esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 3 The Proposed Method
### Spatial Gradient Capture Module  
#### 3.1.1 PCA Operator  
Generally, the aim of PCA is to reduce the dimensionality of large datasets by exploiting the underlying correlations between the variables efficiently while preserving most of the information [23]. However, in this paper, PCA is used as a feature extractor to estimate  
Figure 1: Flowchart of the IVIF method via feature-oriented dual-module complementary. Different line formats represent different sections. A, B, and F represent the infrared image, visible image, and fused image, respectively.  
the weight maps. To the best of our knowledge, PCA has not been utilized for IVIF, but it has already been adopted in MEIF [24].  
First, gray-scale images \(I_{n}(n=1\ldots N)\) are vectorized into column vectors of size \((\widetilde{r}c\times 1)\), where \(\widetilde{r}\) and \(c\) denote the numbers of rows and columns of the image, respectively. Then, all these column vectors are combined in a data matrix of size \((\widetilde{r}c\times N)\) consisting of \(\widetilde{r}c\) objects having \(N\) variables each. After calculating the PCA scores of all objects, each object-variable vector is reshaped to an \((\widetilde{r}\times c)\) image matrix. Next, a Gaussian filter is used to eliminate noise and discontinuities while smoothing the sharp changeovers at the transition regions. Lastly, a sum-to-one normalization is performed at each spatial position \((\widetilde{r},c)\) over all images, and the final PCA weight map \(PCA_{n}(n=1\ldots N)\) is obtained for the fusion operation.
# Relationship Between Ball Speed and Spin in Elite Youth Table Tennis Players Using Optical Sensors
## 1 Introduction  
Table tennis is a complex sport, categorized within the family of racket sports and more specifically as a net game, according to the definition provided by _[PERSON] & Bartlett_[1]. In this sport, players need to manage multiple factors to perform well, just like in other racket sports [2]. Different performance indicators rely on biomechanical, technical, or even tactical skills. Tactical skills involve mastering a wide variety of strokes and using them at the right moment to take over an opponent.  
What makes those shots unique is the ability of a player to put spin on the ball during a short time of contact between the racket and the ball. Mastering this brief period of contact to induce spin is crucial to fully using different strokes to win a point, especially as spin is one of the key performance indicators of a player, along with placement, ball speed, technique, and precision [3]. Therefore, it is crucial to measure performance. Spin has been largely studied from a fluid mechanics perspective [4, 5, 6, 7, 8], which has mainly observed the influence of rotation on a ball's trajectory or determined the complex physics behind the flying time of the ball. However, spin measurement is difficult to perform under real match conditions because the ball's linear speed is high, as is the rotation (around 127 RPS for high spin) [9]. Spin measurement techniques are various and could use the racket combined with inertial measurement units to estimate ball kinetic parameters [10]. However, the mainmeasurement system is still computer vision, which is based on image sensor technologies such as CCD (Charge Coupled Device) and nowadays mostly CMOS (Complementary Metal Oxide Semiconductor) sensors. Many studies use high-speed cameras or motion capture cameras with reflective markers and image-processing algorithms to estimate spin with pixel displacement [11, 12, 13, 14].  
A common misconception among table tennis practitioners posits that players can control ball speed and rotation independently. This study aims to address this issue by introducing a different methodology for measuring spin, which is determined by observing light reflection on the surface of the balls. This is realized using a montage of a low-level component inside image sensors: a photodiode. The primary objective of this research is to establish a correlation between the linear speed of the ball and its spin through experimental testing in controlled conditions. Additionally, we hypothesize that the type of stroke and the incoming type of ball don't influence the ratio between the ball's rotation and linear speed.
