esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Studies
### Optimization Results  
Since the demand function during the former 14 days of the pre-sale period is a continuous function, the last day often presents a breakpoint in the ticket-purchasing rate; so, we divide the simulation into two stages. The first stage is the former 14 days of the pre-sale period, and the second stage is the 15 th day.  
Set the particle population size popsize = 100, the maximum number of iterations should be 500, the inertia factor \(\varpi=0.8\), the learning factor \(\mathrm{c_{1}}=1.49\), and \(\mathrm{c_{2}}=1.49\). We use MATLAB R2016B software to solve the model. Figure 8 shows the iteration process of the solution. Convergence is reached in less than 200 iterations in the first stage of the pre-sale period, while convergence is reached within 100 iterations in the second stage of the pre-sale period. At this time, the objective function value does not change, and the optimal solution is obtained. The total revenue of the train during the pre-sale period is CNY 601,881.  
The ticket price change in each OD section during the pre-sale period is shown in Figure 9, where the blue line is the fixed ticket price, and the red line is the optimal ticket price of each period during the pre-sale period.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**OD** & & & & & & \\
**Section** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **Total** & **Error** \\ \hline (1,2) & 10 & 23 & 65 & 26 & 124 & 2.48\% \\ (1,3) & 20 & 50 & 120 & 31 & 221 & 2.79\% \\ (1,4) & 51 & 118 & 259 & 89 & 517 & 1.97\% \\ (2,3) & 11 & 47 & 39 & 32 & 129 & 3.2\% \\ (2,4) & 15 & 44 & 75 & 31 & 165 & 3.13\% \\ (3,4) & 11 & 38 & 35 & 42 & 126 & 3.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical table of average value and error of ticket-purchasing demand.  
Figure 8: Schematic diagram of the iteration process for solving the model.  
The ticket prices and number of ticket sales of each period during the pre-sale period in each OD section, i.e., the optimization results of the model, are shown in Table 8.
## 2 Materials and Methods
### Electrochemical Performance  
Electrochemical analyses of the anodes were performed using potentiostat instrumentation (Autolab PGSTAT302N, Utrecht, The Netherlands), capable of measuring both linear polarization and cyclic voltammograms in a solution of 3.5% NaCl electrolytes at room temperature. The experimental setup included a half-cell configuration with the anode as the working electrode, Ag/AgCl as the reference electrode, and a platinum wire as the counter electrode. To ensure accurate measurements, the electrolyte solution was saturated with oxygen for 30 min prior to the tests. Polarization measurements were made at a scan rate of 10 mV.s\({}^{-1}\), with a potential range set to 25 mV relative to the open circuit potential (OCP), allowing for determination of the steady-state OCP value. Furthermore, the cathode materials were also subjected to cyclic voltammetry (CV) analysis using the same equipment. CV tests were carried out at a scan rate of 10 mV.s\({}^{-1}\), covering a potential range of \(-1\) V to +1 V in a 3.5% NaCl electrolyte solution saturated with oxygen, and similarly under nitrogen-saturated conditions to compare the effects of different gases on electrochemical behavior. In addition to these electrochemical characterizations, a battery analyzer (BST with 8 channels, Richmond, CA, USA) was utilized to assess discharge time and quality, comprising both anode and cathode materials in a 3.5% NaCl electrolyte solution. The current density was maintained at 2 mA cm\({}^{-2}\) throughout the discharge analysis, allowing for evaluation of the battery performance under controlled conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bath Composition** & \begin{tabular}{c} **ZnSO4** \\ **mol L\({}^{-1}\)** \\ \end{tabular} & \
\begin{tabular}{c} **C6H5 Na5O7** \\ **mol L\({}^{-1}\)** \\ \end{tabular} &
\begin{tabular}{c} **C10H16N2O8** \\ **mol L\({}^{-1}\)** \\ \end{tabular} \\ \hline First route & 0.2 & 0.2 & - \\ Second route & 0.2 & 0.2 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electrolyte bath composition for electrodeposition.
