esa-sceva/satcom-chunk-collection huggingface dataset:
# Conditional GAN-Based Two-Stage ISP Tuning Method: A Reconstruction-Enhancement Proxy Framework
## 3 Methods  
The overall process of our proposed two-stage proxy auto-tuning method for ISP is shown in Figure 1 below.  
Firstly, we replace the end-to-end proxy [20] with a perceptual loss generative adversarial network ReEn-GAN based on the Pix2 pix architecture [32] by introducing adversarial loss, reducing the problems of blurry details and insufficient texture realism in image proxy reconstruction.  
Secondly, we utilize the prior knowledge of ISP to decompose the ISP pipeline into two processes: reconstruction and enhancement. We use distinct network architectures and loss functions tailored to specific objectives to achieve independent learning of the reconstruction and enhancement steps in the ISP proxy. This solves the problem of difficult -optimal parameter search and poor parameter matching caused by equal tuning of all parameters in the end-to-end proxy method.  
Thirdly, we use an alternating training method to train two-stage proxies and achieve global optimal parameter search for the two-stage proxies through complete parameter loss back-propagation.  
In the rest of this section, we will discuss the process and theoretical derivation of the proposed method in detail.
## 3 The Proposed Method
### Generation of the First Constrained Polygonal Space
#### 3.3.3 Complexity of the Obstacles' Avoidance Options  
There are usually four options in total to bypass any obstacle present in a \(W\) such as right, left, up, and down (in the case of hanged obstacles or flying obstacles). Meanwhile, after the space reduction, the number of options to avoid obstacle will likely be reduced, and there can be an increase in the complexity of remaining options. Because of this, a path may be taut and path length can be prolonged. Hence, while determining the CPS complexity, we take into account the complexity of options needed to bypass obstacles. To calculate the complexity (\(C_{AO}\)) of each option, the entropy concept is employed. Entropy is acknowledged as the most effective and accurate measurement for similar tasks in numerous fields. In this work, we consider the urban environment; therefore, the \(P\) cannot go beneath the obstacles since bottom height of each obstacle is zero, and, hence, there are only three options in total to avoid any obstacle. The strategy below is employed to calculate the \(C_{AO}\).  
1. Find the proportion (\(p_{i}\)) value of every avoidance option (i.e., \(AO_{l}\), \(AO_{r}\), \(AO_{l}\)) category using Equation (11). \[p_{i}=\frac{AO_{i}}{b}\] (11) where \(b\) denotes the total \(AO\), and its value can be determined using Equation (12).  
\[b=\sum_{i=1}^{3}AO_{j} \tag{12}\]
2. The complexity \(C_{AO}\) of avoidance options can be calculated using Equation (13): \[C_{AO}=-\sum_{i=1}^{b}p_{i}log_{2}p_{i}\] (13)  
The normalized value of the \(C_{AO}\) lies between 0 and 1, denoted as \(C_{AO}\in[0,1]\). The \(C_{AO}\) with 0 value means that avoidance complexity is low (e.g., all obstacles can be avoided from the same side). In contrast, the \(C_{AO}\) value 1 means that enough variations exist in options to avoid all obstacles, and the path can contain many turns. In the CPS analysis, we take into account the \(C_{AO}\) values.
