esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Datasets  
The dataset in this paper consists of two parts. The first part was collected during a road survey in Zhengzhou using a 4-megapixel Hikvision industrial camera MV-CA060-10 GC over different time periods under natural lighting conditions. The closer the camera to the road surface during collection, the smaller the field of view, the more accurate the overall information, and the clearer the crack texture. After numerous attempts, taking into account the overall layout and the branch details of the crack, it was determined that the optimum shooting distance was 60 cm from the road surface, and the shooting interval was set to 1 s. Finally, a total of 727 images were collected.  
The second part of the dataset involves the Crack500 dataset, which was shot by [PERSON] et al. [49] on the main campus of Temple University in the United States with a mobile phone. We used all the pictures from the Crack500 dataset.  
Neural network training requires a large number of labeled datasets to prevent overfitting from occurring, so the data used in this article were generated through the following operations:  
1. Data preprocessing: if images that are too large are input into the deep convolutional network, there may be insufficient total graphics memory, resulting in training failure. In addition, the data we collected had a certain degree of randomness, which is not conducive to distinguishing between positive and negative samples during network training. Therefore, we resized the images in the original dataset to a resolution of 416 \(\times\) 416 by compression and cropping, and selected 1278 images as our dataset through filtering. In addition, we screened the pavement materials with defects, where 496 images for asphalt, 236 images for concrete, 219 images for tar, and 277 images for gravel.
2. Data augmentation: to meet the requirements of model training, we expanded the cropped dataset by rotating images through 90\({}^{\circ}\) and 180\({}^{\circ}\), and applying horizontal and vertical flips and other image geometric transformations to give a final total of 5110 images. We randomly chose 3582 images as the training set, 765 as the validation set, and the remainder as the test set.  
The heterogeneity of images affects the performance of crack detection [14; 15]; as shown in Figure 7, to comprehensively test the robustness of the algorithm, the types of cracks included transverse, vertical, and network. Potholes, tire marks, shadows, and water stains were included as environmental disturbances in our datasets.
# The Potential of Cannabidiol in the Management of Oral Infections
## 5 CBD Delivery Methods for Oral Infections: Current Strategies and Therapeutic Potential
### Topical Delivery Systems
#### 5.1.1 Mucoadhesive Gels and Ointments  
CBD-infused gels can be applied to inflamed or ulcerated oral mucosa, periodontal pockets, or peri-implant tissues. Mucoadhesive formulations enhance residence time, facilitating sustained release and deeper tissue penetration. In preclinical models, these gels have reduced microbial load and downregulated pro-inflammatory cytokines (e.g., IL-1\(\beta\), IL-6), aiding in infection control and tissue healing [63].
# Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks +
## 3 User Throughput in Single-Band HetNet
### Comparison with Conventional Method  
Figure 8 shows the average and 5-percentile user throughput of the proposed adaptive control CRE using the optimal parameters determined in Section 3.2 compared with those for the conventional method and homogeneous network (HomoNet).  
In the conventional method, six kinds of fixed CSO are set: CSO = 0, 2, 4, 6, 8, and 10 dB. The proposed method (Proposal) refers to the use of adaptive control CRE with the parameters of \(\alpha=0.4\), \(\text{CSO}_{\text{low}}=4\) dB, and \(\text{CSO}_{\text{high}}=6\) dB. The blue bar shows the average user throughput corresponding to the left-side axis, and the orange bar shows the 5-percentile user throughput corresponding to the right-side axis.  
