esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Effect of Temperature on the Sound Transmission Loss of a Curved Plate
###### Abstract  
Curved structures are used in many engineering applications. Temperature affects the performance of structural vibration and acoustics and can damage the equipment when its effects are severe. A theoretical model of the sound transmission loss (STL) of a simply supported curved plate considering the effect of temperature was established. Moreover, a numerical solution was obtained using the modal superposition method. The finite element results were compared to validate the theoretical model. The effects of temperature and acoustic loading on the modal frequency and STL were considered, and the variation laws of the radius of curvature, opening angle, and loss factor on the STL were analyzed. The results showed that the modal frequency increased with decreasing temperature, the frequency ratio of the same-order modal frequency varied approximately linearly with temperature, and the trough of the STL shifted toward high frequencies. The pitch angle substantially influenced the STL, whereas the azimuth angle was negligible. A larger radius increased the effect of temperature. In addition, an increase in the opening angle formed an aggregation of STL at (1, 1)-order modal frequencies. The loss factor affects only the amplitude of the trough position of the STL. Therefore, the proposed theoretical model can aid in the vibroacoustic design of curved plates in a thermal environment.  
sound transmission loss; curved plate; thermal environment Article
# Flutter Reliability Analysis of Xiangshan Harbor Highway Cable-Stayed Bridges in Service
## 2 The Flutter Analysis Framework for Flexible Bridge
### Influence of Damping Ratio on Flutter Stability  
The overall bridge damping under wind load is composed of bridge structural damping and aerodynamic damping under wind. When bridge flutter occurs, the overall damping of the bridge structure changes from positive to negative, so it was necessary to consider the influence of bridge structure damping on the critical wind speed of flutter instability. The damping ratio was set to 0, 0.3%, 0.5% and 1%, respectively, and the change curve of flutter critical wind speed of Xiangshan Harbor Bridge is shown in Figure 15.  
Figure 14: Change curve of flutter critical wind speed with wire breaking rate.  
Figure 15: Change curve of flutter critical wind speed with damping ratio.  
Figure 15 shows that the critical flutter wind speed of Xiangshan Harbor Bridge increased with the increase of damping ratio. When the damping ratio changed from 0 to 1%, the critical flutter wind speed increased from 50 m/s to 73 m/s, and the growth rate of the critical flutter wind speed was faster at the damping ratio 0-0.5% than that at 0.5-1%.
# Wireless Communication Technologies for Smart Grid Distribution Networks +
## 2 Available Wireless Technologies for Smart Grid Communications
### IEEE 802.22
Even though wireless technologies are the principal candidates for the deployment of communication among sensor nodes at the distribution level, it is worth mentioning the features that wired technologies can offer in the same context. The two options described below are considered in this summary.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 2 Materials  
### SVM Classifier  
The support vector machine (SVM) is a supervised learning model whose fundamental principle is to identify a decision boundary that maximizes the classification margin. This ensures that data points from different classes are correctly classified while positioning the boundary as far as possible from the nearest data points, known as support vectors. Maximizing the classification margin improves the model's generalization ability, resulting in better performance on unseen data [13].  
For nonlinear problems, the SVM model introduces a kernel function that maps the original input into a higher-dimensional space. In this space, the decision boundary is determined and used to classify nonlinear data [14]. The SVM model function is expressed as shown in Equation (1).  
\[f(x)=\text{sign}(\sum_{i=1}^{n}\alpha_{i}y_{i}K(x_{i},x)+b) \tag{1}\]  
where:  
* \(f(x)\): The output of the model, representing the predicted class label for the input \(x\).
* \(\alpha_{i}\): The Lagrange multipliers, which represent the weights of the support vectors in the model.
* \(y_{i}\): The class label of the \(i-\)th support vector, indicating its corresponding category.
* \(K(x_{i},x)\): The kernel function, which calculates the similarity between the support vector \(x_{i}\) and the input \(x\) in the transformed high-dimensional space.
* \(b\): The bias term, which helps to adjust the decision boundary away from the origin in the high-dimensional space.  
