esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Boundary Conditions and Meshing  
As shown in Figure 21, the geometry of the beams, loads, and boundary conditions were all symmetrical, so just one-half of a beam was modelled using a standard finite element mesh. This approach significantly decreased the amount of computing time and the computer storage space, especially in the case of the GFRP/Concrete interface which is  
Figure 20: Bilinear traction–separation constitutive law.  
not modeled as a full bond. To choose an appropriate mesh with acceptable accuracy in terms of ultimate load and ultimate deflection, three models with various mesh sizes--fine, medium, and coarse of 15, 25, and 35 mm, respectively--were examined. Figure 21 depicts the concrete volume medium meshes and reinforcing medium meshes for tested beams. The solid three-dimensional eight-node bricks (C3D8R) were adopted in the FE study to model the concrete. The main reinforcement bars were modeled using a bar truss element (T2D3) with the same mesh size as the concrete. GFRP laminate was modeled as a four-nodded doubly thin curved shell, reduced integration, and hourglass control (S4R). The applied load and supports were taken as illustrated in Figure 21a to simulate the experimental program.
## 2 Literature Review
[19] developed the concept of a probabilistic linguistic term set (PLTS) whereby a decision-maker is able to specify different probability values for each of their selected linguistic terms. PLTS was well received in the field of decision making, with the development of many decision-making models combining existing MCDM techniques with the concept of PLTS, such as PL-MULTIMOORA [40], PL-PROMETHEE [41], PL-VIKOR [42], and PL-CODAS [43]. The ELECTRE II method has also been enhanced to perform under the PLTS environment. [PERSON] et al. [44] developed the PL-ELECTRE II model to solve the problem of evaluating therapeutic scheduling for patients suffering from brain-metastasized non-small cell lung cancer. In another study by [PERSON] et al. [45], the authors developed the PLTS-ELECTRE II model for solving a venture capital evaluation problem. The PLTS-ELECTRE II model was able to model both quantitative criteria and qualitative linguistic criteria. An extensive study on the PLTS and its many applications to MCDM can be found in [46]. Even though the PLTS has been fairly successful in modeling real-world decision problems, they suffer from one weakness. Oftentimes, different decision-makers provide their evaluations for a decision problem from different perspectives. They have different skill sets and experiences. The PLTS does not factor this, and therefore, the PLTS was recently combined with the concept of Z-numbers by [PERSON] et al. [18], leading to the Z-Probabilistic Linguistic Term Set (ZPLTS). The concept of Z-numbers was proposed by [PERSON] [47] as a way of assigning a credibility value to an evaluation value, and it has been applied to a range of MCDM models in recent years, resulting in Z-PROMETHEE II [48], Z-TOPSIS [49], and Z-MABAC [50], to name a few. Z-numbers are able to represent information and its credibility, and hence, it provides a solution to problematic results caused by the lack of information credibility in decision-making problems. ZPLTS combines the PLTS with Z-numbers in order to allow decision-makers to express their evaluations along with their credibility, both in the form of linguistic values with associated probabilities. The ZPLTS allows for richer modeling and decision-making capabilities for real-world decision problems. To the best of our knowledge, despite its capabilities, the ZPLTS has not been integrated with the ELECTRE II model. This study proposed the ZPLTS-ELECTRE II, which is an enhanced ELECTRE II model that combines the traditional ELECTRE II method with a Z-Probabilistic Linguistic Term Set. The new ZPLTS-ELECTRE II method is then applied to rank a set of ontologies of the mental health domain. The next section presents ELECTRE II and defines the various concepts that are relevant in the specification of the proposed ZPLTS-ELECTRE II method.
## Appendix B Calculation of TSEB Soil and Canopy Resistances When Estimating H  
The r\({}_{\rm soil}\) (s/m) is calculated using Equations (A10)-(A13) [21]:  
\[r_{soil}=\frac{1}{0.004+0.012\ U_{soil}}\] (A10)  
\[U_{soil}=U_{canopy}\times\exp\left(-a_{ext}\times\left[1-\frac{0.05}{h_{c}} \right]\right)\] (A11)  
\[U_{canopy}=\frac{u_{*}}{k}\times\ln\left(\frac{h_{c}-d}{z_{om}}\right)\] (A12)  
\[a_{ext}=0.28\times\left(CF\times LAI\right)^{\frac{2}{3}}\times h_{c}^{\frac{1 }{3}}\times w_{c}^{-\frac{1}{3}}\] (A13)  
where U\({}_{\rm soil}\) is the mean horizontal wind speed at the ground surface (m/s); U\({}_{\rm canopy}\) is the mean horizontal wind speed at the top of the canopy (m/s); w\({}_{\rm c}\) is the mean leaf width (for maize, 0.09 m); a\({}_{\rm ext}\) is the wind factor (dimensionless).  
