Myint, Lin Min

Preferred name

Myint, Lin Min

Alternative Name

Myint, L. M.

Myint, L.

Myint, Lin Min Min

Myint, Lin M.

Main Affiliation

School of Engineering

linminmin.my@kmitl.ac.th

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Classification of Equatorial Ionospheric Irregularities Using Unsupervised Machine Learning Based on Spatiotemporal ROTI Keograms
(2025-01-01)
Mutasov, Gleb
;
Supnithi, Pornchai
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Budtho, Jirapoom
;
Tongkasem, Napat
;
Nishioka, Michi
Equatorial ionospheric irregularities, particularly those associated with equatorial plasma bubbles (EPB), can significantly disrupt satellite navigation and communication systems. As the demand for reliable Global Navigation Satellite System (GNSS) and communication services grows, the prediction of ionospheric irregularities becomes critical. A key step in the prediction process is to identify distinct spatiotemporal patterns of irregularities, including day-to-day, longitudinal, and seasonal variations. However, with large datasets, manually classification or identification of these irregularities is a complex and challenging task. In this work, we propose unsupervised machine learning techniques to recognize and group irregularity patterns in large, unlabeled Rate of Total Electron Content (TEC) Index (ROTI) keograms. Specifically, two machine learning models: Gaussian Mixture Model and k-means clustering are employed. The ROTI keograms are constructed using GNSS data from two low-latitude receiver stations in Thailand. To reduce redundancy in the keogram images, three feature extraction techniques are applied before the clustering process. A comparative analysis is performed to determine the optimal number of clusters using these models. Based on the results, the optimal combination of feature extraction and clustering technique is determined for the proposed clustering model. The resulting k-means model with contour extractor classifies five distinct patterns of ionospheric irregularity patterns, providing valuable insights for enhancing EPB prediction models and deepening our understanding of ionospheric dynamics. Furthermore, these five irregularity patterns are analyzed in relation to space weather parameters such as the solar radio flux index (F10.7), and the geomagnetic index (Kp). The findings contribute to the development of robust prediction models, improving the reliability of satellite-based communication and navigation systems.
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Simultaneous equatorial plasma bubble observation using amplitude scintillations from GNSS and LEO satellites in low-latitude region
(2023-12-01)
Seechai, Khanitin
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Myint, Lin Min Min
;
Hozumi, Kornyanat
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Nishioka, Michi
;
Saito, Susumu
This study estimates the scale sizes of the plasma density irregularities and the longitudinal width associated with equatorial plasma bubbles (EPBs) in equatorial and low-latitude regions. By analyzing amplitude scintillation S<inf>4</inf> indices and total electron content (TEC) measured from low earth orbit (LEO) satellite’s beacon signals with 400 MHz and Global Navigation Satellite System (GNSS) L1/E1 signals with 1575.42 MHz, recorded by receivers at the KMITL station in Bangkok, Thailand (geographic; 13.73° N, 100.77°E, magnetic: 7.26°N), we investigate the characteristics of these irregularities. We collected data of 154 LEO satellite pass events during nighttime on 21 disturbed days in four equinoctial months in 2021. Based on the presence or absence of the scintillation effects on GNSS and LEO beacon signals, the events are categorized into four classes to estimate the scale size of the plasma density irregularities. The analysis suggests that events with both GNSS and LEO scintillations, as well as events with GNSS scintillation alone, occur predominantly before midnight assuming the presence of the small-scale size of the irregularities within EPB. However, events with only LEO scintillation occur throughout the whole night and some events are observed before the events with both GNSS and LEO scintillations. Post-sunset LEO scintillation alone may be attributed to the onset of EPBs developing at low altitude, while post-midnight LEO scintillation events near the magnetic equator, observed during periods of low GNSS Rate of TEC Index (ROTI) values, are associated with bottom-side ionospheric irregularities but are not linked with EPB. The findings are consistent with previous researches on the generation and decay of electron density irregularities within plasma bubbles. However, this study provides new insights by using specific data sets and analysis techniques, offering a more comprehensive understanding of the association of LEO scintillations with bottom-side ionospheric irregularities near the magnetic equator, not observed in the ROTI map. Graphical Abstract: [Figure not available: see fulltext.]
