esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The proposed gradient-Laplacian attention module (GLAM), integrated into the vision transformer (PViT) network, significantly enhanced edge detection and detail preservation across all datasets (piglet, head, abdomen, chest, and thoracic) by sharpening image edges during the denoising process. This improvement stems from the combined use of Sobel and Laplacian filters, each focusing on distinct edge characteristics. A series of ablation studies validated the effectiveness of the proposed network. First, we compared the performance of the baseline PViT using traditional MHSA with various attention mechanisms, including GAM-PViT (diagonal, vertical, horizontal, VH, DVH), LAM-PViT, and the proposed GLAM-PViT. Next, we evaluated the proposed loss function with different combinations--MSE, SSIM, PL, and MSE+SSIM+PL--to determine optimal performance. Benchmark comparisons were then conducted with state-of-the-art denoising models (RED-CNN, TED-Net, DSC-GAN, BM3D, and DRLEMP) to assess the proposed model's competitiveness.  
The results indicate that leveraging transformer-based models for denoising in low-dose CT imaging can enhance diagnostic accuracy, potentially benefiting patient care across various medical fields. This approach shows particular promise in cancer diagnostics, where early tumor detection, accurate lesion segmentation, staging, and treatment planning are critical. Integrating these models into clinical workflows could reduce false positives and negatives, streamline interpretation, support large-scale screening, and stimulate further advancements in medical imaging research.  
The authors confirm contribution to this paper as follows: study conception: J.A. and P.B.; methodology and investigation: L.M.; data curation: P.B.; analysis and interpretation of results: L.M., J.A., and P.B.; draft manuscript preparation: L.M.; review and editing: J.A. and P.B.; funding acquisition: J.A. All authors have read and agreed to the published version of this manuscript.  
This research was supported by an NSERC Discovery grant RGPIN-202004441 that was awarded to [PERSON].  
The data (thoracic, chest, abdomen, and head) used for this study are openly available from the The Cancer Imaging Archive (TCIA): [[https://doi.org/10.7937/9](https://doi.org/10.7937/9) NPB-2637]([https://doi.org/10.7937/9](https://doi.org/10.7937/9) NPB-2637) (accessed on 26 February 2025). The Piglet dataset was provided by Dr. [PERSON]; further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors would like to thank [PERSON], the Mayo Clinic, and the American Association of Physicists in Medicine for making the CT data available for this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 3 PM2.5 Time Series Prediction Model
### mRMR Feature Selection Method  
In this paper, mRMR method [20] is used for feature selection. Its basic idea is to maximize the correlation between input features and output, and to minimize the correlation between input features. The goal is to find the most representative feature subset. The evaluation of correlation between features is performed by mutual information method, and the appropriate feature subset is selected from original feature set. The maximum relevance and minimum redundancy of mRMR are calculated as follows:  
\[\max D(S,C)=\frac{1}{|S|}\sum_{f_{i}\in S}I(f_{i};C) \tag{4}\]  
\[\min R(S)=\frac{1}{|S|^{2}}\sum_{f_{i},f_{k}\in S}I(f_{i};f_{k}) \tag{5}\]  
where \(f_{i}\) and \(f_{k}\) represent the \(i\)th and \(k\)th feature in set \(S\), respectively. \(C\) denotes the target output. \(I(f_{i};C)\) denotes the correlation function of the \(i\)th feature and output \(C.I(f_{i};f_{k})\) represents the correlation function of the \(i\)th and \(k\)th feature. Mutual information is defined as follows:  
\[I(x;y)=\iint P(x,y)\log\frac{P(x,y)}{P(x)P(y)}dxdy \tag{6}\]Therefore, the expression of mRMR is as follows:  
\[\max_{S}\left[\frac{1}{|S|}\sum_{f_{i}\in S}I(f_{i};C)-\frac{1}{|S|^{2}}\sum_{f_{ i},f_{k}\in S}I(f_{i};f_{k})\right] \tag{7}\]  
Applying the forward selection algorithm to solve the objective function (7), we can get the sort result of the input features. Finally, the optimal input feature subset can be obtained by cross-validation.
