esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling of the Proder-Object System and the Numerical Analysis Method
\begin{array}{cc}k_{1}\!+\!k_{3}&-k_{3}\\ -k_{3}&k_{2}\!+\!k_{3}\end{array} \right]\), \(\textbf{C}=\left[ \begin{array}{cc}c_{1}\!+\!c_{3}&-c_{3}\\ -c_{3}&c_{2}\!+\!c_{3}\end{array} \right]\), \(\textbf{M}=\left[ \begin{array}{c}m_{1}\\ m_{2}\end{array} \right]\), \(\textbf{F}=\left[ \begin{array}{c}F\\ 0\end{array} \right]\). After being Fourier transformed, the matrix Equation (2) can be written, as follows:  
\[\Big{(}\textbf{K}\!-\!\omega^{2}\textbf{M}\!+\!j\omega\textbf{C}\Big{)} \textbf{X}(\omega)=\textbf{F}(\omega) \tag{3}\]  
Accordingly, \(\textbf{Z}(\omega)\), the impedance matrix of Equation (3), can be written as the following equation:  
\[\textbf{Z}(\omega)=\textbf{K}\!-\!\omega^{2}\textbf{M}\!+\!j\omega\textbf{C} \tag{4}\]  
Subsequently, \(\text{det}\textbf{Z}(\omega)\) and \(adj\textbf{Z}(\omega)\), the determinant and adjoint matrixes of \(\textbf{Z}(\omega)\), respectively, can be written, as follows:  
\[adj\textbf{Z}(\omega)=\left[ \begin{array}{cc}k_{1}+k_{2}-\omega^{2}m_{2}+j \omega[c_{1}+c_{2}]&k_{1}+j\omega c_{1}\\ k_{1}+j\omega c_{1}&k_{1}+k_{3}-\omega^{2}m_{1}+j\omega[c_{1}+c_{3}]\end{array} \right] \tag{5}\]  
\[\text{det}\textbf{Z}(\omega)=\Big{[}k_{1}+k_{3}-\omega^{2}m_{1}+j\omega[c_{1} +c_{3}]\Big{]}\Big{[}k_{1}+k_{2}-\omega^{2}m_{2}+j\omega[c_{1}+c_{2}]\Big{]}- \left(k_{1}+j\omega c_{1}\right)^{2} \tag{6}\]  
Afterwards, the system frequency response function \(\textbf{H}(\omega)\) can be written, as follows:  
\[\textbf{H}(\omega)=\frac{adj\textbf{Z}(\omega)}{\text{det}\textbf{Z}(\omega) }=\left[ \begin{array}{cc}H_{11}[\omega]&H_{12}[\omega]\\ H_{21}[\omega]&H_{22}[\omega]\end{array} \right] \tag{7}\]  
In the 2-2 matrix \(\textbf{H}(\omega)\), \(H_{lp}(\omega)\) is the transfer function between the \(l\) th and \(p\) th component of the system. In this case, \(H_{12}(\omega)\), the transfer function between prodder and buried object, can be written, as follows:  
\[|H_{12}|\omega||=\sqrt{\frac{C^{2}\!+\!D^{2}}{E^{2}\!+\!F^{2}}} \tag{8}\]  
where  
\(A=k_{1}+k_{3}-\omega^{2}m_{1}\);  
\(B=\omega(c_{1}+c_{3})\);  
\(C=k_{2}+k_{3}-\omega^{2}m_{2}\);  
\(D=\omega(c_{2}+c_{3})\);  
\(E=AC-BD-k_{3}^{2}+\omega^{2}c_{3}^{2}\); and,  
\(F=AD+BC-2\omega c_{3}k_{3}\).
# Minimum Audible Angle in 3 rd-Order Ambisonics in Horizontal Plane for Different Ambsonic Decoders
## 6 Discussion  
In this study, the arithmetic mean was employed as a measure of central tendency for the analysed parameters, with full awareness of the methodological limitations inherent to its application in small-sample research. To address these limitations and ensure transparency, the steps were implemented as follows:  
* Presentation of individual participant data alongside aggregated means (see Figures 8-10) to visualise inter-subject variability.  
