esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Strengths and Limitations  
A systematic review examines the impact of kinesiophobia, defined as the fear of movement, on PA and quality of life in individuals with chronic diseases, including cardiovascular diseases, cancer, diabetes, and chronic respiratory conditions. Carried out according to PRISMA guidelines, the review includes six major databases such as PubMed and Scopus, resulting in the inclusion of 81 publications. The findings indicate the widespread prevalence of kinesiophobia in various chronic conditions, ranging from 42.85% to 93%, highlighting the need for further understanding of its implications. The strengths of the study lie in its methodological consistency and adherence to systematic review standards. It covers a wide range of chronic diseases and provides data on the prevalence of kinesiophobia using well-established diagnostic tools, such as the Tampa Scale of Kinesiophobia. Furthermore, the inclusion of innovative therapeutic insights, including unconventional interventions such as virtual reality, enriches the discussion on potential ways to alleviate kinesiophobia. However, the study also has several weaknesses. The heterogeneity observed in the included studies, reflected in the broad range of prevalence, suggests inconsistencies in methodologies, sample sizes, and diagnostic criteria. Additionally, disease-specific analyses remain limited, particularly for conditions such as diabetes, which are underrepresented in the review. While the review highlights several therapeutic strategies, there is a lack of standardized guidelines for managing kinesiophobia, and the effectiveness of some interventions appears inconsistent. The dominance of cross-sectional studies in the review also limits the ability to draw causal conclusions about the relationship between kinesiophobia, PA, and quality of life.
## 3 Results and Discussion
### Validation on Different Gas Turbine  
In this section, we selected one year of operational data from a 9 FB heavy-duty gas turbine at a power plant. The data were sampled at 1-h intervals, and after removing start-up and shutdown data, 6504 sampling points were obtained. These points were randomly split into 80% training set and 20% test set. Based on the training set, we established both basic and combined models to predict power output for the test set. The prediction results are shown in Figure 11.  
Figure 11a shows the comparison between predicted power from both basic and combined models against actual power values. The combined model demonstrates better alignment with actual power values at most data points. Figure 11b illustrates the distribution of absolute percentage errors, showing that the combined model reduces overall prediction errors and shifts more prediction points into lower error regions. Using common model evaluation metrics, the Mean Absolute Percentage Error (MAPE) for the basic model is 0.7341% while the combined model achieves 0.5079%, representing a 30.81% improvement. In terms of mean absolute error, the basic model shows 1.75 MW while the combined model achieves 1.23 MW, indicating a 29.98% improvement in accuracy. For the median absolute error, the basic model shows 1.51 MW while the combined model achieves 0.91 MW, representing a 39.39% improvement over the basic model. These results demonstrate that compared to using a single basic model, the combined model achieves higher prediction accuracy on this new turbine model, indicating the generalizability of the combined modeling approach.
# Big Data Perspective and Challenges in Next Generation Networks
## 2 Major Milestones of Next Generation Networks
### Ultra Dense Networks
#### 2.1.1 Base Station Densification Gain  
If there is base station density defined by \(\lambda 1\) and corresponding data rate \(R1\), and a higher base station density \(\lambda 2\) and corresponding data rate \(R2\), then the base station gain can be written as:  
\[\rho(\lambda 1,\lambda 2)=\frac{(R2-R1)/R1}{(\lambda 2-\lambda 1)/\lambda 1} \tag{1}\]  
_\"The densification gain is the slope of the rate increase over density range\"_. For example, if there is a network whose density is doubled and data rate is increased by 50 percent, then the densification gain, \(\rho\), will be 0.5. For cellular networks, the densification gain is generally assumed as \(\rho>0\)[2]. For mmWaves frequency, the densification gain \(\rho\gg 1\) might be possible. As for mmWave frequency, communication is largely limited by noise, so density increases but it may increase signal-to-interference noise ratio dramatically [17].
## 2 Route Generation Methodology
### Higher-Level Path Generation: Genetic Algorithm (GA)  
In the following approach, a higher-level path generation method was used to distribute the vehicles to scan several sub-areas. Each vehicle begins at a starting location (which can be different for each vehicle) and travels to a sub-area, scans the sub-area, and then either continues to the next sub-area or travels to the ending location.  
The higher-level path generation method is based on the GA with some minor modifications. Specifically, modifications to the crossover and mutation operators were made to meet the constraints of this study. The basic principles of the GA and the modifications to the crossover and mutation operators are explained in this section. The higher-level path generation algorithm was adapted from the authors' previous research [32].
