esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Wave Propagation and Structural Response  
To investigate the wave propagation and response of the RC tunnel, a loading history of 100 Hz frequency with an amplitude of 5 m/s was applied. Figure 9 illustrates the free field resultant velocity contours as time progresses. The wave induced by blasting includes the shock wave, compressive stress wave, and the seismic elastic wave. The propagation of shock waves in a medium takes place at supersonic velocities. As the blast wave spreads from the explosive charge, it propagates in the rock medium as a cylindrical wave. The wave further attenuates due to geometric and material damping at the far field, and it turns into an elastic wave.  
Figure 8: Comparison of numerical versus far-field theoretical solution for \(V_{p}\) = 4000 (m/s): (a) 0% damping; (b) 5% damping.  
When a blast wave interacts with a tunnel, it begins to respond to the blast, transforming its energy into kinetic energy. The kinetic energy-time history of the RC tunnel is displayed in Figure 10. The tunnel response starts at \(t\) = 4 ms. The small first peak appears due to the first negative phase of the time history load. The graph shows a rise in the kinetic energy, which reaches the peak at \(t\) = 8.8 ms before gradually decreasing to zero. Kinetic energy peaks at 140 kJ. The tunnel's kinetic energy disappears during plastic deformation.  
The wave propagation process and tunnel response with different time intervals are shown in Figure 11. As the incident wave approaches the tunnel, the wave-structure interaction initiates. It induces an increase in pressure in the vicinity of the tunnel side, which is directed toward the detonation center.  
Figure 10: Kinetic energy time history of RC tunnel.  
Figure 9: Blast-induced wave propagation in rock medium at different time steps: (**a**) 2.5 ms; (**b**) 6.4 ms; (**c**) 10.4 ms; (**d**) 13 ms.  
Figure 12 displays the time histories of tunnel sections at four selected locations, as shown in Figure 3a. Free field velocity was also recorded at 19.4 m from the blast hole to the tunnel's center. Velocity and displacement decreased as the distance from the explosion increased. The highest recorded value was at point A, located closest to the explosion, with a PPV of 75.3 cm and a peak displacement of 2.65 mm. Point C, located at the opposite end of the blasting zone, had the lowest value of PPV 19.1 cm and 0.7 mm displacement. Free field velocity was 29.1 cm less than the velocity with structure.  
Figure 12: Resultant velocity and displacement time histories of the tunnel at selected points: (**a**) resultant velocity; (**b**) resultant displacement.  
Figure 13b. The tensile cracks continued to form as time progressed. At 25 ms, cracks were fully developed along the longitudinal direction (Z direction) on the tunnel's free surface, as shown in Figure 13c,d.
# Supervised Contrastive Learning for 3D Cross-Modal Retrieval
## 4 Experiments
### Experimental Results for Comparison  
We conducted a comprehensive performance evaluation of the proposed method with previous approaches for 3D cross-modal retrieval tasks. To demonstrate the effectiveness of contrastive learning methods, we compared two cross-modal center loss-based methods, CLF [3] and RONO [2].  
Quantitative Comparison: Tables 5 and 6 represent the results of quantitative comparisons. The proposed method achieved the highest mAP performance on the ModelNet10 and ModelNet40 datasets. Based on the comparison results, we can verify the following:  
* The proposed contrastive learning-based method exhibits better results than the center loss-based methods, as it is less sensitive to external elements. Therefore, the proposed method is suitable for 3D cross-modal retrieval.
