esa-sceva/satcom-chunk-collection huggingface dataset:
# A Polishing Processes Optimization Method for Ring-Pendulum Double-Sided Polisher
## 2 Model of Radial-Feed System of the RDP  
The RDP is presented in Figure 1. It mainly comprises the lower-polishing disk with rotation pedestal, the element fixation disk, and the upper-polishing disk. The optical element is positioned on the element fixation disk, and the lower-polishing disk polishes the lower surface through rotation. The upper disk and the element-fixation disk polish the optical element's upper surface by rotational and radial-feed motion simultaneously.  
The radial-feed motion of the element fixation disk is less susceptible to uncertain disturbances compared to that of the upper-polishing disk. We establish a model for the radial-feed system of the latter. The upper-polishing disk radial-feed system of the RDP's structure is demonstrated in Figure 2. The radial-feed system is composed of a radial-feed permanent-magnet synchronous motor, a spherical screw drive, and an upper-polishing disk. The screw is driven by the motor to realize the radial-feed motion.  
The second-order system model proposed in Equation (1) represents an open-loop transfer function of the radial feed's speed and current. The motor driving current \(i_{q}(s)\) provides the input, while the radial-feed motor speed of the upper-polishing disk \(\omega(s)\) represents the output. The remaining physical quantities in Equation (1) have the following meanings: \(T\) is the load torque, \(p_{n}\) is the number of motor poles, \(\psi_{f}\) is the flux of a  
Figure 1: The ring-pendulum double-sided polisher.  
Figure 2: Schematic diagram of radial-feed system of ring pendulum double-sided polisher.  
permanent magnet, \(J\) is the motor's moment of inertia, and \(B\) is the viscous damping coefficient.  
\[\frac{\omega(s)}{i_{q}(s)}=\frac{1.5p_{n}\psi_{f}}{(T_{S}+1)(Js+B)}=\frac{1.5p_{n }\psi_{f}}{JTs^{2}+(J+TB)s+B} \tag{1}\]  
Equation (2) shows the radial-feed system model with its parameters identified by the multi-innovation stochastic gradient descent parameter identification method [35; 36].  
\[\frac{\omega(z)}{i_{q}(z)}=\frac{-2.0491z^{-1}+88.5127z^{-2}}{1-0.0788z^{-1}+0. 1852z^{-2}} \tag{2}\]  
To facilitate the subsequent controller design, we transform the system transfer function into the state space in Equation (3), which adds the interferences of the system state and control quantity into the model.  
\[\begin{array}{c}x(k+1)=Ax(k)+Bu(k)+w(k)\\ y(k)=Cx(k)+v(k)\end{array} \tag{3}\]  
where A = [0.0788, \(-0.1852\); 1.0000, 0], B = [1; 0], and C = [\(-2.0491\), \(88.5127\)]. \(x(k)\) is the system state variable and \(u(k)\) is the system control quantity. \(w(k)\) and \(v(k)\) are system state disturbance and control quantity disturbance, respectively.
## 3 Constitutive Model of Rock Mass Under THMC Coupling
### THM Coupling Models
#### 3.2.4 Creep Constitutive Model Study of Deep Rocks  
The rheological behavior of rocks, particularly under multi-field coupling, is a critical area of study in rock mechanics. Scholars are particularly focused on uncovering the mechanical effects of these multi-field interactions and developing rheological models that accurately describe the behavior of rocks under such conditions [161]. Only through detailed rheological experiments can constitutive models be developed that reflect the long-term deformation characteristics of rocks, thereby guiding practical engineering applications.  
One of the primary factors influencing the creep properties of rocks is their water content [174]. Water affects several aspects of rock behavior, including elastic deformation, the duration of creep, and the magnitude of creep deformation [175]. Typically, researchers investigate the effect of water content by comparing the creep properties of dry rock specimens and water-saturated specimens. This method allows for a qualitative analysis of how water presence alters rock creep characteristics. However, since rock water content varies between these two extremes, it becomes crucial to study the full spectrum of moisture content and its impact on the mechanical properties of rock. A comprehensive understanding of these effects can lead to more accurate creep models that account for the variability of water content in natural and engineered settings.  
In many underground projects, such as tunneling or mining, the groundwater often contains chemical ions that can influence rock properties. Acidic and alkaline solutions, as well as cyclical wetting and drying processes, further complicate the rheological behavior of rocks. Automatic rheological servo testing machines are used to analyze the effects of water on creep deformation and permeability over time to study these influences, providing insight into the long-term stability of underground rock formations.
