esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 3 Sliding-Mode Controller Design
### Equivalent Control Condition  
The equivalent control condition evaluates that the average value of the control signal \(u\) is always constrained within the control signal limits \([0,1]\). Taking into account that the average value of \(u\) is the converter duty cycle \(d\), as given in (10), the equivalent control condition, in practice, evaluates the non-saturation of the duty cycle:  
\[0<u_{eq}=d<1 \tag{31}\]  
i.e., the hysteresis band is \([-\delta\Psi_{r}+\delta\Psi]\). It must be put into evidence that the switching function \(\Psi\) is the same one defined in (19), the change occurs at the comparison limits (from 0 to \(\pm\delta\Psi\)).  
\[\Phi_{H}=\left\{\Psi\leq\delta\Psi\right\} \tag{37}\]  
Based on the reachability conditions reported in (27) and (28), the control law required to reach the hysteresis band is obtained as follows:  
* If \(\Psi>+\delta\Psi\), it is required \(\frac{d\Psi}{dt}<0\) to reach \([-\delta\Psi,+\delta\Psi]\) (the hysteresis band), which is achieved with \(u=0\).
* If \(\Psi<-\delta\Psi\), it is required \(\frac{d\Psi}{dt}>0\) to reach \([-\delta\Psi,+\delta\Psi]\), which is achieved with \(u=1\).  
The previous control law is formalized with the logic equation given in (38), which generates the main control signal \(u\), and requires the calculation of the switching function \(\Psi\). The following subsection describes, in detail, the synthesis of this control law.  
\[u=\left\{\begin{array}{cl}1&\text{if}\quad\Psi<-\delta\Psi\\ \\ 0&\text{if}\quad\Psi>+\delta\Psi\end{array}\right. \tag{38}\]
# Practical Real-Time Phase Drift Compensation Scheme for Quantum Communication Systems
## 4 Conclusions  
In this paper, we propose a real-time phase drift compensation scheme for phase-coding quantum communication systems, in which only existing data from the quantum communication process is used to establish a stable closed-loop control subsystem for phase tracking without any additional transmission efficiency reduction or information exchange process. Our scheme is applied into an FSMI-based phase-coding quantum communication system, leading to a stable and continuous operation, even with a complex environmental disturbance. Note that, in our long-term stability experiment, we obtain an average QBER value of 1.60%, which is approximately equal to \(e_{0}=1.59\%\), and the effectiveness of our scheme has been effectively validated. Additionally, it can be observed from the experimental results that increasing the statistical frequency can effectively improve system  
Figure 6: QBER, its statistical distribution (**a**), and driving voltage of the phase modulator (**b**) with a feedback compensation frequency of 0.1 s per iteration for 16 h.  
performance. However, this also results in a reduced number of statistical samples, leading to larger statistical errors. Therefore, a careful balance is needed in this regard, especially when the repetition rate is lower, which results in relatively smaller counts and larger fluctuations. Another point that needs to be mentioned is that, during the phase tracking process, there is still a possibility of incorrect modulation direction due to sudden phase changes or statistical fluctuations when the phase tracking driving voltage operates near the target value. Consequently, the incorrect modulation direction results in a higher QBER, which is a clear indication of modulation direction errors and can be quickly corrected through subsequent compensation, leading to a relatively stable state. The stability of the experimental results also verifies this point.  
