esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Anti-Sebum Efficacy  
For the position with the toner base applied, the anti-sebum efficacy of the toner base was \(12.0\pm 1.52\%\). This showed that applying the toner base after washing can reduce the sebum recovery of the skin more than only washing the face. This might be caused by the fact that washing may remove the lipid film that protects skin hydration which makes sebum glands produce more sebum to compensate the unbalanced hydration. As a result, washing without the toners stimulates the skin to secrete more sebum than the skin which is washed before the toner base is applied, which is composed of glycerin, one of the effective humectants that can increase skin hydration, which compensates moisture to the skin, corresponding to the results of study with a 4-h experiment, the same period as this study, which revealed that skincare products that moisten the skin can reduce sebum secretion [38].  
The anti-sebum efficacy of the emblica toner was \(23.5\pm 1.24\%\), higher than that of the toner base (\(p<0.05\)) and gave the highest anti-sebum efficacy at 2 h. The anti-sebum efficacy on cheek skin (\(26.9\pm 1.78\%\)) was higher than that on forehead skin (\(20.1\pm 1.34\%\)). It showed that the astringent property of emblica extract in emblica toner can reduce sebum more than toner base, corresponding to a study conducted by [PERSON] et al. [22] which suggested that tannin can form cross-linked bonds with protein, which may result in the astringent effect to the skin in reducing sebum secretion. In addition, applying equal amounts of emblica toner in each position gave less anti-sebum efficacy on forehead skin than that on cheek skin since forehead skin secreted more sebum than cheek skin. The average anti-sebum efficacy of 3% emblica toner in this study was relatively high compared with several studies. Typically, anti-sebum efficacy of more than 10% produced a satisfying result [39]. For a toner with 6% guava leaf extract, the anti-sebum efficacy of the forehead and nose was found to be \(13.10\pm 3.67\%\) and \(21.43\pm 3.21\%\), respectively, after 28-day usage [24]. For a toner with 2%, 4.5%, and 7% green tea extract, the anti-sebum efficacy was found to be \(3.47\pm 0.10\%\), \(8.18\pm 0.44\%\), and \(17.87\pm 0.46\%\), respectively, after 14-day usage, and \(8.48\pm 0.13\%\), \(20.26\pm 1.03\%\), and \(31.57\pm 1.22\%\), respectively, after 28-day usage [25].  
Applying emblica toner on forehead skin and cheek skin after washing resulted in anti-sebum efficacy of facial skin under testing conditions around the 4-h period of the experiment. The anti-sebum efficacy on cheek skin was higher than the anti-sebum efficacy on forehead skin. In addition, the efficacy reached its maximum at 2 h after applying emblica toner, and was statistically significantly higher than the efficacy of toner base throughout the period of the experiment. Moreover, the model of anti-sebum evaluation used in this research can be applied for evaluation of other anti-sebum products for further studies.
## 5 Results
### Fitness Function and Penalty Fitness Function  
The fitness function \(F(X)\) guides solution \(X\) towards optimization, which, in turn, will attain goals in artificial intelligence, such as the SSO, GA, and PSO. All suitable fitness functions vary, depending on the optimization problem defined by the corresponding  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **1** & **2** & **3** & **4** & **5** \\ \hline \(X_{15}\) & 4 & **3** & 2 & 1 & 4 \\ \(X\) & **1** & 4 & **3** & 3 & **2** \\ \(\rho\) & 0.53 & 0.78 & 0.16 & 0.97 & 0.32 \\ New \(X_{15}\) & 1 & 3 & 3 & 4  & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: An example of the SSO update process.  
Figure 2: The flowchart of SSO.  
application. In this study, Equation (7) is adopted here to represent the fitness function, which is to be maximized in the proposed problem. For example, without considering the budget limit, \(F(X_{5})=393\), as discussed in Equation (4) for \(X_{5}=(4,3,4)\).  
