esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 4 Experiments
### Experimental Results
#### 4.5.3 Analysis on Time Complexity
To simplify the symbolic representation, we use \(B\times D\) to denote the shape of latent representations. Since our method is the same as the autoencoder of the DMSC-UDL, we only calculate the time complexity of the self-expressive modules of the two methods. For the reconstructing self-expressive matrices module in our proposed DCRSM, the number of channels in the hidden layer is \(L\), so the time complexity of a single-view is \(O(L(D+L)B^{2})\), while for the self-expressive layer in DMSC-UDL, all training samples are needed in each iteration. The time complexity of single view is \(O(D^{2}N^{2})\). For large-scale datasets\((B\ll N)\), we can reduce the complexity extremely. Moreover, our method freezes the parameters of the encoder while training the reconstruction module, which also reduces the complexity of training process. We also calculate the FLOPs and the number of parameters of our proposed model and the DMSC-UDL network, as shown in Figure 5. When dealing with small-scale datasets, our method and the comparative method have similar FLOPs. However, our method exhibits a significant advantage when extending to large datasets. For instance, on the COIL-100 dataset, our method has 34.23G FLOPs, while DMSC-UDL has 121.47 G FLOPs. On the YTF dataset, our method has 45.4G FLOPs and the comparative approach reaches 170.04 G FLOPs. Moreover, our method has a fixed number of parameters, while the parameters of traditional self-expressive methods such as DMSC-UDL grow quadratically with the  
Figure 4: Clustering Accuracy vs. Running Time on different datasets with various training number \(N\). DCRSM-\(N\) represents DCRSM trained with \(N\) data samples. number of samples in the dataset. The fixed number of parameters and lower FLOPs of our method give it a significant advantage when scaling up to large datasets.
# Bot-Based Emergency Software Applications for Natural Disaster Situations
## 1 Introduction  
During the last few years, there has been a special interest in the development of software applications focused on providing help during and after the occurrence of natural disasters of great magnitude [1; 2; 3]. The normal life of people is destabilized, there is chaos and the need for help, rescue, and relief to the victims of the catastrophe. All of this can cause human, structural, and economic losses that can rise inculactly if not managed properly. In this crisis context, emergency response teams composed of spontaneous volunteer and experts have a key role in helping reduce the impact of the natural disasters. They are typically coordinated by social, public, and national organizations responsible for sending the appropriate resources to the most affected areas and also for reporting the news. Emergency response teams need to work fast, and the decision makers must base their decision on the collected data from different sources as soon as possible [4].  
Nowadays, technology such as mobile devices and smartphones have driven the development of applications devised for large-scale natural disasters to aid victims and also to facilitate the collaboration between spontaneous volunteers [5; 6]. However, in a serious emergency situation, people will always turn to the software they use every day,the software that is most familiar to them. It is unlikely that a person who can potentially participate as a spontaneous volunteer in the face of a serious emergency remembers in advance to install and learn how to use a large-scale disaster application. Especially in a stressful situation, either as an affected person or as a person in charge of volunteer work or being part of an emergency team, people will tend to turn to the software they know best and have available on their smartphones [7; 8]. Social media has also been successfully used to rapidly evaluate the impacts of disasters in real time [9; 10].
# A Study on Highway Driving Assist Evaluation Method Using the Theoretical Formula and Dual Cameras
## 2 Theoretical Background
### Calculate the Lane Distance Using Dual Cameras  
#### 2.3.1 Variables of Camera Image  
To define the variables, it is assumed that the width of the image (I\({}_{\text{width}}\)), height of the image (I\({}_{\text{height}}\)), and focal length of the camera (f) used for lane detection are constant and that the specifications of the vehicle and camera used are known.  
