esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D\({}^{2}\)GAN
## 1 Introduction
Additionally, considering the shortcomings of using a single discriminator, this paper uses dual discriminators to fully obtain the valid information contained in the source images and highlight the lesion information in the fused images. Finally, due to how the fusion rule based on region energy can obtain local region feature information and improve the sharpness of the fused images, this paper uses the threshold adaptive weighting algorithm based on a region energy ratio as the salient region fusion rule. The main contributions can be summarized as follows:  
1. A hybrid multimodal medical image fusion method based on LatLRR and ED-D\({}^{2}\)GAN is proposed, which can effectively realize the fusion of CT and PET images. 2. An image fusion strategy based on a dual discriminator GAN is proposed. Encoder and decoder networks are used in the generator; CNNs are used in dual discriminators, which can effectively preserve the anatomical structure information in CT source images and the functional information of the lesion region in PET source images. 3. A threshold adaptive weighting algorithm based on a region energy ratio is used as the fusion rule of salient region images, which improves the quality of fused images. The remainder of the paper is presented in four sections. The fusion methods of low-rank region images and salient region images are described in detail in Section 2. Section 3 provides and analyses the experimental details and results. The experimental results and future work directions are discussed in Section 4. Finally, this paper is concluded in Section 5.
# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 5 Results and Discussion
### Performance Comparison with Baseline Classifiers and Recent Literature
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Feature Selection Method**} & \multicolumn{2}{c}{**Features**} \\ \hline \multirow{6}{*}{Complete feature set} & \multicolumn{2}{c}{ID, LIMIT\_BAL, SEX, EDUCATION,} & \multicolumn{1}{c}{MARRIAGE, AGE, PAY\_0, PAY\_2, PAY\_3,} \\  & \multicolumn{1}{c}{PAY\_4, PAY\_5, PAY\_6, BILL\_AMT1, BILL\_AMT2,} & \multicolumn{1}{c}{BILL\_AMT3, BILL\_AMT4, BILL\_AMT5,} \\  & \multicolumn{1}{c}{BILL\_AMT3, BILL\_AMT4, BILL\_AMT5,} & \multicolumn{1}{c}{BILL\_AMT6, PAY\_AMT1, PAY\_AMT2,} \\  & \multicolumn{1}{c}{PAY\_AMT3, PAY\_AMT4, PAY\_AMT5, PAY\_AMT6} \\ \hline \multirow{6}{*}{IG} & \multicolumn{2}{c}{SEX, PAY\_0, PAY\_2, PAY\_3, PAY\_4, PAY\_5,} \\  & \multicolumn{1}{c}{PAY\_6, BILL\_AMT1, BILL\_AMT2, BILL\_AMT3,} & \multicolumn{1}{c}{BILL\_AMT4, BILL\_AMT5,} \\  & \multicolumn{1}{c}{PAY\_4, PAY\_5, PAY\_6, BILL\_AMT1, BILL\_AMT2, BILL\_AMT3,} & \multicolumn{1}{c}{PAY\_4, PAY\_4,} \\  & \multicolumn{1}{c}{PAY\_4, PAY\_6, BILL\_AMT4, BILL\_AMT5, BILL\_AMT6,} & \multicolumn{1}{c}{PAY\_4, PAY\_AMT5, PAY\_AMT6} \\ \hline \multirow{6}{*}{GAW} & \multicolumn{2}{c}{PAY\_0, PAY\_2, PAY\_4, PAY\_5, BILL\_AMT1,} & \multicolumn{1}{c}{GAW} & \multicolumn{1}{c}{BILL\_AMT2, BILL\_AMT3,} \\  & \multicolumn{1}{c}{PAY\_4, BILL\_AMT4, BILL\_AMT5, BILL\_AMT6, PAY\_AMT1,} & \multicolumn{1}{c}{PAY\_4, PAY\_4,} \\ \cline{1-1}  & \multicolumn{1}{c}{PAY\_4, PAY\_4, PAY\_4, PAY\_4,} & \multicolumn{1}{c}{PAY\_4,} & \multicolumn{1}{c}{PAY\_4,} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Feature sets from the Taiwan dataset. Figure 5: Comparative analysis of the various methods using the Taiwan dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Sensitivity** & **Specificity** & **AUC** & **G-Mean** \\ \hline AdaBoost & 0.870 & 0.890 & 0.870 & 0.880 \\ LR & 0.625 & 0.837 & 0.640 & 0.723 \\ RF & 0.829 & 0.914 & 0.840 & 0.870 \\ SVM & 0.626 & 0.819 & 0.650 & 0.716 \\ DT & 0.574 & 0.773 & 0.610 & 0.666 \\ ELM & 0.710 & 0.885 & 0.730 & 0.793 \\ IG-ELM & 0.874 & 0.911 & 0.890 & 0.892 \\ GAW & 0.899 & 0.920 & 0.900 & 0.909 \\ Proposed IG-GAW & 0.945 & 0.961 & 0.940 & 0.952 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison using the Taiwan dataset.
