esa-sceva/satcom-chunk-collection huggingface dataset:
# The Presence of Impacted Teeth (Except for Wisdom Teeth) in Orthodontic Patients in Israel
## 3 Results
The overall duration of orthodontic treatment was 4.2 years in the study group compared to 2.4 years in the control (\(p\) < 0.0001). Table 3 shows the distribution of impacted teeth. Most of the impacted teeth were in the maxilla (84.4%); no difference was found between right and left or bilateral impaction. The most commonly impacted teeth in descending order were: maxillary canine, maxillary incisor, mandibular canine, premolars and molars. A total of 55.6% of the patients had one impacted tooth, while the other 44.4% had more than one impacted tooth. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**Number of Patients**} \\ \hline \multirow{3}{*}{Jaw} & Maxilla & 38 (84.4\%) \\ \cline{2-3}  & Mandible & 4 (8.9\%) \\ \cline{2-3}  & Both & 3 (6.7\%) \\ \hline \multirow{3}{*}{Side} & Right & 17 (37.8\%) \\ \cline{2-3}  & Left & 12 (26.7\%) \\ \cline{2-3}  & Bilateral & 16 (35.5\%) \\ \hline \multirow{3}{*}{Type} & Maximillary central Incisor & 8 (11.1\%) \\ \cline{2-3}  & Maximillary lateral Incisor & 1 (1.4\%) \\ \cline{2-3}  & Mandibular lateral Incisor & 1 (1.4\%) \\ \cline{2-3}  & Maximillary canine & 44 (61.1\%) \\ \cline{2-3}  & Mandibular canine & 5 (6.9\%) \\ \cline{2-3}  & Maximillary 1 st premolar & 3 (4.2\%) \\ \cline{2-3}  & Maximillary 2 nd premolar & 3 (4.2\%) \\ \cline{2-3}  & Mandibular 2 nd premolar & 2 (2.8\%) \\ \cline{2-3}  & Maximillary 1 st molar & 1 (1.4\%) \\ \cline{2-3}  & Mandibular 1 st molar & 1 (1.4\%) \\ \cline{2-3}  & Maximillary 2 nd molar & 2 (2.8\%) \\ \cline{2-3}  & Mandibular 2 nd molar & 1 (1.4\%) \\ \hline \multirow{3}{*}{Number of impacted teeth} & 1 & 25 (55.6\%) \\ \cline{2-3}  & 2 & 15 (33.3\%) \\ \cline{2-3}  & 3 & 4 (8.9\%) \\ \cline{2-3}  & 4 & 0 (0\%) \\ \cline{2-3}  & 5 & 1 (2.2\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The distribution of the impacted teeth in the study group. Analyzing the impacted teeth group separately, most patients underwent open or closed exposure (14 and 21, respectively), 5 underwent tooth extraction, and the remaining five underwent no surgical procedure at all (two patients underwent rapid palatal expansion--RPE, and in three patients the tooth erupted spontaneously after orthodontic space opening). In addition, there was no correlation between the type of surgical procedure and the patient's age. Of all patients who underwent surgical procedures for the exposure of impacted tooth, 8 (18.2%) patients underwent an additional surgical procedure (recurrent exposure/extraction). The logistic regression model found that the factors influencing the presence of an impacted tooth were only the presence of other dental anomalies (in the specific patient) and the presence of dental anomalies in their family members, with an odds ratio 6 times higher and 5.7 times higher, respectively (\(p\) = 0.009 and \(p\) = 0.034), while the other variables were not found to affect risk (week of birth, type of birth, medical condition, etc. ).
## 3 Explicit Calculation of Phase Space Representations of Arbitrary Digital Holograms
### On the Application of Phase Space Representations to Digital Holograms  
Rather than studying the entire 4D phase space of DHs, we will consider only 1D cross-sections (see Figure 1b) in the form of individual rows or columns sampled from the DH. Those provide, sufficient information for most analysis tasks. For near-field holograms, the row/column positions correspond to horizontal or vertical slices in scene space and should be chosen according to the objects of interest. For far-field holograms, they correspond to frequency sub-bands and thus reveal information about the entire scene at once. Unless stated otherwise, we will evaluate the PSRs on the middle row of the DHs, for example given a hologram \(H\in\mathbb{C}^{N_{q}\times N_{\xi}}\) as \(SM_{v,w}(H(:,\lfloor N_{q}/2\rfloor);\xi^{px},f_{\xi}^{px})\).