Conceptualization, X.S., C.Z. and G.L.; methodology, X.S. and C.Z.; software, J.G. and F.Z.; validation, X.S., C.Z., D.P. and M.W.; formal analysis, X.S.; data curation, X.S. and C.Z.; writing--original draft preparation, X.S.; writing--review and editing, G.L., D.P., X.S., M.W., F.Z. and C.Z.; supervision, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Postdoctoral Science Foundation (2022M710401); National Natural Science Foundation of China (12205011); and the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (2022 QNRC001).  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
## 4 Experiments
### Generalization Study
#### 4.3.1 Cross-Size Generalization  
To assess the cross-size generalization ability, we generate 1000 instances with 20, 50, 100, 150, and 200 nodes following the uniform distribution, and employ models pretrained on TSP20 and CVRP20 to handle them. We compare our approach with compelling DRL-based methods (i.e., AM, DACT, and POMO), and the experimental results are illustrated in Figure 4. DRL-DM attains an optimality gap of 2.18% on TSP200, outstripping AM, DACT, and POMO by 4.64%, 3.53%, and 2.31%, respectively. In addition, the proposed approach consistently outperforms state-of-the-art POMO by 1.26%, 1.76%, and 2.31% separately on large-scale TSPs with 100, 150, and 200 nodes. As for CVRPs, the optimality gaps of our DRL-DM are 0.49%, 2.26%, 3.18%, 4.05%, and 5.26% on problems with 20, 50, 100, 150,  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**CVRP20**} & \multicolumn{3}{c}{**CVRP50**} & \multicolumn{3}{c}{**CVRP100**} \\ \cline{2-10}  & **Length** & **Gap** & **Time** & **Length** & **Gap** & **Time** & **Length** & **Gap** & **Time** \\ \hline Concorde [14] & 6.10 & 0.00\% & 12 h & - & - & - & - & - \\ Gurobi [28] & 6.10 & 0.00\% & 5 h & 10.37 & 0.00\% & 16 h & - & - & - \\ LKH-3 [29] & 6.10 & 0.00\% & 2 h & 10.37 & 0.00\% & 7 h & 15.65 & 0.00\% & 14 h \\ \hline OR Tools [30] & 6.47 & 6.07\% & 2 min & 11.29 & 8.87\% & 15 min & 17.15 & 9.58\% & 51 min \\ GA [31] & 6.40 & 4.92\% & 2 min & 11.12 & 7.23\% & 15 min & 16.87 & 7.80\% & 49 min \\ SA [32] & 6.36 & 0.52\% & 2 min & 11.05 & 2.28\% & 14 min & 16.78 & 7.22\% & 47 min \\ ACO [33] & 6.32 & 3.61\% & 2 min & 10.98 & 5.88\% & 13 min & 16.70 & 6.71\% & 45 min \\ \hline AM [22] & 6.41 & 5.08\% & 3 s & 10.99 & 5.98\% & 8 s & 16.82 & 7.48\% & 23 s \\ Wu et al. [24] & 6.19 & 1.48\% & 24 min & 10.72 & 3.38\% & 49 min & 16.32 & 4.28\% & 1 h \\ DACT [25] & 6.17 & 1.15\% & 1 min & 10.63 & 2.51\% & 4 min & 16.19 & 3.45\% & 12 min \\ NeuRewritter [35] & 6.16 & 0.98\% & 31 min & 10.58 & 2.03\% & 56 min & 16.12 & 3.00\% & 1 h \\ POMO [23] & 6.16 & 0.98\% & 15 s & 10.52 & 1.45\% & 1 min & 15.93 & 1.79\% & 4 min \\ Ours & 6.13 & 0.49\% & 14 s & 10.44 & 0.68\% & 1 min & 15.78 & 0.83\% & 3 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results among baseline methods on CVRP20-100.  
and 200 nodes, which are 4.59%, 6.41%, 7.29%, 8.01%, and 8.97% lower than those of the milestone AM. These advancements verify the elaborated design of our model especially when generalizing to larger-scale in-distribution scenes.
# Three-Dimensional-Printed Polymeric Cores for Methane Hydrate Enhanced Growth
## 5 Conclusions  
Different types of polymeric cores and variations of pore sizes/effective porosity (including an anisotropy in the coupled channels network) were revealed to be affecting the methane hydrate growth. The presented approach makes it possible to control the direction of hydrate growth as well as to achieve the complete water-to-hydrate conversion (for PLA core) by properly selecting the polymer. The direction of the connected pores seems to influence the transition considerably. It is assumed that varying the type of material, its structure (the shape and size of the channels), and the functionalization of the surface of these channels will make it possible to achieve separation of the hydrate and residual solution directly during the hydrate growth. Thus, studying the formation of hydrates in a porous medium can become the basis for developing gas hydrate technology for water purification. Achieving a high rate of hydrate production on such membranes will also allow them to be considered for storing and transporting gases (methane, hydrogen, natural gas), and carbon dioxide capture.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/polym15102312/s1](https://www.mdpi.com/article/10.3390/polym15102312/s1)]([https://www.mdpi.com/article/10.3390/polym15102312/s1](https://www.mdpi.com/article/10.3390/polym15102312/s1)), effective porosity data (pore diameters distribution (Figure S1) and structure of 3D-printed samples (Figure S2)); additional hydrate morphology photos (Figures S3-S5); Videos S1-S9: formation of methane hydrate growth at 1 \({}^{\circ}\)C and about 9.1 MPa from deionized water with varying 3D-printed polymer cores.  
