esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The PI-Based Task Offloading Algorithm
### Convergence and Complexity Analysis  
The convergence of PITO is naturally guaranteed. In PITO, each satellite \(s\in S\) optimizes \(F(\theta)\) by iteratively adding or removing tasks into or out of its task sequence \(\theta_{s}\). According to Lemma 1, the offloading assignment \(\theta_{s}\) of each satellite \(s\) will be changed as long as \(F(\theta)\) is decreased. Thus, Algorithm PITO achieves convergence when there are no changes to \(\theta\) after one iteration.  
Subsequently, we discuss the complexity of PITO. There are \(m\) satellites and \(n\) delay-sensitive tasks in SEC. In the task inclusion stage, a total of \(m\) satellites independently integrate, at most, \(n\) tasks into their task sequences, and then the complexity is \(\mathcal{O}(mn)\). In the consensus process, each of the \(m\) satellites receives information from up to four neighboring satellites, and updates elements in its vectors for \(n\) tasks; then, the corresponding complexity is denoted as \(\mathcal{O}(4 mn)\). In the task removal process, a maximum of \(m\) satellites remove no more than \(n\) conflicting tasks from their sequences; then, its complexity is \(\mathcal{O}(mn)\). Therefore, the computational complexity in each iteration of PITO is \(\mathcal{O}(mn)+\mathcal{O}(4 mn)+\mathcal{O}(mn)=\mathcal{O}(6 mn)\). Let \(\delta\) denote the number of iterations required for convergence, and then the complexity of Algorithm PITO is \(\mathcal{O}(68 mn)\), i.e., it is of polynomial complexity.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 4 Results
### Path Simulation Comparison  
#### 4.2.1 Path Recovery  
Quickly returning to the original preset trajectory after obstacle avoidance is the key to improving efficiency. As shown in Figure 4, it is a simulation schematic diagram to verify the path restoration method proposed in this paper.  
Figure 4: Effect diagram of path recovery.  
Figure 3: The algorithm convergence curve graph.  
The gray squares represent static obstacles, the red squares represent dynamic obstacles, the blue trajectory is the original path trajectory, and the red trajectory is the optimized trajectory. As can be seen from the figure, when encountering dynamic obstacles, the local artificial potential field method starts to operate for dynamic obstacle avoidance. After the obstacle avoidance is completed, it can quickly and smoothly return to the original preset trajectory, proving the effectiveness of the method proposed in this paper.  
This section will conduct the simulation comparison and verification in three test scenarios to further verify the optimization performance of the LAPGWO algorithm in solving the path planning problem of multiple mobile robots in various environments. The complexity of the obstacles in the three scenarios varies to comprehensively evaluate the generalization ability of the LAPGWO algorithm proposed in this paper and its ability to adapt to the different requirements of the number of obstacles. The three scenarios are as follows: First, there is a simple warehouse environment with a width of 18 m and a length of 9 m. The number of robots is eight, and there are 18 obstacles. The specific scenario is shown in Figure 5. Second, there is a complex warehouse environment with a side length of 23 m. The number of robots is eight, and the number of obstacles is 64. Among them, the green circles represent the initial positions of the robots, and the red squares represent the positions of the target points. The specific scenario is shown in [the corresponding figure]. Finally, there is a non-warehouse environment with a side length of 50 m. The number of robots is eight, and the number of obstacles is 23. A distributed structure is adopted for control. However, considering the computing power of each robot, a unified calculation will be carried out on a single computer. After the calculation, the results will be transmitted to each robot to achieve the effect of distributed control.
