esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Discussion on Correlation  
The observed correlations suggest that soil physicochemical properties play a key role in determining the fate and persistence of pesticide residues. These findings are consistent with previous studies indicating that the environmental behavior of pesticides is strongly influenced by soil composition [63; 64; 65]. In addition to agricultural practices such as pesticide application rates and timing, soil properties such as organic matter content, pH, and nutrient status can influence pesticide adsorption, mobility, and degradation. These findings emphasize the need to consider both anthropogenic inputs and inherent soil characteristics when evaluating pesticide occurrence and predicting their environmental impacts.  
Notably, a positive correlation was observed between soil pH and both available potassium and acetochlor concentrations. This relationship can be attributed to the influence of soil acidity on pesticide speciation and adsorption [66; 67]. SOM exhibited a positive correlation with available potassium and acetochlor, which aligns with the well-documented role of SOM in enhancing pesticide adsorption [68; 69]. Atrazine and alachlor also exhibited a significant positive correlation, suggesting potential similarities in their environmental behavior or co-application patterns [70]. In contrast, available nitrogen and available phosphorus displayed a negative correlation. This inverse relationship might be driven by competitive nutrient utilization dynamics within soil microbial communities, where preferential uptake of one nutrient over another disrupts stoichiometric balances. Furthermore, intensified anthropogenic activities--such as excessive nitrogen fertilizer application in agricultural systems and rapid urbanization--are likely contributing factors. These disturbances can lead to imbalances in the natural nitrogen and phosphorus cycles, altering biogeochemical processes and reducing soil nutrient efficiency [71; 72]. Collectively, these findings underscore the complex interplay between pesticide contamination and soil properties, shaped by both intrinsic soil characteristics and extrinsic human pressures. The observed patterns suggest that agricultural intensification, characterized by repeated fertilizer use and pesticide overapplication, has significantly altered soil geochemistry and ecological functioning. Therefore, systematic monitoring and integrated management strategies are urgently required to mitigate soil degradation and protect soil environmental quality. Effective interventions not only contribute to maintaining and enhancing soil productivity but also support the long-term sustainability of agroecosystems, ultimately safeguarding the health of terrestrial ecosystems for future generations.
## 6 Parametric Study
### Discussion on Coupled Effects of the Pulse Feature and Oblique Incidence  
The influence of intensity parameters on the dynamic response of the structure under different incident angles is investigated as above, and the optimal index is selected for further exploration. Then, the coupled effect of the pulse characteristics and oblique incidence can be discussed based on the selected optimal seismic parameter \(S_{\text{a}}(T_{1})\). Figure 15 shows the fitting line between \(S_{\text{a}}(T_{1})\) and DVR under PL and NP motion at different incident angles and directions. The \(R^{2}\) values at different incident angles exceed 0.7. The fitting line results under various working conditions also exhibit a certain degree of change with increased incident angle. In particular, the response results under oblique PL motion are considerably greater than those under NP motion. When the incident angle ranges from \(30^{\circ}\) to \(45^{\circ}\), the pulse characteristics have no major influence on the seismic response. As the angle increases, the influence of the pulse characteristics gradually appears, causing the difference between the response fitting lines with and without the PL ground motion to widen gradually. Finally, the fitting slopes under the four working conditions are almost equidistantly reduced--that is, the order of the response values is PL in the positive and negative directions and NP in the positive and negative directions.  
The fitting slopes and intercepts are shown in Figure 15f. The analysis shows that the discrete data points adhere closely to the polynomial fitting model, indicating a high level of correlation. Nearly all scattered points align with the fitted curves. As the incident angle increases, the fitting slope increases, accompanied by a decrease in the intercept. The linear increase in the fitting results for the negative angles is particularly noteworthy. Conversely, the growth trend for positive incidences gradually slows with incremental changes in the incident angle.  