* Traditional methods based on center loss do not actively exclude other classes. On the other hand, our proposed method, CSupCon, based on contrastive learning, actively compares with other classes using data augmentation, obtaining better results during evaluation. As shown in Tables 5 and 6, performance improvements in cross-modal retrieval are observed in four out of six tasks for ModelNet40 and six out of six tasks for ModelNet10. The highest mAPs for cross-modal retrieval tasks are underlined in the tables.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Retrieval Tasks**} & \multicolumn{2}{c}{**Softmargin**} \\ \cline{3-5}  & & & **w/o Margin** & **w/ Margin** \\ \hline Image & & Image & 89.67 & 90.37 \\ Image & & Mesh & 89.20 & 89.81 \\ Image & & Point & 90.12 & 90.69 \\ Mesh & & Image & 87.49 & 88.15 \\ Mesh & \(\rightarrow\) & Mesh & 88.32 & 88.78 \\ Mesh & & Point & 88.59 & 89.01 \\ Point & & Image & 89.41 & 89.92 \\ Point & & Mesh & 89.71 & 89.88 \\ Point & & Point & 90.51 & 90.88 \\ \hline \multicolumn{5}{c}{Average mAP} & \multicolumn{2}{c}{89.22} & 89.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study to verify the performances w/ and w/o Softmargin. In all experiments, the batch size was set to 128.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Retrieval Tasks**} & \multicolumn{2}{c}{**FPH Strategy**} \\ \cline{3-5}  & & & **w/o FPH** & **w/ FPH** \\ \hline Image & & Image & 89.32 & 90.37 \\ Image & & Mesh & 89.04 & 89.81 \\ Image & & Point & 88.83 & 90.69 \\ Mesh & & Image & 88.88 & 88.15 \\ Mesh & & Mesh & 89.50 & 88.78 \\ Point & & Point & 88.61 & 89.01 \\ Point & & Image & 89.26 & 89.92 \\ Point & & Mesh & 89.43 & 89.88 \\ Point & & Point & 89.58 & 90.88 \\ \hline \hline \multicolumn{5}{c}{Average mAP} & \multicolumn{2}{c}{89.16} & 89.72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study to verify the performances using the FPH strategy on the ModelNet40 dataset. In all experiments, the batch size was set to 128.
## 3 Methodology
### Prediction Evaluation Indicators  
In machine learning, the reliability of model parameter estimation directly impacts prediction accuracy. This study assesses the performance of the models using four critical metrics: the coefficient of determination (\(R^{2}\)), root mean squared error (\(RMSE\)), variance accounted for (\(VAF\)), and mean absolute error (\(MAE\)). Specifically, \(R^{2}\) quantifies the proportion of data variation explained by the model, \(RMSE\) measures the average magnitude of the prediction errors, \(VAF\) evaluates how well the model captures data variance, and \(MAE\) provides the average absolute deviation between the predicted and actual values. The specific formulas are as follows [35; 36]:  
\[R^{2}=1-\sum_{i=1}^{N}\left(y_{i}-\widetilde{y}_{i}\right)^{2}\bigg{/}\sum_{i =1}^{N}\left(y_{i}-\overline{y}_{i}\right)^{2} \tag{9}\]\[RMSE=\sqrt{\sum_{i=1}^{N}\Bigl{(}y_{i}-\widetilde{y}_{i}\Bigr{)}^{2}}\bigg{/}N \tag{10}\]  
\[\mathit{VAF}=\biggl{[}1-\mathit{var}\Bigl{(}y_{i}-\widetilde{y}_{i}\Bigr{)} \bigg{/}\mathit{var}(y_{i})\biggr{]}\times 100\% \tag{11}\]  
\[MAE=\sum_{i=1}^{N}\Bigl{|}y_{i}-\widetilde{y}_{i}\Bigr{|}\bigg{/}N \tag{12}\]  
where \(y_{i}\) is the \(w\) value, \(\widetilde{y}_{i}\) is the predicted PC value of the model, \(\overline{y}_{i}\) is the average of the PC values, and \(N\) denotes the number of samples in the training or testing stages.
# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 4 Experiments
### Quantitative Comparisons
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **IBLA** & **RGHS** & **ULAP** & **FGAN** & **Water-Net** & **Ucolor** & **MLE** & **PGN** \\  & & [19] & [5] & [20] & [10] & [15] & [8] & [6] & \\ \hline \multirow{6}{*}{EUVP Dark} & PSNR \(\uparrow\) & 16.66 & 15.91 & 17.37 & 21.17 & 20.80 & 20.56 & 14.28 & **21.77** \\  & SSIM \(\uparrow\) & 0.773 & 0.789 & 0.771 & 0.881 & 0.864 & 0.857 & 0.588 & **0.889** \\  & UISM \(\uparrow\) & 7.152 & 7.141 & 7.216 & 7.179 & 7.203 & 7.207 & **7.599** & 7.234 \\  & MUSIQ \(\uparrow\) & 51.72 & 53.05 & 50.81 & 51.76 & 51.23 & 54.60 & 54.15 & **54.83** \\  & COSINE \(\uparrow\) & 0.786 & 0.801 & 0.784 & 0.901 & 0.897 & 0.883 & 0.654 & **0.927** \\ \hline \multirow{6}{*}{EUVP Imagenet} & PSNR \(\uparrow\) & 16.09 & 16.51 & 18.39 & 22.21 & 22.50 & 23.12 & 15.44 & **24.98** \\  & SSIM \(\uparrow\) & 0.634 & 0.714 & 0.707 & 0.767 & 0.817 & 0.783 & 0.584 & **0.839** \\  & UISM \(\uparrow\) & 6.784 & 6.779 & 6.935 & 6.961 & 6.952 & 6.930 & **7.44** & 7.12 \\  & MUSIQ \(\uparrow\) & 44.48 & 45.11 & 43.73 & 46.11 & 45.29 & 46.98 & **48.10** & 47.15 \\  & COSINE \(\uparrow\) & 0.689 & 0.759 & 0.776 & 0.786 & 0.837 & 0.819 & 0.612 & **0.846** \\ \hline \multirow{6}{*}{EUVP Scenes} & PSNR \(\uparrow\) & 19.65 & 18.43 & 19.93 & 25.48 & 22.65 & 26.21 & 14.98 & **27.40** \\  & SSIM \(\uparrow\) & 0.723 & 0.751 & 0.747 & 0.830 & 0.820 & 0.865 & 0.631 & **0.890** \\  & UISM \(\uparrow\) & 6.499 & 6.613 & 6.575 & 7.161 & 7.141 & 7.222 & **7.410** & 7.198 \\  & MUSIQ \(\uparrow\) & 35.56 & 35.13 & 35.64 & 44.85 & 44.24 & 45.69 & 42.56 & **45.89** \\  & COSINE \(\uparrow\) & 0.785 & 0.806 & 0.812 & 0.856 & 0.842 & 0.917 & 0.685 & **0.933** \\ \hline \multirow{6}{*}{UGAN} & PSNR \(\uparrow\) & 15.80 & 16.30 & 18.02 & 22.40 & 22.12 & 23.58 & 15.90 & **24.98** \\  & SSIM \(\uparrow\) & 0.627 & 0.708 & 0.699 & 0.759 & 0.806 & 0.793 & 0.596 & **0.839** \\ \cline{1-1}  & UISM \(\uparrow\) & 6.999 & 7.052 & 7.067 & 6.959 & 6.936 & 6.914 & **7.339** & 7.145 \\ \cline{1-1}  & MUSIQ \(\uparrow\) & 43.34 & 44.32 & 42.65 & 45.14 & 44.61 & 46.17 & **46.96** & 46.26 \\ \cline{1-1}  & COSINE \(\uparrow\) & 0.685 & 0.721 & 0.714 & 0.788 & 0.826 & 0.813 & 0.641 & **0.857** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Image Quantitative evaluation of different methods on the EUVP [10] and UGAN [16] datasets. The best results are highlighted in bold, and the second-best results are underlined. \(\uparrow\) demonstrates the larger the value, the better the effect.
## 2 Data and Methods
### Principles of OTSUI  
The maximum inter-class variance method (OTSU) divides the image into two parts--background and foreground--based on the grayscale characteristics of the image. Variance is a measure of the uniformity of grayscale distribution [20; 21]. A larger inter-class variance between the background and the foreground means a greater difference between the two parts that make up the image. When part of the foreground is incorrectly classified as the background or part of the background is misclassified as the foreground, the difference between the two parts becomes smaller. This method is mainly used to identify single targets such as landslides [22; 23]. The best threshold selection method for this purpose is OTSU, which reduces the probability of misclassification between the foreground and background through inter-class variance. The OTSU algorithm is shown in Equation (1).  
\[\sigma^{2}=P1(M1-MG)^{2}+P2(M2-MG)^{2} \tag{1}\]  
The optimal threshold for segmenting the image is the value of \(\sigma\) that maximizes the given equation. Among them, \(\sigma\) is the threshold for segmentation; \(P1\) and \(P2\) are the background scale and the foreground scale, respectively; \(M1\) and \(M2\) are the background mean and the foreground mean, respectively; and \(MG\) is the mean of the entire image.  
\[P1\times M1+P2\times M2=MG \tag{2}\]  
The expression is shown in Equation (2), and it can be simplified to Equation (6) when Equation (1) is more computationally intensive.  
\[\sigma^{2}=P1\times P2(M1-M2)^{2} \tag{3}\]  
The gray level that maximizes the abovementioned equation is the OTSU threshold.  
Figure 3: Flowchart of the proposed method.