## 3 Improved Snake Optimization Algorithm for Melting Point Optimization Solution
### Actual Production Data Experiment  
Using real production data from a non-ferrous metal smelting plant in China, simulation tests were conducted to compare the performance of the ISO, SO, HHO, GWO, SOA, and SCA algorithms. The algorithm parameters were set according to Table 3. The comparison of slag melting point optimization results is shown in Table 9, and the convergence curve of the zinc smelting slag melting point reduction is depicted in Figure 6.  
Analysis of Table 9 and Figure 6 reveals that in terms of convergence accuracy, the improved ISO algorithm can reduce the slag melting point to 1259.8 \({}^{\circ}\)C, which is the lowest among recent algorithms such as HHO and GWO that have shown promising results in this field. In terms of convergence speed, the ISO algorithm demonstrates a significant improvement compared to GWO, SOA, and HHO. Although its convergence speed is similar to that of the SO and SCA algorithms, the ISO algorithm achieves a much higher precision. It meets the real-time and precision requirements of practical production and has achieved good results, effectively reducing the melting point of the mixed slag, thereby reducing the cost of the smelting plant and enhancing its economic benefits.  
To validate the authenticity and feasibility of the optimization results, experimental slag blending tests were conducted using raw materials from the factory, and the actual slag melting point was determined using a slag melting point and melting speed tester RDS-05. The actual experimental measurement of the slag melting point was found to be 1253 \({}^{\circ}\)C, which differs from the optimized result by 6.8 \({}^{\circ}\)C, with an error of approximately 0.54%, within the normal range of error. Therefore, the improved snake optimization algorithm demonstrates feasibility and authenticity in the slag melting point optimization problem. This method can assist metallurgical production personnel in selecting process parameters  
Figure 6: Comparison of slag melting point optimization results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Iron–Silicon** & **Calcium–Silicon** & **Coal Quantity** & **Oxygen Flow** & **SiO2** & **Melting Point** \\ \hline real\_data & 2.0049 & 0.4462 & 109.80 & 76.0588 & 11.1251 & 1390.0041 \\ GWO & 1.8611 & 0.4591 & 109.13 & 75.4719 & 10.2991 & 1333.5102 \\ SOA & 1.8596 & 0.4757 & 110.08 & 71.0769 & 10.3537 & 1324.2401 \\ SCA & 1.9248 & 0.6490 & 106.93 & 70.8544 & 10.3901 & 1316.3657 \\ SO & 1.9981 & 0.6199 & 105.02 & 72.3678 & 10.4288 & 1299.3442 \\ HHO & 1.9994 & 0.6618 & 104.91 & 73.5412 & 10.4004 & 1281.7811 \\ ISO & 2.0472 & 0.6804 & 104.89 & 71.4551 & 10.3930 & 1259.8102 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of slag Melting Point Optimization Results.  
quickly and effectively. Given the similarity between process parameter optimization problems in other metallurgical fields and zinc smelting slag optimization problems, this algorithm also holds great potential for development in related fields.
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
###### Abstract  
Underwater acoustic communication (UWAC) has a wide range of applications, including marine environment monitoring, disaster warning, seabed terrain exploration, and oil extraction. It plays an indispensable and increasingly important role in marine resource exploration and marine economic development. In current UWAC systems, the terminal nodes are usually powered by energy-limited batteries. Due to the harshness of the underwater environment, especially in the ocean environment, it is very costly and difficult, even impossible, to replace the batteries for the terminal nodes in UWACs, which results in the short lifetime and unreliability of the terminal nodes and the systems. In this paper, we present the application of a wireless powered communication network (WPCN) to the UWAC systems to provide an auxiliary and convenient energy supplement for solving the energy-limited problem of the terminal nodes, where the hybrid access point (H-AP) transfers energy to the terminal nodes in the downlink. In contrast, the terminal nodes use the harvested energy to transmit the information to the H-AP in the uplink. To evaluate the proposed WPCN-based UWAC systems, we investigate the performance of the average bit error rate (BER), outage probability, and achievable information rate for the systems in a frequency-selective sparse channel and non-white noise environment. We derive the closed-form expression for the probability density function (PDF) of the received signal-to-noise ratio (SNR). Based on this, we further derive novel closed-form expressions for the average BER and the outage probability of the systems. Numerical results confirm the validity of the proposed analytical results. It is shown that there exists an optimal signal frequency and time allocation factor for the systems to achieve optimal performance, and a larger optimal time allocation factor is preferred for a smaller hybrid access point (H-AP) transmit power or a larger transmission distance, while a smaller optimal signal frequency is required for a larger transmission distance.  
