esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Attention Mechanisms for Grasp Detection  
The attention mechanism [14] has shown strong performance in natural language processing (NLP) tasks and, subsequently, researchers have applied this framework to grasping pose detection. Recent research on grasp detection [15] has demonstrated that incorporating an attention mechanism into the network can effectively enhance the network's focus on grasp regions. In their work, [PERSON] et al. [16] integrated a channel attention mechanism with a residual structure to establish inter-channel connections by encoding the weight of each channel. However, they did not consider encoding spatial feature information. Building on the SE attention, research [17] introduces the CBAM attention, which comprehensively considers information in both spatial and channel dimensions and utilizes the self-attention mechanism in the CoA module [18] to enhance the feature information recognition ability in the spatial dimension. The evaluation score of this region is obtained by summing the correlation between each region in the feature image and the adjacent region and employing the one-dimensional convolution operation to determine the weight of the space and the channel. AMSC-Net [19] effectively improves the network's attention to the grasping area by fusing the attention mechanism of two dimensions: space and channel. Nonetheless, the attention mechanism of AMSC-Net is achieved through simple convolution, which does not fully exploit contextual information. DSC-Grasp-Net introduces a novel collaborative attention (CA) mechanism [20] which converts the feature tensor into a single feature vector through 2D global pooling to enhance the relevant features.  
In contrast to the aforementioned, this article proposes an attention mechanism that employs a loss-guided approach. By utilizing a loss function to adjust attention, the recognition capability of the regions of interest is enhanced. It modifies feature weights in multiple dimensions to improve focus on crucial feature information.
# Fake News Detection on Social Networks: A Survey
## 3 Fake News Detection
### Fake News Detection Approaches
#### 3.3.1 The Content-Based Methods
[39] showed that although visual content is very important, our understanding of the visual features in fake news detection is still limited. [PERSON] et al. [40] designed a CNN-based neural network framework to fuse the visual information of the frequency domain and pixel domain to detect fake news. [PERSON] et al. [41] proposed the credibility neural network to evaluate the credibility of images onOSNs. They applied the spatial characteristics of CNNs to find physical changes in images and analyze whether images reflect negative emotions. In recent years, the issue of facial manipulation videos has received widespread attention, especially deepfake techniques that use deep learning tools to manipulate images and videos. The deepfake algorithm can use the generative model to replace the face in the target video [42]. As people become increasingly interested in deepfake technology, more and more deepfake detection technologies are underway. Early attempts mainly focused on the inconsistency of features caused by the facial synthesis process, while recently proposed methods center on basic features, where camera fingerprinting and biological signal-based schemes perform better [42]. (3) Multimodal-based  
Currently, most fake news detection schemes focus on a single modality (such as text or visual features only). Due to the combination of multimodal information in social media in recent years, fake news detection based on multimodal information has gradually attracted extensive research. How to effectively fuse the data from different modalities is a challenge. [PERSON] et al. [43] designed a multimodal fake news detection method that combines multimodal attention residuals and multi-channel convolutional neural networks (CNNs). [PERSON] et al. [44] proposed a multimodal approach for detecting fake news using the text and visual features of news stories. [PERSON] et al. [45] proposed a fake news detection model consisting of a bimodal variational autoencoder and a binary classifier. The bimodal variational autoencoder is used for the multimodal representation, with the input being the text of the post and the accompanying images. [PERSON] et al. [46] used unimodal and multimodal methods to classify fake news in a fine-grained way. Experiments showed that the multimodal CNN method that combines text and image data has the best performance. [PERSON] et al. [47] extracted features based on text, images, and user attributes for Sina Weibo as well as image-text correlation features. Finally, a new deep neural network framework was established to detect fake news.
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 5 Conclusions  
This study delved into the detection of ice thickness on transmission lines and introduced a model tailored for edge detection of ice-covered lines. The main contribution of this study is the innovation concerning network structure and loss function. Regarding the network structure, the traditional encoder and decoder configuration was revamped using CG-UNet, wherein a cross-guide module was added at the crosslayer connection to guide the decoder to generate edge images with higher accuracy. Based on ECA-Net, the CGM undergoes targeted enhancements in channel and spatial attention, rendering it more adept at calculating strip-shaped objects. Further, a joint loss function is proposed, combining the weighted cross-entropy loss function with the DoU loss function to effectively discern pixels with high uncertainty near the true edge. Subsequently, experimental validation was conducted on the ice cover dataset, yielding an ODS of 0.934 and an OIS of 0.938. Moreover, the calculation error for ice thickness remained within 7.2%.  
