esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Attack Test
### Sensitivity Analysis  
#### 4.4.1 Key Sensitivity  
Generally speaking, the key sensitivity is a vital feature for the security of an image encryption scheme because it demonstrates the intensity for resisting brute force attack. According to the generation method of the key, when there is even a little change in the plaintext image, the corresponding image after the encryption is completely different. Nevertheless, a slight variation in the hash value will also result in two diverse cipher images, and the plaintext cannot be extracted from the cipher image when using a modified key whose attribute has been slightly changed. Due to the high sensitivity to the input parameters, which is a remarkable property of the hyperchaotic system, even the slightest variation will result in the situation mentioned above.  
The original image of Lena, the corresponding image after encryption, the image after decryption with the correct key, and the three images by using the hash value with only 1 bit changed for the input of the decryption process are display in Figure 13. These  
Figure 12: Distributions of the pixels in the Lena image.  
false decryption results demonstrate that even if there is a small change in the hash value, the image cannot be correctly decrypted. As a result, this novel scheme has strong key sensitivity.
## 4 Results and Analysis
### Result of DEAP Dataset  
#### 4.3.1 Results of Filter-Based Feature Selection Methods on the DEAP Dataset  
Table 6 presents the experimental outputs obtained from filter-based feature selection on the DEAP dataset. We employed the DEAP dataset with filter-based feature selection using SelectKBest and an ANOVA \(F\)-test to demonstrate the potential outputs of the models by selecting the most relevant 50, 100, and 150 features. Applying SelectKBest with ANOVA for feature selection and BO for hyperparameters of machine learning algorithms significantly improved the performance of all the models. When we set the model to choose the most relevant 50 features, the RF model's accuracy increased to 68.29% (+2.16%) for valence, with performance metrics including an F1 score of 67.83% (+2.06%) matching this value due to the model becoming more intricate from hyperparameter tuning. When we set the model to choose the most relevant 100 features, logistic regression achieved the highest accuracy of 65.00% (+4.1%) for valence, corresponding with F1 scores also at 64.65% (+4.45%). Similarly, with 150 features, random forest exhibited improvements of valence 66.36% (+0.23%), arousal 62.72% (+2.38%), and XGBoost valence 65.34% (+2.28%), arousal 63.06% (+2.39%), respectively. These results demonstrate that SelectKBest with the ANOVA \(F\)-test enhances model performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of** & & & & & \\
**Features** & **Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline \multirow{4}{*}{2548} & RF & 98.78 & 98.79 & 98.78 & 98.78 \\  & Logistic & 96.72 & 96.78 & 96.71 & 96.70 \\  & Regression & 99.30 & 99.30 & 99.30 & 99.30 \\  & SVM & 96.67 & 96.70 & 96.66 & 96.66 \\ \hline
1264 & RF & **99.30** & **99.28** & **99.29** & **99.29** \\ \hline \multirow{2}{*}{1274} & Logistic & **97.64** & **97.63** & **97.64** & **97.64** \\  & Regression & **99.34** & **99.33** & **99.34** & **99.34** \\ \hline
1275 & SVM & **97.03** & **97.02** & **97.02** & **97.03** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of machine learning after applying the wrapper-based feature selection method. Improved results compared to models without feature selection are marked in boldface.
# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
## 1 Introduction
Multilevel can produce several thresholds, such as tri-level or quad-level, and break pixels into different classes according to the intensity histogram's global or local level when used to segment complicated images [18; 19]. Multilevel divides the image into several classes based on the global or local intensity histogram, whereas bi-level divides the image into two classes. In order to determine the threshold values for multilevel thresholding segmentation, many studies have used objective functions optimization techniques. Slime Mould Algorithm for image segmentation, an advanced multi-threshold technique, has been recently proposed [20]. A framework for efficient brain tumor classification using MRI images is proposed in [21]. It is based on three steps, first, image improvement of visual quality. Second, the locations of tumors are obtained based on a clustering-based method. Third is feature extraction. A recent deep learning approach [22; 23] uses two different pre-trained deep learning models to extract features from images as vectors. The vectors are then combined using the partial least squares (PLS) method to form a hybrid feature vector. Then, the top tumor locations are revealed via agglomerative clustering. Those methods provide very good accuracy, meanwhile, are heavily time-consuming. An updated Unet algorithm was published recently [23]. The results obtained on brain segmentation are about 84% average Dice index. When used with medical brain images and a real-coded genetic algorithm, the innovative technique of multilevel thresholding for brain segmentation has been demonstrated to yield accurate and promising results [24]. In 2017, multiple OTSU thresholding and cross-correlation were used to identify brain tumors, and in the same year, a multilevel thresholding method based on electromagnetics was created to accurately segment brain MRI data to detect white matter and gray matter [25]. Using modified multi-level Otsu Thresholding and cross-correlation of principal components, tumor identification in MRI images has recently been developed [26]. A comparison between the Patch-Levy Bees Algorithm and the Harmony Search Algorithm for Multilevel Thresholding of Brain Tumor MRI Images was published by [27]. The segmentation of retinal blood vessels using morphology and hybrid multilevel thresholding has been shown to be a reliable technique [28]. The brain MR image was segmented using multilevel thresholding, particle swarm optimization, the Otsu approach, and anisotropic diffusion [29]. This study compares a few CNN and DLA segmentation techniques used on MRI brain tumor pictures to a developed approach for multilevel segmentation of MRI brain tumor images based on an updated harmonic search optimization algorithm [30]. Performance evaluation indicators are used in the comparison, and the result is favorable and competitive in a time of execution and accuracy.
## 2 Material and Methods
### Weather Data
\begin{table}
\begin{tabular}{c c c c c|c c c c c c} \hline \hline \(\beta\) **(\({}^{\circ}\))** & **30** & **35** & **40** & **45** & **50** & \(\beta\) **(\({}^{\circ}\))** & **30** & **35** & **40** & **45** & **50** \\ \hline \multicolumn{10}{c}{**Zone A–Heraklion**} & \multicolumn{10}{c}{**Zone C–Thessaloniki**} \\ \hline JAN & 3.09 & 3.19 & 3.27 & 3.34 & 3.38 & JAN & 2.69 & 2.80 & 2.90 & 2.98 & 3.04 \\ FEB & 3.77 & 3.85 & 3.89 & 3.92 & 3.93 & FEB & 3.28 & 3.37 & 3.43 & 3.47 & 3.49 \\ MAR & 4.60 & 4.60 & 4.58 & 4.54 & 4.47 & MAR & 3.89 & 3.91 & 3.91 & 3.89 & 3.85 \\ APR & 5.63 & 5.54 & 5.42 & 5.27 & 5.10 & APR & 4.85 & 4.79 & 4.71 & 4.60 & 4.47 \\ MAY & 6.23 & 6.04 & 5.83 & 5.60 & 5.33 & MAY & 5.51 & 5.37 & 5.22 & 5.03 & 4.83 \\ JUN & 6.64 & 6.40 & 6.14 & 5.85 & 5.54 & JUN & 6.09 & 5.91 & 5.70 & 5.47 & 5.22 \\ JUL & 6.67 & 6.45 & 6.20 & 5.92 & 5.62 & JUL & 6.31 & 6.14 & 5.93 & 5.70 & 5.45 \\ AUG & 6.53 & 6.39 & 6.21 & 6.00 & 5.77 & AUG & 5.98 & 5.87 & 5.74 & 5.57 & 5.38 \\ SEP & 5.99 & 5.96 & 5.90 & 5.81 & 5.68 & SEP & 5.15 & 5.15 & 5.12 & 5.07 & 4.98 \\ OCT & 4.80 & 4.87 & 4.92 & 4.94 & 4.92 & OCT & 3.87 & 3.95 & 4.01 & 4.04 & 4.05 \\ NOV & 3.80 & 3.92 & 4.02 & 4.09 & 4.14 & NOV & 2.89 & 3.00 & 3.09 & 3.16 & 3.22 \\ DEC & 3.02 & 3.13 & 3.23 & 3.31 & 3.36 & DEC & 2.44 & 2.56 & 2.66 & 2.74 & 2.80 \\ \hline \multicolumn{10}{c}{**Zone B–Athens**} & \multicolumn{10}{c}{**Zone D–Kastoria**} \\ \hline JAN & 3.17 & 3.29 & 3.40 & 3.48 & 3.54 & JAN & 2.99 & 3.13 & 3.24 & 3.33 & 3.40 \\ FEB & 3.73 & 3.82 & 3.88 & 3.93 & 3.94 & FEB & 3.50 & 3.59 & 3.66 & 3.71 & 3.73 \\ MAR & 4.47 & 4.49 & 4.48 & 4.45 & 4.40 & MAR & 4.23 & 4.26 & 4.26 & 4.24 & 4.20 \\ APR & 5.23 & 5.16 & 5.06 & 4.94 & 4.79 & APR & 4.87 & 4.81 & 4.72 & 4.62 & 4.49 \\ MAY & 5.80 & 5.65 & 5.47 & 5.27 & 5.05 & MAY & 5.34 & 5.21 & 5.06 & 4.88 & 4.69 \\ JUN & 6.29 & 6.09 & 5.86 & 5.61 & 5.34 & JUN & 6.18 & 6.00 & 5.79 & 5.55 & 5.30 \\ JUL & 6.40 & 6.21 & 5.99 & 5.75 & 5.48 & JUL & 6.22 & 6.04 & 5.84 & 5.62 & 5.37 \\ AUG & 6.37 & 6.25 & 6.09 & 5.91 & 5.69 & AUG & 6.00 & 5.90 & 5.76 & 5.60 & 5.41 \\ SEP & 5.85 & 5.84 & 5.80 & 5.73 & 5.62 & SEP & 5.23 & 5.23 & 5.20 & 5.14 & 5.06 \\ OCT & 4.69 & 4.79 & 4.85 & 4.89 & OCT & 4.14 & 4.23 & 4.29 & 4.33 & 4.34 \\ NOV & 3.41 & 3.53 & 3.64 & 3.72 & 3.77 & NOV & 3.09 & 3.21 & 3.32 & 3.40 & 3.46 \\ DEC & 2.83 & 2.95 & 3.06 & 3.15 & 3.22 & DEC & 2.58 & 2.70 & 2.81 & 2.90 & 2.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Daily solar energy on the tilt surface (H\({}_{\rm T}\)) for different tilt angles and locations in [kWh/m\({}^{2}\)].
## 5 Analysis of Behavior and Results
### Simulation Results at Autumn/Winter ToU Tariff
#### 5.2.1 Autumn Day Scenario  
An autumn day simulated scenario is implemented by taking under consideration the power consumption profile (Figure 8) for an average day of autumn and the ToU tariff of autumn/winter (Figure 1b). This simulated scenario is implemented without prioritizing the load cover at specific slots of the discharging zones. Consequently, all five weights of the first zone and the 11 weights of the second zone are set 1.  
The operation of the BESS at this average autumn day is presented at the obtained Figure 14a,b and Figure 15. Due to the equal weights for the all the time slots at the first discharging zone the energy stored is supplied almost equally to the five time slots of the zone.  
Figure 14: BESS operation schedule: (**a**) Energy distribution; (**b**) Charging and discharging currents.  
Figure 13: BESS parameters.  
At the end of first the zone the battery stack's load is 19.45 Ah. Hence, the needed current so as to fully charge the BESS during the short first charging zone (2 h) is 40.28 A. Nonetheless, the BESS \(C_{rate}\) limitation (0.33C), forces the implementation of charging with current I = 33 A. Consequently, at the end of the first charging zone the BESS is not fully charged (\(q0\) = 85.45 Ah). At the second discharging zone the BESS distributes again the energy stored almost equally to all slots (around 360 Wh) due to the equal weights. However, at the last time slot (1 a.m.-2 a.m.) significantly reduced energy is supplied (52 Wh). This is explained by the fact that the minimum permissible SOC (\(SOC_{\text{min}}\) = 10%) has been reached. Moreover, the needed current to fully charge the BESS at the second charging zone (6 h) is calculated (I = 15 A) and applied to the KiBaM model.  
