esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Automatic License Plate Detection
## 4 Results and Discussion
### Precision and Recall  
We empirically compute the precision as well as the recall parameters of the compared techniques of several intervals of matching confidence (\(\xi\)). The aforesaid analysis is performed between the forecasted rectangle drawn by our method (\(c\)) along with its ground truth (\(t\)). It is worthwhile to describe here that the matching confidence (\(\xi\)) is calculated using Equation (6):  
\[\xi=\{c\cap t/c\cup t|t\subseteq c\} \tag{6}\]  
For matching confidence (\(\xi=\xi_{0}\)), both the _Precision_ and _Recall_ are calculated using Equations (7) and (8), respectively:  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
where _TP_ denotes True Positives and implies the actual detection of license plate(s). The higher the _TP_ rate is, the higher the _Precision_ and _Recall_ ratio will be. Few examples of _TP_ are actual detected license plates, which are enclosed by green rectangles as shown in Figure 2. The _FP_ represents False positives, which is the case of a non-license plate region being detected as a license plate by the detector. A higher value of _FP_ will result in a low _Precision_ ratio. One such example of _FP_ is found in the fifth image of the last row of Figure 2, where two blue rectangles on the front mirror of a truck are detected as a license plate area by the detector. _FN_ represents False Negatives. A high value of _FN_ will result in low _Recall_ ratio. Few examples of _FN_ are shown in the first two columns of Figure 3a,b where the actual license plates present in the image are not located by the detector due to reflective glare and luminance variations. A good algorithm should have higher values of both _Precision_ and _Recall_. The comparison of the _Precision_ and _Recall_ values for different matching confidence intervals is given in Figure 4a,b. As can be seen in Figure 4a, for the matching intervals from 0.33 to 0.41 and greater than 0.84, the methods in [21; 22] have slightly higher _Precision_ than the proposed method. For all other interval range, the proposed method has comparable _Precision_ values with the compared methods. Moreover, in Figure 4b, it is clear that the proposed algorithm yields higher _Recall_ values when the range of matching confidence interval exceeds 0.40.  
Figure 4: (**a**) Precision and (**b**) recall comparison.
# OTFS: A Potential Waveform for Space-Air-Ground Integrated Networks in 6G and Beyond
## 4 Results and Discussion  
This section presents the simulation results of the above system for performance analysis. The system was implemented in the 6G Exploration Library in MATLAB R2024a [26]. To ensure a fair comparison between OFDM and OTFS, identical system parameters are used across both modulation schemes. The simulation parameters, given in Table 1, are carefully selected to reflect realistic operating conditions in SAGINs in 6G, including vehicular communications, high-speed trains, UAVs, and NTNs. A carrier frequency of 100 GHz is chosen to align with the anticipated use of THz spectrum in 6G for high-capacity communication, while a subcarrier spacing of 480 kHz helps mitigate ICI under extreme Doppler shifts [17]. Larger subcarrier spacings are beneficial in such environments as they allow for more bandwidth and reduce the system's sensitivity to frequency dispersion.  
The Doppler ranges considered (20-2000 km/h) correspond to a variety of real-world mobility scenarios, from vehicular networks (V2X) to high-speed aerial platforms, ensuring our results are applicable to a broad spectrum of mobility conditions. The number of subcarriers (M = 128) and sub-symbols per frame (N = 64) are selected to provide a balance between computational complexity and resolution in the delay-Doppler domain. The Linear MMSE channel estimation method is employed due to its effectiveness in mitigating channel impairments in high-mobility environments. These choices ensure that our results are both theoretically sound and practically relevant for future 6G deployments.
###### Abstract  
Prosthetics, a rapidly advancing field in dentistry, aims to improve patient comfort and aesthetics by addressing the challenge of replacing missing teeth. A critical obstacle in dental implantation is the condition of the jawbone, which often necessitates reconstruction prior to implant placement. Guided bone regeneration (GBR) and guided tissue regeneration (GTR) techniques utilize membranes that act as scaffolds for bone and tissue growth while serving as barriers against rapidly proliferating cells and pathogens. Commonly used membranes, such as poly(tetrafluoroethylene) (PTFE) and collagen, have significant limitations--PTFE is non-bioresorbable and requires secondary removal, while collagen lacks adequate mechanical strength and exhibits unpredictable degradation rates. To overcome these challenges, nanofiber membranes produced via electrospinning using polylactic acid (PLA) were developed. The novel composites were functionalized with bioactive additives, including periclase (MgO) nanoparticles and polylopamine (PDA), to enhance osteoblast adhesion, antibacterial properties, and tissue regeneration. This study comprehensively evaluated the biological, mechanical, and physicochemical properties of the prepared nanofibous scaffolds. Experimental results revealed controlled degradation rates and improved hydrophilicity due to surface modifications with PDA and MgO. Moreover, the nanofibers exhibited enhanced swelling behavior, which promoted nutrient exchange while maintaining structural integrity over prolonged periods. The incorporation of bioactive additives contributed to superior osteoblast proliferation, antibacterial activity, and growth factor immobilization, supporting bone tissue regeneration. These findings suggest that the developed nanofibrous composites are a promising candidate for GBR and GTR applications, offering a balanced combination of biological activity, mechanical performance, and degradation behavior tailored for clinical use.  
