- a distributed algorithm with very low resource overhead, for monitoring the quality of a regression model in terms of its coefficient of determination (R2 statistic). When the nodes collectively determine that R2 has dropped below a fixed threshold, the linear regression model is recomputed via a network-wide convergecast and the updated model is broadcast back to all nodes. We show empirically, using both synthetic and real data, that our proposed method is highly communication-efficient and scalable, and also provide theoretical guarantees on correctness.

An Integrated Approach to Battery Health Monitoring using Bayesian Regression, Classification and State Estimation

The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation via Particle Filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of- life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electro- chemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.

SOHO Charge, Element, and Isotope Analysis System (CELIAS) Solar Extreme-Ultra-Violet Monitor (SEM) Daily Data in CDF Format

The data include daily average raw count rates in Channels 1 - 3 and First Order (26 - 34 nm, average of Ch1 and Ch3) and Central Order (0.1 - 50 nm, Ch2) photon fluxes, as well as SOHO Heliocentric location and distance. This CDF dataset was converted from the original ASCII dataset at the SOHO Archive https://soho.nascom.nasa.gov/data/archive.html. The original ASCII dataset Version 4 Science Product Release Notes are available at https://lasp.colorado.edu/eve/data_access/eve_data/lasp_soho_sem_data/long/LASP_SOHO_SEM_release_notes_v4.pdf. Description of the CELIAS-SEM instrument and scientific scope can befound on the CELIAS home page http://www2.physik.uni-kiel.de/SOHO-CELIAS/ and on the SOHO mission home page https://sohowww.nascom.nasa.gov/. CELIAS Instrument Publication: Hovestadt, D., Hilchenbach, M., Bürgi, A. et al. CELIAS - Charge, Element and Isotope Analysis System for SOHO, Sol Phys 162, 441–481 (1995), https://doi.org/10.1007/BF00733436.

Trajectory Clustering and an Application to Airspace Monitoring

This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data driven methods. Standard procedures are used by air traffic controllers (ATC) to guide aircraft, ensure the safety of the airspace, and to maximize the runway occupancy. Even though standard procedures are used by ATC, the control of the aircraft remains with the pilots, leading to a large variability in the flight patterns observed. Two methods to identify typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to the typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure a safe separation between aircraft.

SOHO Charge, Element, and Isotope Analysis System (CELIAS) Solar Extreme-Ultra-Violet Monitor (SEM) 15-second Data in CDF Format

The data include 15-second average raw count rates in Channels 1 - 3 and First Order (26 - 34 nm, average of Ch1 and Ch3) and Central Order (0.1 - 50 nm, Ch2) photon fluxes, as well as SOHO Heliocentric location and distance. This CDF dataset was converted from the original ASCII dataset at the SOHO Archive https://soho.nascom.nasa.gov/data/archive.html. The original ASCII dataset Version 4 science product release notes are available at https://lasp.colorado.edu/eve/data_access/eve_data/lasp_soho_sem_data/long/LASP_SOHO_SEM_release_notes_v4.pdf. Description of the CELIAS-SEM instrument and scientific scope can be found on the CELIAS home page http://www2.physik.uni-kiel.de/SOHO-CELIAS/ and on the SOHO home page https://sohowww.nascom.nasa.gov/. CELIAS Instrument Publication: Hovestadt, D., Hilchenbach, M., Bürgi, A. et al. CELIAS - Charge, Element and Isotope Analysis System for SOHO, Sol Phys 162, 441–481 (1995), https://doi.org/10.1007/BF00733436.

A Framework for Systematic Benchmarking of Monitoring and Diagnostic Systems

In this paper, we present an architecture and a formal framework to be used for systematic benchmarking of monitoring and diagnostic systems and for producing comparable performance assessments of different diagnostic technologies. The framework defines a number of standardized specifications, which include a fault catalog, a library of modular test scenarios, and a common protocol for gathering and processing diagnostic data. At the center of the framework are 13 benchmarking metric definitions. The calculation of metrics is illustrated on a probabilistic model-based diagnosis algorithm utilizing Bayesian reasoning techniques. The diagnosed system is a real-world electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The proposed benchmarking approach shows how to generate realistic diagnostic data sets for large-scale, complex engineering systems, and how the generated experimental data can be used to enable “apples to apples” assessments of the effectiveness of different diagnostic and monitoring algorithms.