Figure 14: MAA values for a subject moderately experienced with audio engineering (student), with low familiarity with the playback system.  
Figure 13: MAA values for a subject highly experienced with audio engineering, with medium familiarity with the playback system.  
* Cautious interpretation of results, emphasising exploratory trends rather than definitive conclusions. This approach aligns with recommendations for small-sample studies in the psychoacoustic literature [33], where descriptive statistics remain valid when accompanied by rigorous transparency about data dispersion and reproducibility constraints. The ANOVA results indicate that decoder performance varies significantly across spatial positions; however, the main effect of the decoder alone is not significant (\(p=0.697\)). Notably, pairwise comparisons between the AllRAD and SAD show a significant difference (\(p<0.05\)) only at \(75^{\circ}\) and \(90^{\circ}\). The overall effect size is small (\(R^{2}=0.42\)). Additional observations within the subjects are captured, suggesting a possible dependency on familiarity with the playback system and the stereophony experience and offering direction for further research. For subjects highly familiar with the system, the differences between the decoders remain within the same order of magnitude. For listeners with high experience in stereophonic sound, the resolution within the stereo base (up to \(30^{\circ}\)) remains very high for the VBAP-based decoders. For the subjects without experience, the EPAD produces better results than any other decoder for the whole range of the horizontal plane. That may suggest the overall importance of stable energy distribution and minimising the energy vector angular error to produce the best perceptual results. The results of the three subjects (S01, S05, S06) with varied experience with stereophonic and spatial audio may suggest that, unless the listener is highly familiar with the playback system, the particular experience in stereophony may cause increased alertness in phase differences. This, in turn, may reduce the perceptual resolution of the MAA.
## 2 Results and Discussions
### Optimization of SSF Conditions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **SS** & **DF** & **MS** & **F** & **_p_ \\ \hline Model & 141,506 & 9 & 15,722.9 & 8.51 & 0.005 \\ X\({}_{1}\)- Initial moisture content & 35,841.8 & 1 & 35,841.8 & 19.41 & 0.003 \\ X\({}_{2}\)- Incubation temperature & 4703.0 & 1 & 4703.0 & 2.55 & 0.155 \\ X\({}_{3}\)- Incubation time & 48,013.0 & 1 & 48,013.0 & 26.00 & 0.001 \\ X\({}_{1}\)X\({}_{2}\) & 798.3 & 1 & 798.3 & 0.43 & 0.532 \\ X\({}_{1}\)X\({}_{3}\) & 11,200.7 & 1 & 11,200.7 & 6.06 & 0.043 \\ X\({}_{2}\)X\({}_{3}\) & 9070.3 & 1 & 9070.3 & 4.91 & 0.062 \\ X\({}_{1}\)\({}^{2}\) & 4664.4 & 1 & 4664.4 & 2.53 & 0.156 \\ X\({}_{2}\)\({}^{2}\) & 5067.7 & 1 & 5067.7 & 2.74 & 0.142 \\ X\({}_{3}\)\({}^{2}\) & 19,268.9 & 1 & 19,268.9 & 10.43 & 0.014 \\ \hline Residual & 12,928.3 & 7 & 1846.9 & & \\ Lack of Fit & 2181.9 & 3 & 727.3 & 0.27 & 0.844 \\ Pure Error & 10,746.4 & 4 & 2686.6 & & \\ Cor Total & 154,434.3 & 16 & & & \\ \hline \hline \multicolumn{6}{c}{R\({}^{2}\) = 0.916, Adjusted R\({}^{2}\) = 0.809, Predicted R\({}^{2}\) = 0.665} \\ \hline \end{tabular} DF: degrees of freedom, SS: sum of squares, MS: mean squares.  