## 2 Materials and Methods
### Network Architecture  
As shown in Figure 3, our proposal is based on a 3D U-Net architecture similar to the one of [14]. Each block in the encoding path contains two 3 \(\times\) 3 \(\times\) 3 convolutions followed by ReLU. Down-sampling in each resolution step is performed by a 2 \(\times\) 2 \(\times\) 2 max pooling operation with a stride of 2. In the decoder, up-sampling is performed with an up-convolution operation of 2 \(\times\) 2 \(\times\) 2 with a stride of 2, followed by two 3 \(\times\) 3 \(\times\) 3 convolution operations each followed by ReLU. Skip connections are also introduced at each level of the network and allow the decoder to access the higher-resolution features learned by the encoder. We obtain the DVF with a three-channel 1 \(\times\) 1 \(\times\) 1 convolution operation without activation function. Then, the spatial transformer network (STN) applies the learned DVF to warp the baseline CT image and its hematoma mask into the predicted follow-up (FU1) CT image and its predicted hematoma mask. The total number of parameters of the proposed network is 22.4 M. The overall configuration of network training and testing is described in the following section.
## 5 Performance Analysis
### Performance Analysis on Water Pipeline Tracking Application  
The results of the existing comprehensive framework [54], leakage detection method [55], heterogeneous two-tiered routing method [56], WiRoTip [57], and EarnArdui [58] are discussed with the LeakDB dataset collected from GitHub. The URL of the dataset is given as [74]. In total, 500 sensor nodes are deployed to conduct the simulation in water pipeline tracking.  
Figure 19 illustrates the Network Lifetime (\(NL\)) in Equation (8) of five different methods--across varying asset data points, ranging from 100 to 1000. At 1000 data points, the \(NL\)s for these methods are 62 s, 55 s, 49 s, 69 s, and 71 s, respectively. As the number of data points increases, the \(NL\) also shows a corresponding rise. Notably, EarnArdui demonstrates the highest \(NL\) among the methods, attributed to its use of a wireless sensor network testbed for water pipeline monitoring. This setup enhances the efficiency of the water distribution system monitoring, leading to a longer network lifetime. Specifically, EarnArdui outperforms the other approaches by 21%, 44%, 69%, and 11%, respectively, highlighting its superior performance.  
The overall measurement of the \(NL\) for five different existing methods is shown in Figure 20. The recorded \(NL\) are 51 s, 43 s, 37 s, 55 s, and 61 s, respectively. From this analysis, it is evident that the EarnArdui method achieves the longest \(NL\), outperforming the other methods. This indicates a significant improvement in EarnArdui's efficiency, making it more effective in extending network operation than the alternatives.
## 4 Experimental Results and Analysis
### Ship Classification Considering Geometric Features
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Ship Types**} & \multicolumn{6}{c}{**Evaluation Metrics**} \\ \cline{3-8}  & & _Precision_ & _Recall_ & _F\({}_{1}\)-Score_ & **Support Size** & _Accuracy_ \\ \hline \multirow{4}{*}{SVM} & Cargo & 72.80\% & 87.00\% & 79.27\% & 200 & \\  & Tanker & 82.59\% & 83.00\% & 82.79\% & 200 & \\  & Fishing & 54.88\% & 81.50\% & 65.59\% & 200 & \\  & Passenger & 71.30\% & 38.50\% & 50.00\% & 200 & \\  & Tug & 78.71\% & 61.00\% & 68.73\% & 200 & \\ \hline \multirow{4}{*}{RF} & Cargo & 78.07\% & 89.00\% & 83.18\% & 200 & \\  & Tanker & 83.65\% & 87.00\% & 85.29\% & 200 & \\ \cline{1-1}  & Fishing & 56.29\% & 85.00\% & 67.73\% & 200 & **73.10\%** \\ \cline{1-1}  & Passenger & 75.45\% & 41.50\% & 53.55\% & 200 & \\ \cline{1-1}  & Tug & 82.89\% & 63.00\% & 71.59\% & 200 & \\ \hline \end{tabular}
\end{table} 
Table 5: The classification results of the five types of ships considering the geometric features.