In the SVM model, the objective is to identify the optimal decision boundary that maximizes the classification margin while appropriately penalizing misclassified samples. To achieve this, the SVM adopts a dual problem formulation, converting the optimization problem using the Lagrange multiplier method [15]. The objective function of the dual problem, along with its constraints, is expressed as shown in Equation (2).  
\[\begin{split}\max\limits_{\alpha}\sum\limits_{i=1}^{n}\alpha_{i} -\frac{1}{2}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\alpha_{i}\alpha_{j}y_{ i}y_{j}K(x_{i},x_{j})\\ \text{s.t.}\left\{\begin{array}{l}\sum\limits_{i=1}^{n}\alpha_{i} y_{i}=0\\ 0\leq\alpha_{i}\leq C\end{array}\right.\end{split} \tag{2}\]  
The SVM model typically requires the determination of the following functions:  
1. Penalty Parameter: A larger value of this parameter forces the model to fit the training data more strictly, which can lead to overfitting. While this may result in better performance on the training data, it can potentially degrade performance on the test data. Conversely, if the penalty parameter is too small, it becomes more lenient toward misclassifications, resulting in a larger margin that may lead to underfitting of the SVM model.
2. Kernel Function and Kernel Parameters: The kernel function utilized in this paper is the RBF (Radial Basis Function) kernel. The parameter associated with this kernel governs the influence range of data points; a smaller value enhances the generalization performance of the SVM model, while a larger value may degrade its generalization capability [16].
# Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound
## 3 Results
### Blood Analysis  
Blood samples from 10 (of 42) patients were exposed to six different low-frequency ultrasound modes (Table 1) and changes in 20 blood parameters were identified. The blood of each patient was sonicated under six different ultrasound intensities and time variables (A, B, C, D, E, F), in addition to not being exposed to ultrasound (K). To ensure the statistical validity of our analyses, we assessed the normality of the data using the Kolmogorov-Smirnov test. In cases where significant deviations from normality were detected, suitable transformations were applied to address this issue. Subsequently, paired sample \(t\)-tests were conducted to compare means between groups, and if significant differences were observed, an ANOVA was performed to evaluate the overall significance. These steps were taken to meet the assumptions of the statistical tests and ensure the reliability of our data analysis.  
A repeated-measure ANOVA was performed to compare the effect of different ultrasound exposure conditions (Table 1) on different blood parameters. The ANOVA results revealed that there was a statistically significant (\(p\)-value \(<\) 0.05) difference in 15 blood parameters between at least two groups. MATLAB R2018a was used to conduct statistical analyses for these 15 parameters (Table 2). The \(F\)-statistic is the ratio of the mean squared errors, where \(d_{1}\) = 40 (for the numerator) and \(d_{2}\) = 6 (for the denominator).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter:** & \(\mathbf{F_{40,6}}\) & \(\mathbf{p}\)**-Value** \\ \hline RBC & 13.80 & \textless{}0.01 \\ MCV & 12.51 & \textless{}0.01 \\ HCT & 12.61 & \textless{}0.01 \\ MPV & 14.58 & \textless{}0.01 \\ PDW & 15.20 & \textless{}0.01 \\ LPCR & 15.31 & \textless{}0.01 \\ WBC & 29.43 & \textless{}0.01 \\ HGB & 2.25 & 0.039 \\ MCH & 17.34 & \textless{}0.01 \\ MCHC & 2.53 & 0.021 \\ LYM & 16.98 & \textless{}0.01 \\ GRAN & 32.88 & \textless{}0.01 \\ LYM\% & 13.52 & \textless{}0.01 \\ GRA\% & 7.29 & \textless{}0.01 \\ MID\% & 23.49 & \textless{}0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Repeated-measure ANOVA results.  
Figure 2: Blood platelet aggregation with epinephrine (**a**) and low-frequency ultrasound (**b**) in vitro. In (**b**), two samples are sonicated with ultrasound at 60 W electric power (pink, red) and two samples are sonicated with ultrasound at 35 W electric power (green, blue).  
A multiple comparisons test showed that the mean value of 49 ultrasound conditions was significantly different from the control group. Primarily higher-power ultrasound had the greatest effect on test parameters.
