## 3 Dataset
### Dataset Processing  
Data Cropping and Filtering  
Different from the data in Dataset 1, Dataset 2 provides the complete data within 600 s without preprocessing and data extraction. This means that if the data in this dataset is used directly, each sample will reach a dimension of 480,000. Figure 1 is the response curve of different gas sensors:  
Figure 1a is a graph of the sensor response for raw 250 ppm methane gas. It can be seen from the figure that the resistance value of the sensor is not always changing. Only during the desorption process and a short period during the reaction process does the resistance value of the sensor change due to the redox reaction. When in the stable and maximum response stages, the resistance value basically does not change or fluctuates little. Therefore, there is too much redundant information in the 480,000-dimensional data. Excessive data dimensions will not only greatly increase the computational complexity of model training but also cause model training to be slow. Therefore, it is necessary to perform dimensionality reduction operations on the data. In addition, when processing the data in the data set, it is found that due to data preservation reasons, many data samples in  
Figure 1: Sensor response curve (**a**) original 250 ppm methane gas; (**b**) 250 ppm methane gas after cutting; (**c**) 25 ppm methane gas after cutting; (**d**) 250 ppm carbon monoxide gas after cutting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gas Type** & **Number of Samples** & **Concentration (ppm)** \\ \hline Ethanol & 160 & 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100, 112.5, 125 \\ Methane & 160 & 25, 50, 75, 100, 125, 150, 175, 200, 225, 250 \\ Vinyl & 160 & 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100, 112.5, 125 \\ Carbon Monoxide & 160 & 25, 50, 75, 100, 125, 150, 175, 200, 225, 250 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Amounts and concentrations of different gases in dataset 2.  
the public data set do not reach the dimension of 480,000, and there is data loss, which will bring difficulties to the subsequent feature extraction. In order to ensure the uniformity of the dimensions of the sample data, the entire data set is screened and the data samples are trimmed. First, discard the samples with too much missing data, and reduce the entire data set to 600 data samples. After that, the data is trimmed to reduce the amount of data in the two cleaning processes and the stabilization stage, and finally, the dimension of each sample is reduced to 25,000 \(\times\) 8 = 200,000.  
Figure 1b-d shows the data response curves of trimmed 250 ppm methane gas, 25 ppm methane gas and 250 ppm carbon monoxide, respectively. By comparing Figure 1b,c, it can be seen that the sensor response curves of two gases with different concentrations but the same type in the same sensor array are different. It can be seen that the gas with higher concentration makes the resistance value of the sensor change more, but the changing trend of the resistance value of each sensor is roughly the same. However, it can be seen in Figure 1b,d that the response values and changing trends of the sensor arrays are different at the same concentration of carbon monoxide gas and methane gas. The pattern recognition algorithm of machine smell is to classify the gas by extracting the features in the response curve. Table 4 below shows the number of different gas samples after cropping the dataset.