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625 results for “Anomaly”

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zenodo32/100

Pn velocity anomalies and anisotropy

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2024View details →

zenodo32/100

SST Anomalies

SST anomalies for GEV testing

opencc-by-4.0Dec 2021View details →

zenodo32/100

Mooring observed zonal velocity anomaly at 142E/0 averaged over the depths of 400-1000 m

This dataset contains zonal velocity anomaly observed by the mooring at 142E/0. It is supplementary of the paper "Interdecadal modulation of ENSO-related anomalous Equatorial Intermediate Currents in the western Pacific by the PDO" published by the Geophysical Research Letters. Please let me know if you need any more information.

opencc-by-4.0Feb 2022View details →

zenodo32/100

Research Artifacts - An Anomaly-based Approach for Detecting Modularity Violations on Method Placement

This record contains research artifacts of our paper. The description of each file is as follows. <ul> <li>case_study.xlsx: a spreadsheet that contains a list of our detected cases and results of our manual inspection</li> <li>java-large+.tar.gz: a preprocessed dataset used for training our detection model</li> <li>model_parameters.ckpt.gz: trained parameters and a word dictionary of our neural network model</li> <li>src.tar.gz: source code of our neural network model written in Python and a dataset preprocessor written in Java</li> </ul>

opencc-by-4.0Mar 2022View details →

zenodo32/100

syslrn: Learning What to Monitor for Efficient Anomaly Detection [Dataset]

This repository includes the dataset for the paper: <a href="http://doi.org/10.1145/3517207.3526979">D. Sanvito, G. Siracusano, S. Santhanam, R. Gonzalez, R. Bifulco</a> <a href="http://doi.org/10.1145/3517207.3526979">syslrn: Learning What to Monitor for Efficient Anomaly Detection </a> <a href="http://doi.org/10.1145/3517207.3526979">ACM EuroMLSys 2022</a> The dataset contains two directories at the root level: <ul> <li>raw_dataset</li> <li>processed_dataset</li> </ul> Each folder in the raw_dataset directory contains the raw monitoring data used to generate the graph associated to a single experiment together with additional metadata files. Each folder in the processed_dataset directory contains the graph associated to a single experiment as a set of three CSV files: two for the graph edges (pid_childof_pid_df.csv and pid_speakswith_pid_df.csv) and one for the graph nodes (proc_df.csv). We provide below a code snippet to parse a graph from processed_dataset directory. In both folders the name of each sub-folder is based on the following schema: [SCENARIO]_[W]wl/test_[TEST_ID] where: <ul> <li>[SCENARIO] reports the target component for the failure injection (cinder_failure, neutron_failure, nova_failure). ff  indicates instead a failure-free execution</li> <li>[W] reports the number of concurrent workloads</li> <li>[TEST_ID] reports the ID of the specific failure scenario injected (same ID selected by the OpenStack failure injection framework [1] )</li> </ul> Each experiment includes the following data in the raw_dataset sub-folders: <ul> <li>audit_raw_logs_[TEST_ID]/: raw audit monitoring data</li> <li>bpf_tools_[TEST_ID]/: raw ebpf tools monitoring data</li> <li>instance-[INSTANCE_ID]/: workload-specific metadata files, e.g. stdout/stderr (generated by the OpenStack failure injection framework [1] )</li> <li>logs_workload_[TEST_ID]/: OpenStack application logs</li> <li>perf_tools_[TEST_ID]/: raw perf tools monitoring data</li> <li>audit_filtered_[TEST_ID].log: audit data pre-processed by ausearch (e.g. numerical entities are resolved to symbols)</li> <li>failure_[TEST_ID].info: metadata information about the specific failure scenario (generated by the OpenStack failure injection framework [1] )</li> <li>timestamps_[TEST_ID]: timing information</li> </ul> [1] D. Cotroneo, L. De Simone, P. Liguori, R. Natella, N. Bidokhti - How Bad Can a Bug Get? An Empirical Analysis of Software Failures in the OpenStack Cloud Computing Platform [ACM ESEC/FSE 2019]   Example: parsing a graph from processed_dataset directory <pre><code class="language-python">import pandas as pd import networkx as nx def parse_csv(path): processes_df = pd.read_csv('%sproc_df.csv' % path, index_col=0).reset_index(drop=True) speakswith_edges_df = pd.read_csv('%spid_speakswith_pid_df.csv' % path, index_col=0) speakswith_edges_df['type'] = 'speaksWith' childof_edges_df = pd.read_csv('%spid_childof_pid_df.csv' % path, index_col=0) childof_edges_df['type'] = 'childOf' return processes_df, pd.concat([speakswith_edges_df, childof_edges_df], ignore_index=True) def make_graph(nodes_df, edges_df): G = nx.MultiGraph() for _, node in nodes_df.iterrows(): G.add_node(node.pid, **node) for _, edge in edges_df.iterrows(): G.add_edge(edge.pid1, edge.pid2, type=edge.type) return G PATH = 'processed_dataset/ff_1wl/test_1/' nodes_df, edges_df = parse_csv(PATH) G = make_graph(nodes_df, edges_df) nx.draw_networkx(G, node_size=10, with_labels=False)</code></pre>   If you use this dataset for your research, please cite the following paper: <pre><code>@inproceedings{sanvito2022syslrn, title={syslrn: Learning What to Monitor for Efficient Anomaly Detection}, author={Sanvito, Davide and Siracusano, Giuseppe and Santhanam, Sharan and Gonzalez, Roberto and Bifulco, Roberto}, booktitle={2nd European Workshop on Machine Learning and Systems (EuroMLSys '22)}, year={2022}, address = {Rennes, France}, publisher = {ACM}, month = apr, } </code></pre>