## 3. SOH Prediction Method Based on the PSO-GWO-LSSVM Model  
### Least Squares Support Vector Machine Model  
With strong generalization capabilities and excellent robustness, LSSVM has been widely applied to problems such as nonlinear computations and function approximation. The regression principle of LSSVM is as follows: Let the sample be an n-dimensional vector, and let the training set be \(\{(\mathsf{x}_{i},\mathsf{y}_{i})\}\) (i = 1,2,..,n) where \(\mathsf{x}_{i}\) represents the input vector and \(\mathsf{y}_{i}\) is the corresponding output. Through a nonlinear mapping function \(\varphi(x)\), the input vector is mapped to a high-dimensional feature space, where linear regression is performed. The resulting function is given  
According to the (Karush-Kuhn-Tucker, KKT) optimization conditions:  
\[\frac{\partial L}{\partial\omega}=0,\frac{\partial L}{\partial\xi_{i}^{2}}=0, \frac{\partial L}{\partial\lambda_{i}}=0,\frac{\partial L}{\partial b}=0 \tag{15}\]  
Obtain the system of linear equations:  
\[\left[\begin{array}{c}b\\ \gamma\end{array}\right]=\begin{bmatrix}0&E^{T}\\ E&K+I/Y\end{bmatrix}^{-1}\begin{bmatrix}0\\ D\end{bmatrix} \tag{16}\]  
where \(\lambda=\left[\lambda_{1},\lambda_{2}\ldots,\lambda_{n}\right]^{T}\) and \(E=\left[1,1\ldots,1\right]^{T}\) as n \(\times\) 1-dimensional column vector; and \(D=\left[d_{1},d_{2}\ldots,d_{n}\right]^{T}\) as the identity matrix.  
Define the kernel function, \(K(x_{i},y_{j})=\varphi(x_{i})^{T}\varphi(y_{j})\) replaces the dot product operation in the high-dimensional feature space. By optimizing the KKT conditions using the least squares method, the values of \(\lambda\) and \(b\) can be obtained. The resulting prediction model based on LSSVM is given by:  
\[y(x)=\sum_{i,j=1}^{n}\lambda_{i}K(x_{i},x_{j})+b \tag{17}\]  
Due to the excellent interference resistance, strong nonlinear mapping capability, and good generalization ability of the (radial basis function, RBF) kernel, RBF is chosen as the kernel function for LSSVM in this study [34]. The expression for RBF is as follows:  
\[K(x_{i},y_{j})=\exp\left[-\left\|x_{i}-x_{j}\right\|^{2}/\left(2\sigma^{2} \right)\right] \tag{18}\]  
where \(\sigma\) represents the radial basis kernel width. When constructing an RBF-based LSSVM model, the correct selection of \(\sigma\) and \(\gamma\) is crucial.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 2 Model of Cross-Medium Communication Channel
### Verification of Cross-Medium Communication Channel Model
#### 2.4.1 Noise Test at Silent Source  
Initially, the background noise \(N(t)\) in the system needed to be measured as an additive component. This noise served as background noise for the model and its measurement was crucial for an accurate analysis. To capture the time-domain signal of the noise, the waveform of the water surface waves can be recorded using MMWR. This should be done with the speaker, which was energized but did not emit any acoustic signals. However, solely analyzing the time domain signal was insufficient for understanding the noise characteristics. A spectral analysis was necessary to gain further insights into its properties. The obtained test results are presented in Figure 4.  
Based on our measurements, the following conclusions can be drawn. In the time domain, the maximum value of the surface wave amplitude in 5 s is 45 \(\upmu\)m. In the absence of an acoustic signal, the amplitude of the surface wave reaches the micron level. The influence of the environment on water-air cross-medium communication cannot be overlooked. In the frequency domain, noise energy predominantly resides in the low-frequency range from 0 Hz to 45 Hz. To minimize the impact of noise, it is advisable to avoid utilizing this frequency band for information transmission. Consequently, for effective communication, a carrier frequency of at least 100 Hz is necessary. To mitigate noise interference on the transmitted signal and enhance communication quality and stability, it is recommended to configure the filter with a low cut-off frequency of 100 Hz.  
Figure 3: Water-to-air cross-medium communication system based on MMWR. (**a**) Equipment diagram. (**b**) System diagram.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 2 Related Work
### Transformer in Monocular 3D Object Detection  
The recent success of Transformers in computer vision has inspired their application in monocular 3D object detection. Unlike convolutional neural networks (CNNs), Transformers capture global spatial dependencies using self-attention mechanisms, addressing the limitations of local feature extraction. For example, MonoDETR [22] employs a dual-encoder architecture and a depth-guided decoder to improve depth representation and object localization. Similarly, MonoPSTR [23] introduces scale-aware attention and position-coded queries to enhance detection precision and efficiency. Recently, diffusion models [24; 25; 26; 27] have also demonstrated their potential in depth estimation by iteratively refining depth predictions, offering an alternative to traditional depth map generation methods. These models could complement Transformer-based architectures by providing robust depth priors for enhanced detection.  