The penalty fitness function \(F_{\text{P}}(X)\) helps deal with these problems without too many constraints and it is not easy to generate feasible solutions that satisfy the constraints, e.g., Equation (5). Penalty functions can force these infeasible solutions near the feasible boundary back to the feasible region by adding or subtracting larger positive values to the fitness for the maximum or minimum problems, respectively.  
If the larger positive value is not large enough, the final solution may be not feasible. Hence, a novel self-adaptive penalty function based on the budget and the deploy plan is provided below:  
\[F_{\text{P}}(X)=\left\{\begin{array}{ll}F(X)-PENALTY&\text{if }\sum\limits_{(x,y)}\text{COST}(r(x,y))>\text{COST}_{\text{UB}}\\ F(X)&\text{otherwise}\end{array}\right. \tag{10}\]  
where:  
\[PENALTY(X)\ =\ \left[r_{\text{UB}}\sum\limits_{(x,y)}\text{COST}(r(x,y)) \right]^{2} \tag{11}\]  
For example, let \(\text{COST}_{\text{UB}}=10\) in Figure 1. The total cost for the fifth solution \(X_{5}=(4,3,4)\) is \(\text{COST}(X_{5})=\text{COST}(4,3,4)=12\) from Equation (6). Because \(\text{COST}(X_{5})=\)12 \(>10\), we have:  
\[PENALTY(X_{5})=(10\times 12)^{2}=14,400,\]  
The penalty fitness function of \(X_{5}\) is:  
\[F_{\text{P}}(X_{5})=F(X_{5})-PENALTY(X_{5})=393-14,400=-14,007,\]  
Here, the penalty fitness function is that the fitness function subtracts the penalty if the cost is over the \(\text{COST}_{\text{UB}}\).
## 3 Problem Description
### Decision Variables and Constraints  
Constraints need to be imposed on the input space to define the feasible region of the objective function. For a CCS system with \(N\) wells, the input space consists of \(2N\) decision variables and the wells' location may be written as in Equation (15).  
\[\mathbf{x}=(x_{1},y_{1},x_{2},y_{2},\ldots,x_{N},y_{N}) \tag{15}\]  
The first constraint to be imposed on the input space is that two wells cannot take up the same cells on the grid, since the simulator issues a simulation error instantly. As such, the optimizer is instructed to penalize such cases by giving a very negative value for the objective function when the simulator crashes.  
The second and much more troublesome-to-handle constraint is the one regarding the valid position of each well. Since the aquifer's shape is not a square box, there are coordinate values within the grid-enclosing volume that correspond to null (non-existent) grid cells, as seen in Figure 3. The recommended solution would be to add a severe penalty term by introducing a large negative objective function value for inputs in the infeasible area. For a reservoir with an active-to-total-cell ratio of 50%, this ratio corresponds to the probability of a well being placed within the feasible area. If the location of a single well were sought, the optimizer would be able to map the infeasible area quite quickly. However, when N wells have to be placed rather than just one, the probability of selecting all of them inside the feasible space is only \((0.5)^{N}\). Clearly, the timeframe for the determination of the feasible area increases exponentially with the number of wells selected.  
To overcome this challenge, a mapping from this 2D input space to a 1D space is employed while maintaining a one-to-one relationship, using a transformation based on the cell distance from the origin. Subsequently, an ascending-order-sorting operation is applied to the Euclidean norms of each cell to arrange them. Algorithm 1 of the map can be seen below.  
```
Input: Set of 2D cells \(C=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\}\) Output: Mapped one-dimensional values  Calculate Euclidean distance from the origin \((0,0)\) for each cell and store them in array \(D\);  Sort array \(D\) in ascending order;  Initialize \(i=1\); for\(d\) in \(D\)do  Assign \(f(x_{i},y_{i})=i\);  Increment \(i\) by \(1\);  end for
```  
**Algorithm 1**Mapping function \(f:\mathbb{R}^{2}\rightarrow\mathbb{R}\).  
This mapping is one to one (1-1) because each point \((x,y)\in\mathbb{R}^{2}\) is uniquely associated with a real number \(f(x,y)\in\mathbb{R}\). Note that this is not a homeomorphism, as it does not preserve topological properties such as closeness of points.  