## 5 Conclusions  
After having organized this special issue, we can draw several conclusions from the published papers:  
* Almost all the papers have highlighted the need of new technology for underwater communications because the range of applications in underwater environment is growing fast.
* Most researchers have point out the challenges and drawbacks when developing new technologies in this research field.
* The issues that should be taken into account in sensing systems are different depending on the size of the area to be monitored. It can be used one or few independent sensors to monitor a localized zone or a sensor network for large zone monitoring.
* The fact of including video cameras and the increase of the number of sensors placed inside the water has emphasized the need of higher bandwidth to transmit more data in the wireless channels.
* This Special Issue has received more papers focused about underwater architectures and protocols than the rest of the topics proposed in the call for papers.
* Although underwater sensor nodes and underwater sensor networks have been a hot topic since several years ago, it seems that this area will continue to be a hot topic for many more years to come due to the need for more technology in this field.
# Image-to-Image Translation Using Identical-Pair Adversarial Networks
## 4 Experiments and Results
### Experimental Results
However, iPANs w/ G&DP + P still cannot outperform our proposed architecture iPANs. According to the comparison results in Figure 7, our proposed model architecture is more beneficial while using U-NET and perceptual similarity network. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Edges \(\rightarrow\) Shoes**} \\ \hline  & **PSNR** & **SSIM** & **UQI** & **VIF** \\ \hline Pix2 pix & 15.74 & 0.42 & 0.07 & 0.05 \\ \hline PAN & 19.51 & 0.78 & 0.34 & 0.23 \\ \hline Ours & **21.01** & **0.88** & **0.94** & **0.24** \\ \hline \multicolumn{5}{c}{De-raining} \\ \hline  & **PSNR** & **SSIM** & **UQI** & **VIF** \\ \hline ID-cGAN & 22.91 & 0.81 & 0.64 & 0.38 \\ \hline PAN & 23.35 & 0.83 & 0.66 & 0.40 \\ \hline Ours & **31.59** & **0.89** & **0.78** & **0.51** \\ \hline \multicolumn{5}{c}{Aerial photos \(\rightarrow\) Maps} \\ \hline  & **PSNR** & **SSIM** & **UQI** & **VIF** \\ \hline Pix2 pix & 26.20 & 0.64 & 0.09 & 0.02 \\ \hline PAN & 28.32 & 0.75 & 0.33 & 0.16 \\ \hline Ours & **31.9** & **0.79** & **0.64** & **0.23** \\ \hline \multicolumn{5}{c}{Label\(\rightarrow\)Facade} \\ \hline  & **PSNR** & **SSIM** & **UQI** & **VIF** \\ \hline Pix2 Pix & 12.85 & 0.35 & 0.80 & **0.05** \\ \hline PAN & 12.42 & 0.31 & 0.77 & **0.05** \\ \hline Dual-GAN & 12.74 & **0.53** & 0.73 & **0.13** \\ \hline Ours & **14.08** & 0.45 & **0.86** & 0.14 \\ \hline \multicolumn{5}{c}{Night \(\rightarrow\) Day} \\ \hline  & **PSNR** & **SSIM** & **UQI** & **VIF** \\ \hline Pix2 Pix & 9.46 & 0.57 & 0.76 & **0.14** \\ \hline bicycleGANs & 17.33 & **0.64** & 0.60 & 0.12 \\ \hline Ours & **18.13** & 0.54 & **0.88** & **0.14** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with Existing Work.
## 2 Preliminaries and Problem Formulation  
### Symbol Notations and Graph Theory  
Let \(\mathbb{R}\), \(\mathbb{R}^{n}\) and \(\mathbb{R}^{n\times m}\) denote the set of real numbers, \(n\times 1\) real vectors, and \(n\times m\) real matrices, respectively. The diagonal matrix and identity matrix are denoted by \(\text{diag}(\cdot)\) and \(I;\mathbf{1}_{n}=[1,1,...,1]^{\top}\in\mathbb{R}^{n}\) is a vector with all elements are \(1\); \(\|a\|\) denotes the Euclidean norm for a vector \(a\in\mathbb{R}^{n}\); \(\text{card}(S)\) stands for the number of elements in the set \(S\).  