## 4 Conclusions  
During our work, sorbents based on PAN fiber and transition metal ferrocyanides were synthesized for \({}^{137}\)Cs sorption from natural and technological environments. It was shown that the best sorption characteristics were obtained at a concentration of potassium ferrocyanide solution of 0.1-0.2 mol/L and pH--1 for sorbents based on PAN fiber prepared by precipitation of iron(III) hydroxide with ammonia (PAN-Fe (OH)\({}_{3}\)), and pH 1-5 for sorbents based on PAN fiber treated with potassium permanganate (PAN-MnO\({}_{2}\)). For sorbents based on PAN-Fe(OH)\({}_{3}\) fiber, the best cesium distribution coefficients were achieved when cobalt(II) chloride with a concentration of 0.02 mol/L was used for modification (CoFeFC-PAN sorbent); for sorbents based on PAN-MnO\({}_{2}\) fiber, the best cesium distribution coefficients were achieved when cobalt(II) chloride with a concentration of 0.02-0.05 mol/L (CoMnFC-PAN sorbent) and iron(III) chloride with a concentration of 0.02 mol/L (FeMnFC-PAN sorbent) were used for modification PAN. The independence of the properties of obtained sorbents from the temperature regime in the range of 25-80 \({}^{\circ}\)C is shown. The studied structure of the obtained materials using infrared spectroscopy, thermogravimetric analysis, and scanning electron microscopy with energy dispersive analysis showed that during the formation of the ferrocyanide layer, the distribution of all components of the mixture occurs uniformly, regardless of the synthesis conditions. In the IR spectra of the obtained sorbents, peaks in the range of 2000-2150 cm\({}^{-1}\) correspond to the C\(\equiv\)N group coordinated to the metal, which indicates the presence of a complex ion [Fe(CN)\({}_{6}\)]\({}^{4-}\). When studying the influence of solution composition (pH, concentration of Na\({}^{+}\), K\({}^{+}\), NH4\({}^{+}\) cations), it was shown that the synthesized sorbents could be used to recover and concentrate cesium from liquid media in the pH range from one to nine containing macro quantities of sodium, potassium and ammonium ions. The presence of the NH4\({}^{+}\) cation has the greatest effect on the cesium sorption; the influence of Na\({}^{+}\) is minimal. The sorption of \({}^{137}\)Cs microquantities by the obtained sorbents was studied, the effectiveness of the obtained sorbents was shown in comparison with known adsorbents of various brands, and the possibility of using the obtained sorbents for the recovery and concentration of \({}^{137}\)Cs from seawater was demonstrated. In the course of further research, a more detailed study of the physicochemical parameters of cesium sorption is planned using the obtained materials, namely sorption and desorption kinetics, isotherms, selectivity, and sorption dynamics in solutions of complex chemical composition. In addition, \({}^{137}\)Cs sorption efficiency from large volumes (up to 250 L) of natural water will be assessed at different flow rates of the test solution. During the expedition, radioanalytical monitoring of \({}^{137}\)Cs in surface seawater samples will be performed using the obtained sorbents.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 1 Introduction
### Needs of Multi-Band Communication Techniques Underwater  
Due to the constrained conditions underwater, communication systems face problems such as multi-path, frequency fading, doppler shifts, etc. Multi-band techniques can be used as an effective technique with respect to efficiency and throughput, and to increase the performance level. Multi-band can overcome the frequency fading problem by allotting the same information to several frequency-bands.
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 4 Results and Discussion
### Influence of Suppressant Materials on Dust-Mist Coupling Effect  
The results of the dust-mist coupling behavior are shown in Figure 11. Overall, the D32 size increased when dust was added to the mist field for the dust suppressant solutions, compared to the results in Figure 10. The overall trend observed is that as the pressure increases, the increased D32 value decreases. The change trend was maintained in the order of DA \(>\) OP-10 \(>\) SDS \(>\) water.  
As the pressure increased, the mean D32 value of DA decreased from 58.86 \(\upmu\)m at 2.0 MPa to 27.65 \(\upmu\)m at 6.0 MPa. Similarly, the D32 value of OP-10 decreased from 55.86 \(\upmu\)m to 26.25 \(\upmu\)m, the D32 value of SDS decreased from 53.62 \(\upmu\)m to 25.37 \(\upmu\)m, and the mean D32 value of water decreased from 43.29 \(\upmu\)m at 2.0 MPa to 23.61 \(\upmu\)m at 6.0 MPa. For the measurement position at 5.0 cm, the average D32 value of the dust suppressant changed from 50.36 \(\upmu\)m at 2.0 MPa to 22.92 \(\upmu\)m at 6.0 MPa. For the measuring point at 7.0 cm, the average D32 value changed from 54.43 \(\upmu\)m at 2.0 MPa to 29.42 \(\upmu\)m at 6.0 MPa. For the 9.0 cm measurement position, the average D32 value changed from 59.30 \(\upmu\)m at 2.0 MPa to 27.58 \(\upmu\)m at 6.0 MPa. For the 10.0 cm measurement position, the average D32 value changed from 54.92 \(\upmu\)m at 2.0 MPa to 26.08 \(\upmu\)m at 6.0 MPa. For the 15.0 cm measurement position, the average D32 value of the dust suppressants changed from 45.55 \(\upmu\)m at 2.0 MPa to 22.63 \(\upmu\)m at 6.0 MPa, respectively.  
