esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
In summary, the time responses of the generator speed shown in Figure 9c, the output power shown in Figure 11c, results presented in Table 1, and open loop frequency responses indicate that the overall performance of the optimal controllers, particularly MPC and \(H_{\infty}\) controllers, outperform PID controllers, as illustrated in the frequency domain. Figure 13c shows the corresponding control input, i.e., pitch angle demand in mode 3. Generator speed control is achieved by adjusting the pitch angle in mode 3, as aforementioned. Figure 13: Time responses of torque and pitch angle demand (control input) in mode 1 and 2 and mode 3, respectively: MPC, LQG, and \(H_{\infty}\) based on Bladed linearization model; (**a**) mode 1, (**b**) mode 2, (**c**) mode 3. The results discussed so far are summarized in Table 2. It introduces the performance of all optimal controllers using error metrics. As aforementioned, the \(C_{pmax}\) curve is tracked in order to extract as much energy as possible from the wind in mode 1; in mode 2, the constant generator speed (\(\omega_{0}\)) is tracked; and the rated power is maintained in mode 3 by active pitching. Table 2 illustrates that, in mode 1, all optimal controllers appear to be satisfactory in terms of tacking the \(C_{pmax}\) curve. In mode 2, all optimal controllers appear to satisfactorily track the constant generator speed (\(\omega_{0}\)). In mode 3, all optimal controllers appear to satisfactorily maintain the rated power. Figure 14: Open–loop frequency responses of PID, MPC, LQG, and \(H_{\infty}\) controllers based on Bladed linearized model in mode 2. Figure 15: Open–loop frequency responses of PID controller with and without damper.
## 4 Experimental Results and Discussions
### Experiment Comparison of BBO, OBBO, ROBBO and COOBBO
Compared with the classical BBO, the evolution process of COOBBO tends to last shorter, because more candidate paths are tried in each iteration. In a word, the original balance between exploitation and exploration in BBO is destroyed by opposition-based learning, and then COOBBO suffers more exploitation, which leads to better solution convergence, and less exploration, which leads to lower population diversity, in the evolution process. According to our understanding, it is the key reason that the experiment results shown in Table 2 are not consistent with the common view in the field of population-based search algorithms.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 3 Classification of Multi-Objective Evolutionary Algorithms from the View of Search Strategy
### Dominant Relation-Based MOEA Algorithms
#### 3.2.2 Lexicographic Optimization Method  
In the study of MOEA algorithms using dominance relation, lexicographic optimization is a class of multi-objective optimization algorithms that identify the solution to the Pareto front. It starts by considering an objective function and determines the feasible domain of the objective space using other functions as constraints [79]. Depending on their essentialness or significance, the objective functions are identified and optimization is carried out iteratively. Single-objective optimization can more easily find the optimum because only one objective function is considered. However, since multi-objective optimization attempts to search for the Pareto optimal solutions, the use of lexicographic optimization allows multiple objective functions simultaneously aiming for the Pareto optimal solutions.  
Figure 4 presents an example of the lexicographic optimization process for a bi-objective minimum optimization problem, where \(f_{1}(x)\) and \(f_{2}(x)\) are the objective functions and \(f_{1}^{\prime}\) and \(f_{2}^{\prime}\) are the optimal solutions of two objective functions, respectively. To prevent finding the weak Pareto optimum solution, the objective functions are minimized in turn, while the solutions achieved are employed as constraints. The Pareto optimum solution is discovered using the optimal value of one objective function as a constraint  
Figure 3: The operation mechanism of VEGA. The MOP is divided into groups, each sub-objective function is evaluated and selected independently, then a new group is formed, crossover and variation operations are performed, and this proceeds cyclically, resulting in non-inferior solutions to the problem. This figure is adopted from [5].  
after the other objective functions have been minimized. In this way, if there are \(n\) objective problems, \(n\) initial best solutions of each objective function can be available as the beginning point for finding the next Pareto optimal solution of the objective function that is currently optimized.
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
## 6 Conclusions  
This paper studies communication problems of safety related information in VANETs. Under the proposed framework, the IA technology applied in VANETs is realized. Taking advantage of data-driven assisted transmission, the use ratio of route is improved. The simulation results show that this approach can effectively improve VANETs link use and reduce communication delay.  