Figure 16 shows the relationship between the seismic parameter \(S_{\text{a}}(T_{1})\) and displacement index under the four working conditions, with the slope and intercept of the fitting line also being provided. Consistent with the above results, \(S_{\text{a}}(T_{1})\) is proportional to the  
Figure 15: Fitting line of the spectral acceleration and DVR under NF motion with varying incident angles and directions: (**a**) \(15^{\circ}\); (**b**) \(30^{\circ}\); (**c**) \(45^{\circ}\); (**d**) \(60^{\circ}\); (**e**) \(75^{\circ}\); (**f**) The variation trend of the fitting slope.  
displacement response under various earthquake input conditions. When the incident angle exceeds 60\({}^{\circ}\), the incident direction has little effect on the displacement response under PL motion. The slope and intercept variation trends are similar to those of the dam structure damage response. Moreover, the growth trend of the fitting slope is faster under PL motion, which indicates that the displacement response is more sensitive to PL motion.
## 5 Experiments and Results
### Detailed Experimentation and Results Analysis with Pix2 PixHD
#### 5.4.2 Pix2 PixHD with Hybrid Edge and Mask Inputs  
This experiment extended the methodology of the first two experiments by integrating mask inputs with both edge techniques to investigate their combined effect on the Pix2 PixHD model's performance in generating high-quality X-ray images. Each input to the Pix2 PixHD model consisted of three channels: one for the mask data, which assigned a specific pixel value to identify the object's category, and two for edge data, Edge01 and Edge02 (collectively termed \"Hybrid Edge\"), to emphasize textural details and subtle features often missed in the mask-only approach. The hybrid input was processed by the Pix2 PixHD model to evaluate its effect on the quality of the generated images, as shown in Figure 9.  
Figure 8: Comparative analysis of image synthesis with different edge inputs using the Pix2 PixHD Model—column 1: Edge01 results, column 2: Edge02 results, column 3: Hybrid Edge results, and column 4: corresponding ground-truth image.  
Our evaluation focused on comparing the results with those obtained from using only masks, paying particular attention to improvements in image detail, texture, and overall realism.  
The inclusion of this enriched input strategy aimed to enhance Pix2 PixHD's capabilities and compare its performance with the Pix2 Pix model. This approach sets a new benchmark for image synthesis quality and enables further studies in the area of paired X-ray image security translation. Utilising the hybrid edge and mask input, the Pix2 PixHD model achieved a PSNR of 19.93, SSIM of 0.71, LPIPS of 0.12, FID score of 29.36, and FSIM score of 0.54, exhibiting notably sharper and more detailed visual results compared to those from previous experiments with edge data, due to the comprehensive input details provided through the three-channel approach. Conversely, applying the same methodology to the Pix2 Pix model yielded some improvements, with a PSNR of 13.89, SSIM of 0.54, LPIPS of 0.35, FID score of 146.20, and FSIM score of 0.42. However, the results confirmed that utilising a mask with hybrid edge inputs in conjunction with the Pix2 PixHD model delivered the best outcomes, establishing a superior method for generating detailed and accurate X-ray images. Figure 10 shows the effectiveness of our proposed three-channel input.  
Figure 9: Comparative analysis when utilising masks and hybrid edges as three-channel inputs for image synthesis—column 1: Pix2 Pix results, column 2: Pix2 PixHD results, column 3: corresponding ground-truth images.
## 3 Results and Discussion
### Discussion
#### 3.2.2 Different Supporting Stiffness and Damping Coefficients of Rubber O-rings  
Based on the analysis in Section 3.2.1, the flexible support for the winding rotor is of significance to control its vibration. To obtain better performance of the rotor, the structure and process parameters of the rotor should be designed practically. In this section, the different coefficients of supporting parameters of the rubber O-rings in the flexible support were studied further. In practical application, the coefficients of the supporting parameter of the rubber O-ring can be obtained through changing the rubber material and number of rubber O-rings, as well as the structural parameters of the rubber O-ring. The dynamic response of the winding rotor was carried out under the original supporting parameters in Figure 7, 0.5 time and 2 times coefficients of the supporting parameters of the rubber O-ring, and the vibration amplitude-rotational speed curves are shown in Figure 15.  
Through the study of the influence of different supporting stiffness and damping coefficients of rubber O-rings on the winding rotor, research results show as follows:  
* Since the rubber O-rings are installed on the shaft in parallel, the stiffness value of supporting rubber O-rings increases with the increase of their number. Therefore, the critical speed point of the rotor is increased correspondingly, as shown in Figure 15a;
* Comparing with Figure 14, it can be seen that the flexible support effectively suppresses vibration of the winding rotor, and plays an important role in maintaining the system to pass through the critical speed points smoothly and rotate steadily in a wide working speed range. In addition, the winding rotor can rotate steadily in the wide working speed range under the equivalent coefficient of 0.5 time, 1 time and 2 times supporting parameters of the flexible support, which provides more rational choices for designing the flexible support structure and parameters.
