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306 results for “MMS”
8 years of dayside Magnetospheric Multiscale (MMS) unsupervised clustering plasma regions classifications
<p>These files contain the 1-minute resolution dataset (“labeled_sunside_data.csv”) and 15 minute or longer region list (“<region_name>_region_list.csv”) for Toy-Edens et al.'s Classifying 8 years of MMS Dayside Plasma Regions via Unsupervised Machine Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, probe name (mms1, mms2, mms3, mms4), features that go into clustering and post-cleansing methods, spacecraft positions (in GSE, GSM, and magnetic latitude/local time), raw and cleansed clustering labels, and transition name. The 15+ minute region lists contain the name of the plasma region type, the probe name (mms1, mms2, mms3, mms4), and the start and stop epoch of >= 15 minute epoch where the probe is solidly within that region. NOTE: for the 15+ minute region lists we are only looking for changes in plasma regions, this means that missing data may artificially inflate the duration of the epoch, we suggest looking at the full 1-minute resolution dataset to confirm the region timing.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.'s Classifying 8 years of MMS Dayside Plasma Regions via Unsupervised Machine Learning (DOI:10.1029/2024JA032431).</p> <p>This work was funded by grant 2225463 from the NSF GEM program.</p> <p> </p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data.csv description</strong></p> <table> <tbody> <tr> <td> <p><strong>Column Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Epoch</p> </td> <td> <p>Epoch in datetime</p> </td> </tr> <tr> <td> <p> probe</p> </td> <td> <p>MMS probe name</p> </td> </tr> <tr> <td> <p> ratio_max_width</p> </td> <td> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr> <td> <p> ratio_high_low</p> </td> <td> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr> <td> <p> norm_Btot</p> </td> <td> <p>Magnitude of the total magnetic field normalized to 50nT. See paper for more information</p> </td> </tr> <tr> <td> <p> small_energy_mean</p> </td> <td> <p>The denominator in ratio_high_low</p> </td> </tr> <tr> <td> <p> large_energy_mean</p> </td> <td> <p>The numerator in ratio_high_low</p> </td> </tr> <tr> <td> <p> temp_total</p> </td> <td> <p>Total temperature from the DIS moments. See paper for more information</p> </td> </tr> <tr> <td> <p> r_gse_x</p> </td> <td> <p>x position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gse_y</p> </td> <td> <p>y position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gse_z</p> </td> <td> <p>z position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gsm_x</p> </td> <td> <p>x position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> r_gsm_y</p> </td> <td> <p>y position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> r_gsm_z</p> </td> <td> <p>z position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> mlat</p> </td> <td> <p>magnetic latitude of spacecraft</p> </td> </tr> <tr> <td> <p> mlt</p> </td> <td> <p>magnetic local time of spacecraft</p> </td> </tr> <tr> <td> <p> raw_named_label</p> </td> <td> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind, ion foreshock)</p> </td> </tr> <tr> <td> <p> modified_named_label</p> </td> <td> <p>Cleansed cluster assigned plasma region label (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr> <td> <p> transition_name</p> </td> <td> <p>Transition names (e.g. quasi-perpendicular bow shock, magnetopause). See paper for more information</p> </td> </tr> </tbody> </table> <p> </p> <p><strong><region_name>_region_list.csv description</strong></p> <table> <tbody> <tr> <td> <p><strong>Column Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>start</p> </td> <td> <p>Starting Epoch in datetime</p> </td> </tr> <tr> <td> <p>stop</p> </td> <td> <p>Stopping Epoch in datetime</p> </td> </tr> <tr> <td> <p>probe</p> </td> <td> <p>MMS probe name</p> </td> </tr> <tr> <td> <p>region</p> </td> <td> <p>Cleansed cluster name associated with 1-minute resolution “modified_named_label”</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Magnetosheath Jets MMS (5/2015 - 6/2019)
<p>This dataset contains the jets and their classes that are used and analyzed in the paper: <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JA027754">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JA027754</a> </p> <p>The list has been generated using MMS1 satellite with data from 5/2015 to 6/2019.</p> <p>More information can be found in the manuscript and its supplementary material. </p> <p> </p> <p> </p>
A database of MMS bow shock crossings compiled using machine learning
<p>We use a machine learning approach to automatically identify shock crossings from the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of 2797 crossings including various spacecraft related and shock related parameters for each event. Furthermore, for each event we provide an overview plot containing key parameters of the shock crossing.</p> <p>A Technical report detailing the content of the database can be found at the DOI: http://dx.doi.org/10.1029/2022JA030454</p>
Text-fig. 2—The articular surface of the frontal for the prefrontal in anterior view. A, Right frontal of Albertosaurus cf. A. lancensis, LACM 23845. B, Left frontal of Tyrannosaurus sp., MMS 51-2004. The diagonal lines indicate broken surfaces. Abbreviations: ec, endocranial cavity; po, articular surface of frontal for postorbital; prf, articular surface of frontal for prefrontal. MMS 51-2004 includes portions of the laterosphenoid and prootic in addition to the frontal. Bars represent 1 cm. in An albertosaur from the Hell Creek formation of Montana
Text-fig. 2—The articular surface of the frontal for the prefrontal in anterior view. A, Right frontal of Albertosaurus cf. A. lancensis, LACM 23845. B, Left frontal of Tyrannosaurus sp., MMS 51-2004. The diagonal lines indicate broken surfaces. Abbreviations: ec, endocranial cavity; po, articular surface of frontal for postorbital; prf, articular surface of frontal for prefrontal. MMS 51-2004 includes portions of the laterosphenoid and prootic in addition to the frontal. Bars represent 1 cm.