Considering the weak computing power of edge devices, the CG-UNet model was not designed to be lightweight, resulting in its detection speed being unable to achieve real-time detection accuracy. There are still areas that can be optimized in regard to real-time detection, which is the direction for our future research. In practical scenarios, transmission lines do not remain stationary and often sway in the wind, making it difficult to use image detection methods for calculating ice thickness. The next phase involves designing a suitable image detection algorithm that accommodates the oscillating nature of transmission lines, aiming to enhance the accuracy of ice thickness calculations. In addition, we plan to collect more images of ice-covered transmission lines in actual environments, which will be used to train and validate our model to ensure its accuracy and robustness in practical applications.  
Conceptualization, Y.Z. and Y.D.; methodology, Y.J. and L.Z.; software, Y.Z.; validation, Y.D. and Q.L.; formal analysis, Y.Z., Y.J. and Y.L.; resources, Y.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by \"Development of Key Technologies for Resonance-Based Ice Removal Robots\" (24 CXY0923) and \"Research and Development of Key Technologies for Source Storage in New Power Systems for Extreme Environments and Simulation Testing Platform\" (RZ2400005886).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Bridgeless Buck-Boost PFC Rectifier with Positive Output Voltage
## 4 Design Considerations
### Inductor Design  
Firstly, it is assumed that the voltage \(v_{in}\) and the current \(i_{in}\) are purely sinusoidal and in phase. Therefore,  
\[v_{in}=V_{in,pk}\sin\theta \tag{5}\]  
\[i_{in}=I_{in,pk}\sin\theta \tag{6}\]  
where \(V_{in,pk}\) denotes the peak value of \(v_{in}\), \(I_{in,pk}\) indicates the peak value of \(i_{in}\), \(\omega_{L}\) equals \(2\pi f_{L}\),\(f_{L}\) signifies the line frequency inversely proportional to the line period \(T_{L}\), and \(\theta\) equals \(\omega_{L}t\).  
Therefore, the average input power \(P_{in}\) can be obtained:  
\[\begin{array}{ll}P_{in}&=\frac{1}{T_{L}}\int_{0}^{t_{0}+T_{L}}p_{in}(\theta )d\theta\\ &=\frac{1}{\pi}\int_{0}^{\pi}v_{in}\cdot i_{in}d\theta\end{array} \tag{7}\]  
By substituting (5) and (6) into (7), \(P_{in}\) can be rewritten as  
\[\begin{array}{ll}P_{in}&=\frac{1}{\pi}\int_{0}^{\pi}V_{in,pk}\sin\theta\cdot I _{in,pk}\sin\theta d\theta\\ &=\frac{V_{in,pk}\cdot I_{in,pk}}{\pi}\int_{0}^{\pi}\sin^{2}\theta d\theta\\ &=\frac{V_{in,pk}\cdot I_{in,pk}}{\pi}\int_{0}^{\pi}(\frac{1-\cos 20}{2})d \theta\\ &=\frac{V_{in,pk}I_{in,pk}}{2}\end{array} \tag{8}\]  
Secondly, it has been assumed that the circuit works in the BCM at the peak input current under the minimum input voltage \(V_{in,min,pk}\), named \(I_{in,pk,max}\), and the rated output power \(P_{o,nrad}\). By taking the rated-load efficiency under the minimum input voltage, named \(\eta\), into consideration, \(I_{in,pk,max}\) can be represented based on (8) as  
\[I_{in,pk,max}=2\cdot\frac{p_{o,rated}/\eta}{V_{in,min,pk}} \tag{9}\]  
By putting the numerical values into (9), the calculated value of \(I_{in,pk,max}\) can be obtained:  
\[I_{in,pk,max}=2\times\frac{90/0.9}{90\sqrt{2}}=1.57\text{A} \tag{10}\]  
Thirdly, since the circuit works in the BCM only at the peak input current under the minimum input voltage and the rated output power, the maximum value of the peak value of \(i_{L}\), named \(I_{L,pk,max}\), can be represented by  
\[I_{L,pk,max}=\frac{I_{in,pk,max}}{D} \tag{11}\]  
where \(D\) corresponds to the peak value of \(i_{L}\) under the minimum input voltage and the rated output power.  