* Appropriately increasing the values of supporting stiffness can effectively improve the supporting capacity of the winding rotor, but it will increase the vibration amplitude of the system.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## 2 Materials and Methods
### Methods
#### 2.2.4 Summary of the Heading Estimation Algorithm  
In summary, the algorithm for determining the heading angle in real time has the following structure:  
1. acquire the planar acceleration data in NED frame: \(a_{N}(t)\), \(a_{E}(t)\);
2. exploit an available step identification system, such as the one provided by the PLL, that provides the time between each single step \(T_{k}\);
3. for each step detected \(t^{*}_{k}\): 1. compute the Fourier coefficients (\(\alpha_{N,k},\alpha_{E,k},\beta_{N,k},\beta_{E,k}\)) from which it is possible to calculate \[A_{k} =\beta_{N,k}^{2}+\beta_{E,k}^{2}-\alpha_{N,k}^{2}-\alpha_{E,k}^{2}\] \[B_{k} =\alpha_{N,k}\beta_{N,k}+\alpha_{E,k}\beta_{E,k}\] 2. calculate \(t^{*}_{k}\): \[t^{*}_{k}=\frac{T_{k}}{4\pi}\mathrm{atan}\bigg{(}\frac{B_{k}}{A_{k}}\bigg{)}\] 3. if \(t^{*}_{k}<0\) update \(t^{*}_{k}\) adding a quarter of period: \(t^{*}_{k}=t^{*}_{k}+T_{k}/4\) (_atan_ function will give nearest solution);
4. check that the second derivative of the acceleration module calculated in \(t^{*}_{k}\) is less than zero to be sure that refers to a maximum point; otherwise update \(t^{*}_{k}\) adding a quarter of period: \(t^{*}_{k}=t^{*}_{k}+T_{k}/4\);
5. compute the heading angle \(\theta_{k}\): \[\theta_{k}=\mathrm{atan}2(a_{N}(t^{*}_{k}),a_{E}(t^{*}_{k}))\]  
**Remark 1**.: _Notice that the values of the integrals (13), that correspond to the Fourier coefficients, assume a valid meaning only at the end of each step, but nonetheless need the information relating to the time taken to carry out the step to compute sine and cosine functions. For this reason, it was decided to provide the time elapsed on the last step to estimate the Fourier coefficients at the current step. This approximation does not dramatically affect the heading angle estimation due to the regularity of the step frequency._  
**Remark 2**.: _This heading calculation does not take into account the difference between magnetic north and true north (the direction along a meridian towards the geographic North Pole). This difference is called magnetic declination and varies depending on the position on the Earth surface and changes over time. By convention, declination is positive when magnetic north is east of true north, and negative when it is to the west. The magnetic declination in a given area may change slowly over time, possibly as little as 2-2.5 degrees every hundred years, so it could be considered constant for this implementation. A good implementation requires an evaluation of the declination angle when the application starts in order to achieve information about the place where the smartphone is. A website like [[https://www.magnetic-declination.com](https://www.magnetic-declination.com)]([https://www.magnetic-declination.com](https://www.magnetic-declination.com)) (access date: 15 December 2021) can provide this information, in Florence, Italy the declination is +3.20 degrees._  
Figure 7: Rotated ellipse and planar acceleration.
