esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Detection Results at Different Detection Distances and in the Presence of Defects  
We adjusted the position of the electromagnetic ultrasonic transducer and measured the guided wave echo signal every 200 mm on the right end face of the No. 1 flat steel specimen. The test results are shown in Figure 12a.  
According to the test findings, as the electromagnetic ultrasonic transducer moves farther away from the right end face of the flat steel, the echo signal appears later, while the echo amplitude tends to decrease gradually, which is consistent with the theoretical and simulation results.  
For the selected flat steel specimen No. 1, the electromagnetic ultrasonic transducer was placed at the O point and N while using the self-transmitting and self-accepting method for detection. The detection signal is shown in Figure 12b.  
The electromagnetic ultrasonic transducer initiates the SH0 wave, which propagates through the flat steel from the detection point N, interacts with the end face of the flat steel, and generates an echo signal. After propagating back to the electromagnetic ultrasonic transducer, the signal is received and uploaded to the laptop. The propagation distance of the electromagnetic ultrasonic SH0 wave at this time is 800 mm, with a propagation time of 0.259 ms (the difference between the excitation of the electromagnetic ultrasonic transducer signal and the moment corresponding to the peak of the reflected echo signal). The propagation speed of the surface wave in the steel plate is calculated to be 3174.6 m/s, with an error of only 0.14% from the theoretical wave speed of 3170 m/s. The test results demonstrate that the SH0 wave effectively identifies corrosion defects in flat steel, providing high-accuracy defect positioning.
# GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
## 3 Preliminaries
### Secret Sharing  
Common secret sharing [35] methods include additive secret sharing, multiplicative secret sharing, and threshold secret sharing. Additive secret sharing was chosen for this scheme, so multiplication and threshold secret sharing methods do not need to be described here.  
* Additive secret sharing: Suppose there are three users \(A\), \(B\), and \(C\), at this point. Additive secret sharing can make \(C\) obtain the value of \(x+y\) without knowing the local data \(x\) of user \(A\) or the local data \(y\) of user \(B\). Initially, N cloud servers are selected and denoted as \(S_{1},S_{2},\ldots,S_{n}\). Subsequently, users \(A\) and \(B\) randomly partition their local data \(x\) and \(y\) into n segments, respectively, such that \(x=x_{1}+x_{2}+\ldots+x_{n}\) and \(y=y_{1}+y_{2}+\ldots+y_{n}\). The corresponding segments \(x_{i}\) and \(y_{i}\) are then assigned to the \(i\)th server, where \(i\in\{1,2,...,n\}\). User \(C\) sends computation requests to each cloud server, and the cloud server \(S_{i}\) computes \(z_{i}=x_{i}+y_{i}\), subsequently transmitting the result back to user \(C\). After receiving it, user \(C\) calculates the sum locally: \(z=z_{1}+z_{2}+\ldots+z_{n}=x_{1}+y_{1}+x_{2}+y_{2}+\ldots+x_{n}+y_{n}=x+y\). The specific process is shown in Figure 3.  
Figure 3: The flow chart of secret sharing.  
* Multiplicative secret sharing: This primarily utilizes the multiplication triple \((a,b,ab)\), which consists of two random numbers, \(a\) and \(b\), to achieve secure computation.
* Threshold secret sharing: Threshold secret sharing based on polynomials randomly splits a secret \(s\) into \(n\) parts, such that \(s=s_{1}+s_{2}+\ldots+s_{n}\), and distributes these parts to n users. A threshold \(t(1<t<n)\) is set, meaning that as long as any t users are selected from the n users, the original secret \(s\) can be reconstructed using the shares \(s_{i}\) they hold: \(i\in\{1,2,...,n\}\).