## 3 Methods of Determining of Image Number Lines
### The Analysis of Obtained Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Source of Reveal Emission**} & \multirow{2}{*}{**Duration of**} & \multicolumn{2}{c}{**Frequency of**} & \\  & **Displayed Image** & & **Reveal Signal** & **Bandwidth** & **Number Lines** \\ \hline Display of VoIP terminal—menu in form & & & & \\ of icons & Unknown & 800 MHz & 20 MHz & 528 \\ Display of HP laser printer & Unknown & 235 MHz & 10 MHz & 288 \\ M477 fdm—menu in form of text & & & & \\ Display of HP laser printer & Unknown & 235 MHz & 10 MHz & 288 \\ M477 fdm—menu in form of text & & & & \\ Display of HP laser printer M507—menu in form of icons & Unknown & 392 MHz & 10 MHz & 266 \\ \multirow{2}{*}{HDMI standard} & 1280 \(\times\) 1024/60 Hz & 1334 MHz & 50 MHz & 1066 \\  & 1280 \(\times\) 1024/60 Hz & 200 MHz & 100 MHz & 1125 \\ \multirow{2}{*}{DVI standard} & 1280 \(\times\) 1024/60 Hz & 1334 MHz & 50 MHz & 1066 \\  & 1280 \(\times\) 1024/60 Hz & 740 MHz & 50 MHz & 628 \\ \multirow{2}{*}{DVI standard} & 1920 \(\times\) 1080/60 Hz & 1775 MHz & 100 MHz & 1125 \\  & 1280 \(\times\) 1024/60 Hz & 1805 MHz & 100 MHz & 1066 \\ \multirow{2}{*}{VGA standard} & 640 \(\times\) 480/60 Hz & 365 MHz & 50 MHz & 525 \\  & 800 \(\times\) 600/60 Hz & 450 MHz & 20 MHz & 628 \\ \multirow{2}{*}{VGA standard} & 1024 \(\times\) 768/60 Hz & 558 MHz & 10 MHz & 806 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of sources of reveal emissions used in the tests. effective in determining the number of lines of the image obtained in the electromagnetic infiltration process. The choice of an effective method in the determining the number \(B_{Corr}\) of lines in the image reconstructed in the electromagnetic infiltration process was based on calculating the differences between the maximum and minimum values of individual measures calculated for images with the number of lines equal to \(B\) (Table 7). In order to improve the correctness of the selection, the assessment of the analysed methods was also based on the variance \(\sigma^{2}\) values of individual measures calculated as a function of parameter \(B\) (Table 8). The obtained results allowed to indicate methods III and VI as effective methods in the determining the correct \(B_{Corr}\) number. When analyzing the values of the variance \(\sigma^{2}\) and the mentioned differences of the maximum and minimum values, one may wonder whether the methods I, IV, and VII cannot also be used to determine the number of lines in the image. The values of the measures under consideration clearly indicate, through the maximum value, the correct number \(B_{Corr}\). However, the distance between the minimum values and the maximum may, according to the authors, be insufficient in practice for other images reconstructed from the emission revealing signals.
## 3 Results
### Accuracy Assessment for Rigid + Non-Rigid Registration  
As shown comparing Figures 5 and 7, the use of elastic registration following the rigid step improves the performance in each condition. Furthermore, registration performance metrics considering both the rigid and the non-rigid steps, evaluated with different learning rates and mesh sizes, were systematically better in the IM group. Kruskal-Wallis H tests showed that there were statistically significant differences in both DSI (chi-squared = 49.55, \(p<\)0.05, df = 13) and MSD (chi-squared = 49.32, \(p<\) 0.05, df = 13) but not in HD (chi-squared = 8.50, \(p=\) 0.81, df = 13). The analysis of ICC coefficients highlights a weak agreement (\(<\)0.5) between IM and NM conditions. \(P\)-values of pairwise comparisons and ICCs are shown in Figure 8.  
Accuracy indexes of the NM group are generally better for the 3\(\times\)3\(\times\)3 mesh, regardless of the learning rate, and tend to worsen as the mesh grows denser.  
In the IM group, on the other hand, the performance of the deformable registration is better with a thicker mesh and a larger learning rate. The best values of DSI and MSD are found for the IM with learning rate 4 and mesh size 8,8,8 (IM_lr4_ms888) (DSI: 0.862 \(\pm\) 0.026; MSD: 0.784 \(\pm\) 0.136 mm), while HD does not show substantial differences among the registration schemes.  
Accuracy indexes for each individual muscle (Figure 9) show the same trend.  
The images of a sample subject after all registration steps are shown in Figure 10. Multimodal acquisitions are shown registered on their intra-session T1-w scan.  
Figure 6: DSI, MSD (mm), and HD (mm) for each individual considered muscle (Gluteus Maximus, Gracilis, Sartorius, Tensor Fasciae Latae) for rigid registration schemes.  
Figure 8: Statistical significance expressed as p-values of pairwise comparisons using the Wilcoxon rank sum test for **(a)** DSI and **(b)** MSD and intraclass correlation coefficients (ICCs) for **(c)** DSI and **(d)** MSD of the non-rigid registration step considering the 4 muscle ROIs together.  