openother-ncMar 2022View details →

zenodo32/100

Processed data: flood generation processes and flood anomalies

This dataset inludes processed data required to reproduce main results presented in the manuscript by Tarasova et al. "Shifts in flood generation processes exacerbate regional flood anomalies in Europe".

opencc-by-4.0Mar 2022View details →

zenodo32/100

Elastic Anomalies across the α-β Phase Transition in Orthopyroxene: Implication for the Metastable Wedge in the Cold Subduction Slab

Here are the expeirmental data

opencc-by-4.0Apr 2022View details →

dryad32/100

Behavioral analysis for market anomalies with heterogeneous agent model

This paper investigates the effect of heterogeneity and bounded rationality on market anomalies in price formation. We establish a heterogeneous agent model under prospect theory, where asset price fluctuation is determined by evolutionary dynamics system embedded in the model. Specifically, our model includes three types of expectation schemes related to different decision-making progresses: the rational scheme, the emotional scheme and the noise scheme. Agents under emotional scheme make decisions based on the cumulative prospect theory framework, which makes psychological factor tractable. By analyzing the distribution of return rate under selected parameters, we show that the model return shares the same pattern as those of SSE 50 Index. Furthermore, we investigate the factors of option implied volatility smile based on dynamic system, and the results suggest that the smile anomaly may be associated with the tail risk of the underlying asset's return distribution, which is effected by market stability and psychological factors.

opencc-zeroDec 2021View details →

dryad32/100

Sea-surface temperature anomalies mediate changes in fish richness and abundance in Western North Atlantic and Gulf of Mexico estuaries

Aim: Anthropogenic-driven warming of marine systems has resulted in a series of biological and physiological responses that are fundamentally altering ecosystem structure. Because estuaries exist at the land-ocean interface, they are particularly vulnerable to the effects of ocean warming as they can undergo rapid biogeochemical and hydrological shifts due to climate and land-use change. We explored how fish diversity structures—turnover, richness and abundance—have changed in the western North Atlantic and Gulf of Mexico estuaries through space and time and the drivers of change. Location: North Atlantic and Northern Gulf of Mexico Taxa: Fish Results: We found that species richness and abundance, turnover have increased in North Atlantic and northern Gulf of Mexico estuaries in the last 3 decades. These changes were mediated largely by sea-surface temperature anomalies, especially in more northern estuaries where warming has been relatively pronounced. There is also an indication that urbanization, perhaps through habitat fragmentation and/or fisheries activities may be contributing to the increase in fish richness in many of these estuaries. Main Conclusion: The increasing trajectory of turnover in many of the estuaries suggests that the fish communities have changed fundamentally from the baselines. A fundamental change in community composition can lead to an irreversible trophic imbalance or alternative stable states among other outcomes. Thus, predicting how shifting community structures might influence food webs, ecosystem stability and human resource use remains a pertinent task.

opencc-zeroMay 2022View details →

zenodo32/100

Dataset used in Can process mining help in anomaly-based intrusion detection?