**Author Contributions:** Conceptualization, A.S. ([PERSON]), R.K. and A.S. ([PERSON]).; methodology, A.S. ([PERSON]) and R.K.; validation, A.S. ([PERSON]) and A.S. ([PERSON]).; data curation, A.S. ([PERSON]). A.S. ([PERSON]) and A.M.; writing--original draft preparation, A.S. ([PERSON]); writing--review and editing, A.S. ([PERSON]), A.S. ([PERSON]) and A.M.; formal analysis, A.S. ([PERSON]), R.K., T.A., A.S. ([PERSON]) and A.M.; investigation, A.S. ([PERSON]), R.K., E.S., T.H.N. and M.Y.; resources, A.S. ([PERSON]), R.K. and A.M.; visualization, A.S. ([PERSON]), R.K. and T.A.; supervision, A.S. ([PERSON]); project administration, A.S. ([PERSON]) and R.K.; funding acquisition, A.S. ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Russian Science Foundation (grant 22-79-00230).  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Tolerance Parameter and Stopping Criteria  
Parameter \(\epsilon\) controls the tightness of the local community. A big value of \(\epsilon\) allows a big reduction in \(R(\mathcal{C})\) between iterations. It leads to a larger selection of candidate vertices, thus allowing the algorithm to probe a bigger region of the network at the cost of a longer running time. Using a small value of \(\epsilon\) allows a small reduction in \(R(\mathcal{C})\). Therefore, the algorithm will converge faster but might miss some important structures due to the inclusion of fewer candidate vertices. \(\epsilon=0\) is similar to what was performed in [PERSON]'s  
Figure 2: An illustration of the search algorithm. It shows the first three steps in the search for the local community of vertex 1.  
original method, where \(R(\mathcal{C})\) increases monotonically [10]. In a sense, the algorithm is similar to the simulated annealing optimization using the probabilistic technique to approximate the global optimum of a given function [14]. In practice, a single constant optimal value of \(\epsilon\) that fits all scenarios does not exist. It depends on the structure of the network, including the clustering, degree distribution, etc. In the initial stage of the algorithm, where there are big fluctuations between consecutive steps, \(\epsilon\) can be chosen to be large in order to probe a larger area of the network. Later, when \(R(\mathcal{C})\) stabilizes, \(\epsilon\) can take smaller values to fine-tune the results.  
Certain criteria must be met in order to terminate the algorithm. It is possible that all vertices are included in the local community. More generally, if the local modularity \(R(\mathcal{C})\) varies little for a certain number of consecutive steps, we then conclude that running the algorithm for more steps will not improve the performance, and it should be terminated.
## 3 Results and Discussion
### Transport Study of Cr(VI) through Aliquat 336/PolyHIPE Membrane
#### 3.4.3 Modeling and Parameters of Cr(VI) Diffusion
\[D=7.4\times 10^{-8}(\phi M)^{1/2}\frac{T}{\eta V^{0.6}} \tag{23}\]  
where \(M\) and \(\eta\) is the molecular weight and viscosity of solvent, and \(\phi\) is the \"association parameter\" (\(\phi\) = 1). Since the PolyHIPE is filled with Aliquat 336, which can be treated as solvent in this case, \(M\) = 404.16 g/mol and \(\eta\) = 1.5 Pa.s. The value of \(V\) = 0.671 \(\times\) 10\({}^{3}\) cm\({}^{3}\)/mol can be calculated by summing the contribution of atoms, which consist of the solute molecule (i.e., Cr(VI)-Aliquat 336 complex) as suggested by [PERSON] [46]. For 25 \({}^{\circ}\)C and \(T\) = 298 K, \(D\) = 5.95 \(\times\) 10\({}^{-13}\) m\({}^{2}\) s\({}^{-1}\). The value of \(D\) obtained from \(D^{\star}\) seems to be very high, compared to that estimated from [PERSON] equation (Equation (23)). It suggests that the migration of the complex is not a pure diffusion process. One possible explanation is the jumping transport mechanism as suggested by many literature studies [48]. With this mechanism, the transporter molecules act as \"stepping stones\", and the solute moves through the membrane by jumping from one fixed site to another. The characteristic of jumping transport is to be faster than the diffusion [35]. Therefore, for the conditions of the experiments, the assumption of jumping transport seems to be correct.
## 4 Discussion and Conclusions  
In this paper, a vehicle positioning system combining a monocular camera, a binocular camera, and inertial navigation is proposed to solve the problem of GPS lock-out in a long tunnel. In the absence of a GPS signal, when a vehicle changes lanes or overtakes, the system can provide high-precision vehicle lateral positioning and accurately obtain the lane where the vehicle is after a lane change or overtaking. The traditional inertial navigation  
Figure 23: Distance between vehicle and lane line measured by binocular camera when driving.  