# Sliding Mode Control Algorithms for Anti-Lock Braking Systems with Performance Comparisons
## 1 Introduction
While a similar simplified braking model was employed with a recurrent neural network (RNN)-based uncertainty observer, in a hybrid SMC design provided by [24]. Noticing that the adaptive laws of the RNN bring supplementary computational burden rather than account for uncertainties by typical upper bounds. A comprehensive experimental validation on a braking system of a real-life scale laboratory car was proposed by [25]. Although a very detailed model is provided, uncertainty is not accounted for in the SMC design, and with the experimental validation as the main focus, the theoretical stability analysis is missing. Another quasi-SMC ABS controller with endocrine neural network-based estimator was presented in [26]. In order to increase the steady-state accuracy, the estimated modeling error is included in the controller at the cost of computational complexity. A simpler SMC design with included model uncertainties and integral switching surface for chattering reduction was suggested in the work of [17] on a four-wheel car model with simplified dynamics. However, theoretical stability certificates were not provided in the mentioned work. Other control approaches for ABS include nonlinear adaptive solutions [5; 10], data-based approach [1], fractional SMC [4], fuzzy [16] and neuro-fuzzy sliding mode incremental learning [27] and grey system modeling approach for SMC [28]. More recent application-oriented validation of intelligent ABS control is presented in the works [29; 30; 31; 32]. The contribution of this work is threefold. Firstly, the original SMC laws (LSMC and RSMC) are proposed and they have not been applied before to ABS control systems. Secondly, their complete design and stability analysis show that the SMC control is robust enough to account for large uncertainty in the design, without additional uncertainty estimator and without striving for complex models, even though the actuator dynamics is simplified to a simple gain. This operation is required in order to bring the ABS model to a second-order system dynamics favorable to the SMC design, herein proposed in two variants. Thirdly, the comparisons with other four competitive controllers also prove the performance capability attainable with such SMC-based controllers. These controllers are of various types: an adaptive one, a model-free reinforcement learning one, a model-free SMCone and a model-based periodic switching sliding-mode controller. Without a significant computational burden and with proven control performance, such sliding-mode controllers are attractive for industrial implementations, owing to their robustness, simplicity and few tuning parameters. In the following, Section 2 briefly describes the ABS system and the slip regulation control problem, while Section 3 proposes two variants of sliding mode control design. Section 4 validates the two approaches and compares the results with a state-of-the-art adaptive approach. Conclusions are highlighted in Section 5.
## 2 System Reliability Evaluation Method of a Single Failure Criterion
### Copula Theory  
The copula function, first proposed by [PERSON] [32], describes the correlation between variables, and is actually a class of functions that connect joint distributions to their respective marginal distributions. \(X_{1}X_{2}\),...,\(X_{d}\) are the random variables with respective marginal distributions \(\mathrm{F}_{1},\mathrm{F}_{2},\ldots,\mathrm{F}_{d}\) and the multivariate distribution is \(\mathrm{F}(x_{1},x_{2},\ldots,x_{d})\). For all \(\mathbf{x}\in R\), \(R\in(-\infty,+\infty)\), there exists a d-dimensional copula function, which describes the relationship between the multivariate distribution and the corresponding marginal distribution function:  
\[\begin{array}{ll}\mathrm{F}(x_{1},x_{2},\ldots,x_{d})&=P[X_{1}\leq x_{1},X_{ 2}\leq x_{2},\ldots,X_{d}\leq x_{d}]\\ &=\ \mathrm{C}(\mathrm{F}_{1}(x_{1}),\mathrm{F}_{2}(x_{2}),\ldots,\mathrm{F}_{ d}(x_{d})|\theta)\\ &=\ \mathrm{C}(u_{1},u_{2},\ldots,u_{d}|\theta).\end{array} \tag{1}\]  
where \(\mathrm{C}(\cdot)\) is the copula function with domain [0,1]\({}^{\mathrm{d}}\). \(\theta\) is the parameter for the copula. The functions \(\mathrm{F}_{i}(x_{i})=u_{i},i=1,2,\ldots,d\) follow the uniform distribution U(0,1).  
The set of all copula functions contains many families, such as Archimedean copulas, meta-elliptical copulas, quadratic copulas [33], and cubic copulas [34]. Among them, Archimedean copulas and meta-elliptical copulas are commonly used because they are relatively simple in their function construction compared to the other copulas [35; 36]. In this study, the Clayton, Gumbel, and Frank copulas in Archimedean copulas and the Gaussian copula in meta-elliptical copulas were selected as the candidate copulas based on previous studies [20; 23].
# Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adaptation
## 4 Strategies for Overcoming Challenges
### Enhancing EV Charging Procedures--Battery Switching Stations
One of the key players in this space is NIO, a Chinese EV manufacturer, which has implemented battery swapping stations across China. These stations are fully automated and use a robotic arm to remove the depleted battery from the EV and replace it with a fully charged one. NIO claims that the entire process takes less than five minutes, providing a convenient and efficient way for EV drivers to continue their journey. They have installed over 1323 battery swapping stations across China as of March 2023. Another company, called CATL, has developed a standardized battery swapping solution that can be used across different EV models. This approach provides flexibility for EV manufacturers and enables them to implement battery swapping technology without having to develop their own proprietary solutions [56]. Furthermore, a study by McKinsey & Company suggests that battery swapping technology could account for up to 30% of EV charging in China by 2030. The study also notes that battery swapping can provide benefits such as reducing the cost of EV ownership, improving the utilization of EV batteries, and reducing the need for large-scale charging infrastructure [57]. Given China's success in deploying battery swapping technology, other countries could benefit from learning and adopting similar techniques. Battery swapping provides a convenient and efficient alternative to traditional charging methods, which could help accelerate the adoption of electric vehicles and reduce reliance on fossil fuels.
# Near-Optimal Weather Routing by Using Improved A* Algorithm
## 2 Routing Algorithms
### \(A^{*}\) Algorithm
As described above, because the A* algorithm guarantees the admissibility, consistency, and efficiency for optimal path finding, it was used to find the optimal ship routes in this study. Unlike the Dijkstra algorithm, the A* algorithm puts the start node into the _Queue_ with a key of \(f\) value with a heuristic function value instead of path cost \(d\). Then, it selects the adjacent node \(u\) with the minimum path cost. The next step is to update the path cost through edge relaxation in the same way as the Dijkstra algorithm. This process is repeated until the target node is reached. The overall processes of the A* algorithm are as follows. 1. For all \(nodes\in\mathbf{N}\) in graph \(\mathbf{G}\) provided, initialize with \(d(node)=\infty\). 2. For source \(s\), initialize \(d(s)=0\) and \(f(s)=d(s)+heuristic(s)\), then put into _Queue_ as key with value of \(f\). 3. Extract minimum-valued node \(u\) from _Queue_. 4. Execute edge relaxation based on \(d\) at \(u\), putting newly visited nodes into _Queue_. 5. Repeat iii. and iv. until the path reaches the target node \(t\). where \(d\) corresponds to the path cost \(d\) of the Dijkstra algorithm and is the same as the \(g\) function of the A* algorithm in Equation (1). Algorithm 3 shows the pseudocode of the A* algorithm. Here, HEURISTIC is the cost-evaluation function over a path defined with at least two consecutive nodes.
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
## 6 Conclusions  
We present a novel all-optical processor designed to overcome the limitations of current high-speed PONs. This processor leverages optical signal processing techniques to perform either all-optical equalization or chromatic dispersion compensation, offering a significant improvement in system efficiency and power consumption. The processor effectively manages the increased computational demands of DSP by dynamically switching between tasks and selecting optimal parameter values, significantly boosting performance and surpassing the capabilities of its complex electrical counterparts. Experimental validation shows substantial enhancements in signal quality and overall network performance, demonstrating the processor's potential for deployment in future high-capacity access networks. The proposed solution not only addresses the growing demand for higher data rates but also reduces the complexity and power consumption associated with traditional electrical methods. By integrating advanced optical techniques, this work paves the way for scalable and cost-effective implementations in next-generation PON technologies.  
Figure 7: Optical spectrum: (**a**) 50 GBaud (ROP = \(-14\) dBm) and (**b**) 100 GBaud (ROP = \(-11\) dBm).  