This is the dataset used in the paper Can process mining help in anomaly-based intrusion detection?

opencc-by-4.0Jun 2022View details →

zenodo32/100

Cell Anomaly Localisation using Structured Uncertainty Prediction Networks

Fluorescent and brightfield datasets for "Cell Anomaly Localisation using Structured Uncertainty Prediction Networks", part of MIDL 2022.

opencc-by-4.0Dec 2021View details →

dryad32/100

Data from: Effects of climate and topography on the diversity anomaly of plants disjunctly distributed in eastern Asia and eastern North America

Aim: Differences in physiography have been proposed to explain the diversity anomaly for vascular plants between environmentally similar regions of eastern Asia (EAS) and eastern North America (ENA). Here, we use plant species within disjunct genera to examine whether differences in topography contribute to the diversity anomaly and whether the richness–environment relationships differ between regions. Disjuncts are used to ensure that the diversity anomaly relates to post-disjunction evolution and diversification rather than regional differences in clade ages or immigration. Location: EAS and ENA. Time period: Current. Major taxa studied: Plant taxa disjunctly distributed in EAS and ENA. Method: We compiled county-level plant distribution data, and calculated species richness and variables of topography and climate within unit grid cells. We compared estimated coefficients of region effects among models, where richness was fitted with or without topography and climate. Topography and climate were also used to separately model within-region spatial diversity patterns using spatial simultaneous autoregressive error models. Results: The coefficients of region effects varied from -0.776 for the model only including region to -0.309 when topography was controlled for, but remained significant. Climate dominated the spatial diversity patterns in ENA. In contrast, the influence of climate (14.2%) on species richness was weaker than that of topography (18.3%) in warm EAS. Relations to elevation and temperature varied between regions, shifting between positive and negative relationships in several cases. Main conclusion: Our results demonstrate that variability in local topography contributes to the strong regional anomaly in plant species richness between EAS and ENA. Nevertheless, the diversity anomaly persists after controlling for local topography and climate. EAS and ENA also exhibit contrasting richness–environment relationships, providing another divergent aspect between the EAS-ENA disjunct floras. Our findings highlight that regional differences in topography or other environmental factors may underlie the diversity anomaly.

opencc-zeroAug 2022View details →

zenodo32/100

Anomalies in Microservice Architecture (train-ticket) based on version configurations

The work contains ten datasets containing monitoring data (logs, Jaeger Traces and Prometheus KPI data). The datasets contain monitoring data from train-ticket, a benchmark system for microservices. The dataset includes a short description with explanations of identified anomalies. The structure of the folder is as follows: <changed-microservice>_<third-party-library>_<version>_<date-when-data-was-collected> Each folder stores the respective data: <ol> <li>Logs: <ul> <li>original log file: LOGS_<change-microservice>_<third-party-library>_<Version>.txt</li> <li>parsed log files required for Loglizer (anomaly detection technique): <ul> <li>LOGS_<change-microservice>_<third-party-library>_<Version>.txt_structured.csv</li> <li>LOGS_<change-microservice>_<third-party-library>_<Version>.txt_templates.csv</li> </ul> </li> </ul> </li> <li>KPI Data: Monitoring_<change-microservice>_<third-party-library>_<Version></li> <li>Traces: Traces_<change-microservice>_<third-party-library>_<Version></li> </ol>

opencc-by-4.0Aug 2022View details →

dryad32/100

The relative influence of sea surface temperature anomalies on the benthic composition of an Indo-Pacific and Caribbean coral reef over the last decade

Rising ocean temperatures are the primary driver of coral reef declines throughout the tropics. Such declines include reductions in coral cover that facilitate the monopolisation of the benthos by other taxa such as macroalgae, resulting in reduced habitat complexity and biodiversity. Long term monitoring projects present rare opportunities to assess how sea surface temperature anomalies (SSTAs) influence changes in the benthic composition of coral reefs across distinct locations. Here, using extensively monitored coral reef sites from Honduras (in the Caribbean Sea), and from the Wakatobi National Park located in the centre of the coral triangle of Indonesia, we assess the impact of global warming on coral reef benthic compositions over the period 2012-2019. Bayesian Generalised Linear Mixed effect Models revealed increases in sponge, and hard coral coverage through time, while rubble coverage decreased at the Indonesia location. Conversely, the effect of sea surface temperature anomalies (SSTA) did not predict any changes in benthic coverage. At the Honduras location, algae and soft coral coverage increased through time, while hard coral and rock coverage were decreasing. The effects of SSTA at the Honduras location included increased rock coverage, but reduced sponge coverage, indicating disparate responses between both systems under SSTAs. However, redundancy analyses showed intra-location site variability explained the majority of variance in benthic composition over the course of the study period. Our findings show that SSTAs have differentially influenced the benthic composition between the Honduras and the Indonesia coral reefs surveyed in this study. However, large intra-location variance which explains the benthic composition at both locations indicates that localised processes have a predominant role for explaining benthic composition over the last decade. The sustained monitoring effort is critical for understanding how these reefs will change in their composition as global temperatures continue to rise through the Anthropocene.

