esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Calculation Process of the Prestress Loss for Steel Strands
#### 4.1.5. Prestress Loss Due to Shrinkage and Creep of Concrete  
For steel strands, the prestress loss resulted from the concrete shrinkage and concrete creep can be obtained through Equation (10).  
\[\sigma_{I5}=\frac{55+300\frac{\sigma_{\text{pc}}}{f^{\prime}\sigma_{\text{cu }}}}{1+15\rho} \tag{10}\]  
According to Equations (1) and (8)-(10), the total prestress loss for steel strands can be obtained.
# Self-Piercing Riveted Joint of Vibration-Damping Steel and Aluminum Alloy
## 4 Conclusions  
In this study, the SPR joining between dissimilar materials of vibration-damping steel, SPFC590 DP, and Al5052-H32 was carried out, and the mechanical performance, failure mode, and geometrical features of the joints with different die types and material stack configurations were investigated. The important conclusions of this research are as follows:  
1. The SPR joint between the vibration-damping steel and Al5052-H32 showed mechanical performance similar to that between SPFC590 DP and Al5052-H32. For the Al5052-H32 bottom configuration, the SPR joint of the vibration-damping steel had 22.6% lower tensile-shear load than SPFC590 DP. However, it had 3.9% higher tensile-shear load than SPFC590 DP when Al5052-H32 was used as a top sheet. These results suggest that the SPR technique could be a promising solution for dissimilar joining between vibration-damping steel and aluminum.
2. The SPR joints of the vibration-damping steel and Al5052-H32 with the Al5052-H32-top configuration had a consistent superior mechanical performance, mainly due to the increase of the interlock width. In addition, due to the small interlock width, all SPR joints of the vibration-damping steel and Al5052-H32 showed consistent rivet pull-out failure, regardless of the joint configuration.
3. The SPR joints of the vibration-damping steel and Al5052-H32 showed the largest tensile shear load with a flat die (a benchmark die). For the flat die, the increase of die taper angle and diameter reduced the mechanical performance of the joint, owing to the increase of the die's volume, resulting in the smaller interlock width of the joint.  
CRediT authorship contribution statement. D.-H.K., project administration, supervision, conceptualization, methodology, writing--original draft, and funding acquisition. T.-E.J., investigation, formal analysis, data curation, and methodology. M.-G.K., investigation, formal analysis, data curation, and visualization. J.S., formal analysis, visualization, writing--original draft, review, and editing.  
This research work was supported by the Korea Institute of Industrial Technology and the Industry Core Technology Development Program funded by the Ministry of Trade, Industry & Energy (Korea). Grant No. 10063579.  
Figure 17: Comparison of failure modes of the SPR joint (fabricated by type A1 die) for different stack-up combinations. **(a)** SPFC590 DP (top) and Al5052-H32 (bottom), **(b)** vibration-damping steel (top) and Al5052-H32 (bottom), **(c)** Al5052-H32 (top) and SPFC590 DP (bottom), and **(d)** Al5052-H32 (top) and vibration-damping steel (bottom).
## 3 Results
### Comparison between Regular Fully Connected Layers and Global Average Pooling  
Our method used global averaged pooling layers instead of the regular fully connected layers to improve the classification accuracy. To verify the advantage of global averaged pooling, we calculated the classification results with regular fully connected layer to compare with the results based on global average pooling. In this experiment, the dropout technique with an experimental value of 50% dropout probability was adopted with two fully connected layers to compare with the global average pooling classifier. Fully connected layers are usually accompanied by the dropout method to promote convergence. Dropout is a regularization technique to prevent overfitting for neural network models [14; 22]. Neurons are randomly selected with a given probability to be dropped out and ignored during training so that the network could learn multiple independent internal representations and improve the generalization ability.  
