esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Synthesis and Analysis
#### 3.1.10 Differential Evolution  
The differential evolutionary (DE) algorithm is a powerful and efficient population-based optimization algorithm based on evolutionary theory. It is presented as a floating-point encoding evolutionary algorithm for minimizing possibly nonlinear and non-differentiable continuous space functions [122; 123]. [PERSON] [124] introduced DE-KM, a combination of differential evolution algorithm and K-means clustering algorithm. The mutation and crossover operation of DE generates each candidate solution, which is then fine-tuned using the K-means algorithm. [PERSON] et al. [125] proposed a hybrid of DE and one-step K-means algorithm termed CDE (clustering-based DE) for solving unconstrained global optimization problems. The one-step K-means was introduced to enhance DE performance by acting as several multi-parent crossover operators to utilize the population information efficiently. [PERSON], [PERSON], and [PERSON] [126] proposed ACDE-K-means integrating automatic clustering based differential evolution algorithm with K-means algorithm seeking to improve ACDE algorithm's performance by the use of the K-means algorithm for tuning the cluster centroids.  
[PERSON], [PERSON], and [PERSON] [127] hybridized the K-means clustering algorithm and DE for continuous optimization using the DE operators to work on the groups generated by the K-means algorithm for better diversification and escaping from local convergence. [PERSON] et al. [128] proposed an improved K-means clustering algorithm using a hybrid of DE and FOA (fruit fly optimization algorithm) embedded into K-means. [PERSON] [129] proposed a weighted K-means algorithm based on DE with an initial clustering center and strong global search capability. [PERSON] et al. [130] used a u-control chart (UCC) to automatically determine the k activation threshold for ACDE with the cluster number generated serving as the specified k value for the K-means algorithm, thus improving the performance of the clustering algorithm. [PERSON] et al. [131] presented a combination of differential evolution algorithms with adaptive niching and K-means termed DE-NS-AKO for partitional clustering. The K-means-based adaptive niching adjusts each niche size to avoid premature convergence. As reported earlier, [PERSON] et al. [97] presented a combination of DE with a modified K-means algorithm with ABC for color image segmentation. [PERSON] and [PERSON] [132] explored the combination of GA and DE to find the original seed point and determine the required cluster numbers for the traditional K-means algorithm to reduce the possibility of its convergence into local optimal.
# Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles
###### Abstract  
Highway toll stations are equipped with electronic toll collection (ETC) lanes and manual toll collection (MTC) lanes. It is anticipated that connected autonomous vehicles (CAVs), MTC human-driven vehicles (MTC-HVs), and ETC human-driven vehicles (ETC-HVs) will coexist for a long time, sharing toll station infrastructure. To fully leverage the congestion reduction potential of ETC, this paper addresses the problem of ETC lane allocation at toll stations under heterogeneous traffic flows, modeling it as a mixed-integer nonlinear bilevel programming problem (MINLBP). The objective is to minimize total toll station travel time by optimizing the number of ETC lanes at station entrances and exits while considering ETC-HVs' lane selection behavior based on the user equilibrium principle. As both upper-level and lower-level problems are convex, the bilevel problem is transformed into an equivalent single-level optimization using the Karush-Kuhn-Tucker (KKT) conditions of the lower-level problem, and numerical solutions are obtained using the commercial solver Gurobi. Based on surveillance video data from the Liulin toll station (Lianhuo Expressway) in Zhengzhou, China, numerical experiments were conducted. The results illustrate that the proposed method reduces total vehicle travel time by 90.44% compared to the current lane allocation scheme or the proportional lane allocation method. Increasing the proportion of CAVs or ETC-HVs helps accommodate high traffic demand. Dynamically adjusting lane allocation in response to variations in traffic arrival rates is proven to be a more effective supply strategy than static allocation. Moreover, regarding the interesting conclusion that all ETC-HVs choose the ETC lanes, we derived the relaxed analytical solution of MINLBP using a parameter iteration method. The analytical solution confirmed the validity of the numerical experiment results. The findings of this study can effectively and conveniently guide lane allocation at highway toll stations to improve traffic efficiency.  
