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The dataset provided in this study contains variables related to solar power generation, including solar irradiance, temperature, wind speed, and humidity in Riyadh. The data was collected using NASA satellite imagery and various ground stations over a period of time. This dataset is crucial for improving solar radiation forecasting models, particularly by enhancing the prediction of solar power production in Saudi Arabia under varying climatic conditions.

This dataset integrates textual, financial, and macroeconomic indicators to support research on bank failure prediction and financial distress forecasting in Vietnam. It includes financial news from the BKAI News Corpus Dataset (2009–2023) and financial crisis data from "A Dataset for the Vietnamese Banking System (2002–2021)" (Tu Le et al., 2022), covering crisis-related events such as restructuring, special control, mergers, and acquisitions.

This dataset contains high-resolution solar and wind measurement data collected from the Feni region, Bangladesh, spanning from 2017 to 2019. Logged at a 1-minute interval, the dataset provides a comprehensive record of atmospheric and meteorological conditions, essential for renewable energy analysis, climatological studies, and resource assessment.

Artificial Intelligence (AI) has increasingly influenced modern society, recently in particular through significant advancements in Large Language Models (LLMs). However, high computational and storage demands of LLMs still limit their deployment in resource-constrained environments. Knowledge distillation addresses this challenge by training a smaller language model (student) from a larger one (teacher). Previous research has introduced several distillation methods for both generating training data and training the student model.