esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] [20] studied the dynamic behavior of beams on Pasternak-type viscoelastic foundations subjected to time-dependent loads. [PERSON] et al. [21] investigated the vibration responses of a Timoshenko beam on a random viscoelastic foundation subjected to a harmonic moving load. [PERSON] and [PERSON] [22] researched the response of a uniform Timoshenko beam supported by a Pasternak-type viscoelastic foundation subjected to an arbitrarily distributed harmonic moving load. [PERSON] et al. [23] developed a high-order shear deformable beam model and investigated the transient response of a sandwich porous beam acted upon a non-uniformly distributed moving mass. [PERSON] [24] analyzed the steady response of an infinite beam on a viscoelastic foundation with a uniformly distributed mass moving along it. [PERSON] et al. [25] investigated the dynamic behavior of a bidirectional functionally graded sandwich beam under the moving point load. [PERSON] et al. [26] proposed a novel material parameter-dependent model to investigate the nonlinear vibration of a beam under a moving load within a finite deformation framework. [PERSON] et al. [27] investigated the dynamic responses of thick Timoshenko perforated beams under a moving load using the Ritz method. However, there are rarely are works considering the inhomogeneities and non-uniformities in material, foundation, cross-section, and loading simultaneously. The works on beams under moving loads mostly model the loads as concentrated point loads or uniformly distributed loads, except for a few works which consider non-uniformly distributed loads. To the best of the authors' knowledge, works on the dynamic analysis of FGM beams on inhomogeneous viscoelastic foundations subjected to arbitrary non-uniformly distributed loads are nearly unreported. Thus, the objective of this paper is to compare the modeling accuracy of different load models and to analyze the influence of inhomogeneous foundations and material gradient on vibration amplitude and critical velocity. First, given that the inhomogeneities or non-uniformities in material, foundation, cross-section, and load are so common, the paper is going to propose a consistent dynamic model to tackle all the axially varying properties, including foundations, moving distributed loads, functionally graded materials, and cross-sections, based on the Timoshenko beam theory and a Chebyshev spectral method. A series of examples are given to validate the convergence and accuracy of the proposed method. After that, the dynamics of a square AFG beam resting on an axially inhomogeneous foundation subjected to a non-uniformly distributed load are discussed. The effects of non-uniformly distributed moving loads, axially varying foundations, and AFG materials are investigated, including the variations in the maximum displacement and the critical velocity of the beam, etc.
## 3 Results and Discussion
### Characteristics and Mechanism of Erosion  
Figure 6 exhibited the erosion rates at different impact angles. The maximum erosion rate represented the lowest wear resistance, and the impact angle corresponding to the maximum erosion rate revealed the dominance of the wear mechanism.  
According to previous studies, the typically ductility wear mechanism is collectively controlled by cutting, plowing, and plastic deformation. In the range of 15-30\({}^{\circ}\), as the angle increases, the corresponding erosion rate gradually increases; in contrast, the erosion rate gradually decreases in the range of 30-90\({}^{\circ}\)[38; 39]. The results conducted on the three specimens indicated a typical ductile fracture mode, with the maximum erosion rate located at an impact angle of 30\({}^{\circ}\) and the minimum erosion rate observed at 90\({}^{\circ}\). The erosion rates of the specimens were ranked as F \(>\) FR \(>\) FRH as an impact angle lower than 45\({}^{\circ}\) and F \(>\) FRH \(>\) FR as an impact angle greater than 45\({}^{\circ}\). Notably, under all the impact angle conditions, the F specimen exhibited the lowest wear resistance.  
Under low-impact angle conditions, the high hardness brought out the FR specimens' greater wear resistance; on the other hand, under high-impact angle conditions, the FRH specimens with the lowest hardness showed higher wear resistance. It can be concluded that the effect of hardness on the erosion wear resistance depended on the conditions at different impact angles. On the other hand, compared with the erosion results of the traditional Al-Si alloy, the CCRD Al-Si alloy has better wear resistance [26].