# Dynamic Characteristics of Sandstone under Coupled Static-Dynamic Loads after Freeze-Thaw Cycles
## 2 Experimental and Rock Specimens  
### Rock Specimens and Preparation  
The rock used in this study is a uniform, fine to medium-grained sandstone with a light yellow color. All specimens were extracted from a sandstone block with high geometric integrity and uniformity,which were prepared in accordance with the ISRM (International Society for Rock Mechanics) suggested specification [22]. The specimens were used in SHPB (split Hopkinson pressure bar made by the School of Resources and Safety Engineering, Central South University, Changsha, Hunan, China) tests, which have a length/diameter (L/D) ratio of 1.0.  
In order to determine suitable axial pre-compression stresses, static load tests were first carried out using an Instron 1346 hydraulic servo-controlled machine (made by the Instron Limited Company, Boston, MA, USA). The specimens used in static uniaxial compression tests correspond to a length/diameter (L/D) ratio of 2.0, and the average material properties from five specimens tested are summarized in Table 1.  
A typical stress-strain curve (SSC) of sandstone can be divided into four stages, as shown in Figure 1. The threshold stress separating the densification stage (OA) from the elasticity stage (AB) is approximately 21 MPa and that separating the elasticity stage (AB) from the yielding stage (BC) is approximately 56 MPa. Therefore, four different axial pre-compression stresses of 0 MPa, 15 MPa, 35 MPa and 55 MPa were used to study the effect of the pre-compression stress on the dynamic mechanical characteristics. One hundred rock samples divided into 4 groups are shown in Figure 2.
## 3 Proposed Methodology
### Flow and Mathematical Model of WR-SVM
```
Data: Training set \(S=((x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n}))\) Result: Optimal \((\mathbf{w},b)\) and \(R\) radius of MEB initialization; k-fold \(\leftarrow\) 10; Folds \(\leftarrow\) SplitDatatoids(S) forachfoldo  one, all \(\leftarrow\) Split one group from others  Serparable \(\leftarrow\) SeparabilityCheck(one, all) ifSeparablethen \(\xi\gets 0\); \(C\gets 0\); \(K\leftarrow\) linear else \(K=\exp(-\gamma\parallel x_{i}-x_{j}\parallel^{2})\) \(w\leftarrow\sum_{i=1}^{m}a_{i}y_{i}x_{i}\) \(b\gets y_{i}-w.x_{i}\)  Radius of MEB  Step 1: \(\bar{x}=\frac{\sum_{i=1}^{n}x_{i}}{n}\) Step 2: \(\overline{R}=max_{i}\parallel Ax_{i}-A\bar{x}\parallel_{2}^{2}\) \(weight_{i}=\parallel\bar{x}-x_{i}\parallel_{2}^{2}\) Weighted Feature Space: \(\bar{x}^{(w)}=\frac{\sum_{n=1}^{n}x_{i}.weight_{i}}{\sum_{i=1}^{n}.weight_{ i}}\) Weighted Radius of MEB Repeat Step 1 and 2 for weighted feature space
```  
**Algorithm 1**The two-step minimization algorithm for WR-SVM. Figure 10: WR-SVM Mathematical Model.
# Structural Augmentation in Seismic Data for Fault Prediction
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fault Detection Methods** & **Ours** & **Net\_manual** & **Net\_transfer** & **Net\_syn\_our** & **Net\_syn\_Wu** & **Coherence** & **Curvature** \\  & & [62] & [118] & **Net\_syn\_our** & **[68]** & **[19]** & **[25]** \\ \hline Ours & 1.0 & **0.1968** & **0.1209** & **0.2215** & 0.1882 & 0.1070 & 0.0509 \\ Net\_manual [62] & 0.1968 & 1.0 & 0.0807 & 0.1505 & 0.1540 & 0.1174 & 0.0523 \\ Net\_transfer [118  
increase the generalization ability of the method. The virtual faults added to the seismic data can produce sharp discontinuities. In practical applications, moderate smoothing may help to simulate more realistic faults in seismic images because smoothing can blur sharp discontinuities near faults. The fault recognition results in this study are only achieved by a simple U-net, and the use of more complex neural network architectures in future work may further improve the fault recognition results. In this study, there is currently no better way to quantitatively analyze the accuracy of identification results from field data. Although synthetic data have ground truth fault labels, it is not fair to utilize synthetic data for accuracy analysis. In future work, it may be a feasible option to quantitatively evaluate fault identification results from field data using well logging data. Conceptualization, S.W. ; methodology, S.W. ; coding, S.W. and X.S; validation, X.S; writing--original draft preparation, S.W. ; writing--review and editing, S.W., X.S., Z.C. and Y.C. ; visualization, S.W., X.S. and Y.C. ; project administration, Z.C. ; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, China (Grant No. XDA14010302). Not applicable. The training sets can be found at [[https://github.com/sixu0/Fault_aug](https://github.com/sixu0/Fault_aug)]([https://github.com/sixu0/Fault_aug](https://github.com/sixu0/Fault_aug)), accessed on 1 September 2020. We thank the Editors and reviewers for their helpful comments and suggestions. We gratefully acknowledge the Northwest Oilfield Branch Company, Sinopec, for generously supplying the field data and manually interpreted datasets for this study. The authors declare no conflict of interest.