opencc-zeroAug 2022View details →

zenodo32/100

Detecting anomalies in system logs with a compact convolutional transformer - Data

Detecting anomalies in system logs with a compact convolutional transformer - Data Preprocessed data and a pre-trained model for the Larisch, Vitay, Hamker (2022) publication. The data directory contains the Blue Gene/L data set, after tokenization, shuffling, and splitting in a training and test set (BGL_masked_Xtrain.npy and BGL_masked_Xtest.npy, respectively) and the corresponding labels. Additionally, the BGL_masked_Xtest_uniq.npy and BGL_masked_Ytest_uniq.npy contains the test data, where samples from the training set are removed. The model directory contains a pre-trained compact convolutional transformer (CCT) model. The CCT is trained on the proposed BGL training data and uses a 4x4 convolutional kernel.

opencc-by-4.0Oct 2022View details →

zenodo32/100

Dataset related to publication: Landcover-categorized fires respond distinctly to precipitation anomalies in the South-Central United States

Landcover-categorized fires respond distinctly to precipitation anomalies in the South-Central United States Kátia Fernandes and Sen g. Young doi: 10.3389/fenvs.2024.1433920 Abstract Satellite detection of active fires have contributed to advance our understanding of fire ecology, fire and climate dynamics, fire emissions and how to better manage the use of fires as a tool. In this study we use 12 years (2012-2023) of active fire data combined with landcover information in the South-Central United States to derive a monthly, open access dataset of categorized fires. This is done by calculating a fire predominance index used to rank fire prone land covers, which are then grouped into four main landscapes: grassland, forest, wildland and crop fires. County level aggregated analyses reveal spatial distributions, climatologies, and peak fire months that are particular to each fire type. Using the Standardized Precipitation Index (SPI), it is found that during climatological fire peak-month, SPI and fires exhibit an inverse relationship in forests and crops, whereas grassland and wildland fires show less consistent inverse or even direct relationship with SPI. This varied behavior is discussed in the context of landscapes’ responses to anomalies in precipitation, and fire management practices, such as prescribed fires and crop residue burning. In a case study of Osage County (OK) we find that large wildfires, known to be closely related to climate anomalies, occur where forest fires are located in the county and absent in areas of grassland fires. Weaker grassland fires response to precipitation anomalies can be attributed to the use of prescribed burning, which are normally planned under environmental conditions that facilitate control and thus avoided during droughts. Crop fires on the other hand, are set to efficiently burn residue and practiced more intensely in drier years than in wetter, explaining the consistently strong inverse correlation between fires and precipitation anomalies. In our increasingly volatile climate, understanding how fires, vegetation, and precipitation interact has become imperative to prevent hazardous fire conflagrations and to better manage ecosystems.