[PERSON] contributes to the writing of this article, references, analysis of experimental results and data interpretation. [PERSON] contributes to the research idea, direction, acquisition and design of this article. [PERSON] contributes to the literature retrieval, research content, algorithm design, data analysis, algorithm analysis, manuscript writing, etc. [PERSON] contributes to the experimental design, algorithm realization, experimental analysis and manuscript review of this article. [PERSON] contributes to the implementation of experimental algorithm, chart making, data collection and data analysis of this article.  
**Funding:** This work is supported by the National Natural Science Foundation of China under Grant No. 61701162 and the Fundamental Research Funds for the Central Universities (GRANT NO. JZ2019 YYPY0298, JZ2019 YYYY0290).  
**Acknowledgments:** We also want to deliver our sincere gratitude to those who made arduous efforts in reviewing this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of Vehicles**} & \multicolumn{2}{c}{**Proportion of Base**} & \multicolumn{2}{c}{**Ratio of Transmit**} & \multirow{2}{*}{**PTU**} & \multirow{2}{*}{**PTU without IA**} \\  & **Station Session** & & **Links to Sessions** & \\ \hline
30 & 0.65 & 0.74 & 0.55 & 0.65 \\
40 & 0.65 & 0.85 & 0.65 & 0.76 \\
50 & 0.67 & 0.80 & 0.70 & 0.88 \\
60 & 0.70 & 0.69 & 0.80 & 0.93 \\
70 & 0.72 & 0.90 & 0.85 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 8: PTU and Percentages of Sessions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of Vehicles**} & \multicolumn{2}{c}{**Proportion Direct**} & \multicolumn{2}{c}{**Proportion of Forward**} \\  & **Transmission** & **Transmission** \\ \hline
30 & 0.30 & 0.71 \\
40 & 0.47 & 0.53 \\
50 & 0.45 & 0.55 \\
60 & 0.36 & 0.64 \\
70 & 0.34 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Proportion of Direct and Forward Transmission.
# Investigation of Ti65 Powder Spreading Behavior in Multi-Layer Laser Powder Bed Fusion
## 3 Simulation Conditions and Model Validation
### Model Validation  
To validate the accuracy of the coupled DEM-CFD model, both numerical simulations and physical experiments of powder spreading and selective laser melting are conducted under identical conditions. In this study, the powder spreading and printing experiments were performed by the EOS M280, and the microstructure morphology was obtained by the Olympus BX51M microscope. The top-view morphologies of the first powder layer are presented in Figure 5a (\(H_{\text{set}}\) = 60 \(\upmu\)m, \(V\) = 0.100 m/s). In both the simulation and experimental results, powders are randomly distributed on the substrate, exhibiting a certain degree of clustering due to cohesive forces. The quantitative characterization of powder layer porosity is presented in Figure 5b, showing good agreement between the simulation and the experiment, with porosity values of approximately 0.9.  
Figure 4: Illustration of the melting process, where (**a**) powder bed before printing; (**b**) printing process; (**c**) printing strategy.  
Figure 5: Comparison of powder spreading (\(H_{\text{set}}\) = 60 \(\upmu\)m, \(V\) = 0.100 m/s) and selective melting (printing power \(p\) = 250 W, printing speed \(V_{\text{p}}\) = 1.40 m/s) between numerical simulations and physicalexperiments, showing (**a**) morphology and (**b**) porosity of the first powder layer, and (**c**) morphology and (**d**) size of the printed region.  
Figure 5c provides a qualitative comparison of morphologies of the printed regions (laser power \(p=250\) W, printing speed \(V_{\mathrm{p}}=1.40\) m/s). Both experimental and numerical results display a continuous, well-defined melt track. In the experimental image, the track edges are notably rough and exhibit visible spatter. The simulation also captures the overall geometry and irregularities at the edge, although with slightly smoother boundaries. This deviation is largely attributed to the inherent difficulty in fully capturing powder spatter dynamics with the CFD model. A quantitative comparison of the single-track dimensions is shown in Figure 5d. The experimentally measured width and depth of the track are approximately 171 \(\upmu\)m and 109 \(\upmu\)m, respectively. The corresponding simulated values are 176 \(\upmu\)m in width and 107 \(\upmu\)m in depth, demonstrating a strong agreement.
