esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Statistical Properties
### Moments  
The \(r\)th moment about the origin of the PZD is defined as  
\[E(X_{PZD}^{r})=\frac{\beta(\theta+2)+r}{\beta(\theta+2)}\theta^{-\frac{r}{\beta }}\Gamma\left(\frac{r}{\beta}+2\right),\,r=1,2,3,... \tag{5}\]  
and hence the mean of the distribution is  
\[E(X_{PZD})=\frac{\beta(\theta+2)+1}{\beta(\theta+2)\theta^{\frac{1}{\beta}}} \Gamma\left(\frac{1}{\beta}+2\right)\]  
The inverse moment of the PZD is  
\[E\left(\frac{1}{X_{PZD}^{r}}\right)=\frac{\beta(\theta+2)-r}{\beta(\theta+2) }\theta^{r/\beta}\Gamma\left(2-\frac{r}{\beta}\right),r=1,2,3,... \tag{6}\]  
While kurtosis of a random variable measures the degree of tail heaviness, skewness of a random variable measures the degree of long-tail. The coefficient of skewness \((\uplambda_{1})\), coefficient of variation \((\uplambda_{2})\), and coefficient of kurtosis \((\uplambda_{3})\) of the PZD are given, respectively, by  
\[\begin{split}&\beta^{2}(\theta+2)^{2}[\beta(\theta+2)+3]\Gamma \left(2+\frac{3}{\beta}\right)+2[\beta(\theta+2)+1]^{3}\Gamma\left(2+\frac{1}{ \beta}\right)^{3}\\ \uplambda_{1}=&\frac{-3\beta(\theta+2)[\beta( \theta+2)+1][\beta(\theta+2)+2]\Gamma\left(2+\frac{2}{\beta}\right)\Gamma \left(2+\frac{1}{\beta}\right)}{\left(\beta(\theta+2)[\beta(\theta+2)+2] \Gamma\left(2+\frac{2}{\beta}\right)-[\beta(\theta+2)+1]^{2}\Gamma\left(2+ \frac{1}{\beta}\right)^{2}\right)^{3/2}}\\ \uplambda_{2}=&\frac{\sqrt{\beta(\theta+2)[\beta( \theta+2)+2]\Gamma\left(2+\frac{2}{\beta}\right)-[\beta(\theta+2)+1]^{2} \Gamma\left(2+\frac{1}{\beta}\right)^{2}}}{[\beta(\theta+2)+1]\Gamma\left(2+ \frac{1}{\beta}\right)}\\ \uplambda_{3}=&\frac{A_{1}-A_{2}-A_{3}}{\left([ \beta(\theta+2)+1]^{2}\Gamma\left(2+\frac{1}{\beta}\right)^{2}-\beta(\theta+2 )[\beta(\theta+2)+2]\Gamma\left(2+\frac{2}{\beta}\right)\right)^{2}}\\ \end{split} \tag{7}\]  
\[\uplambda_{3}=\frac{A_{1}-A_{2}-A_{3}}{\left([\beta(\theta+2)+1]^{2}\Gamma \left(2+\frac{1}{\beta}\right)^{2}-\beta(\theta+2)[\beta(\theta+2)+2]\Gamma \left(2+\frac{2}{\beta}\right)\right)^{2}}\\ \end{split} \tag{8}\]  
where  
\[A_{1}=\beta^{3}(\theta+2)^{3}[\beta(\theta+2)+4]\Gamma\left(2+\frac{4}{\beta} \right),\]  
\[A_{2}=4\beta^{2}(\theta+2)^{2}[\beta(\theta+2)+1][\beta(\theta+2)+3]\Gamma \left(2+\frac{3}{\beta}\right)\Gamma\left(2+\frac{1}{\beta}\right),\]  
\[A_{3}=3[\beta(\theta+2)+1]^{2}\Gamma\left(2+\frac{1}{\beta}\right)^{2}\left\{ [\beta(\theta+2)+1]^{2}\Gamma\left(2+\frac{1}{\beta}\right)^{2}+2\beta(\theta+ 2)[\beta(\theta+2)+2]\Gamma\left(2+\frac{2}{\beta}\right)\right\}.\]  
Table 1 gives the mean \((\mu_{X})\), standard deviation \((\sigma_{X})\), \(\uplambda_{1}\), \(\uplambda_{2}\), and \(\uplambda_{3}\) of the PZD for various selections of the parameters.  
