esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Metrics  
A confusion matrix is a valuable tool for evaluating the performance of a classification model by comparing the model's predicted outcomes with the actual outcomes [38]. It provides a detailed summary of prediction results, which is especially useful in scenarios with imbalanced classes or when different classification errors have varying consequences. By analyzing the matrix, we can gain insights into the types and frequencies of errors the model makes, aiding in understanding its overall performance. Figure 5 depicts the matrix, which includes key terms such as true positive (TP), true negative (TN), false negative (FN), and false positive (FP). Table 4 provides a summary of the performance measurement metrics used in this study. Accuracy represents the percentage of overall correct predictions, reflecting the model's ability to classify all cases correctly. Precision measures the rate of correct positive predictions among all predicted positive cases, indicating the model's reliability in identifying true positives. Recall highlights the percentage of correctly identified positive cases out of all actual positive cases, showcasing the model's sensitivity. Lastly, the F1-score combines precision and recall to provide a balanced measure of the classifier's precision and robustness, offering insight into the model's overall performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Parameters** \\ \hline DT & Max\_depth = 6 \\ \hline RF & n\_estimators = 42 \\  & random\_state = 42 \\ \hline LRC & random\_state = 42 \\ \hline KNN & n\_neighbors = 5, \\  & metric = minkowski, \(p=2\) \\ \hline GB & n\_estimators = 100, \\  & learning\_rate = 1.0, \\  & max\_depth = 5 \\ \hline XGB & booster = gbtree, max\_depth = 6, \\  & gamma = 0, min\_child\_weight = 1, \\  & subsample = 1.0, colsample\_bytree = 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The values for hyperparameters tuning.  
Receiver operating characteristic area under curve (ROC AUC): The ROC AUC is used to measure the area under the receiver operating characteristic (ROC) curve, which plots the true-positive rate (sensitivity) against the false-positive rate (1-specificity) for different threshold values [39]. It quantifies the model's ability to discriminate between positive and negative classes across all possible thresholds. The ROC AUC score ranges from 0 to 1, where a score closer to 1 indicates better model performance.  
K-fold cross-validation: We use the K-fold cross-validation method to assess the performance and validity of an ML model [40]. We divide the dataset into k equal parts or folds. The model is then trained and validated k times, using a different fold as the validation set and the remaining K\(-1\) folds as the training set. This process generates K performance scores, which are averaged to produce a single performance estimate. K-fold cross-validation helps reduce overfitting and efficient use of data. This method provides a robust evaluation by leveraging various data subsets for training and validation.
## 4 Results and Discussion
### Generation of OCT Images
#### 4.1.3 Comparison with Different Generative Models  
Image quality measurement methods can be roughly divided into three categories, including pixel distance-based, correlation-based, and mean square error-based [32]. To verify the effectiveness of the proposed image generation model, we compared it to Autoencoder [33], DCGAN [34], U-Net [35], and other generative models. Figure 17 presents the results of our proposed OCR generation model and other models.  
The experimental results in Figure 17 show that although the Autoencoder and DCGAN models can generate images similar to real OCT images, they cannot replicate the blood vessels and other small details in the image. Similarly, the U-Net model can capture more detail in the images but is still not ideal because it cannot handle the branches in the blood vessels. In contrast, the images generated using the proposed OCT generative model are smoother, more realistic, and more similar to real images.  
To evaluate the OCT generation effect of different generative models, we use the generated OCT images and the original real OCT images from the test set of 221 fundus images and use Equations (10)-(13) to calculate PSNR, MSE, SSIM, and COSIN for each generated OCT image and the original corresponding OCT image, and finally expressed the results as the average value. Table 2 presents the performance comparison of various generative models under different evaluation criteria. The proposed method achieves the highest performance compared to other methods.  
Because the optic disc area on an OCT scan allows doctors to identify optic atrophy and determine macular thickness, it plays an essential role in glaucoma screening. Therefore, to further evaluate the effectiveness of the proposed generator, we randomly capture ten optic disc regions of size 130 \(\times\) 130 from the generated OCT images. Figure 18 shows two OCT images generated by different methods. The images in the table are generated and cropped to the optic disc area, and the disc size is 130 \(\times\) 130.  