The follower and leader agents are presented by nodes in the set \(\mathcal{F}=\{1,...,N\}\) and \(\mathcal{W}=\{N+1,...,N+M\}\), respectively. The communication between these agents is described by a directed graph \(\mathcal{G}=\{\mathcal{V},\mathcal{E},\mathcal{A}\}\) whose node set is \(\mathcal{V}=\mathcal{F}\cup\mathcal{W}\). The edges in the set \(\mathcal{E}\subset\mathcal{V}\times\mathcal{V}\) represent the communication links between agents. The expression \(\mathcal{A}=[a_{ij}]\in\mathbb{R}^{(N+M)\times(N+M)}(i,j\in\mathcal{V})\) is defined as an adjacency matrix with elements \(a_{ij}=1\) if and only if the directed edge \((j,i)\in\mathcal{E}\), otherwise \(a_{ij}=0\). The in-degree matrix of a graph \(\mathcal{G}\) is defined by a diagonal matrix \(\mathcal{D}=diag(d_{1},...,d_{N+M})\) with diagonal entries \(d_{i}=\sum_{j=1}^{N+M}a_{ij}\). The neighbors of the \(i\)th agent are denoted by the set \(\mathcal{N}_{i}=\{j|(j,i)\in\mathcal{E}\}\). A directed path from node \(i\) to node \(j\) is defined as a sequence of consecutive edges which begin from node \(i\) and end in node \(j\). A directed graph is strongly connected if a path exists between any pair of nodes. The Laplacian matrix is defined as \(\mathcal{L}=\mathcal{D}-\mathcal{A}\). Due to the fact that leaders do not receive any information from other agents, the Laplacian matrix \(\mathcal{L}\) can be partitioned as the following block matrix [8]:  
\[\mathcal{L}=\left[\begin{array}{cc}\mathcal{L}_{1}&\mathcal{L}_{2}\\ \mathbf{0}_{M\times N}&\mathbf{0}_{M\times M}\end{array}\right] \tag{1}\]  
where \(\mathcal{L}_{1}\in\mathbb{R}^{N\times N}\) and \(\mathcal{L}_{2}\in\mathbb{R}^{N\times M}\).  
In order to give the readers a better understanding of this paper, the major notations are listed in Notations part.
## 2 Materials and Methods
### Method Validation
#### 2.5.1 Specificity  
Specificity assesses the capability of the chosen method to detect the analyte of interest in the presence of other substances or closely related compounds. In HPTLC analysis specificity is ensured through the complete separation of the peak(s) of the analyte(s) from other peaks as well as by their distinct retardation factors (R\({}_{\text{F}}\)). In this study, specificity focused on the adequate separation of the four investigated fat-soluble vitamins, D\({}_{2}\), D\({}_{3}\), E, and K\({}_{1}\).
## 4 Experiment Results and Discussion
### Moment-Strain Curves of the End-Plate Joint Component  
#### 4.2.1 Steel Tube Strain  
To determine the stress distribution law of the main limb steel tubes of the end-plate joint component, the bending moment-steel tube strain relationship curves are obtained (as shown in Figure 14). The label \"STS\" in the figure represents the monitoring item steel tube strain, \"1\" represents the longitudinal strain measuring point 1, and the rest are the same.  
It can be seen from Figure 14 that all measuring points in the outer main limb steel tubes show compressive strain, and the strain values of measuring points STS-1 and STS-4 are larger than others. When reaching the ultimate bearing state, the strain values of measuring points STS-1 and STS-4 are \(-2435\)\(\mu\)\(\epsilon\) and \(-2347\)\(\mu\)\(\epsilon\), respectively. The converted stress values are 501.6 MPa and 483.5 MPa, which exceed the yield strength of the steel tube. The internal main limb steel tubes measuring points STS-5-STS-9 all show tensile strain, and the change trend is basically the same. The maximum strain value of all measuring points ranges from 1357 \(\mu\)\(\epsilon\) to 1634 \(\mu\)\(\epsilon\), which does not exceed the yield strength of the steel tube.  
Figure 14: Bending moment–steel tube strain curves of the end-plate joint component.  
Figure 13: Comparison of bending moment–mid-span deflection displacement relationship curves.
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 3 Experimental Framework
### Dataset
Moreover, shorter time intervals, such as months or weeks, can be used instead of trimesters. The summary of the dataset is presented in Table 1. Recall that the training and testing split is done in such a way that prevents data leakage. That is, each city is assigned to a single set (either training or testing). Moreover, the testing set is manually inspected, invalidating areas where the labeling of OSM is not accurate. This allows us to make a fair evaluation of the models, considering only properly labeled areas. The remaining extension percentage after invalidation is also included for the testing set. The geographical distribution of the dataset within the Spanish territory is shown in Figure 6.  
Figure 6: Geographical distribution of the dataset (green training set/red testing set).
