esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experiment  
### Dynamic Simulation Workflow  
To achieve precise simulation of the dynamic behavior of the PTL and FPTLIR, a modular simulation was employed, dividing the simulation process into two main modules, where the red represented the PTL module, and the blue represented the FPTLIR module (Figure 8). First, during the initialization phase, global simulation parameters (e.g., total simulation time and time step size) were defined, the generalized coordinates and initial conditions for both the PTL and FPTLIR systems were set, and geometric and physical models that met actual operational requirements were constructed. Next, the mass matrices of the PTL and FPTLIR were computed in parallel. The mass matrix of the PTL remained constant, while that of the FPTLIR dynamically updated according to its varying attitudes. Following this, the internal and external generalized forces of the PTL and FPTLIR systems were computed in parallel. The internal forces of the PTL included elastic and damping forces, while the external forces consisted of gravity and contact forces. The external forces acting on the FPTLIR included contact forces and control forces, which were updated ateach iterative step. The green-colored contact model dynamically updated contact forces in real time based on the arc coordinates of the PTL's contact points and the generalized coordinates of the FPTLIR. These forces were computed in independent modules and integrated into the system's equations of motion. Subsequently, the equations of motion module combined the mass matrix and generalized forces to form the system's dynamic equations (Equations (60) and (61)), which were solved separately using the generalized-\(\alpha\) method. This method optimized energy dissipation characteristics by adjusting parameters, ensuring high accuracy and numerical stability during temporal integration. Based on the generalized-\(\alpha\) method, the generalized velocity and displacement at the current time step were calculated, the system state was updated, and the simulation time step was dynamically adjusted during periods of rapid change to enhance efficiency and accuracy. Throughout the simulation process, system response indicators (e.g., displacement and force) were continuously evaluated, and the simulation was terminated based on predefined conditions. The results were then output for further analysis.
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 2 Methods
### Statistical Analysis  
The survey results were initially analyzed using descriptive statistical methods to quantify perceived physical exertion (PPE) and effectiveness of the exoskeleton. Further analysis included both one-way and two-way Analysis of Variance (ANOVA) to examine the effects of various factors on PPE. ANOVA is an inferential statistical method that was used to assess differences among groups and conditions, providing a robust understanding of the exoskeleton's impact on worker ergonomics.  
One-way ANOVA is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups. The judgment criteria for the effectiveness of a factor on an output is the F-ratio, which is used to test the hypothesis of equal means. A significant F-ratio suggests that at least one group mean significantly differs from the others.  
Two-way ANOVA is used to investigate the interaction effects between two independent variables on a dependent variable. It extends one-way ANOVA by adding another factor and the interaction term. The mean square for the interaction is used to find the F-ratio for the interaction. A significant F-ratio for the interaction indicates that the effects of one factor depend on the level of the other factor.  
ANOVA results are interpreted using the F-ratio and its corresponding \(p\)-values. A significant \(p\)-value (typically \(<\)0.05) suggests that the null hypothesis of equal means, as a results of a factor effect, can be rejected, indicating statistically significant differences between groups of interaction effects. Post-hoc tests such as Tukey's HSD are used following significant ANOVA results to pinpoint where the difference lies among group means.  
In this study, the factors were \"task\", with the three levels of walking, lifting a box, and carrying a box, and \"support\" that referred to the participants' wearing or not the BExo as a support, and therefore, had the two levels of exo and no exo. The other factor considered was the effect of the BExo on reducing PPE on various body parts; we considered ten body parts, i.e., neck, shoulder, upper back, chest, wrist, lower back, knee, ankle, feet, elbow (10 levels). The total factor combination for preparing data for ANOVA analysis was Task (3 levels) \(\times\) Support (2 levels) \(\times\) Body part (10 levels) leading to 60 total variations. The output to be analyzed for the ANOVA models was PPE, which represented the sum of perceived physical exertion per combination for all participants. This comprehensive approach allowed for detailed understanding and analysis of the effects of different factors like task, support, and body part on perceived physical exertion.