opencc-by-4.0May 2024View details →

zenodo32/100

Dataset: 2023 GPS Anomalies, NOTAMs, and Aircraft Traffic

<h1>Dataset: 2023 GPS Anomalies, NOTAMs, and Aircraft Traffic</h1> The dataset "2023 GPS Anomalies, NOTAMs, and Aircraft Traffic" was collected and generated for the paper "Detecting GPS Anomalies in Aviation Using ADS-B: Correlating Coordinate Gaps and GPS Deviations with NOTAM Warnings." This dataset provides a collection of geospatial and temporal data necessary for analyzing potential GPS anomalies in aviation. The data sources include NOTAMs received from the FAA, and the aircraft traffic and GPS information calculated and extracted from the OpenSky Trino ADS-B database. The FAA_and_ICAO_locations file includes 21,382 records with identifiers, coordinates, and detailed facility information. This dataset serves as a reference for analyzing the geographical distribution of aviation facilities. The Flights_per_Hour_per_Grid file, with 74,219,036 records, provides hourly flight movement counts within specified grids, offering insights into air traffic patterns and potential disruptions. The GPS_Jumps_from_Routes file, comprising 5,878,275 records, documents deviations in flight paths, capturing metrics such as distances, speeds, and timestamps. This data is crucial for identifying potential GPS spoofing incidents by analyzing unusual jumps between consecutive data points. The GPS_Missing_Coordinates file, with 53,232 records, highlights periods of missing GPS signals, indicating possible GPS jamming events. This file includes start and end times, distances between known coordinates, and Navigation Integrity Category (NIC) values to assess data quality during null periods. The NOTAM_ICAO_GPS and NOTAM_USA files, with 30,160 and 234,205 records respectively, provide detailed information on NOTAM areas, including geographic areas, active periods, and categories. This allows for an analysis of the spatial and temporal correlation between NOTAM warnings and GPS anomalies, facilitating a better understanding of the impact of GPS disruptions on aviation safety and operations. <h1>Summary Table</h1> <table> <tbody> <tr> <td> Category </td> <td> File Names </td> <td> Total Records </td> <td> Columns </td> </tr> <tr> <td> FAA and ICAO Locations </td> <td> FAA_and_ICAO_locations.csv FAA_and_ICAO_locations.dpkg </td> <td> 21,382 </td> <td> WKT, id, fid, Location_ID, ICAO_ID, IATA_ID, FAA_Location_Code, Facility_Type, Facility_Name, FAA_New_Location_Code, Coordinates, lat, lon, Region, Country_Code, Country, State_Id, State_Name, City, Location, Effective_Date, Site_Id, ADO, ARTCC_Id, ARTCC_Computer_ID, ARTCC_Name, Tie_In_FSS_Id, Tie_In_FSS_Name, NOTAM_Facility_Id, NOTAM_Service </td> </tr> <tr> <td> Flights per Hour per Grid </td> <td> Flights_per_Hour_per_Grid-2023.csv Flights_per_Hour_per_Grid-2023.dpkg </td> <td> 74,219,036 </td> <td> grid_id, hour, movement_count, geometry </td> </tr> <tr> <td> GPS Jumps from Routes (possible spoofing) </td> <td> GPS_Jumps_from_Routes-2023.csv GPS_Jumps_from_Routes-2023.dpkg </td> <td> 5,878,275 </td> <td> WKT, id, fid, icao24, callsign, time_before_spoofing, time_of_spoofing, distance, time_difference, speed_m_s, time_start, time_end </td> </tr> <tr> <td> GPS Missing Coordinates (possible jamming) </td> <td> GPS_Missing_Coordinates-2023.csv GPS_Missing_Coordinates-2023.dpkg </td> <td> 53,232 </td> <td> WKT, id, icao24, callsign, null_start_time, null_end_time, time_of_previous_not_null_coords, time_of_next_not_null_coords, between_coords_distance_m, null_duration_seconds, between_coords_duration_seconds, avg_nic, min_nic, max_nic, start_time, end_time, start_y, end_x, end_y, start_x </td> </tr> <tr> <td> NOTAM ICAO GPS </td> <td> NOTAM_ICAO_GPS-2023.csv NOTAM_ICAO_GPS-2023.dpkg </td> <td> 30,160 </td> <td> WKT, id, fid, notam_id, category_name, coordinates_center, radius_nm, radius_mod_nm, notam_number, accountability, location_id, icao_id, domestic_text, icao_text, type, category_id, time_start, time_end </td> </tr> <tr> <td> NOTAM USA </td> <td> NOTAM_USA-2023.csv NOTAM_USA-2023.dpkg </td> <td> 234,205 </td> <td> WKT, id, fid, notam_id, category_name, is_circle, coordinates_polygon, coordinates_center, radius_nm, faa_location_code, is_faa_location, location_id, is_restricted_area, restricted_area_id, restricted_area_code, category_id, message, notam_number, notam_accountability, moa, type, time_start, time_end </td> </tr> <tr> <td>   </td> <td>   </td> <td>   </td> <td>   </td> </tr> </tbody> </table> <h1>Details</h1> <h2>1. FAA_and_ICAO_locations.csv and FAA_and_ICAO_locations.dpkg</h2> <ul> <li>Total Records: 21,382</li> <li>Columns:</li> <ul> <li>WKT: Well-Known Text representation of a point in the CSV file, or a geometry field in the DPKG file.</li> <li>id: Unique identifier for each record.</li> <li>fid: Feature identifier.</li> <li>Location_ID: Identifier for the location.</li> <li>ICAO_ID: ICAO (International Civil Aviation Organization) identifier.