**Author Contributions:** Conceptualization, G.B. and G.K.; Methodology, G.B., G.K. and M.S.; Software, G.B.; Validation, G.K. and M.S.; Formal analysis, G.K. and M.S.; Data curation, G.B.; Writing--original draft, G.B.; Writing--review and editing, G.K. and M.S.; Visualization, G.B.; Supervision, G.K., M.S. and H.A.; Project administration, H.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the H2020 ICT PICaboo project (contract no. 101017114) under the photonics PPP.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Method
### Autonomous Vehicle Pipeline and Vehicle Setup
#### 2.1.2 Path Planning
In the proposed algorithm, the shortest path is chosen, i.e., the one with the lowest \(\Lambda\). Furthermore, the planned path must be feasible and collision-free, thus the paths which hit obstacles are discarded. The investigated trajectory planning method is thus able to compute a feasible trajectory that is consistent with the performance of an autonomous steering actuator, as discussed in [40]. Afterwards, the speed profile can be associated to the selected path based on the limitations imposed by the vehicle dynamics. Therefore, during turns, the speed is limited by the maximum lateral acceleration \(\dot{U}_{y,max}\) that is equal to 2 g. In the same way, the reference speed profile is constrained by the longitudinal speed and acceleration on a straight path. The upper bound of the longitudinal speed \(U_{max}\) is 30 m/s, according to the vehicle's maximum speed. The vehicle longitudinal acceleration peaks can reach up to 1.6 g. Moreover, as the vehicle should not go in reverse, the lower bound is imposed equal to 0 m/s. Under these assumptions, the minimum value of the velocity \(U_{min}\) is computed as follows:  
\[U_{min}=\sqrt{\frac{\dot{U}_{y,max}}{\kappa}} \tag{2}\]where \(\kappa\) is the curvature of the computed path. Given the coordinates \((x,y)\) of each sample of the path, \(\kappa\) is geometrically computed as follows:  
\[\kappa=\frac{\left|\dot{x}\ddot{y}-\ddot{x}\dot{y}\right|}{\left(\dot{x}^{2}+\dot {y}^{2}\right)^{\frac{3}{2}}} \tag{3}\]
# A 112 Gb/s DAC-Based Duo-Binary PAM4 Transmitter in 28 nm CMOS
## 5 Conclusions  
To solve the problem of the high bit error of serial transmitters under strong channel attenuation, this paper designed a low-power 112 Gb/s SerDes transmitter adopting a duo-binary PAM4 modulation technology. The transmitter adopted a precoding circuit to reduce the correlation between the front and back symbols and reduce the bit error rate. The problem of jitter caused by charge sharing of the 4:1 MUX was solved by using precharging auxiliary transistors. In order to improve the linearity, a 7-bit weighted SST driver circuit was adopted. Meanwhile, a parallel resistance was added to the driver circuit and a new current path was added to ensure the output swing of the driver and reduce the dynamic power consumption of the driver. The differential nonlinearity and integral nonlinearity of the DAC were 0.38 LSB and 0.68 LSB, respectively. The transmitter adopted a 28 nm process, could work at 112 Gb/s, consumed 226 mW, had an energy efficiency of 2.02 mW/Gb/s, its linearity reached 96.7%, and its bit error rate was lower than \(10^{-12}\).  
[PERSON] analyzed and improved the circuit and wrote the manuscript. [PERSON] helped analyze the circuit and modify the manuscripts. [PERSON] helped with the layout design and proofread the manuscript. [PERSON] participated in technical discussions and helped with the circuit simulation implementation. [PERSON] put forward valuable suggestions and revised the paper proofs. [PERSON] reviewed the manuscript and looked at the relevant literature. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The China Postdoctoral Science Foundation has 67 batches of projects and the fundamental technology research of high-speed SerDes chips for 10E level supercomputing IO.  
The authors declare no conflict of interest.
## 5 Simulations and Discussions  
Simulations of the direct trust model, reputation computation model, and iterative total trust and reputation computation models are shown in this section to illustrate the performance of the proposed model. In the simulations, three classical types of service clients (risk taking, cautious, and conservative) are assumed by concrete values, and the influence of the parameters of these models are discussed. Both improvement and decrement processes of trust and reputation are simulated.
## 3 Materials and Methods
### Research Design
#### 3.1.2 Selection of Spatial Weight Matrix  
The spatial weight matrix is applied to describe the distance relationship between individuals in spatial econometrics. The spatial weight matrix mainly includes the adjacent spatial weight matrix (assuming that the connection between individuals only exists in the regions with a common boundary), the geographical distance spatial weight matrix (assuming that the spatial connection of two regions is inversely proportional to the distance between them) and the economic distance spatial weight matrix. Due to the lack of data in Tibet, China, the adjacent spatial weight matrix cannot reflect the spatial relationship between regions truly, and the economic distance spatial weight matrix is the extension of the geographical distance spatial weight matrix. Therefore, the current research applies the geographical distance spatial weight matrix to carry out spatial econometric analysis. The specific formula of spatial weight matrix applied in this paper is set as follows.  
\[w_{i,j}=\left\{\begin{array}{l}\frac{1}{d_{i,j}},i\
eq j\\ 0,i=j\end{array}\right. \tag{2}\]  
where \(d_{i,j}\) represents the surface distance between province \(i\) and province \(j\). Specific values of \(d_{i,j}\) can be calculated by extracting the longitude and latitude data from the National Basic Geographic System Database.