dental material; tooth restoration; GBR/GTR procedures; membranes; electrospinning +
Footnote †: journal: Applied Sciences  
Article
## 3 Results and Discussion
### Effect of Adsorption Time in a Solution Mixed with EDTA and Cs Ions
It was thought that the decrease in EDTA removal efficiency from EDTA/Cs solutions resulted from the changes in the charge balance of EDTA by the interaction with Cs ions. To investigate whether Cs ions were simultaneously removed from the EDTA/Cs solution during EDTA adsorption by the AERs, the remaining Cs ion concentration in the EDTA/Cs solution was determined using ICP after the adsorption. It was found that more than  
Figure 8: Changes in the removal efficiency of EDTA and the adsorption capacity of AERs from 1 mM EDTA/Cs (1:1) solutions containing various EDTA concentrations. [AERs] = 2 g/L; pH = 7; adsorption time = 5 h.  
Figure 7: Changes in (**a**) removal efficiency of EDTA and (**b**) adsorption capacity of AERs in EDTA/Cs (1:1) with various amounts of AERs. [EDTA] = 0.1, 0.25, and 0.5 mM; pH = 7; adsorption time = 5 h.  
70% of Cs ions were removed during the adsorption of EDTA, and 100% removal was obtained at the higher initial EDTA concentration of 1.0 mM (Figure 10). The result suggests that the Cs ions were adsorbed by the AERs as well as EDTA, indicating that these ions were already bound to EDTA. As the positively charged AERs could not adsorb isolated, positively charged Cs ions because of electrostatic repulsion. Cs ions must have been chelated with EDTA to be removed by the AERs. To elucidate whether the EDTA removal was based on the charge interaction between the latter and AERs, the removal efficiency of EDTA, was investigated using CERs in an EDTA/Cs (1:1) solution with various initial EDTA concentrations. The removal efficiency of EDTA, using CERs, gradually decreased with increasing initial EDTA concentration, and eventually reached 0% (Figure 11). The \(q_{\mathrm{e}}\) of CERs for EDTA removal slightly increased at a very small increment, and saturated at about 0.01 mmol/g. The adsorption performance using CERs was much lower than with AERs, demonstrating that the negatively charged CERs did not interact with the negative EDTA, unlike AERs. Figure 10: Removal efficiency of Cs ions with EDTA by AERs from EDTA/Cs (1:1) containing various initial EDTA concentrations. [AERs] = 2 g/L; pH = 7; adsorption time = 5 h.  
Figure 9: Changes in removal efficiency and adsorption capacity of EDTA using AERs in various ratios of EDTA and Cs (1:1 (\(\blacksquare\), \(\square\)), 1:2 (\(\bullet\), \(\bigcirc\)), and 1:4 (\(\blacktriangle\), \(\bigtriangle\))) with various initial EDTA concentrations (0.01–1 mM). [AERs] = 2 g/L; pH = 7; adsorption time = 5 h.
## 4 Results and Discussion
### Irreversibility Analysis under Different Nozzle Geometries
#### 4.2.1 Nozzle Throat Radius  
It can be seen from Figure 9 that the total entropy generation rate was mainly generated and concentrated at the nozzle's throat. The area of the entropy generation rate gradually increased with the increase in the throat radius, but the peak value of the total entropy generation rate decreased. This directly caused the total entropy production (the volume integrals for the total entropy generation rate) to decrease from 73.99 W/K under a throat radius of 0.025 m to 71.71 W/K under a throat radius of 0.05 m. Moreover, the position of the total entropy generation rate concentration region gradually moved downstream of the nozzle's divergent section.  