Despite their advancements, Transformer-based methods often rely on pre-computed depth maps or handcrafted priors to guide detection [17; 28]. This dependence on external inputs can hinder adaptability to diverse scenarios. AuxDepthNet addresses this limitation by embedding depth reasoning directly into the network via the Auxiliary Depth Feature (ADF) and Depth Position Mapping (DPM) modules. These modules enable the model to implicitly learn depth-sensitive features, eliminating external dependencies and delivering robust, scalable performance.
## 3 Verification by Comparison with Previous Experimental Results
### Scope of the Application of the RC Beam Plane Conservation Assumption  
#### 3.2.1 Examination Outline  
To examine the applicable range of the plane conservation assumption of the RC beam, the experimental results of 134 cases of the bending-failure-type RC beam plastically deformed by a weight collision were collected. The specifications of the RC beams investigated in this study are shown in Appendix A (see Tables A1 and A2). The numerical values of the calculated bending capacity \(P_{\text{u}}\) and shear capacity \(Q_{\text{u}}\) were obtained from the literature, shown in those tables. Table 2 shows the range of the considered specifications of the RC beams for the 134 cases. In this table, \(\alpha\) is the shear-bending capacity ratio obtained by dividing the calculated shear capacity \(Q_{\text{u}}\) by the calculated bending capacity \(P_{\text{u}}\). RC beams with \(\alpha\geq 1.0\) were selected here. In addition, when the velocity of the impactor is higher than approximately 80 m/s, the RC member is often damaged by penetration or perforation, including shear failure and backside spalling, prior to exhibiting bending deformation [14]. Moreover, it was reported that the maximum impact velocity of falling rocks is approximately 25 m/s [3]. Thus, those experiments with impact velocities of less than 25 m/s were considered. The diameter of the impactor is almost the same as the width of the RC beam, and the shape of the bottom surface of the weight was spherical with a small curvature. Each study confirmed that bending deformation was predominantly observed for all RC beams. Figure 7 shows the dimensions for the typical RC beams, considered here. Figure 6: Comparison between the estimated maximum deflection \(\delta_{\text{u}}\) and the measured value \(\delta_{\text{u,exp}}\). Figure 8 shows the relationship between the estimated maximum deflection \(\delta_{\mathrm{u}}\) and the experimental value \(\delta_{\mathrm{u,exp}}\). From the figure, the estimated value \(\delta_{\mathrm{u}}\) up to approximately 40 mm corresponds well with the experimental value \(\delta_{\mathrm{u,exp}}\). On the other hand, when \(\delta_{\mathrm{u}}\) becomes large, \(\delta_{\mathrm{u}}\) may overestimate the experimental ones. It is assumed to be because the RC beams have greatly deformed and damaged, so that the assumption of plane conservation does not hold as in the case of Figure 6.  
However, as the minimum and maximum clear spans of the considered RC beam are 0.9 m and 8 m, respectively, the maximum deflection is expected to be significantly different even if the degree of damage is the same. Therefore, the accuracy of the estimated values and the applicable range must be examined without the results being affected by the shape and size of the beam.
# Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems
## 1 Introduction
_Notations_: A boldface capital letter, \(\mathbf{X}\), is used to denote a matrix, and a boldface lowercase letter, \(\mathbf{x}\), represents a column vector. The \(n\)th row and \(m\)th column entry of \(\mathbf{X}\) is denoted by \(x_{n,m}\), whereas the \(n\)th entry of vector \(\mathbf{x}\) is denoted by \(x_{n}\). The transpose operation is denoted by \((\cdot)^{T}\), and the norm of a vector is denoted by \(\|\cdot\|\). Furthermore, \(\Re(\cdot)\) and \(\Im(\cdot)\) indicate the real and imaginary parts of a matrix or vector, respectively.
## 1 Introduction
As mentioned above, although the existing algorithms exhibit substantial improvement in terms of minimizing the peak sidelobe or sidelobe control in direction of unintended BS, they assume the sensor nodes in traditional WSN are static and the optimization results may cause some sensor nodes to be selected more often such that the network lifetime is reduced. Due to the characteristic of the social dynamic of social sensors for the novel WSN in CPSS, the existing CB optimization methods are not applicable for the novel WSN including mobile social and static physical sensors in CPSS. In this paper, we integrate the social dynamic model into CB to solve the problem. The corresponding transmission optimization method based on reinforcement learning is developed to improve the transmission efficiency of WSN in CPSS. Moreover, the communication mechanism of implementing the transmission scheme and the detailed performance analysis are presented. The remainder of this paper is organized as follows. The related work is presented in Section 2. Section 3 presents the WSN sensing scenario based on CB in CPSS, including the model of social dynamic mobility and the model of CB transmission. In Section 3, we formulate the CB transmission optimization problem. Section 4 presents a transmission schemes of CB based on reinforcement learning to maximize the SNR performance, while considering the constraint of the interference power at the direction of unintended BS in CPSS. Section 5 evaluates the proposed methods via extensive simulations. Finally, we conclude this work in Section 6.