## 4 Applied Strategies for Textile Dye Wastewater Treatment
### Physical Treatment Process
#### 4.1.2 Nanoparticle Utilization  
Nanotechnology is the science of nanoscale materials (size \(\leq\) 100 nm); it has attracted numerous researchers due to the great potentials of nanoparticles in removing textile dyes [68]. Nanoparticles have a wide range of classification [69], they are chemically stable, have a large surface area, can be prepared physically, chemically and biologically (microbes and plants), and are cheap and eco-friendly [70]. The shape, size, structure, purity and arrangement of nanomaterials matters in the process of decolorization [71]. Nanoparticles are mainly applied directly to wastewater to adsorb rejected dyes on their surface for further elimination [72]. They could also be used in filters, membranes and carbon nanotubes for further regeneration and reutilization in dye wastewater discharge cleaning [73]. This original methodology is taking place as a significant, large-scale solution for textile industries due to the speed of cleaning and reduction of certain pollutants to near-zero levels [74]. Nanomaterials could be synthesized based on plant extracts [75; 76], metals and metal oxides [77; 78], carbon [79] and nanocomposites [80]. However, it is important to know that the elimination of nanoparticles after water purification needs to be addressed [81]. Multiple scientific works covered dye degradation using nanocomposites including graphene oxide/zinc oxide (GO/ZnO) [111], polyaniline/SiO\({}_{2}\)[121] and RGO-Ni [130]. [PERSON] et al. [150] used Pd-Ni bimetallic nanoparticles to treat Acid orange 8 (Table 1).
# Situation-Aware Survivable Network Design for Tactical Environments
## 3 Proposed Algorithm
### Situational Backoff Reset Algorithm  
#### 3.1.1 Military Quality of Service  
The IEEE 802.11e EDCA is a representative standard that can improve the QoS. Generally, commercial QoS has features such that it meets performance-based requirements, static priority, and best effort service. However, these characteristics are hard to directly apply in the environment of tactical networks. In the case of MQoS, the priority may vary depending on the mission; requirements are based on importance and urgency, and MQoS should be guaranteed. The MQoS requirements defined by the U.S. Army unified capabilities (UC) reference architecture (RA) are listed in Table 1[28]. Table 1 includes some metrics such as end-to-end delay, end-to-end packet loss, and end-to-end delay jitter, which are defined in Ref. [29]. End-to-end delay is the sum of transmission, processing, and queueing delays in routers; propagation delays in links; and end-system processing delays. End-to-end packet loss is the ratio of packets discarded or lost to total transmitted packets. End-to-end delay jitter is the variation in the delay of packets. Unlike IEEE 802.11e, in which the priority is assigned by traffic type, the priority of tactical traffic is divided into four sections based on its importance and urgency: urgent and important traffic, urgent but unimportant traffic, important but not urgent traffic, and neither urgent nor important traffic. In this work, we consider three service classes from Table 1; voice, multimedia conferencing, and short messaging.
## 3 Results and Discussion
### Multi-Location On-Body RF EMF Exposure Evaluation (Virtual Measurements)
A smaller dispersion of measurement results was also obtained when omitting the location of measurement points on the arms (Figures 6 and 7). Additionally, the underestimation of the exposure level using the average value of the multi-point measurement on the human body in relation to the unperturbed RF EMF (which must be considered in legal evaluations of workers' exposure), was found at a nearly flat level over the frequency range 400-3600 MHz (Figures 6 and 8). Such a systematic underestimation of the exposure level may be compensated for by applying the relevant correction factors. In the considered exposure scenarios, a compensation factor of two may be derived from discussed study (Figure 8, Table 4). However, further studies need to be performed covering a broader set of exposure scenarios and experimental tests in the real work environment. The changes in the distance of measurement points, simulated as small-sensor from the body do not cause significant changes in the distribution of KX1 ss ratios (approx. 10-30%). The distance of the measurement probes from the body is therefore not a critical factor influencing the accuracy of exposure assessment using on-body measurement equipment.
## 2 Problem Formulation
### Path Recognition Problem  
Straight and curvilinear paths are mostly common in use for the line-tracking vision guidance of AGVs. If the path pixels are extracted correctly without being contaminated by a large amount of noise points, it is not a difficult task to estimate the path model by means of the least square method, the RANSAC algorithm or the Hough transform. Unfortunately, the aforementioned methods are vulnerable to the interferences caused by the non-uniform surrounding illumination when the onboard camera is set in the oblique installation way, or to the sight-line occlusion for the onboard camera due to some objects covering the path, or to stripe damage that obviously changes the previous  
Figure 1: The sketch of a vision-based automated guided vehicle(AGV).  
