esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Broadcast Event-Triggered Consensus Control Scheme
### Local Controller, \(L\)  
Local controller \(L_{i}\), which is the distributed control integrated with the event-triggered process, will determine the agents' movement \(A_{i}\) at every even or odd time \(t\) is based on input from the broadcast signal, \(B(t)\in R\). The local controller is denoted by Equation (11), where \(\delta_{i}(t)\in R^{v}\) is the state, \(B(t)\ \in R\) is the broadcast signal, \(u_{i}(t)\ \in R^{n}\) is the output and \(\alpha:R^{v}X\ R\to R^{v}\), \(\beta:R^{v}X\ R\to R^{v}\) and \(\gamma:R^{v}X\ R\to R^{v}\) are functions. \(\alpha\) is the state function, \(\beta\) is the random and deterministic function of the SPSA and \(\gamma\) is the standard consensus protocol with ET. The column of vector of Equation (9) represents the variables  
Figure 3: Event-triggered broadcast controller design.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 5 Discussion  
This section discusses the links between previous research and ours, the strengths and weaknesses of our model, and our future directions for improvement. The experiments in Section 4.4 show that all the methods we used have good effects on improving the accuracy of remote sensing image detection models. The experiments in Section 4.5 show that our model has the highest detection accuracy compared to other state-of-the-art models. Before us, many researchers have also tried to use a combination of various advanced methods to achieve automatic object detection in remote sensing.
# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 3 Experiment
### Evaluation Criteria  
The classification results are evaluated and analyzed from both objective and subjective perspectives. For the analysis of subjective results, the distribution of Gaze points (\(\mathbf{Ra}\), _Gaze point ratio_) and the degree of concentration (\(\mathbf{Co}\), _Concentration_) are compared to analyze the feasibility of the proposed method for classifying gaze points. For the analysis of objective results, the results of the proposed method are compared with the existing methods of the same category, specifically from the quantitative indexes of precision-recall (\(\mathbf{Precision}\)-\(\mathbf{Recall}\)) and weighted values (\(\mathbf{We}\), _Weighted values_).  
(1) \(\mathbf{Ra}\)  
The Gaze point ratio (\(\mathbf{Ra}\)) measures the percentage of classification gaze point, which reflects the degree of attention distribution of the gazed target. The larger the \(\mathbf{Ra}\), the more attention is distributed on the target. \(\mathbf{Ra}\) is computed as,  
\[Ra=\frac{D_{gazepoint\text{ number}}}{Y_{gazepoint\text{ number}}}, \tag{12}\]  
where \(D_{gazepoint\text{ number}}\) is the number of classified gaze points on the target and \(Y_{gazepoint\text{ number}}\) is the total number of gaze points in the images.  
(2) \(\mathbf{Co}\)  
The concentration (\(\mathbf{Co}\)) reflects the distribution density of gaze points classified. The larger the \(\mathbf{Co}\), the smaller the concentration of gaze attention and vice versa:  
\[Co=\frac{1}{D-1}\sum_{i=0}^{i=D}|D_{i}-D_{i-1}|, \tag{13}\]  
where \(|D_{i}-D_{i-1}|\) is the distance between two neighboring gaze points of the classified gaze points.  
(3) \(\mathbf{Precision}\)-\(\mathbf{Recall}\)  
The Precision-Recall (\(\mathbf{PR}\)) is an evaluation based on the overlap between manually labeled and classified gaze points. The higher the accuracy and recall, the better the algorithm's filtering and classification results:  
\[\begin{array}{c}precision=\frac{D_{S}\cap D_{T}}{D_{S}}\\ recall=\frac{D_{S}\cap D_{T}}{D_{T}}\end{array}, \tag{14}\]  
where \(D_{S}\) is the classified gaze points and \(D_{T}\) is the manually marked gaze points.  
Figure 9: Example of the distribution of gaze points for different scene images.  
(4) _We_  
The Weighted values (_We_) reflect the comprehensive performance of the algorithm and are used to count the accuracy and recall rate between the gaze points of different targets and their valid points in the same gaze image, which comprehensively reflects the validity and reliability of the algorithm. They are defined as:  
\[We=\frac{\left(1+\beta^{2}\right)\cdot precision\cdot recall}{\beta^{2}\cdot precision + recall}, \tag{15}\]  
In this paper, \(\beta=0.3\), which focuses more on the precision values.