This mapping allows for dimensionality reduction from a \(2N\)-dimensional input space to an \(N\)-dimensional one. Furthermore, all values in the new input space represent the feasible region, so no more constraints on the input region need to be handled. The mapped values of each cell are shown in Figure 4.  
Figure 3: Feasible region for wells on reservoir grid top view.
## 3 Experimental Results and Analysis  
In practical engineering applications, an algorithm based on feature point matching should be used where the detection environment can be guaranteed to be the same at different times. The experimental platform built in this work is shown in Figure 8. In the figure, in red box 1 is an experimental bracket, a nearly sealed iron box with a camera and an illuminant in it. Sealing reduces the impact of the environment on the algorithm. In red box 2 is the host computer. In the experiment, the radius of the circular hole on the surface of engine cylinder head is detected.  
Figure 8: Experimental platform for visual inspection of engine cylinder head.  
Figure 7: Similarity images of the images with different illuminations.  
First, in this section, the advantages of the improved Haar-like texture feature compared with the traditional Haar feature are analyzed in detail. Secondly, the effect of space mapping method based on image similarity on illumination robustness enhancement is demonstrated. Finally, the detection results of the surface of the engine cylinder head are shown.
# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
## 2 Materials and Methods
### Multi-Objective Optimization Framework
#### 2.2.2 Multi-Objective Optimization and Decision-Making Method
RSM alone provides mathematical models but lacks global optimization capabilities; NSGA-III generates diverse Pareto solutions but offers no mechanism for final selection; weighting methods without objective statistical foundations introduce bias that compromises optimization outcomes. To overcome these limitations, this study implements a sequential methodology where RSM-derived prediction models serve as objective functions for NSGA-III optimization, generating a diverse Pareto-optimal solution set. These solutions are then normalized and processed through CRITIC to calculate objective weights based on statistical analysis of performance indicators. Finally, VIKOR applies these weights to compute compromise indices, enabling a scientifically based selection of the optimal concrete mix proportions that balance conflicting performance requirements for arid environment applications. Figure 2: The detailed process of the CRITIC-VIKOR decision-making method [35].
## 2 Methodology
### Experimental Setup and Measurements
#### 2.2.2 Experimental Equipment  
The experimental equipment diagram is shown in Figure 1. The test engine was installed on an eddy current dynamometer (DY-230 kW, Hwanwoong Mechatronics, Gyeongsangnam-do, Korea). The combustion pressure was measured using a piezo-electric type pressure sensor (Kistler, 6056a, Winterthur, Switzerland) located at the position of the glow plug, and the data were recorded and analyzed by a DAQ board (PCI 6040e, National Instrument, Austin, TX, USA). The levels of NOx and CO were measured using a multi-gas analyzer MK2 (Euroton, Italy), and the level of HC was measured by an HPC501 analyzer (Pantong Huapeng Electronics, China). The smoke opacity level was measured using a partial flow collecting type soot analyzer (OPA-102, Qurotech, Korea) based on the level of opacity. The fuel flow was calculated by measuring the fuel weight change over 10 min on a high-precision digital electronic weighing balance (AND, GP-30 K). The exhaust gas temperature was measured after the turbocharger.