# SMCSPO-Based Robust Control of AUV in Underwater Environments including Disturbances
## 3 Controller and Observer Design
### Smcspo  
In this section, we discuss the integration of the SMC and SPO. For the vehicle system (26), the estimated sliding function is defined as  
\[\mathbf{\hat{S}}\mathbf{=}\mathbf{\hat{E}}\mathbf{+}\mathbf{C}\mathbf{E}, \tag{34}\]where \(\mathbf{C}>0\) and \(\mathbf{\hat{E}}=\mathbf{\hat{X}_{1}}-\mathbf{X_{1d}}\) are the estimated position tracking errors. The control input of the SMC is selected to enforce \(\hat{\mathbf{S}}\hat{\mathbf{S}}<0\). The desired s-dynamics are defined as  
\[\dot{\hat{\mathbf{S}}}\mathbf{=}\mathbf{-}Ksat\big{(}\hat{\mathbf{S}}\big{)}, \tag{35}\]  
where  
\[sat(\hat{s})=\left\{\begin{array}{ll}\hat{s}/|\hat{s}|,&if\ |\hat{s}|\geq\varepsilon_{c} \\ \hat{s}/\varepsilon_{c},&if\ |\hat{s}|<\varepsilon_{c}\end{array}\right. \tag{36}\]  
The saturation function is used because of its desirable anti-chatter properties. In this equation, \(\varepsilon_{c}\) is selected for the boundary layer of the SMC controller, like the \(\varepsilon_{o}\) in the SPO. If the vehicle system is controlled only with SMC without using SPO, the K gain will be increased to control nonlinear terms. This can cause chattering in the control result or control input.  
Using the results of the previous sections, \(\dot{\hat{s}}\) can be computed as  
\[\dot{\hat{\mathbf{S}}}\mathbf{=}\mathbf{\alpha_{3}}\mathbf{\Pi}\mathbf{-}\left(k_{2} \mathbf{+}C\mathbf{k_{1}}\mathbf{-}\mathbf{k_{1}^{2}}\right)\widetilde{\mathbf{X}}_{1} \mathbf{-}\mathbf{k_{1}}\widetilde{\mathbf{X}}_{2}\mathbf{-}\ddot{\mathbf{X}}_{1d}\mathbf{ +}\mathbf{C}\Big{(}\widetilde{\mathbf{X}}_{2}\mathbf{-}\dot{\mathbf{X}}_{1d}\Big{)}\mathbf{ +}\mathbf{\Psi}. \tag{37}\]  
When \(\widetilde{x}_{2}=0\), a control law is selected using (35) and (37), as follows:  
\[\mathbf{\Pi}\mathbf{=}\frac{1}{\mathbf{\alpha_{3}}}\Big{\{}-Ksat\big{(}\hat\mathbf{{S}}\big{)}\mathbf{+}\left(k_{2}\mathbf{+}C\mathbf{k_{1}}\mathbf{-}\mathbf{k_{1}^{2}} \right)\widetilde{\mathbf{X}}_{1}\mathbf{+}\ddot{\mathbf{X}}_{1d}\mathbf{-}C\Big{(} \widetilde{\mathbf{X}}_{2}\mathbf{-}\dot{\mathbf{X}}_{1d}\Big{)}\mathbf{-}\mathbf{\Psi} \Big{\}}. \tag{38}\]
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 6 Performance Evaluation
### Block Error Rate Performance Comparison  
As shown in Figures 13 and 14, we have compared the CBER performance of SCMA system with PIC polar codes, uncoupled polar codes, and LDPC code over AWGN and Rayleigh channels.  
From these figures, the PIC PCCA-PC-SCMA has the best performance at various code rate and channel configurations, e.g., over AWGN channel, when K = 100 and R = 1/2, the PIC CA-PC-SCMA achieves 0.28 dB coupling gain over the CA (SCL)-PC-SCMA at CBER = \(10^{-3}\) as shown in Figure 13a. In the early stage of each CB decoding, parity-check bits are used to correct errors and save the correct decoding path as far as possible. Therefore, PIC PCCA-polar code also achieved an additional 0.22 dB coding gain compared to PIC CA-polar code over SCMA system. Similarly, for the code rate-1/3, PIC PCCA-PC-SCMA can also obtain corresponding gain as shown in Figure 13b. However, at the same time, the performance of uncoupled CA-PC-SCMA based on SCAN decoding is the worst among other systems. Since error blocks cannot provide perfect extrinsic messages to adjacent blocks in a TB, adjacent CB can only be decoded at a higher code rate. In particular, in the lower \(E_{b}/N_{0}\) range, a wrong CB often causes continuous CB errors in a same TB, in other words, these errors are aggregated. Thus, with SCL decoding (for CB), the CBER performance of PIC PC-SCMA systems is worse than that of uncoupled PC-SCMA system. Similarly, we can obtain the same results from Figure 14.  