The results in Table 1 indicate that the distribution is skewed to the right for these parameter value selections, with relatively small values of skewness. In addition, it is noted that the mean and variance values are decreasing as one parameter's values are increasing for a fixed value of the other parameter.
## 3 Results
### AIGC Input-Output Classification
#### 3.3.5 Image to Text  
The content of an image can be described using AIGC techniques, which leverages the power of computer vision and natural language processing. One notable model in this field is the Show and Tell: Neural Image Caption (NIC) [59] developed by the Google research team, which serves as the foundation for image captioning applications.  
The architecture process of NIC [59] involves the utilization of convolutional neural networks (CNN) and long short-term memory (LSTM), a type of recurrent neural network (RNN). The CNN acts as an encoder, extracting visual features from the image. These features capture the salient information necessary for generating meaningful captions. The extracted features are then passed on to the LSTM, which functions as a decoder. The LSTM generates a sequence of words or a description based on the visual features obtained from the CNN.  
Subsequently, attention mechanisms were introduced to further improve image captioning. One such model is the recurrent topic-transition GAN (RTT-GAN) [62], which enhances the caption generation process by incorporating attention-based techniques. The architecture of RTT-GAN [62] includes a generator and two discriminators. The generator recurrently produces sentences using semantic regions, allowing it to focus on different aspects of the image and generate more contextually relevant captions. The two discriminators assess the quality of the sentences generated by the generator, ensuring that the captions are accurate and coherent.  
Another noteworthy model within the realm of visual attention is show, attend and tell [171]. This model also incorporates an visual attention mechanism, specifically the soft attention mechanism. It allows the model to dynamically focus on different regions of the image while generating captions. By attending to relevant image regions, the model can generate more accurate and detailed descriptions. VisualGPT [65] based on pre-trained language model (PLM) which employs an encoder-decoder attention mechanism to generate a suitable caption for an given image.  
Overall, these models demonstrate the advancements in image-to-text generation by combining the power of computer vision, natural language processing, and attention mechanisms.  
Figure 15: Source code for isPrime() function generated by Github Copilot using Codex.
## 3 Results
### First Cadaver Head Scan
#### 3.1.2 Metal Artifact Reduction Algorithm  
All four cadaver heads had dental implants. The metal artifact reduction algorithm was beneficial in all examined data sets (Figure 2) (mean Likert scale value 2 with MAR vs. 1 without MAR). Less beam hardening artifacts were visible due to MAR, the cortex of the mandible and maxilla and the bony structures were better visible (Figure 2A,B). MAR did not miscalculate or disturb the datasets (Figure 2C). Beam-hardening artifacts of metal implants were visible even in the vertebral bodies in the images without MAR, but not in images with MAR. Despite MAR, the bony fixation of the implants was not visible in some places, depending on the angle of rotation and the direction of the beam hardening artifacts (Figure 2A).
# Usage of V2X Applications in Road Tunnels
## 3 Methodology
### Testing Scenarios  
The test was carried out in two main phases. First (pre-test), the equipped vehicle went through the tunnel to check if the OBU was working properly while the GNSS signal was lost. In the second phase, the major test was performed and communication between both vehicles was tested and verified.