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
###### Abstract
This makes it particularly advantageous in practical applications, especially in noisy or data-scarce environments. We employed a hybrid approach leveraging TKEO to amplify fault-related transient features while suppressing noise, enabling the ML models to capture subtle fault signatures under high loads. Moreover, we adopted a dual-segment strategy, systematically evaluating short (2400-point) and long (12,000-point) data segments to balance real-time monitoring needs and in-depth fault pattern analysis. Finally, feature selection is a crucial step in fault diagnosis tasks. We propose a feature simplification method by removing low-contribution features (such as Max and Min) through quantitative analysis; the results of th current study verify its effectiveness in fault classification. This feature optimization method, especially when combined with SVC and RFC models, can enhance computational efficiency and reduce the risk of overfitting. It maintains high accuracy while reducing computational complexity, avoiding the waste of computational resources on redundant features and improving model interpretability. This approach meets the industrial demand for real-time performance and ease of deployment. The remainder of this paper is organized as follows: Section 2 describes the CWRU dataset and methodologies, including TKEO processing, SVM/RF classifiers, and feature selection. Section 3 details the experimental setup and evaluation metrics. Section 4 presents the results, with a focus on the TKEO's performance under varying loads and segmentation lengths. Then, practical implications and limitations are discussed, followed by the conclusion.
## 3 Development of the EMS and Obtained Results for a Specific Case Study
### Optimization of the Charging Schedule Using a GA  
A sensitivity analysis of the effect of the number of generations and chromosomes on the cost and the processing time has been performed. A compromise between the cost and the time has led to 1000 generations and 90 chromosomes. With these parameters, 10 optimization processes that lead to slightly different results have been carried out. Figure 8 shows the best day charging profile obtained. The blue curve represents the power required from the buses while the red one is related to the grid electricity price. When this price is high, the power demanded decreases, and the inverse is also true.  
Figure 8: EVCS optimized charging profile. The left y-axis is used for the blue line, which refers to the EVCS power consumption, and the right y-axis is used for the red line, which refers to the electricity price at each moment.  
The optimized charging profile performs 10% better than the original charging schedule, which can be seen in Figure 11.
## 4 Comparison of Results and Discussion
\begin{table}
\begin{tabular}{l l l c c c c c c c} \hline \hline
**Reference/Year** & **Multiband** & **Technique** & **Maximum Gain (dB)** & **Radiation Efficiency** & \begin{tabular}{c} **Radiation Pattern in** \\ **Boresight for All** \\ **Frequency Bands** \\ \end{tabular} & \begin{tabular}{c} **No. of Antenna** \\ **Elements** \\ \end{tabular} & \
\begin{tabular}{c} **Non- of Antenna** \\ **Elements** \\ \end{tabular} &
\begin{tabular}{c} **Bandwidth (MHz)** \\ **Antenna Spacing** \\ \end{tabular} \\ \hline
[23]/2019 & Yes & Circular fractal array & 5.94 & - & No & 4 & \(-\)500 & 0.5x \\ \hline
[24]/2018 & Yes &
\begin{tabular}{c} Koch-fed obstacle/slot \\ asymmetric ground plane \\ \end{tabular} & 5 & 93\% & No & 4 & \(-\)104 & - \\ \hline
[25]/2019 & No &
\begin{tabular}{c} Fibonacci series with the \\ incorporation of Koch \\ snowflake structure \\ \end{tabular} & 15.18 & 72.12\% & No & 4 & 105 & 0.5x \\ \hline
[26]/2018 & Yes &
\begin{tabular}{c} Serpinski carpet fractal \\ antenna array \\ \end{tabular} & 11 & - & No & 8 & 90 & 0.9x \\ \hline