# Multi-Condition Constrained Pedestrian Localization Algorithm Based on IMU
## 3 Results
### Varying Walking Speeds Experiment  
The experimental results in Figure 11 represent the data collected under normal walking conditions of the test subject. To verify the effectiveness of the MCC algorithm proposed in this article under different walking speeds, slow and fast walking experiments were conducted along the same experimental path. The results from these experiments are shown in Figure 13. Since the sampling frequency of the IMU sensor used in the experiments remained constant, the number of sampling points along the same path reflects the walking speed of the subject. As shown in Figure 13a, during slow walking, the trajectory of using the traditional ZUPT algorithm exhibits significant errors. This is due to the high rate of false detection of zero-velocity intervals in the traditional ZUPT algorithm at slow walking speeds. However, the trajectory by using the MCC algorithm (Figure 13b) significantly corrects these deviations, and the results are very close to the reference trajectory, with a substantial reduction in errors. The experimental results under fast walking conditions follow the same pattern, as can be seen by comparing the results  
Figure 12: (**a**) Accuracy improvement of the MCC algorithm compared to other algorithms under three different error types. (**b**) Starting–end point distance of the trajectories obtained by each algorithm.  
in Figure 13d,e. In conclusion, the MCC algorithm proposed in this article is able to maintain high correction performance across varying walking speeds, demonstrating its strong robustness.  
Figure 13: Experimental results with two different speeds. (**a**) Slow walking trajectory by the traditional ZUPT algorithm. (**b**) Slow walking trajectory by the MCC algorithm. (**c**) Point-wise error values of slow walking trajectory by the MCC algorithm. (**d**) Fast walking trajectory by the traditional ZUPT algorithm. (**e**) Fast walking trajectory by the MCC algorithm. (**f**) Point-wise error values of fast walking trajectory by the MCC algorithm.
# Fast Ferroelectric Liquid Crystal Based Optical Switch: Simulation and Experiments
## 3 Optical Switching in FLC Modulator  
Optical switching was performed in the smectic C* (SmC*) mesophase. The center of mass of the LC molecules in this phase is randomly distributed in the equally spaced smectic layers. The molecular director (\(\mathbf{n}\)) is tilted with an angle \(\theta\)/2 with respect to the normal to the smectic layers and the phase is optically biaxial. The possible states of molecules in SmC* phase is depicted in Figure 3.  
From Figure 3a it is obvious that \(\alpha^{\pm}=\beta\pm\theta\), where \(\theta\) is the molecular tilt angle. In order to measure \(\theta\), one must find out the position (i.e., \(\beta\) angle) of minimum transmittance which occurs at \(\alpha\) = 0. There are two angular positions (of \(\beta\)) for such minimum, first \(\beta=\theta\) gives a minimum for negative state, i.e., \(\alpha^{-}=\beta-\theta=0\), and the second \(\beta=-\theta\) gives a minimum for positive state, i.e., \(\alpha^{+}=-\beta+\theta=0\). Therefore, by measuring the angular positions of rotation stage of polarizing optical microscope which correspond to the negative and positive minimums we can determine \(2\theta\).  
Figure 2: (**a**) Schematic of the wedge shaped cell and (**b**) graph showing the wavelength dependent transmittance of cells of different thicknesses.  
In sufficiently thin samples, a homogeneously polarized ferroelectric state can be switched under the application of an electric field. FLC acts as a dielectric in a transparent capacitor. The applied voltages across the capacitor plates produce linear electro-optical switching of the polarization and tilt and also principal optical axis switching. The optical axis for an FLC is determined by \(n\) and the molecule can be switched between two stable states with a relative angle \(\theta\). Two molecular orientations are possible with respect to the polarization in the SmC* phase, and can be seen in Figure 3b. The net spontaneous polarization (\(P\)) appears in the direction perpendicular to the smectic layer plane. The switching can be easily observed between crossed polarizers. Due to the birefringence of the cell, the ordinary and extraordinary waves travel at different speeds through the cell and interfere at the output. The molecules can be switched by the application of electric field in the direction perpendicular to the surface. When the direction of spontaneous polarization is reversed by the external electric field then the optical axis therefore changes its orientation by twice the tilt angle.
