esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, the stability of the subgrade with ELB is described using theoretical and numerical simulation methods. The following conclusions are drawn.  
1. An analytical model was proposed for stability analysis of the subgrade with ELB, and it can be used for ELB design.
2. The subgrade stability can be improved drastically by the ELB. With the ELB width and height increase, the subgrade stability factors increase gradually. The subgrade stability factors increase more with the width increase than that with the height increase. That is, the ELB width has a greater influence on the subgrade stability factors than the ELB height. For the ELB design, the ELB with bigger width is recommended to ensure subgrade stability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Width B/m** & **Height H/m** & \begin{tabular}{c} **Density** \\ \(\rho\)/kg/m\({}^{3}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Cohesion** \\ \(\sigma\)/kPa** \\ \end{tabular} &
\begin{tabular}{c} **Internal** \\ **Friction \(\varphi\)/\({}^{\circ}\)** \\ \end{tabular} \\ \hline \(K_{1j}\) & 5.71 & 5.8 & 5.80 & 5.85 & 5.87 \\ \(K_{2j}\) & 5.78 & 5.81 & 5.85 & 5.85 & 5.85 \\ \(K_{3j}\) & 5.89 & 5.89 & 5.87 & 5.88 & 5.9 \\ \(K_{4j}\) & 5.95 & 5.95 & 5.93 & 5.89 & 5.86 \\ \(K_{5j}\) & 6.05 & 5.93 & 5.94 & 5.91 & 5.9 \\ \(R_{j}\) & 0.34 & 0.15 & 0.14 & 0.06 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Range analysis results.  
Figure 11: The range for every factor.  
3. The ELB parameter sensitivity order on the subgrade stability is as follows. Width > height > density > cohesion > internal friction. In the ELB designing process, the width and height should be focused on and the density, cohesion, and internal friction can be ignored. This research provides references for ELB designing and is significant for promoting the application of ELB in soft soil areas. In future, research on the ELB designing method can be carried out.  
Conceptualization, F.X.; methodology, F.X., X.W., F.Y. and J.Y.; software, L.H.; validation, R.L. and J.Y.; investigation, X.W. and F.Y.; resources, X.W.; writing--original draft preparation, F.X.; writing--review and editing, X.W. and F.Y.; visualization, L.H. and X.W.; supervision, R.L.; project administration, R.L.; funding acquisition, F.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Anhui Province (Grant No. 2108085 ME190).  
Not applicable.  
The raw data required to reproduce these findings cannot be shared at this time due to time limitations. The processed data required to reproduce these findings cannot be shared at this time due to time limitations. The authors would like to acknowledge the China Railway No. 4 Engineering Group Co., Ltd.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Influence of Cataphoretic Deposition and Degreasing Deposition on Coating Characteristics
Under these conditions, a reduction in thickness is only observed with a 13 min degreasing deposition time, where the minimum thickness of 15.95 \(\upmu\)m is reached after 7 min of KTL deposition. The maximum thickness is produced during a 3 min degreasing deposition. At this degreasing time, the largest thickness increase of 2.15 \(\upmu\)m is observed as the KTL deposition time increases, with the thickness reaching a maximum of 18.68 \(\upmu\)m at 7 min of KTL deposition. When the degreasing temperature is increased to 80 \({}^{\circ}\)C, a graph is produced with a range of values of 0.45 \(\upmu\)m at a minimum coating time of 3 min, with thickness values increasing as the deposition time increases. The smallest thickness of 15.22 \(\upmu\)m is achieved after 13 min of deposition. The highest thickness values are observed with a 3 min KTL deposition time. After approximately 4.8 min of KTL deposition, a cross-point occurs on the graph, with a thickness of around 16.28 \(\upmu\)m at this point. As the deposition time increases, the maximum thickness is achieved with a 13 min deposition duration. After reaching 7 min of KTL, the resulting layer exhibits a thickness of 17.54 \(\upmu\)m. During this specified deposition time, the minimum thickness of 16.96 \(\upmu\)m was obtained with a 3 min degreasing process. The results also confirm the conclusions of [PERSON] et al. [39], [PERSON] et al. [40], and [PERSON] et al. [41]. Figure 9: The influence of cataphoretic deposition time and degreasing temperature on the change of the formed layer at maximum values of the design of experiments methodology.