As depicted in Figure 11d, the change rate of the three dust suppressants exhibited a more pronounced increase compared to that of water. The change rate peaked at the 9.0 cm measurement point and subsequently decreased at the 10.0 cm point for all dust suppressants. Among these, DA demonstrated the highest change rate, with an average value of 27.0%, followed by OP-10 at 20.3%, and SDS at 15.0%. The mean change rate of DA was 1.80 times that of SDS and 1.35 times that of OP-10. The results demonstrate that a higher D32 value corresponds to greater coupling ability of the suppressants. The dust suppressants enhanced the wetting performance on dust particles, thereby significantly improving the coupling behavior between dust and mist and leading to a markedly shorter sedimentation time compared to water, as illustrated in Figure 3. This suggests that better wetting performance is directly proportional to the increased coupling particle size, thereby establishing a proportional relationship between wetting performance and the dust-mist coupling effeciency. In comparison with Figure 2, the experimental data further reveal an inverse relationship between the surface tension and viscosity of the dust suppressant and dust-mist coupling efficiency.
## 3 Model Validation and Results Analysis  
The invariant imbedded T-matrix model is implemented in Fortran95, and to improve the modeling efficiency, the model is further parallelized by the OpenMP technique (OpenMP is a multi-threading implementation technique that allows the compiler to generate code for task and data parallelism). To validate the simulation accuracy of this model, the scattering parameters calculated by the IIM T-Matrix method are compared with those calculated by EBCM and DDASCAT (which have been well tested and are usually taken as a benchmark). The non-spherical particles simulated in this paper include spheroid with different sizes, cylinder, spheroid with a spherical core (inhomogeneous particle), and hexagonal prism (particles with non-rotationally geometry).
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 5 Numerical Simulations
### Comparison of Computation Complexity  
In order to compare the computation complexity of the two-step sub-optimal algorithm and the brute-force two-dimensional search algorithm, we show in Figure 7 the different time costs of two algorithms which obtain the maximal average energy efficiency-based proportional fairness of nodes with different accuracy of the receive threshold and association probability. Our results show that, with the increase of accuracy, the time cost on the brute-force two-dimensional search algorithm increases with increasing exponent rate. The time cost on the two-step sub-optimal algorithm increases slowly and is always less than that of the brute-force search algorithm. This simulation result shows the advantage of our proposed sub-optimal algorithm on time complexity. Meanwhile, Figure 7 further presents that the time cost of two algorithms are almost unchanged with different system parameters such as density of BSs and nodes. This is because that the time complexity of two search algorithms depends on the accuracy of the receive threshold and association probability.  
Figure 6: The energy efficiency proportional fairness of total two-tier heterogeneous WPCNs with respect to the association probability of tier 2 for different values of the transmit power of BSs.
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 2 Materials
### Targets of Classification  
The study area comprises traditional urban types and covers a variety of land cover features on the ground such as buildings, roads, parking lots, shrubs, trees, and open grassy spaces. To define land cover classes in classification, we considered the most important classes for the land cover map and their availability in this study area and selected five major types for classification: buildings, trees, grass, roads, and bare soil. These classes are well-defined land cover types in urban areas and can be identified without ambiguity in reference data. This study manually selected 280 randomly distributed sampling objects comprising 44,158 pixels in the study area. The pixel size was 0.5 \(\times\) 0.5 m (i.e., the pixel size of rasterized LiDAR), and the area of all selected pixels was 11,039.5 m\({}^{2}\). We used 140 objects as the training data and the remaining 140 objects as the test data (Table 3), distributed in areas excluding the water regions (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Density** & \(\lambda_{532}\) & \(\lambda_{1064}\) & \(\lambda_{1550}\) & **Sum** \\ \hline Building & 24.8 & 23.5 & 23.2 & 71.5 \\ Grass & 32.7 & 31.9 & 29.5 & 94.1 \\ Tree & 39.6 & 46.9 & 39.2 & 125.7 \\ \hline \end{tabular}
\end{table}
Table 2: Point density in different land covers (Unit: points/m\({}^{2}\)).