## 2 Methods and Materials
### Size and Depth Estimation Techniques
To convert these pixel dimensions into real-world measurements, the technique relied on similar triangle geometry (please refer to Figure 16). The calculations involved key parameters like the focal length (\(f\)), sensor width (_sensor_), image width (_imagew_), and the distance between the camera and the defect (\(D\)). Real-world defect width (\(W\)) was derived as:  
\[W=w\times\frac{D}{f} \tag{2}\]  
Figure 16: Similar Triangles method to calculate the object’s dimensions [35]. Figure 15: An illustration of the approach for calculating defect depth [19]. For more advanced measurements, the method used effective focal length (_EFL_). It related pixel dimensions to real-world size as:  
\[\mathit{defect}_{w}=\frac{D\cdot dA\cdot\mathit{sensor}_{w}}{f\cdot image_{w}}= \frac{D\cdot dA}{\mathit{EFL}}\,\ \mathit{where}\ \mathit{EFL}=\frac{f\cdot image_{w}}{\mathit{sensor}_{w}} \tag{3}\]  
Lab tests confirmed high accuracy, but field conditions like reflective surfaces and non-perpendicular imaging introduced challenges. Despite these, the approach provided a reliable framework for automated defect measurement with potential refinements. [PERSON] et al. [20] as illustrated in Figure 17, measured the dent depth and also analysed the dent length and area through a step-by-step process. They performed a deviation analysis in Design X software with the colour bar set to a solid colour. This created a colour map that outlined the dent perimeter. Deviations beyond a set tolerance appeared in a single colour, such as dark blue, making them easy to identify. The team converted the colour map into an 8-bit grayscale image using ImageJ software. They noted that sharpening or despeckling could reduce noise but did not apply these in their study. For curved surfaces, they used the Design X Normal To Function to adjust the 3D CAD surface. This ensured that the map aligned perpendicularly with the viewer for accurate measurements. Based on dimensional data from Design X, they defined a unit scale in pixels per millimetre and calculated dent parameters, including area and maximum length, in ImageJ. This method provided accurate measurements for simple and complex dents on flat and curved panels, offering a detailed view of the damage.
## 5 Results and Discussion
### Entropy Generation  
The contours of total entropy generation are shown in Figure 6 for different studied scenarios. The entropy generation occurs due to the difference in temperature, velocity, and magnetic field. However, the entropy generated due to temperature difference is greater than that generated due to velocity and magnetic field. As a result, the dense entropy distribution contour is located near the heat sources (due to high temperature and high thermal boundary layers) and the moving walls (due to the velocity effect and high hydrodynamic boundary layer).  
_Case-1:_ the contours of the total entropy generation seem symmetrical because of symmetrical boundary conditions with the Y-axis. The intensity of entropy generation is generally located near the vertical walls and the heat sources, especially at low \(\widetilde{\mathrm{Ri}}\) and \(\widetilde{\mathrm{Ha}}\). The effect of \(\widetilde{\mathrm{Ha}}\) tends to distribute the entropy in the transverse axis due to increased heat transfer. At high values of \(\widetilde{\mathrm{Ri}}=100\) and \(\widetilde{\mathrm{Ha}}=75\), the entropy generation lines move horizontally (parallel to the base wall) in the middle of the cavity (Figure 6). There is no noticeable remarkable entropy generation near the upper wall due to zero wall velocity and thermally insulated conditions.  
_Case-2:_ the entropy generation appears nonsymmetrical due to nonsymmetrical boundary conditions. However, when the values of \(\widetilde{\text{Ri}}\) and \(\widetilde{\text{Ha}}\) are increased to 100, the entropy generation change and becomes symmetrical. The intensity of entropy generation contours is denser near the right wall than near the left wall. This is due to the movement of the right wall, which distributes heat flow along the wall (Figure 6).  
_Case-3:_ the entropy generation contour is dense near the base wall and the lower part of the vertical walls. This is because the movement of the vertical walls is opposite to the fluid movement, resulting in the dense entropy generation near the base wall (Figure 6).