Figure 7: DSI, MSD, and HD values of the IM, NM elastic registration schemes. The accuracy indexes were evaluated considering the 4 muscle ROIs together.  
Figure 10: T1-w, T2 map, fat fraction, fractional anisotropy, and mean diffusivity map after intra-session rigid registration and rigid and non-rigid registration steps of a sample subject.  
Figure 9: DSI, MSD (mm), and HD (mm) for each individual considered muscle (Gluteus Maximus, Gracilis, Sartorius, Tensor Fasciae Late) for non-rigid registration schemes.
## Materials and Methods
### Image Segmentation and Feature Extraction  
#### 2.3.1 Otsu-Grabcut Algorithm  
At present, the most widely used image segmentation algorithm is the threshold segmentation algorithm, which includes global threshold segmentation and local threshold segmentation. The Otsu method, also known as the maximum between-class variance method, was proposed by the Japanese scholar [PERSON] [27] in 1979. The original Otsu is an adaptive global threshold segmentation method. The algorithm calculates the best threshold in the image and improves the image. The distinction between the background and target pixels is maximized, so as to achieve the purpose of extracting the target in the image. However, for infrared images, the grayscale distribution of the image is more complicated, and the traditional Otsu method is not good at segmentation. Therefore, some scholars have proposed an improved Otsu algorithm based on a two-dimensional histogram. As the horizontal axis, the average gray value of the neighboring pixels is used as the vertical axis to form a two-dimensional histogram. When searching for the threshold, the spatial correlation information of the image is fully used. Therefore, the algorithm has a good segmentation effect for infrared images.  
Figure 3: Flowchart of the wavelet threshold infrared image denoising algorithm (WTD-IFA) algorithm.  
The Otsu algorithm based on the two-dimensional histogram uses the neighborhood deviation \(m(x,y)\) to represent the deviation between the gray value of the pixel and the average gray of the neighborhood. The calculation formula is shown as follow:  
\[m(x,y)=\frac{\sqrt{\left|f(x,y)-v(x,y)\right|}}{v(x,y)}, \tag{16}\]  
where \(f(x,y)\) is the gray value of pixels, \(v(x,y)\) is the average gray level of all pixels in the neighborhood with \((x,y)\) as the center pixel.  
However, the segmented image is a binary image, and a lot of information in the original image is lost. Therefore, a new algorithm combining the two-dimensional Otsu algorithm and Grabcut extraction algorithm is proposed to process the infrared image.
# Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning
## 2 Materials and Methods
### Proposed Method  
The proposed method consisted of two main CNNs: a detector that we called Periapical Root Area Detection Model, and a classifier that we called Periapical Lesion Classification Model. Our model accepts a panoramic radiograph as input and outputs the location of the detected periapical lesion on the given panoramic image. The detector localized PRAs using a bounding-box-based object detection model, while the classifier classified the extracted PRAs as PL or H. We developed our model on GoogleColab PRO + notebook using Python.  
The overall proposed system workflow is shown in Figure 1. The entire panoramic radiograph is used as an input to the presented system. The PRA Detection Model with Faster R-RCNN processes the panoramic radiograph to detect and define the boundaries of each PRA by a bounding box. The proposed system then crops all predicted bounding boxes, which are the PRAs extracted by the system, and then feeds them to the PL Classifier with Inception v3. The PL Classifier in turn classifies each cropped PRA as Periapical Lesion (PL) or Healthy (H). The system finally outputs in red only the bounding boxes corresponding to Periapical Lesions with a confidence score. A confidence score is a percentage calculated by the AI, indicating how sure the AI is that the output is correct.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Type/Format**} & **Offline Preparation** & \multirow{2}{*}{**Online Preparation**} & \multirow{2}{*}{**Resolution**} \\ \hline \multirow{3}{*}{Detector} & Dental panoramas & & Normalize & \\  & JPG images & Cropping & Standardize & \\  & & & Random Flipping & \\ \hline \multirow{3}{*}{Classifier} & \multirow{3}{*}{PRAs JPG images} & & Normalize & \\  & PRAs JPG images & Cropping & Random rotation & \(75\times 75\) \\ \cline{1-1}  & & & Random zoom & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Applied Data Preprocessing Techniques.