The comparison was based on the same five-fold cross-validation dataset, as mentioned above. The highest classification accuracy of the regular fully connected layers was 97.3%, which was less than the highest accuracy of 98.8% of the global average pooling method. Moreover, we also found that the global averaged pooling method had better convergence performance for model training. Figure 6 plots the training accuracy curve and validation accuracy curve of both global averaged pooling and conventional fully connected layers. The global averaged pooling method shows more steady convergence process with less fluctuation. The global averaged pooling method also has a narrower gap between the training and validation curves, implying better generalization ability than the fully connected layers method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** & **Mean \(\pm\) Std.** \\ \hline
93.3\% & 93.2\% & 98.8\% & 93.7\% & 95.9\% & 95.0 \(\pm\) 2.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification Accuracy of the Proposed Method.
# Infrared All-Dielectric Metasurface Beam Splitter Based on Transflective Structures
## 2 Structural Design and Simulation
In this paper, we set the phase spacing between adjacent nanocolumns along the \(y\)-axis to \(\pi/2\) to construct an equal phase plane with a certain slope to make the incident light positively deflected along the \(y\)-axis. According to Figure 2, we select four values with a relatively stable change in transmittance and a phase interval of \(\pi/2\) between them as the radii of the units, and one low transmittance unit cell radius size to reduce the overall transmittance of the metasurface. The metasurface beam splitter after arranging unit cells from small to large radii is shown in Figure 1b, with radii of 0.27 \(\upmu\)m, 0.33 \(\upmu\)m, 0.36 \(\upmu\)m, 0.37 \(\upmu\)m, and 0.38 \(\upmu\)m. Furthermore, the transmittance and additional phase for the various radii are shown in Table 1.  
Figure 1: (**a**) The columnar unit cells of the parallel light-splitting metasurface beam splitter. The materials of the nanocolumn and the substrate are Si and SiO\({}_{2}\), the geometric parameters of the cell are period Px = Py = P = 1 \(\upmu\)m, and the height of the nanocolumn is H = 3.2 \(\upmu\)m. (**b**) The schematic of  
the parallel light-splitting metasurface beam splitter, with blue arrows for transmitted light, black for reflected light, and yellow for incoming light. The design was numerically simulated by the Finite Difference in Time Domain (FDTD) technique method. The light source is a 1.55 \(\upmu\)m linearly polarized plane wave incident on the metasurface along the positive \(z\)-axis. The reflected and transmitted light phase conditions, incidence angle tolerance, and light field intensity distribution were recorded and evaluated in the incident light direction (reflective end) and the transmitted light direction (transmissive end) in the Y-Z plane, respectively.
# Power Control Techniques for Interference Management-
## 4 Power Control Technique Evaluation
### Keywords Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Keywords** & **Occurrences (\%)** & **Residual** & **Standardized Residual** \\ \hline resource management/radio & 274 (35.58\%) & 34.67 & 28.56 \\ resource management power control & 173 (22.47\%) & 19.45 & 16.02 \\ non-orthogonal multiple access/noma & 157 (20.39\%) & 17.04 & 14.03 \\ interference & 127 (16.49\%) & 12.51 & 10.31 \\ 5g/5g mobile & 115 (14.94\%) & 10.70 & 8.82 \\ optimization/nonconvex & 113 (14.68\%) & 10.40 & 8.57 \\ optimization & 106 (13.77\%) & 9.35 & 7.70 \\ device-to-device & 76 (9.87\%) & 4.82 & 3.97 \\ communication/device-to-device/d2d & 62 (8.05\%) & 2.71 & 2.24 \\ power allocation quality of service/qos & 59 (7.66\%) & 2.26 & 1.86 \\ interference manage-ment/interference mitigation & 50 (6.49\%) & 0.90 & 0.75 \\ reinforcement & 42 (5.45\%) & \(-\)0.30 & \(-\)0.25 \\ reinforcement learning & 40 (5.19\%) & \(-\)0.60 & \(-\)0.50 \\
6g mobile & 38 (4.94\%) & \(-\)0.90 & \(-\)0.75 \\ communication/6g & 37 (4.81\%) & \(-\)1.06 & \(-\)0.87 \\ uplink & 34 (4.42\%) & \(-\)1.51 & \(-\)1.24 \\ internet of things (iot) & 32 (4.16\%) & \(-\)1.81 & \(-\)1.49 \\ heterogeneous cellular & 30 (3.90\%) & \(-\)2.11 & \(-\)1.74 \\ networks & 30 (3.90\%) & \(-\)2.11 & \(-\)1.74 \\ throughput & 30 (3.90\%) & \(-\)2.11 & \(-\)1.74 \\ machine learning & 29 (3.77\%) & \(-\)2.26 & \(-\)1.86 \\ massive mimo & 29 (3.77\%) & \(-\)2.26 & \(-\)1.86 \\ cellular network & 28 (3.64\%) & \(-\)2.41 & \(-\)1.99 \\ signal to noise ratio & 27 (3.51\%) & \(-\)2.56 & \(-\)2.11 \\ spectral efficiency & 25 (3.25\%) & \(-\)2.86 & \(-\)2.36 \\ game theory & 24 (3.12\%) & \(-\)3.02 & \(-\)2.48 \\ wireless networks & 23 (2.99\%) & \(-\)3.17 & \(-\)2.61 \\ handover & 18(2.34\%) & \(-\)3.92 & \(-\)3.23 \\ training & 18 (2.34\%) & \(-\)3.92 & \(-\)3.23 \\ energy efficiency & 15 (1.95\%) & \(-\)4.37 & \(-\)3.60 \\ heuristic algorithms & 15 (1.95\%) & \(-\)4.37 & \(-\)3.60 \\ fading channels & 15 (1.95\%) & \(-\)4.37 & \(-\)3.60 \\ oldm & 13 (1.69\%) & \(-\)4.67 & \(-\)3.85 \\ small cells/femtocells & 12(1.56\%) & \(-\)4.82 & \(-\)3.97 \\ millimeter wave & 12 (1.56\%) & \(-\)4.82 & \(-\)3.97 \\ ultra reliable low latency & 11 (1.43\%) & \(-\)4.97 & \(-\)4.10 \\ communication & 11 (1.43\%) & \(-\)4.97 & \(-\)4.10 \\ stochastic geometry & 11 (1.43\%) & \(-\)4.97 & \(-\)4.10 \\ cognitive radio & 11 (1.43\%) & \(-\)4.97 & \(-\)4.10 \\ channel estimation & 11 (1.43\%) & \(-\)4.97 & \(-\)4.10 \\ deep learning/deep & 9 (1.17\%) & \(-\)5.28 & \(-\)4.35 \\ q-learning & 8 (1.04\%) & \(-\)5.43 & \(-\)4.47 \\ spectrum sharing & 5 (0.65\%) & \(-\)5.88 & \(-\)4.84 \\ mobile edge computing & 4 (0.52\%) & \(-\)6.03 & \(-\)4.97 \\ unsupervised learning & 3 (0.39\%) & \(-\)6.18 & \(-\)5.09 \\ graph neural networks & 12 (0.26\%) & \(-\)6.33 & \(-\)5.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Noteworthy keywords depicting frequency of occurrences, percentages, and residuals.
# Quantum Multi-User Broadcast Protocol for the \"Platform as a Service\" Model
## 4 Analysis  
### Analysis of Quantum Bit Error Rate and Secure Key Rate  
The Quantum Bit Error Rate (\(QBER\)) is defined as the number of wrong bits to the total number of received bits and is normally in the order of a few percent. In the following, we use it expressed as a function of rates:  
\[QBER\approx\frac{R_{error}}{R_{sift}}, \tag{13}\]  
where the sifted key corresponds to the cases in which [PERSON] and [PERSON] made compatible choices of bases, hence its rate is half that of the raw key. In a practical quantum key distribution system, e.g. the \(BB84\) protocol, after attenuation and sifting, the sifted key generation rate is given by [26]  
\[R_{sift}=\frac{1}{2}q\cdot f_{rep}\cdot\mu\cdot t_{link}\cdot\eta. \tag{14}\]  
where the factor \(q\) (\(q\leq 1\), typically 1 or \(\frac{1}{2}\) ) must be introduced for some phase-coding setups in order to correct for noninterfering path combinations, \(f_{rep}\) is the pulse rate, \(\mu\) is the mean number of photon per pulse, \(t_{link}\) is the probability of a photon to arrive at the analyzer, and \(\eta\) is the probability of the photon being detected.  