# Microstructure and Properties of AA6061/SiCp Composites Sintered under Ultra High-Pressure
## 4 Conclusions  
This paper mainly studies the microstructure and properties of AA6061/N-SiCp composites. The main conclusions are as follows:  
1. For AA6061/1 vol.% N-SiCp and AA6061/3 vol.% N-SiCp, there is almost no agglomeration, and the Al particles become irregular. Higher volume fraction will lead to a thicker N-SiC layer  
Figure 18: Wear morphology of AA6061/5 vol.% N-SiCp sintered at: 450 (**a**), 550 (**b**), 600 (**c**), 650 (**d**) \({}^{\circ}\)C.  
around the Al particles. The rod-like particles appeared at the junction of the two particles. The Al\({}_{4}\)C\({}_{3}\) phase was not detected for AA6061/1 vol.% N-SiCp and AA6061/3 vol.% N-SiCp. The increase in temperature has no significant effect on the agglomeration of N-SiCp, but the higher temperature can generate the better plastic flow of the Al matrix, the filling of the spheroidized pores, and the larger number of small-sized Al particles. The Al\({}_{4}\)C\({}_{3}\) phase begins to appear at the interface at 550 \({}^{\circ}\)C. As the temperature increases, the Al\({}_{4}\)C\({}_{3}\) phase distribution at the interface becomes dense.
2. The density of the composites decreases with the increment of the volume fraction of N-SiC and increases with the augment in sintering temperature.
3. The hardness and wear property first increase and then decrease as the temperature increases. The hardness reaches up to 52 HRA, and the wear rate is just \(1.0\times 10^{-6}\) g/m at 600 \({}^{\circ}\)C. The wear mechanism is mainly composed of abrasive wear and adhesive wear.  
Data curation, Z.C.; Funding acquisition, Y.W.; Supervision, L.X.; Visualization, G.M.; Writing--original draft, E.Y.; Writing--review and editing, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Research Foundation of China University of Petroleum--Beijing at Karamay, grant numbers XQZX20200016, XQZX20200019 and Science and Technology Plan of Karamay, grant number XQZX20200036. The authors declare no conflict of interest.
## Materials and Methods  
### Overview of the Methodology  
Several advanced machine learning methods, such as Extreme Deep Neural Network (_DNN_), Extreme Learning Machine (_ELM_), Support Vector Regression (_SVR_), _LASSO_ regression (_LASSO_), Random Forest (_RF_), Ridge Regression (_Ridge_), Partial Least Square Regression (_PLSR_), Stepwise Regression (_Stepwise_), Kernel Ridge (_KRidge_), Genetic Programming (_GP_), and Least Square Regression (_LSR_), have been used to learn from 100 samples collected from previous studies [27]. Multiple input parameters, including the pile material, average cohesion (kN/m\({}^{2}\)), average friction angle (\({}^{\circ}\)), average soil-specific weight (kN/m\({}^{3}\)), average pile-soil friction angle (\({}^{\circ}\)), flap number, pile area (m\({}^{2}\)), and pile length (m), have been used. Firstly, the aforementioned advanced machine-learning methods have been utilized for modeling the input parameters, and their effectiveness was assessed through various statistical indicators. To evaluate the predictive ability of the optimal model, the \(k\)-fold cross-validation approach, which is based on five splits, has been employed. Afterward, in order to know which input variables have the biggest effect on the pile-bearing capacity through the proposed model, a sensitivity analysis has been performed via the step-by-step method. Finally, a reliable, easy-to-use, and the graphical interface was designed based on our optimal model in order to help civil engineers and researchers to easily predict the pile-bearing capacity in future studies.