## 2 Modeling
### Bicycle Model  
The six-state bicycle model is obtained by integrating the actuator dynamics with the four-state bicycle model that is linearized using small angle approximation and constant longitudinal velocity ([PERSON] et al., 2019). The state-space representation can be expressed as follows.  
\[\begin{array}{l}\dot{\mathbf{x}}=\mathbf{A}_{m}\mathbf{x}+\mathbf{B}_{m} \mathbf{u}\\ \mathbf{y}=\mathbf{C}_{m}\mathbf{x}\end{array} \tag{3}\]  
where \(\mathbf{x}=\begin{bmatrix}y&v_{y}&\psi&r&\delta_{f}&M_{z}\end{bmatrix}^{T}\) is the state vector, \(\mathbf{u}=\begin{bmatrix}\delta_{f,r}&M_{z,r}\end{bmatrix}^{T}\) is the input vector, and \(\mathbf{y}=\begin{bmatrix}y&\psi\end{bmatrix}^{T}\) is the output vector; \(v_{y}\) and \(r\) are the lateral velocity and yaw rate of the vehicle, respectively; \(y\) and \(\psi\) are the lateral displacement and heading angle of the vehicle, respectively; \(\mathbf{A}_{m}\), \(\mathbf{B}_{m}\) and \(\mathbf{C}_{m}\) are system matrix, input matrix and output matrix as shown below.  
\[\mathbf{A}_{m}=\begin{bmatrix}0&1&v_{x}&0&0&0\\ 0&\frac{-(c_{af}+C_{ar})T_{x}}{mv_{x}}&0&\frac{l_{x}C_{ar}-l_{x}/C_{ar}}{mv_{x}}- v_{x}&\frac{C_{af}}{m}&0\\ 0&0&0&1&0&0\\ 0&\frac{l_{x}C_{ar}-l_{x}C_{af}}{l_{x}v_{x}}&0&\frac{-(l_{x}^{2}C_{af}+l_{x}^{2} C_{ar})}{l_{x}v_{x}}&\frac{l_{f}C_{af}}{l_{x}v_{x}}&\frac{1}{l_{x}}\\ 0&0&0&0&-\frac{1}{\tau_{LHB}}&0\\ 0&0&0&0&0&-\frac{1}{\tau_{LHB}}\end{bmatrix} \tag{4}\]  
\[\mathbf{B}_{m}=\begin{bmatrix}0&0&0&0&\frac{1}{\tau_{L}PS}&0\\ 0&0&0&0&0&\frac{1}{\tau_{LHB}}\end{bmatrix}^{T} \tag{5}\]  
\[\mathbf{C}_{m}=\begin{bmatrix}1&0&0&0&0&0\\ 0&0&1&0&0&0\end{bmatrix} \tag{6}\]  
where \(m\) is the vehicle mass; \(v_{x}\) is the vehicle longitudinal velocity; \(C_{af}\) and \(C_{ar}\) are the cornering stiffness of the front and rear axes, respectively; \(l_{f}\) and \(l_{r}\) are the distances from the C.G. to the front and rear axes, respectively; \(l_{z}\) is the yaw-plane rotational inertia of vehicle. The ranks of controllability matrix \(\mathcal{C}\) and observability matrix \(\mathcal{O}\) of the six-state bicycle model can be calculated as follows.  
\[rank(\mathcal{C})=rank\big{(}[\mathbf{A}_{m}\quad\mathbf{A}_{m}\mathbf{B}_{m }\quad\mathbf{A}_{m}^{2}\mathbf{B}_{m}\quad\ldots\quad\mathbf{A}_{m}^{5} \mathbf{B}_{m}]\big{)} \tag{7}\]  
\[rank(\mathcal{O})=rank\Big{(}[\mathbf{C}_{m}\quad\mathbf{C}_{m}\mathbf{A}_{m} \quad\mathbf{C}_{m}\mathbf{A}_{m}^{2}\quad\ldots\quad\mathbf{C}_{m}\mathbf{A} _{m}^{5}]^{T}\Big{)} \tag{8}\]  
Since both \(\mathcal{C}\) and \(\mathcal{O}\) are full rank of 6, the proposed model is fully controllable and observable.