Figure 14: CBER of SCMA over Rayleigh channel. (a) \(K=100\), \(R=1/2\); (b) \(K=64\), \(R=1/3\).  
Figure 13: CBER of SCMA over AWGN channel. (a) \(K=100\), \(R=1/2\); (b) \(K=64\), \(R=1/3\).  
The TBER performance curves of SCMA system are also given by Figures 15 and 16. Obviously, we can get results similar to CBER performance. At a low \(E_{b}/N_{0}\), although wrong CB may easily cause continuous CB errors in a same TB, the look-back and go-back scheme can also be used to improve the probability of correct decoding once any CB is correctly decoding. PCCA-polar code uses parity check to improve the performance of the base code, thus achieving the goal of reducing TBER. Therefore, even at a low \(E_{b}/N_{0}\), PIC PCCA-PC-SCMA also maintains the best TBER performance. Table 2 shows the specific performance gains of PIC PCCA-PC-SCMA for various code rates and channel configurations at target CBER = \(10^{-3}\) and TBER = \(10^{-2}\).
# Energy Optimisation of Industrial Limestone Grinding Using ANN
### Towards Energy-Efficient Grinding Technologies  
Over the last three decades, the industry has adopted novel communition technologies--high-pressure grinding rolls (HPGRs), stirred media mills, and vertical roller mills--that can achieve 10-40% energy savings relative to traditional ball mills [13; 14]. Complementary advances in liner design and discrete element method (DEM) simulations have improved charge motion and lifter profiles, further lowering specific energy consumption [15]. Despite these technological gains, operational energy efficiency is still highly sensitive to ore hardness variability, feed size fluctuations, and sub-optimal control actions [16]. Consequently, the communition community continues to view real-time optimisation as a major lever for both cost reduction and greenhouse gas mitigation.
# Estimation of Changes in Sediment Transport along the Free-Flowing Middle Danube River Reach
## 4 Results
### Results of the Hydrological Data Trend Analysis  
The results of the trend analysis of the annual characteristic values of water levels are presented in Figure 5, based on data from [5]. The data show a linear decreasing trend at all gauging stations; the trendline slope is negative along the reach except in Novi Sad at the downstream end, where the free-flowing Danube enters the Iron Gate I reservoir. It is readily apparent that the rate of decrease in minimum water levels decreases in the downstream direction. The decreasing rate is more than three times greater on the gravel bed (approximately 3.9 cm/year) than on the sand bed sub-reach, where the average decreasing rate upstream of the confluence of the Drava River is 1.1 cm/year. Along the sand bed reach between Baja and Bogojevo (just upstream of the Drava River confluence), it almost linearly decreases from 1.3 cm/year to 0.9 cm/year. Based on these data, the total change in water level minima (\(\Delta Z\)) for the analyzed 70-year period ranges from 1.45 m at the beginning of the sand bed reach (Paks) to approximately 0.60 m at Bogojevo (Figure 6). Between Mohacs and Apatin, the total drop in water levels changes almost linearly from 0.76 m to 0.70 m. The corresponding average yearly water level decrease (\(\delta Z\)) is around 2 cm/year at Paks, 1.3 cm/year at Baja, and approximately 1 cm/year between Mohacs and Apatin. Downstream of the Drava River confluence, it is less than 0.9 cm/year.  
Variation of trends for the mean values on the gravel-bed sub-reach is almost the same as for minima, while that on the sand-bed reach is similar, except the rates are higher. Since means include the influence of water level maxima, any anomaly in the water level maxima reflects on them. It is visible at Mohacs (Figure 5), where the rate of decrease is approximately 25% greater than expected (0.0146) had not there been this anomaly in the water level maxima.  