# A Novel Dynamic Generalized Opposition-Based Grey Wolf Optimization Algorithm
## 4 Experiments and Discussion  
### Benchmark Functions  
In this section, 23 benchmark functions commonly used in research were applied to evaluate the optimal performance of the proposed DOGWO algorithm [25; 26; 27; 28; 29; 37; 42; 43]. The benchmark functions used were all minimization functions which can be divided into three categories: unimodal, multimodal, and fixed-dimension multimodal. According to [PERSON] et al. [25], the unimodal functions are suitable for benchmarking optimum exploitation ability of an algorithm; similarly, the multimodal functions are suitable for benchmarking the ability of optimum exploration of an algorithm. These benchmark functions are listed in Table 1 where F1-F6 are unimodal functions, F7-F12 are multimodal functions, and F13-F23 are fixed-dimension multimodal benchmark functions. Moreover, the 2-D versions of these 23 benchmark functions are presented in Figure 3 for a better analysis of the form and search space. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Dim 1** & **Range 2** & \(\mathbf{f_{min}}\)3 \\ \hline \(\mathrm{F_{1}}(\mathrm{x})=\sum_{i=1}^{n}\mathrm{x}_{i}^{2}\) & 30 & \([-100,100]\) & 0 \\ \(\mathrm{F_{2}}(\mathrm{x})=\sum_{i=1}^{n}\mathrm{|x_{i}|}+\prod_{i=1}^{n}\mathrm{ |x_{i}|}\) & 30 & \([-10,10]\) & 0 \\ \(\mathrm{F_{3}}(\mathrm{x})=\sum_{i=1}^{n}\mathrm{(\sum_{j=1}^{n}\mathrm{x}_{j} )}^{2}\) & 30 & \([-100,100]\) & 0 \\ \(\mathrm{F_{4}}(\mathrm{x})=\mathrm{max}_{i}\{|x_{i}|,1\leq i\leq n\}\) & 30 & \([-100,100]\) & 0 \\ \(\mathrm{F_{5}}(\mathrm{x})=\sum_{i=1}^{n}\mathrm{x}_{i}^{4}+\mathrm{random}[0, 1)\) & 30 & \([-1.28,1.28]\) & 0 \\ \(\mathrm{F_{6}}(\mathrm{x})=\sum_{i=1}^{n}\mathrm{(\,|x_{i}+0.5\,]\,}^{2}\) & 30 & \([-100,100]\) & 0 \\ \(\mathrm{F_{7}}(\mathrm{x})=\sum_{i=1}^{n}\mathrm{|x_{i}\sin\left(x_{i}\right)+ 0.1x_{i}|}\) & 30 & \([-30,30]\) & 0 \\ \(\mathrm{F_{8}}(\mathrm{x})=\sum_{i=1}^{n}\mathrm{|x_{i}^{2}-10\cos\left(2\pi \mathrm{x_{i}}\right)+10|}\) & 30 & \([-5.12,5.12]\) & 0 \\ \(\mathrm{F_{9}}(\mathrm{x})=-20\exp\left(-0.2\sqrt{\frac{1}{n}\sum_{i=1}^{n}x_{ i}^{2}}\right)-\exp\left(\frac{1}{n}\sum_{i=1}^{n}\cos\left(2\pi x_{i} \right)\right)+20+e\) & 30 & \([-32,32]\) & 0 \\ \(\mathrm{F_{10}}(\mathrm{x})=\frac{1}{4000}\sum_{i=1}^{n}x_{i}^{2}-\Pi_{1}^{ \mathrm{x}}\cos\left(\frac{x_{i}}{i! }\right)+1\) & 30 & \([-600,600]\) & 0 \\ \(\mathrm{F_{11}}(\mathrm{x})=\frac{\frac{\pi}{n}\{10\sin\left(\pi y_{1}\right) +\sum_{i=1}^{n}\left(y_{i}-1\right)^{2}[1+10 sin^{2}(\pi y_{i+1})]\,+\,\,(y_{n} -}}\) & & \\ \(1)^{2}\}+\sum_{i=1}^{n}u(x_{i},10,100,4)\) & & & \\ \(y_{i}=1+\frac{x_{i}+1}{4}\) & 30 & \([-50,50]\) & 0 \\ \(u(x_{i},a,k,m)=\left\{ \begin{array}{l}k(x_{i}-a)^{m};x_{i}>a\\ 0;-a<x_{i}<a\\ k(-x_{i}-a)^{m};x_{i}<a\end{array}
# Modeling and Multi-Objective Optimization Design of High-Speed on/off Valve System
## 3 Construction of the BPNN Model
of the network output is required. These processes facilitate the faster convergence of the network and help mitigate numerical issues. \[RMSE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}{{{{\left({{y_{i}}-{{\hat{y}}_{i}}} \right)}^{2}}}}} \tag{12}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{N}{{{{\left({{y_{i}}-{y_{i}}}\right)}^{2}}}}}{ \sum\limits_{i=1}^{N}{{{{\left({{y_{i}}-{{\overline{y}}_{i}}}\right)}^{2}}}}} \tag{13}\]  
The BPNN was trained and tested separately with various sample sizes, maintaining a training set to test set ratio of 7:3. The performance of the BPNN model in testing flow and current is illustrated in Figure 10. It is evident that as the sample size increases, there is a decreasing trend in the overall RMSE. At a sample size of 3000, the RMSE values for flow and current testing with the BPNN model are 0.181 and 0.223, respectively. These outcomes signify that the predictive performance of the BPNN model for flow and current improves with an increase in the sample size, thus effectively improving the effectiveness of the model fitting results. A total of 2100 samples were selected as the training set, while 900 samples were designated for the purpose of testing the BPNN model. Figure 11 provides a visual representation of the fitting between predicted values and target values, accompanied by their respective R\({}^{2}\) values. It is evident from the figure that the BPNN model exhibits a strong linear fit for both the current (a) and flow (b) tests, with R\({}^{2}\) values approaching 1. Based on the analysis conducted, it can be concluded that the established BPNN model demonstrates a notable level of prediction accuracy, effectively capturing the nonlinear relationship between the structural parameters and performance of the HSV. Figure 10: Train the BPNN model with different sample sizes and test its performance on Gain and NF.
