esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Results  
#### 5.3.1 Synthetic Experiments  
Our proposed model, MagEMNet, a dual-channel convolutional neural network (CNN) with two convolutional layers, was trained on a joint dataset that integrates both magnetic and electromagnetic response data. This combined dataset allows MagEMNet to capture complex interdependencies between various physical properties, thereby improving the accuracy of parameter inversion.  
Following training, MagEMNet was evaluated on an independent test set, achieving a coefficient of determination \(R^{2}=0.92\) and a mean square error (MSE) of 0.956, reflecting the model's strong predictive performance and ability to generalize well to unseen data.  
To further assess the effectiveness of MagEMNet, we randomly selected 100 samples from the test set for additional predictions. The predicted values were then compared with the true values, as depicted in Figure 8.  
The attention weights for SE Block 1 are illustrated in Figure 9. The focus is concentrated on specific channels with high weights. The attention weights for SE Block 2 are displayed in Figure 10. This block demonstrates a more even distribution of attention across multiple channels.  
Figure 8: Part of results for joint inversion: (**a**) \(x\) (\(m\)), (**b**) \(y\) (\(m\)), (**c**) \(z\) (\(m\)), (**d**) \(L_{1}\), (**e**) \(L_{2}\), (**f**) \(L_{3}\), (**g**) \(\alpha\) (\({}^{\circ}\)), (**h**) \(\beta\) (\({}^{\circ}\)).  
For spatial parameters, the model showed high consistency between the predicted and true values of the target's position coordinates \(x\), \(y\), and \(z\). The dipole moment \(L_{3}\) exhibited slightly lower accuracy due to its larger magnitude, which introduces greater variability in predictions. Conversely, the parameters \(L_{1}\) and \(L_{2}\), with lower magnitudes, were more susceptible to the influence of \(L_{3}\), resulting in minor deviations for a few samples. Overall, the deviations were minimal and did not significantly impact the prediction quality.  
Moreover, MagEMNet achieved strong accuracy in predicting the rotational angle \(\alpha\) and inclination angle \(\beta\), with no substantial discrepancies noted across the sample set. These findings validate the efficacy of MagEMNet in accurately inverting target parameters, confirming its robustness and suitability for subsurface exploration applications.  
We systematically evaluated the performance of our proposed MagEMNet inversion model by conducting comparative analyses with several established inversion methods, include DNN [7], LM [36], and BAD [37]. This evaluation includes both qualitative and quantitative assessments to highlight MagEMNet's efficacy in accurately resolving subsurface parameters.  
Table 3 presents the experimental results obtained using these methods on our dataset. For quantitative analysis and comparison, we calculated response estimation outcomes based on the specified metrics. As shown in Table 3, our method consistently outperformed others in terms of quantitative performance.  
Figure 10: Attention map for SE Block 2.  
Figure 9: Attention map for SE Block 1.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 3 Antenna Configurations  
Two complementary case studies were implemented to investigate an antenna integrated into the OLED. The non-transparent antenna was designed with an all-copper film. The optically transparent and invisible antenna uses a honeycomb-shaped mesh structure. The all-metal version served as the reference standard for characterizing RF properties.
## 5 Scattering of Surface Waves by a Cavity of Arbitrary Shape
### Distributed Loading Approximation  
An approximation using distributed loading is now considered. The tractions first calculated at every point on the surface of the cavity are shifted to the surface of the half-space. The reciprocity theorem is then applied for these equivalent surface loads to obtain the displacement amplitudes of the scattered field. In the concentrated loading approximation, the loads that generate the scattered field are all applied at the origin of the coordinate system. For the current approximation, the loads are applied over an area, which is the projection of the cavity on the surface of the half-space. This method is believed to be valid for shallow cavities. The obtained theoretical predictions were compared with the experimental results in order to understand the limitations of the proposed approach.  