Figure 22: Lane line image after processing: (**a**) Contour image of the lane line; (**b**) Parallax image of lane lines.  
positioning algorithm is greatly affected by the accuracy of inertial components, and the error will increase with the accumulation of time. The high-precision inertial components will increase the cost of the system, which is one of the reasons why the current inertial navigation is still not popular. In this paper, the vehicle lateral positioning algorithm based on the heading angle calculated by inertial navigation has high lateral positioning accuracy and is less affected by inertial components. The positioning error will not increase with the accumulation of time, which can reduce the cost of the positioning system and has good engineering application value. With the help of a binocular camera and a monocular camera, the system has better anti-interference. When the vehicle is in a congested or low-speed section in the tunnel, the monocular camera may fail to recognize the lane line due to the obstruction of the vehicle in front. At this time, the binocular camera can be used to measure the distance between the vehicle and the lane line, judge whether the vehicle keeps driving in the lane, and assist in judging whether the vehicle changes lanes according to the distance information; therefore, the system also has good robustness.  
Conceptualization, X.J., [PERSON] and [PERSON]; methodology, [PERSON]; programming, [PERSON]; validation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; supervision, [PERSON]; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research is financially supported by the National Natural Science Foundation of China (grant no. 51575288).  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors also express great gratitude to the research team and the editors for their help.  
The authors declare no conflict of interest.
# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 5 Experiments and Results
### Object Detection Result
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Clear Image**} & \multicolumn{2}{c}{**Rain Image**} & \multicolumn{2}{c}{**Fog Image**} \\ \cline{2-7}  & **mAP @0.5** & **mAP @0.95** & **mAP @0.5** & **mAP @0.95** & **mAP @0.5** & **mAP @0.95** \\ \hline Pedestrian & 0.782 & 0.455 & 0.740 & 0.425 & 0.730 & 0.415 \\ \hline Bicycle & 0.871 & 0.651 & 0.858 & 0.635 & 0.842 & 0.622 \\ \hline Car & 0.954 & 0.675 & 0.952 & 0.678 & 0.937 & 0.664 \\ \hline Bus & 0.976 & 0.850 & 0.979 & 0.856 & 0.974 & 0.865 \\ \hline Truck & 0.966 & 0.807 & 0.963 & 0.805 & 0.854 & 0.811 \\ \hline All & 0.910 & 0.688 & 0.898 & 0.680 & 0.886 & 0.675 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Object detection accuracy with clear images, rain images and fog images. Figure 12: Object detection results—first row: pedestrian; second row: bicycle; third row: car; last row: bus; first column: clear images; second column: rain images; last column: fog images. Figure 13: Object detection results with dehazed rain images—first row: pedestrian; second row: bicycle; third row: car; last row: bus; first column: images dehazed using base model; second column: images dehazed using our proposed model 1; last column: images dehazed using our proposed model 2. Furthermore, we performed an additional object detection experiment on the RTTS dataset. We did not re-train our proposed model 2 for this dataset. Table 13 shows a comparison of the accuracy of object detection with the original hazy images and dehazed images generated using our proposed model 2. Figure 15 shows some visual results obtained with the original hazy images and the dehazed images generated using our proposed model 2. The numerical and visual results demonstrated that the dehazed images generated using our proposed model could result in better object detection than the original hazy images. Figure 14: Object detection results with dehazed fog images—first row: pedestrian; second row: bicycle; third row: car; last row: bus; first column: image dehazed using base model; second column: images dehazed using our proposed model 1; last column: image dehazed using our proposed model 2.
## 4 Experimental Results and Discussion
### Evaluation of the Objective Color Correction Network
#### 4.2.5 Analysis of Real Tongue Images  
To provide more in-depth insights on our OCC, we will investigate its performance of real tongue images in the views of subjective and objective analysis. We have collected a set of tongue images in the open and closed lighting environment. They are shown in Figures 10 and 11, respectively. The OCC corrected images of the closed environment are shown in Figure 12.  