\end{table} 
Table 2: Analysis of variance (ANOVA) for the fitted design model for optimization of SSF conditions. stage of SSF, the peptides will be further hydrolyzed into amino acids that may cause a decrease in the peptide content. This result also indicates the importance of optimizing the fermentation time (incubation time) as a significant factor.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 3 Proposed Algorithm
### Initialization  
The initial estimate has an impact on the computational complexity, the convergence speed, and the number of subsequent iterations required in massive MIMO signal detection. Traditional detection algorithms generally have zero vectors as initial values, which are far away from the final estimate, so convergence is slower. When the matrix A is a Hermitian positive definite matrix, the GS repetitive scheme converges for any initial solution [27]. For this reason, two initialization schemes are proposed below. Using the initial estimate of the zero vector as the reference object, we simulate the performance of the two initialization strategies discussed in Section 4.1. Scheme 3 refers to repetitive computation where the beginning value is zero.  
1. In a given massive MIMO, the quantity of transmitting and receiving antennas is set. Thus, the initial solution is estimated by means of a linear transformation of the number estimating the initial solution, which can avoid matrix inversion operations and further limit computing power. \[s^{(0)}=\frac{y_{MF}}{B+U}\] (9)  
Figure 2: Block diagram of the CJGS detector.  
2. The diagonal elements of the matrix \(A\) are dominant in massive MIMO communication. Therefore, the elements of matrix A can be grouped into diagonal elements that are not negligible and non-diagonal elements that are negligible, i.e., \(A\approx D\). The computational complexity of inverting the matrix could be decreased by replacing \(A\) with \(D\). \[s^{(0)}=D^{-1}y_{MF}\] (10)
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 5 Experiments
### Hyperparameters Selection  
All the models were built using the default values for hyperparameters provided by the scikit-learn package in Python. We aimed to create the models as simple as possible to test if the simpler models can classify the traffic with high accuracy, and that is the purpose of using the default parameters. The list of hyperparameters for all the models is listed below, whereas the selected values for those parameters are mentioned in Table 1.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **n\_Estimators** & **Criterion** & **Max Depth** & \
\begin{tabular}{l} **Min Samples** \\ **Split** \\ \end{tabular} &
\begin{tabular}{l} **Min Samples** \\ **Leaf** \\ \end{tabular} \\ \hline RF & 100 & Gini & None & 2 & 1 \\ \hline ET & 100 & Gini & None & 2 & 1 \\ \hline GBT & 100 & friedman\_mse & 3 & 2 & 1 \\ \hline XGBT & 100 & friedman\_mse & 3 & 2 & 1 \\ \hline LGBM & 100 & friedman\_mse & 3 & 2 & 1 \\ \hline \hline \end{tabular}  
* n\_estimators: Number of trees in the model.
* criterion: Quality measurement of the split.
* max\_depth: Maximum depth of the tree.
* min\_samples\_split: Minimum number of samples required to split.
* min\_samples\_leaf: Minimum number of samples required for a leaf.  
\end{table}
Table 1: Hyperparameters values for each model.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 2 Model Order Selection
### Information Criteria  
Let us start with information criteria that are common to model order selection.
## 3 Detouring Service via Multiple Paths
### An Extension of a Routing Protocol RDSR [27]  
DMP is based on the general routing protocols in the normal case or in any area except for the boundary of the jamming zone. Thus, we illustrate the extension of a sensor routing protocol, called Reactive Direction based Sensor Routing (RDSR), in order to reinforce the resistance against jamming with DMP. RDSR is one of the routing schemes considering the energy efficiency on sensor networks, as shown in Figure 4. We choose the RDSR with hierarchical topology, unlike the base DMP with rectangular zones. The assumed network consists of a base station, several manager nodes and lots of sensor nodes randomly distributed. The whole network is divided into four quadrants, namely (\(+\) +), (\(+\) -), (\(-\) +), and (\(-\) -), and then each quadrant is divided into sectors. Thus, each node tries to route the traffic toward the manager node if it exists within a 1-hop distance. Otherwise it finds the node with the smallest sector number as a next hop node or just selects a node with the same sector ID. If there is any detouring service in RDSR, the traffic from the start node is blocked at the (\(+3\) -\(3\)) manager node if a jamming attack is mounted at the (\(+2\) -\(2\)) zone, as shown in Figure 4.  
Figure 4: Rerouting example with extended RDSR in the jamming attack with \(\alpha=2\).  
Figure 3: An example of forward zones according to victim zones (destination zone 1, victim zone is 22, 23 and 32 for each respective case.).  