## 3 Numerical Simulation and Validation
### Model Validation  
The comparison between the damage patterns of the specimens obtained from the 30 mm mesh size model and the simulated results is depicted in Figures 17-19. As shown in the figure, the dimensional errors of the spalling area on the underside of the top plate of Chamber 2 for specimen G are \(-2.90\%\) in the short direction and \(-5.03\%\) in the long direction. For specimen PCG, the errors are \(-11.8\%\) and \(-11.3\%\), respectively, while for specimen PCGR, the errors are \(-0.32\%\) and \(7.96\%\). The possible reasons for these discrepancies include uneven concrete casting and variations in the mass of the explosive. In summary, the simulated perforation and indentation on the top plate closely match the experimental findings [30].  
The comparison of the experimental and simulated temporal evolution curves for vertical displacement and acceleration response at the central point of the baseplate in box chamber 2 of the specimen, as presented in Figure 20. For specimen G, the peak vertical displacement and peak acceleration deviations compared to test results are \(10.59\%\) and \(15.00\%\), respectively. For specimen PCG, the deviations are \(-11.73\%\) and \(-4.20\%\), while for specimen PCGR, the deviations are \(-6.66\%\) and \(-8.62\%\), respectively. These results suggest that the failure behaviors and dynamic reactions of the specimens G, PCG, and PCGR in the numerical simulations closely align with the experimental results, underscoring the reliability of the finite element model and the simulation method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameter** & \(\rho\)/(kg. m\({}^{-3}\)) & \(E\)/Pa & \(\
u\) & \(\sigma_{\
u}\)/Pa & \(E_{\
u}\)/Pa & \(\beta\) & \(C\) & \(P\) & \(\epsilon_{F}\) & **VP** \\ \hline Value & 1020 & \(2.3\times 10^{8}\) & 0.4 & \(6\times 10^{6}\) & \(3.5\times 10^{6}\) & 0 & 98.2 & 4.5 & 0.85 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material parameters of polyurea.  
Figure 17: The damage patterns of experimental and simulated results for specimen G.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Parameter** & \(\rho\)/(kg. m\({}^{-3}\)) & \(E\)/GPa & \(A\) & \(\beta\) & \(C\) & \(N\) & \(\sigma_{\
u}\)/GPa & \(T\)/GPa & \(\epsilon_{F}\) \\ \hline Value & 1986 & 5.18 & 0.63 & 1.56 & 0.0054 & 0.826 & \(7.5\times 10^{-2}\) & \(6\times 10^{-3}\) & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Material parameters of brick support.  
Figure 19: The damage patterns of experimental and simulated results for specimen PCGR.  
Figure 20: Experimental and simulated data for vertical displacements and accelerations of the specimens.  
Figure 18: The damage patterns of experimental and simulated results for specimen PCG [30].
## 1 Introduction
[25] redefined an oscillator based on the amplitude-controlled phase oscillator, which generated swimming and walking behaviors in salamander robots [26], and used the Proximal Policy Optimization (PPO) algorithm to learn the oscillator parameters. [PERSON] et al. [27] used DRL algorithms to train robot jumping and landing, successfully transferring the learned skills to the SpaceBok robot. In 2021, [PERSON] et al. [28] developed a control algorithm based on RL and the artificial neural network; the RL policy was determined according to the expected optimal motor angle of the quadruped robot, and the neural network was trained with this policy as training data, enabling self-balancing control of the robot. While DRL algorithms can facilitate the learning of various motor skills by robots, it is important to note that learning from scratch requires a large number of interactions between the robot and the environment to obtain sufficient training samples. Consequently, the learning process can be time-consuming. In this paper, to mimic the properties of biorhythm movement, we propose a new kind of adaptive learning algorithm that combines deep reinforcement learning (DRL) and a rhythm controller based on a cosine oscillator for controlling the locomotion of a quadruped robot. The rhythm controller is responsible for coordinating the movements of the robot's four legs, while DRL is employed to learn the parameters of the controller and enable the robot to acquire various motor skills. The main contributions of this paper are as follows:  
1. Eight cosine oscillators are used to construct the quadruped robot's locomotion rhythm controller, and then the coordination of different legs is coupled just by the phase relationship among the eight joints. This simple weak coupling has fewer restrictionson the robot movement, which makes the generation of legged robots' locomotion rhythm simpler and easier, and it is also easy to realize all kinds of gaits, as well as applying to most kinds of legged robots. 2. The Soft Actor-Critic (SAC), a kind of DRL algorithm, is used to train the parameters of the rhythm controller, which addresses the challenge of automatic acquisition and adjustment of the controller parameters. The reward function designed in this paper not only considers the robot's general locomotion abilities, such as attitude balance and yaw control, but also takes its adaption to complex terrain into account. Therefore, a state estimation method is proposed, and the achieved slope information is integrated into the reward function to finally enable the robot to better cope with an unknown environment. The rest of this paper is organized as follows. Section 2 introduces the concrete structure of the robot and the gait planning. Section 3 introduces quadruped robot locomotion learning algorithms in detail, including the rhythm controller based on a cosine oscillator, the slope information estimation method and the deep reinforcement learning algorithm. Section 4 shows the simulation results of the locomotion learning algorithm on the robot. Finally, Section 5 gives a brief conclusion.