The r\({}_{\rm x}\) term (s/m) is calculated using Equations (A14) and (A15) below [21]:  
\[r_{x}=\frac{C^{\prime}}{LAI}\bigg{(}\frac{\Delta}{U_{d+z_{om}}}\bigg{)}^{\frac{1 }{2}}\] (A14)  
\[U_{d+z_{om}}=U_{canopy}\times exp\bigg{[}-a_{ext}\times\bigg{(}1-\frac{d+z_{om }}{h_{c}}\bigg{)}\bigg{]}\] (A15)  
where C\({}^{\prime}\) is a weighing coefficient (set to 90 as indicated by [77]) and U\({}_{\rm d+zom}\) is the mean horizontal wind speed at the height equal to d + z\({}_{\rm om}\) (m/s).  
The r\({}_{\rm c}\) term is calculated using Equations (A16) and (A17) [78]:  
\[\frac{r_{c}}{r_{ah}}=\begin{cases}3.09\times\frac{r_{*}}{r_{ah}}+2.41\times \sqrt{\frac{r_{*}}{r_{ah}}}+0.62,&LAI<2\\ 2.74\times\frac{r_{*}}{r_{ah}}-5.90\times\sqrt{\frac{r_{*}}{r_{ah}}}+7.04,&LAI \geq 2\end{cases}\] (A16)  
\[r_{*}=\rho_{a}C_{pa}\bigg{[}\frac{e_{s}-e_{a}}{\gamma(R_{n}-G)}\bigg{]}\] (A17)  
where \(r_{*}\) is the climatic resistance (s/m).
# Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo
## 4 Numerical Results
### Multivariate Linear Quantile  
Suppose the error terms \(\epsilon_{i=1,\ldots,T}\stackrel{{ i.i.d.}}{{\mapsto}}\mathrm{ALD}(0,0.1,0.75)\). As the Bayesian approach requires a likelihood, we use the ALD to make the Bayesian inference, and the quantiles of interest are \(\tau=0.1\) and \(\tau=0.12\); hence, we use the relationship between the two ALDs mentioned in Section 2.  
We independently generate 300 and 3000 observations, using R, issued from the model defined in Equation (5) with \(X^{\prime}\beta=1+2x_{1}+1.5*x_{2}+x_{3}+6x_{4}+2x_{5}+3x_{6}+2x_{7}+x_{8}+6x_{ 9}+4x_{10}\).  
Since we are using simulated data, the theoretical quantiles are: \(\beta_{0,0.1}=0.1940388\), \(\beta_{0,0.12}=0.2669674\), \(\beta_{1,0.1}=\beta_{1,0.12}=2\), \(\beta_{2,0.1}=\beta_{2,0.12}=1.5\), \(\beta_{3,0.1}=\beta_{3,0.12}=1\), \(\beta_{4,0.1}=\beta_{4,0.12}=6\), \(\beta_{5,0.1}=\beta_{5,0.12}=2\), \(\beta_{5,0.1}=\beta_{5,0.12}=2\), \(\beta_{6,0.1}=\beta_{6,0.12 Figure 5: \(\hat{R}\) for multivariate model with 300 observations.  
Figure 6: Histograms showcasing diverse \(\beta\) values for the multivariate model with 300 observations.  
Additionally, an examination of the trace plots in Figure 8 reveals consistent and stable behavior in the MCMC chains, showcasing no discernible oscillation, drift, or other abnormalities. This pattern suggests a thorough exploration of the parameter space, indicating successful convergence to the target distribution.  
The small variance observed in Table 1 further supports the reliability of the results. Furthermore, the MCSE is 0 for all \(\beta\)s, indicating that the estimated values are likely close to the true values, as shown in Table 2. The sampler parameters for our model were configured with a \(n_{leapfrog}\) value of approximately 50 for all chains, indicating that each iteration of the sampler took around 50 leapfrog steps to explore the parameter space.  
From the obtained results, the trace plots do not seem to indicate non-stationary behavior.  
Furthermore, upon comparing the results presented in Table 2 for 300 observations with those for 3000 observations, we observe an improvement in estimation accuracy with higher observation counts, indicated by lower RMSE values and reduced variance across most quantiles. This phenomenon is expected, as larger observation sizes generally lead to improved estimations.  
