esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Metal Ions in Spin-on Hardmask Using Functionalized Porous Silica Adsorbents
## 2 Experimental
### Preparation of the Adsorbents  
MCM-41 was synthesized by scaling up a previously reported method [14]. CTAB (3.06 g, 8.40 mmol) was dissolved in a 1 L beaker containing ammonia solution (171.84 mL, 28%) and distilled water (630 mL). The mixture was stirred for 30 min at 50 \({}^{\circ}\)C. Subsequently, TEOS (14.1 g, 66 mmol) was then added dropwise and stirred at 400 rpm for 3 h at 75 \({}^{\circ}\)C. The resulting precipitate was separated by vacuum filtration, thoroughly washed with ethanol, and dried at room temperature for 24 h.  
For the synthesis of SBA-15, P-123 (12 g) was dissolved in a mixture of hydrochloric acid (62.5 mL, 35%) and distilled water (297.5 mL) in a 500 mL beaker. After complete dissolution, TEOS (25.7 mL, 24 g) was added, and the solution was stirred at 400 rpm at 40 \({}^{\circ}\)C for 24 h. The resulting mixture was transferred to a polypropylene (PP) bottle and maintained at 100 \({}^{\circ}\)C for 48 h without agitation. After cooling to room temperature, the solid product was separated by filtration, thoroughly washed with ethanol, and air-dried overnight [15].  
To remove the surfactant and template, solvent extraction was performed on the as-synthesized MCM-41 and SBA-15 samples [16]. The pre-synthesized materials (8 g) were added to a three-neck flask containing 180 mL of ethanol, followed by the addition of HCl (8 mL, 35%). The mixture was stirred and heated to 75 \({}^{\circ}\)C using a reflux condenser at a heating rate of 3 \({}^{\circ}\)C min\({}^{-1}\). The temperature was maintained at 75 \({}^{\circ}\)C for 3 h for MCM-41 and 6 h for SBA-15. After cooling to room temperature, the materials were filtered, thoroughly washed with ethanol, and dried overnight.  
For surface functionalization, 1.5 g of mesoporous silica was dispersed in 150 mL of dry toluene in a 250 mL three-neck flask. Then, 6 mL of MPTMS was added for thiol functionalization. The flask was equipped with a reflux condenser and heated to 110 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C min\({}^{-1}\) under stirring for 24 h. The resulting material was filtered, washed with ethanol, and air-dried overnight [17]. The same procedure was applied for amine and methyl functionalization using APTES and MTES, respectively.
## 4 Results and Discussion
### Geometric Comparison of the Obtained Models
In this regard, it is seen as a disadvantage of using the same degree for the Bezier curve for all nodules since each nodule has a different number of points in its contour and sometimes a specific degree is not appropriate for other nodule due to its particular characteristics. In contrast, when genetic algorithms are used to optimize the selection of Bezier degree for each graphite nodule, that problem is avoided as can be seen in Table 1, where using GA a curve degree of 9 was set for the nodule A, since this value best approximates the contour, as a higher order for this specific nodule would produce a \(CSF_{\text{est}}\) that is too low, which, in consequence, would generate an increased relative error. In this way, a higher degree for this kind of nodule is not considered as a candidate for the GA. Further, using the proposed methodology, each nodule was approximated with optimal Bezier curve degrees that are not too small to obtain exaggeratedly smooth curves, such as degree of 4, which have very large \(CSF_{\text{est}}\) and approaches to an idealistic model, but are not too large to fall into curves that simulate errors due to the discretization process, such as degree of 18, where \(CSF_{\text{est}}\) decrease significantly.