underwater acoustic communication (UWAC); wireless powered communication network (WPCN); OFDM; average BER; outage probability +
Footnote †: journal: Marine Science and Engineering
## 1 Introduction
However, it is necessary to divide the plate into meshes according to the required accuracy and then perform scanning calculations. This results in a larger calculation cost and a lower localization speed. Based on the localization performances of TDOA method and beamforming method, this paper proposes a joint localization method. The proposed method not only improves the accuracy of the TDOA method, but also greatly reduces the localization calculation coat of the beamforming method. The organization of this paper is as follows. The principle of TDOA method and beamforming method are introduced in Section 2. Section 3 analyses the performances of TDOA method and beamforming method based on simulated signals. Section 4 proposes the joint localization method. Section 5 carries out simulation and experimental verification. Finally, the conclusions are drawn in Section 6.
## 4 Experiments and Findings
### Experimental Findings on CSI-HAR Dataset  
The results of the CSI-based HAR dataset demonstrate the superior classification capabilities of the CNN-GRU-AttNet model, as presented in Table 4. The findings highlight the CNN-GRU-AttNet model's outstanding performance, with an average accuracy of 99.62%, precision of 99.61%, recall of 99.61%, and F1-score of 99.61% across all human movements. Furthermore, a comparative analysis indicates that the CNN-GRU-AttNet model exhibits exceptional efficacy in classifying HAR tasks, surpassing the achievement of the five baseline DL models.  
Figure 8 illustrates the confusion matrix of the CSI-HAR dataset based on the proposed CNN-GRU-AttNet model. The matrix's diagonal elements correspond to the model's accuracy in identifying individual human actions. The findings indicate that the CNN-GRU-AttNet model is effective in capturing both the spatial and temporal features of the WiFi CSI signal. Specifically, the model achieves 100% accuracy in recognizing run, sit down, standup, and walk activities. However, there needs to be more clarification between lie down and sit down activities. The misclassification could be explained by the overlapping patterns between sit down activities, characterized by sudden sitting and prolonged immobility, and lie down activities.  
The accuracy and loss metrics of the CNN-GRU-AttNet model are illustrated in Figure 9. The graph in Figure 9 depicts the accuracy values for both the training and validation data. Notably, the model achieves convergence within a relatively short timeframe, specifically within 100 epochs. Additionally, Figure 9 demonstrates that the training loss exhibits higher values compared to the validation loss, which is a reasonable observation. This elevated training loss can be attributed to the multi-phase learning process aimed at understanding the distinct characteristics of CSI signals associated with various human actions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Recognition Performance (Mean \(\pm\) Std)**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline CNN & 95.67\% (\(\pm\)1.57\%) & 95.97\% (\(\pm\)1.46\%) & 95.76\% (\(\pm\)1.51\%) & 95.66\% (\(\pm\)1.60\%) \\ LSTM & 84.12\% (\(\pm\)1.22\%) & 84.56\% (\(\pm\)1.14\%) & 84.17\% (\(\pm\)1.24\%) & 84.10\% (\(\pm\)1.18\%) \\ BiLSTM & 90.44\% (\(\pm\)0.86\%) & 90.49\% (\(\pm\)0.86\%) & 90.38\% (\(\pm\)0.84\%) & 90.34\% (\(\pm\)0.88\%) \\ GRU & 89.21\% (\(\pm\)2.86\%) & 89.14\% (\(\pm\)2.92\%) & 89.12\% (\(\pm\)2.87\%) & 89.06\% (\(\pm\)2.89\%) \\ BiGRU & 95.39\% (\(\pm\)0.92\%) & 95.38\% (\(\pm\)0.94\%) & 95.37\% (\(\pm\)0.96\%) & 95.31\% (\(\pm\)0.95\%) \\ CNN-GRU-AttNet & 99.62\% (\(\pm\)0.26\%) & 99.61\% (\(\pm\)0.26\%) & 99.61\% (\(\pm\)0.27\%) & 99.61\% (\(\pm\)0.26\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance results of both the proposed CNN-GRU-AttNet model and the five baseline models on the CSI-HAR dataset.  
Figure 8: Confusion matrix of the proposed model on CSI-HAR dataset.
