esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Finite Element Analysis and Field Monitoring
### Distribution of Monitoring Points
The rise-span ratio of each of the first two construction stages is basically the same, and all the influencing factors have not changed, so it is basically maintained at about 30 mm, which is also close to the measured value. In the article, by studying the rise-span ratio of the top arch, there is a significant difference in the surface settlement caused by the top arch construction stages at different rise-span ratios. At the rise-span ratio of 0.28, the settlement caused by the top arch construction is only 11.57 mm, accounting for about 22.67% of the total settlement, which is obviously smaller than the settlement caused by the top arch construction under other rise-span ratio, so the reasonably designed rise-span ratio can effectively control the ground surface settlement and help to protect a series of civil facilities such as underground pipelines. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Pilot Tunnel** & **Beam and Column** & **Top Arch** & **Main Body** \\ \hline \(\mu=0.22\) & 37.03\% & 4.83\% & 37.58\% & 20.56\% \\ \(\mu=0.25\) & 40.51\% & 5.06\% & 33.04\% & 21.39\% \\ \(\mu=0.28\) & 59.00\% & 6.15\% & 22.67\% & 12.17\% \\ \(\mu=0.31\) & 44.27\% & 5.31\% & 29.65\% & 20.78\% \\ \(\mu=0.34\) & 42.12\% & 5.58\% & 30.65\% & 21.64\% \\ \(\mu=0.37\) & 42.28\% & 4.20\% & 28.39\% & 25.13\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage of ground settlement at each construction stage. When the rise-span ratio is 0.28, the construction phase of the guide tunnel group (i.e., the first construction phase) and the construction phase of the top arch (i.e., the third construction phase) are the two main phases of settlement growth, and the settlement of these two phases accounts for approximately 81.67% of the settlement of the entire construction process, which indicates that a reasonable rise-span ratio can increase the resistance of the arch, which is conducive to the stability of the entire structure and the mechanical system of the structure. The lower the settlement caused by the main structure, the more favourable it is for the subsequent construction of the large excavation area. The top arch rise-span ratios of 0.22, 0.25, 0.28, 0.31, 0.34, and 0.37 show a trend of decreasing and then increasing settlement in the construction of the top arch, which also indicates that the top arch rise-span ratio needs to be set at a reasonable range. From Figure 9, it can be seen that the measured and simulated values have the same trend, and the actual design of each rise-span is between 0.28 and 0.31, because the finite element calculation does not consider the dynamic load and groundwater, which leads to the error between the simulated and measured values. However, the measured values and the rise-span ratio of 0.22 or 0.31 are basically the same, and the larger and smaller rise-span ratios increase the settlement caused by the construction, which also proves that a reasonable design of the rise-span ratio can control the settlement of the ground surface.
## 3 Results and Discussion
### Impact on Seed Germination
#### 3.1.2 Relative Growth (%)  
Data pertaining to the percentage relative wheat shoot and root growth are shown in Table 3, where it can be deduced that they both decreased gradually with increasing the concentration of either SPs or Cu NPs. Their lowest values were obtained at the concentration of 0.06 mg/mL where SPs showed a percentage relative growth of \(13.88\pm 8.24\) and \(41.86\pm 19.82\%\), while Cu NPs recorded comparable values of \(13.22\pm 9.91\) and \(36.72\pm 18.51\%\) for the wheat shoot and root, respectively. This implies that both SPs and Cu NPs have a negative impact on root and shoot growth and their highest inhibition effects were attained at 0.06 mg/mL. In addition, the germination index order (combined seed germination and root elongation) for the tested concentrations of SPs and Cu NPs was as follows: SPs \(0.004>\) SPs \(0.002>\) SPs \(0.03>\) NPs \(0.002>\) NPs \(0.004>\) NPs \(0.03>\) SPs \(0.06>\) NPs \(0.06\). The negative effect of Cu NPs on seed germination and growth was also observed with silver nanoparticles that were applied to 11 species of wetland plants [36], while the reduction in the germination index with an increasing concentration of Cu NPs was also reported in previous work on wheat seeds [37], which is in agreement with our study. The results of seed germination studies (Table 3) also revealed that the application of low concentrations of SPs slightly enhanced the relative germination rate in wheat relative to the control. The application of low concentrations of Cu NPs, on the other hand, reduced the germination rate relative to the control. This inhibitory effect was previously observed in studies where the effects of copper nanoparticles and ZnO NPs on the germination percentage of wheat and corn seeds were individually investigated, respectively [38; 39]. At the highest concentration of 0.06 mg/L, both SPs and Cu NPs had the same effect on the germination rate. This behavior is consistent with the shoot and root relative growth results discussed earlier. The inhibitory effects of SPs and Cu NPs on seed germination can be owed to their glucose content since it is the major component present in SPs constituting 49.3% by mol as detected by HPLC analysis. This has been previously evidenced in the literature since plant glucose was shown to affect many processes, including germination, early seedling growth, flowering and senescence [40; 41]. A genetic analysis revealed that sugar signaling in plants is closely related to plant hormone biosynthesis and signaling, in particular to the hormone of abscisic acid, ABA [42; 43]. The sugar mediated delay in germination was explained through the inhibitory role of the plant hormone ABA, and the stimulatory effects of Gibberell acid (GA) and ethylene [44].