# Automatic Identification of Children with ADHD from EEG Brain Waves
## 6 Result  
Table 4 shows the accuracy of the classifier for holdout validation. Here, we can observe that for 90% variance, we got the highest test accuracy--93.2%. The lowest test accuracy was 85.5% for 80% variance using holdout cross-validation.  
Beyond 90% variance, as we increased the PCA variance, and the test accuracy decreased because the dataset results in overfitting. We performed holdout cross-validation (with 90% PCA variance) ten times to see the classifier accuracy in different runs, and each time the training test and validation set were randomly divided into 70:15:15. Table 5 shows the performance of holdout cross-validation. It shows that for each time running holdout cross-validation, the test accuracy was around 93% for the SVM classifier. The mean and the STD for performing holdout 10 times were 93.2% and 0.44, respectively, which shows that there was no bias in the classifier model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**\% Variance**} & \multirow{2}{*}{\
\begin{tabular}{c} **Accuracy** \\ **(Validation)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy** \\ **(Test)** \\ \end{tabular} } & \multicolumn{4}{c}{**Confusion Matrix (Test)**} \\ \cline{3-8}  & & & **TP** & **TN** & **FP** & **FN** \\ \hline
80\% & 88.49\% & 88.06\% & 967 & 1263 & 166 & 124 \\ \hline
85\% & 92.18\% & 92.51\% & 1011 & 1312 & 122 & 75 \\ \hline
90\% & 93.17\% & 93.24\% & 994 & 1354 & 139 & 33 \\ \hline
95\% & 88.29\% & 88.36\% & 867 & 1358 & 266 & 29 \\ \hline
97\% & 85.0\% & 85.5\% & 769 & 1344 & 342 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the SVM classifier for different PCA variance after holdout cross-validation.  
Figure 4: K-fold (10-fold) cross-validation process [55].  
Table 6 shows the accuracy of the classifier for k-fold cross-validation. Here, we also got the highest test accuracy at 90% variance, which is 94.2%, and the lowest test accuracy at 84.4% for 97% variance for using k-fold cross-validation. The test accuracy also decreased as we increased the PCA variance because the dataset results in overfitting after 90% variance.  
From Tables 4 and 6, we observe that both cross-validation methods have the highest accuracy for 90% variance. The 10-fold validation method has a 1% higher accuracy than the holdout method.
## 3 Results and Discussion
### Sensitivity Analysis  
The accuracy and efficiency of the multiscale compositional numerical simulator were validated in the previous section. This section focuses on the impact of capillary forces, emphasizing the proposed simulator's importance in tight oil reservoir development. It is divided into two main parts: the first part examines the influence of capillary forces on phase equilibrium calculations, while the second part utilizes the previously established multiscale numerical simulator, incorporating capillary forces, to analyze a typical tight oil reservoir model. By referencing actual tight oil reservoir parameters, this part explores the effect of capillary forces on oil recovery.  
Figure 5: Comparison of Component Distribution between Different Simulators at Various Production Times (In the legend, MS denotes the multiscale model presented in this paper).
# Economic Loss of Pine Wood Nematode Disease in Mainland China from 1998 to 2017
## 3 Results
### Economic Loss of Pine Wood Nematode Disease at Provincial Scale in Mainland China from 1998 to 2017  
According to the evaluation, the economic losses of pine wood nematode disease in various provinces of China from 1998 to 2017 showed an upward trend. Zhejiang, Guangdong and Jiangsu provinces were the areas with the most severe economic losses in disease (Figure 4). Zhejiang's average annual economic loss was CNY 2.14 billion, accounting for 28% of the national economic loss, of which direct economic loss was CNY 480 million and indirect economic loss was CNY 1.66 billion. The average annual economic loss in Guangdong Province was CNY 1.81 billion, accounting for 23% of the national economic loss. The average annual economic loss in Jiangsu Province was CNY 1.22 billion, accounting for 15% of the national economic loss, of which the direct economic losses accounted for 3%, and indirect economic losses accounted for 12% (Table A8).  