## 4 Results and Discussion
### Mechanical Analysis  
This study aimed to investigate the material locking effect between the staple pins. All the staples were inserted vertically to the printed layers, as shown in Figure 5. The flexural and compressive loads were conducted vertically to the printed layers. When a stapled beam was under a bending load (such as a three-point bending test), the inner composite with a staple was under a pulling operation, as shown in Figure 16a. To analyze the mechanism of the staple reinforcement, a simplified model was built, as shown in Figure 16b. There are gaps around the staple pins at positions **a** and **a**/. Line **b** represents the strip interface. This structure is under a pulling operation. The symbols \(f_{a}\), \(f_{b}\), \(f_{c}\), \(f_{e}\), and \(f_{f}\) represent the maximum failing stresses under pulling loads at positions **a**, b, c, e, and **f**, respectively; \(f_{a}\) is determined by the composite strength at positions **a** and **a**/ around the staple pins when the pins are pulled out of the composite; \(f_{b}\) represents the interface bonding stress on the connected strips; \(f_{c}\) is obtained when the staple body fails under the pulling operation; and \(f_{e}\) represents the failure of point e, where the staple pin is a defect of the cement. In addition, \(f_{f}\) corresponds to pure composite failure.  
If \(f_{b}=0\) and \(f_{e}=f_{f}\), the failure stress of this structure under pulling operation is affected by \(f_{a}\) and \(f_{c}\), which are denoted as \(f_{ac}\). If \(f_{a}=0\) and \(f_{e}=f_{f}\), \(f_{b}\) is the failure stress of the interface bonding. If \(f_{a}\
eq 0\), \(f_{b}\
eq 0\), and \(f_{e}=f_{f}\), the maximum pulling stress is \(f_{abc}\). Thus, the staple improvement ratio \(r\) of the 3 DCP-SIT can be calculated using Equation (1):  
\[r=\frac{f_{abc}-f_{b}}{f_{b}}\times 100\% \tag{1}\]  
A low \(f_{b}\) can significantly increase the mechanical improvement value, which is proved by the maximum bending stress improvement of 120% from IL-2 to IL-3, where the interfaces were poorly fabricated by slowing the material extrusion speed compared to IL-0/1. The interfacial bonding could be affected by the printable material proportions and printing parameters [51]. The faster the setting of the material, or the longer the time interval [52], the lower was the bonding of the interface, that is, a low \(f_{b}\).
# Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler
## 2 Materials and Methods
### Problem Statement
However, since the burden on the controller is increased and the E2E latency is only obtained when the packet reaches its destination, SARL also uses estimated delay. Estimated delay is calculated at every step at every node. The estimated delay reflects the queuing delay, so it either maintains or increases as the step increases. Since the estimated delay information changes steadily at every step, the RL agent uses the estimated delay information together with the queue information.
## 2 Signal Model and Related Work
It is observed from (3) that the correlation peak might be deteriorated by the unpredictable strong NBI signal, and hence cause degradation to the detection performance. In order to solve the problems of the conventional methods, the preamble is specifically designed and optimized in this paper, along with the proposed synchronization method to improve the synchronization performance against the NBI impacts, as described in detail in the following.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 2 Materials and Methods
### Channel Domain Transfer Learning
#### 2.2.2 Construction of Metric Criteria  
For any defect image xi in the target domain (\(1\leq i\leq n\)), it is input into the generator of the source domain network. After undergoing computation by the \(j\)-th convolution kernel of the \(k\)-th convolutional layer, it produces a feature map \(F^{jk}\). Subsequently, \(xi\) is input into the generator of the target domain network, and after undergoing computation by the \(j\)-th convolution kernel of the k-th convolutional layer, it yields a feature map \(F^{\prime/jk}\). The consistency in feature distribution between the source and target domains is quantified using Equation (8).  
\[L_{\text{C}}=\sum_{k=1}^{M}\sum_{j=1}^{N}\left(w_{j}\cdot||F^{jk}-F^{\prime/jk }||_{2}^{2}\right) \tag{8}\]  
In this context, \(w_{j}\) represents the weight allocated to the \(j\)-th convolution kernel, \(N\) denotes the total number of convolution kernels, and \(M\) represents the total number of convolutional layers. \(\|F^{jk}-F^{\prime/jk}\|_{2}^{2}\) signifies the Euclidean distance between corresponding features of the source and target networks.  