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 4 Numerical Simulations
### Performance over Different UWA Channel  
Besides the path-based channel model in the above experiments, the statistical UWA channel model is developed in [20]. This channel model considers detailed ocean physical parameters and deployment conditions. Through setting different deployment parameters as shown in Table 3, the channel impulse responses are shown in Figure 8. These channels present different sparse structures. Pass signals over these channels and CS-based channel estimation is utilized. The BER performance comparisons are shown in Figure 9. From the figure, we can see the proposed detector achieves better BER results compared with the conventional AMP methods. ML has the best BER results over all test channels. Over channel 1, considering the case when BER = \(3\times 10^{-3}\), the performance gap between the proposed detector and the LLR is about 1 dB, which is better than the conventional AMP-based detectors with 3 dB gains. Over channel 2, the proposed detector has approximate BER results with the LLR detectors and achieves 4 dB gains compared with the IM-MMSE-AMP detector at BER = \(10^{-2}\).  
We test the performance over the real-test channel in Wuyuan Bay, Xiamen, China. The channels are sensed and collected by the transceivers at a distance of 1000 m. The transmitter and the receivers are deployed about 4m below the surface. One example of a channel is shown in Figure 10a. From the channel impulse response, we can see the signals are reflected by the sea surface and the bottom. Pass the OFDM-IM signals over the real channel and add different Gaussian noise with a defined SNR level, the BER results are shown in Figure 10b. From the result, we can see the proposed detector keeps its superior performance compared with conventional AMP-based detectors, and most approximates the LLR and ML detectors.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Channel 1** & **Channel 2** \\ \hline Spreading factor & 1.7 & 1.7 \\ Sound speed in water (m/s) & 1500 & 1500 \\ Sound speed in bottom (m/s) & 1200 & 1200 \\ Surface variance (m\({}^{2}\)) & 0.0125 & 0.0125 \\ Bottom variance (m\({}^{2}\)) & 0.00625 & 0.00625 \\ Number of intra-paths & 20 & 20 \\ Mean of intra-path amplitudes & 0.025 & 0.025 \\ Variance of intra-path amplitudes & 0.000001 & 0.000001 \\ Distance (km) & 1.5 & 3 \\ Height of transmitter (m) & 45 & 58 \\ Height of receiver (m) & 60 & 59 \\ Depth of water (m) & 100 & 103 \\ \hline \hline \end{tabular}
\end{table}
Table 3: UWA channel parameters.  
Figure 8: Channel impulse response with different setting parameters. (**a**) Channel 1; (**b**) Channel 2.
# Analysis and Improvement of a Two-Stage Centrifugal Compressor Used in an MW-Level Gas Turbine
## 6 Results  
This study presented an optimized design method for centrifugal compressors based on 1D calculations and analyses. A low/high-pressure-stage centrifugal compressor in an MW-level gas turbine was optimized by the proposed method. According to the calculation results of the MW-level gas turbine cycle, three typical load conditions of 100%, 90%, and 60% were calculated for the original and optimized designs. The main conclusions are summarized as follows.  
For the low-pressure-stage centrifugal compressor, the analysis showed that the diameter ratio of the vaned diffuser was overly large, which led to an increase in loss and low efficiency. The diameter ratio was reduced in the optimized design. To optimize the ratio of the throat area between the impeller and diffuser, a tandem diffuser was used to replace the original single-stage diffuser. After optimization, the pressure ratio increased by more than 3%, and efficiency improved by more than 2%.  
For the high-pressure-stage centrifugal compressor, the calculation results of the 1D optimal design system showed that the divergent angle of the vaned diffuser was overly large and led to unit performance degradation. In the optimized design, the divergent angle was reduced by using a vaned diffuser with splitter vanes. After optimization, the pressure ratio and efficiency increased by more than 4%.  
Through the coupling calculations at 100%, 90%, and 60% loads, the performances of optimized design were significantly improved compared to those of the original design in the low/high-pressure-stage centrifugal compressor. Under the three load conditions, the pressure ratio of the unit increased by approximately 8%, and efficiency improved by approximately 4%.  
Conceptualization, X.S.L. and C.W.G.; Methodology, W.Z. and X.D.R.; Investigation, W.Z. and X.D.R.; Validation, W.Z. and X.D.R.; Writing-Original Draft Preparation, W.Z. and X.S.L.; Writing-Review & Editing, W.Z. and X.S.L.; Visualization, X.S.L.; Supervision, C.W.G.  
This research was funded by [National Natural Science Foundation of China] grant number [51736008].  
The authors declare no conflicts of interest.  