The average user throughput decreases according to an increase of the CSO from 0 to 10 dB. One reason for this phenomenon is the increase of the number of UE in the CRE zone whose user throughput may be degraded. By increasing the CSO up to 4 dB, the 5-percentile user throughput increases, although the average user throughput decreases. One reason is that UE with poor user throughput connected to macro-eNB is connected to pico-eNB. This results in the improvement of the user throughput of the UE. However, a higher CSO decreases the 5-percentile user throughput in the conventional CRE. Likewise, if we attempt to improve the 5-percentile user throughput using a fixed CSO, the conventional CRE causes a deterioration in the average user throughput. However, the adaptive control CRE can solve such kinds of trade-off problems, i.e., improve the 5-percentile user throughput by 5% while maintaining the average user throughput compared with those of conventional method (a CSO of 4 dB), as shown in Figure 8. Compared with a CSO of 0 dB, the proposed method can improve 5-percentile user throughput by 1.5 times approximately, although the average user throughput slightly decreases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**MCS Index** & **Modulation** & **Coding Rate** & **Efficiency (bps/Hz)** \\ \hline
0 & \multicolumn{3}{c}{Out of range} \\ \hline
1 & \multicolumn{3}{c}{1/13} & 0.152 \\
2 & QPSK & 1/5 & 0.377 \\
3 & \multicolumn{3}{c}{7/16} & 0.875 \\ \hline
4 & \multicolumn{3}{c}{3/8} & 1.500 \\
5 & 16-QAM & 1/2 & 2.000 \\
6 & \multicolumn{3}{c}{3/5} & 2.400 \\ \hline
7 & \multicolumn{3}{c}{5/9} & 3.333 \\
8 & \multicolumn{3}{c}{9/20} & 3.700 \\
9 & 64-QAM & 13/20 & 3.900 \\
10 & \multicolumn{3}{c}{3/4} & 4.500 \\
11 & \multicolumn{3}{c}{17/20} & 5.100 \\ \hline
12 & \multicolumn{3}{c}{7/10} & 5.600 \\
13 & \multicolumn{3}{c}{256-QAM} & 19/24 & 6.333 \\
14 & \multicolumn{3}{c}{13/15} & 6.933 \\
15 & \multicolumn{3}{c}{23/25} & 7.360 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modulation and coding scheme (MCS) table in downlink [20].  
Figure 7: Average and 5-percentile user throughput versus \(\text{CSO}_{\text{high}}\) as a function of \(\text{CSO}_{\text{low}}\) (**a**) when \(\alpha=0.2\); (**b**) \(\alpha=0.4\); (**c**) \(\alpha=0.6\); (**d**) \(\alpha=0.8\).
# Mechanical Behavior of Bi-Layer and Dispersion Coatings
## 2 Materials and Methods  
### Materials  
The Ti Grade II (EkspresStal, Lubor, Poland) was used as a substrate. Specimens in form of 40 mm in diameter and 4 mm thick slices were cut from the rod and divided into quarters using a precision cutter (Brillant 220, ATM GmbH, Mammelzen, Germany). The surface was ground using abrasive paper SiC up to grit # 800 on a metallographic grinding machine (Saphir 330, ATM GmbH, Mammelzen, Germany). Then, specimens were rinsed with acetone (Chempur, Piekary Slaskie, Poland), distilled water, dried in the air, and pickled in 5% HF (Chempur, Piekary Slaskie, Poland) for 30 s to remove oxide layers from the surface, and finally rinsed with distilled water.  
The multi-wall carbon nanotubes (MWCNTs) had an outer diameter of 10-15 nm, an inner diameter of 2-6 nm, a length of 1-10 \(\upmu\)m, and the number of walls 3-15 (3D-Nano, Krakow, Poland). Nanocopper (nanoCu) had a mean grain size of 80 nm (Hongwu International Group Ltd., Guangzhou, Guangdong, China). Titanium dioxide (TiO\({}_{2}\)) of rutile structure (3D-Nano, Krakow, Poland), possessed a grain size in the range of 1 to 2 nm.  