</li> <li>IATA_ID: IATA (International Air Transport Association) identifier.</li> <li>FAA_Location_Code: FAA location code.</li> <li>Facility_Type: Type of facility (e.g., airport, heliport).</li> <li>Facility_Name: Name of the facility.</li> <li>FAA_New_Location_Code: New location code by FAA.</li> <li>Coordinates: Coordinates of the location.</li> <li>lat: Latitude of the location.</li> <li>lon: Longitude of the location.</li> <li>Region: Geographical region of the location.</li> <li>Country_Code: Country code of the location.</li> <li>Country: Country name of the location.</li> <li>State_Id: State identifier.</li> <li>State_Name: Name of the state.</li> <li>City: City name.</li> <li>Location: General location information.</li> <li>Effective_Date: Effective date of the record.</li> <li>Site_Id: Site identifier.</li> <li>ADO: Airport District Office.</li> <li>ARTCC_Id: ARTCC (Air Route Traffic Control Center) identifier.</li> <li>ARTCC_Computer_ID: ARTCC computer identifier.</li> <li>ARTCC_Name: Name of the ARTCC.</li> <li>Tie_In_FSS_Id: Tie-in Flight Service Station identifier.</li> <li>Tie_In_FSS_Name: Name of the Tie-in Flight Service Station.</li> <li>NOTAM_Facility_Id: NOTAM (Notice to Airmen) facility identifier.</li> <li>NOTAM_Service: Indicates if NOTAM service is available (Y/N).</li> </ul> </ul> <h2>2. Flights_per_Hour_per_Grid-2023.csv and Flights_per_Hour_per_Grid-2023.dpkg</h2> <ul> <li>Total Records: 74,219,036</li> <li>Columns:</li> <ul> <li>grid_id: Identifier for the grid.</li> <li>hour: Timestamp for the hour.</li> <li>movement_count: Number of flights in each 0.5x0.5 degree grid during each hour of year 2023.</li> <li>geometry: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.</li> </ul> </ul> <h2>3. GPS_Jumps_from_Routes-2023.csv and GPS_Jumps_from_Routes-2023.dpkg</h2> <ul> <li>Total Records: 5,878,275</li> <li>Columns:</li> <ul> <li>WKT: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.</li> <li>id: Unique identifier for each record.</li> <li>fid: Feature identifier.</li> <li>icao24: ICAO 24-bit aircraft address.</li> <li>callsign: Callsign of the aircraft.</li> <li>time_before_spoofing: Timestamp before the spoofing event.</li> <li>time_of_spoofing: Timestamp of the spoofing event.</li> <li>distance: Distance of the jump in meters.</li> <li>time_difference: Time difference between two coordinates in seconds.</li> <li>speed_m_s: Speed in meters per second.</li> <li>time_start: Start time of the record.</li> <li>time_end: End time of the record.</li> </ul> </ul> <h2>4. GPS_Missing_Coordinates-2023.csv and GPS_Missing_Coordinates-2023.dpkg</h2> <ul> <li>Total Records: 53,232</li> <li>Columns:</li> <ul> <li>WKT: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file.</li> <li>id: Unique identifier for each record.</li> <li>icao24: ICAO 24-bit aircraft address.</li> <li>callsign: Callsign of the aircraft.</li> <li>null_start_time: Start time of missing GPS coordinates.</li> <li>null_end_time: End time of missing GPS coordinates.</li> <li>time_of_previous_not_null_coords: Time of the last known good GPS coordinates before the null period.</li> <li>time_of_next_not_null_coords: Time of the first known good GPS coordinates after the null period.</li> <li>between_coords_distance_m: Distance between the previous and next known good coordinates in meters.</li> <li>null_duration_seconds: Duration of the null period in seconds.</li> <li>between_coords_duration_seconds: Duration between the previous and next known good coordinates in seconds.</li> <li>avg_nic: Average Navigation Integrity Category (NIC) during the period.</li> <li>min_nic: Minimum NIC during the period.</li> <li>max_nic: Maximum NIC during the period.</li> <li>start_time: Human-readable start time of the null period.</li> <li>end_time: Human-readable end time of the null period.</li> <li>start_y: Latitude of the start point.</li> <li>end_x: Longitude of the end point.</li> <li>end_y: Latitude of the end point.</li> <li>start_x: Longitude of the start point.</li> </ul> </ul> <h2>5. NOTAM_ICAO_GPS-2023.csv and NOTAM_ICAO_GPS-2023.dpkg</h2> <ul> <li>Total Records: 30,160</li> <li>Columns:</li> <ul> <li>WKT: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.</li> <li>id: Unique identifier for each record.</li> <li>fid: Feature identifier.</li> <li>notam_id: NOTAM identifier.</li> <li>category_name: Name of the NOTAM category.</li> <li>coordinates_center: Center coordinates of the NOTAM area.</li> <li>radius_nm: Radius in nautical miles.</li> <li>radius_mod_nm: Modified radius in nautical miles.</li> <li>notam_number: NOTAM number.</li> <li>accountability: Accountability of the NOTAM.</li> <li>location_id: Location identifier.</li> <li>icao_id: ICAO identifier.</li> <li>domestic_text: Text of the NOTAM for domestic purposes.</li> <li>icao_text: Text of the NOTAM for ICAO purposes.</li> <li>type: Type of NOTAM.</li> <li>category_id: Identifier for the NOTAM category.