## 3 Proposed Method
### Gated Fusion Unit  
In CNNs, the gating mechanism is usually implemented by some particular neurons. These neurons can decide whether information can pass through them. If gating neurons fire less, they can block the flow of information, preventing some information from being passed on to the next layer. Inspired by the Gated Linear Unit (GLU) [44] and self-attention [45], the GFU was designed for underwater enhancement, as shown in Figure 2d. The Gated Fusion Unit further extracts the input color information and texture features through a \(3\times 3\) convolutional layer and depthwise separable convolution. Next, it fuses the weighted features with the original features and controls the weights of different features through a gating mechanism. Finally, the Gated Fusion Unit takes the processed feature vector as the output. It is worth mentioning that the function of the Gated Fusion Unit is to enhance the quality and clarity of the feature maps at different stages and to filter the noise and irrelevant information so that the details of helpful information can be highlighted.
## 2 Data and Methods
### Methods
#### 2.2.3 Evaluation and Explanation  
We defined:  
\[\eta_{ij}=\frac{\zeta_{ij}^{+}}{\zeta_{ij}^{+}+\xi_{ij}^{-}}. \tag{6}\]  
Hence, we can depict the annual trend under different indicators by comparing \(\overline{\eta_{ij}}=\frac{\eta_{ij}}{\sum_{i=1}^{4}\eta_{ij}}\). Alternatively, we can evaluate the development under primary index (Table 1) by defining a parameter \(\chi_{ik}=\frac{\sum\omega_{i}\overline{\eta_{ij}}}{\sum\omega_{i}}(k=1,2, \cdots 6)\).  
We, furthermore, defined a parameter as:  
\[\sigma_{j}=\frac{\sum_{i=1}^{4}\eta_{ij}}{4}. \tag{7}\]  
This can help us evaluate the amplitude of a gap among different years from different perspectives. If the \(\sigma_{j}\) is small, the average level of all schools is far from the highest level, the gap among different years is significant, and vice versa.  
By combining the Euclidean Distance and Grey Correlation Degree, we defined a couple of parameters:  
\[\begin{cases}\gamma_{i}^{+}=\epsilon\psi\big{(}D_{i}^{-}\big{)}+(1-\epsilon) \psi\big{(}r_{i}^{+}\big{)}\\ \gamma_{i}^{-}=\epsilon\psi\big{(}D_{i}^{+}\big{)}+(1-\epsilon)\psi\big{(}r_{ i}^{-}\big{)},\end{cases} \tag{8}\]  
where \(\psi(x)=\frac{x-min(x)}{max(x)-min(x)}\), \(\epsilon\in[0,1]\) describes the weight between the distance (TOPSIS) and curve shape (GRA). In this study, we set \(\epsilon=0.5\).  
Finally, we can obtain the comprehensive performance score for each year as:  
\[P_{i}=\ \psi\Bigg{(}\frac{\gamma_{i}^{+}}{\gamma_{i}^{+}+\gamma_{i}^{-}} \Bigg{)}\times 40+60. \tag{9}\]  
A higher score indicates a better performance.
# Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images
## 3 BoVW-Based Cattle Identification
### Bag-of-Visual-Words Representation  
The BoVW technique has been shown to be successful for a wide range of computer vision applications, including image retrieval [45] and object classification [46; 47] as well as action recognition [48; 49] with outstanding performance and low storage requirements. Simply, in the basic BoVW model, some local features are extracted from an image using a feature extractor (e.g., SURF) and then the extracted local features are clustered into visual words. That is, the image is described by a histogram of visual word counts instead of low-level features. In this context, this visual vocabulary representation provides a global representation from the local features or a \"mid-level\" representation that can bridge the large semantic gap between the low-level features extracted from the image and the high-level concepts.  
