esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments & Analysis
### Metrics  
When evaluating text recognition models, accuracy, precision, recall and the F-measure are commonly used metrics. To fully understand these metrics, it is important to examine the confusion matrix. The confusion matrix is a graphical illustration that shows the association between predicted and actual categories, and it is also referred to as an error matrix or contingency matrix.  
The confusion matrix typically has columns representing the predicted categories and rows representing the actual categories. The sum of each column represents the total  
Figure 3: Text instances of nameplates and labels are depicted in this image, which represents a typical example from our industrial nameplate dataset. The instances of text are particularly challenging to predict due to their complexity, which is characterized by variations in color, shape, background, and density with special distributions. These challenging aspects of the dataset require advanced text detection and recognition techniques to achieve accurate results.  
number of predictions made for each category, while the sum of each row represents the total number of occurrences of each actual category. The numbers in each cell indicate the count of actual data points that were correctly classified as belonging to the corresponding predicted category (Table 2).  
Accuracy: the proportion of correctly predicted samples in all samples; this assessment indicator suffers greatly when the positive and negative samples are out of proportion.  
\[Accuracy=\frac{TP+NP}{TP+TN+FP+FN} \tag{12}\]  
Precision: the measure of how well the model identifies true positive cases out of all positive predictions.  
\[Precision=\frac{TP}{TP+FP} \tag{13}\]  
Recall: considers the ratio of positively anticipated samples to all positive samples.  
\[Recall=\frac{TP}{TP+FN} \tag{14}\]  
The statistic called F-measure is sometimes also referred to as F-ScoreHmean. The weighted harmonic average of Precision and Recall is known as the F-Measure. It is referred to as the F1 value when \(\beta=1\). A higher F1 score indicates improved performance.  
\[F_{measure}=\frac{(1+\beta^{2})\times Precision\times Recall}{(\beta^{2}\times Precision +Recall)} \tag{15}\]
## 4 Simulation Setup
### Comparison Methods  
The performance of the proposed CNN-based method was compared with three baseline schemes: equal power allocation (EPA) assigns uniform power from the CPU to all APs and from APs to UEs regardless of their spatial or channel conditions; max-min power allocation is a fairness-oriented approach that allocates power to maximize the minimum SINR among all users; finally, the third method, referred to as \"CPU-AP Equal + AP-UE CNN\", applies equal power from the CPU to each AP while using a CNN to determine the AP-to-UE power distribution.
## 5 Analysis of Factors Affecting Tunnel Deformation
### Enclosure Deformation Pattern Effects  
This section explores the various deformation patterns of the retaining structure and their impacts on the existing tunnel outside the pit. Figure 11(a) illustrates the horizontal displacement of the diaphragm wall's middle section at different excavation depths, considering displacement towards the pit to be positive. The figure indicates that, during shallow excavations, the retaining structure's horizontal displacement exhibits a cantilevered distribution, with the maximum displacement at the top. As excavation deepens, this maximum horizontal displacement gradually transitions from the top towards the pit's bottom, exhibiting a combined cantilever and inward convex distribution pattern.  
Figure 11: Time history curve of tunnel displacement (A-A section).  
Figure 12: Horizontal displacement of diaphragm wall.  
Figure 12b presents the horizontal displacement of the diaphragm wall at various excavation depths (post-completion) across different cross-sectional positions. The displacement variation at the top of the diaphragm wall is minimal. However, near the pit's center, the diaphragm wall shows substantial horizontal displacement at the bottom, suggesting a composite distribution pattern. Conversely, the diaphragm wall near the pit's edges displays a more cantilevered pattern due to significant bending moments at the pit's center, which result in maximum horizontal displacement. As excavation reaches the pit's bottom, enhancing monitoring of the retaining structure and adjacent tunnels becomes essential. Strategies such as optimizing support design or employing techniques like soil retention at the pit's bottom or reinforcement are crucial to mitigate the maximum horizontal displacement at the diaphragm wall's middle section.  