The economic loss of the pine wood nematode disease also showed a decreasing trend from the east to the west. The average annual economic loss in Fujian Province was CNY 550 million, followed by Jiangsu Province. In addition, Anhui Province, Liaoning Province, Shandong Province, Jiangxi Province, and Chongqing Municipality also suffered huge losses, and the average annual economic loss was more than CNY 200 million. Among them, except for Chongqing and Liaoning Provinces, the other provinces are located in the southeast of China. Regarding Liaoning Province, although it was a new epidemic area in 2016, its annual economic loss was CNY 320 million, accounting for 4% of the national average annual economic loss of pine wood nematode disease.  
ratios of indirect economic losses to direct economic losses were Shanghai, Henan and Anhui province, the values for which were 1.2, 2.6, and 2.9.
## Appendix B  
The expression for the NDA-EVM given in Equation (14) can be viewed as the sum of two error sums. The first sum consists of a sum of the errors over singly truncated decision regions (i.e., \(D_{0,R}\) and \(D_{k,R}\)), and the second sum consists of a sum of the errors over doubly truncated decision regions \(D_{i,R}\). Because the second sum is always positive and its value is much smaller than that of the first sum, Equation (14) can be reduced to  
\[\begin{array}{ll}\xi[k]^{2}>\frac{2}{k+1}\{\sum\limits_{j=0}^{k}&[\sigma_{n} (-b+\lambda_{jk,R})\varphi(\frac{-b-\lambda_{jk,R}}{\sigma_{n}})+(\lambda_{jk,R}^{2}+\sigma_{n}^{2})Q(\frac{-b-\lambda_{jk,R}}{\sigma_{n}})]\\ &+\sum\limits_{j=0}^{k}\left[\sigma_{n}(b+\lambda_{jk,R})\varphi(\frac{b- \lambda_{jk,R}}{\sigma_{n}})+(\lambda_{jk,R}^{2}+\sigma_{n}^{2})Q(\frac{b- \lambda_{jk,R}}{\sigma_{n}})\right]\}\end{array}. \tag{12}\]  
Considering \(\lambda_{jk,R}=b[\alpha(j-k)+(\alpha j-k)]<0\) and \(Q(x)\) decrease monotonically. Therefore, \(Q[(-b-\lambda_{jk,R})/\sigma_{n}]>Q[(b-\lambda_{jk,R})/\sigma_{n}]\) similarly, \(\varphi[(-b-\lambda_{jk,R})/\sigma_{n}]>\varphi[(b-\lambda_{jk,R})/\sigma_{n}]\).  