We can enforce the RDSR in the security view with our DMP. After detecting a jamming attack and determining the detour nodes, each detour node detours the traffic destined for the base station to forward nodes determined according to the process shown in Table 2.  
In the basic DMP, we assume a rectangular shaped zone for detouring normal traffic, however our method of defense can be easily adjusted to zones with other shapes such as hexagonal zones or arbitrary shaped zones, because the forward zones are determined among the adjacent neighbor zones. With respect to the zone shape, only the set of neighbor zones differs.  
Thus, for the extension of RDSR, we assume each sector of each quadrant is divided into two zones. On the network shown in Figure 4, the candidate forward zones for zone 0 become the neighbor zones 1, 4 and 5 except for jamming zones 2 and 3. Figure 4 depicts the routes by the extension of RDSR with DMP as an illustration of jamming. The _start node_ with traffic decides the forward zones among the neighbor zones and then it detours the traffic to the forward nodes in the decided forward zones 1 and 5, ignoring the original RDSR on the boundary of the jamming area. This process is repeated until the general next-hop of a forward node is not a node in the jamming area. After the route moves beyond the jamming area, the remaining routing follows the original RDSR.
## 2 Materials and Methods
### PET Acquisition Simulation
#### 2.3.3 Evaluation  
Several methodologies were employed to evaluate the voxelized images against the reconstructions. A visual check of the resemblance between the voxelized images and the  
Figure 3: Scanner simulated in GATE.  
reconstruction was performed to compare the activity distribution between the two cases. Additionally, a line profile was also obtained. A line profile shows the intensity of an image along a specified line or path. We compared the line profile of both the voxelized and reconstructed images to validate the results from the visual check.  
In order to obtain a quantitative metric for all available images, another method of evaluation was the use of the Structural Similarity Index (SSIM), which indicates the similitude between two images. Its range is between 0 and 1, indicating that the maximum value equals a perfect copy of the reference.  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c2)}{(\mu_{x}^{2}+\mu_{y} ^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{2}\]  
where the following apply:  
* \(\mu_{x}\) is the pixel sample mean of x.
* \(\mu_{y}\) is the pixel sample mean of y.
* \(\sigma_{x}^{2}\) is the variance of x.
* \(\sigma_{y}^{2}\) is the variance of y.
* \(\sigma_{xy}\) is the covariance of x and y.
* \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\) are two variables to stabilize the division with a weak denominator.
* L is the dynamic range of the pixel values (typically, this is \(2^{\#bits\ per\ pixel}-1))\).
* \(k_{1}=0.01\), and \(k_{2}=0.03\) by default.  
Lastly, we also calculated the Frechet Inception Distance (FID); this metric is used to check the quality and diversity of datasets, as it performs feature extraction and utilizes the means and covariance matrix of the feature matrix of each image. For the interpretation of this metric, the closer it is to zero, the more similar the images from the datasets are. When the FID < 10, the datasets are highly similar and of high quality [25; 26].  
\[FID=|\mu-\mu_{w}|^{2}+tr(\Sigma+\Sigma_{w}-2(\Sigma\Sigma_{w})^{\frac{1}{2}})\]
## 2 Theoretical Foundations
### Flow Theory  
[PERSON] [11] took the lead in elaborating the concept of flow theory, defining it as a holistic and deep psychological state experienced by individuals when they are fully immersed in a specific activity. The core of this definition covers four key elements: a sense of control, concentration, curiosity, and intrinsic interest drive. The flow state is the optimal form of experience in a situation with immediate feedback. It is characterized by deep concentration, a strong sense of control over the environment, distortion of time perception, and temporary dissolution of self-consciousness [16; 17]. This state widely exists in sports competition, occupational work, shopping behavior, game entertainment, personal hobbies, computer operation, and other activities, reflecting the universal applicability of flow as a unique psychological phenomenon [18].  
[PERSON] et al. [19] further define flow experience as the psychological state of the experiencers in engaging activities that significantly promotes the concentration of attention and the enhancement of pleasure. Given the critical role of flow experience in shaping engaging experiences, a deeper understanding of the drivers that trigger the flow experience for consumers is strategically vital for for-profit organizations to create unique, highly engaging services or products. This process not only helps to optimize the user experience but also significantly increases customer satisfaction and loyalty.  