Suppose that a point source is applied at \((x_{0},y_{0})\), or \((r_{0},\theta_{0})\) using cylindrical coordinates, and the observation point is located at \((r,0)\), as illustrated in Figure 3. Here, the observation point is chosen with \(\theta=0\) for the purpose of simplicity. Note that solutions for other points, i.e., \(\theta\
eq 0\) can be easily gained by using Equations (2) and (3). The following relations are found in Figure 3 for the case of \(\theta=0\):  
\[r^{\prime}=\sqrt{r_{0}^{2}+r^{2}-2 rr_{0}\cos\theta_{0}},\ \ \cos\theta\,=\frac{r-r_{0}\cos\theta_{0}}{ \sqrt{r_{0}^{2}+r^{2}-2 rr_{0}\cos\theta_{0}}}. \tag{7}\]  
For an incident surface wave propagating in the positive \(x\)-direction with amplitude \(A_{in}\), the point forces corresponding to the tractions on the surface of the virtual cavity at \((x_{0},y_{0},z_{0})\) can be calculated using:  
\[f_{i}(x_{0},y_{0},z_{0})=-t_{i}(x_{0},y_{0},z_{0})dS,\ \ i=x,y,z. \tag{8}\]  
It is convenient to also represent the expressions of \(f_{i}(x_{0},y_{0},z_{0})\) and \(t_{i}(x_{0},y_{0},z_{0})\) given by Equation (8) in the cylindrical coordinate \((r,\theta,z)\). For a time-harmonic load at the origin of the coordinate system, the vertical displacements follow from Equations (2) and (3). In the case of a time-harmonic load applied at \((r_{0},\theta_{0},0)\) as in Figure 3, the total vertical displacement of the scattered field at the observation point \((r,0,z)\) is the summation of the surface wave fields generated by \(f_{i}\) (\(i=x,y,z\)). Thus:  
\[u_{z}=\frac{-kW(z)}{4i\mu E}\iint\limits_{S}\!\!\!\Big{[}U(0)\big{(}t_{x}\cos \theta\prime+t_{y}\sin\theta\prime\big{)}H_{1}^{(1)}(kr\prime)+W(0)t_{z}H_{0}^ {(1)}(kr\prime)\Big{]}dS. \tag{9}\]  
It should be noted that the displacement given in Equation (9) is dependent on frequency since its expression appears with a frequency term via the wavenumber \(k\).
# Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning
## 3 System Model  
As shown in Figure 1, after the user establishes a connection with a server, the computing task is unloaded to the server, and then the server creates a dedicated VM for the calculation of the task. During the period of establishing a connection to the end of the service, users still upload their location information regularly, and the server sends back the navigation information regularly. With the continuous movement of users, the distance from the server will become farther and farther, resulting in an increase in transmission delay, so as to reduce the quality of user experience. Therefore, the above problems can be solved by actively migrating VMs closer to mobile users.  
In order to better represent the mobility of users and the dynamic changes of the network, time is divided into time periods according to the same interval and numbered according to time order, and the i-th time period is expressed as \(slot_{i}\). As shown in the figure, consider a network structure composed of MEC server \(\mathcal{H}=\{1,2,\cdots,n,\cdots,H\}\). MEC server \(n\) can be represented by a triple \((cap_{n},r_{n},l_{n})\), where \(cap_{n}\) represents the remaining computing capacity of the server (in cycles per s), \(r_{n}\) represents the remaining disk space of the server (in byte), and \(l_{n}\) represents the location of the server. Each MEC node connects to a local Base Station (BS) or wireless access point via a high-speed local area network (LAN). The VMs to be migrated on the MEC server \(n\) within the time period \(Sl_{i}\) are recorded as \(\mathcal{T}_{n}=\left\{T_{n}^{1},T_{n}^{2},\ldots,T_{n}^{u}\right\}\), the VM \(T_{n}^{u}\) is represented by the quintuple \(T_{n}^{u}=\left(\lambda_{n}^{u},c_{n}^{u},d_{n}^{u},s_{n}^{u},\omega_{n}^{u}\right)\), where \(\lambda_{n}^{u}\) represents the size of the VM configuration file (unit byte), \(c_{n}^{u}\) represents the calculation cycle (unit cycle) of the CPU required to perform the computing task, and \(d_{n}^{u}\) represents the maximum delay (unit seconds) that the task can tolerate. \(s_{n}^{u}\) represents the current movement speed of the device that generated the task (in meter per second), and \(c_{n}^{u}\) represents the current direction of movement of the device that generated the task, expressed as the angle between the clockwise direction and the north (in 2 degrees).
