esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Damping Layer Parameters
### The Effect of the Damping Layer's Thickness (\(h_{d}\) Is Variable)
#### 3.3.2 Case of \(h_{d}(x_{d1})=h_{d}(x_{d4})=0\)  
Consider that the thickness \(h_{d}(x)\) fluctuates in accordance with several distinct functional forms:  
Figure 6: The graphs of functions \(h_{d1},\cdots,h_{d4}\) given in Equation (26).  
Figure 7: The impact of damping layer thickness variation on the system modal loss factor. The damping layer position is limited between \(x_{d1}=0.14\,m\) and \(x_{d4}=0.26\,m\) and \(h_{d}(x)\) is taken as \(0.0002\), \(h_{d1},\cdots,h_{d4}\), respectively. \(h_{d1},\cdots,h_{d4}\) are given in Equation (26).  
\[h_{d5}(x) =-\frac{1}{150}|x-0.2|+\frac{1}{2500}, \tag{27}\] \[h_{d6}(x) =-\frac{1}{12}(x-0.2)^{2}+\frac{3}{10000},\] \[h_{d7}(x) =\frac{1}{6}(|x-0.2|-0.06)^{2},\] \[h_{d8}(x) =-\frac{100}{81}|x-0.2|^{3}+\frac{1}{3750},\hskip 56.905512 ptx\in[0.14,0.26]\]  
The plots of the functions given in Equation (27) are depicted in Figure 8. Figure 9 illustrates the improvement in the ABH effect achieved through various thickness variation functions, with the damping layer's maximum thickness positioned at the ABH tip. Varying the damping layer's thickness based on the functions \(h_{d5}\) through \(h_{d8}\) achieves better outcomes than maintaining a constant thickness throughout. This demonstrates that a uniform application of damping material across the entire region \([x_{d1},x_{d4}]\) is not optimal. With a fixed total amount of damping material, concentrating more damping at the ABH tip and tapering off as we move away from it leads to a markedly improved performance over a uniform distribution. Figure 9 shows that among the different thickness variation functions, the ABH effect is most significant when the thickness of the damping layer is adjusted according to the function \(h_{d7}(x)\). The findings from this subsection reveal that selecting the appropriate thickness variation function for the damping layer has a substantial influence on the ABH effect, warranting further investigation into optimizing the shape of the function.  
Figure 8: The graphs of functions \(h_{d5},\cdots,h_{d8}\) given in Equation (27).
## 4 Conclusions  
1. The spillway erosion mainly occurs at the bottom of the spillway chamber and the middle section, and the most serious erosion wear occurs near the ground sill. Compared with the bottom of the spillway, the gate leaf wear is relatively slight, but due to the importance of the radial leaf in hydraulic construction, the erosion wear of the gate leaf should not be underestimated;
2. Under the same other conditions, the erosion amount increases first and then declines with the increase in sediment particle size, and the maximum erosion amount increases linearly with the increase in sediment concentration;
3. The maximum erosion of the spillway is positively correlated with the flow velocity. At the bottom of the spillway, the erosion increment decreases with the increase in the flow velocity. At the gate, the erosion increases exponentially with the increase in the flow velocity;
4. The opening of the radial leaf will affect the amount and location of sediment erosion on the spillway. When the gate opening degree is small (0-3 m when the water level is 6.5 m), the erosion at the bottom of the spillway reduces with the increase in the gate opening. When the opening reaches a certain range, the maximum erosion at the bottom of the spillway is usually stable. At the same time, with the increase in the opening, the highest erosion area at the bottom of the spillway changes gradually from the bottom sill of the gate to the rear section of the gate;
5. In this paper, a mechanism model of spillway erosion is built through numerical simulation, which can predict the erosion of the gate to a certain extent. Further research in this paper can be carried out by collecting information such as sediment diameter, sediment concentration, flow velocity and gate opening degree of the spillway at each working time. Then the erosion simulation model is used to simulate  
Figure 16: Flow field velocity near the gate with different opening degrees.  
the erosion of the spillway so as to realize more economical and effective operation and maintenance of the spillway.  