Nevertheless, the factors contributing to the flow experience within the AR context remain largely unexplored [20]. A study by [PERSON] et al. [21] examined AR facilitation to  
Figure 1: The technology acceptance model (TAM).  
enhance the flow experience's unique features and its positive impact on various consumer outcomes, concluding that vividness, interactivity, and novelty positively impact mindful flow states. This study will further explore the perceptual factors that lead to an AR-enhanced flow experience in the consumer context of cultural heritage purchases.
## 3 Feature Extraction
### Feature Vector Arrangement  
After nine signal characteristics of PQD signals were obtained by the MRA process, the statistical parameters of signals were evaluated based on the signal's characteristics. In this work, we focused on 11 statistical parameters including the energy (\(E\)), entropy (\(Ent\)), standard deviation (\(s\)), mean value (\(m\)), kurtosis (\(KT\)), skewness (\(SK\)), root-mean square (\(RMS\)), range (\(RG\)), log-energy entropy (\(LEnt\)), crest factor (\(CF\)), and form factor, as shown in Table 2. \(i,\,j=1,\,2,\,3,\,\ldots,\,l\) are the wavelet decomposition numbers at level \(l\). \(N\) is the signal coefficient quantity in each decomposed data. \(X\) is the signal collected from eight detailed signals (\(D1\), \(D2\), \(\ldots,\,D8\)) and one approximate signal (\(A8\)).  
The feature vector of each statistical parameter was then constructed by the 11 detailed signals and one approximate signal, as shown in Table 3. It is shown that the overall features having 99 data are organized based on the type of statistical parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Statistical Equation** & **Parameter** & **Statistical Equation** \\ \hline Energy & \(E_{i}=\sum_{j=1}^{N}\left(\left|X_{ij}\right|^{2}\right)\) & RMS & \(RMS_{i}=\left(\frac{1}{n}\sum_{j=1}^{N}\left(X_{ij}\right)^{2}\right)^{\frac{ 1}{2}}\) \\ Entropy & \(Ent_{i}=-\sum_{j=1}^{N}\left\{X_{ij}^{2}\log X_{ij}^{2}\right\}\) & Range & \(RG_{i}=Max\left(X_{ij}\right)-Min\left(X_{ij}\right)\) \\ Standard deviation & \(\sigma_{i}=\left(\frac{1}{n-1}\sum_{j=1}^{N}\left(X_{ij}-\mu_{i}\right)^{2} \right)\right)^{\frac{1}{2}}\) & Log-energy entropy & \(LEnt_{i}=-\sum_{j=1}^{N}\log\left(X_{ij}^{2}\right)\) \\ Mean & \(\mu_{i}=\frac{1}{n}\sum_{j=1}^{N}\left(X_{ij}\right)\) & Crest factor & \(CF_{i}=\frac{X_{max}}{RMS_{i}}\) \\ Kurtosis & \(KT_{i}=\frac{E\left(X_{ij}-\mu_{i}\right)^{4}}{\sigma_{i}^{2}}\) & Form factor & \(FF_{i}=\frac{X}{RMS_{i}}\) \\ Skewness & \(SK_{i}=\frac{E\left(X_{ij}-\mu_{i}\right)^{3}}{\sigma_{i}^{2}}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equations for statistical parameter.  
Since the feature vectors calculated from the statistical parameters might have a large difference in scale, they have to be normalized before passing through the classifier, as given in Equation (3);  
\[Z_{i}=\frac{F_{i}-F_{\text{min}}}{F_{\text{max}}-F_{\text{min}}} \tag{3}\]  
where \(Z_{i}\) is the normalized data, \(F_{i}\) is the feature vectors, \(F_{\text{min}}\) and \(F_{\text{max}}\) are the feature vectors having the minimum and maximum value. Therefore, the overall feature set can be obtained using Equation (4).  