heterogeneous traffic; queueing theory; user equilibrium; MINLBP; lane allocation; transportation planning and management
## 7 Simulation Results
### Results Obtained for an Unknown Parameter \(\epsilon\) and a Fixed Parameter \(\delta\)  
We investigate different values of the parameter \(\delta\) to determine the associated values of the parameter \(\epsilon\) and the matrix gain \(\mathcal{K}\), in compliance with the LMI-based stability requirements formulated in Theorem 2, which considers a fixed parameter \(\delta\) and an unknown parameter \(\epsilon\). We provide the obtained numerical results in Table 4 by addressing the LMI problem obtained from Theorem 2 and by integrating the extra LMI conditions specified by (95a) and (95b). Similarly, the LMI-based minimization problem is found to be unfeasible for all \(\delta\leq 0.61\) and \(\delta\geq 1.64\). Moreover, for all feasible cases, the feedback gain \(\mathcal{K}\) is small and realizable; hence, the obtained outcome is quite satisfactory.  
Therefore, we provide the graphical results given in Figure 4 using the gain \(\mathcal{K}\) corresponding to the value of \(\delta=0.9\), as presented in Table 4 for the adopted controller described by (25). As before, the figures clearly demonstrate that the actuated knee exoskeleton is effectively regulated to the desired condition. Additionally, regulation endeavor \(u\) is almost negligible at the equilibrium point.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\delta\) & \(\mathcal{K}\) & \(\epsilon_{\text{min}}\) \\ \hline
0.61 & (l) & (l) \\
0.62 & \(\left[\begin{array}{cc}-2.75&-1.67\\ -2.00&-1.04\\ -1.95&-1.00\\ 0.9&-1.96&-1.01\\ 1.1&-2.06&-1.09\\ 1.3&-2.27&-1.26\\ 1.5&-2.77&-1.68\\ 1.6&-3.91&-2.66\\ 1.63&-7.10&-5.35\\ 1.64&(\text{(l)})\end{array}\right]\) &
\begin{tabular}{c} 3.85 \\ 2.13 \\ 2.03 \\ 2.28 \\ 2.77 \\ 3.91 \\ 4.30 \\ 5.35 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical results of the feedback gain \(\mathcal{K}\) and the optimal/minimal value \(\epsilon_{\text{min}}\) of the parameter \(\epsilon\) obtained for some values of the parameter \(\delta\) by resolving the LMI minimization issue in Theorem 2 and incorporating the two LMIs (95a) and (95b) for reducing the size of \(\mathcal{K}\).  
Figure 3: Simulation results obtained based on the first designed method. Temporal variation of: (**a**) the angular position error \(\phi\), (**b**) the angular velocity error \(\phi\), (**c**) the affine state-feedback controller \(u\), and (**d**) the lumped disturbance \(\Delta_{t}\).
## 4 Results and Discussion
### Parametric Analysis
#### 4.3.2 Effect of Water Flow Rate  
In the literature, the hot water flow rate values through solar collectors range from 20 to 80 L/h per m\({}^{2}\) of collector area are recommended for panels connected in parallel, as in our case. The variation of the solar fraction, with the hot water flow rate through the solar collector array, is indicated in Figure 10. The water flow rate varied from 20 to 55 (L/h)/m\({}^{2}\) of collector area. A change of water flow from 20 to 40 (L/h)/m\({}^{2}\) causes only a 0.9% increase in solar fraction; increasing the flow rate over an optimum value (40 (L/h)/m\({}^{2}\)) will lead to drops in a solar fraction of about 0.2%. It is evident that the results obtained depict small changes in solar fraction and allow us to affirm that this parameter does not present a significant impact on solar coverage; it is in alignment with the results obtained by [PERSON] [40,41].