## 5 Anomaly Detection Model Test Results
### Long Short-Term Memory (LSTM)  
Figure 15 shows the accuracy according to the training progresses of LSTM-m2, and Table 10 shows the training results of the models per the LSTM layers and hidden units configured as shown in Table 5. The numbers 10-90 shown above the graph's \(x\)-axis in the figure means the number of epochs. In Figure 15, we can see that the accuracy according to the training progress is oscillating between about 60 and 90% and not converging. Training LSTM is done by dividing the training data into several subsets and finding parameters that minimize the loss function for each training data subset. The reason for accuracy oscillating is optimized LSTM parameters for each subset do not converge and continue to change. This means that the LSTM cannot properly find the pattern of target input/output data. Accordingly, we can see that the accuracy per the models in Table 10 is also low. All training results show high TPRs but low TNRs.  
Figure 14: Training results of the MLP-based anomaly detection models: (**a**) TPR and TNR for each model training; (**b**) TNR according to training iteration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \begin{tabular}{c} **TPR** \\ **(Average)** \\ \end{tabular} & \
\begin{tabular}{c} **TNR** \\ **(Average)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(Average)** \\ \end{tabular} \\ \hline MLP-m1 & 97.6 & 70.1 & 90.5 \\ MLP-m2 & 97.5 & 62.3 & 87.9 \\ MLP-m3 & 98.4 & 67.2 & 90.5 \\ MLP-m4 & 98.0 & 67.2 & 90.5 \\ MLP-m5 & 97.8 & 72.0 & 91.0 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Test results of MLP-based anomaly detection models.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 6 Results and Discussion  
This section presents and analyzes the results of the network performance for the different setups. Firstly, we study the single-user case scenario. Then, it explores the network with multiple users. Finally, we discuss if the expectations of the system performance have been accomplished.
## 2 Establishment of Numerical Simulation Model  
### Model of Vehicle-Track Coupled Dynamics  
In order to simulate the dynamics of railway vehicles on flexible tracks and keep track of the inherent wheel-rail interaction, we simulate each of the two subsystems--vehicle and rail--with appropriate software concurrently and exchange the interfacing data at discrete communication points. The track model has been implemented in ANSYS, allowing a sufficiently accurate approximation of its flexibility, while the vehicle's motion, including its complex wheel-rail interaction, is reproduced in the Universal Mechanism (UM) software [18; 19].Using this co-simulation method, the wheel-rail interaction forces resulting from wheel polygons are determined by UM software. These forces are then entered into a slab track finite element model. A diagram of the analytic workflow is shown in Figure 2.  
Figure 1: Typical wheel polygons with different harmonic orders: (**a**) fourth-order; (**b**) 11 th-order; and (**c**) 22 nd-order.  
The vehicle-track system dynamics model consists of one high-speed electric multiple-unit carriage consisting of two rigid bogies and four elastic wheelsets running on a straight track (see Figure 3). The vehicle model of Chinese high-speed electric multiple units (EMUs)considers all translational and rotational degrees of freedom of the bodies. Each wheel of the wheelsets has a worn type tread, with the polygonal shape over the circumference at the center of the transverse profile modeled by a sinusoidal function to describe the harmonic wheel radius deviation from the nominal radius. Hence, the excitation caused by a polygonal wheel radius as a function of time \(Z(t)\) is defined by  
\[Z(t)=\Delta r_{n}\sin[n[v/R]t+\varphi_{n}], \tag{1}\]  
where \(\Delta r_{n}\) is the OOR amplitude of the \(n\)th-order polygonized wheel, \(n\) is the order of the polygonized wheel, \(v\) is the running speed, \(R\) is the nominal rolling radius, and \(\phi_{n}\) is the phase angle. As the effect of phase shift is not considered in this paper, \(\phi_{n}\) is set to zero. All the wheels of a train are assumed to be polygonal in this dynamic model.  