# One-Dimensional Consolidation of Multi-Layered Unsaturated Soil with Impeded Drainage Boundaries
## 4 Verifications and Examples  
### Verifications  
In this part, two consolidation cases of three-layered soil strata are investigated to verify the correctness of the derived semi-analytical solution against the available analytical and numerical solutions. One case is from the work of [PERSON] et al. (2015), which has a fully drained top surface and an undrained bottom boundary. For seeking an analytical solution, this case takes the same coefficient of air volume change and that of water volume change for different soil layers. The other case is from the work of [PERSON] et al. (2016), which caters to impeded drainage top and bottom boundaries. In addition, the coefficient of air volume change and that of water volume change are considered to be different in different soil layers. For this case, [PERSON] et al. (2016) proposed an IDQM solution. For these two cases, the specifications of the soil profile and material parameters are given in Table 1[25; 26]. For the first case, the drainage parameters at the top surface and bottom boundary are taken as infinities and zeros (i.e., \(R_{\text{ta}}=R_{\text{tw}}=\infty\) and \(R_{\text{ba}}=R_{\text{bw}}=0\)), respectively. This indicates that the top surface is fully drained while the bottom base is undrained to pore-air and pore-water. The applied loading is a constant load of 100 kPa, and at the initial time, both the excess pore-air and pore-water pressures are given as zeros. On the basis of Equation (23), the excess pore pressures are attained and the results are graphically shown in Figure 2a,b in the forms of excess pore pressure distributions. As a comparison, these figures also plot the results of [PERSON] et al. (2015) from the analytical solution. For the second case, the top surface is taken as fully drained to pore-air and pore-water, while the bottom base is given as undrained to pore-water but impeded drained to pore-air. The applied loading is an exponential load with the asymptotic value of 100 kPa. The excess pore-air and pore-water pressures are taken as zeros at the initial time. Similarly, the results of excess pore pressures are calculated from Equation (23). For the graphic presentation, the point \(z=9.5\) m was chosen to be investigated. In Figure 3a,b, the evolutions of excess pore-air and pore-water pressures are depicted. As a comparison, the results of [PERSON] et al. (2016) from the IDQM solution are also given. As seen in Figures 2 and 3, excellent agreements are obtained in these comparisons, which demonstrates the correctness and reliability of the derived semi-analytical solution. Therefore, the proposed solution is feasible to predict the 1D consolidation behavior of multi-layered unsaturated soil stratum with the general impeded drainage boundary conditions.
## 2 Image Registration Algorithm Based on Deep Learning and Local Homography Transformation  
In supervised learning-based image registration, sample labeling is required first. However, the cost of labeling samples manually is too high, and it is usually difficult to ensure the labeling accuracy, as well as to collect enough diverse images for registration. To solve this problem, an image registration algorithm based on deep learning and local homography transformation is proposed in this paper. Firstly, a sample and label generation method for deep learning is designed. In this method, direct linear transformation (DLT) and moving direct linear transformation (MDLT) are used to automatically generate more reasonable and effective samples and labels for deep learning, and then supervised learning is used to train CNN so as to obtain the image registration model, with which the local homography transformation-based image registration can be achieved.
## 4 Discussion
### Correlation between GPP and LST, PR, SRAD  
In this paper, the correlation calculation of selected driving natural factors of GPP was carried out. The results show that the mean value of GPP has a high correlation with land surface temperature [37], and there is a significant correlation in the Yanshan and Tai-hang Mountains. However, the overall correlation is low, and the correlation coefficient ranges from 0 to 0.2, while there is a high significant correlation in the central and southern plains region. The results showed that the distribution of gross primary productivity and land surface temperature has a strong correlation [38]. The correlation between GPP and precipitation shows that the whole region presents a significant correlation, but the correlation is low, indicating that although the increase of GPP in 20 years is related to the increase of precipitation, there is no strong significant correlation, and the regional difference is small, indicating that the precipitation gap is not obvious for the whole study area. For the correlation between downward surface shortwave radiation and GPP, the variation trend of GPP in the whole region has a very strong and significant correlation with downward surface shortwave radiation in the past 20 years. This result indicates that downward surface shortwave radiation has no obvious spatial difference but has a very strong driving effect on the long-term change of GPP [39].