Suppose we have a sequence \(\mathbf{X}=(x_{1},x_{2},...,x_{n})\) of \(d\)-dimensional feature vectors obtained by a feature extractor from an image, where \(x_{i}\in\mathbb{R}^{d}\). The main objective of the BoVW technique is to quantize each sequence \(\mathbf{X}\) based on a specific vocabulary dictionary \(V=\{\
u_{1},\
u_{2},...,\
u_{N}\}\subset\mathbb{R}^{d}\) of \(N\) visual words. To achieve this objective, each sequence \(\mathbf{X}\) can be represented by a histogram of probabilities \(p(\
u|x)\). In this way, the BoVW histogram \(\mathcal{H}\) summarizes the whole image by counting how many times each of the visual words occurs in that image:  
\[\mathcal{H}=\frac{1}{K}\sum_{i=1}^{k}h(x_{i}) \tag{2}\]  
where  
\[h(x_{i})=\left(\begin{array}{c}p(\
u_{1}|x_{i})\\ p(\
u_{2}|x_{i})\\ \vdots\\ p(\
u_{N}|x_{i})\end{array}\right) \tag{3}\]  
The most well-known method for building the visual vocabulary is to use \(k\)-means clustering because of its simplicity and convergence speed. Other methods, such as hierarchical or spectral clustering, can also be used for this task [48]. In this case, the center of every cluster is used as a visual word. The clustering step is to quantize the feature space into a small discrete number of visual words. It should be noted that the choice of data plays an important role in creating the visual vocabulary [50].  
Figure 2 describes the proposed BoVW-based cattle identification approach. The local features of all the images in the database are extracted using SURF/MSER mechanisms. Then, a 500-word visual vocabulary is created by reducing the number of features via feature space quantization using \(k\)-means clustering. Finally, a classification mechanism is considered.
## 3 AIS Sensor Data Acquisition and Preprocessing
### Data Normalization  
To accelerate model convergence and solve errors caused by large differences in magnitude between model samples, the sample data were linearly normalized by the following formula to the interval [0, 1]:  
\[x^{\prime}=\frac{x-x_{\min}}{x_{\max}-x_{\min}}, \tag{23}\]  
where \(x\) is the sample data and \(x\in\mathbb{R}^{n}\); \(x^{\prime}\) is the normalized data and \(x^{\prime}\in[0,1]\); and \(x_{\max}\) and \(x_{\min}\) are the maximum and minimum values in the sample points, respectively.
## 3 Discussion of the Noise Results
### Effect of the Pore Diameter
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Configuration** & \(\mathbf{V_{\infty}}\) **= 10 m/s** & \(\mathbf{V_{\infty}}\) **= 20 m/s** & \(\mathbf{V_{\infty}}\) **= 30 m/s** & \(\mathbf{V_{\infty}}\) **= 40 m/s** & \(\mathbf{V_{\infty}}\) **= 50 m/s** & \(\mathbf{V_{\infty}}\) **= 60 m/s** & \(\mathbf{V_{\infty}}\) **= 70 m/s** \\ \hline Baseline & 71.81 & 78.14 & 73.76 & 76.79 & 82.10 & 88.32 & 92.10 \\ L04M04N04 & 71.88 & 77.87 & 73.04 & 76.81 & 82.11 & 88.31 & 92.10 \\ L06M06N06 & 71.45 & 75.98 & 72.23 & 76.53 & 82.00 & 88.32 & 92.16 \\ L08M08N08 & 71.02 & 75.51 & 70.90 & 76.44 & 82.00 & 88.21 & 92.13 \\ L00M06N06 & 71.82 & 75.75 & 69.66 & 76.55 & 82.08 & 88.20 & 92.20 \\ L00M00N06 & 71.85 & 74.85 & 69.65 & 76.60 & 81.84 & 88.33 & 92.16 \\ L00M06N00 & 72.46 & 78.07 & 73.34 & 76.57 & 82.12 & 88.41 & 92.21 \\ L06M00N00 & 72.38 & 78.13 & 72.45 & 76.82 & 82.20 & 88.30 & 92.18 \\ L04M05N06 & 72.05 & 75.72 & 69.53 & 76.46 & 81.97 & 88.27 & 92.29 \\ L06M05N04 & 72.23 & 75.81 & 69.83 & 76.48 & 81.82 & 88.25 & 92.20 \\ L04M06N08 & 71.93 & 76.99 & 72.24 & 76.69 & 82.08 & 88.39 & 92.29 \\ L08M06N04 & 72.71 & 77.84 & 73.54 & 77.43 & 82.12 & 88.53 & 92.33 \\ L04M08N12 & 70.50 & 74.96 & 71.50 & 76.56 & 82.15 & 88.46 & 92.32 \\ L12M08N04 & 71.24 & 77.35 & 74.15 & 77.62 & 82.00 & 88.43 & 92.32 \\ L06M06N06-\(c_{1}\)4 & 72.61 & 78.10 & 73.42 & 76.74 & 82.07 & 88.58 & 92.39 \\ L06M06N06-\(c_{1}\)1 & 71.01 & 77.28 & 72.15 & 76.67 & 81.96 & 88.36 & 92.13 \\ L06M06N06-\(c_{2}\)20 & 72.15 & 77.39 & 72.61 & 76.88 & 82.09 & 88.57 & 92.27 \\ L06M06N06-\(c_{3}\)5 & 71.54 & 76.71 & 72.33 & 77.17 & 82.05 & 88.60 & 92.31 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of the OASPL for the different configurations at \(\alpha=0^{\circ}\).
