{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:20:48Z","timestamp":1759450848206,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017592","name":"Institute of Physics Belgrade","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100017592","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Data"],"abstract":"<jats:p>The binary anomaly detection (classification) of ionospheric data related to Very Low Frequency (VLF) signal amplitude in prior research demonstrated the potential for development and further advancement. Further data quality improvement is integral for advancing the development of machine learning (ML)-based ionospheric data (VLF signal amplitude) anomaly detection. This paper presents the transition from binary to multi-class classification of ionospheric signal amplitude datasets. The dataset comprises 19 transmitter\u2013receiver pairs and 383,041 manually labeled amplitude instances. The target variable was reclassified from a binary classification (normal and anomalous data points) to a six-class classification that distinguishes between daytime undisturbed signals, nighttime signals, solar flare effects, instrument errors, instrumental noise, and outlier data points. Furthermore, in addition to the dataset, we developed a freely accessible web-based tool designed to facilitate the conversion of MATLAB data files to TRAINSET-compatible formats, thereby establishing a completely free and open data pipeline from the WALDO world data repository to data labeling software. This novel dataset facilitates further research in ionospheric signal amplitude anomaly detection, concentrating on effective and efficient anomaly detection in ionospheric signal amplitude data. The potential outcomes of employing anomaly detection techniques on ionospheric signal amplitude data may be extended to other space weather parameters in the future, such as ELF\/LF datasets and other relevant datasets.<\/jats:p>","DOI":"10.3390\/data10100157","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T14:16:29Z","timestamp":1759241789000},"page":"157","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Class Labeled Ionospheric Dataset for Machine Learning Anomaly Detection"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4769-0152","authenticated-orcid":false,"given":"Aleksandra","family":"Kolarski","sequence":"first","affiliation":[{"name":"Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4825","authenticated-orcid":false,"given":"Filip","family":"Arnaut","sequence":"additional","affiliation":[{"name":"Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5332-068X","authenticated-orcid":false,"given":"Sreten","family":"Jevremovi\u0107","sequence":"additional","affiliation":[{"name":"Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7785-4456","authenticated-orcid":false,"given":"Zoran R.","family":"Miji\u0107","sequence":"additional","affiliation":[{"name":"Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7938-5748","authenticated-orcid":false,"given":"Vladimir A.","family":"Sre\u0107kovi\u0107","sequence":"additional","affiliation":[{"name":"Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"ref_1","unstructured":"Kelley, M. (1989). The Earth\u2019s Ionosphere, Elsevier."},{"key":"ref_2","unstructured":"Pr\u00f6lss, G. (2012). Physics of the Earth\u2019s Space Environment, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mitra, A.P. (1974). Ionospheric Effects of Solar Flares, Springer.","DOI":"10.1007\/978-94-010-2231-6"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/S0065-2199(08)60047-0","article-title":"Ion Chemistry in the D Region","volume":"12","author":"Reid","year":"1976","journal-title":"Adv. At. Mol. Phys."},{"key":"ref_5","unstructured":"Helliwell, R.A. (1965). Whistlers and Related Ionospheric Phenomena, Stanford University Press."},{"key":"ref_6","first-page":"741","article-title":"Lightning, Trimpis and Sprites","volume":"1993","author":"Strangeways","year":"1996","journal-title":"Oxford Univ. Press"},{"key":"ref_7","unstructured":"Goodman, J. (2006). Space Weather & Telecommunications, Springer."},{"key":"ref_8","unstructured":"Cannon, P., Angling, M., Barclay, L., Curry, C., Dyer, C., Edwards, R., Greene, G., Hapgood, M., Horne, R.B., and Jackson, D. (2013). Extreme Space Weather: Impacts on Engineered Systems and Infrastructure, Royal Academy of Engineering."},{"key":"ref_9","unstructured":"McMorrow, D. (2011). Impacts of Severe Space Weather on the Electric Grid, JASON."