**Author Contributions**: Formal analysis, Y.Z.; Investigation, C.Z.; Resources, T.W.; Software, M.W.; Supervision, Y.Z.; Writing--Original draft, C.Z.; Writing--Review & editing, C.Z. and H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was funded by Project of Integrated Standardization and New Mode Application of Intelligent Manufacturing of Ministry of Industry and Information Technology of China, grant number 2018037. This research was also funded by Water Conservancy Science and Technology Project of Henan Provincial Water Resources Department, grant number GG202068 No.2018037.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 5 Performance Evaluation
### Estimation Model Results Analysis  
#### 5.2.1 Performance of Different Methods  
We adopted the different methods mentioned in Section 5.1.4 as the approach to the estimation model. The results are shown in Table 4. So the error can be further reduced, we use MAE as the criterion of the loss function.  
Linear smoothing has the worst performance because it takes the average of past moments into account; exponential smoothing has slightly better performance because it favors information from the nearer moments.  
The performance of SVR that adopts machine learning methods is excellent. However, LSTM considers the information of past moments, and the MAE is even lower and performs best according to general presence. To show its sustainability, we will discuss the performance of each method in each location around the world in the next part.
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 4 Results and Discussion
### Latency Overhead Analysis
#### 4.2.3 Encryption/Decryption Latency  
When assessing the overall latency of privacy-preserving methods, encryption latency \(L_{enc}\), and decryption latency \(L_{dec}\) are crucial. For a given security parameter \(\lambda\), the encryption and decryption latencies can be articulated as:  
\[L_{enc}=f(\lambda,n,d) \tag{28}\]  
\[L_{dec}=g(\lambda,n,d) \tag{29}\]  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Computational Latency** & **Communication Latency** & **Encryption/Decryption Latency** & **Total** \\ \hline Transformer [4] & 0.13 & 0 & 0 & 0.13 \\ FHE [32] & 13.55 & 0 & 113.78 & 127.33 \\ TEE [34] & 0.18 & 0.12 & 0.03 & 0.33 \\ Proposed Method & 0.05 & 0 & 0.08 & 0.15 \\ SOTER [33] & 0.11 & 0.08 & 0.08 & 0.27 \\ DP [35] & 0.18 & 0 & 0 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Latency Detail.  
where \(f\) and \(g\) are the complexity functions of encryption and decryption algorithms, respectively. The proposed method employs a lightweight encryption scheme to reduce \(L_{enc}\) and \(L_{dec}\), thus facilitating fast and secure operations in line with the efficiency goals of the model.  
Empirical results from the performance comparison table indicate that the proposed method generates a minimal latency of 0.15 milliseconds, only slightly higher than the 0.13 milliseconds of the unencrypted baseline transformer model, and significantly lower than the 127.33 milliseconds of the FHE method. This substantiates the effectiveness of the proposed optimizations in mitigating the overhead introduced by encryption operations, while preserving the computational efficiency of the underlying transformer architecture.  
In summary, the proposed method provides a comprehensive solution to the challenges of latency faced by privacy-preserving models in NLP tasks. By achieving a balance between computational efficiency and security considerations, a practical implementation has been realized, facilitating the development of real-time applications without compromising user privacy.
## 4 Experiments and Results
### Bimanual Tasks with Teleoperation Execution  
On the basis of the individual teleoperation experiment of the robotic arms and grippers, an integrated teleoperation experiment with the dual arms and dual grippers was conducted to test the performance of the whole teleoperation system for bimanual tasks. A common action set, with target approaching, grasping, lifting, and holding, conducted by dual arms and dual grippers was designed as a bimanual task. A 5.2 kg aluminum frame was introduced as the target object. The experiment of the teleoperation-based bimanual tasks is shown in Figure 10.  