## 2 Materials and Methods
Investigations of the surface topology of the deposited thin films have been carried out using atomic force microscopy (AFM) S.I.S. Surface Imaging NanoStation II operated in non-contact mode using a Si cantilever (Aachen, Germany). The Gwyddion 2.56 software was used to calculate the roughness parameters (Czech Metrology Institute, Brno, Czech Republic). The morphological analysis of the deposited provided information about key parameters, such as topography, roughness, and uniformity, which are essential for understanding their behavior in various applications. The chemical composition of the resulting films was studied using the FEI Helios NanoLabTM 600 DualBeam scanning electron microscope (SEM/FIB) (Hillsboro, OR, USA) equipped with an energy dispersive X-ray (EDX) detector with surface area of 30 mm\({}^{2}\) (Ametek, model ELECT PLUS, Berwyn, PA, USA). The energy bandgaps of oxide films were calculated from the optical absorption spectra measure with a Jasco 670 UV-VIS spectrophotometer (Jasco International Co., Ltd., Tokyo, Japan), applying the Tauc plot method. This spectrometer covered a wavelength range from 190 nm to 2500 nm. X-ray diffraction (XRD) analysis was carried out on a Bruker AXS D8 Discover diffractometer (Bruker corporation, Billerica, MA, USA), using Cu K\(\alpha\)1 radiation (\(\lambda\) = 0.15406 nm). The instrument was operated at a beam voltage of 40 kV and a beam current of 40 mA. Complementarily, the hydrophobic or hydrophilic properties of oxide-based thin films produced by aerosol deposition were studied by measuring the contact angle, using a KRUSS DSA25 (drop shape analyzer) (KRUSS GmbH, Hamburg, Germany), equipped with a camera. This procedure involves placing a water droplet on the surface of the deposited oxide-based thin film and measuring the angle between the surface and the tangent to the droplet at the contact point. This research enhances the understanding of the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
interactions between the oxide-based thin films and the environment, helping to optimize their properties for different applications, such as electronic devices, sensors, or surface coatings with specific functionalities, including microfluidic applications. By combining the data obtained from wetting edge angle measurements and morphological analysis, comprehensive information about the performance and characteristics of these materials is received, opening new directions for future research in materials science and technology.
## Results
### Characteristic of CHC/GO/CNC Nanocomposite Hydrogels
#### 3.2.2 Biocompatibility and Antibacterial Properties of the CHC/GO/CNC Nanocomposite Hydrogel  
From the results of the 3T3 fibroblast survival assay (Figure 8), it was found that 5% DMSO addition (positive control) showed high cytotoxicity against 3T3 fibroblasts. However, it showed great biocompatibility in the sample of GO/CNC (without CHC). When the CHC addition was 0.05-0.1 wt%,  
Figure 6: The sedimentation test at the initial state and after 10 h incubation in the water solution; and the Lumisizer® test (the sedimentation rate) of (**a**) GO; (**b**) GO/CNC, and the CHC/GO/CNC nanocomposite suspension with the different CHC concentrations of (**c**) 0.05 wt%; (**d**) 0.1 wt%; and (**e**) 0.2 wt%.  
Figure 7: Water absorption ability of pristine chitosan (CS), hyaluronic acid (HA), and the nanocomposite hydrogel (CHC/GO/CNC) at the relative humidity of (**a**) 90% and (**b**) 23%. \({}^{\star}\) The statistical analysis (Student’s \(t\)-test) showed a significant difference (\(p<0.05\)) between the two observed groups (chitosan with HA and CHC/GO/CNC, respectively) at the relative humidity of (**a**) 90% and (**b**) 23%.  
the cell viability (%) was higher than the negative control (100%), which can increase the growth of 3T3 fibroblasts up to 10-20%. When the concentration was higher than 0.5-2 wt%, the cell viability dropped slightly to 98-100%, but it still showed no cytotoxicity to 3T3 fibroblasts.  
In addition, according to the JIS Z2801: 2010 antibacterial quantitative analysis method, the bacteriostatic effects of CHC/GO/CNC nanocomposite hydrogels to various microorganisms (_Escherichia coli_, _Staphylococcus aureus_, _Candida albicans_) are shown in Table 1. The CHC/GO/CNC nanocomposite (2 wt% of CHC) inhibits a variety of microorganisms, of which gram-negative bacteria (_Escherichia coli_) are more easily inhibited than other microorganisms, presumably because the surface of the CHC has a positive charge from its zeta potential and is in contact with the negatively charged cell wall of microorganisms. However, the negative charge of GO/CNC addition is beneficial to the inhibition of gram-positive bacteria (_Staphylococcus aureus_). The bacteriostatic ratio (%) of CHC/GO/CNC nanocomposite hydrogels is about 97.5%, which is slightly lower than that for gram-negative bacteria (_Escherichia coli_ > 99.9%). Furthermore, because the 2D-materials of GO nanosheets are like a knife, it is easy to slice the cell wall of the fungus (_Candida albicans_). The bacteriostatic ratio (%) of CHC/GO/CNC hydrogels for fungus is about 94.1%, which can inhibit fungal growth. This interaction causes leakage of protein and other cellular components, which in turn causes the cell membrane to rupture and the microbes die.