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Engine Parameters** & **Unit** & **Specification** \\ \hline Engine Type & - & Hyundai In-line 4 Cylinder, WGT \\ Maximum Power/Torque & kW/Nm & 84.6 (@4000 rpm)/260(@2000 rpm) \\ Bore x Stroke & mm \(\times\) mm & 83 \(\times\) 92 \\ Displacement & cc & 1991 \\ Compression Ratio & - & 17.7: 1 \\ Number of Injector nozzle holes & - & 5 \\ Injector type & - & Solenoid \\ Injector hole diameter & mm & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Engine specification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Unit** & **Specification** \\ \hline \multirow{5}{*}{Compressor wheel} & Type & - & Waste Gate Turbocharger (MHI) \\  & Inducer/Exducer dia. & mm & 33.00/44.01 \\ \cline{1-1}  & Blade numbers & EA & 6 + 6 (Trailing angle 60 degree) \\ \cline{1-1}  & Maximum boost & bar & 5 \\ \cline{1-1}  & Material & - & Forged aluminum \\ \cline{1-1}  & Inducer/Exducer dia. & mm & 36.5/32.2 \\ \cline{1-1}  & Blade numbers & EA & 11 \\ \cline{1-1}  & Cooling type & - & Oil cooled system \\ \hline \hline \end{tabular}
\end{table}
Table 3: Turbocharger specification.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 4 Theoretical Analysis
### Position Of Assisted Sensors  
**Theorem 3**.: _If only need one assisted node \((N_{m}=1)\), select inner centre of \(\Delta s_{i}s_{j}s_{k}\) as the position of first assisted sensor, When \(N_{m}>1\) then the position of assisted sensor must lie on the angle bisector of \(\Delta s_{i}s_{j}s_{k}\), which is the line connects \(P_{0}\) to \(S_{i}\)._  
Proof of Theorem 3.: First, when there is only one sensor enough to cover the \(S_{ uncovered}\), apparently it should take the inner center of \(S_{\Delta s_{i}s_{j}s_{k}}\) as the first assisted sensor location and where is the optimal choice, for the reason that \(P_{0}\) has the same distance from three sides which can achieve maximal coverage in the triangle. Second, in order to proof that \(P_{1}\) lies at angular bisector \(\overline{(P_{0}S_{1})}\), we introduce two assisted angles \(\alpha\), \(\beta\), if we can proof that only when \(\alpha=\beta\), the bow area is minimal then we can deduce that \(P_{1}\) is on the angle bisector \(\overline{P_{0}S_{1}}\). Assume that the distance between \(P_{1}\) and \(S_{1}\) is \(d\), we can get the area of \(S_{\Delta P_{1}R_{1}^{2}R_{2}^{2}}\) and \(S_{sector2}\), \(S_{\Delta P_{1}R_{1}^{1}R_{2}^{1}}\) and \(S_{sector1}\).  
\[\left\{\begin{array}{rl}S_{\Delta P_{1}R_{1}^{2}R_{2}^{2}}&=&\text{${{dsin }(\alpha)\sqrt{R_{s}^{2}-d^{2}sin^{2}(\alpha)}}$}\\ S_{\Delta P_{1}R_{1}^{2}R_{2}^{2}}&=&\text{${{dsin}(\beta)\sqrt{R_{s}^{2}-d^{ 2}sin^{2}(\beta)}}$}\\ S_{sector1}&=&\frac{R_{2}^{2}}{2}(\pi-2\arcsin(\frac{{{dsin}(\beta)}}{{{{{{{ {dsin}}}}}}}}))\\ S_{sector2}&=&\frac{R_{2}^{2}}{2}(\pi-2\arcsin(\frac{{{dsin}(\beta)}}{{{{{{ {ds}}}}}}}))\end{array}\right. \tag{7}\]  
we can get the \(S_{\widehat{R_{1}^{1}R_{2}^{2}}}=S_{sector1}-S_{\Delta P_{1}R_{1}^{1}R_{2}^{2}}\), in a similar way \(S_{\widehat{R_{1}^{1}R_{1}^{2}}}\) can be got. Finally, the blue area  
\[\begin{split} S_{Bouvarea}&=S_{\widehat{R_{1}^{1}R_{1}^{2}} }+S_{\widehat{R_{1}^{1}R_{2}^{2}}}\\ &=\sum_{\phi=\alpha,\beta}\frac{R_{s}^{2}}{2}(\pi-2\arcsin(\frac{{{ dsin}\phi}}{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{                                                                                                        \ \;\;\;\;\;\;\;\;\;\;}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}  
position is on line \(\overline{P_{0}S_{2}}\) and the fourth node is on line at \(\overline{P_{0}S_{3}}\), the fifth node lies on perpendicular bisector of \(P_{0}\) and \(P_{1}\). Additionally, the distance far from \(\overline{P_{0}P_{1}}\) is \(\frac{3}{2}R_{s}\) just as Figure 6 demonstrates, the rest can be deduced by the same manner. In this way, the coverage is relative maximization and redundant coverage is relative minimization in current triangle.