Fourthly, the peak-to-peak value of \(i_{L}\), named \(\Delta i_{L}\), can be expressed as  
\[\Delta i_{L}=\frac{V_{o}(1-D)T_{s}}{L} \tag{12}\]  
According to (21), (22) and the specifications given at the beginning of Section 4, the value of \(C_{o}\) can be worked out by setting \(I_{o}\) at the rated output current:  
\[C_{o}=\frac{I_{o}}{\omega_{L}\Delta v_{o}}=\frac{1.125}{2\pi\times 60\times 2.4}=1243.4\mu\text{F} \tag{23}\]  
Eventually, two Nippon Chemi-Con 650 \(\mu\)F capacitors, connected in parallel, are selected as \(C_{o}\).
# An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
## 4 Optimisation Approach  
As mentioned above, each UAV is configured by using four parameters. Thus, the complete system configuration is represented by the following status vector:  
\[\vec{x}=\{D_{th_{i}},D_{min_{i}},F_{RD_{1}},Spd_{1},\ldots,D_{th_{N}},D_{min_{N} },F_{RD_{N}},Spd_{N} \tag{5}\]  
where \(D_{th_{i}}\) is the distance threshold, \(D_{min_{i}}\) is the minimum distance to the rogue drone, \(F_{RD_{i}}\) is the attracting/repelling force, and \(Spd_{i}\) is the speed, all for each UAV \(i\) in the swarm. Hence, the length of the status vector will depend on the number of UAVs in the swarm (\(N\)).  
The proposed evaluation function for this formation problem is shown in Equation (6). Three terms are added and then averaged for the \(M\) evaluated scenarios (different initial conditions). In doing so, we expect to obtain a more robust solution as the optimisation algorithm will be converging to more general solutions instead of a particular one. The three terms in \(F(\vec{x})\) are the minimum error (\(Em(\vec{x})\)) and maximum error (\(EM(\vec{x})\)) distance of any UAV in the swarm to the rogue drone, with respect to the desired distance \(D_{RD}\), and an extra term (\(D(\vec{x})\)) to foster the UAVs to be spread throughout the surface (sphere) instead of forming local clusters. Because we wish to minimise these terms, the lower the value of \(F(\vec{x})\) the better:  
\[F(\vec{x}) = \frac{1}{M}\sum_{j}^{M}[Em_{j}(\vec{x})+EM_{j}(\vec{x})+D_{j}( \vec{x})] \tag{6}\] \[Em(\vec{x}) = |\min\delta(i,RD)-D_{RD}|,\quad i\in\{1\ldots N\}\] (7) \[EM(\vec{x}) = |\max\delta(i,RD)-D_{RD}|,\quad i\in\{1\ldots N\}\] (8) \[D(\vec{x}) = |2.0\times D_{RD}-\min\delta(l,m)|,\quad\forall l,m\in\{1\ldots N\},l\
eq m. \tag{9}\]  
Having defined the solution vector (problem representation) and the evaluation function, it can be seen that this is a difficult problem to optimise (solve) by an exact method, especially if we take into account its high dimensionality. Hence, in the next section we describe the proposed optimisation technique, a hybrid evolutionary algorithm.
# Numerical Study of the Flow and Blockage Ratio of Cylindrical Pier Local Scour
## 4 Numerical Results
### Clear-Water Case
#### 4.2.2 Morphodynamic Validation
As illustrated by Figure 11b, initially, the scour evolution in the numerical results fits well with the experimental results of [PERSON], 2018 [18], but the model eventually over-predicts the maximum scour depth. With regard to the characteristic lengths, the numerical results present a better fit for \(L_{y}\) than \(L_{x}\). This is because the numerical results present a rounder scour hole than the one in the experiment. As a comment, we note that the maximum depth of the predicted scour hole is often situated in a region that, in the experimental case, was obscured by the pier, and thus could not be measured. Figure 12 shows a comparison between the numerical results of the present study and the experimental results with equivalent flow and sediment conditions reported in the thesis of [PERSON], 2018 [18]. Both the scour hole and the dune have a similar magnitude and angle, with some discrepancy around the limits of the scour hole and downstream of the dune. The numerical and experimental data in the figure both correspond to an instant in which the scour hole has not yet reached an equilibrium. Figure 11: (**a**) Time evolution of \(\xi_{m}\), \(L_{x}\), and \(L_{y}\). (**b**) Comparison between the numerical and experimental data. Figure 13 shows a topographical comparison at the same time as Figure 7:0 The shape and magnitude of the predicted scour hole is very similar to that of the experimental results. However, while the position and magnitude of the dune in the numerical results agree with the experimental ones, the width is different. A shallow scour hole is present at the lee-side of the dune, which is in agreement with the experimental results, although the one predicted is larger. This second scour hole seems to be a result of the non-slip conditions at the lateral walls, as these conditions model the physical walls of a narrow channel. This scour hole is an example of wake scour, as the wake of the cylinder is trapped inside the channel, producing scour at the lee-side of the dune. A 3D view is shown in Figure 7:1  
Figure 7:2 Comparison between the numerical results at a non-dimensional time \(t/T=3500\) and the experimental results reported in Lachausée, 2018 [18] at time \(t=2.4\) h. The horizontal profiles were extracted along the lines \(y=0\) (**a**), \(x=0\) (**b**), and \(x=-9\) mm (**c**).