## 2 Mathematical Method
### Static and Perturbed Reynolds Equation  
With the shaft and pads perturbation, the pressure is expressed as static pressure \(\overline{p}_{0}\) and complex perturbed pressure \(\widetilde{p}_{d}\). \[\overline{p}=\overline{p}_{0}+\widetilde{p}_{d}e^{i\Omega\overline{t}} \tag{7}\]  
The partial derivative method was extended in [23] embracing dynamic variations of complete variables. For the lubricating oil film, the dynamic variation of both film thickness and viscosity originate from turbulence coefficients \(k_{x}\), \(k_{z}\) as well as \(\overline{h}^{3}/\overline{\mu}\). The linearized complex perturbation expansions of turbulence coefficients are combined responses to perturbed film thickness \(\widehat{\tilde{h}}_{d}\) and perturbed viscosity \(\widetilde{\tilde{\mu}}_{d}\). \[ \begin{array}{l}\frac{1}{k_{x}}=\frac{1}{k_{x0}}\left(1+b_{1}\left(\frac{ \widetilde{\tilde{\mu}}_{d}}{\tilde{\tilde{\mu}}_{0}}-\frac{\widetilde{\tilde {h}}_{d}}{\tilde{\tilde{\mu}}_{0}}\right)e^{i\Omega\overline{t}}\right)\\ \frac{1}{k_{z}}=\frac{1}{k_{z0}}\left(1+b_{2}\left(\frac{\widetilde{\tilde{ \mu}}_{d}}{\tilde{\tilde{\mu}}_{0}}-\frac{\widetilde{\tilde{h}}_{d}}{\tilde{ \tilde{\mu}}_{0}}\right)e^{i\Omega\overline{t}}\right)\end{array}  \tag{8}\]  
where \(k_{x0}\) and \(k_{z0}\) are static turbulence coefficients at the equilibrium position. When the \(Re\) < 2000, the \(k_{x0}=k_{z0}=12\) for the laminar state and the corresponding \(b_{1}\) and \(b_{2}\) in Equation (8) are equal to zero. \[ \begin{array}{l}k_{x0}=12+a_{1}\left(Re_{c}\frac{\widetilde{\tilde{\mu}}_{0 }}{\tilde{\tilde{\mu}}_{0}}\right)^{b_{1}}\\ k_{z0}=12+a_{2}\left(Re_{c}\frac{\widetilde{\tilde{\mu}}_{0}}{\tilde{\tilde{ \mu}}_{0}}\right)^{b_{2}}\end{array}  \tag{9}\]  
where the \(Re_{c}=\rho\omega RC_{p}/\mu_{a}\) is the average Reynolds number. Figure 1: Schematic of tilting pad bearing (load between pad). The relation between perturbed viscosity \(\vec{\mu}_{d}\) and perturbed pressure \(\vec{p}_{d}\) was constructed through a dynamic mapping [23] expressed by the dimensionless partial derivative of viscosity to pressure at \(\overline{p}_{0}\). \[\overline{\mu}=\overline{\mu}_{0}+\vec{\mu}_{d}e^{i\Omega\overline{t}}=\overline {\mu}_{0}+\overline{\partial}_{\overline{p}}\overline{\mu}(p_{0},T)\vec{p}_{d }e^{i\Omega\overline{t}} \tag{10}\]  
Taking Equations (4), (7), (8) and (10) into Equation (3), the static and perturbed Reynolds equation is separated, which method of derivation and solution was verified in [23]. \[\frac{\partial}{\partial\theta}\Bigg{(}\frac{\overline{h}_{0}^{3}}{k_{x0} \overline{\mu}_{0}}\frac{\partial\overline{p}_{0}}{\partial\theta}\Bigg{)}+ \left(\frac{d}{t}\right)^{2}\frac{\partial}{\partial\lambda}\Bigg{(}\frac{ \overline{h}_{0}^{3}}{k_{x0}\overline{\mu}_{0}}\frac{\partial\overline{p}_{0 }}{\partial\lambda}\Bigg{)}=\frac{1}{2}\frac{\partial\overline{h}_{0}}{ \partial\theta} \tag{11}\]  
\[
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 4 SS-FGSM Performance Analysis
### ABER Performance Analysis  
In this subsection, we will analyze the ABER performance of the SS-FGSM ML-based detector (MLSS-FGSM). The ABER is a function of the probability of corrupted bits due to detecting the transmit active antennas, spreading codes of real and imaginary parts of the modulated signal, and the modulated signal itself. In other words, ABER is a function of physical transmitted modulated bits \(log_{2}(M)\) and mapped data bits, \(2 log_{2}(\mathcal{N})+(N_{t}-1)\) including the bits transmitted by indices of antennas and the bits transmitted by the indices of spreading codes. We denote the probability associated with erroneous antenna detection by \(\mathcal{F}_{a}\); the probability associated with erroneous spreading code by \(\mathcal{F}_{c}\); and finally, the probability associated with the modulated bits by \(\mathcal{F}_{m}\). The ABER of the proposed SS-FGSM scheme can be formulated as:  
\[ABER=\frac{(N_{t}-1)}{R_{DLES-FGSM}}\;\mathcal{F}_{a}+\;\frac{2 log_{2}( \mathcal{N})}{R_{DLES-FGSM}}\mathcal{F}_{c}+\;\frac{log_{2}(M)}{R_{DLES-FGSM}} \mathcal{F}_{m}. \tag{22}\]In the MLSS-FGSM, the probability of the errors in the bits transmitted in the index of antenna \(\mathcal{F}_{a}\) and probability of error of the modulated symbols \(\mathcal{F}_{m}\) are an independent probability. The probability of the errors in the bits transmitted using the index of the code \(\mathcal{F}_{c}\) are dependent probability and it depends on the probability of error on estimated index antenna \(\mathcal{F}_{a}\).  