</li> <li>time_start: Start time of the NOTAM.</li> <li>time_end: End time of the NOTAM.</li> </ul> </ul> <h2>6. NOTAM_USA-2023.csv and NOTAM_USA-2023.dpkg</h2> <ul> <li>Total Records: 234,205</li> <li>Columns:</li> <ul> <li>WKT: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file.</li> <li>id: Unique identifier for each record.</li> <li>fid: Feature identifier.</li> <li>notam_id: NOTAM identifier.</li> <li>category_name: Name of the NOTAM category.</li> <li>is_circle: Indicates if the NOTAM area is a circle (1) or not (0).</li> <li>coordinates_polygon: Coordinates of the polygon vertices.</li> <li>coordinates_center: Center coordinates of the NOTAM area.</li> <li>radius_nm: Radius in nautical miles.</li> <li>faa_location_code: FAA location code.</li> <li>is_faa_location: Indicates if it is an FAA location (1) or not (0).</li> <li>location_id: Location identifier.</li> <li>is_restricted_area: Indicates if it is a restricted area (1) or not (0).</li> <li>restricted_area_id: Restricted area identifier.</li> <li>restricted_area_code: Code for the restricted area.</li> <li>category_id: Identifier for the NOTAM category.</li> <li>message: NOTAM message.</li> <li>notam_number: NOTAM number.</li> <li>notam_accountability: NOTAM accountability.</li> <li>moa: Military Operations Area (MOA) identifier.</li> <li>type: Type of NOTAM.</li> <li>time_start: Start time of the NOTAM.</li> <li>time_end: End time of the NOTAM.</li> </ul> </ul>   Note that all the files are zipped as CSV. The GPKG version is also available where geographical information is present. Due to the size of the file Flights_per_Hour_per_Grid-2023, the command line program ogr2ogr may be the best choice to upload the data into a database. Below is an example of a SQL script and a command to upload this file into a PostgreSQL table. Update the placeholders your_DB_table, your_DB_name, your_DB_user, your_DB_password, your_DB_hostname with the actual information. CREATE TABLE your_DB_table (grid_id TEXT, date TIMESTAMP, movement_count INTEGER, geometry GEOMETRY(POLYGON, 4326)); "C:\Program Files\QGIS 3.36.2\bin\ogr2ogr" -f "PostgreSQL" PG:"dbname=your_DB_name user=your_DB_user password=your_DB_password host=your_DB_hostname port=5432" C:\Flights_per_Hour_per_Grid.gpkg -nln your_DB_table -a_srs EPSG:4326 -dim 2 -progress -append <h1>Author</h1> Eugene Pik <a href="https://orcid.org/0000-0001-6296-919X">https://orcid.org/0000-0001-6296-919X</a> <a href="https://www.linkedin.com/in/eugene/">https://www.linkedin.com/in/eugene/</a> eugene.pik@mevocopter.com <h1>DOI</h1> <a href="https://doi.org/10.5281/zenodo.11411991">https://doi.org/10.5281/zenodo.11411991</a> <h1>References</h1> Following references were used to update NOTAMs with WKT polygons: airport-data.com. (n.d.). USA airports by FAA code. https://www.airport-data.com/usa-airports/faa-code/A.html DoD. (2019). Flight information publication area planning special use airspace. NATIONAL GEOSPATIAL-INTELLIGENCE AGENCY. https://www.cnatra.navy.mil/assets-global/docs/area-planning-1A-20190815.pdf FAA. (n.d.-a). Airport Data and Information Portal. https://adip.faa.gov/agis/public/#/airportSearch/advanced FAA. (n.d.-b). US ICAO location finder. https://www.notams.faa.gov/common/icao/USA.html FAA. (2017a, September 30). Encodes/decodes—Aeronautical data [Template]. https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/loc_id_search/Encodes_Decodes/ FAA. (2017b, October 12). Aeronautical information manual—Official guide to  basic flight Information and ATC procedures. https://www.faa.gov/air_traffic/publications/media/AIM_Basic_dtd_10-12-17.pdf#page=142 FAA. (2021, July 26). Order JO 7350.9Z - Location identifiers [Template]. https://www.faa.gov/regulations_policies/orders_notices/index.cfm/go/document.information/documentID/1040529 FAA. (2023a). Pilot’s handbook of aeronautical knowledge. https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/phak FAA. (2023b, June 1). Airport Data [Template]. https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/Airport_Data/ FAA. (2024, February 16). Order JO 7400.10F - Special Use Airspace [Template]. https://www.faa.gov/documentLibrary/media/Order/Order_7400.10F_2024_-_final_-signed.pdf ICAO. (2022). North Atlantic (NAT) air navigation plan Volume I (Doc 9634). https://www.icao.int/EURNAT/EUR%20and%20NAT%20Documents/NAT%20Documents/_eANP%20NAT%20Doc9634/Doc9634%20NAT%20eANP%20Vol%20I.pdf ProAirPilot.com. (2024). Complete list of NOTAM abbreviations. https://proairpilot.com/notam-abbreviations.html SkyVector. (n.d.). Search for Airports by ICAO ID or name. https://skyvector.com/airports   Below is the reference to our source of the aircraft traffic and GPS anomalies data, the OpenSky ADS-B database. Schäfer, M., Strohmeier, M., Lenders, V., Martinovic, I., & Wilhelm, M. (2014). Bringing up OpenSky: A large-scale ADS-B sensor network for research. IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, 83–94. https://doi.org/10.1109/IPSN.2014.6846743