We can see that the tongue images captured in the open environment suffer from high dynamic lighting variety. Some tongue images are pale, some are reddish, and some are yellowish. The tongue images of the open environment are more susceptible to blurring. Lighting effects and image quality pose challenges for subsequent automated analysis, while, as shown in Figure 11, for the tongue images captured in the closed environment, the color consistency has been greatly improved. We can see that the effects of light changes are almost completely eliminated. From the OCC corrected images shown in Figure 11, we can observe that some red regions in the tongue body become more significant. For example, in the tip of the 1 st and 5 th images, the side of the 2 nd image in Figure 11, the red parts are realistic reproduced. The tooth marks in the 3 rd and 4 th images are more obvious. The color of the tongue coating in the 4 th and 5 th images looks unchanged. The above observations implied that the results of our OCC may be good for further color classification.  
The subjective evaluation by assessing the color perception is hard to quantify. It is difficult to calculate the color difference between two color images directly. Like [PERSON] et al. [11], we will calculate the average color to represent each tongue image, and then analyze the distribution of their color centers. Here, the CIE _xy_Y color space is employed to obtain the \((x,y)\) coordinate for each pixel.  
The distribution of the open tongue images, the closed tongue images, and the OCC corrected images are shown in Figure 13. We can see that the tongue image in an open environment is distributed over a large area, while the images captured in the closed environment and their corrected version attempt to be more clustered into a central image. The stable light source provided by the closed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Illumination** & \(\Delta E_{ab}^{*}\) & **Uncorrected** & **POLY [7]** & **SVR [12]** & **K-PLSR [16]** & **OURS** \\ \hline ILL1 & \(\Delta E_{mean}\) & 12.07 & 7.05 & 2.98 & 3.25 & **1.96** \\  & \(\Delta E_{max}\) & 24.50 & 20.74 & 10.04 & 6.58 & 7.56 \\ ILL2 & \(\Delta E_{mean}\)
## 2 Kinematics Analysis
### Inverse Kinematic Analysis  
With the posture \(\mathbf{q}=(x,y,z,\alpha,\beta,\gamma)\) of the moving plate, the length vector of the six outriggers \(\mathbf{l}=(l_{1},l_{2},l_{3},l_{4},l_{5},l_{6})\) can be calculated \(\overset{\rightharpoonup}{l_{i}}=\overset{\rightharpoonup}{B_{i}}\overset{ \rightharpoonup}{A_{i}}\), so the vector relationship of outriggers can be described as follows:  
\[\overset{\rightharpoonup}{B_{i}}\overset{\rightharpoonup}{A_{i}}=\overset{ \rightharpoonup}{O\mathbf{O}^{\prime}}+\overset{\rightharpoonup}{O^{\prime}} \overset{\rightharpoonup}{A_{i}}-\overset{\rightharpoonup}{O\mathbf{B_{i}}} \tag{1}\]  
Which is:  
\[\overset{\rightharpoonup}{l_{i}}=\overset{\rightharpoonup}{t_{i}}+\overset{ \rightharpoonup}{R\overset{\rightharpoonup}{A_{i}}}-\overset{\rightharpoonup}{O \mathbf{B_{i}}} \tag{2}\]  
where:  
\[\mathbf{R}=\begin{bmatrix}cos\gamma\ cos\beta&cos\gamma\ sin\beta\ sin\ sin\ - sin\gamma\ cos\alpha&sin\ \gamma\ sin\ \alpha+cos\gamma\ sin\beta\ cos\alpha\\ sin\gamma\ cos\beta&cos\alpha\ cos\gamma+sin\gamma\ sin\beta\ sin\ \alpha&sin\gamma\ sin\beta\ cin -cos\gamma\ sin\alpha\\ -sin\beta&cos\beta\ sin\alpha&cos\beta\ cos\alpha\end{bmatrix}\]  
\(\mathbf{R}\) is z-y-x Euler transformation matrix, \(\overset{\rightharpoonup}{t_{i}}=(x,y,z)\) is the position vector of the top platform. Let:  
\[\mathbf{P_{i}}=\overset{\rightharpoonup}{R\overset{\rightharpoonup}{A_{i}}}\]  
So, the length of the six outriggers is  
\[\begin{array}{c}\mathbf{l_{i}}=\sqrt{\sum\limits_{k=1}^{3}\left[\mathbf{P_{i}}(k)- \mathbf{B_{i}}(k)\right]^{2}}\\ i=1,\ 2,\ 3\ldots 6\end{array} \tag{3}\]  
(3) is the inverse kinematic solution formula of outrigger length. Obviously, it is easy and fast for computer to calculate the length of six outriggers corresponding to the given posture by solving (3).  
Figure 1: Simplified structure and vector geometry diagram of the SGP. (**a**) Main components of SGP. (**b**) Kinematic vector diagram of single outrigger.