The probability errors in the received bits transmitted using the index of the codes \(\mathcal{F}_{c}\) can be in two cases: Case 1: if the detection of transmitting active antennas is correct, but the spreading code is detected incorrectly; Case 2: if the detection of the active antennas and spreading codes both are incorrect. Hence, the errors probability \(\mathcal{F}_{c}\) in the received bits transmitted via the index of the codes can be written as:  
\[\mathcal{F}_{c}=\mathcal{F}_{a}+\frac{(1-\mathcal{F}_{a})}{\mathcal{N}}. \tag{23}\]  
and \(\mathcal{F}_{a}\) can be calculated as [36]:  
\[\mathcal{F}_{a}\leq\ \frac{1}{2^{(N_{t}-1)}}\sum_{\subseteq=1}^{2^{(N_{t}-1)}} \sum_{\|=1}^{2^{(N_{t}-1)}}\frac{\mathcal{S}(q\rightarrow\overline{q})\left( \frac{1}{4\lambda_{a}}\right)^{N_{t}}\left(\begin{array}{c}2N_{r}-1\\ N_{r}\end{array}\right)}{(N_{t}-1)}, \tag{24}\]  
where \(\lambda_{a}=\frac{1}{2}\bigg{(}1-\sqrt{\frac{\sigma_{a}^{2}}{1+\sigma_{a}^{2} }}\bigg{)}\) and \(\sigma_{a}^{2}=\frac{\gamma}{2}\big{|}\chi_{q}(t)\big{|}^{2}\). \(\mathcal{S}(q\rightarrow\overline{q})\) denotes the number of bits in error between the transmitted bits via the antenna index and its estimation at the receiver side.  
The error probability in M-ary modulation can be written as [10]:  
\[\mathcal{F}_{m}=\ \Bigg{(}\bigg{(}1-\frac{1}{\sqrt{M}}\bigg{)}erfc\Bigg{(} \sqrt{\frac{3E_{s}}{N_{o}(M-1)}}\Bigg{)}\Bigg{)} \tag{25}\]  
where \(E_{s}\) is the energy symbol and \(N_{o}\) is the noise variance.
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 3 Material: WLS200S Lidar
### Scanning Strategy  
During the Passy-2015 field experiment, the WLS200S was operated with the following strategy:  
* An horizontal Plan Position Indicator scan (PPI) every 10 min. This was obtained by the lidar beam scanning in azimuth, between 250\({}^{\circ}\) and 60\({}^{\circ}\) with respect to the north, while keeping the elevation angle at 0\({}^{\circ}\) (in red in Figure 4). Horizontal PPIs allowed the structure of the horizontal valley wind, 40 m AVL, to be investigated.
* A set of three Vertical Range Height Indicator scans (RHI) every 30 min, obtained by maintaining a constant azimuth angle of the lidar beam and scanning vertically between 0\({}^{\circ}\) and 90\({}^{\circ}\) in elevation (in green in Figure 4). RHI scans were performed in three azimuth directions: 295\({}^{\circ}\), 350\({}^{\circ}\), 28\({}^{\circ}\) in order to capture the vertical structure of the wind in the along-valley direction (azimuth 295\({}^{\circ}\)), along the north slopes (azimuth 350\({}^{\circ}\)) and in the eastern part of the basin close to the Servoz passageway that leads to the upstream part of the valley (Chamonix). The baselines of RHI scans are indicated by the green lines in Figure 1.