The secure key rate in our protocol is the quantum communication rate of the whole system. This depends on the rate of key distribution when each root node communicates with its children, that is the rate of the sifted key generation.
## 3 Results
### Analyses Using X-ray Microtomography
#### 3.4.2 Diameter of the Pores  
Despite the homogeneity of the inner structure, slices of the three types of effervescent tablets analyzed were also examined. The arrangement of two types of tablets during microtomographic analysis and images from the microtomographic scans showing the surface of the tablets are presented in Figure 5.  
We observed that the surface of expired tablets is heterogeneous, with many defects and numerous pores.  
The diameters of the exposed pores were also analyzed. Figure 6 presents the mean diameters of the pores observed in the two types of tablets analyzed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Tablet** & **Density (g/cm\({}^{3}\)) N = 3, M \(\pm\) SD** \\ \hline Unexpired & 1.268 \(\pm\) 0.010 \\ Expired & 1.261 \(\pm\) 0.009 \\ Unexpired tablets dried in a vacuum oven at 50 \({}^{\circ}\)C for 24 h & 1.273 \(\pm\) 0.009 \\ Expired tablets dried in a vacuum oven at 50 \({}^{\circ}\)C for 24 h & 1.269 \(\pm\) 0.009 \\ \(p\) & **<0.001** \\ \hline \hline \end{tabular}  
M—mean; SD—standard deviation; h—hours. Significant difference is in bold.  
\end{table}
Table 3: The mean density of the inner structure of the analyzed effervescent tablets.  
Figure 5: Arrangement of two types of tablets during microtomographic analysis (**A**) and images from the microtomographic scans showing the surface of the analyzed types of effervescent tablets: (**B**) unexpired; (**C**) expired.  
Figure 6: Mean diameters of pores observed on the surface of the analyzed tablets evaluated with X-ray microtomography (\(n\) = 3 of each tablet’s type). Significant difference is in bold.  
We observed differences in mean pore diameter between the expired and unevpired effectiveness tablets. The expired tablets were characterized by a larger pore diameter compared to the unevpired tablets (0.095 mm vs. 0.074 mm, respectively; \(p=0.007\)). In the unevpired tablets, the diameter of the pores correlated negatively with the density of the inner structure; the higher the density, the lower the diameter on average (\(r=-0.326\), \(p=0.042\)).
## 3 Joint Optimization of EE-SE
### Quadratic Transform
#### 3.2.1 Optimization of Transmit Precoding for Given Phase-Shifting Values  
To optimize the objective in problem (P6) over \(\mathbf{W}\), we remove the terms that are independent of \(\mathbf{W}\) and get  
\[\begin{split}\text{(P7)}&\min_{\mathbf{W}}\,y^{2} \mu\sum_{k=1}^{K}||\mathbf{w}_{k}||^{2}+(1-a)\sum_{k=1}^{K}\omega_{k}a_{k} \epsilon_{k}\\ &\text{s.t. \eqref{eq:P7}}.\end{split} \tag{15}\]  
Then, we rewrite \(e_{k}\) as below. Therefore, we can rewrite the objective function and transform problem (P7) equivalently into new problem (P7) below.  
\[\begin{split} e_{k}&=|u_{k}^{*}\mathbf{h}_{k}^{H} \mathbf{w}_{k}-1|^{2}+\sum_{i=1,i\
eq k}^{K}\left|u_{i}\mathbf{h}_{k}^{H} \mathbf{w}_{i}\right|^{2}+\sigma_{0}^{2}|u_{k}|^{2}\\ &=\sum_{i=1}^{K}|u_{i}|^{2}\mathbf{w}_{i}^{H}\mathbf{h}_{k} \mathbf{h}_{k}^{H}\mathbf{w}_{i}-2\Re\{u_{k}\mathbf{w}_{k}^{H}\mathbf{h}_{k} \}+1+\sigma_{0}^{2}|u_{k}|^{2}\end{split} \tag{16}\]  
\[\begin{split}\text{(P7)}&\min_{\mathbf{W}}\,\sum_{k=1 }^{K}\mathbf{w}_{i}^{H}(y^{2}\mu\text{I}_{M}+(1-a)|u_{k}|^{2}\sum_{i=1}^{K} \omega_{i}\alpha_{i}\mathbf{h}_{i}\mathbf{h}_{i}^{H})\mathbf{w}_{k}\\ &\quad-2\Re\Big{\{}(1-a)\omega_{k}a_{k}u_{k}\mathbf{w}_{k}^{H} \mathbf{h}_{k}\Big{\}}+(1-a)\omega_{k}a_{k}(1+\sigma_{0}^{2}|u_{k}|^{2})\\ &\text{s.t. \eqref{eq:P7}},\end{split} \tag{17}\]  
where \(\mathbf{h}_{i}=\mathbf{h}_{\text{d},i}+\mathbf{G}\mathbf{\Theta}\mathbf{h}_{i,i}\) and \(\text{I}_{M}\) denotes an \(M\times M\) identity matrix.  