# Adversarial Attack for Deep Steganography Based on Surrogate Training and Knowledge Diffusion
## 3 Adversarial Attack Method for Deep Steganography
### Adversarial Attack Based on Surrogate Training and Knowledge Distillation
#### 3.3.2. Initialization through Surrogate Training  
Surrogate models can be trained to enact black-box transfer adversarial attacks. Only black-box untargeted transfer attacks can be achieved through surrogate training. However, black-box targeted transfer attacks are more challenging, and the surrogate model needs to fit the target model more accurately. In order to solve the problem of secret image content analysis and targeted attack under the premise of obtaining a small number of instances [11], a targeted black-box transfer attack method based on surrogate training and knowledge distillation is proposed. Unlike model integration [52; 53], to improve the transferability of adversarial examples and implement black-box transfer attacks, the goal of surrogate training is for the surrogate model to approximate the target model as closely as possible without pursuing transferability to multiple models.  
First, the attacker initializes the surrogate model \(F^{\prime}\) using the surrogate dataset. Then, multiple instances created by the target model \(([C^{\prime},S]\ or\ [C^{\prime},S^{\prime}])\) are used to perform knowledge distillation [54] in order to optimize the surrogate model \(F^{\prime}\) so that it gradually approaches the target model \(F\) (Figure 3).
## 4 Measurement of the Model and Performance Evaluation
### Function Evaluate  
**DataSetOriginal.** Table 4 shows the loss value, which is 0.72, the percentage of successes, which is 70%, and the percentage of mean squared error, which is 23.26%, according to the established metric.  
**DataSetFilter.** Table 5 shows the loss value, which is 0.36, the percentage of successes, which is 87%, and the percentage of mean squared error, which is 11.08%, according to that metric.
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## 4 Discussion
### Variable Structure PID Controller  
Next, the simulation results of controller (16) in Section 3.2 are given in this section. The first step is to select the control parameters, and the principle to select control parameters could be concluded as follows: (1) generally a larger differential term could bring a better convergence rate, but the demanded torque is larger and the chattering issue would be aggravated; (2) a larger integral term would bring better robustness to the disturbance, but the system would slow down; (3) a larger proportional term would bring steadier accuracy, but the overshot would be enlarged; (4) the overall control parameters selection could refer to some classic work such as reference [20]. Hence, the control parameters should be selected as follows:  
\[\begin{array}{l}k_{d}=10,k_{p}=5,k_{l}=0.2\\ \alpha=0.2,\beta=0.05,k_{1}=0.1\end{array} \tag{42}\]  
In addition, in order to check system stability, other Lyapunov function parameters should be selected as follows:  
\[l_{1}=0.05,l_{4}=1 \tag{43}\]  
Based on constraint (25), it could be calculated that  
\[\begin{array}{l}l_{2}=l_{1}k_{d}+k_{p}=5.5\\ l_{3}=l_{1}k_{l}+l_{4}k_{p}=5.001\end{array} \tag{44}\]  
If (42)-(44) were substituted into constraints (28) and (29), then it could be found that both are satisfied, and the simulation results of controller (16) proposed in this paper are given as follows.  