# Stochastic Natural Vibration Analyses of Free-Form Shells
## 4 Results and Discussion  
As presented above, three main stochastic factors that may occur on shells are explored, including geometric imperfection of shells, shell thickness deviation, and elastic modulus deviation. The distribution of initial geometric imperfection throughout the shell is determined by the consistent mode imperfection method [24], while the shell thickness deviation and elastic modulus deviation are assumed to be uniformly distributed over the shell. Since each of uncertainty error sources is determined by several independent factors, they are assumed to follow normal distribution [25]. Referring to the existing work and code [26; 27], their respective mean values and standard deviations are determined, as shown in Table 1, where initial geometric imperfection is described by the maximum height deviation of the shell; the thickness and elastic modulus deviation is represented by the relative error between actual and design values, with \"+\" and \"\(-\)\" meaning the actual value larger and smaller than the design value, respectively.  
The presented stochastic analysis method is implemented on two widely used free-form shells. One is a free-form shell with negative Gaussian curvature and the other one is a combination of two positive-Gaussian domes. Both of the models are generated by the optimization approach, which is presented in Section 2. The natural vibration analysis is implement by ANSYS  
\begin{table}
\begin{tabular}{c c c c} \hline
**Uncertainty Factors** & **Value Ranges (Guarantee Rate)** & **Mean Values** & **Standard Deviation** \\ \hline Initial geometric imperfection & \(-100\) mm \(\sim\) + \(100\) mm (\(95.0\%\)) & \(0\) & \(51.0\) mm \\ Thickness deviation & \(-3.57\%\)\(\sim\) + \(5.71\%\) (\(95.0\%\)) & \(1.07\%\) & \(0.02369\) \\ Elastic modulus deviation & \(-15.00\%\)\(\sim\) + \(15.00\%\) (\(99.6\%\)) & \(0\) & \(0.05000\) \\ \hline \end{tabular}
\end{table}
Table 1: Stochastic models of uncertainties.
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 4 Experiments and Results Analysis
### Different Numbers of Learning Clients on Training Accuracy and Time Overhead  
PFMLP can support multi-party machine learning. In addition, theoretically, as the number of clients increases, the learning algorithm should guarantee similar accuracy and even shorter time overhead during model training. Here, we design a comparative experiment with single node (MLP), two clients (2-Client-PFMLP) and four clients (4-Client-PFMLP) on the metal fatigue strength dataset. In addition, the experimental results are shown in Table 8. Here, the local accuracy is the accuracy rate of the model's prediction accuracy on the local test data set. The logical accuracy is the average accuracy on each client. The detailed results are shown in Figure 12.  