Fabrication of the Bi-Layer MWCNTs/TiO\({}_{2}\) Coating and MWCNTs\({}_{\text{Cu}}\) Dispersion Coating  
The electrophoretic deposition (EPD) method was used to prepare coatings, for which synthesis parameters are shown in Table 1. The Ti Grade II substrate was used as an anode and stainless steel as a counter-electrode for the MWCNTs coating, and inversely for both other coatings. The electrodes were placed parallel to each other within a distance of 5 mm and connected to the DC power source (MCP/SPN110-01C, Shanghai MCP Corp., Shanghai, China).  
The EPD process was conducted at parameters selected based on the preliminary studies. The bi-layer coating MWCNTs_TiO2 was prepared in two steps. First, the electrophoretic suspension of 0.1 g MWCNTs in 40 mL of distilled water was prepared and the EPD process on Ti Grade II substrate proceeded (designation: MWCNTs). Second, the same samples were deposited in suspension consisted of 0.15 g TiO2 dispersed in 50 mL of isopropanol together with 0.5 mL of surfactant -Polysorbate 20 (Tween 20, Sigma-Aldrich, Poznan, Poland).  
To prepare the dispersion coating MWCNTs_Cu, 0.1 g of MWCNTs, 0.005 g of nanoCu, and 0.5 mL of Polysorbate 20 were dispersed in 40 mL of distilled water and deposited using the EPD method on Ti Grade II substrate.
## 4 Results and Discussion
### Deriving the Optimal Hyperparameter  
GridSearchCV was used to adjust the hyperparameter to derive the optimal hyperparameter based on XGBoost. Table 4 shows the types of hyperparameters that were to be adjusted, along with their descriptions.  
The optimal hyperparameters for each illness were derived using GridSearchCV. Table 5 shows the R\({}^{2}\) for the final hyperparameters. Comparison with Table 4 shows the improved R\({}^{2}\). This study found that a significant prediction was derived only when R\({}^{2}\) was above 0.65, and illnesses with an R\({}^{2}\) below 0.65 were excluded. Thus, a total of four illnesses for children acute bronchitis, the common cold, rhinitis and tonsillitis, and inner ear inflammation, and a total of six illnesses for older adults high blood pressure and heart disease, the common cold, esophageal inflammation and gastritis, acute bronchitis, eczema and dermatitis, and chronic bronchitis are regarded as having been significantly analyzed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **Parameter Name** & **Description** & **Value** \\ \hline General & booster & Tree model based (gbtree), Linear model based (gblinear) & [gbtree, gblinear] \\ \hline Booster & colsample\_bytes & Random sampling of columns required to create trees & [0.5, 0.7, 0.9, 1] \\ \hline Booster & gamma & Minimum loss reduction value that will determine whether to further split the leaf nodes of trees & [0, 0.1, 0.2, 0.3] \\ \hline Booster & learning\_rate & Coefficient used by weak predictors to correct error values & [0.001, 0.01] \\ \hline Booster & n\_estimator & Number of weak predictors & [900, 950, 1000, 1050] \\ \hline Booster & subsample & Ratio of sampling data & [0.5, 0.7, 0.9, 1] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Description of hyperparameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**a. Children**} \\ \hline \hline \multirow{3}{*}{**Illness**} & \multicolumn{6}{c}{**Final Hyperparameter**} \\ \cline{2-9}  & \multirow{2}{*}{**Booster**} & **Colsample** & **Gamma** & **Learning** & **n\_esti** & **SUB** & **R\({}^{2}\)** \\  & & **Bytes** & **Gamma** & **Rate** & **Mator** & **Sample** & \\ \hline Acute bronchitis & gbtree & 1 & 0.2 & 0.001 & 1050 & 0.9 & **0.7768** \\ Common cold & gbtree & 1 & 0.1 & 0.001 & 1050 & 0.7 & **0.7308** \\ Rhinitis, tonsillitis & gbtree & 0.9 & 0.2 & 0.001 & 1050 & 0.9 & **0.6679** \\ Inner ear inflammation & gbtree & 1 & 0.3 & 0.001 & 1050 & 0.7 & **0.6605** \\ Chronic bronchitis & gbtree & 0.9 & 0.2 & 0.001 & 1050 & 1 & 0.4612 \\ Intestinal inflammation & gbtree & 1 & 0.2 & 0.001 & 1050 & 0.9 & 0.3141 \\ Influenza, pneumonia & gbtree & 1 & 0.1 & 0.001 & 1050 & 0.9 & 0.4092 \\ Eczema, dermatitis & gbtree & 0.7 & 0.1 & 0.001 & 1050 & 1 & 0.3641 \\ Chicken pox & gbtree & 0.5 & 0.3 & 0.001 & 1050 & 0.7 & 0.2768 \\ Conjunctivities & gbtree & 0.9 & 0 & 0.001 & 1050 & 0.9 & 0.278 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Final hyperparameter for each illness.