## 3 Experimental Validation
### Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Testing Samples (30\%)**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **Kappa Score** \\ \hline Class-1 & 97.29 & 79.15 & 75.93 & - \\ \hline Class-2 & 97.06 & 79.76 & 69.07 & - \\ \hline Class-3 & 97.38 & 76.90 & 77.45 & - \\ \hline Class-4 & 97.19 & 79.63 & 72.88 & - \\ \hline Class-5 & 96.63 & 75.18 & 69.21 & - \\ \hline Class-6 & 97.15 & 77.78 & 78.53 & - \\ \hline Class-7 & 96.98 & 73.82 & 79.05 & - \\ \hline Class-8 & 97.33 & 78.75 & 80.77 & - \\ \hline Class-9 & 97.54 & 80.43 & 78.20 & - \\ \hline Class-10 & 97.23 & 76.26 & 82.90 & - \\ \hline Class-11 & 96.71 & 75.17 & 71.19 & - \\ \hline Class-12 & 97.27 & 76.09 & 81.94 & - \\ \hline Class-13 & 97.48 & 79.26 & 76.70 & - \\ \hline Class-14 & 97.33 & 76.62 & 82.72 & - \\ \hline Class-15 & 96.71 & 74.43 & 73.94 & - \\ \hline Class-16 & 97.06 & 76.80 & 82.99 & - \\ \hline
**Average** & **97.15** & **77.25** & **77.09** & **75.64** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Result analysis of the SSODTL-CC model with distinct classes under 30% of testing dataset-2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Dataset-1** & **Dataset-2** \\ \hline SVM & 95.98 & 77.34 \\ \hline ENEA-OO & 97.07 & 86.49 \\ \hline SVRFMC & 98.20 & 86.95 \\ \hline CNN & 98.08 & 87.72 \\ \hline CNN-CRF & 98.80 & 94.67 \\ \hline SSODTL-CC & 99.23 & 97.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of the SSODTL-CC technique with recent algorithms in terms of \(acc_{y}\). Figure 6: Comparative analysis of the SSODTL-CC technique under dataset-1.
## 2 Materials and Methods
### Data Adjustment Techniques  
The modeling of collected data is completed by the RSM [24; 25] and with ANNs [26; 27].  
RSM and ANNs have been used to model systems in a wide range of fields. Both techniques have proven to be efficient in the representation of these systems. On one hand, RSM uses linear regression to obtain the mathematical model with the minimum error. On the other hand, ANNs have been adopted as a tool to model complex systems, which learn the behavior of the system through a training phase based on gathered data on the variables of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment Number** & **Culture Time (h)** & **Humidity Percentage** &
\begin{tabular}{c} **Amount of** \\ Conidiospores \\ \end{tabular} \\ \hline
1 & 48 & 70 & 6,750,000 \\
2 & 48 & 75 & 5,500,000 \\
3 & 48 & 80 & 2,750,000 \\
4 & 56 & 70 & 52,500,000 \\
5 & 56 & 75 & 56,  
Nevertheless, these techniques present some disadvantages. In the case of RSM, it is assumed that the relationships in the system can be approximated by polynomial equations, which may not always be true for highly complex or nonlinear systems. The dimensionality is another drawback for RSM models because if the number of variables increases, the number of terms in the equation also increases, creating complex polynomials.  
In the case of ANNs, the training can be computationally intensive and time-consuming, requiring powerful hardware to carry out the search for the ANN with the best performance. Moreover, the black box nature of ANNs presents problems such as understanding how the mapping is found by the ANN and how the weights can be interpreted by users.  
Despite the disadvantages these techniques can present, they are powerful and versatile tools for modeling systems and making predictions.
## 6 Discussion  
Although the current study demonstrates the feasibility and effectiveness of integrating NSGA-II optimization with FLAC3D numerical modeling for tunnel excavation in fractured slate zones, several limitations and areas for further improvement remain.  
First, the modeling framework is deterministic and based on average input parameters derived from laboratory tests. In practice, spatial variability in geological conditions, such as heterogeneous distribution of rock and soil types, clay sensitivity, or the presence of weak interlayers, can significantly affect tunnel behavior and stability. These effects are not explicitly considered in the current simulation.  