The size of the frame was 600 mm \(\times\) 312 mm \(\times\) 120 mm. It is really challenging to lift and hold such a big and heavy frame with normal configuration of one arm and one gripper, especially under the unstructured teleoperated environment. The size is too big for one gripper to hold it. The weight is too heavy and the shape is asymmetrical. Slipping would occur during the lifting and holding.  
However, as shown in Figure 10 and the attached video, the integrated teleoperation system with dual arms and dual grippers can effectively handle such a big and heavy frame to perform specific bimanual tasks with target approaching, grasping, lifting, and holding. During the manipulation, the two grippers cooperate with each other. The weight of the frame is shared by the two grippers. Potential slipping and dropping are properly avoided. Hence, two arms and two grippers are better than one for such assistive bimanual tasks. It is much easier to keep balance during the manipulation procedures.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{5}{c}{**RRI Value**} \\ \cline{2-6}  & **Thumb** & **Index** & **Middle** & **Ring** & **Pinkie** \\ \hline Apple & 1 & 1 & 1 & 1 & 1 \\ Toy ball & 1 & 1 & 1 & 1 & 1 \\ Fan & 1 & 1 & 1 & 0 & 0 \\ Pineapple & 1 & 1 & 1 & 1 & 1 \\ Pepper & 1 & 1 & 1 & 1 & 1 \\ Egg & 1 & 1 & 1 & 0 & 0 \\ Cup & 1 & 1 & 1 & 1 & 1 \\ Spoon & 1 & 1 & 0 & 0 & 0 \\ Umbrella & 1 & 0 & 1 & 1 & 0 \\ Drinking water & 1 & 1 & 1 & 1 & 1 \\ Balloon & 1 & 1 & 1 & 1 & 1 \\ Tennis & 1 & 1 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RRI value of the five fingers during the teleoperation experiment.
## 4 RNN Structures Used for Traffic Prediction
### Hybrid Models Including RNN Techniques  
In the field of traffic prediction, RNNs and their variants, such as LSTMs and GRUs, are widely adopted due to their excellent performance in handling time series data. However, as the complexity of traffic data continues to increase, models relying solely on RNNs are no longer sufficient to meet prediction requirements entirely. To further enhance prediction accuracy and effectively address the complexities of traffic data, researchers are beginning to explore methods that combine RNNs with other mechanisms and models. These methods may include but are not limited to combining RNNs with traditional machine learning (ML)  
Figure 5: Comparison of RNN, LSTM, and GRU neural network structures.  
techniques, CNNs, GNNs, and attention mechanisms aimed at achieving better results in traffic prediction tasks, as shown in Table 2.
## 3 Results and Discussion
### Comparison of Forecast Results
#### 3.3.2 Discussion on Short-Term Forecasting Results  
In this section, the periodic conditions for applying the algorithm to short-term prediction are discussed based on the ARIMA model. Then the proposed improved model is adopted and compared with SVR and ARIMA models.  
Relevant studies show that the forecasting accuracy of SARIMA is affected by the number of periods in the lag time series. Therefore, the relationship between the number of periods in the forecasting time series and the forecasting accuracy is further explored in the dataset. Figure 8 shows the relationship between different forecasting periods.  
Figure 6: The long-term prediction performance of the proposed DS-SVR model and the SARIMA model in the medium-sized type.  
Figure 7: A comparison of long-term prediction errors between the proposed DS-SVR model and the SARIMA model in the medium-sized type.  
According to the results, with the same input sample, the training error and the prediction error have tendencies to increase with the increase of prediction periods, which is also in line with the characteristics of the autoregressive model affected by the number of lag periods and further illustrate that ARMA model is not suitable for providing long-term decision support. In this case, it is necessary to find the best autocorrelation lag time in use. The error of this research model in three months has basically met the demand, and shorter forecast periods are worthless to the implementation of production lines. Therefore, the optimal results are used to set the number of short-term prediction lag periods and compare them with the SVR algorithm again. Figure 9 shows the comparison curves. The results of quantitative analysis in RMSE show that the prediction error of SVR is 1596, and the prediction error of SARIMA is 933. The results show that the SARIMA has better short-term forecasting accuracy (Figure 10).  