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 2 System Model and Problem Formulation
### Optimization of \(\{Q_{k}\}\) and \(\{\rho_{k}\}\) with Fixed \(\{P_{k}\}\)  
Once \(\{P_{k}^{*}\}\) obtained, we then could optimize \(\{Q_{k}\}\) and \(\{\rho_{k}\}\) in the next step. As aforementioned, to guarantee the convergence of MUD as well as to simplify the derivation, we initially set a relatively larger iteration number so that the assumption in (20) can hold. Such setup facilities the optimization of \(\{P_{k}^{*}\}\) but leads to unnecessary power cost during EH phase. Hence with \(\{P_{k}^{*}\}\) obtained in the previous step, we then track the actual iteration number that each RN needs to achieve its target SINR threshold based on (14). In other words, it is not required for the iterative MUD to be converged and the iteration would stop as long as the target SINR is reached. As \(f(\cdot)\) is obtained beforehand, such recursive tracking process is of low complexity. On the other hand, the optimal power splitting ratio can be linearly expressed as  
\[\rho_{k}^{*}=1-\frac{\Pi_{k}^{*}}{\xi|H_{k}|^{2}N\sum_{k=1}^{K}P_{k}^{*}}\, \forall k \tag{33}\]  
where \(\Pi_{k}^{*}\) is determined by the actual iteration number \(Q_{k}^{*}\) of each RN through tracking (14). The updated \(\{\rho_{k}^{*}\}\) and \(\{Q_{k}^{*}\}\) are fed back to the previous step to once again optimize \(\{P_{k}\}\). Note that the EH constraint should be updated by substituting the new parameters \(\{\rho_{k}^{*}\}\) and \(\{Q_{k}^{*}\}\) into (14), and \(P_{\text{EH}}^{*}\) should also be re-calculated accordingly. The above procedure is cyclicly repeated until \(P_{\text{SN}}\) could not be reduced. Through such iterative optimization process, \(\{P_{k}\}\), \(\{\rho_{k}^{*}\}\) and \(\{Q_{k}^{*}\}\) can be jointly optimized. The proposed sub-optimal scheme is concluded in Algorithm 1. We then discuss the impact of \(\{Q_{k}\}\) and \(\{\rho_{k}\}\) on the power allocation solution in our proposed algorithm. As aforementioned, the minimum transmit power of SN is bounded by the larger one among \(P_{\text{EH}}^{*}\) and \(P_{\text{ID}}^{*}\), which are separately calculated considering their respective requirement, as shown in Section 3.1. The gap between them, suggests the unnecessary power cost of either one has to pay to fulfill the need of another. Intuitively speaking, the optimal power allocation should be the one to minimize such a gap. Note that both \(P_{\text{EH}}^{*}\) and \(P_{\text{ID}}^{*}\) are obtained with respect to certain pre-defined \(\{Q_{k}\}\) and \(\{\rho_{k}\}\). Hence although the EH and ID requirements, i.e., \(\{\gamma_{k}\}\) and \(\{E_{c}\}\) are determined, there is still chance to narrow the gap by varying the value of \(\{\rho_{k}\}\) and \(\{Q_{k}\}\), where the former may help to re-balance the portion of each RN's power for the purpose of EH and ID, and the latter may help to renovate the EH requirement. ```
1:Set \(I=0\), \(P_{\text{SN}}^{0}=\infty\); \(\rho_{k}=\rho\), \(Q_{k}=Q\), \(\forall k\) ;
2:Input \(f(\cdot)\).
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System
### Derivation of the Motion Differential Equations
#### 3.3.7 Motion Differential Equation for the Carrier, \(h\)  
The components of the reactive force acting on the carrier, \(h\), from the planet gear, \(pi\), along the carrier coordinate axes is as follows:  
\[\left\{\begin{array}{l}F_{bxrpi}=k_{bxp}\dot{\sigma}_{xhpi}+c_{bxp}\dot{ \sigma}_{xhpi}\\ F_{yhpi}=k_{byp}\dot{\sigma}_{yhpi}+c_{byp}\dot{\sigma}_{yhpi}\\ F_{bzrpi}=k_{bzp}\dot{\sigma}_{zhpi}+c_{bxp}\dot{\sigma}_{zhpi}\\ M_{b\theta 2 rpi}=k_{b\theta 2r}\dot{\sigma}_{zhpi}+c_{b\theta 2 rpi}\dot{\sigma}_{zhpi} \end{array}\right., \tag{32}\]  
The support force of the bearing acting on the carrier, \(h\), is as follows:  
\[\left\{\begin{array}{l}F_{bxh}=c_{bxh}(\dot{x}_{h}-\omega_{h}y_{h})+k_{bxh} x_{h}\\ F_{yh}=c_{byh}(\dot{y}_{h}+\omega_{h}x_{h})+k_{byh}y_{h}\\ F_{zyh}=c_{zyh}\dot{z}_{h}+k_{zyh}z_{h}\\ M_{b\theta 2h}=c_{b\theta 2h}\dot{\theta}_{2h}+k_{b\theta 2h}\dot{\theta}_{2h}\end{array}\right., \tag{33}\]where \(k_{bxth}\), \(k_{byth}\), \(k_{b2h}\), and \(k_{b6 zh}\) represent the support stiffnesses of the bearing in the respective directions, and \(c_{bxth}\), \(c_{byth}\), \(c_{bzth}\), and \(c_{b6 zh}\) represent the support dampings of the bearing.  