## 4 Experimental Results and Discussion
### Feature Selection Analysis
After a decrease, the average precision and average recall ratio increased again, because the SVM imported the slack variable and the penalty factor to construct the decision boundary in soft intervals, which improved the robustness and generalization of the classifier. Figure 9: Correlation between features and classification accuracy. The accuracy of the SVM classifier is compared under different ratios of training and testing sets to further investigate the effectiveness of the heuristic selection method. The percentages of the training set were increased by 5%, 10%, 15%, 20%, 25%, 30%, and 35%. 1000 trials were performed for each training set percentage to reduce random effects. Figure 11a,b presents the bar graphs of the average accuracy for different percentages of training and testing sets. The error standard deviations are also marked in figures. As discussed in Figure 11, the top 24 features are selected during training and testing. Overall, the training and test accuracies with heuristic selection were both better than the values without selection. When the training percentage is set as 5%, the training accuracy is larger than the testing accuracy, as shown in Figure 11a as well as Figure 11b, which reveals that the training model is underfitting. With the increased percentage of the training sets, the testing accuracy is enhanced. Meanwhile, the classification error is decreased. When the training percentage increases to 30%, the training and test accuracies approach one another when the feature heuristic selection method is used. Nevertheless, neither the training nor the test accuracy are consistent, regardless of the increasing percentages of the training set.
## 3 The Proposed Rolling Bearing Fault Feature Extraction Method under Variable Speed Conditions
### The Optimization Algorithm Derivation  
Equation (8) can be further expressed as an unconstrained optimization problem as  
\[\underset{\mathbf{X}}{\text{minimize}}\ \frac{1}{2}\|\mathbf{Z}-\mathbf{X}\|_{F}^{2}+ \lambda_{0}\|\mathbf{X}\|_{*}+\lambda_{1}\|\mathbf{X}\|_{L1}. \tag{9}\]  
The three terms in Equation (9) are all convex, which means the whole optimization problem is convex and has a globally optimal solution. The ADMM is a high-efficiency computational framework, which allows parallel computing [36]. The convergence of ADMM has been proved in ref. [37]. It can be seen from Equation (9) that the sparsity and low-rankness of the expected fault feature are simultaneously constrained, which makes the optimization more difficult. To decouple the sparsity and low-rankness constraint, an additional constraint \(\mathbf{X}=\mathbf{Y}\) should be introduced, which can be further written as  
\[\underset{\mathbf{X},\mathbf{Y}}{\text{minimize}}\ \frac{1}{2}\|\mathbf{Z}- \mathbf{X}\|_{F}^{2}+\lambda_{0}\|\mathbf{Y}\|_{*}+\lambda_{1}\|\mathbf{X}\|_ {L1},\ \text{subject to}\ \mathbf{X}=\mathbf{Y}. \tag{10}\]  
Then, the argumented Lagrangian function \(L\) is  
\[L(\mathbf{X},\mathbf{Y},\mathbf{D})=\frac{1}{2}\|\mathbf{Z}-\mathbf{X}\|_{F}^{ 2}+\frac{\rho}{2}\|\mathbf{X}-\left(\mathbf{Y}+\mathbf{D}\right)\|_{F}^{2}+ \lambda_{0}\|\mathbf{Y}\|_{*}+\lambda_{1}\|\mathbf{X}\|_{L1}, \tag{11}\]  
where \(\mathbf{D}\) is the Lagrange multiplier, \(\rho\) is the plenty parameter related to the convergence speed. The problem (9) can be solved according to Equation (11) by alternatively solving three sub-problems by iteration as follows:  
\[\begin{split}\mathbf{X}^{(k+1)}&=\arg\underset{ \mathbf{X}^{(k)}}{\text{min}}\ L\Big{(}\mathbf{X}^{(k)},\mathbf{Y}^{(k)}, \mathbf{D}^{(k)}\Big{)}\\ &=\arg\underset{\mathbf{X}^{(k)}}{\text{min}}\ \frac{1}{2}\Big{\|}\mathbf{Z}-\mathbf{X}^{(k)}\Big{\|}_{F}^{2}+\frac{\rho}{2} \Big{\|}\mathbf{X}^{(k)}-\left(\mathbf{Y}^{(k)}+\mathbf{D}^{(k)}\right)\Big{\|} _{F}^{2}+\lambda_{1}\Big{\|}\mathbf{X}^{(k)}\Big{\|}_{L1}\\ \mathbf{Y}^{(k+1)}&=\arg\underset{\mathbf{Y}^{(k)}} {\text{min}}\ L\Big{(}\mathbf{X}^{(k+1)},\mathbf{Y}^{(k)},\mathbf{D}^{(k)} \Big{)}\\ &=\arg\underset{\mathbf{Y}^{(k)}}{\text{min}}\ \frac{\rho}{2}\Big{\|}\mathbf{X}^{(k+1)}-\left(\mathbf{Y}^{(k)}+\mathbf{D}^{( k)}\right)\Big{\|}_{F}^{2}+\lambda_{0}\Big{\|}\mathbf{X}^{(k+1)}\Big{\|}_{*}^{ \prime}\end{split}, \tag{12}\]  
Figure 1: The faulty signal of rolling bearing with variable speed. (**a**) The signal and speed with the line in blue the signal waveform (adhering the left \(y\)-axis), the line in orange the shaft rotating speed (adhering the right \(y\)-axis); (**b**) the fault feature in the OFSC; (**c**) the normalized singular value of the fault feature in the OFSC.  