Figure 8: Curves of SARIMA prediction error with lag periods variation. (**a**) indicates the market capacity forecast error on the test set with lag periods variation; (**b**) indicates the market capacity forecast error on the forecast set with lag periods variation.  
Figure 9: The short-term prediction performance of the proposed DS-SVR model and the SARIMA model in the medium-sized type.  
To sum up, SVR results are significantly better than SARIMA for long-term prediction. On the contrary, SARIMA has achieved better results in short-term prediction.
## 4 Experiments
### Implementation Details
#### 4.2.2 Training Setup  
During training, all the samples are limited to max length 300 frames. For data augmentation, all the frames in the video are first resized to \(256\times 256\) and then randomly cropped to \(224\times 224\) during training and center cropped to \(224\times 224\) in testing. Specifically, for temporal augmentation, we first randomly repeat the sequence by 20% and then randomly remove 20% content. We utilize Adam to optimize our model, and the weight factors \(\lambda_{1}\) and \(\lambda_{2}\) are empirically set to be \(10^{-4}\) and 0.05, respectively. The initial learning rate is \(5\times 10^{-5}\), and the learning rate is reduced by half per epoch after 40 epochs. In this experiment, a single Nvidia RTX 3090 with 24 G memory is used to accelerate model training and inference.
## 3 Hardware Architecture
### Mobile Receiver Unit
#### 3.3.2 Single Board Computer  
The localization algorithm runs onboard the mobile receiver unit. The single board computer needs to be compact enough to fit the geometrical constraints of \(\mu\)AUVs. Its main tasks consist of powering and interfacing the DVB-T dongle via the USB interface, running all required computations for localization and providing the position estimates to other modules. The SBC Raspberry Pi Zero is  
Figure 11: Onboard localization module: a software-defined radio provides I/Q samples to a single board computer.  
Figure 12: Covered and uncovered version of a NooElecTM NESDR Mini DVB-T dongle used as an software-defined radio (SDR) with an R820 tuner, RTL2832 demodulator and USB interface.  
chosen as the computing unit. With a size of \(66\,\mathrm{mm}\ \times\ 30\,\mathrm{mm}\ \times\ 5\,\mathrm{mm}\) and a weight of \(9\,\mathrm{g}\), it fulfills all physical requirements for the use in \(\mu\)AUVs. It has a single-core CPU, which runs at \(1\,\mathrm{GHz}\), and it has \(512\,\mathrm{MB}\) of RAM. The Raspberry Pi runs Raspbian, a Debian-based operating system.
## 2 Optimal Control Model for PMCU Path Planning
### Path Obstacles Avoidance Constraints  
Currently, most path-planning studies based on optimal control use circles or spheres to model obstacles [38; 43; 47] when such methods result in a large amount of space occupied by the obstacles in the model. This method of describing obstacles is not acceptable for the narrow and complex cruise deck environment during PMCU pushing. The main obstacle on the deck of a cruise ship is mainly the steel structures, which are usually relatively regular in shape and can usually be directly regarded as rectangular obstacles. Some areas need to be reserved for subsequent construction, which can also be rectangular obstacles according to the construction scribing. Compared to circular obstacle description methods, rectangular obstacles occupy less space [32], which is better for PMCU pushing path planning.  
For rectangular obstacle avoidance constraints, most of the current studies use the P-norm form for the constraint description, i.e., the P-norm analytic is used to represent the rectangular obstacle contour [26; 48]. However, this method requires that \(p\rightarrow\infty\) when representing a rectangle, which often produces invalid solutions in the computation process.  