# Camera Calibration with Phase-Shifting Wedge Grating Array
## 3 Experiments
### PWG Arrays VS Horizontal/Vertical Phase-Shifting Fringe Patterns  
In this experiment, fringe patterns generated with the same phase shift of \(2\pi/3\) and feature point locations were chosen to compare with three-step PWG arrays. Both of them were phase-shifting patterns, and the fringe patterns consisted of horizontal and vertical phase-shifting fringe patterns. We performed the experimental procedures in the same way that is described in Section 3.1. Nine images per viewpoint consisted of three PWG images, three horizontal fringe pattern images and three vertical phase-shifting fringe pattern images were captured and ninety images were obtained in total. Figure 7 shows the captured images of the two phase-shifting patterns.  
Then, we extracted the feature points from the images: the centers for three-step PWG arrays and the centers for three-step phase-shifting fringe patterns with the feature detection method in paper [16], which extracted the two-dimensional (2D) phase-difference pulse signals that were optimized by  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PatternParameter** & \(f_{u}\) **(Pixel)** & \(f_{v}\) **(Pixel)** & \(\mathbf{n_{0}}\) **(Pixel)** & \(\mathbf{v_{0}}\) **(Pixel)** & \(\mathbf{k_{1}}\) & \(\mathbf{k_{2}}\) & **RMSE (Pixel)** \\ \hline Checkerboard & 2964.7 & 2964.3 & 1011.3 & 545.5 & \(-\)0.145 & 0.755 & 0.066 \\ Clicks & 2963.5 & 2962.8 & 1012.8 & 544.8 & \(-\)0.146 & 0.754 & 0.063 \\ Three-step & 2968.2 & 2968.0 & 1010.3 & 546.5 & \(-\)0.155 & 0.827 & **0.054** \\ PWG arrays & 2965.1 & 2964.8 & 1012.1 & 546.3 & \(-\)0.156 & 0.832 & **0.045** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Camera intrinsic parameters estimated from different patterns.  
Figure 6: Re-projection errors of different patterns. (**a**) Checkerboard; (**b**) circles; (**c**) three-step PWG arrays; and, (**d**) four-step PWG arrays.  
Figure 7: The captured images of different phase-shifting patterns. (**a**) three-step PWG arrays; (**b**) three-step horizontal phase-shifting fringe patterns; and, (**c**) Three-step vertical phase-shifting fringe patterns.  
interpolation as feature points. The workload of capturing images for PWG arrays was reduced by half when compared with the fringe patterns. Figure 8 shows the 2D phase-difference pulses.  
Table 2 shows the intrinsic parameters estimated from the two phase-shifting fringe patterns. The RMSE for the presented method is smaller than that for the method in paper [16]. The difference in RMSEs was caused by the precision of feature detection. The method in paper [16] extracted the feature points based on the sum of two 2\(\pi\)-phase point images. While the addition operation brought more noise, which directly influenced the accurate extraction of feature points than the presented method that used one 2\(\pi\)-phase point image. Therefore, the proposed method is accurate and reliable.