# Camera Calibration with Phase-Shifting Wedge Grating Array
## 2 Principle  
### Camera Model  
The camera is modeled as the classic pinhole. Let \(\mathbf{P}=\left(X_{W},Y_{W},Z_{W},1\right)^{T}\) and \(\mathbf{P}=\left(u,v,1\right)^{T}\) be the homogeneous coordinates of the 3D world point and the 2D image point, respectively. The relationship between \(\mathbf{P}\) and \(\mathbf{P}\) can be mathematically described as [1]:  
\[\lambda\mathbf{P}=\mathbf{K}[\mathbf{R}\quad\mathbf{t}]\mathbf{P}, \tag{1}\]  
\[\mathbf{K}=\left[\begin{array}{ccc}f_{u}&\gamma&u_{0}\\ 0&f_{v}&v_{0}\\ 0&0&1\end{array}\right], \tag{2}\]  
\[\mathbf{R}=\left[\begin{array}{ccc}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{array}\right],\mathbf{t}=\left[\begin{array}{c}t_{1}\\ t_{2}\\ t_{3}\end{array}\right], \tag{3}\]  
where \(\lambda\) is an arbitrary scale factor, \(\mathbf{K}\) called the intrinsic parameters contains focal length \((f_{u},f_{v})\), principal point \((u_{0},v_{0})\), and skew angle parameter \(\gamma\). The rotation matrix \(\mathbf{R}\) and the translation vector \(\mathbf{t}\) denote the extrinsic parameters. Generally, camera lens consisting of several optical elements does not obey the ideal pinhole model, which will introduce nonlinear distortions to correct the captured images [3]. Radial and tangential distortions are the two most common that can be approximated as [19]:\[\left[\begin{array}{c}u^{\prime}\\ v^{\prime}\end{array}\right]=\left[\begin{array}{c}u(1+k_{1}r^{2}+k_{2}r^{4})+2p _{1}uv+p_{2}(r^{2}+2u^{2})\\ v(1+k_{1}r^{2}+k_{2}r^{4})+p_{1}(r^{2}+2v^{2})+2p_{2}uv\end{array}\right], \tag{4}\]  
where \((u,v)\) and \((u^{\prime},v^{\prime})\) are the distorted and undistorted image point in normalized image coordinates, \(k_{1}\) and \(k_{2}\) are the radial distortion coefficients, \(p_{1}\) and \(p_{2}\) are tangential distortion coefficients, and \(r=\sqrt{\left(u-u_{0}\right)^{2}+\left(v-v_{0}\right)^{2}}\).