Table 3 lists the similarity measures of the optic disc region between generated and real OCT images. Obviously, the proposed method achieves better generative results than other architectures.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **SSIM** & **PSNR** & **MS-SSIM** & **COSIN** \\ \hline
**Autoencoder** & 22.2\% & 65.67\% & 24.0\% & 97.7\% \\
**DCGAN**[34] & 22.7\% & 64.72\% & 24.7\% & 97.3\% \\
**U-Net**[35] & 21.3\% & 65.54\% & 22.4\% & 97.6\% \\
**Our method** & **27.2\%** & **65.78\%** & **28.7\%** & **97.8\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of images generated by different architectures.  
Figure 17: (**a**–**c**) Real OCT images. (**d**–**f**) Generated OCT by Autoencoder. (**g**–**i**) Generated by DCGAN. (**j**–**l**) Generated by Unet. (**m**–**o**) Generated by the proposed method.
## 3 Fuselage Barrel Test Case: Geometrical Description and Finite Element Model
### Finite Element Description  
The finite element model of the fuselage section is introduced in Figure 4A,B. The entire model consists of 1,976,157 nodes and 995,858 elements. The elements used for the aluminium components are discretised with three-dimensional elements with eight nodes and reduced integration scheme available in the Abaqus library (SC8R); the elements have a dimension of about 10 mm \(\times\) 10 mm. Planar shell elements with rigid body constraint were adopted to model the impact plate between representing the impacted rigid ground. With reference to the computational grid of the composite components of the fuselage section, eight nodes continuum shell elements with a reduced integration scheme were adopted. These elements are general-purpose shells allowing finite membrane deformation and large rotations, and thus, they are suitable for nonlinear geometric analysis. These elements include the effects of transverse shear deformations and the effects of thickness change. Connections between the sub-components were simulated with tie-constraints allowing to couple a master surface with a slave one by taking into account with all the degrees of freedom. The connections between the vertical standions and the passenger floor/frames were simulated by fastener elements allowing to introduce breakage criteria based on maximum force in each reference direction.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(\mathbf{G}_{1C}^{T}\) [kJ/m\({}^{2}\)] & \(\mathbf{G}_{1C}^{C}\) [kJ/m\({}^{2}\)] & \(\mathbf{G}_{2C}^{T}\) [kJ/m\({}^{2}\)] & \(\mathbf{G}_{2C}^{C}\) [kJ/m\({}^{2}\)] \\ \hline I & UNIDIRECTIONAL UD & 24 & 10 & 3 & 6 \\ I & Woven & 8 & 8 & 8 & 8 \\ II & UNIDIRECTIONAL UD & 44 & 16 & 3 & 6 \\ II & Woven & 12 & 12 & 12 & 12 \\ III & UNIDIRECTIONAL UD & 26 & 12 & 3 & 6 \\ III & Woven & 6 & 6 & 6 & 6 \\ \hline \end{tabular}
\end{table}
Table 6: Fracture toughness energies.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Stacking Sequence**} \\ Skin & [90/45/0/45]s \\ Stringer & [45/45/0/0/90/0]s \\ Frames & [90/45/0/45/45/90/45/45/0-45/45/45]s \\ Components in Woven Fabric Material & [0/45/0/-45]s \\ \hline \end{tabular}
\end{table}
Table 5: Sub-components stacking sequences.
# Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay
## 1 Introduction
The microscopic properties of soil could be obtained using more established image processing techniques, but there is no standardized procedure or step for image preprocessing and binarization, particularly for determining the threshold value of image segmentation. In this paper, the microscopic structure of municipal waste-contaminated clay was adopted as the research object. A novel method for processing SEM images and determining threshold values was proposed and applied in the processing of SEM images of clay contaminated with municipal waste using the Image Pro Plus (IPP) and Pores (Particles) and Cracks Analysis System (PCAS) software packages. To investigate the variation trend in the microstructure of municipal waste-contaminated soil with different seepage depths and different CaCO\({}_{3}\) and CH\({}_{3}\)COOH concentrations at the later stage of municipal waste pollution degradation, the apparent void ratio and fractal dimension were chosen as parameters. Additionally, the effective threshold range was established and verified. The research method is simpler and more accurate for determining the threshold value of image segmentation, and the research conclusions are very important for improving the reliability of the extraction of the microstructure parameters of contaminated soil.
# Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
## 4 Model Introduction and Application
### Transformer Model
#### 4.2.2 Multi-Head Attention Mechanism  
As shown in Figure 12, it is the framework of the multi-head attention mechanism. The multi-head attention mechanism is based on the self-attention mechanism. In the self-attention mechanism, the input is fed into three different fully connected Linear layers to create query, key and value. Query and key are multiplied by dot product matrix to generate a score matrix of scores. Then the scores matrix is processed, and the process can be expressed as Equation (7):  
\[\frac{\textit{scores}}{\sqrt{d_{k}}}=\textit{scale scores} \tag{7}\]  
Then SoftMax calculation is performed on the scaled scores to obtain the attention weight. Finally, the attention weight is multiplied by the value vector to obtain the final output vector. The output of self-attention can be expressed as Equation (8):  
\[Attention\left(Q,K,V\right)=\text{SoftMax}\left(\frac{QK^{T}}{\sqrt{d_{k}}} \right)V \tag{8}\]  
where \(Q\in\mathbb{R}^{T\times d}\), \(K\in\mathbb{R}^{T\times d}\) and \(V\in\mathbb{R}^{T\times d}\). The SoftMax function can be expressed as Equation (9):  
\[\text{SoftMax}(z_{i})=\frac{e^{z_{i}}}{\sum_{j=1}^{k}e^{z_{j}}} \tag{9}\]  
where \(z_{i}\) is the \(i_{th}\) element in the input vector, and K is the dimension of the vector. The SoftMax function performs an exponential operation on each element in the input vector and then divides each exponential value by the sum of all exponential values, resulting in a probability distribution with each element in the range [0, 1].  
To extend the calculation to multi-head attention, the query, key, and value vectors need to be divided into \(n\) vectors before applying the self-attention mechanism. Subsequently, these divided vectors undergo the aforementioned self-attention mechanism. Each self-attention process is referred to as a \"head\", and each head produces an output vector. The \(n\) heads are calculated in parallel and then combined to generate the output, constituting the multi-head attention mechanism. The formula of multi-head attention is expressed as Equations (10) and (11).  
\[MultiHead(Q,K,V)=\textit{Contact}(\textit{head}_{1},\textit{head}_{2},\textit {head}_{3},\ldots,\textit{head}_{n})W^{O} \tag{10}\]  
\[\textit{head}_{i}=Attention\left(QW_{i}^{O},\textit{K}W_{i}^{K},VW_{i}^{OV}\right) \tag{11}\]  
where \(i=1,2,3,4,\ldots,h\), \(W^{O}\), \(W_{i}^{O}\), \(W_{i}^{K}\) and \(W_{i}^{V}\) are the weights of corresponding networks, \(W^{O}\in\mathbb{R}^{d_{model}\times d_{k}}\), \(W_{i}^{O}\in\mathbb{R}^{d_{model}\times d_{k}}\), \(W_{i}^{K}\in\mathbb{R}^{d_{model}\times d_{k}}\) and \(W_{i}^{V}\in\mathbb{R}^{d_{model}\times d_{k}}\). Contact function is the splicing of results generated by parallel calculation of all heads.
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 2 Hydrogen Tanks Classification
### Comparison between Type III and Type IV Tanks  
To understand the differences and respective advantages of Type III and Type IV tanks, it is helpful to compare their key characteristics directly. Table 3 provides a detailed comparison, highlighting the advantages and disadvantages of Type III and Type IV tanks in terms of weight, system gravimetric capacity, cost, pressure cycle resistance, volumetric capacity, and temperature resistance.  