# Satellite Image Categorization Using Scalable Deep Learning
## 6 Conclusions  
Satellite image analyses have a variety of potential applications [28; 32; 34; 53]. Satellite images present many additional challenges as compared with ordinary digital images. This paper presents an end-to-end deep-learning-based satellite image categorization method. The proposed method relies on visual clues from the spatial information of satellite images. Satellite Image Classification Dataset-RSI-CB256 is employed for experiments. The dataset has high intra-class and low inter-class variations. This makes the analysis of satellite images even more challenging. However, the proposed method shows encouraging results.  
The proposed model applies scalable networks for feature extraction. EfficientNets are a type of convolutional neural network that specializes in image classification tasks while being designed to be computationally efficient. The network is composed of three main parts: the stem, blocks, and head. The network's width, resolution, and depth can be adjusted using compound coefficients. The input parameter \(\phi\) determines the size of the network, with a larger \(\phi\) resulting in wider, deeper, and higher-resolution networks. EfficientNet B0 is built on residual blocks from MobileNet and includes squeeze and excitation blocks. Additionally, it utilizes the DConv and PWConv techniques for convolution and a ReLU activation in the PWConv layer.  
The proposed model is compared with many popular deep learning models, such as Inception V3, ResNet-50, and MobileNet. The experimental results testify to the proposed methodology's dominance. The proposed methodology gives a 0.997 F1 score, the highest among all the models studied.  
The presented study can be extended by applying it to different datasets. It can also be tested for more than four classes of satellite images. Moreover, other deep learning models can also be analyzed. It can also be a good research direction to see the effect of different backbone models in EfficientNets.  
Multi-resolution remote sensing image samples can also be employed for image classification. There is a growing volume of multi-resolution image data. A multi-resolution methodology exploiting the advantage of such a substantial amount of data would be useful.  
Data curation, S.T. and S.K.; Formal analysis, A.J. and D.K.A.; Funding acquisition, D.K.A.; Methodology, S.K.; Project administration, S.T.; Supervision, M.H.; Writing--original draft, S.T. and S.K.; Writing--review and editing, A.J. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable for studies not involving humans or animals.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Extraction Yield  
The yield values of the extracted pectin, using MAE and MAE with ultrasonic pretreatment (MAUPE), are shown in Figure 1. The yield of extracted pectin significantly increased from 10.85 \(\pm\) 0.7 to 14.4 \(\pm\) 0.3%, when the duration of microwave heating (MAE) increased from 3 to 5 min. In the extraction process, microwave heating creates significant pressure inside a watermelon rind and it can alter the raw material physical properties, such as porosity, and improve the penetration of the solvent into the tissue, energy transfer to the mixture and the release of target compounds in the extraction medium [22]. According to the obtained results and analysis of variance, MAUPE can significantly increase the extraction yield, and the pretreatment time showed a greater effect on pectin yield than ultrasonic power. The maximum yield of pectin (19.08% \(\pm\) 0.5) was achieved using pretreatment for 20 min and a power of 300 W before the microwave-assisted extraction (at 500 W for 5 min). [PERSON] et al. (2012) showed that the extraction yield of pectin from watermelon rind was 19.6% by MAE at a power of 500 W, pH of 1.5 and extraction time of 7 min [23]. A similar magnitude of extraction yield (19.75%) was reported by [PERSON] et al. using the conventional method to extract pectin from watermelon rind (pH 2, 80 \({}^{\circ}\)C for 60 min) [4]. Moreover, [PERSON] et al. optimized the conventional extraction from watermelon rind, with a temperature of 90 \({}^{\circ}\)C, a pH of 1.0, a time of 150 min and an SLR of 1:20. The extraction yield was 17.6% [5], which was lower than the yield of pectin extraction with the ultrasonic pretreatment. Therefore, pretreatment could reduce the extraction time.  