[27]/2014 & Yes &
\begin{tabular}{c} Fractal tree antenna array \\ \end{tabular} & 9.3 & - & No & 4 & \(-\)40 & - \\ \hline
[28]/2019 & No &
\begin{tabular}{c} Complementary Sierpinski \\asket fractal antenna array \\ \end{tabular} & 9.98 & 68\% & No & 4 & \(-\)400 & - \\ \hline
[29]/2022 & Yes &
\begin{tabular}{c} Rectangular and circular \\ patch antenna array \\ \end{tabular} & 5.71 & 99\% & No & 16 & 980 & 0.3x \\ \hline
[30]/2019 & Yes &
\begin{tabular}{c} Rectangular slotted patch \\ antenna array \\ \end{tabular} & 4.5 & - & No & 2 & 900 & 0.2x \\ \hline
[31]/2021 & Yes &
\begin{tabular}{c} Tri-band hexagonal fractal \\ antenna array \\ \end{tabular} & 4.2 & - & No & 2 & 300 & - \\ \hline
[32]/2018 & Yes &
\begin{tabular}{c} Sierpinski carpet fractal \\ antenna array \\ \end{tabular} & 2.82 & - & No & 2 & \(-\)40 & 0.7x \\ \hline
[28]/2018 & No &
\begin{tabular}{c} Patch antenna arrays based \\ on fractal metamaterial \\ EEG structures \\ \end{tabular} & -5.5 & -81\% & No & 4 & 1250 & 0.5x \\ \hline
[9]/2022 & Yes &
\begin{tabular}{c} Metasurface-inspired \\ fractal antenna \\ \end{tabular} & 7.7 & -80\% & Yes & - & 244 & - \\ \hline
[60]/2020 & No &
\begin{tabular}{c} Koch-soworld-based \\ fractal antenna \\ \end{tabular} & 11 & 71\% & Yes & 4 & 165 & 0.5x \\ \hline
[This World/2022 & Yes &
\begin{tabular}{c} Combined \\ Multiboards-Serpinski carpet antenna \\ \end{tabular} & 9.61 & 96\% & Yes & 4 & 182 & 0.7x \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of proposed four-element MSFAA with existing research literature where other researchers have proposed antenna arrays using fractal structures.
## 3 Simulation Models
### Initial and Boundary Conditions  
The simulations of cooling energy charge and discharge of the PCM are performed separately. With reference to the experimental test described in Section 2, as regards the implementation of the conduction-based model, the PCM initial temperature for the charge simulation is fixed equal to the temperature measured at the start-up experimental test, while, for the discharge simulation, the PCM initial temperature is fixed equal to the measured temperature at the start-up of step 3. The boundary conditions are set according to the experimental test. In particular, the bottom and top surfaces of the cylindrical computational domain relative to the PCM are considered to be adiabatic, while the boundary condition relative to the lateral surface is set according to Equation (10):  
\[q_{l}=h_{l}(T-T_{\infty}\ ),\quad r=r_{\text{max}},0\leq z\leq z_{\text{max}} \tag{10}\]  
where \(q_{l}\) is the heat flux relative to the lateral surface, \(h_{l}\) is the heat transfer coefficient relative to the lateral surface, \(T_{\infty}\) is equal to the air temperature inside the climatic chamber, and \(r\) and \(z\) are the radial and axial coordinates, respectively. The convective heat transfer coefficient \(h_{l}\) is fixed to 30.2 W/(m\({}^{2}\) K) in the cooling energy charge, and to 29.1 W/(m\({}^{2}\) K) in the discharge. These values of \(h_{l}\) were calculated by means of a correlation for cylinders subjected to transverse external forced flow [27], and were obtained using a measured average air velocity inside the climatic chamber of 3.3 m/s.  
The above conditions are also applied for the implementation of the conduction-convection-based model. In this case, the initial velocity is set to zero in both charge and discharge, while a no-slip wall boundary condition is applied to all the external surfaces of the computational domain delimiting the phase change material.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Charge** & **Discharge** \\ \hline \(T_{M}\) (\({}^{\circ}\)C) & 12.5 & 15 \\ \(\Delta T_{M}\) (\({}^{\circ}\)C) & 1 & 5.5 \\ C & \(10^{3.7}\) & \(10^{3.8}\) \\ \hline \end{tabular}
\end{table}
Table 2: Values of \(T_{M}\), \(\Delta T_{M}\) and \(C\) employed in the numerical simulations.