Therefore, this paper adopts the area relationship of contour vertices to express the obstacle avoidance constraints of PMCU pushing [49]. Whether a collision with an obstacle is determined by comparing the size of the sum of the triangle area produced by the points on the planned path and the four vertices of the rectangular obstacle with the area of the rectangle, as shown in Figure 3. The scenario shown in Figure 3A is that the vertex E collides with the obstacle, so the sum of the four triangle areas is equal to the area of the obstacle rectangle. The scenario shown in Figure 3B does not collide; the sum of the areas of the four triangles is greater than the area of the rectangle.  
Thus, the obstacle avoidance constraint for the cabin pushing process can be described as:  
\[S_{\triangle AEB}+S_{\triangle BEC}+S_{\triangle ECD}+S_{\triangle DEA}-S_{ \square ABCD}>0 \tag{11}\]  
In Equation (11), \(S_{\triangle AEB}\), \(S_{\triangle BEC}\), \(S_{\triangle CED}\), and \(S_{\triangle DEA}\) are the areas of triangles \(AEB\), \(BEC\), \(CED\), \(DEA\), respectively. \(S_{\square ABCD}\) is the area of rectangle \(ABCD\). Point \(E\) is the PMCU outer contour vertex, and the entire cabin pushing process should ensure that all four vertices of the PMCU rectangular outer contour satisfy the obstacle avoidance constraints.
# A Communication Framework for Image Transmission through LPWAN Technology
## 3 Proposed Framework  
The proposed framework (Figure 1) is designed in four stages. In stage I, image processing and classification techniques are implemented. In stage II, compression techniques are used in order to reduce the image information, and thus facilitate data transmission through LoRa. Stage III includes image transmission implemented on SDR platforms. Finally, in stage IV, postprocessing and reconstruction techniques are implemented.
# Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks
###### Abstract
Such topologies essentially can be used to represent the topologies emerging in diverse applications that are popular nowadays or will become popular in the near future. We investigate the impact of the network architecture, the type of network, as well as parameters relevant to predefined security measures. Apart from the MRF approach, there are other families of approaches that can provide effective frameworks for modeling malware propagation: for instance, the Birth-Death  
Figure 1: Network topology of considered cyber-physical system. Immigration (BDI) model [3, 4], adversarial or game theoretic models[5, 6, 7], and lately machine learning approaches [8, 9]. Each family of modeling approaches has advantages and limitations, and eventually, there is no one-size-fits-all solution. For instance, one approach may yield low computational complexity (e.g., the MRF), while others may model more naturally the competitive behavior developed (e.g., game theoretic). The MRF approach is a fair alternative in the sense that it is distributed, has low computational complexity, yields satisfactory convergence with few iterations and amble modeling flexibility. The contributions of this paper are summarized as follows:  
* We present a Markovian modeling framework for the propagation of malware in multi-layer networks and demonstrate it for a two-layer cyber-physical topology. * We develop an implementation of the proposed framework and study malware propagation in different types of two-layer networks, which combine different types of graphs, e.g., random with power-law, random geometric graph, random graph, small-world, scale-free and regular. * Through analysis and simulation, we identify the critical parameters affecting the malware evolution, paving the way for developing better mitigation measures. The rest of this paper is organized as follows. In Section 2, we present previous works and distinguish them from the contribution of this paper, while in Section 3, we first provide the basics for the selected network topologies and then explain the choice of topology combinations considered. Section 4 describes the considered malware propagation MRF model and presents the results obtained for each separate considered system topology. In Section 5, we present and discuss the cumulative results of our research, and finally, in Section 6, we conclude the paper and suggest directions for future work.
# Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System
## 2 Materials and Methods
### Proposed Automatic Facial Expression Recognition System
#### 2.2.2 Data Collection and Analysis  
The system runs in the real-time classroom environment taking input from the camera to automatically identify the facial expressions of students in the classroom. The proposed system is capable of storing the data in a database which is used to perform the statistical analysis. The accuracy of the proposed system is compared with well-known FERs such as Azure, Face++, and Face Reader. Moreover, a senior psychologist was provided a recording of the same classroom lecture and, using the proposed system, was asked to assess the selected facial expressions. This helped us to identify the accuracy of the proposed system.