\[Feature=\begin{bmatrix}F_{1}&F_{2}&F_{3}&F_{4}&F_{5}&F_{6}&F_{7}&F_{8}&F_{9}&F_{ 10}&F_{11}\end{bmatrix} \tag{4}\]
## 1 Introduction  
Micropillar arrays have been of interest for photovoltaic applications because structured patterning of a planar silicon surface leads to enhanced light trapping efficiency [1; 2; 3; 4; 5; 6; 7]. This enhancement leads to an increase in efficiency between 1.5-11% [8] and in turn enables solar cells based on thin layers (<100 \(\upmu\)m), thereby greatly reducing start-up materials cost [9; 10; 11]. Among the various surface structures possible, micropillar arrays with radial p-n junctions are particularly attractive, because the light absorption and carrier collection become decoupled [12]. In other words, incident light is absorbed along the long wire axis, while generated charge carriers can be collected along the relatively short radial direction [13]. The main challenge in forming radial p-n junctions is achieving shallow doping that will allow the formation of a quasi-neutral region (QNR) and space charge region (SCR) in its tiny geometry [14; 15]. Figure 1a is a representation of the desired radial p-n junction [15]. Figure 1b is a representation of the radial p-n junction obtained when the pillars are doped using the same procedure that is used to dope planar silicon wafers [16; 17]. Another challenge is reducing the surface defects generated by the direct etching of the semiconductor, which may cause a lower carrier collection efficiency and degradation of the cell performance [18; 19; 20; 21]. Current etching and doping techniques result in rough surfaces with major defects that act as favorable sites for surface recombination. This paper aims to demonstrate that silicon dioxide (SiO\({}_{2}\)) can be used as a layer to achieve shallow (dopant depth <200 nm) and heavy (>10\({}^{20}\) cm\({}^{-3}\)) doping in micropillars. The reason the above metrics were used (<200 nm and >10\({}^{20}\) cm\({}^{-3}\)) is because a study has shown that junctions with depth larger than 200 nm resulted in 20% reduced efficiency [22]. Another study [23; 24; 25] also revealed that junctions with low dopant concentration (<2 \(\times\) 10\({}^{19}\) cm\({}^{-3}\)) did not give properly functioning diodes.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 3 Example Analysis
### Experimental Analysis and Verification
#### 3.2.2 Construction and Optimization of FL-XGB-VPN-Encrypted Traffic Identification Model
The optimal combination of parameter values for the FL-XGB model is searched using Bayesian optimization, and the set of parameters of the FL-XGB model and its optimal search range are shown in Table 6.  
By Bayesian optimization algorithm, the maximum depth of the tree Max_depth is 10, the learning rate Eta is 0.3, the minimum leaf weight Min_child_weight is 4, the minimum loss function descent value Gamma is 7.7, Subsample is 0.5, and the feature sampling ratio colsample_bytree is 0.2 after the optimization. The optimal combination of parameters after optimization is shown in Table 7.  
The optimal set of parameters obtained from Bayesian optimization is input into the FL-XGB model to obtain the ROC curve of the model. The area of the ROC curve indicates its characteristics. When the area enclosed by the ROC curve is 0.5 for random classification, the identification ability of the model is 0. The closer the area is to 1 indicates that the identification ability of the FL-XGB model is stronger. From Figure 15, the area of the ROC curve is close to 1, indicating that the FL-XGB model has a strong identification effect on VPN-encrypted traffic. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value Ranges** \\ \hline Max\_depth & (1, 15) \\ Eta & (0, 1) \\ Min\_child\_weight & (0.1, 20) \\ Gamma & (0, 20) \\ Subsample & (0, 1) \\ colsample\_bytree & (0, 1) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters and their value ranges. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Max\_depth & 10 \\ Eta & 0.3 \\ Min\_child\_weight & 4 \\ Gamma & 7.7 \\ Subsample & 0.5 \\ colsample\_bytree & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The optimal combination of parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cross Entropy** & **Focal Loss (\(\lambda\)=2)** \\ \hline Accuracy & 0.8852 & 0.9543 \\ Precision & 0.9045 & 0.9522 \\ F1-Score & 0.9062 & 0.9509 \\ Recall & 0.9097 & 0.9496 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross entropy and Focal Loss function performance.