Figure 11: Per-iteration time spent on fatigue dataset for different sizes of hidden layers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Dataset** & **Local Accuracy** & **Logical Accuracy** \\ \hline MLP & \(D_{0-400}^{Fatigue^{\prime}}\) & 0.858 & - \\ MLP & \(D_{0-200}^{Fatigue^{\prime}}\) & 0.833 & - \\ MLP & \(D_{200-400}^{Fatigue^{\prime}}\) & 0.783 & - \\
2-Client-PFMLP & \(D_{0-200}^{Fatigue^{\prime}}\) & 0.867 & 0.850 \\
2-Client-PFMLP & \(D_{200-400}^{Fatigue^{\prime}}\) & 0.833 & 0.850 \\ \hline MLP & \(D_{0-100}^{Fatigue^{\prime}}\) & 0.767 & - \\ MLP & \(D_{0-200}^{Fatigue^{\prime}}\) & 0.933 & - \\ MLP & \(D_{200-300}^{Fatigue^{\prime}}\) & 0.800 & - \\ MLP & \(D_{300-400}^{Fatigue^{\prime}}\) & 0.600 & - \\
4-Client-PFMLP & \(D_{0-100}^{Fatigue^{\prime}}\) & 0.833 & 0.850 \\
4-Client-PFMLP & \(D_{100-200}^{Fatigue^{\prime}}\) & 0.967 & 0.850 \\
4-Client-PFMLP & \(D_{200-300}^{Fatigue^{\prime}}\) & 0.867 & 0.850 \\
4-Client-PFMLP & \(D_{300-400}^{Fatigue^{\prime}}\) & 0.733 & 0.850 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The accuracy of PFMLP with different learning clients on the metal fatigue dataset.  
From Figure 12, the multi-client-PFMLP algorithm has significantly improved the prediction accuracy. The logical accuracy rate is almost the same for two and four learning clients. Compared with the local training of the divided data, the local accuracy of PFMLP training has been improved. Especially in extreme cases, the accuracy is amplified. For example, in a 4-Client-PFMLP experiment, the last learning client obviously has outlier data, and the accuracy of PFMLP is 13.3% higher than that of MLP. The second learning client has a local accuracy rate up to 93.3%, and using PFMLP still improves by 3.4%.  
In addition, it can be seen from Table 8 that the number of clients has almost no effect on the performance of the model trained by PFMLP. They are all close to the performance of the model trained by a single MLP that collects data from all participants. Meanwhile, since the core idea of N-Client-PFMLP is based on batch expansion, once each client has less data, they will learn a smaller size of batch per round. Thus, the time overhead of the training process will be reduced.
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 3 Proposed Scheme
### Listless SPIHT compression algorithm  
SPIHT is the benchmark algorithm for compression of still images. It makes use of ordered auxiliary lists to encode significant information in a bit plane manner. The movement of list nodes, addition of new nodes to the list and deletion of a node from the list while processing the ordered bit planes in SPIHT causes a lot of memory management problems. To overcome this listless variant of SPIHT, called NLS is proposed in [36]. NLS reduces memory management problem by using state table markers for encoding coefficients. Further, NLS need less dynamic memory storage for the image. The proposed watermark algorithm makes use of modified NLS for compressing the image form lossy to lossless. Precise rate control can be achieved while compression is lossy. The modified NLS is similar to NLS except that the refinement pass is executed prior to sorting pass. This ensures better compression performance in modified NLS.
## 4 Discussion
### Limitations Related to Measurement Methods  
The ability to extract objective measures through technological devices has led to a reevaluation of traditional assessment methods like observational techniques and parental reports, which are thought to have limitations like the lack of precision needed for in-depth analysis [36; 37]. As in this systematic review, where most of the articles used accelerometers (three articles used pedometers and two heart-rate monitors; the remaining article used accelerometers), the PA data extracted from microelectromechanical devices such as accelerometers has experienced an exponential growth [61; 62].  
However, the measures through accelerometers should be considered with caution since different issues, such as sampling frequency [63; 64] or the used algorithm [64] can affect the extracted data, and most of the articles do not provide the most important information. In fact, the validity and reliability of accelerometers could change in different circumstances, making validation difficult to extrapolate if the environment changes from the validation study to the place where the data will be extracted. In this way, the authors encouraged the use of previously published surveys [64] to warrant the inclusion of relevant information when using technological devices.
# On the Reliability Assessment of Artificial Neural Networks Running on AI-Oriented MPSoCs
## 5 Experimental Results
### Ranking of the Criticality of Single Neurons
#### 5.1.3 Final Network-Oriented Score Map
Specifically, a fixed percentage of critical neurons was set to zero in three scenarios: the proposed methodology (CoA + NoA), the proposed methodology without the CoA, and the Taylor-based [48]. Then, the accuracy of the neural network over the entire test set was computed. Specifically, we removed 2%, 5%, 10%, 20%, 50%, and 70% of critical neurons from the respective ordered network-oriented lists. For the purpose, two different network-oriented score maps were created following our proposed approach, each one with a growing set of critical neurons (\(t=10\%\), \(t=20\%\)). The aim was to demonstrate that with a growing \(t\), we obtained a more robust network-oriented score map. Figure 8 shows the results of our FI simulations with the dropout model for the MNIST, SVHN, and CIFAR-10 CNNs. Moreover, its effectiveness is compared against [48] (green line) and our proposed methodology without the contribution of the class-oriented analysis (red line). As it turns out, the accuracy that the CNN under assessment achieves is always lower when removing the same percentage of critical neurons from our network-oriented score map. It means that, first, the ordering of the critical neurons greatly affects the reliability of the system; second, our final score map holds (in the highest positions) neurons that are critical not only to the entire neural network, but also to individual output classes. Finally, the time required to perform the process described in [48] is \(3\times\), \(4.1\times\), and \(4.7\times\) larger than the proposed one for the custom LeNet-5, ConvNet, and All-CNN, respectively.