Figure 7: Autocorrelation plots for varying \(\beta\) values (\(\beta_{1}\) on the left, \(\beta_{2}\) next to \(\beta_{1}\), and so on) in the multivariate model, showing chain 1 **(top)**, chain 2 **(middle)**, and chain 3 **(bottom)** with 300 observations.
# Harnessing Energy Balance and Genetic Algorithms for Efficient Building Demolition
## 2 Materials and Methods
### Optimization with an Enhanced Genetic Algorithm
For instance, in a specific iteration, if the coordinates for the center of the smallest area are (20, 10), and those for the largest coverage area are (100, 32), the crossover is performed as shown in Figure 8, where the red part represents the coordinate conversion to 6-digit binary numbers. Step 2. Mutation Operation: To preserve population diversity and circumvent local optimum solutions, we introduced a mutation procedure subsequent to the crossover operation. This mutation process is carried out by randomly selecting specific bits and toggling their values. In particular, we randomly choose one bit from the lower six bits of the energy release source point coordinates and modify its value. The mutation probability is set at 0.04, indicating a 0.04 likelihood of undergoing a mutation procedure in each iteration. Through this approach, we can investigate new potential solutions while ensuring the stability of the overall region, leading to more refined optimization. During the mutation process, we utilize a standard binary mutation strategy, that is, one bit is randomly selected from the lower six bits and toggle; if the bit is 0, it is changed to 1, and if it is 1, it is changed to 0. For example, assume that in a particular iteration, the new coordinates for the energy release source point obtained through the crossover operation are (55, 34). We randomly select the third bit of the x-coordinate (counting from the right) and change it from 1 to 0, resulting in new energy release source point coordinates of (49, 34). As shown in Figure 9, where the red part is the corresponding binary representation of the coordinates. Through the integration of crossover and mutation operations, we can progressively converging to the global optimum solution. Furthermore, this optimization approach  
Figure 8: Crossover operation. Figure 9: Mutation Operation. rooted in genetic algorithms showcases notable robustness and adaptability, rendering it apt for a variety of real-world applications. Step 3. Energy Balancing Process: We incorporate the new coordinates for energy release points derived from the crossover and mutation operations and then initiate the energy balancing process. The specific steps of this process are delineated in Section 2.2. Step 4. Iterative Optimization: We continuously execute the aforementioned steps to refine the positioning of the energy release points. This process continues until the current energy release area satisfies Equation (4) and all boundaries meet the energy balance criteria. The specific process is shown in Figure 10.  
This section outlines the distinct steps of the genetic algorithm optimization, including gene selection, crossover, mutation, and iteration, to ensure the logical flow and uniformity of the entire optimization procedure. Each phase of the process is meticulously executed, adhering to established rules and guidelines to uphold the scientific integrity and precision of the optimization.
## 3 Second Step of One-Bounce Scattering Signal Identification Algorithm
### Localization of Scatterer
Furthermore, remove the assumption that \(x_{s}\), \(y_{s}\) and \(d_{1}\) are independent, let  
\[z_{1}=x_{s}^{0}+e_{1},\;z_{2}=y_{s}^{0}+e_{2},\;z_{3}=d_{1}^{0}+e_{3}, \tag{16}\]  
where \(e_{1}\), \(e_{2}\) and \(e_{3}\) are estimation errors of \(z\), then the vector \(\varphi^{\prime}\) denoting inaccuracies in \(z\) is found to be  
\[\varphi^{\prime}=h^{\prime}-G^{\prime}z^{0}, \tag{17}\]where  
\[h^{\prime}= \begin{bmatrix}{(z_{1}-x_{1})}^{2}\\ {(z_{2}-y_{1})}^{2}\\ {z_{3}}\end{bmatrix} ,\,C^{\prime}= \begin{bmatrix}1&0\\ 0&1\\ 1&1\end{bmatrix} ,\,z^{\prime}= \begin{bmatrix}{(x_{s}-x_{1})}^{2}\\ {(y_{s}-y_{1})}^{2}\end{bmatrix} . \tag{18}\]  
The covariance matrix of \(\varphi^{\prime}\) is  
\[\boldsymbol{\varphi}^{\prime}=E\Big{[}{\varphi^{\prime}}{\varphi^{\prime}}^{ T}\Big{]}=4B^{\prime}\text{cov}(z)B^{\prime}, \tag{19}\]  
where \(B^{\prime}=diag\{x^{0}-x_{1},y^{0}-y_{1},r_{1}^{0}\}\), thus the weighted least square (WLS) estimate of \(z^{\prime}\) is  
\[z^{\prime}={{({G^{\prime}}^{T}\boldsymbol{\varphi}^{\prime-1}}{G^{\prime}})}^ {-1}{{G^{\prime}}^{T}\boldsymbol{\varphi}^{\prime-1}}H^{\prime}. \tag{20}\]  
Then after some operations, the final position estimate of scatterer S is obtained from \(z^{\prime}\) as  
\[ \begin{bmatrix}x_{s}\\ y_{s}\end{bmatrix} =\sqrt{z^{\prime}}+ \begin{bmatrix}x_{1}\\ y_{1}\end{bmatrix} \text{or} \begin{bmatrix}x_{s}\\ y_{s}\end{bmatrix} =-\sqrt{z^{\prime}}+ \begin{bmatrix}x_{1}\\ y_{1}\end{bmatrix}  \tag{21}\]  
The proper solution selected is the one that follows the prior information about the scatterer.