# Strans-YOLOX: Fusing Swin Transformer and YOLOX for Automatic Payement Crack Detection
## 3 Methodology
### STrans-YOLOX
#### 3.2.1 Swin Transformer Block  
A CNN can effectively capture local information, although it has limitations in terms of modeling the long-range dependencies. To solve this issue, Swin Transformer blocks are added into the CNN-based structure, which can enhance the long-range dependencies and detailed information. Our design ideas are: (1) to use a CNN to efficiently learn the spatial features from high-resolution images; and (2) to use Swin Transformer to process and aggregate the feature information captured by the CNN. The fusion design combines the advantages of convolution and self-attention. By means of spatial downsampling using the CNN, the model allows the self-attention to focus on small-resolution feature maps with long-range dependencies and an appropriate computational cost. Therefore, we introduce the Swin Transformer block in the last residual CSP bottleneck block and replace some CSPLayer modules in the FPN with the Swin Transformer blocks. The architecture of the Swin Transformer block is illustrated in Figure 3, which consists of windows multi-head self-attention (W-MSA), shifted windows multi-head self-attention (SW-MSA), and multilayer perceptron (MLP). The LayerNorm (LN) layer is inserted between the modules to make the training process steadier. A residual connection is employed after each module. The W-MSA constrains the computation of the self-attention to a window, with the following output:  
\[\hat{x}^{l}=\text{W-MSA}(LN(x^{l-1}))+x^{l-1} \tag{1}\]  
The output \(x^{l}\) of the \(l^{th}\) MLP layer is computed as:  
\[x^{l}=\text{MLP}(LN(\hat{x}^{l}))+\hat{x}^{l} \tag{2}\]  
Figure 2: The architecture of STrans-YOLOX.  
Then, in the next cascaded Swin Transformer module, SW-MSA is added with a shift operation compared with W-MSA to establish information interaction between the different windows without increasing the computation overhead. The process can be expressed as follows:  
\[\hat{x}^{l+1}=\text{SW-MSA}(LN(x^{l}))+x^{l} \tag{3}\]  
\[x^{l+1}=\text{MLP}(LN(\hat{x}^{l+1}))+\hat{x}^{l+1} \tag{4}\]  
where \(\hat{x}^{l+1}\) denotes the output of \(l+1^{th}\) SW-MSA, and \(x^{l+1}\) is the output of \(l+1^{th}\) MLP. Swin Transformer solves the problem of the large computation of the transformer by dividing the image into several windows and constraining the computation of the self-attention in the windows. In the second calculation operation of the self-attention, the shifted window method is utilized to segment image patches with different boundaries so as to realize cross-window communication. By introducing Swin Transformer, the model can effectively model the long-range dependencies, and it has better performance for pavement crack and edge detection in complex scenes.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 3 Decentralization of Virtual Linkage Approach
### Decentralized Coordination Architecture  
In this paper, instead of using hierarchical architecture in which the desired destination of each agent is informed by the corresponding virtual link server, a decentralized architecture is adopted in the proposed approach. As compared to the hierarchical architecture, there does not exist virtual linkage and virtual link servers, each agent only needs to exchange information with its local neighbors.  
Figure 3 shows the architecture diagram of the proposed decentralized virtual linkage approach. Each agent instantiates a local copy of consensus module, denoted as \(F_{i}\). The consensus module \(F_{i}\) is responsible for calculating the instantiation of virtual linkage states \(\xi_{i}=(r_{i},q_{i},\Theta_{i},\lambda_{i})\) for the \(i\)th agent, with the inputs of instantiations of virtual linkage states \(\xi_{j}=(r_{j},q_{j},\Theta_{j},\lambda_{j})\) produced by its local neighbors. The main aim of consensus module is to drive each instantiation of virtual linkage state to converge into the desired states \(\xi^{d}=(r^{d},q^{d},\Theta^{d},\lambda^{d})\).  
The states of the virtual linkage are defined as a coordination variable \(\xi=(r,q,\Theta,\lambda)\), where \(r\) and \(q\) are the desired position and attitude of the virtual linkage's reference frame, respectively, \(\Theta=\big{(}\theta_{1},\theta_{2},\ldots,\theta_{k-1},\theta_{k}\big{)}\) is the desired virtual joint angles, where \(k\) is the number of virtual links in the virtual linkage. In addition, \(\lambda=\big{(}\lambda_{1x},\lambda_{1y},\lambda_{1z},\ldots,\lambda_{kx}, \lambda_{ky},\lambda_{kz}\big{)}\) is a vector which represents the expansion rates of all virtual links. The benefit of introducing \(\lambda\) lies that a group of agents is able to reconfigure into more formation shapes since the length and width of each virtual link can be specified now.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 2 Dynamics Analysis of the Variable-Sweep Wing UAV  
The L-30A UAV, featuring a variable-sweep wing design, can adjust its sweep angles during flight to optimize aerodynamic efficiency and stability. This dynamic capability enhances flight performance but introduces significant nonlinearity and complexity due to changes in the center of mass and aerodynamic forces. To accurately simulate these complex dynamics, a multi-rigid-body dynamics model is essential. This model considers the interactions among various rigid bodies under different flight states, enabling precise simulation of the L-30A UAV's dynamic response.