The extraction rate can be assessed by dividing the yield value by time. In the early stages (from \(t=0\) to \(t=3\) min) of pectin extraction, the process rate was higher than in later ones. This is probably due to the lower viscosity of extraction media at this stage compared to the latter. Ultrasonic pretreatment (300 W for 20 min) increased the extraction rate from 2.88 to 3.8% per minute.  
This non-thermal pretreatment affects the cell wall of the raw material, and promotes the interaction between the substrate and the solvent by generating cavitation in the mixed solution [5], finally increasing the mass transfer coefficient [24]. The cavitation effect includes a high temperature and local pressure, turbulence in the fluid, high stress near the air bubble and microjets near the solid surface, which affect the raw material and result in the facility of the extraction process [25]. Therefore, MAUPE can be used to improve the pectin extraction yield. Compared to other sources of pectin, such as passion fruit and banana, the pectin yields were 14.8 and 11.63%, respectively [26; 27], and the watermelon rind showed a higher extraction yield of pectin.
# A Graduated Non-Convexity Technique for Dealing Large Point Spread Functions
## 4 E-CATILED Technique
### Choice of the Blur Matrix Approximation
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(\|T_{n}-C_{n}(T_{n})\|_{F}\) & \(\|T_{n}-H_{n}(T_{n})\|_{F}\) & \(\|T_{n}-G_{n}(T_{n})\|_{F}\) & \(\|T_{n}-V_{n}(T_{n})\|_{F}\) \\ \hline \(n=20\) & 2.28601 & 2.26095 & 2.10745 & 2.08025 \\ \hline \(n=25\) & 3.17788 & 3.15482 & 2.92053 & 2.89542 \\ \hline \(n=30\) & 4.07270 & 4.05158 & 3.73644 & 3.71341 \\ \hline \(n=35\) & 4.95798 & 4.93865 & 4.54353 & 4.52243 \\ \hline \(n=40\) & 5.79877 & 5.78109 & 5.31037 & 5.29105 \\ \hline \(n=45\) & 6.59117 & 6.57494 & 6.03320 & 6.01547 \\ \hline \(n=50\) & 7.30809 & 7.29317 & 6.68763 & 6.67133 \\ \hline \(n=100\) & 11.56697 & 11.55943 & 10.60308 & 10.59485 \\ \hline \(n=1000\) & 13.68293 & 13.68225 & 13.43137 & 13.43068 \\ \hline \end{tabular}
\end{table} 
Table 3: Mean error obtained by the various approximations concerning 10,000 instances of randomly generated Toeplitz matrices \(T_{n}\) with decreasing entries in \([0,1]\). we deduce that, for \(n\) very large, \(G_{n}(T_{n})\) is the best choice considering both the quality of the approximation and the computational cost. Thus, we define the following approximation of the energy function \(E\) in (1):  
\[E^{(2+h)}(\mathbf{x})=\|\mathbf{y}-\widetilde{A}\mathbf{x}\|^{2}+\sum_{c\in \mathcal{C}}\psi^{(2)}(D_{c}(\mathbf{x}),D_{c-1}(\mathbf{x})),\]  
where \(h\geq 0\) is the update step of the CATILED algorithm, and \(\widetilde{A}\) is the approximation of the blur matrix \(\widehat{A}\), where all the symmetric Toeplitz matrices in the blocks of matrix \(\widehat{A}\) are approximated using the \(\gamma\)-matrices given by Theorem 5. In the proposed GNC algorithm, the parameter \(p\) varies from \(2+h\) to 0 with step \(h\). We call such an algorithm E-CATILED (_Extended Convex Approximation Technique for Interacting Line Elements Deblurring_).