# Influence of Xanthan Gum-Based Soil Conditioners on the Geotechnical Properties of Soils
## 4 Results and Discussion
### Liquid Limit of Untreated Soils and Mixtures  
Figure 4 illustrates the liquid limits for the untreated soils and mixtures with SC_R in low and high dosages. The impact of the SC on the liquid limit is strongly affected by the index properties of the untreated soil. In the case of silt with sand S1, the SC has a negligible effect on the liquid limit, regardless of the dosage. Small differences in measured values of \(\mathrm{w_{L}}\) could be a consequence of the specimen's heterogeneity. Unlike soil S1, the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Soil** \\ **Identificator** \\ \end{tabular} & \begin{tabular}{c} **Sampling** \\ **Location** \\ \end{tabular} & \begin{tabular}{c} **Natural Water** \\ **Content** \\ **w\({}_{0}\) (\%)** \\ \end{tabular} & \begin{tabular}{c} **Liquid Limit** \\ **w\({}_{L}\) (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Plastic Limit** \\ **w\({}_{P}\) (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Particle Density** \\ **\(\rho_{S}\) (g/cm\({}^{3}\))** \\ \end{tabular} \\ \hline S1 & Ljubelj & 43 & 67 & 41 & 2.52 \\ S2 & Catalonia & 10 & 31 & 15 & 2.59 \\ S3 & Heldburg & 18 & 26 & 13 & 2.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Index properties of soils used in the research.  
Figure 3: Particle size distribution for the soils S1, S2, and S3.  
liquid limit of soils S2 and S3 increases with increasing SC_R content. Treatment of S2 and S3 with low and high dosages of SC_R increased the liquid limit by approximately 10% and 35%, respectively.  
This finding aligns with the study [19], which reported a liquid limit (w\({}_{\text{L}}\)) of 22% for an untreated mixture of 50% river sand and 50% residual soil. The study observed an increase of 13% and 39% in w\({}_{\text{L}}\) with the addition of 0.5% and 2% XG, respectively.
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 4 Optimization Methods
### Multi-Objective Performance Metrics  
Several multi-objective quality metrics exist, which can be categorized based on the quality aspect that they assess, i.e., convergence (distance to the optima), diversity, and both convergence and diversity altogether [33]. In this work, we consider the three commonly used three indicators which measure the complementary aspects of the yielded solutions, namely, Inverted Generational Distance (IGD) [63], Spread [45] and Hypervolume (HV) [33]. Since the exact Pareto front can only be computed on small size instances, for large instances a reference front is obtained by combining the approximated Pareto fronts resulting from the heuristic and metaheuristic algorithms.  
