## 3 Results and Discussion
### Data Analysis and Processing  
During combustion experiments, the dataset may encounter outliers due to experimental apparatus inaccuracies or human operational errors. Such data points deviate from the general distribution pattern of the dataset. Experimental data that include outliers can impact the model training; therefore, this research processed the aforementioned data and conducted outlier removal. Since the four factors cannot be considered simultaneously, the dataset is grouped by different \(T\), \(P\), \(X_{H_{2}}\), and \(\varphi\), and outliers are screened out for each group. Our screening method involves fixing three conditions to obtain a scatter plot of a specific condition versus LBV and calculating the 95% confidence interval, as shown in Figure 4, which shows the relationship between \(\varphi\) and LBV at \(P=1\) atm, \(T=298\) K, \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Author(s)** & **Year** & \(\mathbf{X_{H_{2}}}\) & \(\boldsymbol{\varphi}\) & \(\boldsymbol{T}\) (K) & \(\boldsymbol{P}\) (MPa) & **LBV (cm/s)** \\ \hline [PERSON] et al. [34] & 2019 & 0.025–0.4 & 0.7–1.6 & 298 & 0.1–0.5 & 6.0–33.90 \\ [PERSON] et al. [35] & 2020 & 0.4–0.6 & 0.6–1.6 & 298–493 & 0.05–0.2 & 8.85–64.98 \\ [PERSON] et al. [36] & 2020 & 0.05–0.6 & 0.8–1.4 & 298 & 0.1–0.7 & 4.81–140.21 \\ [PERSON] et al. [37] & 2021 & 0.05–0.3 & 0.8–1.4 & 298–423 & 0.05–0.15 & 7.38–50.01 \\ [PERSON] et al. [38] & 2022 & 0.1–0.4 & 0.8–1.8 & 298–443 & 0.1 & 9.44–38.75 \\ [PERSON] et al. [39] & 2022 & 0.1–0.7 & 0.7–1.4 & 298–423 & 0.1 & 5.56–30.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental data on LBV from academic sources. Figure 3: Significance testing between different datasets. \(X_{H_{2}}=0.3\). Samples that fall outside this confidence interval are considered outliers and are removed. The blue area represents the 95% confidence interval for the dataset in this group, and the red data points are the excluded outliers. Using this method, 67 sets of outlier data were eliminated, leaving 486 sets of data. The excluded outliers will not appear in the dataset of the model and will not participate in the training, evaluation, and analysis of the model. Table 2 delineates the spectrum of collected experimental observations and enumerates the remaining datasets following the exclusion of outliers. The structured dataset was partitioned into segments for training, testing, and validation purposes, with a distribution ratio of 7:1.5:1.5, as shown in Table 3. We use training and testing sets for model training and parameter selection, and validation sets for model result validation. By using this dataset partitioning method, it is possible to avoid overfitting of the model due to incomplete evaluation of the model. This study meticulously examines the relationship between four key factors (hydrogen content \(X_{H_{2}}\), temperature \(T\), pressure \(P\), and equivalence ratio \(\varphi\)) to uncover the underlying characteristics of the dataset involved. The findings, as depicted in Figure 5, reveal that \(X_{H_{2}}\) has a substantial impact on LBV, indicating its importance as a primary driver in \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Pressure & 1-7 bar \\ Temperature & 298–493 K \\ Hydrogen fuel fraction & 0.025–0.6 \\ Equivalence ratio & 0.6–1.8 \\ LBV & 4.81–140.21 cm/s \\ No. of exp. points & 486 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The ranges of the dataset parameters used by NHLBVNet. Figure 4: Outlier screening in the group with \(P=1\) atm, \(T=298\) K, \(X_{H_{2}}=0.3\). the experimental data. Temperature's impact ranks as the second most substantial factor, suggesting that thermal conditions must be closely monitored to maintain optimal LBV. In Figure 5, we also conducted a significance analysis, where the more asterisks present indicate a more significant correlation between the feature variables.