# Recent Progress in On-Chip Erbium-Based Light Sources
### Epitaxial III-V Light Sources  
III-V compound semiconductor lasers have developed rapidly in recent decades and are gradually commercialized. Silicon-based epitaxial growth of III-V compound semiconductor light sources have been pursued for many decades for the potential to integrate with other silicon-based optoelectronic devices. However, compared with Si, III-V compound materials have large lattice mismatches and thermal expansion coefficient mismatches, which results in a threading or misfit dislocation density of 10\({}^{8}\)-10\({}^{10}\) cm\({}^{-2}\) when grown on the Si substrate [15]. Numerous approaches have been used to solve these problems, such as special surface treatment [16], strained superlattices [17; 18], low-temperature buffers [19] and growth on patterned substrates [20].  
The first quantum dot (QD) laser monolithically grown on Si was reported in 1999 with its peak emission at 1.013 \(\upmu\)m [21]. In 2011, [PERSON] et al., achieved the first electrically pumped 1.3 \(\upmu\)m InAs QD laser grow directly on Si substrate [22]. Since then, numerous approaches have been investigated to improve device performance. In 2017, [PERSON] et al., succeeded in demonstrating the first Si-based micro-disk lasers with minima Jth of 600 A/cm and emission covering 1320-1350 nm [23]. In 2018, an electronically pumped distributed feedback (DFB) laser array on Si substrate was realized, which could last at room temperature with threshold current as low as 12 mA [24]. In 2019, [PERSON] et al., achieved the first tunable QD single-wavelength laser with 16 nm tuning range directly grown on Si under continue wave (CW) electrical injection at room temperature [25].  
Although a lot of progress has been made in the epitaxial III-V light sources, much effort is needed to make this technology affordable for high volume, high-performance photonic integrated circuits (PICs), such as fewer defects, lower thresholds, higher temperature stability and uniformity. Additionally, low-loss active-passive coupling schemes need to be developed to integrate with other silicon-based components [26].
## 5 Results and Discussion  
### Software Tool  
An underground mine typically consists of portals, shafts, adits, slopes, levels, galleries, roadways, and face area. To generate an optimal deployment model, it is necessary to consider the structure of the mine, with all its components, as well as characteristics regarding inclination, length of the tunnels, and their dimensions. For this reason, the tool proposed in this work starts from the generation of the blueprint from the coordinates obtained from a total station or from a blueprint (AutoCAD 2022 or higher), to which conventions are included so that the tool can interpret it. With this, it is possible to obtain the real structure of the mine, including inclinations and dimensions, as well as have an overall view of the mine and the tunnels that comprise it. The objective is to generate the deployment for the entire mine in a single process. In their multilevel deployment work, [PERSON] and [PERSON] [43] assume parallel tunnels in an odd number to apply their strategy, as shown in Figure 6. This approach is different from the reality of most underground mines. For his part, [PERSON] and [PERSON] [45] and [PERSON] et al. [46] propose a deployment  
Figure 5: Size of mines used to test the tool. (**a**) Small-sized mine. (**b**) Medium-sized mine.  
strategy that must be implemented in tunnels independently, assuming that the inclination does not change.  
It is highlighted that the tool makes a precise and detailed mapping of the mine, considering crucial aspects such as its inclinations and specific dimensions. This approach provides a realistic understanding of the mine structure, which is critical for long-term planning. A benefit of this approach is the ability to estimate implementation costs. By knowing the number of nodes needed, sensor type, and their locations, a more accurate cost forecast can be made, avoiding unnecessary expenditure on equipment and resources.  
Applying separate deployment strategies means having multiple networks. Our proposal generates a single network. Implementing a unified network reduces the need for numerous equipment and infrastructure systems, reducing the initial investment and maintenance costs. This resource efficiency saves money and simplifies technical support and maintenance operations. In addition, as the mine expands or changes, the network can quickly adapt to these new conditions without configuring additional systems. This represents an advantage regarding adaptability and responsiveness to changing mine needs and short-term planning.  
Finally, we want to highlight that the deployment proposed by the methodology can be used to support the worker safety system and that by regulation, it must be part of the initial planning of mining work.