The theoretical ratio of the impeller and the diffuser throat areas when  
\(A_{d}{}^{*}/A_{i}{}^{*}\)  
the impeller and the vaned diffuser choke at the same time  
\(B\)  
hub to shroud passage width  
\(b^{*}\)  
ratio of vaneless diffuser inlet width to impeller exit width  
\(B\)  
aerodynamic blockage  
\(c_{f}\)  
skin friction coefficient  
\(C\)  
absolute velocity  
\(C_{p}\)  
specific heat at constant pressure  
\(C_{m}\)  
absolute meridional velocity  
\(C_{\theta}\)  
absolute tangential velocity  
D diameter  
\(d_{HB}\)  
hydraulic diameter  
\(D_{f}\)  
diffusion factor  
\(\Delta\)h\({}_{th}\)  
euler work  
\(L_{B}\)  
impeller flow length  
\(L_{Z}\)  
axial length of impeller  
\(m\)  
mass flow rate  
\(U\)  
Impeller periphery velocity  
\(W\)  
relative velocity  
\(Z\)  
number of blade
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 3 Proposal Method and Discussion
### Optimization with Respect to RIS Phase Shift Matrix
* The iterative optimization process starts in line 6 and continues until the maximum number of iterations \(T\) is reached. * In lines 7-17, we perform operations for each particle \(i\) in the population. - Lines 7-8 update the velocity and position of the particle based on its previous position, the best position it has encountered (\(\mathbf{q}_{\text{best},i}\)), and the best position in the entire swarm (\(\mathbf{z}_{\text{best}}\)). This helps particles explore the solution space. - Line 9 evaluates the fitness value of the particle's current position. - Lines 10-11 update the historical best position of the particle based on whether the current position or the previous best position is better. This helps particles remember their best-performing positions. - After evaluating and updating each particle, we find the best position among all particles in line 12 and update the historical best position of the entire population (\(\mathbf{z}_{\text{best}}^{(t+1)}\)) in line 15. - The adaptive parameter is adjusted in lines 16 based on the performance of the swarm according to Algorithm 4. - The dynamic adjustment of \(\omega\) aims to strike a balance between exploration and exploitation during the PSO algorithm's execution. It helps fine-tune the search process to improve convergence and find optimal solutions effectively. The AO algorithm is presented in Algorithm 5. ```
1. Initialize \(c=0\);
2. if\(\textbf{z}_{\text{best}}^{(t+1)}<\textbf{z}_{\text{best}}^{(t)}\)
3. \(flag=1\);
4. else
5. \(flag=0\);
6. end if
7. if\(flag=0\)
8. \(c=c+1\);
9. else
10. \(c=\max\{c-1,0\}\);
11. if\(c<2\)
12. \(\omega=2\omega\);
13. else if\(c>5\)
14. \(\omega=\omega/2\);
15. end if
```  
**Algorithm 4**Adjust adaptive parameter. ```
1. Input:\(K,N,M,\eta,P_{\text{Bs}},P_{\text{ULE}},P_{m}(b),P_{\text{max}},\sigma^{2},\{R_{ min,k}\}_{k=1}^{K},\mu_{R,\mu}^{k},\mu_{T,n},\epsilon>0\);
2. Output:\(\boldsymbol{\rho}^{\star},\boldsymbol{P}^{\star}\), and \(\boldsymbol{\Phi}^{\star}\)
3. repeat
4. Fixing the RIS coefficients \(\boldsymbol{\Phi}=\boldsymbol{\Phi}_{0}=\frac{\pi}{2}\cdot I_{N}\)
5. Find the \(\boldsymbol{\rho},\boldsymbol{P}\) according to MDSA Algorithm and
5. SQP Algorithm
6. Fixing the power allocation \(\boldsymbol{\rho},\boldsymbol{P}\), given initial \(\boldsymbol{\Phi}_{0}\), update the RIS coefficients \(\boldsymbol{\Phi}\) via PSO Algorithm
7. Until the objective value of (P1) with the obtained \(\boldsymbol{\rho},\boldsymbol{P}\), and \(\boldsymbol{\Phi}\) reaches convergence. 8. \(\boldsymbol{\rho}^{\star}=\boldsymbol{\rho},\boldsymbol{P}^{\star}=\boldsymbol{P}\), and \(\boldsymbol{\Phi}^{\star}=\boldsymbol{\Phi}\)
```  
**Algorithm 5**Alternating optimization algorithm.