# Numerical Simulation Analysis Method for Rockburst Control in Deeply Buried Caverns
## 2 Mechanism and Control Measures of Rockburst
### Rockburst Mechanism and Evaluation Methods
[29] used the strain energy storage index \(Wet=\Phi p/\Phi s\) to evaluate the rockburst propensity and postulated that a strong rockburst will occur when Wet is more than five; here, \(\Phi p\) is the elastic strain energy accumulated by loading before the rock strength reaches its peak and \(\Phi s\) is the plastic deformation energy dissipated by unloading after the rock strength reaches the peak. [PERSON] et al. [30] found through experiments on stress-strain curves of rock blocks that some test blocks did not experience rockburst even when Wet was more than five. According to rockburst cases that occurred in the project and some previous experimental research results, to facilitate numerical analysis and calculation and also to correspond with Equation (1), we modified the strain energy storage index proposed by [PERSON] into Equation (3):  
\[Wet=Wp/Ws\left\{ \begin{array}{ll}2\sim 3.5&\text{Grade I light rockburst}\\ 3.5\sim 5&\text{Grade II medium rockburst}\\ 5\sim 6.5&\text{Grade III strong rockburst}\\ >6.5&\text{Grade IV extremely strong rockburst}\end{array} \right. \tag{3}\]  
where \(Wp\) is the elastic strain energy released when the surrounding rock is loaded to its peak strength during the excavation process, corresponding to the elastic strain energy \(\Phi p\). According to the increment of stress and strain in each stage of excavation, we can calculate it as follows:  
\[W_{p}=0.5\oint\Delta\sigma\cdot\Delta\varepsilon\text{d}t=0.5\Sigma_{1}^{n} \Delta\sigma\cdot\Delta\varepsilon \tag{4}\]  
where \(Ws\) is the energy dissipated during the cracking and deformation of surrounding rock after the stress reaches its peak strength. \(Ws\) corresponds to the plastic deformation energy \(\Phi s\) which we can obtain from the equation: \(Ws=W-Wp\). The total energy during the loading process of the surrounding rock is \(W=0.5\sigma_{p}^{2}/E\) (where \(\sigma_{b}\) is the peak strength of the rock mass and \(E\) is the elastic modulus of the rock mass).
## 4 Analysis of Experimental Results
### Load Characteristic Analysis
When ice-propeller milling occurs in water under atmospheric pressure, the larger the propeller rotational speed and the inflow velocity, the larger the advance coefficient, and the smaller the mean and peak values of the propeller thrust and torque coefficients, which is consistent with the actual situation. When ice-propeller milling occurs in water under the decompression condition, the smaller the number of cavitation, and the smaller the mean and peak values of the propeller thrust and torque coefficients. Figure 14c-f compare the mean and peak values of the thrust and torque of a single blade with variable parameters in ice-propeller milling conditions. At the same propeller rotational speed and inflow velocity, the mean values of thrust, tangential force, \(X\)-direction torque, and \(Y\)-direction torque of a single blade are the largest under atmospheric pressure, and the peak values are different. When \(n=19.36\) r/s and \(V=1.36\) m/s, the peak values of the thrust, tangential force, and \(Y\)-direction torque of a single blade are the largest under the decompression condition, followed by that under atmospheric pressure, and it is the smallest in air. This is mainly because when \(\sigma=0.44\), cavitation begins to appear at the local position of the back of the blade, but it is in the first stage of development, and the lift coefficient continues to increase [34]. The peak value of the torque in the \(X\)-direction is the  
Figure 8:2 Schematic diagram of load distribution when blade profile is milling ice. largest under atmospheric pressure, followed by that under the decompression condition, and the smallest value is in air. When \(n=22.15\) r/s and \(V=1.8\) m/s, the peak values of the thrust, tangential force, and \(X\)-direction torque of a single blade are the largest under atmospheric pressure, followed by that under the decompression condition, and the smallest value is in air. The peak value of the torque in the \(Y\)-direction is the largest under the decompression condition, followed by that under atmospheric pressure, and the smallest value is in air. It is concluded that the peak load of a single blade is closely related to the milling environment. Compared with the ice milling load in air, the mean and peak values of the axial force, tangential force, \(X\)-direction torque, and \(Y\)-direction torque decrease with an increase in the rotational speed of the propeller. With a decrease in the cavitation number, the mean and peak values of the thrust, tangential force, \(X\)-direction torque, and \(Y\)-direction torque decrease. Figure 8:3 Mean and peak values of propeller and single blade loads with different parameters: (a) \(K_{T}\); (b) \(K_{Q}\); (c) \(K_{TX}\); (d) \(K_{TT}\); (e) \(K_{QX}\); (f) \(K_{QY}\).