Considering that \(\mu>0\), \((1-a)>0\), \(\omega_{i}\geq 0\), \(\alpha_{i}>0\), new problem (P7) is convex. Then, we can use standard convex optimization algorithms [32] to find the optimal value of \(\mathbf{W}\).
## 2 Materials and Methods
### Monitoring the Degradation Behavior of PAB with the MB Sensor in Vitro  
Figure 3 shows the experimental platform. PAB was fully fixed on the experimental platform and was compressed by the external force. It was surrounded by an exciting coil and a sensing coil. The outer coil comprised 0.5 mm diameter copper wire (line 25) with 200 turns and was used for excitation. It produced an alternating magnetic field (3 \(\times\) 10\({}^{2}\) A/m) caused by a sinusoidal signal  
Figure 2: Polylactic acid (PLA) artificial bone (PAB) prototype applied to repair a bone defect (red line is the magnetoelasticity-based (MB) sensor).  
(200 Hz and 2 V, peak to peak) and amplified by a power amplifier (TAPCO Juice TM, Loud Technologies Inc. USA). The inner coil consisted of 0.25 mm diameter copper wire (line 33) with 200 turns and was used for sensing. It was connected with the spectrum analyzer to capture changes in the magnetic field. The results were stored in a computer for data analysis. In this study, the degradation of PAB caused a change in stress on the MB sensor, which led to the varying permeability. This change in the permeability of the MB sensor caused the change of the spatial magnetic field, which could be measured with the sensing coil. Therefore, the degradation of PAB was monitored by the MB sensor wirelessly. Importantly, no cables or batteries were needed for the MB sensor throughout the monitoring process.
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 5 Experimental Evaluation
### Prediction of Image Privacy Level
#### 5.2.3 Results  
At first, we evaluated the effectiveness of privacy level prediction considering one kind of feature in isolation, and then considered them in conjunction.  
Hue-Saturation Features  
In a first experiment, we evaluated the feature extraction method described in Section 4.1.1. Results are shown in Table 4.  
The best results were obtained using random forest, considering the values of precision, recall, F\({}_{1}\) score, and MCC. However, the results were only slightly better than the ones achieved by a random classifier, meaning that hue-saturation features alone are not sufficient for reliably recognizing the privacy level of images.  
Face Detection Features  
In a second experiment, we evaluated the use of face detection features, described in Section 4.1.2. The achieved results are shown in Table 5.  
Previous research showed that the presence of faces inside images are a good hint to determine the privacy level. As expected, results with these features improved with respect to the use of hue-saturation features. In this case, the random forest classifier achieved the best results, obtaining an F\({}_{1}\) score of \(0.67\). Moreover, the MCC value of around \(0.40\) clearly indicates a significant correlation between the predictions and the ground truth. Nonetheless, by inspecting the image dataset, we noticed that the presence of faces was not always sufficient to distinguish private vs. public images, since (even without faces or human presence) other elements in the picture may determine privacy issues. Hence, face detection features are not sufficient by themselves for this classification task.