**Inverted Generational Distance (IGD)**[63]: This metric measures the distance between the obtained approximated solutions and the Pareto front. IGD is defined in Equation (3), where \(d_{i}\) is the Euclidean distance from point \(i\) in the approximated front to the closest one in the Pareto front, and \(n\) is the number of solutions in the Pareto front. \(IGD=0\) indicates that the evaluated Pareto front consists only of solutions from the optimal Pareto front.  
\[IGD=\frac{\sqrt{\sum_{i=1}^{k}d_{i}^{2}}}{n}\enspace. \tag{3}\]  
**Spread**[45]: This metric measures the diversity of the obtained approximated front and is defined as:  
\[\blacksquare=\frac{d_{f}+d_{l}+\sum_{i=1}^{N-1}\left|d_{i}-\bar{d}\right|}{d_{f}+d_ {l}+(N-1)\bar{d}}\enspace, \tag{4}\]  
where \(d_{i}\) is the Euclidean distance between consecutive solutions, \(\bar{d}\) is the mean of these distances, and \(d_{f}\) and \(d_{l}\) are the Euclidean distances to the _extreme_ solutions of the Pareto front. A zero value indicates an ideal distribution, i.e., pointing out a perfect spread of the  
Figure 5: Uniform mutation process.  
solutions in the evaluated set of solutions.  
\[HV=\text{volume}\left(\bigcup_{i=1}^{|Q|}v_{i}\right). \tag{5}\]  
**Hypervolume**[64]: This metric assesses both convergence and diversity of a Pareto front. It calculates the m-dimensional volume (in the objective space) covered by the solutions in the evaluated Pareto front \(Q\) and a dominated reference point \(W\). For each solution \(i\in Q\), a hypercube \(v_{i}\) is constructed with the reference point \(W\) and the solution \(i\) as the diagonal corners of the hypercube. The hypervolume is calculated as the union of all hypercubes, as shown in Equation (5). The higher the hypervolume the better the algorithm performed.
# An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
## 3 Proposed Method
### Improved DBNet for Natural Scene Text Detection  
Although the multi-channel MSER model has removed most of the non-textual components of the image and reduced the complexity of the text detection problem, it is not sufficient to extract the textual regions of the street sign image, which motivated us to use an improved DBNet for further text detection. The DBNet algorithm is a segmentation-based network that can not only detect text in arbitrary directions but also use deformable convolution to enhance the detection of text with extreme aspect ratios. In addition, DBNet transforms the non-differentiable fixed binarization segmentation into an approximate differentiable binarization function, avoiding the problem of non-differentiable gradients, and it makes the final output image robust to the threshold value. While simplifying the post-processing, DBNet has better results on the lightweight backbone network, which accelerates the detection speed of the model. Therefore, we chose the DBNet model as the base network for street sign text detection in this paper, and the DBNet network structure is shown in Figure 4. However, the DBNet model adopts an FPN for feature fusion. When the FPN performs top-down feature fusion, the network expression ability is lost due to the mandatory reduction in feature channels, and there are missed and false detections for small-scale texts. In addition, the FPN is limited by the one-way information flow and the loss of low-level spatial information due to multiple sampling operations, which also have a certain impact on the accuracy of text detection.  
In this study, the Resnet-18 [28] lightweight network was selected as the backbone network for feature extraction, which effectively reduced the computational complexity of the model. In order to be able to cope with text instances with extreme aspect ratios, all 3 \(\times\) 3 convolutional layers were replaced with deformable convolution layers in the conv3, conv4, and conv5 stages of the feature extraction network of Resnet-18 [29] to improve the network's ability to focus on relevant image regions. At the same time, the HLFE and FPRE modules were introduced to enable the feature pyramid network of this model to fully utilize the high-level semantic information and contextual semantic information of the network. The structure diagram of the improved DBNet network is shown in Figure 5.  
Figure 4: DBNet model network structure diagram.
# Combined Mie Resonance Metasurface for Wideband Terahertz Absorber
## 4 Conclusions  
In summary, we propose a combined metasurface consisting of an aluminum substrate and an array of TiO\({}_{2}\) blocks to achieve a wideband terahertz absorber. During our experiments, we found that the absorption bandwidth with this design was nearly 2.5 times and 5 times wider than a traditional single block when combining four and nine blocks, respectively. The widening multiple could be further improved by increasing the number of combinations in structures (variable parameters included number, spacing, dimensions etc.). We also investigated the effect of the incident angle and polarization on the performance of the absorber. The absorption bandwidth could be widened effectively in any polarization; when the incident angle was 45\({}^{\circ}\), the absorption rate could still reach 75% or more.  
[PERSON] designed the structure and performed the numerical simulations; [PERSON] put forward the idea, analyzed the data, and wrote the article; [PERSON] and [PERSON] checked the spelling, grammar of this article and put forward some comments.  
