## 2 Materials and Methods
### Experimental Equipment and Data Description
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{\begin{tabular}{c} **CO\({}_{2}\)** \\ **(g)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **RFM** \\ **(rpm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Torque** \\ **(N-m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Power** \\ **(kW)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **No. 1** \\ **Cylinder** \\ **MCP (bar)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **No. 1** \\ **Exhaust Gas** \\ **Temperature** \\ **(°C)** \\ \end{tabular} } \\ \hline
1 & 11:06:19 & 807.7 & 136.8 & 351.2 & 3035 & 164.3 & 355.3 & \\
2 & 11:06:20 & 807.6 & 136.8 & 350.8 & 5030 & 164.2 & 355.3 & \\
3 & 11:06:21 & 807.7 & 136.8 & 350.4 & 5025 & 16.1 & 355.2 & \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
3253 & 12:05:20 & 807.8 & 137.5 & 347.2 & 4977.2 & 161.8 & 354.9 & \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
21,879 & 21:35:40 & 808.0 & 136.5 & 345.6 & 4947.3 & 157.8 & 355.6 & \\ \hline \end{tabular}
\end{table} 
Table 2: Engine and exhaust emissions data for experiment. To ensure efficient training and reproducibility of the deep learning model, all experiments were conducted in a controlled computing environment. The hardware used included an Intel Core i9-12900K CPU, and an NVIDIA GeForce 2070 SUPER GPU with 8 GB of memory. On the software side, the model was developed using Python 3.9.13 and implemented with TensorFlow 2.18.0 and Keras 3.8.0. CUDA version 12.8 was utilized to enable GPU-accelerated computation. The detailed specifications of the hardware and software environment are summarized in Table 4.  
The dataset comprises one training dataset with the training dataset consisting of 109 rows and 11,958 columns and a test dataset with 109 rows and 9921 columns. As shown in Table 5, the Anderson-Darling, and Shapiro-Wilk tests for parametric techniques, along with the Kolmogorov-Smirnov (K-S) test for nonparametric techniques, confirmed \begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Specification** \\ \hline CPU & Intel Core i9–12900K \\ GPU & NVIDIA GeForce RTX 2070 SUPER (8 GB) \\ RAM & 32 GB \\ Operating system & Window 10 Pro 64-bit \\ Python version & 3.9.13 \\ Deep learning framework & TensorFlow 2.18.0/Keras 3.8.0 \\ CUDA version & 12.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hardware and software configuration used for training and evaluating the proposed deep learning model. Figure 1: Experimental setup for data acquisition. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **SEMTECH DS+** & **HORIBA OBS-ONE** \\ \hline Measuring method (CO\({}_{2}\)) & NDIR & NDIR \\ Maximum range & 18\% vol. & 20\% vol. \\ Accuracy & \(<\)\(\pm\)2\% of reading or \(\leq\)\(\pm\)0.3\% of full & Within \(\pm\)0.3\% of full scale or \(\pm\)2\% of \\  & scale (whichever is larger) & readings (whichever is larger) \\ Zero drift & \(\leq\)\(\pm\)0.1\% vol. (over 4 h) & Within \(\pm\)0.5\% of full scale per 4 h \\ Span drift & \(\leq\)2\% of span value or \(\leq\)\(\pm\)0.1\% & Within \(\pm\)1\% of full scale per 4 h \\  & vol.(whichever is larger, over 4 h) & Within \(\pm\)1\% of full scale per 4 h \\ Response time & T\({}_{0-90}\leq 10\) s & Within 6 s \\ Recording frequency & 1–5 Hz & 100 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specification of the portable emission-measuring systems. that the training data did not follow a normal distribution. Consequently, [PERSON]'s correlation analysis was conducted to identify the factors influencing CO2 emissions from vessel engines. The Anderson-Darling test is commonly used to assess whether a dataset follows a specific distribution, as described by the following formula [30; 31; 32].