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This dataset comprises a collection of CSV files containing paired time-series measurements essential for nonlinear compensation research in electrochemical seismometers (MET). Each CSV file, named according to specific magnitude-frequency combinations (magX_freqY.csv), contains two columns: 'origin' representing the original system response and 'target' representing the desired compensated output.

This study presents a deep learning-based framework for detecting vehicle deceleration patterns using Ultra-Wideband (UWB) Channel Impulse Response (CIR) analysis. Unlike traditional GPS or IMU-based systems, which struggle in GPS-denied environments such as tunnels, the proposed method leverages UWB CIR signal variations to classify two key driving behaviors: rapid deceleration and gradual deceleration. All data were collected from real-world experiments using UWB devices installed on actual vehicles at a professional highway testing site.

The data covers the period from January 4, 2021, to August 16, 2023. It includes the carbon trading prices from the Hubei carbon market and other relevant feature data that may influence carbon prices. The feature data has undergone preliminary screening and consists of Brent crude oil prices, natural gas prices, Rotterdam coal prices, EU Emission Allowances, the China Securities 300 Index, and the Euro exchange rate.

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.