The daily energy cost for the household with and without the BESS was calculated and found 2.065 \(\upepsilon\) with the BESS and 2.295 \(\upepsilon\) without the BESS. Thus, the daily profit was 0.23 \(\upepsilon\) and for the whole autumn would be 20.93 \(\upepsilon\). The higher economic profit for the consumer during a typical autumn day compared to a spring/summer day is a result of the corresponding ToU tariff which permits two full charge/discharge cycles per day instead of one. The estimated capacity loss for the first charge/discharge cycle of the day is 0.0864 Ah while the set point is 0.0984 Ah. Additionally, for the second cycle of the day, the capacity loss is 0.0676 Ah and the corresponding set point is 0.0659 Ah. Finally, the estimated capacity loss for the whole day is 0.154 Ah while the capacity loss set point is 0.1643 Ah.
# Estimating Tail Probabilities of Random Sums of Phase-Type Scale Mixture Random Variables +
###### Abstract  
We consider the problem of estimating tail probabilities of random sums of scale mixture of phase-type distributions--a class of distributions corresponding to random variables which can be represented as a product of a non-negative but otherwise arbitrary random variable with a phase-type random variable. Our motivation arises from applications in risk, queueing problems for estimating ruin probabilities, and waiting time distributions, respectively. Mixtures of distributions are flexible models and can be exploited in modelling non-life insurance loss amounts. Classical rare-event simulation algorithms cannot be implemented in this setting because these methods typically rely on the availability of the cumulative distribution function or the moment generating function, but these are difficult to compute or are not even available for the class of scale mixture of phase-type distributions. The contributions of this paper are that we address these issues by proposing alternative simulation methods for estimating tail probabilities of random sums of scale mixture of phase-type distributions which combine importance sampling and conditional Monte Carlo methods, showing the efficiency of the proposed estimators for a wide class of scaling distributions, and validating the empirical performance of the suggested methods via numerical experimentation.  
subexponential distribution; phase-type distribution; rare event simulation; conditional Monte Carlo; importance sampling
## 2 Numerical Analysis
### Results of Numerical Analysis  
#### 2.5.1 Inter-Story Stiffness and First Natural Period  
Table 6 shows the values of the inter-story stiffness in the simulation models. The results show that the first floor of the isolation structure has a rather large stiffness value of 157.61 kN/mm, which is 2.61 times the stiffness value of the first floor of the non-isolated model due to the shorter calculated column value after inserting the isolation layer in the first-story column (Figure 3b). With the addition of the isolation layer, the first floor of the isolation structure (157.61 kN/mm) is much larger than its above floors (81.62 kN/mm). While the first floor of the non-isolated structure (60.28 kN/mm) is smaller than its above floors (81.62 kN/mm) when the non-isolated structure has no isolation layer.  
\begin{table}
\begin{tabular}{c c} \hline
**Story** & **Inter-Story Stiffness (kN/mm)** \\ \hline
2–7 story of the model & 81.62 \\ Isolation layer & 1.53 \\ First floor of the isolation structure & 157.61 \\ First floor of the non-isolated model & 60.28 \\ \hline \end{tabular}
\end{table}
Table 6: Inter-story stiffness of the model structure.  
Figure 7: Acceleration response spectra.  
Through the modal analysis, the first natural periods of the whole non-isolated model and story isolation model were obtained as 0.61 s and 0.71 s, respectively. This result is expected because the stiffness of the story isolation model is smaller than that of the non-isolated model. They are then compared with the results obtained from the fabricated structure model in Section 3.4.
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
## 2 Overall System Architecture
In the first phase, jamming is applied to the communication between the drone and the remote controller and to the GPS signals. The second phase occurs in parallel to the spoofing steps and corresponds to jamming the control signal only. The fourth step consists of identifying the location of the invasive drone. The system determines the location of the system using a set of sensors that are managed using the sensor module. The fifth step is one of the main functions of the system and is the responsibility of the Tx module. In this case, this module starts by emitting spoofing signals targeted at the UAV, enabling the system to gain control over the drone. Finally, the sixth step comprises the generation of activity logs (date, time, signals issued, operator and location) which are sent to the supervisory entity. Figure 2: System flowchart.