###### Abstract  
Researchers are facing significant challenges to develop robust energy-efficient clustering and routing protocols for Wireless Sensor Networks (WSNs) in different areas such as military, agriculture, education, industry, environmental monitoring, etc. WSNs have made an everlasting imprint on everyone's lives. The bulk of existing routing protocols has focused on cluster head election while disregarding other important aspects of routing including cluster formation, data aggregation, and security, among others. Although cluster-based routing has made a significant contribution to tackling this issue, the cluster head (CH) selection procedure may still be improved by integrating critical characteristics. Nature-inspired algorithms are gaining traction as a viable solution for addressing important challenges in WSNs, such as sensor lifespan and transmission distance. Despite this, the sensor node batteries cannot be changed when installed in a remote or unsupervised area due to their wireless nature. As a result, numerous researches are being done to lengthen the life of a node span. The bulk of existing node clustering techniques suffers from non-uniform cluster head distribution, an imbalanced load difficulty within clusters, concerning left-out nodes, coverage area, and placement according to a recent study. Metaheuristic algorithms (DE, GA, PSO, ACO, SFO, and GWO) have the advantages of being simple, versatile, and derivation-free, as well as effectively utilizing the network's energy resource by grouping nodes into clusters to increase the lifespan of the entire network. In this paper, we explore recently used hybridization techniques (DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, etc.) for bio-inspired algorithms to improve the energy efficiency of WSNs. This paper also discusses how critical issues can be addressed by speeding up the implementation process, how more efficient data can be transferred, as well as how energy consumption can be reduced by using bio-inspired hybrid optimization algorithms.  
hybridization; nature-inspired algorithms; energy utilization; WSNs +
Footnote †: journal: Electronics Letters
## 4 Conclusions  
A frequency measurement system based on frequency-to-time mapping was proposed to realize the ultra-fast multifrequency measurement. A relationship between the unknown signal and the time difference of two optical pulses was established with the assist of a frequency scanning heterodyne approach. The unknown microwave frequency was inversely mapped by FTM relationship. The proposed frequency measurement system can measure single frequency signal as well as multiple frequency signals with a wideband, high resolution, and small compact. The frequency measurement range ranged from 4 to 12 GHz with the measurement error less than 90 MHz. Meanwhile, two-tone microwave signal was also measured with the resolution less than 150 MHz. The proposed method to measure frequency information in time domain with the help of partly integrated heterodyning technology provides an alternative for microwave frequency measurement in radar and electronic warfare systems.  
Conceptualization and methodology, N.S.; software, T.H.; validation, N.S.; resources, M.L., W.L. and N.Z.; writing--original draft preparation, N.S.; writing--review and editing, N.S.; visualization, N.S.; supervision, N.S.; project administration, N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2018 YFB2201802) and the National Natural Science Foundation of China under 62075212, 61705217.  
The authors declare no conflict of interest.
# Integrated Deep Ultraviolet Doublet Metalens for Projection Imaging
## 1 Introduction
We analyze the efficiency of the integrated doublet metalens, and the light intensity per unit area of the emitted light is 7.39 times the intensity of the incident light unit area. Projection imaging is realized for the reticle placed in the integrated doublet metalens under the incidence of parallel light. The doublet metalens realizes the function of projection imaging through an integrated array. We can adjust the size and focal length of the two metalenses according to the required imaging ratio to obtain a pattern consistent with the corresponding ratio of the propagation distance. It has the advantages of being ultra-light, ultra-thin, and easy to integrate and opens up a new path for deep ultraviolet lithography, micro-nano optical imaging, and optoelectronic equipment. Compared with the traditional deep ultraviolet lens group, this design has the advantages of a small volume, simple system structure, stable system, etc.