# Tailorable Brillouin Light Scattering in a Lithium Niobate Waveguide
## 3 Results
When the waveguide width is \(w=1.5\)\(\upmu\)m, the electric field distribution generated by the pump light is shown in Figure 5a, and Figure 5b shows the finite element simulation of the acoustic-optic coupling rate changing with waveguide width. The red dotted star line represents the relationships between frequencies in the acoustic wave models and waveguide width of each mode order m, utilizing \(\Omega_{p}=mV_{1}/(2w)\), in which \(V_{1}=6570\) m/s represents the speed of sound in lithium niobate and m represents the integer of the mode order. Figure 5c represents the five acoustic modes (\(m=1\), \(2\), \(3\), \(4\), \(5\)) produced when the waveguide width is \(1.5\)\(\upmu\)m under the action of the MB effect and the PE effect, as shown in Figure 5b. The five red dotted star lines correspond to each other. It can be seen from Figure 5c that the first-order acoustic breathing mode and the TE optical mode have better photochemical coupling efficiency; in this case, the maximum strain component overlaps  
Figure 4: Contribution of different effects to the BSBS gain coefficient in the stimulated Brillouin scattering process, including the moving boundary effect (orange short line), radiation pressure effect (red short line) and the entire Brillouin contribution of gain (short green line) when the waveguide width \(w=2818\) nm. the optical mode to the same extent. Therefore, we obtained a higher acoustic-optic coupling efficiency, as shown in Figure 5b. On the other hand, when used for coupling between a third-order acoustic mode and surrounding TE modes, there is competition between the PE effect and the MB effect. The product of the boundary and the volume is opposite to the geometric phase, resulting in a self-cancellation effect. In addition, in the forward scattering state, a simple suspended waveguide is not enough to completely confine the elastic phonon wave within the waveguide. It can be seen from Figure 6 that each waveguide produces a series of regular Brillouin resonances [23]. As shown in Figure 5c, due to the light field having spatial symmetry, only phonons with uniform spatial symmetry can produce effective Brillouin coupling [24]. As the size of the waveguide changes, the colored curves in Figure 6 represent different resonance characteristics. As shown in Figure 3, the change in the size of the suspended waveguide achieves 1-15 GHz frequency resonance, as well as nonlinear tunability. Figure 5: Based on forward Brillouin scattering: (**a**) the electric field distribution diagram of pump light, waveguide width \(w=1.5\) μm; (**b**) The finite element simulation of the photoacoustic coupling rate varies with the width of the waveguide in the suspended waveguide structure. The red dotted star line is the frequency calculated by satisfying the Fabry–Perot multi-order mode. The formula \(\Omega_{p}=mV_{1}/(2w)\) can be approximated for simulation, where \(V_{1}=6570\) m/s; (**c**) When the waveguide width is 1.5 μm, the mode distribution diagram of the phonon mode determined in b that meets each phase-matching is determined.
## 3 Results and Discussion
### Numerical Simulation Results  
The computed near-field pressure distribution due to explosion is shown in Figure 9. The propagation of the shock wave within the tunnel structure at different times is clearly observed. Table 5 shows the experimental and numerical results for portal blast pressure. The numerical results obtained by LS-DYNA and shown in Table 5 also illustrate that the pressure at the portal was attenuated when the scaled distance Z increased. In \(Z=2.15\) to \(3.26\) m/kg\({}^{1/3}\), the relative difference between numerical simulation and the experiment was less than 6%. Numerical results are in agreement with the experimental results and can be extended for further analysis.  
In order to expand the overpressure response where were not available in experiments. In numerical study increased the scaled distance Z to \(4.95\) m/kg\({}^{1/3}\) and decreased the scaled distance Z to \(0.97\) m/kg\({}^{1/3}\). The numerical results are shown in Figure 10, and the polynomial regression equation of the maximum coefficient of determination (R-squared value \(0.99\)) was used as the best regression equation for portal overpressure, as shown in Equation (10).  
\[\mathrm{P_{LS-Dyna}}=2.64Z^{6}-57.38Z^{5}+510.24Z^{4}-2377.5Z^{3}+6158.1Z^{2}- 8516Z+5097 \tag{10}\]  
Figure 8: Peak overpressure ratio vs. L/D.  
Figure 9: Fluid–structureinteraction for external tunnel explosion process (50 gC4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Z** & **PLS-Dyna** & **Portal** & **Difference** \\ **(m/kg\({}^{1/3}\))** & **(kPa)** & **(kPa)** & **(\%)** \\ \hline
3.26 & 75.81 & 71.66 & 5.79 \\
2.59 & 108.45 & 107.84 & 0.57 \\
2.40 & 123.39 & 117.64 & 4.89 \\
2.26 & 139.59 & 135.97 & \(-0.28\) \\
2.15 & 154.48 & 162.04 & \(-4.67\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overpressure comparison between PLS-Dyna and Portal.  