# Using Geospatial Information to Map Yield Gain from the Use of _Azospirillum brasilense_ in Furrow
## 2 Method
### Generation of Yield Gain Surfaces by Kriging  
The objective was to evaluate the application of a geostatistical technique (interpolation by Kriging) to perform yield inference considering different treatment scenarios in the study areas.  
It is worth noting that the purpose of this analysis was to simulate the yield of the particular area, considering two situations: yield of an area that was not treated (STD) or treated with the application of _A. brasilense_ (TS). For each area, two yield surfaces were generated considering different locations for interpolation. Initially, 80% of the points contained in the locations where treatment was not applied were considered for interpolation. Subsequently, 80% of the points contained in the locations where treatment was applied were considered (Figure 2B,E). The 20% of the remaining data distributed in each treatment were used to validate the models.  
Before generating the interpolated surfaces, a semivariogram analysis was performed to define the mapping functions that best reproduced the yield structure analyzed. The spatial dependence between the data was modeled based on the semivariogram. This allowed the interpolation of productivity values at unsampled locations. Through the covariances, the statistical relationships existing between the spaced samples of successive predetermined values were measured, allowing the determination of the best linear combination of nearby sample values to estimate a value at an unknown location [7].
## 4 Mathematical Calculation of PSD
### Polar NRZ-L PSD  
NRZ-L represents a bit with the single waveform in \(\mathbf{T_{b}}\), the plus duration for polar NRZ-L of PSD. Equation (3) shows the PSD of NRZ-L. In Equation (3) \"A\" represents the 1 bit, which normalizes the unity average of PSD signal. NRZ-L requires a double voltage level for illustration of the 0 s and 1 s in the bitstream [25].  
\[(f)=A^{2}T_{b}(\frac{sin\pi fT_{b}}{\pi fT_{b}})^{2} \tag{3}\]
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 4 Discussion
### Configuration Selection of ASDNet Architecture
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **AUC** & **pAUC** & **Flops** & **\# Params.** \\ \hline \multicolumn{5}{c}{**Different Configurations of Layers**} \\
7 & 91.99 & 84.27 & 85.12 M & 0.49 M \\
8 (ASDNet) & **94.42** & 87.18 & 85.4 M & 0.51 M \\
9 & 94.10 & **87.21** & 85.7 M & 0.56 M \\
10 & 93.83 & 86.98 & 85.79 M & 0.59 M \\ \hline \multicolumn{5}{c}{**Different Configurations of Channels**} \\
64 & 92.80 & 86.32 & 22.12 M & 0.14 M \\
128 (ASDNet) & **94.42** & 87.18 & 85.4 M & 0.51 M \\
192 & 94.07 & 87.30 & 189.65 M & 1.11 M \\
256 & 93.84 & 86.57 & 335.07 M & 1.94 M \\ option A & 93.99 & 87.17 & 28.62 M & 0.69 M \\ option B & 92.70 & 84.01 & 6.68 M & 0.13 M \\ option C & 94.12 & **87.77** & 307.77 M & 1.23 M \\ \hline \multicolumn{5}{c}{**Different Configurations of Kernel Sizes**} \\
5 & 93.04 & 85.37 & 125.46 M & 0.57 M \\
7 & 94.38 & 87.05 & 232.52 M & 0.8 M \\ ASDNet & **94.42** & **87.18** & 85.4 M & 0.51 M \\ option D & 93.63 & 85.76 & 166.31 M & 0.74 M \\ option E & 93.63 & 86.76 & 243.55 M & 0.74 M \\ \hline \hline \end{tabular} For different configurations of channels, “64”, “128”, “192”, and “256” represent models with constant channels across all layers. “Option A” uses the configuration [32, 64, 128, 192, 256, 192, 128, 128], “Option B” has [32, 32, 32, 64, 64, 128, 128], and “Option C” follows [256, 256, 192, 192, 160, 160, 128, 128]. For different configurations of kernel sizes, “5” and “7” represent models with fixed kernel sizes of 5 and 7, respectively. ‘Option D’ uses an increasing kernel size sequence [3, 3, 5, 5, 7, 7, 9, 9], while ‘Option E’ alternates between increasing and decreasing kernel sizes [3, 5, 7, 9, 9, 7, 5, 3].  