The rails are considered as Timoshenko beams without irregularities. A special bushing-type force was used for the simulation of rail pads and fasteners, which connect with the under-rail base. The kinematic characteristics of the rails, rail pads, and fasteners correspond to those in the slab track finite element model. The Kik-Poitrowski model is adopted to calculate the vertical interaction forces within the wheel-rail contact patches, and tangent forces are calculated based on [PERSON]'s rolling contact theory [20]. Slab track parameters, such as the parameters of rails and their cross-sections, and the vertical and lateral stiffness of the track were consistent with the finite element track model built in ANSYS.  
Figure 2: Diagram of modeling workflow.
# Compact and Accurate Scene Text Detector
## 2 Related Work
### Regression-Based Approach  
Compared to segmentation-based approaches, a regression-based approach directly finds the word boxes. That is, it finds several points (e.g., top-left points and bottom-right points) of potential word boxes. The existing studies of this approach mainly focused on effectiveness, and thus adopted heavy backbone models (e.g., Resnet, VGGnet) to enhance accuracy. In [8; 9] anchor boxes of various aspect ratios were utilized and achieved about a 77\(\sim\)81% F1 score using VGGnet as a backbone. In [20] a way to use the mask branch of Mask R-CNN for character segmentation was proposed, and achieved an 86\(\sim\)91% F1 score using the ResNet as a backbone. In [21] a text context module (TCM) to detect texts was proposed, and achieved an 87\(\sim\)92% F1 score using ResNet as a backbone. The studies above commonly focused only on improving effectiveness (e.g., the F1 score) and thus increased model complexity without considering efficiency.  
Regression-based approaches generates multiple candidates for word boxes, so it is necessary to filter inappropriate candidates out. There have been studies for effectively selecting such promising candidates. For example, [10] proposed a method of merging small word boxes to detect long shaped word boxes, and achieved a 75\(\sim\)85% F1 score. In [4] the corner-based Region Proposal Network (CRPN) was proposed to divide each word box into smaller word boxes. An 82\(\sim\)91% F1 score using line level detection and recalled non-maximum suppression was achieved in [22]. The studies above commonly focused only on improving effectiveness (e.g., the F1 score) by employing complex post-processing (e.g., filtering, merging, dividing).  
There have been few studies aimed at efficient models for text detection. In [10] a single shot detector (SSD) was utilized and it was shown that their proposed model is efficient in terms of the inference time. In [12], a geometry map was proposed and Pvanet is adopted as a backbone. Their proposed model exhibited a comparable F1 score (e.g., 75% F1 score) and has much more compact structure than existing models. Such compact structure is mainly obtained by adopting Pvanet as a backbone, showing that it is important to carefully choose the backbone. In [14] the ResNet was adopted as a backbone to improve effectiveness without losing inference time. Using ResNet as a backbone led t a better F1 score, but it turned out that it causes efficiency degradation in terms of the number of parameters and FLOPS. Even though the above studies focused on developing efficient models, there are two considerable factors. First, there is still room for investigating better backbones for improved efficiency; for example, MobileNet and MobileNet2 are well-known methods that are efficient as well as effective. Second, the above studies did not carefully design the decoders even though the decoder strongly impacts the results; for example, the decoders of existing studies usually consist of traditional convolutional layers.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 3 Rotational Stiffness of Corrugated Steel Flange Joint  
### Fine Numerical Simulation Analysis  
The corrugated steel flange joint (CSF) is a commonly used connection method in corrugated steel lining structures [12]. Specifically, the CSF consists of CSPs, flange plates, and bolts. The flange plates are welded to both ends of the CSP, and the connection between two corrugated steel linings is achieved by bolts securing the flange plates, as shown in Figure 7.  