## 4 Results
### Hypothesis Testing
#### 4.2.8 Hypothesis 3 Results  
Effort expectancy did not have a statistically significant effect on older adults' behavioral intent to _use_ Internet-enabled technologies, \(B=-0.026\), \(t(308)=-0.409\), \(p=0.683\) (see Table 10). The null hypothesis (H3\({}_{0}\)) that effort will not influence use was accepted.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Behavioral Intent (1) & 1.000 & 0.709 & 0.522 & 0.644 & 0.568 & 0.690 & 0.597 & 0.775 \\ Performance Expectancy (2) & 0.709 & 1.000 & 0.606 & 0.579 & 0.525 & 0.727 & 0.512 & 0.598 \\ Effort Expectancy (3) & 0.522 & 0.606 & 1.000 & 0.367 & 0.720 & 0.614 & 0.409 & 0.418 \\ Social Influence (4) & 0.644 & 0.579 & 0.367 & 1.000 & 0.391 & 0.561 & 0.364 & 0.647 \\ Facilitating Conditions (5) & 0.568 & 0.525 & 0.720 & 0.391 & 1.000 & 0.560 & 0.501 & 0.462 \\ Hedonic Motivation (6) & 0.690 & 0.727 & 0.614 & 0.561 & 0.560 & 1.000 & 0.500 & 0.613 \\ Price Value (7) & 0.597 & 0.512 & 0.409 & 0.364 & 0.501 & 0.500 & 1.000 & 0.525 \\ Habit (8) & 0.775 & 0.598 & 0.418 & 0.647 & 0.462 & 0.613 & 0.525 & 1.000 \\ \hline \multicolumn{8}{c}{Note. \(p<0.001\).} \\ \end{tabular}
\end{table}
Table 8: Correlations among research variables.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized**} & \multicolumn{2}{c}{**Standardized**} & \multirow{2}{*}{**Sig.**} & \multirow{2}{*}{**95\% Confidence Interval for B**} \\  & \multicolumn{2}{c}{**Coefficients**} & & & & & **Lower Bound** & **Upper Bound** \\ \cline{2-7}  & **B** & **Std. Error** & **Beta** & & & **Lower Bound** & **Upper Bound** \\ \hline (Constant) & \(-0.811\) & \(0.167\) & & \(-4.864\) & \(0.000\) & \(-1.139\) & \(-0.483\) \\ Performance expectancy & \(0.244\) & \(0.055\) & \(0.206\) & \(4.427\) & \(0.000\) & \(0.135\) & \(0.352\) \\ Effort expectancy & \(-0.026\) & \(0.063\) & \(-0.019\) & \(-0.409\) & \(0.683\) & \(-0.150\) & \(0.098\) \\ Social influence & \(0.138\) & \(0.040\) & \(0.138\) & \(3.475\) & \(0.001\) & \(0.060\) & \(0.217\) \\ Facilitating conditions & \(0.184\) & \(0.061\) & \(0.132\) & \(2.999\) & \(0.003\) & \(0.063\) & \(0.305\) \\ Hedonic motivation & \(0.153\) & \(0.057\) & \(0.126\) & \(2.694\) & \(0.007\) & \(0.041\) & \(0.264\) \\ Price value & \(0.140\) & \(0.045\) & \(0.114\) & \(3.099\) & \(0.002\) & \(0.051\) & \(0.229\) \\ Habit & \(0.378\) & \(0.043\) & \(0.374\) & \(8.696\) & \(0.000\) & \(0.292\) & \(0.464\) \\ \hline \multicolumn{7}{c}{Note. \(F(7,308)=133.50\), \(p<0.001\), \(R^{2}=0.75\).} \\ \end{tabular}
\end{table}
Table 9: Linear regression results.
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 3 System Calibration and Image Reconstruction Methods
### System Calibration Method
#### 3.2.3 Nonlinear Optimization  
Since \(C(\psi)\) characterizes the distortion of the reconstructed image, the optimal estimate of the system parameter vector, called the calibrated system parameter vector \(\hat{\psi}\), can be obtained by minimizing it:  
\[\hat{\psi}=\arg\min_{\psi}(C(\psi)). \tag{14}\]  
This is a nonlinear least-squares problem. As the number of variables increases, the problem becomes more complex. In the modeling of the practical scanning trace, it can be found that for the three variables, \(v\),\(a\), and \(f\), the mapping relationship is actually related only to \(v/f\) and \(a/f\). Therefore, the sampling frequency \(f\) can be fixed to the setting value with only \(v\) and \(a\) to be optimized, thus reducing one variable. To address this issue, the Levenberg-Marquardt Algorithm [22] is employed after comparison to other optimization algorithms. It requires an initial guess of \(\psi\) which can be set as the ideal system parameter vector \(\psi_{0}\).