\[\mathbf{D}^{(k+1)}=\mathbf{D}^{(k)}-(\mathbf{X}^{(k+1)}-\mathbf{Y}^{(k+1)}), \tag{14}\]  
where \((\cdot)^{k}\) denotes the \(k\)-th iteration.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 4 MASU Evaluation Using Simulations
### Simulations Setup
#### 4.1.2 Mobility Patterns  
The nodes' movements were modelled using the BonnMotion tool [31], which includes well-known models that represent people's mobility patterns [32]. For the simulations, the nodes' speed was set between 0 m/s (static nodes) and 1.5 m/s (walking speed) and three mobility patterns were used: RandomWalk, SLAW, and Nomadic. Table 3 shows the parameters used for the setup of the mobility models considered.  
This configuration helped us simulate a dynamic network, where some existing communication links can be lost and new links can appear. Such a configuration represents a realistic scenario (e.g., opportunistic collaboration in a university campus) where people can move within the physical space, and eventually interact with other people that they meet.
# Auxiliary Information-Enhanced Recommendations
###### Abstract
To bridge the aforementioned drawbacks of existing works, in this paper, we aim at developing an accurate sequential recommendation algorithm by effectively extracting and aggregating useful information from multi-modal auxiliary information, as well as modeling the complex interaction relations embedded in them. To be specific, we devise a memory fusion network for recommendation (MFN4 Rec) by effectively integrating the relevant information from three modalities, i.e., item IDs, item images and item description texts, and modelling the complex relations between and within modalities. MFN4 Rec is built on a typical work in multi-modal sequence representation learning, i.e., a memory fusion network for multi-view sequential learning [4], for multi-modal representation learning for sequential recommendations. To be specific, MFN4 Rec contains a multi-GRU layer, a multi-view gated memory network (MGMN), and a prediction module. The multi-GRU layer contains three GRU-based RNNs, while each RNN models the sequential dependencies by taking the modal-specific representation of each item as the input of each step. MGMN is designed to model and extract the interaction relations across different modalities. The outputs from both the multi-GRU layer and MGMN are combined together as the input of the prediction module for the next-item prediction. Benefiting from the information memory and spreading mechanism of the memory network, MFN4 Rec is able to not only effectively handle the relations within and across modalities, but also effectively model the dynamic sequential dependencies in sequences, and thus make the multi-modal auxiliary information contribute more to the sequential recommendations. The contributions of this work are summarized below:  
* We propose a memory fusion network for recommendation (MFN4 Rec) to effectively model auxiliary multi-modal information for accurate sequential recommendations. * A multi-GRU layer is designed to effectively model the sequential dependencies with each modality. A multi-view gated memory network (MGMN) is particularly devised to effectively model the complex interaction relations across different modalities. Extensive experiments have been conducted on two real-world e-commerce transaction datasets. The results have demonstrated the superiority of our proposed SRS algorithm over the state-of-the-art ones when performing sequential recommendations.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 6 Limitations and Challenges  
This section presents the limitations and challenges encountered while developing this authentication protocol for WSNs. It critically assesses the proposed approach and identifies issues or challenges that may limit its effectiveness.  
1. **Limited Resources:** WSNs have resource constraints regarding memory, processing power, and battery life. Therefore, security protocols must be designed with these limitations in mind, to ensure they do not consume too much energy or memory.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Notations** & **Explanation** & \(\approx\)**C Computation Time** \\ \hline \(T_{H}\) & Hash function & 0.056 ms \\ \(T_{SM}\) & Scalar multiplication & 13.405 ms \\ \(T_{SDD}\) & Symmetric enc/dec & 1.657 ms \\ \(T_{TE}\) & Fuzzy extractor & 13.405 ms \\  & function & & \\ \(T_{BP}\) & Bilinear pairing & 32.713 ms \\ \hline \hline \end{tabular}
\end{table}
Table 8: Approximated computation costs for different procedures.  