## 4 Discussion  
The characterization results for all 11 devices (Table 3) indicate a certain degree of deviation from the sRGB color space. Notably, the average parameters across all measured devices do not align with the default sRGB gamma value or its chromatic assumptions. These findings provide quantitative evidence that the sRGB color space is not the most suitable for color rendering on modern displays, particularly in mobile OLED and LCD screens.  
In terms of perceptual evaluation, the CAAD model (based on averaged parameters from 11 Apple devices) delivered a color reproduction accuracy nearly equivalent to the fully device-specific MDCIM when tested on the iPhone XS Max [13]. Using the CAAD model yields very similar results: 97.4%, and the visual difference is reduced to 1.29. This finding is particularly relevant, as it suggests that accurate color reproduction may be achievable without performing individual spectroradiometric characterization, which can be time-consuming and resource-intensive.  
It is important to consider that the performance improvements observed with the CAAD model stem from two key factors: (1) the use of averaged device-specific display characterization parameters, and (2) the adaptation to ambient lighting conditions. While the current study does not independently quantify the contribution of each component, their combined effect leads to a significant enhancement in perceptual color fidelity over the sRGB baseline.  
The results also indicate that the CAAD model significantly improves color matching over sRGB. The percentage of reasonable or good color matches increases from 4.0% with sRGB to 84.2% with the averaged model.  
These results reinforce the broader implication that the sRGB approach for color rendering is insufficient for current display technologies. As new display systems continue to evolve, the need for flexible, context-aware models becomes increasingly urgent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method to Calculate** & & **Percentage with** & **Percentage with** \\
**Digital Color** & & **Visual Score \(\geq\) 4** & **Visual Score \(\leq\) 2** \\ \hline sRGB color space & 4.33 & 85.3\% & 4.0\% \\ \hline CAAD & 1.29 & 4.9\% & 84.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Visual scores when using digital color representations using the sRGB color space, or when using the CAAD model.
# Collaborative Multi-Robot Formation Control and Global Path Optimization
## 4 Global Path Planning for Multi-Robot Formations  
### Graph Theory  
The robot formation problem can be viewed as how the graph topology interacts with the local feedback control protocols of the nodes to generate the overall behavior of the interconnected nodes. The topology diagram can be defined as \(D(V,E)\), where \(V=\{v_{1},v_{2},\ldots,v_{N}\}\) denotes the set of \(N\) nodes; \(E\) is the set of edges. Assume that the graph is a simple directed graph \((v_{i},v_{j})\
ot\in E\) and there are no redundant edges between the same nodes. The element \((v_{i},v_{j})\) in the set \(E\) in the formation represents the robots whose information flows from node \(v_{j}\) robot to node \(v_{i}\). The out-degree \(d_{i}^{o}\) of a node robot represents the number of edges with it as the data sender, and the in-degree \(d_{i}\) represents the number with it as the data receiver.  
Determine the weights of the edges as \(a_{ij}\), and then represent the graph as a matrix \(A=[a_{ij}]\). When \((v_{i},v_{j})\
ot\in E\), \(a_{ij}=1\); otherwise \(a_{ij}=0\).  
\[d_{i}=\sum_{j=1}^{N}a_{ij},\ d_{i}^{o}=\sum_{j=1}^{N}a_{ji} \tag{20}\]  
Next, a diagonal matrix \(D=diag\ d_{i}\) and a Laplace matrix \(L=D-A\) are defined. The eigenvalues of \(L\) are analyzed and the Jordan form of the Laplace matrix is defined as follows:  
\[L=MJM^{-1} \tag{21}\]  
Figure 5: Path planning using improved ACO in the complex grid map.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**ACO**} & \multicolumn{2}{c}{**APF-ACO**} \\  & **Best Path** & **Iterations** & **Best Path** & **Iterations** \\ \hline Simple map & 14.5721 & 27 & 12.7280 & 13 \\ Complex map & 14.7286 & 29 & 14.4854 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of ACO and APF-ACO.  
where the Jordan Matrix \(J\) and the transformation matrix \(M\) are  
\[J=\begin{pmatrix}\lambda_{1}&1&&\\ &\lambda_{2}&1&\\ &&\ldots&1\\ &&&\lambda_{N}\end{pmatrix},\] \[M=[v_{1},v_{2},\ldots,v_{N}]\]  
where eigenvalues \(\lambda_{i}\) and the right eigenvectors \(v_{i}\) of \(M\) satisfy \((\lambda_{i}I-L)v_{i}=0\), and \(I\) is the unit matrix. The left eigenvector \(w_{i}\) of \(M\) satisfies \(w_{i}(\lambda_{i}I-L)=0\).