\end{table} 
Table 7: Evaluation of ASDNet performances for different configurations of layers, channels, and kernel sizes. Boldface denotes the best results.
## 5 The Proposed Deep-Learning-Based Handover Mechanism for Real-Time Video Conference Services
Figure 16: _Cont._  
Figure 15: Predicted RSRP results for the next 2 s: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM. Figure 16: Predicted RSRP results for the next 4 s: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM. Figure 17: _Cont._  
Figure 17: Predicted RSRP results for the next 6 s: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM. Figure 18: Predicted RSRP results for the next 8 s: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM. From Figures 15-19, the four deep learning models all show a significant or considerable gap between the real data and the predicted data as the prediction time increases. However, the predicted data of the RNN model stays relatively close to the real data in general. Conversely, the overall prediction result of the CNN model produces a relatively larger deviation from the real data. The proposed deep-learning-based handover mechanism is implemented using the Mininet-WiFi for LTE network simulation. When the UE sends back the received RSRP value to the eNB in every second, the eNB will make RSRP predictions for the next 2, 4, 6, 8, and 10 s based on the RSRP values. Based on the predicted RSRP values, the corresponding MOS values are calculated according to Equation (7). According to the RSRP values received from the UE, the eNB needs to predict whether the eNB can meet the required MOS for video conferencing services in the next few seconds. If not, it activates  
Figure 19: Predicted RSRP results for the next 10 s: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM. the handover mechanism before the signal quality is degraded to avoid the MOS value going too low.
# Evolution Mechanism of Interlayer Properties of CRTS III Slab Track during Construction
## 3 Results and Discussion
### Influence of Key Factors on the Evolution of Interlayer Damage
#### 3.3.2 Shrinkage Performance of SCC  
The shrinkage performance of SCC is related to the curing conditions. When improperly cured, the increment of shrinkage deformation can exceed 60% [40; 41], directly affecting the interfacial cracking and damage. In this section, based on the standard shrinkage deformation with proper curing, shrinkage deformations of 125% and 150% were constructed to represent improper curing while keeping the bonding performance constant (the reduction coefficient is 1). The corner of the slab was the focus of the section, and the cracking factor and vertical displacement under different shrinkage loads were extracted, as shown in Figure 18.  
Figure 17: Typical index at the corner of the slab under different interface reduction coefficients. (**a**) \(D_{I}\); (**b**) vertical displacement.  