From the above and by using \(\varphi(x)\leq Q(x)\) after manipulation, Equation (12) becomes  
\[\xi[M]^{2}>\frac{1}{k+1}\sum\limits_{j=0}^{k}\frac{(\sigma_{n}-\lambda_{jk,R} )^{2}}{\sqrt{2\pi}}\exp(-(\frac{b-\lambda_{jk,R}}{\sqrt{2}\sigma_{n}})^{2}). \tag{13}\]  
Considering that \((1+k-(2j-k)\alpha)_{\max}=1+k\alpha+k\), Equation (13) can be rewritten as  
\[\xi[k]^{2}>\frac{1}{k+1}\sum\limits_{j=0}^{k}\frac{(\sigma_{n}-\lambda_{jk,R} )^{2}}{\sqrt{2\pi}}\exp(-\frac{b^{2}k^{2}\alpha^{2}}{2\sigma_{n}^{2}})\exp(- \frac{b^{2}[(1+k)^{2}+2k(1+k)]}{2\sigma_{n}^{2}}). \tag{14}\]  
According to the Cauchy-Schwarz-Buniakowsky inequality [32], \((\sum\limits_{k=1}^{n}a_{k}b_{k})^{2}\leq\sum\limits_{k=1}^{n}a_{k}^{2}\sum \limits_{k=1}^{n}b_{k}^{2}\). Let \(b_{k}=1\), we get \((\sum\limits_{k=1}^{n}a_{k})^{2}\leq n\sum\limits_{k=1}^{n}a_{k}^{2}\), and Equation (14) can be reduced to  
\[\xi[k]>\frac{1}{(k+1)}\exp(-\frac{b^{2}[(1+k)^{2}+2k(1+k)]}{4\sigma_{n}^{2}}) \exp(-\frac{b^{2}k^{2}\alpha^{2}}{4\sigma_{n}^{2}})(\sum\limits_{j=0}^{k} \frac{(\sigma_{n}+bk)}{\left(2\pi\right)^{1/4}}+\sum\limits_{j=0}^{k}\frac{(-(2j -k)bk)}{\left(2\pi\right)^{1/4}}) \tag{15}\]Since \(\sum\limits_{j=0}^{k}{(-(2j-k)bk)/(2\pi)^{1/4}}=0\), we can obtain the lower bound on the NDA-EVM as follows:  
\[\xi[M]>\frac{\sigma_{n}+bk}{(2\pi)^{1/4}}\exp(-\frac{b^{2}[(1+k)^{2}+2k(1+k)]+b^{ 2}k^{2}\alpha^{2}}{4\sigma_{n}^{2}}). \tag{11}\]
# Dual Attention Equivariant Network for Weakly Supervised Semantic Segmentation
## 5 Conclusions  
In this paper, we proposed a dual attention equivariant network model to effectively improve the accuracy of CAM by considering both channel and spatial information of different feature maps. Specifically, we first designed a channel-spatial attention module for DAEN to extract accurately features of target objects by considering the correlation among feature maps in different channels, and then integrated CSM with equivariant regularization and pixel-correlation modules to achieve more accurate and effective pixel-level semantic segmentation. Extensive experiments on PASCAL VOC 2012 and LUAD-HistoSeg datasets demonstrate that our proposed model outperforms existing state-of-the-art models for image-level weakly supervised semantic segmentation.  
Conceptualization, G.H.; methodology, Z.Z.; data curation, M.Z.; formal analysis, L.Z.; funding acquisition, G.H., [PERSON] ([PERSON]), and [PERSON] ([PERSON]); investigation, Z.Z.; resources, G.H.; software, Z.Z.; project administration, L.Z.; visualization, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Guangxi Natural Science Foundation Program under Grant No. 2025 GXNSFBA069090, the Guangxi Key Laboratory of Trusted Software under Grant No. KX202324, the Key Research and Development Program of Guangxi under Grant No. GuiKeAD22035118, and the Key Laboratory of Equipment Data Security and Guarantee Technology, Ministry of Education under Grant No. GDZB2024060600.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **TE** & **NEC** & **LYM** & **TAS** & **Average Result** \\ \hline HistoSegNet [42] & 45.6 & 36.3 & 58.3 & 50.8 & 47.7 \\ SEAM & 47.6 & 49.5 & 48.3 & 56.9 & 50.6 \\ \hline
**Our DAEN** & 48.2 & 51.1 & 49.4 & 58.7 & **51.9** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance comparison in terms of mIoU (%) of the proposed DAEN model with other weakly supervised semantic segmentation models on LUAD-HistoSeg dataset.  
Figure 7: Comparison of segmentation results on PASCAL VOC 2012 verification set. (**a**) Original images. (**b**) Ground truth labels. (**c**) Segmentation labels produced by the SEAM model. (**d**) Segmentation labels produced by the DAEN model.