opencc-by-4.0Jun 2024View details →

zenodo32/100

Preliminary Results of Marine Gravity Anomaly and Bathymetry from SWOT Wide-Swath altimeter data

We explore the potential of Ka-band radar interferometry (KaRIn) data from the SWOT mission for marine gravity and bathymetry applications. Our evaluation includes determining the deflection of vertical (DOV) using the least squares collocation (LSC) method, recovering gravity anomalies with the inverse Vening-Meinesz (IVM) approach, and predicting bathymetry through the gravity-geological method (GGM). SWOT-derived gravity anomalies in the study area, located between 20ºN - 30ºN and 135ºE - 145ºE, lies at the convergence of the Pacific and Eurasian tectonic plates.  KaRIn data (only seven months) recovered gravity anomalies with an accuracy of 2.37 mGal, which is better than current models. The details of the method are discussed in a manuscript submitted to GRL.

opencc-by-4.0Jun 2024View details →

zenodo32/100

IMAD-DS: A Dataset for Industrial Multi-Sensor Anomaly Detection Under Domain Shift Conditions

IMAD-DS is a dataset developed for multi-rate multi-sensor anomaly detection (AD) in industrial environments, that considers varying operational and environmental conditions known as domain shifts. Dataset Overview: This dataset includes data from two scaled industrial machines: a robotic arm and a brushless motor. It includes both normal and abnormal data recorded under various operating conditions to account for domain shifts. These shifts are categorized into: Robotic Arm: The robotic arm is a scaled version of a robotic arm used to move silicon wafers in a factory. Anomalies are created by removing bolts at the nodes of the arm, resulting in an imbalance in the machine. Brushless Motor: The brushless motor is a scaled representation of an industrial brushless motor. Two anomalies are introduced: first, a magnet is moved closer to the motor load, causing oscillations by interacting with two symmetrical magnets on the load; second, a belt that rotates in unison with the motor shaft is tightened, creating mechanical stress. The following domain shifts are included in the dataset: Operational Domain Shifts: Variations caused by changes in machine conditions (e.g., load changes for the robotic arm and speed changes for the brushless motor). Environmental Domain Shifts: Variations due to changes in background noise levels. Combinations of operating and environmental conditions divide each machine's dataset into two subsets: the source domain and the target domain. The source domain has a large number of training examples. The target domain, instead, has limited training data. This discrepancy highlights a common issue in the industry where sufficient training data is often unavailable for the target domain, as machine data is collected under controlled environments that do not fully represent the deployment environments.   Data Collection and Processing: Data is collected using the STEVAL-STWINBX1 IoT Sensor Industrial Node. The sensor used to record the dataset are the following. ·        Analog Microphone (16 kHz) ·        3-axis Accelerometer (6.7 kHz) ·        3-axis Gyroscope (6.7 kHz) Recordings are conducted in an anechoic chamber to control acoustic conditions precisely Data Format: Files are already divided into train and test sets. Inside each folder, each sensor's data is stored in a separate '.parquet' file. Sensor files related to the same segment of machine data share a unique ID. The mapping of each machine data segment to the sensor files is given in .csv files inside the train and test folders. Those .csv files also contain metadata denoting the operational and environmental conditions of a specific segment.

opencc-by-sa-4.0Jul 2024View details →

zenodo32/100

Climatological radiation covariance between temperature and radiative heating anomaly composites

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

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Last verified 2026-04-29Open record