## 3 Results and Discussion
### Effects of Twist Number on the Water Vapor Transmission Rate of Functional Elastic Knits  
Water vapor adsorbs to the surface of knits when they come into contact. The knits diffuse and evaporate the moisture. If water vapor fails to penetrate the knits, it undergoes condensation in the air layer between the skin and the knit. The residual moisture makes the wearers uncomfortable when their body temperature decreases [11; 28]. The skin of the human body has a water vapor transmission rate of 215-350 g/m\({}^{2}\) day [11; 30; 31; 32], and any lower water vapor transmission rates of knits would retain moisture. The water vapor transmission rate of knits was dependent on the types of yarns, specification of the yarns, and the structure and thickness of the knits [12; 32]. Hence, this study investigated the effects of two parameters, namely, the type of yarns and the specification of the yarns.  
Figure 12 shows that all functional elastic knits have a high water vapor transmission rate. The control groups had a greater water vapor transmission rate than the experimental groups. Moreover, the control group of non-processed bamboo charcoal yarns outperformed the control group of non-processed quick-dry yarns, particularly in terms of the water vapor transmission rate. For the experimental groups, the water vapor transmission rate of the elastic knits had a decreasing trend when their constituent yarns were composed of a high twist number. The functional composite yarns had a firm structure after they were twisted or wrapped. However, the advantages of bamboo charcoal yarns and quick-dry yarns were no longer present when they were firmly compressed at a twist number beyond 15 T.P.I., which eventually caused a similar water vapor transmission rate of functional elastic knits.  
The adsorption and evaporation of the moisture were dependent on the micro-structure and the hydrophilic properties of different yarns. Non-processed bamboo charcoal yarns had a porous microstructure that helped with the seizure of vapors. Similarly, non-processed quick-dry yarns had a channeled-like structure over the surface, which increased the specific areas. Therefore, both fibers had good diffusion and evaporation. In sum, for the experimental groups, regardless of whether it was the wrapped or the twisted yarns, all the functional elastic knits attained a water vapor transmission rate that was qualified for use on human skin, preventing condensation of the water vapor.
### GC-MS Analysis  
The EtoAc extract of _Streptomyces_ sp. strain ess_amH1 was completely dissolved in the organic solvent and analyzed by GC-MS on a GC-Clarus 500 (Perkin Elmer, column type-Elite-5 ms (30 m \(\times\) 250), detector: mass detector: turbo mass gold-Perkin Elmer, software: Tubomass 5.4.2). The identification of chemical constituents was based on matching their recorded mass spectra with those attained from the NIST (National Institute and Standard Technology), version-year 2005 [19].  
Antimicrobial Activity of the Extract of Streptomyces_ sp. _Strain ess_amH1 against Bacterial and Fungal Pathogens_ (_Disc Diffusion Assay_)  
The disc diffusion assay was used to determine the antibacterial and antifungal activity of the solvent extract of _Streptomyces_ sp. strain ess_amH1 by following [PERSON] et al.'s (2018) method [20]. Then, 1.0 mL of individual bacterial cultures such as _Streptococcus pneumoniae_ (ATCC 49619), _Staphylococcus aureus_ (ATCC 25923), _Salmonella typhi_ (ATCC 14028), _Klebsiella pneumoniae_ (ATCC 13883), and _Pseudomonas aeruginosa_ (ATCC 27853), as well as a fungal culture, i.e., _Candida albicans_, were mixed with 300 mL of nutrient agar medium and transferred into the 90 mm petri dishes. Then, sterilized Whatman no. 3 filter paper discs (6 mm size) were soaked in different beakers containing various concentrations of solvent extract of _Streptomyces_ sp. strain ess_amH1. The concentrations of 5, 10, 40, and 80 ug per disc and 20, 40, 80, 200, and 400 ug per disc were used for antibacterial activity and antifungal activity respectively. Soaked discs were taken out with sterilized forceps, air-dried, and located on the Petri dishes with different microbes. The plates were incubated at 37 \({}^{\circ}\)C for 24 h. The control was carried out by soaking the paper disc in ethyl acetate and placing it against each of the test microbes to ensure that ethyl acetate did not have any antibacterial/ antifungal activity; streptomycin (50 ug/disc)/fluconazole (100 ug/disc) was used as a positive control. The assay was repeated three times. The zone of inhibition (mm) produced by the solvent extract was used to measure antibacterial and antifungal activity.