Curren density from MMS FGM data of 2015-2016 using curlometer method
<p>Valid date range:</p> <p>2015/09/06-2015/09/15<br> 2015/09/24-2015/09/29<br> 2015/10/09-2015/10/13<br> 2015/10/22-2015/11/17<br> 2015/11/26-2015/12/15<br> 2015/12/17-2015/12/18<br> 2015/12/20-2016/01/12<br> 2016/01/20-2016/02/17<br> 2016/02/26-2016/03/22<br> 2016/04/02-2016/04/19<br> 2016/04/29-2016/05/03<br> 2016/05/14-2016/05/17<br> 2016/05/25-2016/06/14<br> 2016/06/22-2016/07/26<br> 2016/08/03-2016/09/30<br> 2016/10/01-2016/10/05<br> 2016/10/08-2016/10/19<br> 2016/10/23-2016/11/15<br> 2016/11/19-2016/12/13<br> 2016/12/17-2016/12/27</p> <p>Contains the following data (time resolution 1 min):</p> <p>Epoch</p> <p>Position</p> <p>Current density (from MMS FGM data using curlometer method)</p> <p>Magnetic field</p> <p>Interplanetary magnetic field</p> <p>Solar wind pressure</p> <p>AE index</p> <p>SYM_H index</p> <p>Footpoint on the equatorial plane</p> <p>Tetrahedral configure factors</p> <p>DivB/curB</p>
Datasets of Man-in-the-middle Attacks Targeting Modbus TCP/IP and MMS protocols in the Smart Grid
<p>The sustainable development of smart grids requires the massive deployment of renewable energy, in a highly distributed manner, introducing new challenges for the system operation. Therefore, the integration of information and communication technologies in sites with Distributed Energy Resources (DERs) is needed to monitor and control the DERs operation. In this scheme, a local controller is installed at each DER site to interact with the centralized applications at the grid level and the power equipment at the site level. This local controller uses client–server protocols (e.g., Modbus TCP/IP and IEC 61850 Manufacturing Message Specification (MMS)) to communicate with different power equipment in the Private Area Network (PAN) of the site. Such protocols often lack information confidentiality and integrity mechanisms. As a result, the smart grids become vulnerable to cyber-attacks. </p> <p>This repository contains datasets created to evaluate the detection and classification of man-in-the-middle attacks, operating in eavesdropping mode, targeting MMS and Modbus TCP/IP protocols in the PAN of the smart grid. Five Flow-based features were used to create these datasets, as shown in Table 1, in addition to the ARP poisoning indicator feature:</p> <table> <caption>Table 1</caption> <tbody> <tr> <td>Feature</td> <td>Description</td> </tr> <tr> <td>IRTT</td> <td>Time for establishing one connection</td> </tr> <tr> <td>TTOC</td> <td>Time for receiving all responses in one connection</td> </tr> <tr> <td>MITR </td> <td>Minimum time between requests in one connection</td> </tr> <tr> <td>MATR </td> <td>Maximum time between requests in one connection</td> </tr> <tr> <td>NROC </td> <td>Number of requests in one connection</td> </tr> </tbody> </table> <p>**NOTE** If you use this dataset in your research/publication please cite us using the following:<br> Mohamed Faisal Elrawy, Lenos Hadjidemetriou, Christos Laoudias, Maria K. Michael,<br> Detecting and classifying man-in-the-middle attacks in the private area network of smart grids,<br> Sustainable Energy, Grids and Networks,2023,pp.1-13, https://doi.org/10.1016/j.segan.2023.101167</p>
Simulation data for MMS June 17 2017 event
<p>The data from a VPIC simulation modeling the MMS observation event on June 17 2017 of magnetotail magnetic reconnection.</p>
ICAD 2024 MMS Binaural Audification
<p>ICAD 2024 Auralization of Magnetic Multiscale Satellite Data: Toward Integrated Audification in Space Science</p> <p>Authors: Kristina Collins, Robert L. Alexander, Jaye Verniero, Robert M. Candey</p> <p>Video Production: Robert L. Alexander, Kristina Collins </p> <p>MMS Visualization: NASA's Scientific</p> <p>Visualization Studio Visualizer: Tom Bridgman (Global Science and Technology, Inc.) </p> <p>Scientist: Tai Phan (University of California at Berkeley)</p> <p>Producer: Joy Ng (USRA)</p> <p>Writer: Mara Johnson-Groh (Wyle Information Systems)</p>
