Multi-sensor dataset for normal air, Methyl Mercaptan and Hydrogen Sulfide gas classification - Hussain, Mazhar (Mid Sweden University, 2023)
The dataset includes time-series data collected by four different sensors, which measure two target gases, Hydrogen Sulfide and Methyl Mercaptan, in the presence of air. To obtain measurements, each gas was individually exposed to the multi-sensor setup while in the presence of air. The dataset is particularly useful for gas classification tasks, as deep learning and data fusion techniques can be applied to identify the target gases.

The dataset comprises time-series data collected by four sensors, which measure two target gases, Hydrogen Sulfide (H2S) and Methyl Mercaptan (CH3SH) in the presence of air. To obtain measurements, each gas was individually exposed to the multi-sensor setup, while maintaining room temperature. Table 2 in the description file presents the distribution of data samples for the target gases collected from the multi-sensor system against the true gas concentration in parts per million (ppm) at two different humidity levels. The dataset file is available in CSV format and contains 9 columns with a total of 654440x4 gas samples. The CSV file also includes additional information on temperature, humidity, and true concentrations of Hydrogen Sulfide and Methyl Mercaptan. Out of the 654440x4 samples, there are 151682x4 samples of Methyl Mercaptan, 126142x4 samples of Hydrogen Sulfide, and the remaining samples are normal air samples.

The dataset was originally published in DiVA and moved to SND in 2024.
Multi-sensor dataset for normal air, Methyl Mercaptan and Hydrogen Sulfide gas classification
Keywords: Data fusion; Data fusion; Hydrogen Sulfide; Hydrogen Sulfide; Multi sensor; Multi sensor; Gas classification; Gas classification; Methyl Mercaptan; Methyl Mercaptan; Deep learning; Deep learning; Other Electrical Engineering, Electronic Engineering, Information Engineering; Annan elektroteknik och elektronik