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 2 Technical Considerations for RIS
### Noise and Power in RIS
Therefore, the total power consumed by the RIS system can be found as \(P_{total}=P_{d}+P_{s}=P_{d}+(P_{c}+P_{td})\)The power consumption classification for the PIN diode, varactor, and RF switch-based RIS is shown in Figure 36. Since different RISs use different types of drive circuits, the total drive circuitry is related to the type of reconfigurable components. For example, a PIN diode-based RIS unit cell can be driven by a shift register. A varactor diode-based RIS unit cell can be driven by a DAC and operational amplifier (OP Amp), PWM signal, and level regulator, or CMOS logic circuit, while an RF switch-based RIS unit cell can be driven by an FPGA and shift register. Furthermore, the drive circuit power consumption is related to the number of control signals. Specifically, it is related to the number of reconfigurable components \(N_{c}\) and the control degrees of freedom of the RIS (unit cell control, row control, column control, sub-array control). RIS unit cells divided into the same group can use the same control signal, and \(N_{g}\) represents the number of RIS unit cells in the same group with the same control signal. \(N_{s}\) is the number of control signals generated with each driving circuit. The static power consumption relationship for each reconfiguration scheme is shown in the equation below [148]. \[P_{s}=P_{c}+P_{td}=\left\{ \begin{array}{c}P_{c}+\left[\begin{array}{c} \sum\limits_{j=1}^{N}B_{j}\\ \frac{1}{N_{g}N_{s}}\end{array}\right]P_{d}\left(PIN/Varactor\right)\\ P_{c}+\left[\begin{array}{c}\sum\limits_{N}^{N}\\ \frac{1}{N_{g}N_{s}}\end{array}\right]P_{d}\left(RF\ Switch\right)\end{array}  \right\} \tag{2}\]  
Earlier, it was said that if the number of RIS unit cells grows infinitely, i.e., the area grows, the communication performance improvement effect of RIS increases; however, as the area grows, the overall power consumption of RIS increases, which eventually leads to higher RIS hardware power consumption. Therefore, it is necessary to optimize the relationship between the number of RIS unit cells, performance, and power consumption. Table 7 shows a comparison of the power consumption of the control circuit according to the reconfiguration method. Figure 36: Power consumption of RIS [148]. (**a**) Typical RIS power consumption. (**b**) PIN diode-based RIS power consumption. (**c**) Varactor diode-based RIS power consumption. (**d**) RF switch-based RIS power consumption.
# Bioactive Peptides from _Lupinus_ spp. Seed Proteins-State-of-the-Art and Perspectives
## 3 Lupin Bioactive Peptides and Their Properties
### ACE-Inhibitory Peptides  
Cardiovascular diseases are a very common problem worldwide. The interest in antihypertensive peptides increases as they are thought to be a safer alternative to the commercial drugs. These peptides are able to inhibit the angiotensin-converting enzyme (ACE) which converts decapeptide angiotensin I into octapeptide angiotensin II [32], a substance that causes blood pressure to rise. Peptides with ACE-inhibitory activity can be isolated from a wide range of plant and animal sources. Angiotensin II also have the ability to bind to LDL and to form modified lipoprotein which can slow down plaque formation and delay the development of atherosclerosis [50].  
Many studies claimed that lupin protein-derived hydrolysates possess an inhibitory activity towards ACE. A study identified 10 peptides (from _L. mutabilis_), derived via enzymatic hydrolysis, possessing an ACE-inhibitory activity--RLGN, VNEGA SEIGGA, SAPRST, GALGLGH, PQNLDL, AGGPQQR, PSELSGAH, LPKHSDAD, and LIFPGSAD [33]. Pepsin hydrolysates of _L. albus_, _L. angustifolius_, and _L. luteus_ proved to be more efficient, resulting in a mean IC\({}_{50}\) value of 186 \(\pm\) 10 \(\upmu\)g/mL [32]. Fractions in the molecular weight range of 2-3 and 3-5 kDa of blue lupin proteins also possessed ACE-inhibitory activity (IC\({}_{50}\) values from 450 to 600 \(\upmu\)g/mL) [51]. Alcalase hydrolysates showed ACE inhibitory activities with IC\({}_{50}\) values ranging from 0.10 to 0.21 mg/mL [52].