From Equation (8), it can be observed that when the features in the source and target domains are dissimilar, despite having a large Euclidean distance between features, the channel weights corresponding to dissimilar features are small. Channel weights effectively mitigate the influence of dissimilar features on the measure of feature distribution consistency. When the features in the source and target domains are similar, although the channel weights corresponding to similar features are relatively large, the Euclidean distance between similar features is very small, resulting in a minimal value for the measure of feature distribution consistency. Therefore, Equation (8) is applicable for assessing feature distribution consistency, ensuring convergence, and facilitating the transfer of similar features between the source and target domains, thereby reducing the impact of a small sample size on defect image reconstruction accuracy.  
However, the measure of feature distribution consistency, \(L_{\text{c}}\), is still influenced by dissimilar features. To further enhance defect image reconstruction accuracy, Equations (1) and (8) are both incorporated as the loss function of the target domain network, as illustrated in Equation (9). Equation (9) not only accounts for the transfer learning of similarfeatures but also considers the impact of the generator's output error on reconstruction accuracy, ultimately improving defect reconstruction precision.  
\[\min(G^{*}+\alpha L_{C}) \tag{9}\]  
In the equation, the recommended value for \(\alpha\) is 0.1.
# Key Technologies for 6G-Enabled Smart Sustainable City
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & Fifth Generation \\
6G & Sixth Generation \\ AI & Artificial Intelligence \\ AR & Augmented Reality \\ CS & Compressed Sensing \\ DL & Deep Learning \\ eMBB & Enhanced Mobile Broadband \\ GAN & Generative Adversarial Network \\ GEO & Geostationary Orbit \\ HAPS & High-Altitude Platform Station \\ HRLLC & Hyper-Reliable and Low-Latency Communication \\ ICT & Information and Communication Technology \\ IoE & Internet of Everything \\ IoT & Internet of Things \\ IMT & International Mobile Telecommunications \\ ISAC & Integrated Sensing and Communication \\ ISL & Inter-Satellite Link \\ ITU-T & International Telecommunication Union Telecommunication standardization sector \\ JTC & Joint Technical Committee \\ KPI & Key Performance Indicator \\ LEO & Low Earth Orbit \\ LOS & Line of Sight \\ LSTM & Long Short-Term Memory \\ MEO & Medium Earth orbit \\ MIMO & Multiple-Input Multiple-Output \\ mMTC & Massive Machine-Type Communications \\ mmWave & Millimeter Wave \\ NOMA & Non-Orthogonal Multiple Access \\ NTN & Non-Terrestrial Networks \\ QoS & Quality of Service \\ RIS & Reconfigurable Intelligent Surface \\ RNN & Recurrent Neural Network \\ RSMA & Rate-Splitting Multiple Access \\ RTD & Round-Trip Delay \\ SCP & Satellite Channel Predictor \\ SDG & Sustainable Development Goals \\ SSC & Smart Sustainable City \\ SSC-MM & SSC Maturity Model \\ TC & Technical Committee \\ THz & Terahertz \\ TN & Terrestrial Network \\ UAM & Urban Air Mobility \\ UE & User Equipment \\ URLLC & Ultra-Reliable and Low Latency Communication \\ VAWC & Vision-aided Wireless Communication \\ VR & Virtual Reality \\ WG & Working Group \\ XR & Extended Reality \\ \end{tabular}
# A Novel Virtual Sensor for Estimating Robot Joint Total Friction Based on Total Momentum
## 3 Simulation and Analysis
### Sinusoidal Trajectory Tracking Experiment  
#### 4.2.1 Motor Low-Velocity Experiment  
The desired low-velocity sinusoidal position trajectory was 16 \(\pi\)nsin (0.8\(\pi\)t), and its first derivative was the desired velocity. Using the observer described in Section 2.1 and setting \(K_{\mu}\) to 50, the observed friction in the single-joint system is shown in Figure 11a. To observe the relationship between the actual motor velocity and estimated friction, we normalized these two variables in the range of \(-1\) to 1 and show them together in Figure 11b.  
The trend of the observed friction torque is always the opposite of that of the actual motor velocity. Furthermore, the observed friction torque abruptly changes when the actual motor velocity reaches zero. This phenomenon is consistent with the actual friction characteristics. In addition, it should be explained here that all the initial peak values and errors occurring from 0 s to 0.1 s in the figures below are caused by dynamic tracking of the controller, and it is more reasonable to only consider the static errors after 0.1 s. After compensation of the observed friction torque, the RMSE of motor trajectory tracking static errors were obtained and are listed in Table 5 and shown in Figure 11c,d. The figures and table suggest that the position tracking performance of the controller with observed friction compensation did not obviously improve compared to the case without observed friction compensation. Specifically, the RMSE of static position tracking error decreased by 0.1% in Figure 11c. However, the velocity tracking performance of the controller with observed friction compensation considerably improved compared to the case without observed friction compensation, and the RMSE of static velocity tracking error decreased by 33.1%, as shown in Figure 11d.  