PIC simulation results of an MMS southward IMF KH wave event
<p>Simulation data for Runs-A to D shown in the paper entitled "Multi-scale evolution of Kelvin-Helmholtz waves at the Earth's magnetopause during southward IMF periods" are uploaded. </p> <p>The simulations are performed using the VPIC code (githab.com/lanl/vpic).</p> <p>Values are normalized by B=2B_0, N=N_0 (magnetosheath component), the electron inertial length based on N_0 (d_e) and the speed of light c=2VAe=40VA.</p> <p>The ".gda" data are 2x smoothed, unformatted data written by fortran as "write(*) f".</p> <p>The ".dat" data are for the plots of time evolution curves shown in the paper.</p>
Demo of Spatial Audification in OpenSpace: MMS Mission
<p><strong>This is an audio demo; listen with headphones.</strong> The audio begins around the 0:55 mark.</p> <p>In <a href="https://doi.org/10.5281/zenodo.11194309" target="_blank" rel="noopener">Collins et al 2024</a>, we demonstrated a spatial audification of data from NASA's Magnetospheric Multiscale (MMS) mission produced with open-source tools in Python. In that demo, however, the sound sources for each satellite are placed in a static and representative position. Here, we use <a href="https://www.openspaceproject.com/">OpenSpace</a> to associate each audio stream with its respective spacecraft, so that the audification may be experienced with spatial fidelity on a flexible timescale.</p> <p>This proof-of-concept uses the <a href="https://opensoundcontrol.stanford.edu/spec-1_0.html#introduction">Open Sound Control protocol</a> to send positional data of the sound sources from OpenSpace to <a href="https://doc.sccode.org/">SuperCollider</a>, a method also used in <a href="https://icad2024.icad.org/wp-content/uploads/2024/06/ICAD_2024_paper_6.pdf">Elmquist et al 2024</a>. </p>
Simulation data of electrostatic wave dispersion relation measurement by MMS
<p>We simulate electrostatic wave packets with an ion-acoustic like dispersion relation traveling at different directions in the probe coordinate system. We then set up a model spacecraft and measure the dispersion relation of the simulated wave packets using a method based on spin-plane interferometry. </p>
Antimicrobial MMs - Flow Cytometry
<p>Flow cytometry data subset. Acquired using a Sony SA3800 spectral analyzer (Sony Biotechnology, CA, USA).</p>
Dataset of "A Metabolites Merging Strategy (MMS): Harmonization to enable studies intercomparison"
<p>Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.</p>
Database of MMS busty bulk flows including their ionospheric footpoints
<p>The new version includes</p> <ul> <li>The field-line tracing boundary was corrected</li> </ul>
Multiple Micronutrient Supplementation (MMS) Evaluation Among Bangladeshi Pregnant Women
ClinicalTrials.gov study NCT05108454. IPD Sharing: YES. Countries: 1. Publications: 1.
Supporting Information for "Terrestrial bow shock parameters from MMS measurements: dependence on upstream and downstream time ranges"
<p>Summary plots and parameter tables for each of the 51 shocks analyzed in the statistical study "Terrestrial bow shock parameters from MMS measurements: dependence on upstream and downstream time ranges."</p>
ROS-Specific Huntingtin Interactions: Mass spec analysis of MMS-treated mouse striatal cells
<p>Mass spec identification of DNA damage-specific huntingtin protein-protein interactions. </p>
Bow shock ripples and their modulation of whistler wave packets: MMS observations
<p>Dataset for the paper "Bow shock ripples and their modulation of whistler wave packets: MMS observations".</p> <p>The data are for the Figures 1 and 2 in the manuscript.</p> <p>These files can be processed by Matlab with the help of the IRFU-matlab package, which is available at https://github.com/irfu/irfu-matlab.</p> <p> </p>
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
Maternal Adherence to MMS in Nepal
ClinicalTrials.gov study NCT06327646. IPD Sharing: NO. Countries: 1. Publications: 1.
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