The low hydrogen storage density and self-weight metal liner of the Type III hydrogen tanks make it challenging to achieve the objectives of high efficiency and economy. In contrast, the liner of Type IV hydrogen tanks is made of plastic, which has several benefits, including being lightweight, low cost, long-lasting, and resistant to corrosion and fatigue [50; 65].  
This comparison highlights the trade-offs between the two tank types, making determining the most suitable option for specific applications easier, based on their distinct characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature/Aspect** & **Type III Tanks** & **Type IV Tanks** \\ \hline
**Weight** & Heavier (due to metallic liner) & Lighter (due to polymer liner) \\ \hline
**System Gravimetric** & Lower (due to & Higher (due to reduced \\
**Capacity**[22; 63] & higher weight) & weight of the polymer liner) \\ \hline
**System Cost**[63; 66] & Higher (because of the cost of & Lower (despite thicker \\  & the metallic liner) & composite layer) \\ \hline
**Pressure Cycle** & Lower (susceptible to & Higher \\
**Resistance**[22] & hydrogen embrittlement) & (better fatigue life) \\ \hline
**System Volumetric** & Potentially higher (thinner liners & Lower (due to thicker \\
**Capacity**[38; 63] & allow more fuel volume) & composite layers) \\ \hline
**Temperature** & Better (metallic liner provides & Lower (polymer liners are \\
**Resistance**[63] & significant heat protection) & less heat-resistant) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between Type III and Type IV hydrogen storage tanks.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.2 Comparative Experiments between the Original and Improved Models  
The original YOLOX-Nano algorithm has a small parameter size and exhibits good performance in both detection accuracy and speed. The improved RS-YOLOX-Nano algorithm, presented in this paper, shows an increased parameter size of 0.12 MB compared to the original YOLOX-Nano, with a detection speed of 62.45 fps, a decrease of 1.03 fps. It can effectively and promptly detect debris flow geological disasters in remote sensing images in most scenarios. Despite a reduction in detection speed, it meets the real-time detection requirements for relevant scenarios and is capable of performing real-time detection tasks for debris flow geological disasters, suitable for hardware deployment. Most importantly, as shown in Table 2, the enhanced RS-YOLOX-Nano network achieves an \(mAP\) of 86.04% for debris flow geological disasters, an increase of 3.53 percentage points over the previous \(mAP\) of 82.51%, thereby demonstrating superior detection and recognition capabilities for such disasters.
## 5 Experiments
### Experiment to Evaluate Minimum Detectable Marker Size  
Experiments were conducted to evaluate the detection performance of the RCM for small, low-resolution markers in images. In the experiment, the image size of the color markers was varied by changing the distance between the camera and the color markers. RCM detection was performed 100 times for each image size, and the detection rate was measured. The experimental system is depicted in Figure 10.  
Figure 11 illustrates the proportion of RCMs identified by the proposed methodology for each color marker rendering size, thereby confirming that the detection rate of the proposed methodology declines markedly for markers smaller than 10.2 pixels.  
Figure 11: Results of RCM detection rate by rendering size.  
Figure 10: Experimental system for minimum marker size.  
Additional experiments were conducted to verify the effect of inaccuracies in attitude estimation with the ArUco markers used in the experiments on the proposed method. The experimental setup and configuration were the same as in Figure 10, but RCM detection was performed with noise added to the AR marker attitude estimation results. The noise was based on the value from the inertial navigation system, and an error of \(\pm\)0.5 degrees with respect to the attitude angle and \(\pm\)40 mm with respect to the spatial coordinate system was added. The results of the additional experiment are shown in Figure 12.  
Figure 12 shows that the minimum detectable marker size remained unchanged at about 10 pixels in the additional experiment. In other words, it was confirmed that deviation in the attitude angle used in RCM detection can be tolerated to some extent. As demonstrated in Table 2, the requisite detection performance of the proposed methodology for low resolution is less than 14.5 pixels. Consequently, the proposed methodology is capable of attaining the necessary performance.
# SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks
## 3 Vulnerability Prediction Model  
### Overview  
To precisely predict vulnerabilities, we have to catch the characteristic of vulnerable binary. To this end, we propose to obtain three kinds of features--the semantic features, the statistical features, and the structural features--from binary programs to represent vulnerabilities. We need to extract information of the three features from the binary program. Figure 2 shows the structure of the entire model.  