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s12045-017-0443-8","article-title":"Ionosphere and Radio Communication","volume":"22","author":"Bora","year":"2017","journal-title":"Resonance"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s10712-016-9396-9","article-title":"On the Use of VLF Narrowband Measurements to Study the Lower Ionosphere and the Mesosphere\u2013Lower Thermosphere","volume":"38","author":"Silber","year":"2017","journal-title":"Surv. Geophys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0032-0633(73)90024-X","article-title":"Lower Ionosphere Electron Densities from Rocket Measurements Employing LF Radio Propagation and DC Probe Techniques","volume":"21","author":"Hall","year":"1973","journal-title":"Planet. Space Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7807","DOI":"10.1029\/94JA02810","article-title":"Balloon Measurements above the South Pole: Study of Ionospheric Transmission of ULF Waves","volume":"100","author":"Bering","year":"1995","journal-title":"J. Geophys. Res. Sp. Phys."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wait, J.R., and Spies, K.P. (1965). Characteristics of the Earth-Ionosphere Waveguide for VLF Radio Waves, US Department of Commerce, National Bureau of Standards.","DOI":"10.6028\/NBS.TN.300"},{"key":"ref_15","unstructured":"Budden, K.G. (1961). Radio Waves in the Ionosphere, Cambridge University Press."},{"key":"ref_16","unstructured":"Budden, K.G. (1961). The Wave-Guide Mode Theory of Wave Propagation, Logos Press."},{"key":"ref_17","first-page":"13","article-title":"Standardization Framework of Ionospheric Very Low Frequency (VLF) Signal Amplitude Classes for Machine Learning-Based Anomaly Detection: From Calm Ionospheric Conditions to Solar Activity-Induced Dynamics","volume":"55","author":"Arnaut","year":"2025","journal-title":"Contrib. Astron. Obs. Skaln. Pleso"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Arnaut, F., Kolarski, A., and Sre\u0107kovi\u0107, V.A. (2023). Random Forest Classification and Ionospheric Response to Solar Flares: Analysis and Validation. Universe, 9.","DOI":"10.3390\/universe9100436"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Arnaut, F., Kolarski, A., and Sre\u0107kovi\u0107, V.A. (2024). Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion. Data, 9.","DOI":"10.3390\/data9010017"},{"key":"ref_21","unstructured":"Vidra, N., Clifford, T., Jijo, K., Chung, E., and Zhang, L. (2024). Improving Classification Performance With Human Feedback: Label a Few, We Label the Rest. arXiv."},{"key":"ref_22","unstructured":"(2023, March 24). National Oceanic and Atmospheric Administration National Centers for Environmental Information, Available online: https:\/\/www.ncei.noaa.gov\/."},{"key":"ref_23","unstructured":"Georgia Institute of Technology, University of Colorado Denver, and Stanford University (2023, March 24). Worldwide Archive of Low-Frequency Data and Observations. Available online: https:\/\/waldo.world\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e2020SW002706","DOI":"10.1029\/2020SW002706","article-title":"Long short-term memory neural network for ionospheric total electron content forecasting over China","volume":"19","author":"Xiong","year":"2021","journal-title":"Space Weather"},{"key":"ref_25","first-page":"1","article-title":"The short-term prediction of low-latitude ionospheric irregularities leveraging a hybrid ensemble model","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tang, J., Li, Y., Ding, M., Liu, H., Yang, D., and Wu, X. (2022). An ionospheric TEC forecasting model based on a CNN-LSTM-attention mechanism neural network. Remote Sens., 14.","DOI":"10.3390\/rs14102433"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/LGRS.2020.2992633","article-title":"Implementation of hybrid deep learning model (LSTM-CNN) for ionospheric TEC forecasting using GPS data","volume":"18","author":"Ruwali","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e2020SW002501","DOI":"10.1029\/2020SW002501","article-title":"Forecasting global ionospheric TEC using deep learning approach","volume":"18","author":"Liu","year":"2020","journal-title":"Space Weather"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/10\/157\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T04:10:46Z","timestamp":1759378246000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/10\/157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,30]]},"references-count":28,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["data10100157"],"URL":"https:\/\/doi.org\/10.3390\/data10100157","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2025,9,30]]}}}