Based on Figures 5 and 6, it could be found that the system is stable under the controller proposed in this paper. The system convergence time is about 50 s, which is largely improved compared to the standard PD controller (almost 40% of the PD controller and 60% of the finite time controller); this verifies that the method proposed in this paper could improve the system convergence rate with a variable structure controller. In addition, the system steady accuracy at 100 s is about 1.5 \(\times\) 10\({}^{-5}\) rad/s of angular velocity and 5 \(\times\) 10\({}^{-4}\) of attitude quaternion, i.e., 0.01 deg of the Euler angle, which is at the same level compared to the standard PD controller. By comparing the simulations results in this  
Figure 4: Curve of attitude quaternion under a finite time controller from [20].  
section and Section 4.1, it could be found that the chattering issue of angular velocity is largely relieved, and this verifies the effectiveness of the integral term. Based on the integral partition algorithm, the integral term does not slow down the system convergence rate; meanwhile, system robustness to the disturbance is maintained.  
Figure 5: Curve of angular velocity under the controller proposed in this paper.  
Figure 6: Curve of attitude quaternion under the controller proposed in this paper.
## 5 Simulation Results and Discussion  
### Simulation Setup  
The proposed model was evaluated using a two-hour excerpt of the TAPASCologne vehicular mobility trace generated for the city of Cologne, Germany [24]. TAPASCologne is an initiative by the Institute of Transportation Systems at the German Aerospace Center (ITS-DLR), aimed at reproducing realistic car traffic in the greater urban area of the city of Cologne in Germany. Vehicle mobility was generated synthetically using the SUMO microscopic vehicular mobility simulator [28], and traffic demand was generated using the Travel and Activity PAtterns Simulation (TAPAS) methodology. The original trace covers a region of 400 square kilometers of the city of Cologne for a period of 24 h, comprising more than 700,000 individual car trips. This dataset is the most standardized dataset we could find, with detailed location and speed measurements over a time window that captures most of the traffic patterns that can be experienced in a city with urban and suburban areas. The time granularity between vehicular location measurements is one second, which is reasonable in capturing vehicles' mobility over time. Vehicle speed varied between 0 km/h and 90 km/h, covering most traffic scenarios. The two-hour excerpt was enough to establish autocorrelation and evaluate its effect on location prediction and error measurements.  
The two-hour excerpt trace was preprocessed to extract five traces corresponding to five individual vehicle trips. The five individual vehicles are chosen at random using a uniform distribution-based random number generator, and are labeled \(\
u_{1}\) to \(\
u_{5}\) throughout the remainder of the paper for ease of reference. A single vehicle trip consists of a time-stamped sequence of \(xy\) position coordinates in meters as well as the vehicle's speed in meters per second. The selected vehicles' total trip times are 257 s each. The experiments were conducted using Matlab (R2013a). The estimation of the \(AR(p)\) model order was performed using the System Identification Toolbox for \(p~{}=~{}1\): 10, with the location information for one vehicle used for cross validation. The AIC values for the estimated model were compared for the five vehicles and the majority of them exhibited a best model fit at \(p~{}=~{}4\), as illustrated in Figure 5.  
We chose two of the five vehicles and used their location measurements to generate the model parameters and validate its performance. The two vehicles used for model estimation and validation were designated \(\
u_{1}\) and \(\
u_{2}\), respectively. The location measurements for vehicle \(\
u_{1}\) were used to infer the model parameters, and the location measurements for vehicle \(\
u_{2}\) were used to validate the model performance using the parameters generated by \(\
u_{1}\)'s measurements. We estimated the unknown model coefficients and noise variance for \(p~{}=~{}1,2,4\) for the location information of both vehicles using the Yule Walker equations.
# Effect of Diagonal Pattern Training on Trunk Function, Balance, and Gait in Stroke Patients
## 2 Materials and Methods
### Sample Size Calculation  
The sample size of this study was determined using G-power software (G\({}^{*}\) Power 3.1.9.2, Heinrich-Heine-Universitat, Dusseldorf, Germany) [17]. First, a pilot study was conducted in 12 stroke patients to determine the effect size. Using the independent sample t-test, a total of 42 study subjects were calculated as 21 experiment subjects and 21 control participants, where the effect size was 0.90, significance level was 0.05, and the power was 0.80. The subjects considered to be suitable for this study were randomly assigned (randomization website: [[http://www.randomization.com](http://www.randomization.com)]([http://www.randomization.com](http://www.randomization.com))) to the experiment group (diagonal pattern training) and the control group (single plane training).