## 4 Experimental Results  
In this section, we evaluate the performance of the proposed EDADS-1 and EDADS-2. We performed the simulations using MATLAB 2017b. The proposed schemes were evaluated through experiments using the standard 14-bus, 39-bus, 57-bus and 118-bus IEEE test systems. Experiments' results have been averaged over 20 iterations for each case bus system. Figure 5 illustrates the IEEE 39-bus system [38], also known as the New England 10-machine system. Because of space limitations, figures for the other IEEE bus systems employed for testing in this work are not included. To simulate the operation of the power network, we used the Matpower 6.0 toolbox [39] to generate the configuration of these test systems (especially the Jacobian matrix). We employed the AC power flow model and used DC power flow analysis to approximate the state vectors and measurement dataset. In a \(B\)-bus system, state variable vector \(\delta\in\mathbb{R}^{n}\) is composed of \((B-1)\) bus voltage phase angles, and the meter measurement vector consists of active power injections into the buses and branch active power flows. To conduct a fair comparison with a real-world power network scenario, we used the stochastic loads with uniform load distributions similar to [7], i.e., in the range \([0.9\times\text{B}_{0}-1.1\times\text{B}_{0}]\), where \(\text{B}_{0}\) is the base load. In these simulations, the active power measurement features, including the  
Figure 4: Flowchart of the Euclidean distance-based anomaly detection Scheme 2, EDADS-2.  
active power injections into the buses and active power flows on the branches, are the input to the GA for the selection of optimal features.
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 3 Proposed Methodology
### Attention Mechanism
#### 3.4.2 Channel Attention-Aware Weight Excitation (CAWE)  
Each class-specific response (in our case, there are two classes of nodules and background) is related to each channel of local features extracted by the encoder layers. Different semantic class responses are correlated with each other [29]. Thus, to improve the feature representation of each class-specific response, the proposed CAWE block can properly highlight the interdependence between channels in feature maps and explicitly model inter-dependencies between channels. The process of CAWE shown in Figure 5 and can be illustrated as follows.  
Like PAWE, the proposed CAWE block includes two sub-blocks, a channel attention block (CAB) and a weight excitation block (WEB). In the CAB block, the input \(Y\in\mathbb{R}^{C\times H\times W}\) is reshaped in the initial two steps and permuted in the second part into \(Y_{1}^{c}\in\mathbb{R}^{(H\times W)\times C}\) and \(Y_{2}^{c}\in\mathbb{R}^{C\times(H\times W)}\), where the superscript \(c\) is defined for \"channel\". Afterwards, a matrix multiplication between \(Y_{1}^{c}\) and \(Y_{2}^{c}\) is performed. The channel attention map \(E^{c}\in\mathbb{R}^{C\times C}\) can be defined as:  
\[e_{i,j}^{c}=\frac{\exp{(Y_{1,i}^{c}\cdot Y_{2,j}^{c})}}{\sum_{i=1}^{C}\exp{(Y_ {1,i}^{c}\cdot Y_{2,j}^{c})}}, \tag{5}\]  
where the outcome of the \(i\)th channel on the \(j\)th is produced by \(e_{i,j}^{c}\). A multiplication of the transposed version of the input feature maps, \(Y_{3}^{c}\) reshaped to \(\mathbb{R}^{C\times(H\times W)}\), and the resulting \(E^{c}\) of (5) is performed. Consequently, the final channel attention map can be defined as:  
\[F_{CAB,j}=\alpha_{c}\sum_{i=1}^{C}e_{ij}^{c}Y_{3,j}^{c}+Y_{j}, \tag{6}\]  
where \(\alpha_{c}\) quantifies the weight of the channel attention map of the input feature map \(Y\). The final WEB sub-network feature map can similarly be obtained from (3).  
Figure 5: Illustration of the proposed CAWE block.  
Finally, an element-wise sum operation is performed between the CAB and WEB output features maps to produce the final CAWE features, as follows:  
\[\hat{Y}_{CAWE,j}=F_{CAB,j}+F_{WEB,j}, \tag{7}\]  
This process emphasizes generating channel-dependent feature maps using weighted excitation versions of the features of all channels and boosting the feature difference among the channels.