# Hierarchical Federated Learning-Based Intrusion Detection for In-Vehicle Networks
## 3 Materials and Methods
### In-Vehicle Intrusion Detection System  
In this paper, we evaluate the performance of our proposed IDS as detailed in [15]. The proposed IDS consists of a multistage approach, where the first stage is responsible for detecting and classifying known attacks using an Artificial Neural Network (ANN), while the second stage acts as an anomaly detector to identify novel, unknown attacks using a Long Short-Term Memory (LSTM) autoencoder. The first stage of our proposed in-vehicle IDS classifies traffic data as either normal traffic or any known attack class that the ANN model has been trained on. Any data classified as normal by the first model will be re-examined by the second model (LSTM autoencoder) to detect unseen attacks that bypassed the initial model, providing an additional layer of protection. The first stage is a signature-based multiclassifier model, while the second stage is an unsupervised anomaly detection model. Figure 4 shows the workflow of proposed multistage IDS. The proposed IDS achieves an F1-score of 0.95 and a 99.99% detection rate for previously unseen attacks, all within a compact model size of 2.98 MB, making it highly suitable for deployment in real-world applications. This efficient model size fits well within the memory constraints of vehicle-level systems, which typically have over 1 GB of available RAM [31]. More details about the proposed IDS can be found in [15].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature** & **Description** & **Type** \\ \hline Timestamp & Time & float \\ \hline CAN ID & CAN message identifier & hexadecimal \\ \hline \multirow{2}{*}{DLC} & The length of the data field, measured & integer \\  & in bytes & \\ \hline Data & Payload (64-bit) & hexadecimal \\ \hline \multirow{2}{*}{Flag} & T or R, & \\  & T: Attack, R: Normal & string \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data features, descriptions, and types.  
Figure 4: Workflow of the proposed multistage in-vehicle IDS.
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 5 System Model
### Content Delivery Model  
If the vehicle sends a content request, the corresponding controller will process it. RSU makes the downloading decision on the basis of a request. If the content is recently cached by a node in its coverage area, RSU will send the node's location (longitude and latitude) to the requesting node; then the requesting node will fetch the content from a specific node through V2V communication. Popular files (PF) can be fetched by vehicles at any location within the coverage area of MBS; however, location files (LF) are served by RSU. First, RSU will check in neighboring RSUs, in case of non-availability, PF is downloaded from MBS. After that, if there is again a request for the same content, it will be stored in RSU's cache and will entertain the user with minimum delay. In the case of successful V2V communication, the probability of downloading files will be minimum (\(P_{V2V}\) < 1 and >0), while in V2I communication the probability will be maximum (\(P_{V2I}\) = 1), and the probability of V2I is calculated using Equation (4).  
\[P_{V2I}=1-P_{r}\left(A_{ji}>S_{f}\right) \tag{4}\]  
where \(A_{ji}\) represents the amount of data transmitted during V2V communication and \(S_{f}\) denotes the size of the file in packets
## 3 Results and Discussion  
In order to analyze results and to observe the system accuracy, they are graphically represented, making a comparison between the predicted values, in red, and the original values (target values), in blue, with labels \"predicted value\" and \"original value\", respectively. The time period represented in these graphs is eight days, the first eight days of the year. Although errors are calculated with the information belonging to all days of the every year. Because of the great number of experiments and cases, it will only be represented graphically the forecasting corresponding to the use of the regression function which gets best results.  
Despite getting several types of errors, only the values of the MSE are represented in tables for the classification systems explained in Section 2 (i.e., SVM linear, SVM RBF, SVM polynomial, regression tree and fit linear model). In all cases, results are presented with seven decimal places due to the similarity between the results.  
When the SVM predictor is used, the optimal kernel parameters are obtained using a loop that contains the training and testing modes. It modifies the values of the kernel parameters leading to values that achieves smallest error, which are the best. Being \(\gamma\), \(r\), and \(d\) the parameters of the kernel.  
This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation as well as the experimental conclusions that can be drawn.