Notably, friction disturbance torque acts inside the velocity loop, which results in friction disturbance torque having more effects on the performance of the velocity loop than that of the position loop. When friction disturbance torque is not compensated for by the controller, velocity errors will increase obviously because of the disturbance. However, the position loop is built on the velocity loop and position errors can be suppressed well by the position loop gain coefficient after velocity loop adjusting. After compensating for the observed friction torque, the velocity errors will be suppressed well. Then, the position loop can adjust the position errors more easily and position errors almost do not change compared with the no compensation condition. Therefore, the controller position tracking  
Figure 10: Observed total friction torque mapped to the motor side at high velocities.  
performance was not significantly improved; however, the controller velocity tracking performance was notably improved after observed friction torque compensation.
###### Abstract  
The presence of optimized distributed generation (DG) with suitable distribution network reconfiguration (DNR) in the electrical distribution network has an advantage for voltage support, power losses reduction, deferment of new transmission line and distribution structure and system stability improvement. However, installation of a DG unit at non-optimal size with non-optimal DNR may lead to higher power losses, power quality problem, voltage instability and incremental of operational cost. Thus, an appropriate DG and DNR planning are essential and are considered as an objective of this research. An effective heuristic optimization technique named as improved evolutionary particle swarm optimization (IEPSO) is proposed in this research. The objective function is formulated to minimize the total power losses (TPL) and to improve the voltage stability index (VSI). The voltage stability index is determined for three load demand levels namely light load, nominal load, and heavy load with proper optimal DNR and DG sizing. The performance of the proposed technique is compared with other optimization techniques, namely particle swarm optimization (PSO) and iteration particle swarm optimization (IPSO). Four case studies on IEEE 33-bus and IEEE 69-bus distribution systems have been conducted to validate the effectiveness of the proposed IEPSO. The optimization results show that, the best achievement is done by IEPSO technique with power losses reduction up to 79.26%, and 58.41% improvement in the voltage stability index. Moreover, IEPSO has the fastest computational time for all load conditions as compared to other algorithms.  
distribution network reconfiguration; distributed generation; improved evolutionary particle swarm optimization; voltage stability index 2018 804 doi:10.3390/app8050804
## 2 Materials and Methods
### Methods
#### 2.3.2 Mann\(-\)Kendall Test  
The Mann\(-\)Kendall test, which shows the insensitivity to outliers, is a non-parametric statistical test. Theil\(-\)Sen trend analysis is often combined with Mann\(-\)Kendall testing, which has been increasingly used to investigate the variation trend and test the statistical significance of a long time series data [51,52]. The Mann\(-\)Kendall test was originally proposed by [PERSON] [53] and enhanced by [PERSON] and [PERSON] [54]. Moreover, the samples of the long time series data do not need to follow a normal distribution or a linear variation trend. The equation is as follows:  
\[Z=\begin{cases}\dfrac{S-1}{\sqrt{var(S)}},\ S\ >\ 1\\ \ \ \ \ \ \ \ \ \ \ \ \ 0,\ S=0\\ \dfrac{S+1}{\sqrt{var(S)}},\ S\ <\ 0\\ \end{cases} \tag{2}\]  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sign\big{(}\text{CI}_{j}-\text{CI}_{i}\big{)} \tag{3}\]  
Figure 2: Flowchart of this study.  