Currently, there is no standardized algorithm available for determining the rotational stiffness (\(k_{\theta}\)) of the CSFJ, which typically requires mechanical testing, which is slow and time-consuming. In contrast, the three-dimensional fine FE model method offers a simpler and more efficient alternative by fully accounting for local structural details. FE models of the CSFJ (Figure 7) were developed based on the study of [PERSON] [26]. (1) Meshing: Solid elements (C3D8R) were employed to model all components. The mesh sizes for the CSP, flange plates, and bolts were 4 mm. The rollers were defined as rigid bodies, so the mesh size was deemed non-critical. (2) Interaction: Tie constraints were utilized to establish connections between the flange plates and bolts. Additionally, tie constraints were applied to connect the bottom of the CSP to the support. Hard contact and a tangential friction coefficient were specified for other interfaces. (3) Boundary conditions: The corrugated steel was free at both ends. The left-side roller was fully fixed, while the right-side roller could only move horizontally. (4) Material properties: the material constitutive was consistent with Section 2.2.2.  
Figure 6: Influence of wave thickness and RDP on equivalent stiffness coefficient of CSP.  
The comparison of deflection curves (Figure 8) and destruction modes (Figure 9) demonstrate the fidelity of the FE model calculations.  
By modifying the geometry of the previously mentioned FE model, a new model was developed specifically for the CSFJ in the Qipanshan Tunnel, where the CSP dimensions are 380 mm \(\times\) 140 mm \(\times\) 5 mm, as depicted in Figure 7. The reversed arch configuration of the CSP model faithfully replicates practical engineering conditions.  
Figure 8: Comparison between full-scale test and numerical simulation of CSFJ.  
Figure 7: Schematic illustration of three-dimensional refined FE model of CSP flange joint.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 6 Conclusions  
Many vast territories within Canada, Russia, China, and India are isolated regions and are disconnected from the rest of the world. Today, it is essential to establish adequate telecommunication systems in these places, namely, reliable and fast enough techniques to reconnect them to the rest of the world. For now, the only solution is the satellite, but it comes at a very high cost. Therefore, the future will have to offer affordable, green, and reliable solutions. As part of this article, a state of the art of the solutions that can be implanted in an isolated environment was conducted with a critical analysis based on several criteria. This study made it possible to identify a suitable solution: the captive  
Figure 18: Ultra-thin and flexible photovoltaic panels [86].  
balloon. This solution has many advantages for these isolated regions, but it also presents challenges requiring research work. The materials to make the envelope of the captive balloon already exist, but it will be necessary to make sure that the photovoltaic panels installed on the airfoil do not modify the characteristics of the textile used, such as:  
* Its resistance to cold and
* Its ability to retain gas, which will have a direct impact on flight autonomy.  
Safety is also an item requiring further study. It will be necessary to ensure that the balloon does not exceed 120 m in height, a height beyond which authorizations are required from Transport Canada. Similarly, the balloon's stabilization in flight facing the wind will require developing an innovative, intelligent control system with, possibly, an artificial intelligence layer. Finally, powering the system over a large area could pose problems with different weather conditions from one balloon to another. The choice of a telecommunication system that provides the greatest range while consuming the least possible energy is also a challenge. It will also have to be at the current standard and follow the evolution of technology, which is very fast in this sector. Without this, the disconnection of isolated environments will persist.  
Conceptualization, L.F., H.I., and M.I.; methodology, L.F. and H.I.; software L.F.; validation, L.F., and A.I.; formal analysis, L.F.; investigation, L.F.; resources, H.I.; data curation, A.I.; writing--original draft preparation, L.F.; writing--review and editing, M.I.; visualization, A.I.; supervision, H.I.; project administration, L.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This study does not report any data.  
The authors declare no conflict of interest.