This research was funded by Public Projects of Zhejiang Province (LGG18F050003), National major scientific research instrument development project of Natural Science Foundation of China (61727816), and the National Natural Science Foundation of China (No. 61475128 and No. 61875251).  
This work was supported by Public Projects of Zhejiang Province (LGG18F050003), National major scientific research instrument development project of Natural Science Foundation of China (61727816), and the National Natural Science Foundation of China (No. 61475128 and No. 61875251).  
The authors declare no conflict of interest.
## 3 Experiments
### Tailoring the System to the SCI Context  
The primary objective is to determine whether the robotic joint controller enables users to achieve the target \(x_{e}\to x_{e}\), while minimizing their own motions, i.e., bringing \(x_{c}\to x_{c}\). In terms of error coordinates, this corresponds to stabilizing \(e_{e}\) and \(e_{c}\) at zero. This study aims to assess how intuitively users interact with the system, focusing on how quickly and effectively they learn to control it with minimal prior training. Key performance indicators include target accuracy, task completion time, and success rates.  
Given the focus on the SCI context, we made several changes to the control algorithm. These include reducing sensor requirements and introducing a dead-band zone around the target posture to mitigate involuntary reflex motions, a common challenge for SCI subjects, thereby enhancing controller stability. Additionally, accurately fine-tuning the weight matrices allows us to adjust the control system's response to the user's motions. This helps prevent the system from reacting to involuntary movements, such as muscle spasms, which the SCI subject may experience. By appropriately adjusting the gains, we can slow down the control system's response, ensuring that brief, rapid involuntary motions, like spasms, do not cause significant changes in the robot's movements. Instead, the control algorithm is more effectively triggered by sustained, intentional motions. The same tuning is applied for all 12 unimpaired subjects and the two SCI participants. Additionally, as many SCI subjects retain partial control of the upper arm, we aimed to maximize the use of available degrees of freedom DoFs. Unlike [PERSON] et al. (2017), where the frame \(x_{c}\) was aligned with the user's shoulder, we opted to shift it to the next Inertia Measurement Units IMU sensor, placed on the right upper arm. Other sensors along the arm (e.g., wrist and hand) are required for the XSens system but do not actively contribute to the control algorithm. Additionally, extensive empirical tuning of the control gains was required to optimize the algorithm's response to motion requests.
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
## 2 System and Signal Model
### Information Transmission in TWR
Amplify-and-Forward ProtocolWhen applying the AF protocol, the relay utilize an amplifier factor \(G\) to directly amplify \(y_{IP}\) as \(x_{R}=G\cdot y_{IP}\) without any decoding and then transfer the amplified signal to both source nodes with the harvested energy. After information processing, the relay exhausts the harvested energy to forward the processed information to both source nodes. With the consideration of channel reciprocal, the received signals at the source node \(S_{i},i=1,2\), is given by  
\[ \begin{split} y_{S_{i}}&=h_{i}\sqrt{P_{R}}(x_{R}+ \tau_{tx,r})+\tau_{rx,i}+n_{i}\\ &=h_{i}\sqrt{P_{R}}x_{R}+h_{i}\sqrt{P_{R}}\tau_{tx,r}+\tau_{rx,i}+ n_{i}\end{split}  \tag{6}\]  
where \(P_{R}\) is the relay's transmit power, which is calculated as \(P_{R}=2E/T\), \(\tau_{tx,r}\sim\mathcal{CN}(0,\kappa_{1}^{2}),i=1,2\) is the HI caused by the transmitter of \(R\), and \(\tau_{rx,i}\sim\mathcal{CN}(0,\kappa_{2}^{2}|h_{i}|^{2}P_{R})\) are the HI caused by the receiver of \(S_{i}\), \(n_{i}\sim\mathcal{CN}\left(0,\sigma_{i}^{2}\right),\ i=1,2\), is the noise generated at source \(S_{i}\). And  
\[x_{R}= \begin{cases}x_{1}\oplus x_{2},&\text{for DF protocol}\\ G\cdot y_{IP},&\text{for AF protocol}\end{cases}  \tag{7}\]  
where  
\[ \begin{split} G&=[((1-\rho)(1+\kappa_{1}^{2})+\kappa_{2}^{2 })(|h_{1}|^{2}P_{1}+|h_{2}|_{2}^{p})+\sigma_{r}^{2}]^{-\frac{1}{2}}\\ &=[(\bar{\rho}+\kappa^{2}-\rho\kappa_{1}^{2})(|h_{1}|^{2}P_{1}+|h_{2}|_{ 2}^{p})+\sigma_{r}^{2}]^{-\frac{1}{2}}\end{split}  \tag{8}\]  
where \(\bar{\rho}=1-\rho\), \(\kappa^{2}=\kappa_{1}^{2}+\kappa_{2}^{2}\). Since the signal power is usually much greater than the noise power, \(\sigma_{r}^{2}\) in (4) and (8) will be omitted in the following analysis.