The vanishing point and center point required for the calculation can be obtained using the lane detected through the lane detection algorithm. Figure 1 shows the detected right and left lanes extended above the image, with which the coordinates of the vanishing point A (x1, y1) and the coordinates of the center point B (x2, y2) can be obtained.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Height (cm)**} & **Baseline** & **Angle** & \multicolumn{2}{c}{**Error Rate (\%)**} \\ \cline{3-5}  & **(cm)** & **(Degree)** & **Average** & **Maximum** \\ \hline \multirow{8}{*}{30} & \multirow{8}{*}{10} & 3 & 13.28 & 48.62 \\  & & 7 & 14.07 & 45.36 \\  & & 12 & 14.15 & 53.04 \\ \cline{2-5}  & \multirow{8}{*}{20} & 3 & 6.89 & 23.66 \\  & & 7 & 9.64 & 27.3 \\  & & 12 & 4.14 & 10.53 \\ \cline{2-5}  & \multirow{8}{*}{30} & 3 & 10.34 & 35.83 \\  & & 7 & 9.5 & 33.35 \\  & & 12 & 8.34 & 33.99 \\ \hline \multirow{8}{*}{40} & \multirow{8}{*}{10} & 3 & 5.55 & 13.77 \\  & & 7 & 7.84 & 15.38 \\  & & 12 & 8.18 & 26.9 \\ \cline{2-5}  & \multirow{8}{*}{30} & 3 & 4.93 & 15.19 \\  & & 7 & 2.38 & 6.17 \\  & & 12 & 5.49 & 19.52 \\ \cline{2-5}  & \multirow{8}{*}{30} & 3 & 3.34 & 13.9 \\  & & 7 & 1.88 & 5.69 \\  & & 12 & _0.86_ & **2.15** \\ \hline \multirow{8}{*}{50} & \multirow{8}{*}{10} & 3 & 10.62 & 32.49 \\  & & 7 & 3.77 & 10.94 \\ \cline{1-1}  & & 12 & 8.19 & 27.67 \\ \cline{1-1} \cline{2-5}  & \multirow{8}{*}{20} & 3 & 2.47 & 5.81 \\ \cline{1-1}  & & 7 & 2.45 & 5.84 \\ \cline{1-1}  & & 12 & 5.23 & 18.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results for selecting the optimal location for dual cameras.  
In the preceding study [16], the lane detection equation in the image was proposed through Curve Fitting. Using this equation, the positions of the x-coordinate pixel and the y-coordinate pixel of the lane in the image may be obtained. C (x3, y3) and D (x4, y4) may be obtained through the pixel position of the lane. At this time, the origin is in the upper left corner of the image. Figure 2 shows C, D and origin point.
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 1 Introduction
Finally, the use of cooperative approaches may led to significant improvements in terms of communication security [14; 15], representing a fundamental challenge to be addressed also in underwater scenarios.
# Laser Fabrication of Titanium Alloy-Based Photothermal Responsive Slippery Surface
## 2 Methods  
### Sample Preparation  
A 40 mm \(\times\) 40 mm \(\times\) 1 mm titanium alloy slice was used for sample preparation. First, it was cleaned with ethanol before laser processing. The laser treatment mode was set to the laser ablation mode to fabricate micro-nano-structures on the titanium alloy surface. The parameters were set as: scanning speed 50 mm/s, frequency 20 kHz, power 10 W. The scheme for laser processing process is shown in Figure 1a. Figure 1b shows a schematic diagram of the reflection of light on smooth surface and the anti-reflection effect on the laser-structured surface. Briefly, the antireflection effect can be attributed to the grooved microstructures and the nanoscale roughness on the surface.
# Boosting Model Interpretability for Transparent ML in TBM Tunneling
## 4 Results
### SHapley Additive exPlanations (SHAP)  
The waterfall plot in Figure 7 illustrates how individual features contribute to the model's prediction for a specific example. The baseline value, E[f(x)] = \(-\)7.75, represents the model's average prediction across all feature combinations and serves as a reference point. The predicted value, f(x) = \(-\)4.766 (top right corner), is the model's output for this specific input. The difference between f(x) and E[f(x)] is explained by the sum of the Shapley values for all features. The \(y\)-axis lists the features and their values for the analyzed datapoint. Red and blue arrows represent SHAP values, indicating whether a feature increases (red) or decreases (blue) the prediction, with absolute SHAP values showing the magnitude of each feature's impact. In this case, cohesion of the overburden (kN) had the largest effect, followed by distance from the **invert to WT (m)** and the **distance from the shaft (m)**. Notably, the cohesion of the overburden also made the smallest contribution in this instance. SHAP values are datapoint-specific and vary across observations.  