# A Proactive Approach to Extended Vehicle Routing Problem with Drones (EVRPD)
## 4 Materials and Methods--Implementation of the EVRPD Model
### AMPL and Mathematical Programming  
The first approach belongs to the exact methods. Its main purpose was to obtain results that will enable the verification of the approximate methods used for this model and the basis for analysis of the operator using small-sized examples. Its essential feature and important element is coding constraints (1a)-(15) and questions Q1-8 of the model using AMPL. The AMPL model is universal, and it can be solved by many mathematical programming solvers such as: CPLEX, Gurobi, Cypress, Baron, Ilog, and LINDO (commercial solvers), as well as Neos, Gecode, and CBC (open source) [10]. The AMPL model for the EVRPD is included in Appendix A. However, Gurobi is the fastest and most powerful mathematical programming solver available for LP (linear programming), QP (quadratic programming), MILP (mixed integer programming), MIQP (mixed integer programs with quadratic terms in the objective function), and MIQCP (mixed integer programs with quadratic terms in the constraints) problems. Two mathematical programming environments, Gurobi [51] and LINGO [52], were used for the implementation.  
Gurobi has an extensive set of algorithms, and its own procedures and functions were used to solve discrete optimization problems. Like other solvers, it uses variants of known methods such as branch-and-bound, cutting planes, etc. Solving the discrete optimization problem using Gurobi was divided into certain steps and blocks. The first step was pre-solving, which aimed to tighten the formulation and reduce the problem size (the procedures and functions used were: _Presolve_, _PrePasses_, _AggFill_, _Aggregate_, _DualReductions_, _PreSparsify_, and _ImproveStartTime_). The next step was to solve the continuous relaxations (the procedures and functions used were: _Method_ and _NodeMethod_). At this stage, ignoring integrity occurred, and a bound was given on the optimal integral objective. The next step was to cut off relaxation solutions (the procedures and functions used were: _Cuts_, _CutPasses_, _CutAggPasses_, and _GomoryPasses_). After which, the branching variable selection took place, which was crucial for limiting the search tree size. Here the _VarBranch_ procedure was applied. In the last step, primal heuristics (_Heuristics_, _MinRelNodes_, _PumpPasses_, _RINS_, _SubMIPNodes_, and _ZeroObjNodes_) were used to find integer-feasible solutions.  
LINGO is a comprehensive tool designed to make building and solving linear, non-linear (convex, nonconvex, and global), quadratic, quadratically constrained, second-order cone, semi-definite, stochastic, and integer optimization models faster, easier, and more efficient.  
It should be mentioned that more than 2500 companies in over 40 industries turn data into smarter decisions with Gurobi products [51]. By contrast, the number of researchersand educational institutions using this product is difficult to estimate. A schematic diagram of the exact proposed approach is shown in Figure 1.
## 3 Results and Discussion
### Strength Prediction
For larger overlaps (20-25 mm), the adheren' consistently yielded without failing in the adhesive region, demonstrating the dominance of the adheren's flexibility and its susceptibility to yielding, as shown in Figure 11. However, the adhesive failed for a 10 mm overlap, indicating that the shorter overlap could not provide sufficient load distribution. Also, the four methods produced the same force-displacement curve for larger overlaps since the adhesive did not fail due to the adheren's thickness. In summary, for the 0.5 mm thick adherends, none of the lap joints experienced adhesive failure across the tested overlaps. Instead, the adherends yielded plastically, preventing the stress from reaching levels sufficient to initiate cohesive damage in the adhesive layer. Consequently, the force-displacement responses were virtually identical among the different overlap lengths and adhesive modeling approaches (Figure 12). These observations underscore that thinner adherends can yield well before the adhesive's ultimate strength is reached. By contrast, shorter overlaps in thicker adherend configurations (Figure 10) are more prone to early adhesive failure, as they concentrate stresses at the overlap edges. In the next section, we delve into the stress profiles in these joints to illustrate how overlap length and adheren't thickness influence the failure mechanism. Then, we focus on the detailed analyses on thicker adherends, where the cohesive zone (CZ) representations display more pronounced differences in predicting the peak load and failure evolution. Figure 11: Force–displacement curves of the different adhesive model representations for (**a**) 25 mm overlap, 1 mm thick adheren; (**b**) 10 mm overlap, 1 mm thick adherend. Figure 12: Force–displacement curves of the different adhesive model representations for (**a**) 25 mm overlap, 0.5 mm thick adherend; (**b**) 10 mm overlap, 0.5 mm thick adherend.