## 2 Synchronization and Reliability Provided by Power Supply System Design  
### Sources of Power Supply  
To ensure reliability, similar to the subway system, the MCS is supplied with power with voltage levels of 22 kV from the bulk supply substation (BSS), receiving two high-grid-voltage sources of 110 kV and 220 kV [12; 13; 14] in alternating current (AC) at a frequency of 50 HZ, synchronized with the existing Line 1 and Line 2 of the HCMC Metro. The 22 kV of power will be distributed along the alignment through the main cable ring network for feeding traction and auxiliary loads. Keeping in view the reliability requirements, two bulk supply substations (BSSs) are proposed to be set up for the line [24; 25; 26; 27; 28]. The plan includes one BSS located near the Binh Trieu maintenance depot in Phase 1, and the remaining BSS located near the end station of the PVD line at Gia Dinh Park in Phase 2. In Phase 1, the route will be 8.07 km long from the Linh Xuan intersection to Binh Trieu station, including a BSS with five traction substations along the road and one at the depot. The 22 kV internal power distribution system (PDS) will be distributed along the alignment through the 22 kV ring main cable network for feeding traction and auxiliary loads [44].  
Train operation requires converting 22 kV power to 750 V DC at the traction substations, which then supply the power lines along the railway tracks. In addition, the 22 kV AC power is simultaneously reduced to 400 V AC in three phases and supplied to the equipment for communication, signaling, and other uses by an uninterruptible power supply. Two substations supply enough electricity to power the entire system, and either substation can do so [14; 44].
## 1 Introduction
Currently, the pile diameters of actual sea-crossing bridge foundations are changing greatly, increasing from less than 2 m to more than 6 m, and meanwhile, the construction conditions are shifting from inshorne river estuaries with shallow water and relatively moderate waves and currents to fjord waterways with deeper water, high waves, and rapid currents, thereby making the marine-environment challenges for construction more complex and severe. Therefore, there is an urgent need for further research into wave-current loads and their development patterns forsuper-large-diameter piles in the deep-water foundations of modern sea-crossing bridges under the combined action of strong waves and currents. The Xihoumen Strait is a well-known area of rapids in China, with a 100-year return period and a current rate of 3.3 m/s. Currently under construction, the Xihoumen Rail-cum-Road Bridge uses 18 piles with a super-large diameter of 6.3 m for the foundation of its main tower, built on the seabed composed of bedrock with a water depth of 60 m, as shown in Figure 1. Globally, this pile diameter and construction water depth are the largest to date in the construction of pile foundations for sea bridges; they are more than twice those for the foundations of the existing Hangzhou Bay Bridge and East Sea Bridge in China, and they exceed the 4.5-m pile diameter and 45-m construction water depth of the main pier of the Pingtan Straits Rail-cum-Road Bridge. The aims of this study are to (i) give insights into the characteristics of wave-current loads on super-large-diameter piles in deep water and under strong waves and currents and (ii) explore a practical method for calculating such loads. Taking one of the 6.3-m super-large-diameter piles of the Xihoumen Rail-cum-Road Bridge as the research object, this paper begins by considering the variation laws and influencing factors for the force on the pile under the actions of regular waves either alone or combined with a uniform current in the same direction as determined via flume model experiments. Then, based on the modified Morison formula and linear wave theory, the hydrodynamic coefficients \(C_{D}\), and \(C_{M}\) of the super-large-diameter pile under wave-current actions are derived and analyzed using the least-squares method. In addition, the lift coefficient \(C_{L}\) of the super-large-diameter pile under wave-current actions is derived and analyzed based on the Kutta-Joukowski formula. Finally, how the transverse force influences the peak value and direction of the resultant force is analyzed.