The beeswarm plot (Figure 8) ranks features by their overall impact on predictions and visualizes how feature values affect outcomes. Each dot represents an observation, with the horizontal axis showing the SHAP value (feature contribution) and color indicating value levels (red for higher, blue for lower). The plot highlights feature relationships and their influence. For instance, higher **\"Invert to WT (m)\"** values strongly negatively impact predictions, while lower values contribute positively. Conversely, high **cohesion (kN)** in the overburden has a significant positive effect, while low values are negative. The **\"Penetration Speed (mm/rev)\"** feature shows minimal impact regardless of its value. Features are ranked  
Figure 6: Actual vs. predicted values of the two best performing models.  
Figure 7: A SHAP waterfall plot between an expected and predicted value of a single example.  
by global importance, with the most influential at the top. This visualization effectively captures global feature impacts and local contributions across observations.  
The decision plot (Figure 9) illustrates how features contribute to a model's prediction. The \(x\)-axis represents the model's output (settlements) in log odds, while the \(y\)-axis lists features in descending order of importance. SHAP values accumulate as you move from the bottom (expected value) to the top, showing each feature's contribution to the final prediction. The plot begins at the base value and each feature pushes the prediction higher or lower. For example, the \"Invert to WT (m)\" feature consistently increases the prediction by 0.547 mm. While informative for understanding feature impacts, the plot is more effective when combined with other visualizations.
# A Survey of Defensive Measures for Digital
## 3 The Scope: Examples of Digital Persecution in the Global South
### Mexico  
A 2017 New York Times article entitled _Using Texts as Lures, Government Spyware Targets Mexican Journals and Their Families_ describes how the Mexican government purchased a spyware program, called Pegasus, to target their own citizenry [25]. Pegasus, created by the NSO group, was sold to Mexico's federal agencies on the condition that it only be used for investigating terrorists and drug cartels. However, Pegasus has been targeted against journalists, academics, and human rights lawyers, as well as their families. Briefly, the Pegasus spyware is installed on a smartphone when a user clicks on a malicious link in a text message. The message is made to pique the interest of the recipient, such as claiming that the link contains a photo of armed men outside the recipient's house. Clicking the link installs the spyware and leads to a page that appears broken. Once installed, Pegasus monitors texts, emails, calls, calendars, and contacts. It can clandestinely turn on the microphone and camera, effectively turning the phone into a surveillance device. While the Mexican government's involvement has not been confirmed yet, the circumstantial evidence is significant. For one, the NSO Group asserts that only a government agency can use the spyware. Furthermore, all of the targeted non-criminals are people critical of the government. This includes lawyers investigating the unsolved disappearance of 43 students that were abducted by Mexican police.
# The Limits of SEMA on Distinguishing Similar Activation Functions of Embedded Deep Neural Networks
## 3 Identification Using Timing
### Limitations  
Although some functions such as the SELU function and Fast Sigmoid function are easily recognized from the timing patterns, we believe the other functions will be hard to define.  
First, the distinct signatures of the timing behavior for activation functions could depend on the implementation method. The two activation functions used in [15], the Sigmoid and Tanh functions, were distinguished by the differences in the timing pattern. The Tanh function has a symmetric pattern compared to the Sigmoid function. However, [PERSON] et al. implemented the Tanh function differently, causing the symmetric pattern to break and have a similar timing pattern to the Sigmoid function [16]. They insisted that the timing pattern for activation functions can change depending on the implementations, making them hard to characterize. The Taylor series approximation, for example, is one way to implement the exponential function. By using such an approximation, the Sigmoid function could be computed in a uniform timing, making the function a common pattern with no uniqueness. By trading off the computation time, the ReLU and Leaky ReLU functions could also be processed in a uniform timing by adding a dummy operation for either negative or positive inputs. Using such countermeasures, this timing attack is not as effective.  