From Figure 18a, it can be observed that with the increase in shrinkage load, the magnitude of \(D_{I}\) at the same age increases, and the development rate becomes faster. In terms of magnitude, the relative cracking rate shows nonlinear growth with increasing shrinkage load. To facilitate the analysis of interfacial damage evolution rates under different shrinkage loads, the relative cracking rate is defined as follows:  
\[\Delta v_{cracking}=\frac{t_{standard}^{cracking}}{t_{n}^{cracking}} \tag{3}\]  
where \(t_{n}^{cracking}\) represents the corner of the slab cracking time corresponding to the nth contraction load and \(t_{standard}^{cracking}\) represents the slab cracking time corresponding to the standard shrinkage curve. By calculation, it can be concluded that when the shrinkage load is 1.25 times the original load, the relative cracking rate is 1.66 times that of the standard shrinkage load; when the shrinkage load is 1.5 times the original load, the relative cracking rate is 2.01 times that of the standard shrinkage load. The rate of increase in the relative cracking rate is much greater than the rate of increase in the shrinkage load. Therefore, in the process of formulating SCC, it is essential to strictly control the use of cementitious materials to ensure the volume stability of SCC and prevent excessive shrinkage deformation. From Figure 18b, it can be observed that under the standard shrinkage load, there is no abrupt vertical displacement of the slab corner, indicating that the standard shrinkage load has not reached the critical shrinkage displacement for interlayer debonding. When the shrinkage load increases to 125% of the standard shrinkage load, the corner displacement undergoes an abrupt change. Furthermore, as the shrinkage load further increases to 150%, the time of displacement abruptly changes. This is because the faster shrinkage load enables the SCC to reach the critical shrinkage displacement earlier, thereby advancing the timing of the abrupt change in displacement.
## 5 Results
### Simulation Results
#### 5.2.4 Damping Analysis  
The effect of the damping factor on the simulated results of the two boundary conditions was investigated by assigning various damping factors to the model and comparing the displacement predictions. 0.13 assigned to the damping ratio 1 (\(\xi_{1}\)) in the first frequency step \(f_{1}\) = 50 Hz and 0.25 to damping ratio 2 (\(\xi_{2}\)) in the last frequency step \(f_{2}\) = 500 Hz. Then, the first damping ratio (\(\xi_{1}\)) varied from 0.1 to 0.4 and the second (\(\xi_{2}\)) varied from 0.225 to 0.400. Figure 14 shows the \(z\)-component of the displacement amplitude with respect to the variation of the damping ratios in the two boundary conditions.  
High dependency on the damping factor is visible from the graphs in both boundary conditions, especially in frequencies higher than 200 Hz. While, as it should be, this dependency is much lower in the low frequencies, especially in the connected pipette condition. The displacement predictions by damping ratios 0.13 for 50 Hz and 0.25 for 500 Hz were well fit with the measured displacement, which is specified with the green dotted line in figure. This shows that the two damping ratios extracted from the previous studies [54; 55] are precise.
# Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption
## 5 Adaptive Particle Swarm Optimization Algorithm for Solving EVRPTW-DC  
This section details the adaptive particle swarm optimization (PSO) algorithm for our proposed EVRPTW-DC model. PSO was first introduced by [PERSON] and [PERSON] in 1995, and simulated the cooperation mechanism in foraging behavior in a swarm of birds and fish to search for the optimal solution [25].  
In standard PSO, each particle has two attributes: position and velocity. The swarm searches for the best solution in an \(n\)-dimensional space. The position of a particle represents a solution and is adjusted by velocity to search for new solutions. The particle records the best position, _pbest_, it reached, and the best position experienced by the entire swarm, _gbest_, is also recorded [26].  
In each iteration, the velocities \(\overrightarrow{v}\) and positions \(\overrightarrow{x}\) of the particles are updated by the following rules:  
\[\overrightarrow{v}_{id}^{t+1}=\omega\overrightarrow{v}_{id}^{t}+\varphi_{1} \gamma_{1}\bigg{(}\mathit{pbest}_{id}^{t}-\overrightarrow{x}_{id}^{t}\bigg{)} +\varphi_{2}\gamma_{2}\bigg{(}\mathit{gbest}_{id}^{t}-\overrightarrow{x}_{id}^ {t}\bigg{)} \tag{67}\]  
\[\overrightarrow{x}_{id}^{t+1}=\overrightarrow{x}_{id}^{t}+\overrightarrow{v }_{id}^{t+1} \tag{68}\]  
where _pbest_ is the best solution a particle has found and _gbest_ is the best solution the swarm has found. \(\omega\), \(\varphi_{1}\), and \(\varphi_{2}\) are important parameters for PSO. \(\omega\) is the parameter called inertia weight, which serves as a memory of particle velocity from the last iteration, preventing the particle from drastically changing direction. \(\varphi_{1}\) and \(\varphi_{2}\) are acceleration coefficients, affecting the self-learning ability and social-learning ability of the particle, respectively. \(\gamma_{1}\) and \(\gamma_{2}\) are random numbers within 0 to 1.  
The proposed adaptive PSO for EVRPTW-DC consists of similar attributes and parameters but with additional steps to check battery constraints as shown in Figure 5.