# Multi-Source Neural Model for Machine Translation of Agglutinative Language
## 1 Introduction
However, the above works mostly pay attention to the high-resource machine translation tasks with large-scale parallel data and sufficient semantic analysis tools, which lacks the consideration of agglutinative language translation with complex morphology and limited resources. In this paper, we propose a multi-source neural model for the machine translation task on agglutinative language. We consider that enhancing the ability of the NMT model in capturing the semantic information of the source-side sentence is beneficial to compensate for both the corpus scarcity and data sparseness. The followings are our main contributions:  
* Focusing on the complex morphology of the agglutinative language, in contrast to the standard NMT model that uses a single encoder, we utilize a multi-source NMT framework consisting of a word-based encoder and a knowledge-based encoder to encode the word feature and the linguistic features, respectively, which aims to incorporate the source-side linguistic knowledge into the NMT model. * For the purpose of enriching each source word's representation in the NMT model, we extend the input embedding layer of the knowledge-based encoder to allow for the word-level linguistic features of lemma, POS tag and morphological tag. * In the consideration of enhancing the NMT representation ability on the source-side sentence, we use a serial combination method to hierarchically combine the conditional information from the encoders, which helps to learn a high-quality context representation. Firstly, the representation of the source-side linguistic features integrates with the representation of the target sequence. Secondly, the resulting vector integrates with the representation of the source sequence to generate a context vector. Finally, the context vector is employed to predict the target word sequence. Experimental results in Turkish-English and Uyghur-Chinese machine translation tasks show that the proposed approach can effectively improve the translation performance of the agglutinative language, which indicates the validity of the multi-source neural model on using the source-side linguistic knowledge for morphologically rich languages.
## 4 Numerical Solution to the Viscous Free Interface Model
### Discretization by Moving the Interface
Typical values are \(\Delta t^{0}=10^{-4}\) s for the initial time step and \(\Delta Y=10^{-4}\). The solution to Equations (22)-(24) is obtained as follows. We can solve the system (22) together with the boundary conditions (24a) and (24c) for any value of \(b^{n+1}\) since it is a linear system in \(U^{n+1}\) with right-hand side containing \(U^{n}\), the source term and the advection term (remember that the advective derivative is treated explicitly). This leads to a tridiagonal linear system with a matrix that results only from the time derivative and the diffusive terms. This matrix, which is diagonally dominant, has an inverse with nonnegative entries. Thus, the solution \((U_{j}^{n+1})_{1\leq j\leq n_{Y}}\) can be expressed linearly with nonnegative coefficients in terms of the coefficients \((a_{j}^{n+1/2})_{1\leq j\leq n_{Y}}\) which appear on the right-hand side and which depend on the still unknown interface position \(b^{n+1}\). According to (23) and since \(U_{j}^{n}\) is nondecreasing with respect to \(j\) (it should remain nondecreasing because we assume \(\partial_{Z}S\leq 0\)), \(U_{j}^{n+1}\) is thus, for all \(j=1\ldots n_{Y}\), a nondecreasing continuous and piecewise linear function of \(b^{n+1}\), with two different formulae corresponding to whether \(b^{n+1}\) is greater or smaller than \(b^{n}\). In particular, the remaining boundary condition (24b), which is equivalent to \(U_{1}^{n+1}=0\) owing to (24a), determines a unique solution \(b^{n+1}\), see Figure 8. The value of \(b^{n+1}\) can be computed explicitly by solving the linear system twice with two different right-hand sides and using linear interpolation. The first solve uses the right-hand side evaluated with the temporary value \(b^{n}\) for \(b^{n+1}\), yielding a temporary value for \(U_{1}^{n+1}\). If the obtained value is negative (left panel of Figure 8), the second solve is performed using the value \(h\) for \(b^{n+1}\), otherwise the value \(0\) is used (right panel of Figure 8). Once \(b^{n+1}\) is known, we determine the entire profile \(U_{j}^{n+1}\) for all \(j=1\ldots n_{Y}\) by linear interpolation.