# Side-Channel Vulnerabilities of Unified Point Addition on Binary Huff Curve and Its Countermeasure
## 7 Conclusions  
In this paper, we present two vulnerabilities of unified point addition on the binary Huff curve; these vulnerabilities are exploitable by ROSETTA and HCCA. In particular, we found these vulnerabilities of unified point addition on the binary Huff curve as presented in [9]. As countermeasures, we propose \(w\)RPC and present a new unified point addition method for the binary Huff curve. Additionally, we show the proposed unified point addition method and \(w\)RPC applied to the side-channel atomic algorithm. The proposed method is secure against ROSETTA and HCCA. In addition, the proposed unified point addition method has no additional cost compared to the previous one. However, \(w\)RPC does incur additional cost. Depending on the size of the base field of an elliptic curve, the proposed method is about 0.2\(\sim\)4.4% slower than the original one. However, it is about 8.5\(\sim\)17.5% faster than unified point additions using blinding operands as a countermeasure. Additionally, we present our analyses of the vulnerabilities of unified point addition on other elliptic curves, such as Weierstrass, Hessian, Edwards, Jacobi intersections, Jacobi quartic, and binary Edwards elliptic curves in the Appendix A.  
[PERSON] and [PERSON] performed the experiments, analyzed the data, and wrote the paper. H.K. analyzed the data and verified the paper.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017R1C1B2004583).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(n\) & **Algorithm** & \(M\) & **Additional Cost** & **Total Cost** & **Ratio** \\ \hline  & [8,9] & 64 & - & 1088 & 1.000 \\
233 & [8,9] using [10] & 81 & - & 1377 & 1.266 \\  & proposed method & 64 & 48 & 1136 & 1.044 \\ \hline  & [8,9] & 81 & - & 1377 & 1.000 \\
283 & [8,9] using [10] & 100 & - & 1700 & 1.235 \\  & proposed method & 81 & 54 & 1431 & 1.039 \\ \hline  & [8,9] & 169 & - & 2873 & 1.000 \\
409 & [8,9] using [10] & 196 & - & 3332 & 1.160 \\  & proposed & 169 & 78 & 2951 & 1.027 \\ \hline  & [8,9] & 324 & - & 5508 & 1.000 \\
571 & [8,9] using [10] & 361 & - & 6137 & 1.114 \\  & proposed method & 324 & 108 & 5616 & 1.020 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The computational cost of the algorithms of the binary Huff curve.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 7 Evaluation
### The Genetic Approach  
The process of scheduling fog jobs \(\mathbf{J}\) in the cloud layer such that the QoS penalty cost \(\mathcal{C}\) and the energy penalty cost \(\epsilon\) are mitigated is an NP problem. The huge number of fog jobs \(\mathbf{J}\) received at the cloud layer makes it difficult to formulate cost-optimal schedules in a timely manner. However, the permutation genetic algorithm, as a meta-heuristic search strategy, demonstrates its effectiveness in such cases [67; 68; 69]. The genetic algorithm and virtualized-queue design scheme proposed in [41; 65; 66] demonstrate their effectiveness in efficiently exploring and exploiting the scheduling space such that a near-optimal schedule of jobs is formed in a reasonable time, which are adopted in this paper to find a cost near-optimal schedule of fog jobs \(\mathbf{J}\) at the cloud layer.  
As such, a fitness function is formed to evaluate the quality of each virtualized queue (chromosome). The fitness value \(f_{r,G}\) of a chromosome \(r\) in a generation \(G\) represents the penalty cost \(\mathcal{C}\) of the QoS and the penalty cost \(\epsilon\) of energy, each of which computes a normalized fitness value \(F_{r}\) for each schedule candidate. Accordingly, Russian Roulette is used to select a set of schedule candidates to produce the population of the next generation using crossover and mutation operators. Two fitness values are presented: \(fQ_{r,G}\) to represent the fitness of the cost \(\mathcal{C}\) of QoS penalty and \(fE_{r,G}\) to represent the fitness of the cost \(\epsilon\) of energy penalty. The _Single-Point_ crossover and _Insert_ mutation genetic operators are utilized to evolve the schedule of fog jobs \(\mathbf{J}\) at the cloud layer. The rates of such operators are both set to be 0.1 of the population size in each generation. The population size is set to 10, and the maximum number of tours is set to 3000.