Second, the uncertainty associated with geological modeling and parameter selection--such as cohesion, friction angle, and stiffness of the surrounding rock--could influence the optimization outcomes. As a technical limitation, the presented model does not incorporate probabilistic or stochastic components that would help quantify confidence intervals or risk in excavation design. Referring to the attractiveness of 3D modeling [22; 23], the potential of the methodologies with a promising perspective in future geo-modeling under uncertain conditions can be highlighted [21; 25; 26].  
Third, while the Python-FLAC3D integration greatly improves computational efficiency through automation and parallel processing, the computational cost can still be significant when evaluating many generations of individuals in large-scale 3D simulations. This highlights the need for balancing model fidelity with optimization performance in practical applications.  
Finally, this study focuses on a case-specific validation using field data from the Longshu Tunnel. Although the observed deformation trends are consistent with the simulation results, a comparative evaluation with other numerical models or optimization frameworks (e.g., SPEA2, MOPSO) was not conducted. This will be considered in future studies to further validate the methodology's generalizability.
## 4 Empirical Propagation Models
### Empirical Models for Vegetation  
The article [42; 43] shows the most commonly used propagation models in vegetated environments for planning and deploying WSNs. These models exhibit high efficiency in path loss estimation, along with low mathematical complexity. They are known as the \"Modified Exponential Decay Model (MED)\" due to their format:  
\[Att_{MED}=xf^{y}d^{z} \tag{7}\]  
where \(Att_{MED}\) is the attenuation in dB added by vegetation on top of the Free Space Path Loss (FSPL), \(f\) is the frequency in GHz for the Weissberger model and in MHz for the other models, \(d\) is the depth distance into the vegetation in meters, and \(x,y\), and \(z\) are parameters that should be adjusted based on measurements taken in each scenario where their use is required, as stated in [38].
# Cutting-Edge Trajectory Optimization through Quantum Annealing
###### Abstract  
This paper introduces an innovative approach to explore the capabilities of Quantum Annealing (QA) for trajectory optimization in dynamic systems. The proposed method involves transforming trajectory optimization problems into equivalent binary optimization problems using Quadratic Unconstrained Binary Optimization (QUBO) representation. The procedure is general and adaptable, making it applicable to a wide range of optimal control problems that entail the satisfaction of dynamic, boundary, and path constraints. Specifically, the trajectory is discretized and approximated using polynomials. In contrast to the conventional approach of determining the polynomial degree (\(n\)) solely based on the number of boundary conditions, a specific factor is introduced in our method to augment the polynomial degree. As a result, the ultimate polynomial degree is calculated as a composite of two components: \(n=l+(m-1)\), where \(m\) denotes the count of boundary conditions and \(l\) signifies the number of independent variables. By leveraging inverse dynamics, the control required to follow the approximated trajectory can be determined as a linear function of independent variables \(l\). As a result, the optimization function, which is represented by the integral of the square of the control, can be formulated as a QUBO problem and the QA is employed to find the optimal binary solutions.  
trajectory optimization; polynomial approximation; inverse dynamics; QUBO formulation; quantum annealing +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: email: _Applied_
# Effects of Interference and Mitigation Using Notch Filter for the DVB-S2 Standard
## 2 Literature Review and Related Works
* It considers more essential channel effects on the transmitted signal. As a drawback, the physical layer of the receiver must contain all required blocks for receiving data in real situations. The behavior of blocks is deeply analyzed in the presence of CWI. Deviation from the optimum work is explained in the term of packet loss or error from the correct detection. Thus, signal constellation vulnerability and system degradation caused by the other blocks are described mathematically and by simulation. * It introduces more systematic parameters measurements for adjusting the r parameter and simple models are presented to clarify how to measure and define them. For example, the amount of residual interference after filtering is very important to know for digital receivers that have more sensitive blocks.