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 1 Introduction
Following extensive training and optimization, neural networks acquired the capability to comprehend and analyze both the warning details and code snippets information encoded within the context embeddings. Our model was trained by neural networks to classify static analysis warnings and provide actionable insights for developers, enabling them to focus on critical issues. The resulting outputs from these models are further processed through fully connected layers for classification, leading to the detection and identification of actionable warnings. Ultimately, the identified actionable warnings are presented to developers for prompt resolution while mitigating the volume of warnings that developers are required to handle. From what has been mentioned above, our approach significantly enhances the efficiency of handing static analysis alarms, thereby improving the software reliability. This study makes the following principal contributions:  
* We propose a novel approach for effectively handling static analysis alarms that combines the utilization of warning features, context embeddings generated by the pre-trained model, and diverse neural network architectures. A reproducible package containing our results can be found at [[https://github.com/Don2025/ASAT-DL](https://github.com/Don2025/ASAT-DL)]([https://github.com/Don2025/ASAT-DL](https://github.com/Don2025/ASAT-DL)) (accessed on 1 June 2024);
* To comprehensively assess the approach's effectiveness, we conducted an evaluation on the Defects4] dataset, which consists of 835 real-world software defects extracted from 17 open-source projects. By utilizing SpotBugs, we generated a total of 152,518 warnings on the bug version of the Defects4] dataset and subsequently located the corresponding code snippets associated with these alarms. This evaluation offers an empirical substantiation of our approach to tackle static analysis alarms, thereby demonstrating its inherent potential to elevate software reliability within real-world development processes;
* Our approach offers practical implications for developers by providing actionable warnings that are more reliable, which facilitates the prompt resolution of critical issues and the improvement of software quality.
## 2 Materials and Methods
### Study Design
#### 2.2.1 Hamstring Test  
The hamstring test followed the WIMU(r) Hamstring Test guide protocol [55]. The participant was placed lying down in a supine position. A WIMU(r) system (RealTrack Systems, Almeria, Spain) was placed over the distal tibia strapped with an elastic band to measure the inclination. The system is a small, wireless device with more than 20 integrated sensors. The sensors include a 1000 Hz 3D accelerometer, a 1000 Hz 3D gyroscope with 2000 degrees per second resolution, a 3D magnetometer, and a barometer that works with an integration of sensors to improve the information. All data regarding the angle reached in each repetition for both legs were sent via Bluetooth to a personal computer, in real time, and were then recorded using SPro software (RealTrack Systems, Almeria, Spain). Participants performed five ballistic hip flexions, maintaining the knee extension. The pelvis and the contralateral leg were fixed by a researcher to avoid pelvic movement [55]. The WIMU(r) system simultaneously recorded the hip flexion angle. This protocol was performed on both legs equally. The average of the five repetitions before and after the RSA test of the maximal angulation (deg), maximal velocity (deg/s), average velocity (deg/s), time to maximal velocity (ms), and angle at maximal velocity (deg) of each leg separately and of the average of both legs were analysed.
## 5 aPDT Performance in Clinical Studies
### PS in Nanoemulsions  
A method to carry the PS in photodynamic therapy is in nanoemulsions. Nanoemulsions are \"thermodynamically stable colloidal dispersions composed of two immiscible liquids\" [75; 76]. In nanoemulsions, one of the liquids is presented as a small droplet with a range size inferior to 100 nm dispersed within the other liquid [76]. This approach is currently explored to carry hydrophobic drugs and PS in an aqueous biological environment [77]. Moreover, the oil-based nanoemulsions present antimicrobial activity because of the hydrophobic character and interaction with the phospholipid bilayer of bacterial membranes [75].  
Positively and negatively charged nanoemulsions composed of chloro-aluminum phthalocyanine were tested against _Staphylococcus aureus_ in biofilm and planktonic forms. Promising antibacterial activity outcomes were achieved when the positively charged nanoemulsion composed of chloro-aluminum phthalocyanine was used at 31.8 m and a wavelength at 660 nm was applied for 26 min [78].  