The numerical results are in agreement with the experimental results. A simple way to estimate the overpressurein steel tunnels was proposed. The proposed methodology is both useful and efficient and can be further developed for designing tunnel structures that are protected against explosions.  
Methodology, B.-C.S.; software and writing--original draft preparation, C.-W.H.; writing--review and editing, P.-W.W.; project administration, T.-A.C.; funding acquisition, H.-H.L. All authors have read and agreed to the published version of the manuscript.  
The financial support of the Ministry of Science and Technology, Taiwan, under grant NSC 99-2623-E-606-005-D is gratefully acknowledged.  
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no conflicting professional or other personal interest of any nature.
# Analysis of Random Dynamics of Cell Segmented by a Modified Active Contour Method
## 3 Results & Discussion
### Effects of Denoising  
Using the radial symmetric method, image analysis is performed not only for the cells themselves, but also for the background. To exclude the effects of the background noise in image analysis, a denoising process using a Poisson noise filter is applied before localization, as shown in Figure 5. After denoising, background noise is rarely seen, and the edge of the cell looks clear. Based on the denoised image, the trajectory, MSD, and VACF of an MCF-7 cell are acquired for the three localization methods, as shown in Figure 6. The effect of denoising is clearly seen in the trajectory calculated by the radial symmetric method when Figure 6a is compared to Figure 4a. Even though the circular Hough transform method depicted in Figure 6b and the modified active contour method in Figure 6c show differences from the trajectories in Figure 4b,c, the difference is not as large as that observed in the radial symmetric method. This can be quantitatively confirmed by the MSD data in Figure 6d-f. The curves are fitted using Equation (3), and the diffusion coefficient D and the mean localization error \(\sigma\) are shown in Table 1. From the diffusion coefficient of three methods, cells randomly move with 4D of \(\sim\)0.5. The mean localization error of the radial symmetry method, circular Hough transform method, and modified active contour are reduced to 0.1292, 0.7117, and 0.1963, respectively. Specifically, the mean localization error by radial symmetry is reduced by 0.0973, from 0.2265 to 0.1292, while the circular Hough transform method and modified active contour method show a reduction in localization error by 0.0350 and 0.0090, respectively. As mentioned, the radial symmetric method considers the whole image including the noisy background, and the effect of background noise becomes significant when analyzing the intensity gradient. In other words, if denoising is applied to these images, the level of localization error caused by the radial symmetric method (\(\sigma=0.1292\)) can be reduced to the level of that caused by the modified active contour method without denoising (\(\sigma=0.1153\)). To compare the trajectory difference according to the localization methods after denoising, the representative trajectories are presented in Figure 6j.  
Figure 5: (**a**) A bright field image of MCF-7 cells and (**b**) the image after denoising.  
Figure 6: Random dynamics of MCF-7 cells analyzed by the tracking methods after denoising. The (**a–c**) trajectories, (**d–f**) (dashed line) MSD for raw image and (solid line) MSD for denoised image, and (**g–i**) VACFs (\(\delta\) from 0.04 to 2 s) calculated by the radial symmetry, circular Hough transform, and modified active contour methods (N = 36) (**j**) The comparison of the representative trajectory achieved from 3 different localization methods.
## 3 Methods
### Matching Cost Calculation Based on the Improved Joint Similarity Measure
To obtain the joint similarity measure composed of \(C_{CAD}(p,d)\) and \(C_{CH}(p,d)\), the gray values of each color channel of the color image are normalized, and then the sum of the absolute values of the gray difference of each color channel is calculated. The result is called CAD, and the ratio of Hamming distance of the corresponding census transform to the length of a bit string is obtained, as shown in Equation (20). The result is called CHD, in which \(N\) is the length of the bit string. \[C_{CH}^{\prime}(p,d)=\frac{C_{CH}(p,d)}{N} \tag{21}\]  
To avoid the influence of noise and unmatched points, truncated absolute differences (TAD) are introduced here, and the matching cost is shown in Equation (22). \[C(p,d)=\lambda min\{T_{a},C_{CAD}(p,d)\}+(1-\lambda)min\{T_{h},C_{CH}^{\prime} (p,d)\} \tag{22}\]  
Among them, \(T_{a}\) is the set CAD truncation absolute error value, and \(T_{h}\) is the set CHD truncation absolute error value, which is to adjust the proportion between CAD and CHD.