* Meanwhile, a set of slanted PPI scans was obtained every hour by scanning the lidar beam in azimuth between 250\({}^{\circ}\) and 60\({}^{\circ}\) and gradually increasing the elevation angle between 1\({}^{\circ}\) and 15\({}^{\circ}\). An example of PPI scan at elevation 5\({}^{\circ}\) is represented in black in Figure 4.  
This strategy provided a three-dimensional volume of measurement within the valley every hour, with a resolution of 100 m. The system was set with 59 ranges gates, of size 100 m, giving a potential maximum range of 6 km.
## 3 Problem Formulation
### Objective  
According to the system model and UAVs' characteristics, this paper formulates the optimization objective as follows. The primary objective is to minimize the mission makespan, defined as the maximum completion time across all UAVs at each stage \(h\). The completion time \(T_{i}^{h}\) for UAV \(U_{i}\) is the total time spent traveling and processing tasks along its route \(R_{i}^{h}\). Thus, the makespan at stage \(h\) is  
\[T_{\text{max}}^{h}=\max_{U_{i}\in\mathcal{U}}T_{i}^{h}, \tag{5}\]  
where \(T_{i}^{h}\) is the total time UAV \(i\) spends traveling and processing tasks in stage \(h\).  
To ensure energy efficiency, we also consider the total energy consumption as a secondary objective. The energy consumed by UAV \(U_{i}\) during stage \(h\) is  
\[E_{i}^{h}=\sum_{(p_{m},p_{n})\in R_{i}^{h}}e_{m,n}+\sum_{D_{j}\in R_{i}^{h}}e_ {j,\text{proc}}. \tag{6}\]  
The combined objective function at each stage \(h\) is formulated as  
\[J^{h}=T_{\text{max}}^{h}+\alpha\sum_{i=1}^{N_{U_{i}}}E_{i}^{h}, \tag{7}\]  
where \(\alpha>0\) is a weighting coefficient that balances the trade-off between makespan and energy consumption.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 2 Load Period Partitioning Method Based on Improved Fuzzy C-Means Clustering
### Solution Process for Load Period Division Method Using Improved Fuzzy C-Means Clustering  
The specific steps of the time period partitioning method based on improved fuzzy C-means clustering are as follows, and the flowchart is shown in Figure 1:Step 1: Comprehensive similarity calculation. Based on the load characteristics of each time period and the optimal network structure for that time period, a feature vector is constructed. According to Equations (1)-(6), the comprehensive similarity \(S_{ij}\) between any two time periods \(i\) and \(j\) is calculated, and a similarity matrix \(S\in R^{N\times N}\) is constructed.  
Step 2: Introducing time-series smoothing. Based on the original similarity matrix, the information of adjacent time periods is fused according to Equation (7) to construct a time-weighted similarity matrix, which enhances the continuity of similarity in the time dimension and ensures the feasibility and rationality of the partition results in scheduling applications.  
Step 3: Initialize clustering parameters. Set initial parameters such as fuzzy index m, maximum iteration times, convergence threshold, etc., and randomly assign initial membership degree \(u_{ij}\) to each time period, so that for any time period \(i\), \(\sum_{j=1}^{C}u_{ij}=1\) is satisfied.  
Step 4: Similarity weighted center calculation. In each iteration, the similarity matrix is weighted based on the membership degree of the time period, and the fusion similarity \(S_{ij}^{f}\) between time period \(i\) and cluster center \(j\) is calculated according to Equation (8) as the update basis for each iteration.  
Step 5: Update the membership matrix. Update the membership degree \(u_{ij}\) based on the fusion similarity, and determine whether the convergence condition is met. Otherwise, continue iterating.  
Step 6: Adaptive cluster selection. Evaluate the performance of clustering results under multiple candidate clusters using evaluation indicators such as contour coefficients, and select the optimal cluster number \(C\) to improve the quality of partitioning.  
Step 7: The final output includes the clustering clusters to which each time period belongs and their corresponding membership degrees, completing the time period partitioning process, which is optimized based on similarity and time information.
# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
## 3 Multi-Modal System EEG with Facial Expressions--Proposed Model
### EEG-Based Emotion Recognition
It specifically focuses on EEG data collected from the nine optimum electrodes, namely, AF3, F7, F3, FC5, T7, O2, T8, FC6, and F4, with a particular emphasis on the beta frequency range. The architecture begins with an input layer to receive EEG topography generated based on the data from these electrodes as a colored image represented digitally by the three color layers. It subsequently employs two convolutional layers, Conv1 and Conv2, to extract spatial features from the input data. Activation layers, Act1 and Act2, introduce non-linearity into the model. Max-pooling layers, MaxPool1 and MaxPool2, reduce the spatial dimensions of the feature maps. The Flatten layer prepares the data for fully connected layers, followed by Dense1 and Dense2, which process the flattened data for the final output. The table provides additional information, including the total number of parameters in the network (972), and distinguishes between trainable and non-trainable parameters. This architecture is tailored for EEG analysis, particularly in the context of beta frequency range topography. The designed network was trained and validated on a newly collected database with a total number of 16,764 topographies for high and low valence levels. To ensure robust evaluation, the database is divided into training and validation sets, accounting for 80% (13,411 topographies) and 20% (3353 topographies), respectively. This division facilitates both extensive model learning and independent validation, thereby enhancing generalization capabilities and fine-tuning. This comprehensive methodology manifests itself as an efficacious emotion recognition system rooted in topographic EEG data. Impressively, the system had an accuracy of 72.53% when using the full 14 electrodes while it attains 87.43% accuracy when using only 9 electrodes in distinguishing high and low valence levels.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 3 Methodology
### Construction of Combined Model
#### 3.3.3 Construction of CM-GA-ELM Model  
The initial population of the standard GA is randomly generated, and the coverage of the generated population is large, which may lead to the initial population not necessarily containing the optimal solution. If the initial population does not contain the optimal solution and the genetic algorithm cannot cover the global optimal solution in a certain algebra, it is bound to cause premature convergence and affect the regression accuracy of the model. In order to eliminate the outliers caused by model parameters in the GA-ELM prediction model and improve the accuracy and reliability of the prediction model, this paper proposes a linear-weighted combination prediction method (CM), which is simple and can solve the above problems. The solution is as follows:  
1. Elimination of abnormal predicted values Assuming that \(n\) times of GA-ELM prediction models were operated and \(n\) groups of GA-ELM prediction models were established, different solutions were obtained. The \(i\)th prediction value was \(x_{i}\), and the average value of \(n\) times of prediction value was \(\overline{x}\). Equation (9) was used for calculation. To improve the reliability of prediction results, abnormal values in the results were eliminated. First, Equation (10) was used to calculate the mean square error \(R\) of multiple prediction results, and the results greater than 3 times the mean square error were eliminated.  
\[\overline{x}=\frac{x_{1}+x_{2}+\cdots+x_{i}+\cdots+x_{n}}{n} \tag{9}\]  
\[R=\pm\sqrt{\frac{\left[v_{i}v_{i}\right]}{n-1}} \tag{10}\]  
where \(v_{i}=x-x_{i}\).
2. Combination of predicted values Assuming that M predicted values were excluded, the average of the remaining \(J\) (\(J=n-M\)) predicted values could be calculated, and the final predicted value could be obtained by Equation (11):  
\[\overline{x}=\frac{1}{\int\limits_{i=1}^{J}x_{i}}\quad(i=1,2,\cdots,J) \tag{11}\]  
Above all, the CM-GA-ELM combined prediction model could be used to predict the subsidence coefficient of the surface movement basin.
## 7 Security Considerations for Proposed Mobility Schemes
### Authentication of Messages that Modify a MN's Routing Entry in the MSPs  
In the proposed scheme, signaling messages, such as _MN_moved_ and _path_setup_ messages, create or modify the routing state of a MN in an MSP and establish a path between source and destination MSPs. Thus it is necessary to ensure that these signaling messages are sent by an authentic source. Thus whenever an MSP sends the signaling message (that modifies or creates the routing state) to the next hop MSP; in the path between source and destination MSPs it encrypts the message with the pairwise key of itself and its next hop MSPs. The message format is shown below:  
\(\text{MSP}_{\text{i}}\)\(\rightarrow\)\(\text{MSP}_{\text{j}}\): \(\{\textit{MN\_moved}\}_{\text{$<K_{\text{ij}}>$}}\), MAC\(\{\) K\({}_{\text{ij}}\), C\(\}\)\(\{\textit{MN\_moved}\}_{\text{$<K_{\text{ij}}>$}\}\)  
When the next MSP receives the packet, it establishes the authenticity of the message by verifying whether or not it has received the messaged from a trusted MSP by computing the MAC using the pairwise key. Also it creates or updates the routing state of the MNs. It then forwards it to its next hop MSP, towards the destination MSP, by encrypting the message with a pairwise key shared between itself and the next hop MSP. This process enables creation or alteration of routing state entries of MNs securely.