The other limitation is that thousands of measurements with uniformly distributed activation function inputs are required to plot the distribution of the calculation time. In [15], timing measurements were performed when the network was processing random inputs in the range, i.e., \(x\in\{-2,2\}\), and [PERSON] et al. found that this input refers to the inputs to the activation function [16]. To plot the timing behavior for different activation functions based on the inputs, there would be a need to calculate the uniform input to the activation functions. They argued that without the knowledge of the weights, the bias, and the number of neurons in the layer, this timing attack will be difficult. Even with the knowledge of the weights, bias, and network structure, it would be difficult to obtain all of the inputs needed to make the profile of the timing behavior. To plot this graph specifically as in this previous work, a total of 2000 inputs are needed, making this graph difficult to plot, as a complex calculation is needed.  
Accordingly, we used an attack method to substitute the timing-based attack. The attack is a SEMA-based approach first proposed by [PERSON] et al. [16] that has potential to overcome these limitations.
## 4 Results  
To rigorously assess our model's performance, we employed four essential metrics: accuracy, precision, recall, and the F1 score.  
Accuracy: This fundamental classification metric measures the proportion of correct predictions (true positives and true negatives) out of the total dataset. Our model achieved a remarkable accuracy of 97.1% during validation (Figure 1), a trend that persisted in testing with an accuracy of 97.4%. This signified exceptional efficacy in correctly classifying emotional states.  
Precision: Precision quantifies a model's exactness. It is the ratio of true positives (TP) to the sum of true positives and false positives (TP + FP). Our model's high precision scores (Tables 1 and 2) demonstrated a low false-positive rate, indicating that its predictions of specific emotional states were highly reliable.  
Recall: Also termed sensitivity, recall measures a model's ability to identify all relevant instances (true positives). It is calculated as TP/(TP + FN). Our model's high recall values (Tables 1 and 2) confirmed its capacity to detect most instances of each emotional state, missing few true positives.  
F1 Score: The F1 score harmonizes precision and recall, making it especially valuable for potentially imbalanced datasets. Our model's strong F1 scores (Tables 1 and 2) reflected its ability to maintain both precise predictions and comprehensive identification of relevant instances.
# Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
## 4 Numerical Analysis
### Parametric Analysis
#### 4.2.3 Effect of Tamping Energy  
In order to investigate how the tamping energy influences the dynamic compaction effects to saturated ground with a weak interlayer, the tamping energy of DC ranged from 1000 kN\(\cdot\)m to 4000 kN\(\cdot\)m in the numerical simulations. The left half of each figure presents the foundation without an embedded weak layer, while the right half represents the foundation with an embedded weak layer (see cases 9 to 12 in Table 4).  
Figures 16 and 17 show that as the tamping energy increased, the zone of \(\rm I_{r}=7.7\%\) below the embedded weak layer increased significantly. The soil above the embedded weak layer experienced the largest compaction. The relative degree of reinforcement in the embedded weak layer was close to 0. It is proved that a further increase in the tamping energy has little effect on reinforcement effect in the embedded weak layer. It should be noted that \(\rm I_{r}\) below the embedded weak layer was only marginally affected by tamping energy while it is 1000 kN\(\cdot\)m, which suggests that there was not a significant change of reinforcement effect in the embedded weak layer by small tamping energy. However, the  
Figure 15: Distributions of excess pore water pressure ratio in grounds with weak interlayer of different initial position. (**a**) 0.4 m; (**b**) 1 m; (**c**) 2.0 m; (**d**) 3.0 m.  
reinforcement effect below the embedded weak layer was improved obviously when the damping energy increased to 4000 kN\(\cdot\)m. The reinforcement effect could be improved obviously by properly increasing damping energy.  
Figure 18 shows that the excess pore pressure ratio in the upper soil of the weak layer was close to 0 after 10 blows due to its high permeability, the soil in which the layer was effectively reinforced. For the embedded weak layer, the dissipati  
Figure 16: Distributions of I\({}_{\text{r}}\) of embedded weak layer for different damping energy. (**a**) 1000 kN\(\cdot\)m; (**b**) 2000 kN\(\cdot\)m; (**c**) 3000 kN\(\cdot\)m; (**d**) 4000 kN\(\cdot\)m.  