Figure 13 displays the tunnel section's deformation near the pit's center post-excavation magnified 200 times. The simulated and measured maximum horizontal displacements of the tunnel are 15.29 mm and 21.1 mm, respectively, both exceeding the 10 mm control limit. Notably, horizontal displacement exceeds vertical displacement, which is consistent with the typical deformation characteristics of shallow-buried tunnels in settlement-prone areas [28]. This pattern results from the composite distribution of the retaining structure, where significant top displacement induces extensive horizontal displacement in the nearby tunnel, causing it to expand and flatten horizontally. Although minimal horizontal convergence deformation occurs relative to the overall movement, the tunnel primarily undergoes a holistic shift. The most significant horizontal and vertical displacements, along with horizontal convergence, are observed at the pit's center. Given the substantial horizontal displacement of the retaining structure near the pit's middle, the deformation of the existing tunnel directly relates to that of the adjacent pit retaining structure, highlighting the critical need for diligent monitoring of the tunnel's deformation near this area.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.11. Support User Anonymity and Untraceability  
The user's real identity \(ID_{i}\) is never transmitted on a public channel. Instead, users reveal themselves with a temporary identity \(UID_{i}\) generated by the \(CS\) during the user registration phase. In addition, the temporary identity is updated in every session. Therefore, our scheme guarantees user anonymity and untraceability.
## 7 Results and Discussion
### Comparison with CLS and TSVD Result  
As mentioned above, many approaches could be use to solve deconvolution, however, because of the large noise level, most of methods could not show well reconstruction results. In this subsection, the comparison of recovery performance between the proposed approach in this paper and the ones in [17; 18] is performed. Table 2 presents the comparison result, in which compared to the other two approaches, the proposed method has the best recovery performance. It is worth noting that the recovery results of the CLS and TSVD have importance dependence on the parameter \(\gamma\) and \(p\). The same phenomenons that adjusting the parameter \(\gamma\) controls the influence of the noise could be found in Figures 4 and 5 of [18]. Moreover, differing form the waveforms reconstructed using the CLS and TSVD, the one obtained by the proposed approach is smooth so that the feature parameters of the waveform could be directly estimated from the recovered waveform without any other procedures such as the linear fit to estimate LES and TES of the waveform in [8].
## 5 Results and Discussion  
### Ensemble Size  
To employ a statistical sample of the state of the system, the EnKF functions by integrating ensembles of states that are independent of each other; therefore, the model is run N (number of ensembles) times. The ensemble size is generally identified empirically and is based upon a compromise between a reasonable representation of the state and computation cost [19]. Increasing the size of the ensemble (N) decreases the errors in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Assimilation Type** & \
\begin{tabular}{c} **Measurement Type and Number** \\ \end{tabular} & **Measurement Location** &
\begin{tabular}{c} **Assimilation Duration and Frequency** \\ \end{tabular} \\ \hline
**Validation test** & Pseudo Lagrangian & 6 drifters & From point A to B in Figure 1 & 3 h/1 min \\ \hline
**Set-Up I** & Eulerian & 1 ADV & Point C near bridge in Figure 1 & 18 h/1 min \\ \hline
**Set-Up II** & \
\begin{tabular}{c} Eulerian + Pseudo Lagrangian \\ \end{tabular} & 8 drifters + 1 ADV &
\begin{tabular}{c} From point A to B and Point C in \\ Figure 1 \\ \end{tabular} & 21 h/1 min \\ \hline \hline \end{tabular}  
* Time that drifters travelled in channel.  
\end{table}
Table 3: _Cont._Monte Carlo sampling at the rate of \(\frac{1}{\sqrt{N}}\)[36]. We determined ensemble size by performing a sensitivity analysis using ensemble sizes of 5, 10, 25, 50 and 100.  
With respect to our Base-Test (Section 4.1), the results indicate that for an increasing number of ensembles the analysis error reduces. Figure 3 provides RMSE for both velocity components when the size of the ensemble varies. The most striking improvement in RMSE occurs when we increase N up to 25. Further increases in the ensemble size had no significant improvement; hence, we conclude that the best improvement was achieved with 25 ensemble members.