Three types of entropy generation make up the total entropy production: viscous dissipation, heat transport, and phase change entropy generation. These are illustrated in Figure 10 in the form of a stacked column chart. It was found that phase change entropy generation accounted for the largest proportion of entropy, followed by viscous dissipation, and then heat transport. This was found to be true under all of the different nozzle throat radii conditions. The viscous dissipation and the phase change entropy generation were mainly generated in the steam condensation shock region, whereas the heat transport  
Figure 9: The total entropy generation rate and total entropy production under different nozzle throat radii.  
entropy generation was mostly produced at the boundary between the liquid phase and gas phase. As shown in Figure 10, the heat transport and the phase change entropy generation gradually reduced, while the viscous dissipation entropy generation increased with the increase in throat radius. Heat transport and phase change entropy generation reduced from 48.19 and 5.39 W/K to 47.31 and 2.11 W/K, respectively. The viscous dissipation entropy generation increased from 20.41 W/K (the original value) to 22.36 W/K. It is worth noting that the reduction of the phase change entropy generation caused the decrease of the liquid mass fraction and the suppression of the steam's condensation. In addition, it can be more clearly seen from Figure 11 that the liquid mass fraction and the total entropy production both reduced when the nozzle throat radius increased from 0.025 to 0.05 m, but the opposite result was obtained in terms of the mass flow rate. This indicates that properly increasing the nozzle throat radius is beneficial to improving the performance of the nozzle, suppressing the intensity of the spontaneous condensation of steam and reducing energy waste.  
Figure 11: The mass flow rate, total entropy production and liquid mass fraction under different nozzle throat radii.  
Figure 10: The three types of entropy generation under different nozzle throat radii.
# Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City
## 3 Results and Discussion
### Source Apportionment of Heavy Metals
#### 3.4.3 Source Identification by PCA  
The sources of heavy metal pollution in soil were identified by PCA. The Kaiser-Mever-Olkin value (0.608) and [PERSON]'s test (\(p<0.001\)) both showed that the results obtained by PCA were feasible and reasonable. As shown in Table 4 and Figure 7, three factors were identified, and varimax rotation provided a factor loading that corresponded to the principal components. Eigenvalues greater than one were obtained by PCA, accounting for 78.196% of the total variance.  
Principal component 1 (PC1) explained 26.830% of the total variance. The Cu, Pb and Cd loads were higher, and Zn also had a medium load. PC1 may be the traffic source [12; 50]. The gasoline containing Pb is a significant source of Pb in soil, so Pb is often used to identify traffic sources [51; 52]. Cu may come from the vehicle's brake system and radiator [53; 54]. Cd might come from diesel fuel and lubricating oil leakage [39; 42]. Cd could be released from vehicle tires due to the friction between tires and the road surface. Therefore, PC1 was identified as the vehicle emission.  
PC2 explained 26.090% of the total variance, and Cd, Mn and Zn were the main loading elements. Mn and Cd may come from coal combustion [39]. The suspended particles from the steel-making process contain Cd and Zn, which leads to their enrichment in the surrounding soil [55]. Therefore, PC2 might be the industrial source. PC3 was mainly  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{3}{c}{**Factor Load after Rotation**} \\ \cline{2-4}  & **PC1** & **PC2** & **PC3** \\ \hline Cu & 0.853 & \(-\)0.084 & 0.309 \\ Pb & 0.853 & 0.326 & \(-\)0.047 \\ Cd & 0.596 & 0.564 & \(-\)0.152 \\ Mn & 0.007 & 0.839 & \(-\)0.069 \\ Zn & 0.231 & 0.831 & 0.127 \\ Cr & 0.113 & \(-\)0.011 & 0.910 \\ Ni & 0.012 & 0.008 & 0.894 \\ Eigenvalues & 1.878 & 1.826 & 1.769 \\ \% of variance & 26.830 & 26.090 & 25.276 \\ Cumulative\% & 26.830 & 52.920 & 78.196 \\ \hline \hline \end{tabular} Note: PCA loadings \(N>0.4\) are shown in bold.  
\end{table}
Table 4: The result of principal component analysis.  
Figure 6: Dendrogram results of cluster analysis for eight heavy metals in the study area.  
loaded by Cr and Ni, explaining 25.276% of the total variance. According to the previous description, PC3 was the natural source.
# Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews
## 4 Experimental Evaluation
### Experimental Setting  
We implemented our proposed MPCAR model using the PyTorch framework. All models are trained using Adam [40]. We set the training period for all models to 20 epochs. In the interaction-only model, the dimensionality of the user hidden vector and the item hidden vector is set to 50 uniformly. For the CNN-based models (DeepCONN, D-ATT, and NARRE), we set the number of convolutional kernels to 50 and the size of each convolutional kernel to 3. The word vectors are pretrained with the GloVe model. We added dropout layers after the CNN and fully connected layers for each model and set a dropout rate of 0.2. We adopted a fixed L2 regularization of \(10^{-6}\) to regularize these models. For our proposed model, we tested it on four batch sizes (32, 64, 128, 256) and four learning rates (0.0001, 0.0005, 0.001, 0.005) to find the best parameters. The number of hidden factors is set to 10. The number of pointers \(p\) is adjusted among [1; 3; 5; 8; 10]. On most datasets, 2-3 pointers allow the model to achieve the best performance. In addition, we will use the Appendix A to describe Dropout, L2 regularization, the number of hidden factors and the number of pointers in detail.
## 2 Continuous Formulation of Topology Optimization Problem Statements and Sensitivities
### Sensitivity Analysis  
The derivative of the cost function in the direction \(\tilde{\rho}\in L^{\infty}(\Omega)\) is then defined as:  
\[DJ(\rho,u(\rho))\tilde{\rho}=D_{\rho}J(\rho,u(\rho))\tilde{\rho}+D_{u}J(\rho,u (\rho))\;D_{\rho}u(\rho)\tilde{\rho},\quad\forall\quad\tilde{\rho}\in L^{ \infty}(\Omega) \tag{11}\]  
Taking the derivative in the equilibrium equation, in order to find \(D_{u}J(\rho,u(\rho))\;D_{\rho}u(\rho)\tilde{\rho}\), leads to:  
\[[D_{\rho}a(\rho,u(\rho),v)-D_{\rho}l(\rho,v)]\tilde{\rho}+D_{u}a( \rho,u(\rho),v)\;D_{\rho}u(\rho)\tilde{\rho}=0 \tag{12}\] \[\forall\;v\in H_{0}^{1}(\Omega),\;\forall\;\tilde{\rho}\in L^{ \infty}(\Omega).\]  
Since \(a(\rho,u(\rho),v)\) is linear, it can be rewritten as:  
\[D_{u}a(\rho,u(\rho),v)\;D_{\rho}u(\rho)\tilde{\rho}=a(\rho,D_{\rho}u(\rho) \tilde{\rho},v); \tag{13}\]  
therefore, Equation (12) can be rewritten as follows:  
\[-a(\rho,D_{\rho}u(\rho)\tilde{\rho},v)=[D_{\rho}a(\rho,u(\rho),v)-D_{\rho}l( \rho,v)]\tilde{\rho},\;\;\forall\;v,\tilde{\rho}. \tag{14}\]  
Solving Equation (14) for all values of \(\tilde{\rho}\) would be too expensive. For this reason, the use of the adjoint method is preferred. To do so, the adjoint variable \(\lambda\) is defined, the solution of:  
\[\begin{split}& a(\rho,\lambda,w)=-D_{u}I(\rho,u(\rho))w\;\; \forall\;w\in H_{0}^{1}(\Omega)\Leftrightarrow\\ &\Leftrightarrow-a(\rho,D_{\rho}u(\rho)\tilde{\rho},\lambda)=D_{u} J(\rho,u(\rho))\;D_{\rho}u(\rho)\tilde{\rho},\;\;\forall\;\tilde{\rho}.\end{split} \tag{15}\]Then, taking \(w=D_{\rho}u(\rho)\check{\rho}\) in Equation (15):  
\[\begin{split} D_{u}J&(\rho,u(\rho))\ D_{\rho}u(\rho) \check{\rho}=-a(\rho,\lambda,D_{\rho}u(\rho)\check{\rho})=\\ &=-a(\rho,D_{\rho}u(\rho)\check{\rho},\lambda)=\\ &=[D_{\rho}a(\rho,u(\rho),\lambda)-D_{\rho}l(\rho,v)]\check{ \rho}\end{split} \tag{16}\]  
where the self-adjoint property of \(a(\rho,\lambda,w)\) and Equation (12) are applied.  
The generic optimization process, which can be defined in four main steps, is proposed as follows:  
* Find \(u(\rho)\in H^{1}_{0}(\Omega)\), the solution of: \(a(\rho,u,v)=l(\rho,v),\ \forall\ v\in H^{1}_{0}(\Omega)\);
* Find \(\lambda\), the solution of: \(a(\rho,\lambda,w)=-D_{u}J(\rho,u(\rho))w\), \(\forall\ w\);
* Compute the derivative as \(DJ(\rho,u(\rho))\check{\rho}=[D_{\rho}f(\rho,u(\rho))+a_{\rho}(\rho,u(\rho), \lambda)-l_{\rho}(\rho,\lambda)]\check{\rho}\);
* Update the design variables using the gradient information.  