Figure 17: Cloud lines of I\({}_{\text{r}}\) = 7.7% of embedded weak layer for different damping energy.  
not obvious, and the excess pore pressure ratio reached its maximum values, increasing significantly with increases of the damping energy. If the damping energy was too large, the dissipation of excess pore pressure of soil would be incomplete, especially in the soft interlayer foundation with high mud content. In other words, the damping energy should not be too large, so as to avoid the complete destruction of soil structure or the formation of \"rubber soil\". For the soil below the weak layer, the residual excess pore water pressure still remained at a high level, which required a certain time to dissipate due to the existence of the embedded weak layer. This prevented the further development of the reinforcement effect at depths below the weak layer.
## 2 Data Analysis Techniques
### Correlation  
In this paper, we use the correlation method which is one of data analysis methods to select the effective feature. Correlation refers to the similarity between data, so features with a high correlation  
\begin{table}
\begin{tabular}{c c|c c} \hline \hline \multicolumn{2}{c}{**Higher-Order Cumulant**} & \multicolumn{3}{c}{**Expression**} \\ \hline Second-Order Cumulant & \(|C_{20}|\) & \(|M_{20}|\) \\  & \(|C_{21}|\) & \(|M_{21}|\) \\ \hline Fourth-Order Cumulant & \(|C_{41}|\) & \(|M_{40}-3M_{20}^{2}|\) \\  & \(|C_{42}|\) & \(|M_{42}-M_{20}^{2}-2M_{21}^{2}|\) \\ \hline \multirow{4}{*}{Stichth-Order Cumulants} & \(|C_{60}|\) & \(|M_{60}-15M_{20}M_{40}+30M_{20}^{3}|\) \\  & \(|C_{61}|\) & \(|M_{61}-10M_{20}M_{41}-5M_{21}M_{40}+30M_{21}M_{20}^{2}\) \\  & \(|C_{62}|\) & \(|M_{62}-6M_{20}M_{42}-8M_{21}M_{41}-M_{26}M_{40}+6M_{20}^{2}M_{22}+24M_{21}^{2 }M_{20}|\) \\  & \(|C_{63}|\) & \(|M_{63}-9M_{21}M_{42}+12M_{21}^{2}-3M_{20}M_{42}-3M_{22}M_{41}+18M_{20}M_{22}M_{ 22}|\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The expressions for various cumulants.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**BPSK**} & **QPSK** & **8-PSK** & **16-QAM** & **64-QAM** \\ \hline \(|C_{20}|\) & 1 & 0 & 0 & 0 & 0 \\ \(|C_{21}|\) & 1 & 1 & 1 & 1 & 1 \\ \(|C_{40}|\) & 2 & 1 & 0 & 0.68 & 0.62 \\ \(|C_{41}|\) & 2 & 0 & 0 & 0 & 0 \\ \(|C_{42}|\) & 2 & 1 & 1 & 0.68 & 0.62 \\ \(|C_{60}|\) & 16 coefficient between feature values are relatively inefficient in the AMC processes. The Pearson correlation for the variables \(X,\ Y\) is [19]:  
\[\text{cor}(X,Y)=\ \frac{C(X,Y)}{\sigma_{X}\sigma_{Y}}, \tag{1}\]  
where \(C(X,Y)\) is the covariance of the variables \(X\) and \(Y\). Thus, (1) can be expressed as:  
\[\text{cor}(X,Y)=\ \frac{\sum_{i=1}^{k}(x_{i}-\overline{x})(y_{i}-\overline{y}) }{\sigma_{X}\sigma_{Y}}, \tag{2}\]  
where \(\sigma_{X}\) and \(\sigma_{Y}\) are the standard deviations of \(X\) and \(Y\). From this correlation coefficient, information on other data can be obtained through one data. The proposed method uses correlation as a data analysis method to select effective features [19].
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## 1 Introduction
Moreover, the paper will also explain how to construct the proposed network using NFV/SDN technologies as enablers. This paper is organized as follows. First, Section 2 presents an overview of how NFV/SDN technologies are suitable for the dynamic deployment of the UAV network by referring to our conventional work as well as the recent literature. Section 3 describes the architecture and system model in this study, Section 4 explains the proposed method, Section 5 presents the numerical results, and, finally, Section 6 concludes the paper.