Figure 2: The flowchart of line-tracking vision guidance.  
appearance of the path because of wear and tear. In order to imitate these interferences occurring in the realistic industrial fields, a set of path images were sampled on the high-illumination condition, or contaminated using blue, red or white wastes, as shown in Figure 3.  
Figure 3a,d shows the images collected for the straight and curvilinear paths on the normal illumination without any interference. Figure 3b,e was captured in a location with intense lighting, while Figure 3c,f shows the images of guide paths surrounded or overlapped by wastes. Either the non-uniform illumination or various wastes on the floor inevitably bring about the under- or over-segmentation problems in the threshold segmentation of path images. Hence, the model estimation approach is expected to be robust against these image noises. Particle swarm optimization (PSO) is a biomimetic approach imitating the predation behavior of birds, which is widely used for various multidimensional continuous space optimization problems based on particle swarm and fitness [27]. A segment-adjusting-and-merging optimizer is embedded into the genetic PSO evolutionary iterations to improve the search efficiency for the polygonal approximation problem [28]. PSO is also applied to compute an appropriate location of knots automatically for data fitting through B-splines, which is reported to be robust to noise points [29].  
In fact, PSO is usually adopted as a parameter estimation approach for a known class of model. It implies that before PSO is used for the parameter estimation of guide paths, the class of path model, line or curve has to be recognized in advance. Hence, it is necessary to construct a robust path classifier to discriminate between a straight path and a curvilinear one. Usually, curvilinear paths are used for the turning operation of AGVs from one path to another, on which the relatively large deviations can be tolerated. In order to simplify the path model used in model estimation, a curvilinear path is approximated by means of piecewise straight lines. As a result, only the parameters of line model need to be solved.
## 2 Methodology
### General Framework for Image Deconvolution
#### 2.1.2 The Richardson-Lucy Algorithm: Procedure  
The Richardson-Lucy (RL) algorithm [PERSON] and [PERSON] (1975); [PERSON] (1975) is an iterative deconvolution technique optimized for low-flux data, such as many X-ray observations of SMC sources. It is based on maximum likelihood estimation under Poisson statistics. The algorithm iteratively updates the image intensity \(I_{\text{true}}\) by the following equation:  
\[I_{\text{true}}^{(k+1)}(x,y)\ =\ I_{\text{true}}^{(k)}(x,y)\cdot\frac{I_{ \text{obs}}(x,y)*PSF^{T}(x,y)}{I_{\text{true}}^{(k)}(x,y)*PSF(x,y)}\,, \tag{3}\]  
where \(PSF^{T}(x,y)\) is the transpose of the \(PSF\) matrix. The numerator represents the cross-correlation of the observed image with the \(PSF^{T}\) matrix, while the denominator represents the deblurred estimate of the true image.  
The execution steps for the RL algorithm are as follows:  
* **Initialization:** Start with an initial guess for the true image, typically a uniform image or a given \(I_{\text{obs}}(x,y)\).
* **Update rule:** Correct the estimate iteratively based on the ratio of observed data to the simulated blurred estimate.
* **Stopping criterion:** Stop the iteration when the changes in \(I_{\text{true}}(x,y)\) are negligible or when the residual error is sufficiently small.  
The RL algorithm assumes an accurately known PSF and works quite well in images with superimposed Poisson noise. Various extensions of this algorithm can also handle spatially varying PSFs. This work uses the Python'scikit' image restoration package called skimage.restoration.richardson_lucy (2016) (version 0.24.0, downloaded from the URL [[https://scikit-image.org/](https://scikit-image.org/)]([https://scikit-image.org/](https://scikit-image.org/)), accessed on 26 October 2024).