# Concrete Crack Identification and Image Mosaic Based on Image Processing
## 2 Materials and Methods  
### Identification of Crack Width  
#### 2.1.1 Image Binarization  
Crack width is obtained as the distance between the edge pixels nearest to the center skeleton pixel, as shown in Figure 1. In order to obtain crack size, the crack edge must be detected. In the broad sense, the sudden change of an object is called the edge. In image processing, the edge is often detected by abrupt changes in the grey value. Edge detection must meet two conditions: it must effectively suppress noise and locate the edge as accurately as possible. Image binarization [31; 32] is one of the most commonly used image processing methods. In the binarization process, a specified threshold is set for the pixel value of the greyscale image. When the greyscale pixel value is lower than the corresponding threshold, the binarization result is zero (black). In contrast, the binarization result is one (white) when the pixel value is higher than the threshold [24]. Therefore, image binarization is the primary task of calculating crack width. For the acquired image, after the correction and the obtaining of the grayscale image, binarization can be performed.  
The Canny edge detection algorithm is the most widely used edge detection algorithm. The algorithm adds non-maximum suppression and double threshold improvement based on the first order differentiator. Non-maximum suppression can eliminate the nonlocal maximum in the first order differentiator, and the double threshold can reduce the missed detection rate of the edge. However, the Canny operator requires manual threshold setting. A threshold setting that is too high can result in edge loss, and one that is too low cannot detect the true edge, thus reducing detection efficiency. However, the Otsu method [33] can avoid this situation. In this paper, the combination of the Otsu method and Canny operator [34; 35] was used for edge detection. The results are shown in Figure 2. It can be seen that this method could accurately identify the edges.
## 3 Results and Discussion
### AFM Analysis and Spectroscopic Ellipsometry (SE) Measurements  
Atomic force microscopy measurements were performed in order to evaluate the morphology and the roughness of the samples. Figure 5 shows the 2D AFM images (topography) at the scale of 1 \(\times\) 1 \(\upmu\)m\({}^{2}\), together with characteristic line-scans (surface profiles) collected along the fast scanning direction, at the position indicated in each image by a horizontal red line. Due to the fact that the AFM images were very smooth, they are presented in the so-called \"enhanced color\" TM view mode, which uses the change of a pixel relative to its neighbors. Representative line scans are shown below 2D AFM images, presenting in detail the surface profile of the scanned samples.  
Figure 5: The 2D AFM topographic images at the scale of 1 \(\times\) 1 \(\upmu\)m\({}^{2}\) together with random line scans, plotted below each image, for the sol–gel silica materials (TEOS/DMVES/OTES (**A**), TEOS/DMVES/HDTMES (**B**)) deposited as films on PVC substrates.  
At the investigated scale, of \(1\times 1\) um\({}^{2}\), both samples were smooth, as can be observed from the \(z\)-scale of the images and of each plotted line scan (see Figure 5).  
The PVC substrate coated with the sol-gel silica material containing TEOS/DMVES/OTES (sample A) exhibited a root-mean-square (RMS) roughness of 0.47 nm and a peak-to-valley parameter (the height difference between the lowest and the highest points in the image) of 2.76 nm, while the PVC substrate coated with the sol-gel silica material containing TEOS/DMVES/HDTMES (sample B) had a slightly higher RMS roughness of 2.52 nm and a higher peak-to-valley parameter of 13.42 nm. The low corrugation degree of each sample is also well reflected by the line scans plotted below each image, which exhibited surface features in the nanometer range of \(\sim\)2 nm for sample A (from \(-1\) to 1 nm, see the z-scale) and of \(\sim\)10 nm (from \(-7.5\) to 2.5 nm) for sample B, reflecting the same tendency, with sample B being slightly rougher than sample A.  
The thickness and roughness of the PVC substrates coated with the sol-gel silica materials (TEOS/DMVES/OTES (sample A) and TEOS/DMVES/HDTMES (sample B)) were obtained, together with the mean squared error (MSE), and the results are shown in Table 4.  
The results in Table 4 show that the thickness of sample A was higher than that of sample B. From Figure 4 and Table 4, it can be concluded that the transmittance for the PVC substrate coated with the sol-gel silica material TEOS/DMVES/HDTMES (sample B) decreased as the thickness increased, because of increasing reflectance [35].  
The results shown in Figures 4 and 5, and Table 4 reveal that increasing the thickness and roughness of the hybrid film led to a decrease in the transmittance.