First, we use ida-python [38] (for IDA-PRO 7.0) to disassemble the binary program. Second, the assembly instructions and statistical characteristics of all target functions need to be extracted from the program. Among these, the selection of the target function will be explained in the experiment section. We use networkx [39] to retain the control information (structure information) of each function and the different attributes of each basic block (including assembly instructions and statistical vectors); the assembly instructions are collected for the subsequent semantic extraction step and will be used as the input of the language model after tokenization. Third, after obtaining the outputs (instruction embedding), we apply mean pooling to the outputs in the same basic block and form semantic block embeddings. Then, we merge these embeddings with the statistical vectors of the basic block to obtain the whole embedding of each basic block. All the block embeddings in a function and the link between the basic blocks together form an ACFG. The structural features will be input as an adjacency matrix in the following GNN. Although we say the input of the GNN is an ACFG graph, the real input is a feature matrix and an adjacency matrix. After passing the matrices through the GNN, they will finally go through a softmax layer to obtain the vulnerability prediction score of each function.  
Next, we introduce the specific design details for each kind of feature separately.
## 4 Discussion
### Connectivity of mangrove Forests in Ecuador  
The connectivity of mangrove forests as a result of long-term changes has increased the number of connected mangrove patches in Ecuador relative to the increase in total mangrove area, and despite the increase of fragmentation. Along river and aquaculture channels destined for pools, often only a narrow strip of mangrove or even a few un  
Figure 8: Differences between mangrove area in 1996 and 2020. (**A**) Eastern part of Santa Cruz Island, in the Galapagos Islands; (**B**) mouth and estuary of the Guayas River.  
connected mangrove forests remain. This process occurs, for example, in the province of Guayas due to its high increase in shrimp farming activity and lack of land use control. According to [PERSON] et al. [51], in the 1980s and 1990s the major threat to this riparian system was the replacement of mangrove forests by shrimp farming activities, which caused a marked loss of biodiversity in sensitive ecosystems of high priority for preservation worldwide.  
We observed that the global and local connectivity of Ecuadorian mangrove forests increased throughout Ecuador and in all provinces. Connectivity is fundamental for processes such as species dispersal and migration, development of population genetic structure, source-sink dynamics, and possible responses to climate change [52]. Increased fragmentation can lead to increased connectivity, creating more heterogeneous landscapes [53]. However, the increase in fragmentation can be due to two different dynamics. In one, new patches are created in areas close to existing patches, fostering new connections and creating more corridors between patches; therefore, species are more likely to benefit from improved connectivity [54]. The second dynamic is that recovery areas (new patches) are created in areas far from existing ones; these areas will have more difficulty recovering and maintaining their biodiversity [55]. This is observed by increasing the number of components, which occurred especially in the Galapagos Islands. This is the reason why the average connectivity of the patches in the Galapagos decreased, because new mangrove areas were very poorly connected to other mangrove areas, presenting low connectivity per patch and decreasing the average connectivity between 1996 and 2020. Nevertheless, although these patches were poorly connected, they had a positive influence on global connectivity [56].
## 5 Research Challenges and Future Directions
### Resource Allocation Complexity  
In order to allocate resources effectively and in a manner that is suitable for implementation, attention must be taken to properly manage the computational complexity of resource allocation [147; 151]. Most of the approaches, however, have considerable computational and implementational complexity, which extends the processing time and indirectly raises the cost of deployment. More cautious approaches with reduced complexity and quick algorithms are needed to prevent a negative impact on performance [152]. These methods may speed up computation while decreasing complexity; however, the complexity of the underlying algorithm was not evaluated. Therefore, measuring algorithm complexity to ascertain implementation ability is an exciting future study subject. Joint clustering with resource allocation and coordinated multipoint-enabled HetNets are two examples of approaches that have been studied to decrease overhead signals, which are a key cause of the network's implementational complexity. In order to minimize the overhead and computational complexity of potential 6G HetNets, prospective radio RM can use computational intelligence approaches.