## 3 Beam Layout and Frequency Allocation Optimization Method Based on User Distribution
### Optimization of Beam Layout and Frequency Allocation  
The number of users that can actually be connected to the satellite \(N_{sum}\) is also related to the number of users currently distributed under different beams; thus,  
\[N_{sum}=\sum\limits_{i=1}^{K}\big{[}\text{min}\big{\{}\begin{array}{cc}C_{c }^{i}/C_{0}&\rho_{i}\pi R_{i}^{2}\end{array}\big{\}}+\text{min}\big{\{} \begin{array}{cc}C_{s}^{i}/C_{0}&U_{i}-\rho_{i}\pi R_{i}^{2}\end{array} \big{\}}\big{]} \tag{18}\]Define \(\eta\) to facilitate the representation of the adjacent location relationship between the beams.  
\[\eta^{j}_{i}=\left\{\begin{array}{cc}1&,if\,B^{i}_{c}\,\,\,\text{is}\,\,\, \text{adjacent}\,\,\,\text{to}\,\,\,B^{j}_{s}\\ 0&,if\,B^{i}_{c}\,\,\,\text{is}\,\,\,\text{not}\,\,\text{adjacent}\,\,\,\text{to}\,\,\,B^{j}_{s} \end{array}\right. \tag{19}\]  
By adjusting the frequency resource allocation ratio of the central area and the edge area \(B^{i}_{s},B^{i}_{c}\) and the frequency resource allocation center area radius \(R_{i}\), the number of users connected to a single satellite is the optimization target. The complete optimization problem is  
\[\left\{\begin{array}{l}Object:\max(N_{sum})\\ s.t.\,\,\,\text{C1}:0<B^{i}_{s}+B^{i}_{c}\leq B_{s},\forall B^{i}_{s}\subset B,B^{i}_{c}\subset B\\ \text{C2}:\sum\limits_{i}^{K-2}\,\,B^{i}_{s}+\eta^{i+1}_{i}B^{i+1}_{s}+\eta^{ i+2}_{i}B^{i+2}_{s}\leq B_{0}\\ \text{C3}:0<R_{i}<R_{0},i=1,2,...,n\end{array}\right. \tag{20}\]  
where constraint C1 indicates that the frequency band resources allocated to the central and edge areas of the same beam cannot overlap and cannot exceed the total amount of frequency resources that can be allocated; constraint C2 indicates that the frequency band resources allocated to adjacent edge areas cannot overlap; and constraint C3 indicates that the central area cannot exceed the sub-beam.  
In the above optimization problem, the bandwidth allocated to the edge area \(B^{i}_{s}\) of each beam cell and the frequency resource allocation center area radius \(R_{i}\) are both variables to be optimized. There are 19 beam cells in total, and the variables are independent of each other. In addition, the objective function in Equation (20) is nonlinear and can be solved on the star. Therefore, we use the genetic algorithm to solve for the maximum number of access users. The specific parameter settings are shown in Table 2, and the steps are as follows.  
* Randomly initialize the frequency resource allocation center area radius and the frequency resources allocation in the central and edge areas of the 19 beams.
* Calculate the value of the objective function for the initial population.
* Individual selection, chromosomal crossing and genetic variation are performed on the population to generate a new population of offspring.
* Determine whether the number of iterations has been reached. If so, the process is complete and the optimal solution has been found. Otherwise, repeat the above steps.
# A Force Measurement Method Based on Flexible PDMS Grating
###### Abstract  
With the rapid development of flexible materials, various high-performance biocompatible flexible sensors have been proposed for specific measurement applications. Among these materials, polydimethylsiloxane (PDMS) is one of the most popular polymers by curing the mixture of pre-polymer (base) and cross-linker (curing agent). In this paper, a force measurement method based on PDMS grating is introduced. The PDMS grating is cast from a commercial master grating, which is precise, low-cost, and easy to follow. The elastic modulus can be controlled by the curing temperature and the mixing ratio. The PDMS grating is tested using a tension testing machine. As the stretching force increases, the grating line-spacing simultaneously increases and the diffraction light spot shifts. By capturing the light spot shift using a camera, the relationship between light spot position and stretching force is established and evaluated. Experimental results show that the linearity (R\({}^{2}\)) of the proposed method is better than 0.998, adding that the sensitivity is \(\sim\)0.5-0.7N/mm and the accuracy is up to 0.05N.  