# Multi-Technology Multi-Operator Site Sharing: Compliance Distance Analysis for EMF Exposure
## 5 Conclusions  
The EMF exposure compliance boundary for multi-technology, multi-operator site sharing was discussed and analyzed using calculations that considered realistic configurations, and the study covered two types of site sharing: macro and indoor IBS sites. For a typical site operating with 2G/3G/4G/5G, the compliance distance results for the general public zone were less than those for the occupational zone. The ICNIRP limits were slightly more restricted than the FCC limits. For a macro site with a single MNO, the general public's horizontal R\({}_{\text{CD}}\) distances were 14.8 m and 13.8 m according to the ICNIRP and FCC, respectively. Furthermore, for a macro site shared by four MNOs, the R\({}_{\text{CD}}\) distances were 29.61 m and 27.69 m according to the ICNIRP and FCC, respectively. In addition, for all shared macro site scenarios, the 5G N3500 MHz had the highest contributions to the TER of 34% and 39% at the horizontal R\({}_{\text{CD}}\) according to the ICNIRP and the FCC, respectively. For the indoor IBS site with a single MNO, the general public's horizontal R\({}_{\text{CD}}\) distances were 0.87 m and 0.8 m according to the ICNIRP and FCC, respectively. In addition, for an IBS shared by four MNOs, the R\({}_{\text{CD}}\) distances were 1.74 m and 1.6 m according to the ICNIRP and FCC, respectively. Moreover, for all shared IBS site scenarios, the 5G N3600 MHz had the highest contributions to the TER of 26% and 30% at the horizontal compliance distances according to the ICNIRP and FCC, respectively. In the future, the research team will investigate the R\({}_{\text{CD}}\) for the accumulated radiation considering the neighboring sites.  
Conceptualization, M.S.E. and R.A.S.; Methodology, M.S.E. and R.A.S.; Investigation, M.S.E. and R.A.S.; Data curation, M.S.E.; Validation, M.S.E., R.A.S. and S.E.; Writing--original, M.S.E.; Writing--review and editing, M.S.E., R.A.S. and S.E.; Supervision, R.A.S. and S.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. The authors declare no conflict of interest.
## 3 Results and Discussions
### Geographical Prediction Model
Both plots illustrate the variation of the training and validation sets’ loss along the epochs. the achieved accuracy of 91.45% (+/\(-\)8.83%) was not as high as the ones obtained by utilizing the best five and ten features resulting from the ANOVA algorithm. An important aspect which was taken into consideration when developing the above-mentioned models is the fact that all ANNs had the same configuration characteristics (e.g., number of hidden neurons, learning rate, probability of dropout), except for the input layer. These were chosen in accordance to the best-found structure for the Romania-France classification of honey samples.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 3 Path Planning Design
### Search Path for UAV  
We divide the area to be covered by the search in a uniform grid shape. The distribution of this grid shape is uniform in space, so this grid-based partitioning method can also be approximately classified as cell decomposition. The first proposal for a grid-based partitioning method to solve the CPP problem was provided in [42]. The authors proposed a beginning cell and a target cell in their method. The wavefront algorithm is applied from the target cell to the beginning cell, with the grid serving as a representation of the coverage region. In order to reach the beginning cell, a \"wavefront\" is propogated from the target cell across unoccupied cells and through any other barriers. Specifically, each grid cell is given a unique number by using the \"wavefront\" transmission from the destination cell to the beginning cell, as shown in Figure 4. Prior to assigning 1 to all surrounding cells, 0 is first assigned to the target cell. The remaining cell 1's neighbors that have not been given a number will be given the number 2, after which, up until the \"wavefront\" reaches the beginning position, the procedure is progressively repeated. The sequence of numbers assigned to the cells in this area is the sequence of positions reached by the UAV in the area and is also the flight path of the UAV.  