\[sign\big{(}\mathrm{CI}_{j}-\mathrm{CI}_{i}\big{)}=\left\{\begin{array}{c}1, \ \mathrm{CI}_{j}-\mathrm{CI}_{i}\ >\ 0\\ 0,\ \mathrm{CI}_{j}-\mathrm{CI}_{i}=0\\ -1,\ \mathrm{CI}_{j}-\mathrm{CI}_{i}\ <\ 0\end{array}\right. \tag{4}\]  
\[var(S)=\frac{n(n-1)(2n+5)-\sum_{i=1}^{m}t_{i}(t_{i}-1)(2t_{i}+5)}{18} \tag{5}\]  
where \(Z\) denotes the standard normal test statistic; \(S\) is the test statistic; \(var\) is the variance function; \(n\) is the length of the time series; \(sign\) denotes the symbolic function; \(\mathrm{CI}_{j}\) and \(\mathrm{CI}_{i}\) are the CI data in year \(j\) and \(i\), respectively; \(m\) refers to the datasets that repeat during the study period; and \(t_{i}\) refers to the datasets that recur in group \(i\). In this study, \(\alpha\) was defined as 0.05, and \(\ 1\,Z\,1>Z_{1-\alpha/2}\) indicates the significant variation trend of CI time series.  
Here, we used a combination of the above two methods to analyse the spatial and temporal variation characteristics of foliage CI in the Sanjiang Plain from 2001 to 2015 based on MATLAB software. Based on criteria in previous literature [55] and the actual features of the study area, areas with \(\beta<0\) and \(\ 1\,Z\,1>1.96\) were classified as significantly degraded areas, areas with \(\beta>0\) and \(\ 1\,Z\,1>1.96\) were classified as significantly improved areas, areas with \(\beta<0\) and \(\ 1\,Z\,1\leq 1.96\) were classified as lightly degraded areas, and areas with \(\beta>0\) and \(\ 1\,Z\,1\leq 1.96\) were classified as the lightly improved areas.
## 2 Preliminary
### Graph Convolution  
Here, we briefly review the notions and evolutions of graph convolution. The graph Fourier transform of a graph signal \(x\) is defined as \(\hat{x}=U^{T}x\), and the inverse graph Fourier transform is \(x=U\hat{x}\)[8]. According to the convolution theorem [8], the graph convolution operator represented as \(\ast_{G}\) is defined as  
\[x\ast_{G}f=U\Big{(}\big{(}U^{T}f\big{)}\odot\big{(}U^{T}x\big{)}\Big{)}, \tag{1}\]  
where \(f\) denotes the graph convolution kernel in the spatial domain and \(\odot\) is the element-wise Hadamard product.  
Spectral CNN [9] replaces \(U^{T}f\) by a diagonal matrix \(g_{\theta}\) so that the Hadamard product can be written as matrix multiplication,  
\[y=x\ast_{G}f=Ug_{\theta}U^{T}x, \tag{2}\]  
where \(L\) is the normalized graph Laplacian matrix and \(g_{\theta}=\text{diag}\big{(}\{\theta\}_{i=1}^{N}\big{)}\) defined in the spectral domain denotes the frequency response function of the graph filter \(g_{\theta}(L)\).  
However, there are two limitations with the above convolution kernel: (i) it does not have the spatial localization and (ii) it is computationally expensive [10]. To circumvent these issues, \(g_{\theta}\) is well-approximated by a Chebyshev polynomial in ChebyNet [10], which is defined as  
\[g_{\theta}(\Lambda)=\sum_{p=0}^{P-1}\theta_{p}\Lambda^{p}, \tag{3}\]  
where \(P\) is a hyper-parameter and \(\theta_{p}\in\mathbb{R}^{N}\) is the vector of polynomial coefficients. Correspondingly, the graph convolution of ChebyNet [10] is defined as  
\[y=Ug_{\theta}U^{T}x=U\!\!\left(\sum_{p=0}^{P-1}\theta_{p}\Lambda^{p}\right)U^ {T}x=\sum_{p=0}^{P-1}\theta_{p}L^{p}x. \tag{4}\]  
GCN [3] only considers the first-order polynomial by setting \(P=2\) and \(\theta=\theta_{0}=-\theta_{1}\); thus, the simplified graph convolution is defined as  
\[y=Ug_{\theta}U^{T}x=\theta U(I-\Lambda)U^{T}x=\theta(I-L)x, \tag{5}\]  
where GCN [3] constrains the number of parameters further to address overfitting and accelerate computation.  
To strengthen the low-pass performance of the graph filter and improve graph smoothness, AGC [7] modifies the frequency response function of GCN [3] as  
\[y=Ug_{\theta}U^{T}x=\theta U\!\!\left(I-\frac{1}{2}\Lambda\right)U^{T}x= \theta\Big{(}I-\frac{1}{2}L\Big{)}x. \tag{6}\]