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Corrigendum to “Spatio-temporal characteristics of ionospheric irregularities in low latitude regions during the peak of solar cycle 25” [Adv. Space Res. 76(1) (2025) 254–268, (S0273117725004168), (10.1016/j.asr.2025.04.062)]
(2025-09-01)
Tongkasem, Napat
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Supnithi, Pornchai
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Thammavongsy, Phimmasone
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Nishioka, Michi
;
Perwitasari, Septi
The authors regret that the following was omitted from the acknowledgment section: This research project is also financially supported by National Research Council of Thailand (NRCT) under grant N41A640235. The authors would like to apologise for any inconvenience caused.
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Deep learning-based prediction models for the vertical total electron content using GNSS satellite observations
(2026-08-01)
Mutasov, Gleb
;
Myint, Lin Min Min
;
Budtho, Jirapoom
;
Perwitasari, Septi
;
Nishioka, Michi
Ionospheric Total Electron Content (TEC) is a crucial parameter for characterizing the state of the ionosphere and assessing its impact on satellite-based navigation systems and on communication technologies. In equatorial and low-latitude regions, ionospheric irregularities, particularly equatorial plasma bubbles (EPBs), pose significant challenges for satellite navigation and communication due to their capacity to cause rapid TEC fluctuations and signal degradation. Since these effects are especially pronounced during ionospheric and geomagnetic disturbances, making accurate TEC prediction is an essential task for improving the reliability of GNSS-based positioning and space weather applications. This study presents a machine learning-based framework for one-day-ahead prediction of TEC with a 30-min resolution over the magnetic equator and low-latitude regions, with a focus on Southeast Asia. Unlike global models, our approach is tailored to local GNSS observations and directly predicts TEC values along specific satellite-receiver paths, defined by geographic location and satellite visibility. We integrate ionospheric pierce point (IPP) coordinates, geomagnetic indices, and solar activity indicators as features to enhance temporal and spatial forecasting accuracy. To address the nonlinear and nonstationary nature of TEC variations, we investigate and compare three deep learning architectures: a Transformer-based time-series model, a Temporal Kolmogorov–Arnold Network (TKAN), and a Long Short-Term Memory (LSTM). Additionally, the predictions are benchmarked against the empirical IRI-2020 model and a persistence baseline. The results demonstrate that both the Transformer and TKAN models outperform the LSTM and empirical approaches, particularly during different geomagnetic and ionospheric conditions, showing improved robustness and generalization. The proposed framework highlights the potential for accurate, resource-efficient TEC prediction in low-latitude regions and opens a pathway for further improvements by integrating multi-GNSS observations and additional space weather parameters.
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Classification of the equatorial plasma bubbles using convolutional neural network and support vector machine techniques
(2023-12-01)
Thanakulketsarat, Thananphat
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Supnithi, Pornchai
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Myint, Lin Min Min
;
Hozumi, Kornyanat
;
Nishioka, Michi
Equatorial plasma bubble (EPB) is a phenomenon characterized by depletions in ionospheric plasma density being formed during post-sunset hours. The ionospheric irregularities can lead to disruptions in trans-ionospheric radio systems, navigation systems and satellite communications. Real-time detection and classification of EPBs are crucial for the space weather community. Since 2020, the Prachomklao radar station, a very high frequency (VHF) radar station, has been installed at Chumphon station (Geographic: 10.72° N, 99.73° E and Geomagnetic: 1.33° N) and started to produce radar images ever since. In this work, we propose two real-time plasma bubble detection systems based on support vector machine techniques. Two designs are made with the convolutional neural network (CNN) and singular value decomposition (SVD) used for feature extraction, the connected to the support vector machine (SVM) for EPB classification. The proposed models are trained using quick look (QL) plot images from the VHF radar system at the Chumphon station, Thailand, in 2017. The experimental results show that the combined CNN-SVM model, using the RBF kernel, achieves the highest accuracy of 93.08% while the model using the polynomial kernel achieved an accuracy of 92.14%. On the other hand, the combined SVD-SVM models yield the accuracies of 88.37% and 85.00% for RBF and polynomial kernels of SVM, respectively. Graphical Abstract: [Figure not available: see fulltext.].

Myint, Lin Min

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