We define the target region to be a rectangle divided into a grid of \((m\times D)\times(n\times D)\) by square cells with side length \(D\). Thus, the width of the region is \(W=m\times D\) and the length of the region is \(L=n\times D\). A vehicle equipped with \(N\) UAVs and capable of charging the UAVs can park at the center of the detection area on one side outside the detection area. The corresponding coordinates are \(S=(x_{S},y_{S})\). The UAVs depart from this location. The cell \((i,j)\) is the cell located in row \(i\) and column \(j\) of the grid. Therefore, the center position of cell \((i,j)\) is \(C_{i,j}=(i\times D+\frac{D}{2},j\times D+\frac{D}{2})\).  
The centroids of all cells in this target region can be represented as an \(m\times n\) dimensional matrix:  
\[A(x,y)\ \rightarrow\ \left[\begin{array}{cccc}C_{0,n-1},C_{1,n-1}&\cdots&C_{ m-1,n-1}\\ \vdots&\vdots&\vdots&\vdots\\ C_{0,1}\,\ C_{1,1}&\cdots&C_{m-1,1}\\ C_{0,0}\,\ C_{1,0}&\cdots&C_{m-1,0}\end{array}\right] \tag{1}\]  
where \(A_{11}\) to \(A_{m1}\) denotes the near row to the starting point \(S\). We propose five paths for the collaborative search of multiple UAVs.  
Figure 4: Area division method
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
###### Abstract  
In response to the challenge of handling large-scale 3D point cloud data, downsampling is a common approach, yet it often leads to the problem of feature loss. We present a dynamic downsampling algorithm for 3D point cloud maps based on an improved voxel filtering approach. The algorithm consists of two modules, namely, dynamic downsampling and point cloud edge extraction. The former adapts voxel downsampling according to the features of the point cloud, while the latter preserves edge information within the 3D point cloud map. Comparative experiments with voxel downsampling, grid downsampling, clustering-based downsampling, random downsampling, uniform downsampling, and farthest-point downsampling were conducted. The proposed algorithm exhibited favorable downsampling simplification results, with a processing time of 0.01289 s and a simplification rate of 91.89%. Additionally, it demonstrated faster downsampling speed and showcased improved overall performance. This enhancement not only benefits productivity but also highlights the system's efficiency and effectiveness.  
voxel filtering; 3D point cloud downsampling; dynamic downsampling; improved voxel filtering +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
We numerically investigated light absorption in metallic subwavelength gratings covered with a lossy dielectric medium. TMM was used to simulate the structure, where ITO and Al were chosen for a lossy dielectric and metal, respectively. The results show that subwavelength grating reflectors can be considered as semi-infinite homogeneous effective media whose optical responses can be obtained through numerical calculation. Based on these results, light absorption in dielectric-coated subwavelength gratings was calculated using an analytic expression for the reflectance of an asymmetric FP resonant cavity. Comparative numerical simulations allow us to determine how the coated dielectric film and subwavelength grating contribute to overall absorption. In addition, perfect absorption conditions were studied, revealing that the subwavelength grating structure can support critical coupling by acting as a dispersive absorbing substrate, which can be artificially engineered. We are convinced that our numerical study will provide a useful reference for designing subwavelength grating-based optical devices.  
Conceptualization, C.-Y.H.; Numerical Simulation, C.-Y.H.; Discussion, C.-Y.H., Y.-H.K., J.H.C., C.S.H.; Writing-Original Draft Preparation, C.-Y.H.; Writing-Review & Editing, C.-Y.H., Y.H.K., J.H.C., G.H.K., J.-H.Y., J.-E.P., H.-O.K., C.-S.H.; Project Administration, Y.-H.K.; C.-S.H. This research was funded by the Korea government (MSIT).  
This work was supported by The Cross-Ministry Giga KOREA Project (1711073921, Development of Telecommunications Terminal with Digital Holographic Table-top Display) and (1711072846, Development of Full-3D Mobile Display Terminal and Its Contents).  
The authors declare no conflict of interest.  
Figure 7: Plots of (**a**) magnitude and (**b**) phase of \(D(t_{\mathrm{d}}\lambda)\) defined in Equation (3) in order to investigate the critical coupling condition for perfect absorption. (**c**) and (**d**) show the corresponding results for the case of a flat Al reflector without the grating structure (\(w=0\)). In (**a**,**c**), white- and red-colored dashed lines indicate where the magnitude is 1 and the phase is \(2\pi m\) (\(m\) is an integer), respectively.