Figure 7: Computation cost comparison [49], [50], [51], and [52].  
2. **Limited Physical Security:** Sensor nodes are often deployed in unattended environments vulnerable to physical attacks. Security protocols should be designed to withstand physical attacks, such as tampering, destruction, or theft of the nodes.
3. **Communication Overhead:** Security protocols often introduce additional communication overheads, leading to increased latency, energy consumption, and reduced network performance. Therefore, it is essential to design security protocols that minimize communication overheads, while providing sufficient security.
## 2 Results and Discussion
### Inhibition on XO of Screened Potential XO Inhibitors  
Since the screened potential XO inhibitors were identified as gardenin B and eupatorin, the XO inhibitory activities of them were evaluated. As a result, gardenin B and eupatorin exhibited inhibition on XO with IC\({}_{50}\) values of 1.488 \(\upmu\)g/mL and 11.197 \(\upmu\)g/mL, respectively. According to reported literatures, eupatorin showed inhibition on XO [28]. The experiment results showed that both of these compounds could actually inhibit XO and they could prove the effectiveness of this screening method.
# Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images
## 1 Introduction
However, road vectors play a vital role in the construction of geographic information systems and the comprehensive analysis of information, and they need to be taken seriously [35, 44]. Moreover, the methods mentioned above cannot completely overcome road extraction difficulties, such as occlusions, shadows, and varying resolutions and widths. Considering the problems described above, to improve the labeling efficiency of the road dataset, we propose a new method of interactive road extraction and automatic road network generation. The main contributions of this paper follow:  
1. A complete road network extraction framework with high accuracy and availability is proposed. With only a few seed points, the whole road can be obtained quickly. First, the width and seed points of a road are set interactively, and the skeleton of the road is extracted by using regional growth and morphological algorithms. Then, a single-road vector is obtained after vector tracking, vector simplification, endpoint modification, and road connection. Finally, the road network is generated by using intersection connection and buffer algorithms. 2. To further improve the effectiveness of the proposed method, we adopt the road segment modification and road network construction strategy using the combination of grid image level and vector level. At the raster level, morphological algorithms are used to acquire the initial road segment, and at the vector level, further corrections and connections are completed based on road geometric features. For example, considering the 'T', 'Y', and '+' shape of the intersection, an intersection connection algorithm is proposed. 3. The strategy proposed in this paper can be successfully applied to the extraction of rural areas, suburbs, and urban areas. At the same time, it also has a certain degree of correction effect on occlusion and shadow problems. The algorithm for extracting a single road can extract roads with a length of more than 4000 pixels at a time, which is fast and convenient, and has great potential for the application of labeling images for deep learning.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 3 Taxonomy of Cyber-Physical Attacks
### Physical System Layer
#### 3.1.1 Sensor Attack  
A sensor attack is defined as a manipulation of sensor measurement \(y(k)\) on the network, which is represented as the addition of a sensor-attack signal to the legitimate sensor measurement, as follows:  
\[\hat{y}(t)=Cx(t)+y^{a}(t), \tag{3}\]  
where \(\hat{y}(t)\) is the modified sensor measurement, and \(y^{a}(t)\) is the sensor-attack signal from the attacker. The purpose of a sensor attack is to deceive the computing system that is conducting state estimation, and therefore an estimation error causes calculation faults in the control input signal \(u(t)\) for the actuating process (2). Then, in the physical system (1), a fault control input signal in a sensor attack can cause malfunctions in the physical systems, such as divergence in the state trajectory of the physical system.  
As cyber-physical security research advances, more sophisticated sensor attacks are being developed. Among the many cyber-physical security studies of the physical system layer, most focus on sensor attacks. For example, the pole-dynamics attack (PDA) [57; 58] is one of the latest and most sophisticated sensor attacks, where a malicious user generates the attack signal by utilizing matrices \(A\) and \(C\) of a physical system (1). Although the  
Figure 2: Cyber-physical attacks in hierarchical CPS architecture.  
PDA rapidly alters state \(x(t)\) in the physical system, conventional model-based anomaly detection methods such as the residual-based detector and the \(\chi^{2}\) detector cannot detect a PDA.