Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1337
  Single-Ended Fault Detection and Fault Location in Transmission Lines Using Approximate Derivative

  Akdağ, Mustafa; Mamiş, Mehmet Salih; Akmaz, Düzgün

  Fault location in power transmission lines (PTLs) relies on impedance or traveling wave (TW) principles. TW approaches offer superior accuracy and high robustness against fault resistance. While multi-ended methods require precise terminal synchronization, single-ended TW (SETW) methods utilize measurements from one terminal, requiring accurate distinction of reflected waves. This study employs the computationally efficient approximate derivative (AD)—the difference between consecutive samples—for SETW fault detection and location. Normally near zero, the AD of modal signals produces sharp transitions during faults. Comparing AD output to a threshold achieves fault detection. The AD then identifies arrival times of the incident and reflected TWs. When using TW theory to distinguish reflections from the fault point and remote end, the fault distance is calculated from their arrival time difference. Validated through 293 diverse ATP simulated fault scenarios, the approach delivered highly accurate results despite using a lower sampling rate than established methods, utilizing an exceptionally short data window—only 2.03 ms for a 300 km line. Finally, operational boundaries for the signal-to-noise ratio (SNR) in noisy conditions are established.

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• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1302
  Translation-Aware Multilingual Sentiment Analysis: A Comparative Study of Machine Translation Systems and Transformer Ensembles

  Aslan, Serpil; Kartoğlu, Celal Şamil; Can, Ümit

  Multilingual sentiment analysis remains challenging because of cross-linguistic differences, uneven data availability, and potential shifts in meaning introduced during translation. Rather than proposing a new model, this study examines the performance of translation-based pipelines and ensemble Transformer models in multilingual settings. The primary objective is to investigate differences in downstream sentiment classification performance between translation systems within a consistent evaluation framework. Texts in Arabic, Chinese, French, and Italian are standardized through label harmonization and basic preprocessing and subsequently translated into English using Google Translate and the open-source LibreTranslate. Sentiment classification is performed using three Transformer-based models: DeBERTa-v3, RoBERTa, and BERTweet. The experiments are formulated as a binary polarity-classification task comprising positive and negative instances, with neutral samples excluded because their annotations are not directly comparable across the heterogeneous source datasets. Predictions from the individual models are combined through majority voting without introducing a new ensemble method. Experiments on the resulting binary datasets demonstrate that the ensemble achieves the highest mean weighted F1-score, with low fold-to-fold variation under both translation pipelines. Google Translate yields higher and more consistent classification performance across the evaluated subsets, whereas LibreTranslate exhibits greater variation. Given that the datasets differ in domain and genre, these variations cannot be attributed solely to linguistic differences. Overall, the findings reveal differences in downstream sentiment classification performance between the two translation pipelines and indicate that the ensemble combines high mean performance with low fold-to-fold variation within the evaluated binary setting.

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• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1153
  A Deep Learning Framework for Local Earthquake Magnitude Estimation Using Three-Component Waveforms

  Çelik, Yusuf

  This study presents a two-stage deep learning framework for accurate and generalizable estimation of local earthquake magnitudes from three-component seismic waveforms, within the context of ground-based remote sensing systems. In the first stage, phase transition boundaries are identified at the sample level to enable consistent temporal alignment of the signals. In the second stage, earthquake magnitude estimation is performed using 30 s waveform segments aligned with the early portion of the signal and enriched with spectral and statistical features. The model was initially trained on the globally diverse dataset STEAD and later fine-tuned using a subset of KOERI waveforms, and its performance was evaluated on an independent KOERI test set. The results demonstrate high prediction accuracy, with a mean absolute error of approximately 0.09 and a coefficient of determination (R2) of about 0.95, indicating strong agreement between predicted and true magnitudes. The model maintains stable performance across varying signal characteristics and geographic regions, highlighting its strong transferability. These findings suggest that seismic sensor networks can be effectively utilized as remote sensing systems for rapid and reliable earthquake characterization.

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