The effect of nanoemulsions composed of clove oil and zinc phthalocyanines against _E. faecalis_ and methicillin-resistant _S. aureus_ (MRSA) light-irradiation was attempted to improve aPDT effectiveness [79]. Zinc phthalocyanines present great photobiological activity to be applied in photodynamic therapy. However, they are not miscible with aqueous solutions, which make their handling difficult. The maximum zinc phthalocyanines loaded was 5% and clove oil, 5%, resulting in a nanoemulsion of 30 nm and maintaining the drop size as lower than 50 nm. The minimum inhibitory concentration against _E. faecalis_ was 1.09 m\(\mathrm{g}\)/mL and 0.065 m\(\mathrm{g}\)/mL against MRSA. When zinc phthalocyanine was within the nanoemulsion, there was higher antibacterial activity than free zinc phthalocyanine. Moreover, as aforementioned, nanoemulsions can present antibacterial activity due to their hydrophobicity. In this study, blank nanoemulsions also presented antibacterial activity [79].
## 1 Introduction
[13] analyzed mapping relationships between three-dimensional acceleration information from the tire inner liner and tire forces, achieving high-precision online estimation through combined physical models and machine learning techniques. It can be observed that tire force estimation is based on actual physical quantities measured by sensors, combined with appropriate estimation algorithms. Therefore, the measured physical quantities and their sensitivitiesto tire forces significantly influence the estimation accuracy of tire forces. In the studies mentioned above, sensors were predominantly installed at the center of the tire inner liner, without sufficient theoretical analysis regarding the optimal sensor type, quantity, and placement locations. Although [PERSON] et al. [14] compared the correlation between strain measurements in specific tire areas and three-dimensional tire forces based on strain sensors, their method assumed a simple linear relationship between strain and tire forces, neglecting the inherent complexity and nonlinearity of actual tire behaviors. [PERSON] [15] performed experimental analysis on the effects of different loads and speeds on tire strain but did not analyze the sensitivity relationship between strain and tire forces. Therefore, to overcome the blindness of the sensor-targeting configuration in intelligent tires, further research is required to identify tire force-sensitive areas and their corresponding sensitive response variables, which is crucial for achieving precise tire force estimation in intelligent tire technologies. This paper proposes an intelligent tire force estimation method that integrates sensor-measured tire dynamic response parameters with machine learning techniques. A 205/55 R16 radial tire was selected as the research object, and its finite element model was established using the ABAQUS/2020 simulation software with a parametric modeling approach. The validity of the model was verified through indoor static contact and stiffness tests. The variance-based global sensitivity analysis method, along with the multiplication dimensionality reduction-based global sensitivity index solution method and finite element analysis, was used to investigate the sensitivity of acceleration, strain, and displacement responses in the contact deformation area of the tire inner liner to changes in longitudinal and lateral forces. Based on the results of the global sensitivity analysis, the characteristic values from the response signal curves associated with longitudinal and lateral force sensitivity were extracted and used as input variables for the RBF neural network. Three intelligent tire force estimation models, each with different sensor combinations and targeted configurations, were established to determine the optimal sensor configuration for the accurate estimation of the tire's longitudinal and lateral forces.
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Modelling the Car-Following Behavior
#### 3.2.1 Information of State in the Downstream
In the various information-based interaction relationships, the new characteristics of car-following behavior need to be comprehensively explored.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## Data and Methods
### NetCDF Geo-Cubes Database  
To facilitate post-processing, our regions of interest are divided into areas of equal size of \(10\times 10\) km with a resolution of 50 m (or 250 \(\times\) 250 pixels). For each area, we extract and stack the velocity maps and associated metadata into \"cubes\" whose dimension z therefore corresponds to the number of calculated speed maps. The cubes overlap by 5 pixels or 1.25 km to avoid edge problems during post-processing. This standardized dataset where all maps are stored on a common grid can be easily used to extract time series of the surface flow velocity or calculate time-averaged maps. The cubes are stored in the GDAL-compatible netCDF format following the CF (Climate and Forecast) metadata conventions to allow greater portability and facilitate distribution in the community. A cube file contains metadata about the cube itself (dimensions, corner coordinates, number of speed maps), surface displacement maps in meters per year in \(x/y\) directions, associated errors, projection information (UTM area, datum etc.), processing directory in our system, dates of master and slave images, repeat cycle and sensor name.