The motion equation for the carrier, \(h\), is as follows:  
\[\left\{\begin{array}{l}m_{h}\big{(}\ddot{x}_{h}-2\omega_{h}\dot{y}_{h}-\omega _{h}^{2}x_{h}\big{)}+\sum\limits_{i=1}^{N}F_{bxtpi}+F_{bxh}=0\\ m_{h}\big{(}\ddot{y}_{h}+2\omega_{h}\dot{x}_{h}-\omega_{h}^{2}y_{h}\big{)}+ \sum\limits_{i=1}^{N}F_{bytpi}+F_{byh}=0\\ m_{h}\ddot{z}_{h}+\sum\limits_{i=1}^{N}F_{bzhtpi}+F_{bzh}=0\\ J_{h}\theta_{zh}+r_{h}\sum\limits_{i=1}^{N}(F_{bytpi}\cos\psi_{pi}-F_{bxtpi} \sin\psi_{pi})+M_{b6 zc}=T_{out2}\end{array}\right., \tag{34}\]  
Taking the TMS, transmission errors, bearing stiffness, and damping into account, the motion differential equations of each subsystem can be assembled to obtain the overall dynamic motion differential equations of the entire system.  
\[M\ddot{X}+\big{(}C_{b}+C_{m}\big{)}\dot{X}+\Big{(}K_{b}+K_{m}-\omega_{h}^{2}K_ {w}+\omega_{h}K_{d}\Big{)}X=F+T, \tag{35}\]
# On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers
## 6 Co-Simulation Platform  
The design for improved solutions of safety related features has been significantly eased thank to the usage of appropriate simulation platforms, enabling engineers to design, test and validate the control architectures through models in a singular platform, and therefore reducing the development cost and the time to market.  
Here, we propose a co-simulation platform for Model-In-the-Loop (MIL), where autonomous vehicle has been tested in a realistic traffic scenario. This co-simulation environment, represented in Figure 6, has been built leveraging the following four main components :  
* MATLAB/Simulink platform, a widely used framework to model dynamical systems and to design control architectures. Indeed, through an easy to use of its graphical interface, it is possible to develop controllers according to the well-known Model-based Control Design approach. The vehicle dynamics model, implemented in the MATLAB/Simulink environment and employed for the evaluation of the control logic performance in vehicle-following maneuvers, is an efficient 15 degrees of freedom lumped-parameter full vehicle model (LPFVM), described in [60] with a MF-based tire model [54]. The LPFVM is based on a set of Ordinary Differential Equations (ODEs) governing the dynamic equilibrium of the vehicle chassis, including three translational and three rotational equilibrium conditions, and of each wheel, comprising a translational equilibrium along the vertical direction and a rotational equilibrium around the spindle axis. Furthermore, the braking actuation is achieved through a standard hydraulic system made of a master cylinder, a reservoir, a pump and two valves for each wheels which are used to build braking pressure. Details on its model, and the related parameters, can be found in [61] and references therein.
* SUMO (Simulation of Urban MObility), an open-source road traffic simulation package, enabling the user to model entities such as vehicles, traffic lights, road networks, vehicle routing. Each entity is simulated microscopically, meaning that it is possible to control each of them singularly, while the whole scenario is emulated by its internal engine built upon realistic driving models.
* Friction estimator module, allowing the on-board estimation of the current tire-road interaction state and the potential friction value.  
In particular, the Simulink environment has been adopted to describe a highly detailed dynamical behavior of the autonomous vehicle under control, while SUMO emulates the traffic scenario and the road network, where the actual road grip in different scenario can change to mimic the effect of different environmental conditions to be studied.  
The interaction between the different modules for the co-simulation is allowed by _Traci_, an integration tool provided by SUMO. The library _TraCl4 Matlab_ has been employed to couple the vehicle, the road and the SUMO environment model in Simulink.