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 5 Experiments
### Experiments and Results Analysis
#### 5.3.2 Experiments on the Public Dataset TrashNet
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Categories/Indicators** & **DenseNet121** & **InceptionV3** & **MobileNetV2** & **ResNet50** & **Ours** & **Support** \\ \hline \multirow{3}{*}{Cardboard} & Precision & 0.99 & 0.99 & 0.91 & 0.97 & **1.00** & \\  & Recall & 0.91 & 0.96 & 0.93 & 0.86 & **1.00** & 80 \\  & F1 & 0.95 & 0.97 & 0.92 & 0.91 & **1.00** & \\ \hline \multirow{3}{*}{Glass} & Precision & 0.98 & 0.96 & 0.86 & 0.90 & **1.00** & \\  & Recall & 0.99 & 0.92 & 0.95 & 0.96 & **1.00** & 100 \\  & F1 & 0.99 & 0.94 & 0.90 & 0.93 & **1.00** & \\ \hline \multirow{3}{*}{Paper} & Precision & 0.93 & 0.97 & 0.94 & 0.90 & **1.00** & \\  & Recall & **0.97** & **0.97** & 0.86 & 0.93 & **0.97** & 119 \\  & F1 & 0.95 & 0.97 & 0.89 & 0.92 & **0.99** & \\ \hline \multirow{3}{*}{Plastic} & Precision & **0.97** & 0.91 & 0.87 & 0.88 & **0.97** & \\  & Recall & 0.98 & 0.97 & 0.86 & 0.87 & **1.00** & 97 \\  & F1 & 0.97 & 0.94 & 0.86 & 0.88 & **0.98** & \\ \hline Accuracy & - & 0.96 & 0.95 & 0.89 & 0.91 & **0.99** & 396 \\ \hline \multirow{3}{*}{MA} & Precision & 0.97 & 0.96 & 0.89 & 0.91 & **0.99** & \\  & Recall & 0.96 & 0.95 & 0.90 & 0.91 & **0.99** & 396 \\  & F1 & 0.96 & 0.95 & 0.89 & 0.91 & **0.99** & \\ \hline \multirow{3}{*}{WA} & Precision & 0.97 & 0.96 & 0.90 & 0.91 & **0.99** & \\  & Recall & 0.96 & 0.95 & 0.89 & 0.91 & **0.99** & 396 \\ \cline{1-1}  & F1 & 0.96 & 0.95 & 0.89 & 0.91 & **0.99** & \\ \hline \hline \end{tabular}  
* The best results are marked in bold.  
\end{table} 
Table 7: Evaluation report of the five models on the public TrashNet dataset.
# Requirements for Dental CAD Software: A Survey of Korean Dental Personnel
## 1 Introduction  
Digital dental workflows, which are more commonly used for manufacturing dental prostheses than conventional workflows [1; 2; 3], typically use a three-dimensional (3D) scanning process to produce virtual casts and dental computer-aided design and manufacturing(CAD/CAM) processes to create dental prostheses [4; 5; 6]. Among these processes, the design process is typically performed using dental CAD software [7; 8; 9; 10], and the final prostheses are developed based on virtual casts. These CAD processes are performed by dentists and dental hygiists in clinics [11; 12; 13; 14; 15] and dental technicians in laboratories to manufacture temporary and permanent dental prostheses, respectively [16; 17; 18]. Therefore, the efficacy of CAD software can be theoretically improved by configuring it to the environment and purpose, although, to the best of our knowledge, no studies have examined this.  
Previous evidence on dental CAD software suggests that learning efficacy can differ according to the type of software used and dental personnel [19; 20; 21], with the working time varying between the user interface/user experiences and the parameter settings for each type of software [19]. Moreover, factors such as the clinical experience and computer literacy of dentists, technicians, and students play a significant role in their ability to learn the design processes [20; 21]. The addition of artificial intelligence (AI)-based functions in CAD software [22; 23; 24] further highlights the importance of considering the variety of factors that can affect learning.  
In the digital workflow for manufacturing dental prostheses, various types of dental CAD software have been reported to affect the quality of dental prostheses [25]. In addition, AI functions are being applied during the CAD work process to improve the design results of dental CAD software [26]. However, according to survey results, dentists still lack understanding of the digital workflow for manufacturing dental prostheses [27], and predental students lack education on overall digital dentistry, including CAD software [28]. In addition, dental personnel, including dentists, need to understand various functions of dental CAD software to plan treatment appropriately for patients [29]. Therefore, through the present study, it is necessary to confirm the perceptions of various dental personnel about the functions required or considered important for dental CAD software.  
Standards for dental CAD software released by various manufacturers are still ambiguous regarding the criteria dental personnel should consider when purchasing such software. Moreover, the information required by various dental personnel remains insufficient for the development and advancement of dental CAD software. Therefore, this study aimed to evaluate the needs of dental personnel in terms of dental CAD software. The null hypothesis was that the individual survey items in each category would not differ in importance.