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
## 6 Conclusions  
This paper proposes an improved ant colony algorithm to solve the SDVRP. Different customer selection strategies are designed according to the characteristics of the SDVRP, and then the adaptive selection threshold is designed according to the characteristics of each stage of the ant colony algorithm. To guide the early stages of the algorithm, an initial pheromone allocation method based on a greedy strategy is introduced. Furthermore, to overcome the problem of local optima, the pheromone backtracking mechanism and adaptive pheromone volatile factor are proposed to enhance the algorithm's ability to escape local optima. Finally, a 2-opt strategy is integrated to further improve the solution quality. The IACA achieves an average reduction of 1.58%, 4.28%, and 3.64% in the best path length in small, medium, and large-scale tests, respectively. In addition, the IACA has a stronger ability to escape local optima, and it can jump out of local optima even in the middle and late stages of an algorithm run.  
However, the algorithm still has certain limitations. From the medium and large-scale experiments, it can be seen that although IACA achieves better solution quality, there is still room for improvement in solution time. The reasons for the long solution time may include: (1) The 2-opt search has low efficiency and consumes more time as the customer scale increases. (2) The heuristic information focuses too much on local features and cannot effectively guide the ants, resulting in the need for more iterations.  
Therefore, in future research, we will explore improving the algorithm by combining more diverse search methods or integrating deep reinforcement learning techniques to better balance solution quality and solution time. Furthermore, this paper focuses on the basic SDVRP model, while real-world scenarios are often more complex. Future research  
Figure 7: (**a**) Solution diversity curve of the S51D1 dataset. (**b**) Solution diversity curve of the S76D1 dataset. (**c**) Solution diversity curve of the S101D1 dataset.  
will consider incorporating common constraints such as multiple depots, time windows, and uncertain demands into the model for further investigation.  
Conceptualization, X.M. and C.L.; methodology, X.M. and C.L.; software, C.L.; validation, C.L.; formal analysis, X.M. and C.L.; investigation, X.M. and C.L.; resources, X.M. and C.L.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, X.M. and C.L.; visualization, C.L.; supervision, X.M.; project administration, C.L.; funding acquisition, X.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China, grant number 2020 YFB2103604.  
Not applicable.  
The website for obtaining the dataset we used in our research has been provided in the article.  
The authors declare no conflicts of interest.
# Design, Manufacture and Wind Tunnel Test of a Modular FishBAC Wing with Novel 3D Printed Skins
## 4 Material Testing  
To obtain the properties of the materials manufactured by the authors, the following tests were performed: _(a)_ carbon fibre axial, transverse and shear tensile tests, and _(b)_ Armadillo TPU (stringers) and Ninjaflex TPU (skin) tensile tests.  
The material properties of the 8552/IM7 Unidirectional (UD) carbon fibre prepreg were obtained by following the ASTM D3039 [28] and ASTM D3518 [29] Standards for tensile and shear properties of fibre-reinforced polymers, respectively, whereas the skin and stringer materials were tested in accordance with ASTM D638 Standard for tensile properties of plastic [30]. To measure both axial and transverse strains, an Intertum Video Gauge point tracking optical system was used. This system consists of one camera that is placed perpendicular to the test specimen's in-plane face. By tracking points within this face, the in-plane axial and transverse strains can be obtained. Along with the loads measured by the test machine's load cell and the specimen dimensions, these strains can be used to obtain stress-strain curves. From these stress-strain curves, the fibre-direction (\(E_{11}\)), matrix-direction (\(E_{22}\)) and shear (\(G_{12}\)) modulus can be obtained for the CFRP specimens. Additionally, the in-plane Poisson's ratio (\(\
u_{12}\)) can be obtained from both (\(E_{11}\)) and (\(E_{22}\)). Since both skin (Ninjaflex) and stringer (Armadillo) materials are isotropic, only \(E\) and \(\
u\) are obtained from experimental data, and \(G=E/2(1+\
u)\). A summary of all the experimentally measured material properties can be found in Table 2. Figures 13 and 14 show examples of the carbon fibre axial and the Ninjaflex tensile tests, respectively.  
Figure 12: FishBAC wind tunnel wing model—spanwise view.