# Dynamic Weight and Mapping Mutation Operation-Based Salp Swarm Algorithm for Global Optimization
## 2 Salp Swarm Algorithm
### The Position Update of Leaders  
Salps form a chain-like structure and leaders are guided by the food source \(F=[F_{1},F_{2},\cdots,F_{D}]^{T}\) to update their position adaptively. The position of leader equation is shown below:  
\[X_{i,j}=\begin{cases}F_{j}+c_{1}((ub_{j}-lb_{j})c_{2}+lb_{j}),&c_{3}\geq 0.5 \\ F_{j}-c_{1}((ub_{j}-lb_{j})c_{2}+lb_{j}),&c_{3}<0.5\end{cases} \tag{3}\]where \(i=1,2,\cdots,N/2\), \(j=1,2,\cdots,D\), and \(X_{i,j}\) denotes the position of the \(i\)-th salp of leaders in the \(j\)-th dimension. Moreover, leaders are the top \(N/2\) salps according to the objective function value. \(F_{j}\) is the location of the prey in the \(j\)-th dimension. The random numbers \(c_{2}\) and \(c_{3}\) are evenly distributed within the boundary of [0, 1] at each iteration, and they control the step size and the next direction, respectively, for leaders.  
Formula (3) shows that the leader's position update is only related to the location of the food. In this paper, \(c_{1}\) is the convergence factor in the optimization algorithm, which plays a role in balancing global exploration and local development, \(c_{1}\) is a value that gradually decreases with the iteration of the algorithm (dynamic update step) and \(c_{1}\) is a 2-0 decreasing function, \(l\) represents the current iteration number, and \(l_{max}\) represents the maximum iteration number. The mathematical expression can be expressed as:  
\[c_{1}=a\times e^{-(\frac{b\times l}{l_{max}})^{2}} \tag{4}\]  
where \(l\) represents the present iteration number and \(l_{max}\) is the maximal iteration number. In SSA, \(a\) and \(b\) are invariables (\(a=2\) and \(b=4\)).
## Appendix A Model Sensitivity Analysis  
Here, we recall the results of the sensitivity analysis of the model with respect to the resolution of the computational mesh and the parallelization of the simulation presented in [18], which are fully relevant for this work. The accuracy of the simulation is directly related to the resolution of the computational mesh. However, the refinement of the computational mesh increases the computational requirements for the realization of the simulation. Using MPI simulation parallelization requires decomposing the computational domain into multiple computational meshes, which causes a loss of simulation accuracy [22; 25; 26]. In order to maximize the computational efficiency without inadequate loss of simulation accuracy, a set of 6 simulations with 20, 30, and 50 cm mesh resolutions using 1, 12, and 48 computational meshes, respectively, was realized (Table A1). The volume flow rate used in the HVAC settings was adjusted according to the actual dimensions of the jet fans corresponding to the given mesh resolutions while maintaining the same value of the quantity \(\mathrm{Av_{j}}^{2}\) to maintain the thrust of the jet fans and bulk airflow velocities in all the scenarios and to achieve the same tunnel pressure increase. For the entire simulation (1500 s), a constant dynamic pressure of \(-3\) Pa was set at the left portal of the tunnel. After 1000 s, all the jet fans started operating, reaching their full power within 20 s and then operating at full power until the end of the simulation. The time intervals of 900-1000 and 1250-1500 s were used to determine time-averaged values of \(\mathrm{v_{nat}}\) and \(\mathrm{v_{jet}}\), respectively, where \(\mathrm{v_{nat}}\) is the time-averaged airflow velocity of the steady natural flow created by a pressure of \(-3\) Pa at the portal and \(\mathrm{v_{jet}}\) is the time-averaged velocity of the steady airflow caused mainly by the thrust of jet fans. As it can be seen from Table 3, the simulated velocities for the steady natural airflow cases differ only slightly from those of the scenarios considered to be the most accurate, even for the 50 cm mesh resolution. For the higher airflow velocities generated by the jet fans, there are just minor differences (about 1%) for 20 and 30 cm resolution simulations; and a slight overestimation (by about 0.3 m/s) can be seen in the 50 cm resolution simulation, but it is still a good estimate of the airflow velocity. Since the decomposition of the computational domain results in only small inaccuracies in the simulations, the simulation with 12 meshes and the 30 cm mesh resolution appears to be optimal, and therefore these settings were chosen for the final simulations in [18] and in this study.