## 3 Results and Analysis
### General Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & **Variables** & **Mean** & **Std.** & **Variables** & **Mean** & **Std.** \\ \hline TIS * & V mean (\(\mathrm{km}/\mathrm{h}\)) & 84.98 & 5.44 & MLCD (s) & 4.40 & 1.54 \\ GDS & V std (\(\mathrm{km}/\mathrm{h}\)) & 5.05 & 1.03 & MLCD (m) & 5.81 & 1.57 \\ TIS & V std (\(\mathrm{km}/\mathrm{h}\)) & 5.05 & 1.03 & MLCL (m) & 92.34 & 26.71 \\ GDS & 4.03 & 0.97 & MLCL (m) & 124.82 & 33.11 \\ TIS & \(\mathrm{DCC}\max\) (m/s\({}^{2}\)) & \(-1.02\) & 0.27 & MLCA (\({}^{\circ}\)) & 16.72 & 4.51 \\ GDS & \(\mathrm{DCC}\max\) (m/s\({}^{2}\)) & \(-0.86\) & 0.21 & MLCA (\({}^{\circ}\)) & 13.25 & 3.65 \\ TIS & \(\mathrm{RV}\) (\(\mathrm{km}/\mathrm{h}\)) & 5.35 & 2.95 & MLCD (s) & 4.30 & 1.54 \\ GDS & 3.29 & 1.93 & MLCD (s) & 4.89 & 1.57 \\ TIS & 61.48 & 9.90 & MLCL (m) & 92.34 & 26.71 \\ GDS & 52.61 & 7.40 & MLCL (m) & 95.88 & 27.79 \\ TIS & 2.78 & 0.40 & MLCA (\({}^{\circ}\)) & 14.12 & 4.51 \\ GDS & 2.52 & 0.33 & MLCA (\({}^{\circ}\)) & 14.45 & 4.25 \\ TIS & 3.85 & 0.89 & MLCL & 1.55 & \(-0.13\) & 0.14 & \(-0.05\) \\ GDS & Gap (s) & 4.36 & 0.87 & MLCL & 1.0 & 0.05 & \(-0.04\) \\ \hline \hline \end{tabular}
* TIS refers to tunnel–interchange section scenarios; GDS refers to general diversion scenarios.  
\end{table} 
Table 3: Descriptive statistics.
# SACT: A New Model of Covert Communication Based on SDN
## 7 Conclusions  
Anonymous communication technology plays an important role in protecting information security. In traditional networks, after the deployment of an anonymous communication system, if the configuration of firewalls, routers, and switches in the network needs to be modified, it will be a complicated, costly, and time-consuming task. The traditional tracking technology of network watermark relies on the deployment of tracking equipment, and additional equipment such as a watermark embedder must be added to the corresponding network environment to take over all network traffic. It has caused additional resource consumption and easily aroused the vigilance of criminals, which has prevented large-scale implementation.  
Targeting the difficulty to deploy poor reusability and low scalability of the traditional network, in this paper, we studied the Software Defined Network and network watermarking. By taking advantage of SDN to separate the control layer and the data layer, we integrated the network watermark with the SDN to achieve the tracking of covert communication. Hiding relevant details from users, SDN can switch the current state at any time without changing any deployment. Making use of its good expansibility, this paper proposes a watermarking scheme to further improve the detection and anti detection of the tracking watermark, which lays a good foundation for the application and expansion of the watermark. The security and effectiveness of the SACT are verified through detection  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **CTC** & **Non-Zero** & **SACT** \\ \hline effective & Yes & Yes & Yes \\ concealment & Yes & Yes & Yes \\ low-cost & No & Yes & Yes \\ dynamic & No & No & Yes \\ Easy to deploy & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of features of different anonymous communication channels.  
rate, false alarm rate, and anti attack test, which proves that the system can solve the deployment problem of anonymous tracking and improve the availability and scalability of covert communication.  
Conceptualization, L.S. and Y.S.; methodology, L.S., Y.S. and Z.X.; software, L.S., Y.S., Z.X. and Y.L.; validation, L.S. and H.C.; formal analysis, L.S., H.C. and Y.S.; investigation, Y.S. and Y.L.; resources, Y.S. and Y.L.; data curation, Y.S. and Z.X.; writing--original draft preparation, L.S. and Y.S.; writing--review and editing, L.S., Y.S. and H.C. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of Shandong Province under Project ZR2019 MF034, in part by the Research Funds from Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS201811, in part by the National Natural Science Foundation of China (NSFC) under grant No. 61772551.  
The authors declare no conflict of interest.