## 4 Evaluation
### Embedding Capacity  
The embedding capacity refers to the number of bits which are embedded in the ECG signal. Since we embedded the watermark sequence with patient information into the lowest frequency wavelet coefficients in level seven, the embedding capacity is calculated as \(4096\div 2^{7}=32\left(\text{bits}\right)\).
# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 3 Methods  
Pests in agricultural fields exhibit considerable variation in type and size, and identifying and detecting small-sized targets and pests against complex backgrounds present significant technical challenges in agricultural pest management. To address these challenges, optimizations and enhancements to the Yolox-tiny model have been described, aimed at improving detection and recognition capabilities for small target pests and pests in complex backgrounds.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 4 Experiments
### Model Implementation
#### 4.2.2 Parameter Settings  
The input parameters of the model were set as follows: maximum construction budget \(B=20\) km, continuity constraint \(k_{c}=6\), \(k_{com}=2\), adjustment factor \(\alpha=1.0\), and traffic flow limit \(f=90\) vehicles/h, as used in reality for bus lane planning in Shenzhen. To execute the GA, the maximum generation number \(T\) was 1000, population number \(N\) was 200, crossover rate was 0.5, and mutation rate was 0.1. A high mutation rate may hinder the convergence of the genetic process to the true optimal Pareto front. The effects of these parameters on the results are discussed in Section 5.2. In addition, the algorithms were developed in the C++ language, and a PC with 2.80 GHz and 16.00 GB of RAM was used for the experiment.
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 5 Simulation Results and Analysis  
### Simulation Experiments  
This paper considers a RIS-assisted wireless network where a gNB is deployed in the center of the coverage area. The cellular cell radius is 300 m, and the RIS is located 20 m away to increase the base station's coverage. The eMBB users are randomly distributed within the coverage area, and the URLLC traffic model is modeled as satisfying a Poisson distribution with parameter \(\lambda\). Other relevant simulation parameters are set as shown in Table 1, and the outdoor scenario is simulated using the SIMRIS channel simulator [23]. Other relevant parameters are described separately in each subsection.
# Visual Sentiment Analysis Using Deep Learning Models with Social Media Data
## 6 Experimental Results and Discussion
### Visual Features from the First Convolutional Layer of VGG-19  
The weight values of the filters in the first convolutional layer in Block 1 of the VGG-19 model were normalized to have values between 0 to 1. Figure 8 shows a sample input image from the dataset, and Figure 9 shows the first six filters out of the 64 filters in the first convolutional block. The Matplotlib library was used to plot each of the three filter channels, namely R, G, and B. Each row in the diagram represents a filter, whereas the column values indicate each channel (i.e., R, G, and B). The presence of small weights is shown as dark squares, and the large weights appear as light squares.
## 5 Conclusions  
The microhardness and elastic modulus mapping over the cross-section area along the longitudinal axis were employed in order to determine the possible effect of root canal wall thickness on MTA setting. Within limitations of this study, the interfacial layer of MTA which is in contact with root canal wall thinner than 1.2 mm has reduced mechanical properties, the same as in contact with simulated physiological conditions. This effect was observed up to 190 \(\upmu\)m inwards from the root canal wall/MTA interface and leads to decrease in microhardness up to by 32% and for modulus of elasticity up to by 27%. The observed phenomenon can occur in clinical scenario during treatment of immature teeth, endodontic surgery with extensive preparation of retrograde cavity, or direct pulp capping with limited residual dentin thickness around perforation. It seems that, in these clinical situations, might be use of thicker layers of MTA beneficial, but clinical impact of deteriorated mechanical properties of MTA on the boundary is questionable, and further research is desirable. In further research of MTA and other calcium silicate cements, we recommend keeping in mind that permeability of root canal wall can affect the setting of material.  