The first two steps correspond to solving the state and adjoint problems, respectively. The third step is the gradient calculation, which is defined in terms of the state and adjoint variables. The fourth and final step consists of using the gradient to determine the next value of the design variables, which can be performed by any suitable optimization algorithm (such as the algorithms described in Section 5).
## 2 Related Work  
### Mapping Applications in the Literature  
Within the WSN field, mapping applications found in the literature are ultimately concerned with the problem of mapping measurements onto a model of the environment. [PERSON] [1] proposed the construction of isobar maps in WSNs and showed how in-network merging of isobars could help reduce the amount of communication. Furthermore, [PERSON] _et al._[2] proposed an efficient data-collection scheme, and the building of contour maps, for event monitoring and network-wide diagnosis, in centralised networks. Solutions, such as distributed mapping, have been proposed to the general mapping domain. However, many solutions are limited to particular applications and constrained with unreliable assumptions. The grid alignment of sensors in [1], for example, is one such assumption.  
In the wider literature, mapping was sought as a useful tool in respect to network diagnosis and monitoring [2], power management [3], and jammed-area detections [4]. For instance, contour maps were found to be an effective solution to the pattern matching problem that works for limited resource networks [5]. Rather than resolving these types of isolated concerns, in this work the WSN is expected not only to produce map type responses to queries but also to make use of the data supporting the maps for more effective routing, further intelligent data aggregation and information extraction, power scheduling and other network processes.  
These are examples of specific instances of the mapping problem and, as such, motivate the development of a generic distributed mapping framework, furthering the area of research by moving beyond the limitations of the centralised approaches.
# THz-TDS Techniques of Thickness Measurements in Thin Shim Stock Films and Composite Materials
## 3 Experiment System and Measurement
### Measurement Method  
Figure 3 exhibits the T-ray measurement system, which demonstrates the reflection mode. When the test was carried out in this system, the T-ray was created from the emitter and sent to the receiver. At this time, the test was carried out by matching the focal point of the emitter and the receiver with the desired specimen. Then, the angle of inclination of the T-ray lens was determined as 16.6\({}^{\circ}\). Figure 4 shows the Shim stock films and GFRP composites. The thicknesses of the Shim stock films were 0.127 mm, 0.254 mm, 0.381 mm, 0.508 mm, and 0.762 mm, and the thicknesses of GFRP composites were 2.02 mm, 3.08 mm, 5.74 mm, and 5.92 mm, respectively. Figure 5 shows typical A-scan data, which is the reflection mode of the GFRP composites of the T-ray. The thickness of the specimen was 3.0 mm.  
Figure 4: Samples of (**a**) Shim Stock films and (**b**) GFRP composites with various thicknesses.  
Figure 5: T–ray pulses from the transmitted GFRP composites (\(n=2.13\)\(\Delta t=42.7\) ps, \(t=3.0\) mm).  
Figure 3: Overview of the THz measurement method [2].
## 4 Results  
### Analysis of Residual Stress Distribution  
The analysis of residual stress distribution is a critical step in evaluating the impact of the deep rolling process on the mechanical performance and longevity of railway rails. Residual stresses, particularly compressive residual stresses, play a significant role in improving fatigue resistance by preventing crack propagation on the rail surface. In this study, the distribution of compressive residual stresses across several positions on the rail surface was measured and analyzed to understand the effect of the deep rolling process.  
The experimental compressive residual stress values were measured at seven distinct positions on the surface of the deep-rolled railway rails. The compressive stresses at these points varied significantly, ranging from 282.5 MPa at position P.1 to a maximum of 667 MPa at position P.5. This variability highlights the complex nature of the stress distribution induced by deep rolling. The positions of the measurements were taken along a transverse direction from the center of the rail head, at intervals corresponding to distances from the rolling path. Points closer to the center exhibited higher compressive stress, while the stresses at the outer positions (P.1 and P.7) were lower, indicating a more concentrated compressive zone towards the center. Table 6 shows compressive residual stress distribution.  
The provided graph illustrates the distribution of residual stress across the measurement points. The x-axis represents the position along the rail surface (in millimeters), while the y-axis displays the corresponding compressive residual stress values (in MPa). The trend in the graph shows that compressive residual stress is highest at the central positions (P.2, P.3, P.4, and P.5), with the stress gradually decreasing towards the outer positions (P.1 and P.7).  
Central Region (P.2 to P.5): The compressive stresses are consistently high, with values exceeding 600 MPa. This suggests that the deep rolling process was highly effective in inducing compressive