The annual characteristic values of discharges on the reach between Baja and Apatin did not change significantly during the investigated period, as annual maxima show a slightly increasing trend, while mean values and minima fluctuate around almost constant values (\(Q_{\min}\) around 1100 m\({}^{3}\)/s and \(Q_{\mean}\) around 2300 m\({}^{3}\)/s) [5].
# Target State Optimization: Drivability Improvement for Vehicles with Dual Clutch Transmissions
## 5 Results  
### Software in the Loop  
To compare the TSO algorithm with the best benchmark algorithms of Section 3, the TSO algorithm is applied on the optimization problem of Section 2 within the software in the loop environment.  
Table 4 is introduced to illustrate the results. Again, as in Table 3, the columns illustrate the average values (\(\overline{x}_{S}\) and \(\overline{x}_{F}\)), the standard deviation (\(\sigma_{S}\) and \(\sigma_{F}\)), and the result range (\(min_{S}\) and \(max_{S}\)) of the \"successful iterations\" and the \"first success\" over the five test runs.  
It is observable that the activation function (Section 4.4) has a significant impact on the results. While the TSO algorithm with the ReLU activation function outperforms the GA and the best tested RL algorithm (with regard on successful iterations), the TSO algorithm with the sigmoidal activation function lacks in performance which is observable in Figure 13.  
Although the standard deviation is very high compared to the other algorithms (one outlier with only 36 successful iterations, which is significantly low compared to the other test runs), the results show that for a fast and reliable success the ReLU activation function has to be used for the TSO algorithm. It is also noticeable that the first successful iteration generated is almost the same for the NSGA-II and the TSO algorithm with the ReLU activation function.  
Different to the activation function, the hyperparameters explained in Section 4.2 are adjusted during the optimization. The hyperparameter-tuning is carried out three times. The progress of the hyperparameter tuning for the TSO algorithm with the ReLU activation function is illustrated in Table 5 for two different test runs.  
Due to the success within the software in the loop environment, the algorithms NSGA-II and TSO are further tested in different test vehicles. Within the test vehicles the last combination of the hyperparameters (layers = 4, neurons = 425, learning rate = 0.0012) is tested to optimize the calibration parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \(\overline{x}_{S}\) & \(\sigma_{S}\) & \(min_{S}\) & \(max_{S}\) & \(\overline{x}_{F}\) & \(\sigma_{F}\) \\ \hline NSGA-II & 104.2 & 15.4 & 84 & 126 & 21.0 & 13.8 \\ SAC & 28.8 & 4.2 & 22 & 32 & 46.4 & 31.1 \\ TSO (sigmoidal) & 7.8 & 5.5 & 2 & 16 & 221.6 & 332.0 \\ TSO (ReLU) & 250.0 & 160.4 & 36 & 442 & 20.6 & 11.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the TSO algorithm with the benchmark algorithms.  
Figure 13: Comparison of the influence of the activation functions of the TSO algorithm on the re–sults: (**a**) sigmoidal activation function, (**b**) ReLU activation function.
## 3 Results
#### 3.2.1 Particle Size Analysis  
PEG-b-PLGA NPs and PEG-b-PLGA NPs encapsulated with geranin were of optimal size for drug delivery, with values of \(102.7\pm 12.36\) nm and \(134.2\pm 1.45\) nm, respectively. The analysis was done on freeze-dried samples. No abnormality in sizes was detected. The DLS data provides an analysis of NP sizes of all NPs loaded at the time. Therefore, DLS data is ideal at representing an overall NP size (Supplementary data; Figures S1 and S2). Polydispersity index (PDI) values were \(0.30\pm 0.02\) and \(0.24\pm 0.01\), respectively. According to [PERSON] and [PERSON] (2008), the NP size range of 100 to 200 nm is the most suitable for oral drug delivery. The PDI indicates the size distribution of the particles, and a PDI value \(>0.5\) indicates a broad size distribution that may contain large particles or aggregates that could be gradually sedimenting, which is not suitable for DLS analysis. Thus, the data showed that both blank PEG-b-PLGA NPs and PEG-b-PLGA NPs encapsulated with geranin were within the optimal size range for drug delivery. The size of NPs and the PDI value play crucial roles in drug delivery applications because physical properties enable the drug carrier to cross various biological barriers within the body.