PDMS; elastic modulus; diffraction grating; force measurement +
Footnote †: journal: Applied Sciences
## 4 Evaluation Metrics
### Mean Structural Similarity  
Mean structural similarity quality measurement (_MSSIM_) [23] evaluates image quality based on information structure degradation. The _MSSIM_ evaluation metric is more stable than _SSIM_, especially for images with different resolutions.  
\[MSSIM=\frac{1}{M}\sum_{i=1}^{M}\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2} )}{(\mu_{x}^{2}+\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{20}\]  
where \(\mu_{i}\) and \(\sigma_{i}\) denote the mean and standard deviation of its intensity for the \(i\)th local window pixel, respectively. \(C_{i}\) is a constant to prevent errors. The upper and lower boundary range of the _MSSIM_ value is \([0,1]\).
## 3 Results  
RF FDTD simulations were performed to test the design of the proposed volume coil on the basis of a nonsymmetrical shape. We acquired \(\mid B_{1}^{+}\mid\) field maps from the yurt coil and compared its performance with the head birdcage coil. We used the human model DUKE from the virtual family provided by the EM simulation software.  
The \(\mid B_{1}^{+}\mid\) field maps were computed with an input power normalized to 1 W for each BC coil. The field maps were normalized with the square root of the maximum \(SAR_{10\text{g}}\) of the whole head model, as given by  
\[\widehat{\left|B_{1}\right|}=\left|B_{1}^{+}\right|/\sqrt{SAR_{10\text{g,max}}} \tag{2}\]  
This normalization makes it easier to compare the performance of each type coil. Using this visualization, we were able to analyze which coils can produce uniform fields.  
Simulations with the human model were conducted for all the coils, and the \(\mid B_{1}^{+}\mid\) fields were extracted in the \(xy\)-plane slices along the head model (\(z\)-axis). Figure 3a,b shows the performances of the head and yurt BC coils, respectively. Each column represents a different slice of the brain region.  
Figure 3: \(\mid B_{1}^{+}\mid\) fields at different slices of human head model for **(a)** head BC coil and the **(b)** yurt BC coil.  
Table 1 shows the mean \(\mid B_{1}^{+}\mid\) value and the relative uniformity (RU) for each coil at the whole head model. The virt coil produces a higher field intensity than the head BC. The comparison with the RU shows that the yurt BC coil has better field uniformity compared with the reference head BC coil. Therefore, the yurt coil demonstrated the optimal performance. The values measured for the yurt BC coil indicated better performance compared with the head BC coil.  
Additionally, Figure 4 shows the \(\mid B_{1}^{+}\mid\) field in the \(zy\)-plane for each coil. The first aspect of the figure to note is the field intensity produced by each coil. The central field effect can be observed clearly in the head BC coil. Notably, however, the field from the yurt BC coil increases in the cortex area, which has a higher field intensity compared with the head BC coil, as can be seen by comparing Figure 4a,b.  
Next, SAR\({}_{10g}\) maps were obtained from each coil, as shown in Figure 5. The axial slices in Figure 5 correspond to the maximum SAR from each of the coil. The SAR value of the head coil was higher than yurt coil. The peak SAR values in the head model volume were 0.31 and 0.09 W/Kg for the head BC and yurt coil, respectively. Moreover, the mean SAR values for the head BC and yurt coil were 0.068 and 0.02 W/kg, respectively. The occurrence of the peak SAR value in this area was unsurprising because the coil geometry is narrower at the top ring of the yurt BC coil relative to the other designs.  
Figure 4: \(\mid B_{1}^{+}\mid\) fields in the sagittal view for (**a**) head BC coil and the (**b**) yurt BC coil.  
Figure 5: SAR\({}_{10g}\) maps in the axial, sagittal, and coronal views of a human model for (**a**) head BC coil and (**b**) yurt BC coil.