Conceptualization, R.Z. and J.S.; methodology, R.Z., D.M., R.C., J.T., and K.F.; software, R.C., J.T., and K.F.; formal analysis, K.F. and O.V.; investigation, D.M., R.C. and J.T.; writing--original draft preparation, R.Z. and J.S.; writing--review and editing, R.Z., K.F., R.C., J.T., O.V., and K.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by University of Palacky, grant number IGA_LF_2020_014; by the Operational Program Research, Development and Education of Ministry of Education, Youth and Sports of the Czech Republic, grant numbers CZ.02.1.01/0.0/0.0/17_049/000842; CZ.02.1.01/0.0/0.0/16_019/0000754; CZ.1.05/2.1.00/19.0377.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. This data can be found here: [[https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) VmO8 kuVysGVJVe0 pQbVlZvvMdk4 bsrC4a/view?usp=sharing]([https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) VmO8 kuVysGVJVe0 pQbVlZvvMdk4 bsrC4a/view?usp=sharing).  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Discussion
### Cephalometric Parameters  
Many previous studies have documented differences in cephalometric variables between JIA patients and control patients [5; 6; 13; 15; 21; 22; 23; 24; 25; 26; 27]. However, our data did not demonstrate statistically significant differences in cephalometric values between the two groups in any variable other than posterior facial height (\(p=0.024\)). A difference in the posterior facial height has been previously demonstrated in other studies [13; 15; 23]. One possible explanation for the lack of differences in cephalometric values could be that the control group represented an already retrognathic sample. It does appear that both our JIA and control groups have a tendency toward mild mandibular retrognathia, with the ANB angles of the JIA group and control group measured at 3.21 and 3.69 degrees, respectively. Additionally, the absence of cephalometric differences between the groups may suggest that the treatment regimens employed in the treatment of JIA patients were effective at minimizing craniofacial growth disturbances secondary to JIA.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Variable**} & \multicolumn{1}{c}{**Control**} & \multicolumn{1}{c}{**JIA**} & \multicolumn{1}{c}{\(p\)**-Value**} & \multicolumn{1}{c}{**ODDS Ratio**} \\  & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** & **(Confidence Range)** \\ \hline FMA (Degrees) & 22.97 (7.33) & 22.88 (5.79) & 0.958 & 1 (0.34–2.97) \\ Overbite (mm) & 2.81 (2.37) & 2.13 (1.28) & 0.159 & 0.77 (0.15–3.53) \\ Overjet (mm) & 4.15 (2.33) & 4.43 (3.90) & 0.734 & 0.68 (0.25–1.84) \\ Oropharynx Airway Volume (mm\({}^{3}\)) & 14,047.31 (5586.36) & 11,098.03 (6268.15) & 0.051 & 3.40 (1.18–9.81) \({}^{*}\) \\ Nasopharynx Airway Volume (mm\({}^{3}\)) & 5827.03 (425.85) & 4392.19 (1203.04) & 0.004 \({}^{*}\) & 4.59 (1.54–13.67) \({}^{*}\) \\ Total Upper Airway Volume (mm\({}^{3}\)) & 19,874.34 (6790.35) & 15,490.22 (6945.79) & 0.013 \({}^{*}\) & 3.22 (0.77–13.50) \({}^{*}\) \\ Minimal Cross-Sectional Area (mm\({}^{2}\)) & 202.50 (103.03) & 143.75 (103.23) & 0.026 \({}^{*}\) & 2.96 (0.95–9.21) \({}^{*}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._