## 2 Materials and Methods
### Determination of Acoustic Parameters through Transitory Excitation Method
#### 2.3.1 Pre-Analysis by Finite Element Simulation  
The determination of acoustic properties through the transitory excitation method requires knowledge of the mode shape the corresponding natural frequency of samples. The site of the antinodes and nodes was identified, which is very important for the transitory excitation method. Pre-analysis was achieved with a numerical simulation based on the finite element method (FEM) using the software ANSYS Academic R16.2. A modal analysis of free- free vibration was implemented. The modal analysis needs the 9 independent elastic constants of wood and the physical property, the density. The modal analysis computes the natural vibrations of a body (mode shapes) at the relative frequencies (natural frequencies). The free- free vibration means that the geometry of the body does not contain constrains.  
As a first step, the geometry of the model volume as a rectangle, defined by the coordinates x (radial direction), y (L), and z (tangential), was built up. The element type, SOLID 185 (solid structural brick with eight nodes), was chosen to produce the discretization of the region. A physical model based on linear-elastic orthotropic material was created by defining the material's constant density and _MOE_ in three directions (X, Y, Z), shear modulus (\(G\)), and the Poisson's coefficient (_NU_) (both in three planes: XY, YZ, XZ). The values of the _MOE_, \(G\), and _NU_ in the Norway spruce samples correlated with [PERSON]'s 1948 \"Engineering Parameters of Solid Wood\" table [6; 32]. To describe the variability of the material, two different material models and analyses were employed: one with the lowest density results and one with the highest. The algorithm of the block Lanczos was used to find the natural frequencies and mode shapes. Twelve simulations were carried out. The mode shapes were: first bending mode, torsion mode, second bending mode, first transversal bending mode, second torsion mode, third bending mode, third torsion mode, second transversal bending mode, fourth bending mode, fourth torsional mode, and the longitudinal one. The focus was given to the first bending mode and the L mode.  
Four of the computed mode shapes are shown in Figure 2. Predictably, two different values of density were applied: the lowest measured (365 kg m\({}^{-3}\)) and the highest measured (589 kg m\({}^{-3}\)), from which different frequencies were derived. The higher the density, the lower the frequency. The first bending mode of the lowest density corresponded to 391 Hz and the highest to 308 Hz; the average value of density (463 kg m\({}^{-3}\)) corresponded to the frequency of 341 Hz.  
Following the simulation results, the behavior of the laths between diverse density values was intuited. Specifically, after the modal simulation, the relationships between density, mode shape, and frequency were observed, as already summarized in the figure above. Moreover, the modal analysis indicated the antinodes and nodes for each mode shape.
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
## 4 New Polarization Encoding Technique  
Based on the fundamental physics of light polarization, a novel encoding system has been developed. Light is a transverse electromagnetic wave with electric and magnetic field vibrations happening at right angles to each other and in any plane at right angles to the direction of motion of the light [21; 22]. A novel approach based on the FDW code is used for the same wavelength to create zero cross-correlation properties. Unlike the other DW family codes, FDW is ideal for LAN and can support a higher bit rate. Figure 1 shows the polarization encoding-decoding scheme's block architecture.  
Every two users (basic user, Kb) will be given a specific degree of light rotation to utilize the polarization rotator [21]. This will mean that the wavelengths do not overlap with each other and the original code to be compressed, which is shown in Table 6. The combiner is used to combine all channels into a single-mode optical fiber. The splitter is used to sort all data to a respective receiver. A detection technique is used for decoding the data with the addition of linear polarization for filtering specific degrees of the same wavelength. To determine the performance of the system, an analyzer is added to analyze the number of BERs for each user [22; 23].