## 5 Results and Discussion
### MDDA with Receiver-Only Scenario  
In this scenario, the receiver solution was subjected to the MDDA and then compared with the solution obtained from all visible satellites. The horizontal RMSE improved by 13%, while the 3D position RMSE experienced a significant improvement of 43% over the the solution from all visible satellites based on the Google weighting matrix. To further explore the challenges, a low-cost GPS receiver was employed, limiting the maximum number of accessible channels to 10. Instead of utilizing all observed satellites on the horizon, various satellite selection (SS) approaches were examined to assess the algorithm's performance. The results, presented in Table 2, illustrate the efficacy of the MDDA compared to alternative algorithms. In terms of the 2D position RMSE, the MDDA demonstrated a 12.5% improvement over the EL-based algorithm and a 8.7% improvement over the CNR-based algorithm. Similarly, for the 3D position RMSE, the MDDA showcased a 5.5% improvement over the EL-based algorithm and about 4% improvement over the CNR-based algorithm. It is noteworthy that these improvements were achieved solely utilizing the GPS constellation as the data source.  
In Figure 7, the visibility of all satellites and the eliminated satellites using MDDA during samples of epochs are shown.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **2D Position** & **3D Position** \\ \hline \multicolumn{3}{c}{All Visible Satellites} \\ \hline All Satellites & 4.58 & 11.89 \\ \hline MDDA-Based & 3.96 & 6.78 \\ \hline \multicolumn{3}{c}{**Selected Satellites According to Max. Number of Rx Channels (10 Satellites)**} \\ \hline
10 EL-Based & 5.67 & 7.59 \\ \hline
10 CNR-Based & 5.43 & 7.40 \\ \hline
10 MDDA-Based & 4.96 & 7.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Receiver-only 2D and 3D position RMSE in meters.  
In Figure 8, the satellite visibility chart for the trajectory is presented.  
Figure 8: Satellite visibility chart.  
Figure 7: Satellite visibility and elimination.
## 4 Experiments
### Optical Effects of Various Simulation Attacks  
We show the results of simulation attacks in this part. Various attacks simulated for the attack layer are briefly explained. These distortions are generally classified into two categories: normal and special attacks.  
Normal attacks (NA). (A). JPEG (parameters: 30, 50 and 60). Photographs are usually saved in a JPEG compression after being shot, and JPEG compression will cause image loss to the captured photographs. (B). Noise layer. (Gaussian noise: 1, 2, 3, \(\ldots\), 10). Image noise represented as random blobs on a smooth surface can severely affect image quality. The intensity of the noise depends on exposure time, camera settings and the temperature. (C). Cropping attack (parameters: 0.06, 0.11, 0.16, 0.26 and 0.55). Only some images on the display will be captured as full images due to the shooting environment. (D). Blurring (parameters: 1, 1.2, 1.6, 2 and 2.8). The inconsistent bending of light leads to blurred images, and motion blur and defocus blurs are the common blur types of blur that degrade image quality. (E). Rotation (parameters: \(-\)5, \(-\)2, 5, 8 and 10), scaling (parameters: 0.25, 0.5, 1.25, 1.5 and 2) and translation (RST) distortions. RST distortions are common traditional geometric attacks. (F). Brightness and contrast (brightness = 0.3, contrast = 1.5). The whole range of tones in the image will rise or fall accordingly when the brightness is corrected. Midtones are eliminated and images will present a high percentage of dark or black and white or highlights with minimal midtones when the contrast is high. (G). Sharpening (1, 4, 12, 25 and 30). Sharpening is a distortion that increases the apparent sharpness of an image.  
Special attacks (SA). The image suffers from space conversion processes and lead to a combination of strong attacks, such as perspective deformation, moire (U-Net), lens distortion, visible watermark (parameters: 1, 3, 9, 12, 15 and 16), light (brightness and contrast), color changing (hue = 0.1, luminance = 0.5) and display distortion (scaling), when an image displayed on the screen is shot.  
We provide attacked images, the specific results of the attack are illustrated in Figure 12.  
Figure 11: PNSR of the moiré network.