## 4 Potential Application of the Study Results  
The studies conducted have demonstrated that ambient temperatures may influence the removal of contaminants transported with stormwater from urban areas through permeable treatment zones. Analyzing the results of conducted tests, the contaminants load _CL_ (milligrams of added contaminants per grams of reactive material) treated to a certain level of contaminants concentration was determined (Table 5). The permissible concentrations of contaminants transported with sewage to water or to soil were assumed, 0.5 mg Cu/L, 2 mg Zn/L, 10 mg NH\({}_{4}\)-N/L and 5 mg PO\({}_{4}\)-P/L, respectively. Values of the _CL_ may have a practical significance. Although at higher loads the maximum sorption capacity of the materials was not exceeded, the equilibrium concentration exceeded the permissible level. The conclusions drawn from the analysis of _CL_ values are consistent with conclusions based on changes of \(Q_{max}\) values. The highest impact of the temperature decrease was observed for Cu removal on activated carbon and Zn removal on limestone sand and the lowest for NH\({}_{4}\)-N removal on diatomite, limestone sand, halloysite and activated carbon. The values of \(CL\) and \(Q_{max}\) determined at low and high temperatures may be helpful during design stage of the dimensions and maintenance frequency of treatment zone operated in cold region.  
Moreover, the studies confirmed that contaminants present in stormwater from urbanized areas cannot be removed with one universal reactive material. Only the use of a mixture of several reactive materials can ensure the removal of a larger spectrum of contaminants. The conclusion is consistent with those presented by other researchers [17; 18; 20; 29; 30]. The obtained test results indicate that the highest removal efficiency of Cu and PO\({}_{4}\)-P has activated carbon, of Zn--limestone sand, and of NH\({}_{4}\)-N--zeolite.
## 3 Experimental Results and Discussion
### Skeleton Curves  
The skeleton curves of all specimens are symmetrically distributed under tension and compression, which can better show the elastic stage, the strengthening stage, and the plastic stage of the specimen. The skeleton curves of Specimens L1, L2, and L6 have no plastic stage, as shown in Figure 11, which was induced by the brittle failure of the link flange-to-end plate weld and because the plasticity of the specimens was not fully developed. In contrast, the skeleton curves of Specimens L3, L4, L5, L7, and L8 with welding access holes are more distinct in each stage. Table 5 summarizes the initial stiffness, the yield bearing capacity \(F_{y}\), the maximum bearing capacity \(F_{u}\), the yield displacement \(\Delta_{y}\), the ultimate displacement \(\Delta_{u}\), and the plastic rotation angle \(\gamma_{\mathrm{P}}\) of the specimens. The bearing capacity of L8 was larger than that of L5 and L7, and the bearing capacity of L5 and L7 were larger than that of L3 and L4. It was explained that the length ratio \(\zeta\) was smaller and the bearing capacity was larger. In addition,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **L1** & **L2** & **L3** & **L4** & **L5** & **L6** & **L7** & **L8** \\ \hline \(E\) & 0.91 & 1.00 & 1.59 & 1.98 & 1.57 & 0.78 & 1.68 & 1.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy dissipation coefficient.  
Figure 10: Calculation diagram of energy dissipation coefficient E.  
the stiffener spacing for Specimens L3 and L4 was 200 mm and 150 mm, respectively, and the peak load difference between them was 13.6%. The stiffener spacing for Specimens L5 and L7 was 200 mm and 150 mm, respectively, and the peak load difference between them was 9.2%. Nevertheless, the yield loads of Specimens L3 and L4 are approximately the same, as are those of Specimens L5 and L7. It is shown that stiffener spacing has a greater influence on the peak load than on the yield load. This occurs because of the comparatively close stiffener arrangement of the specimen, and the buckling of the web can be limited as the peak load is increased. The bearing capacities of L1 and L2 were less than those of L3 and L4, and the bearing capacity of L6 was less than that of LB5, LB7, and LB8, respectively, which shows that the welding access hole is conducive to delaying the damage of specimens.  
The initial stiffness of Specimens L1, L2, and L6 was not considered because of the brittle failure in the elastic stage. According to the skeleton curve, the other specimens underwent the elastic stage and the linear strengthening stage. The initial stiffness of L8 was larger than that of L5, L7, L3, and L4, which shows that the smaller the length ratio \(\zeta\), the larger the initial stiffness. The skeleton curve trends of both L3 and L4 are the same, as were those of L5 and L7. It is clear that the stiffener spacing has little effect on the initial stiffness.
## 2 Related Work  
### Object Detection in UAV Image  
The field of target detection has seen significant advancements; in particular, convolutional neural networks (CNNs) have surpassed traditional object detection methods. CNN-based object detection schemes have been dominated by anchor-based detectors, which can generally be divided into one-stage object detection algorithms based on regression analysis [11, 12, 13, 14] and two-stage object detection algorithms based on region suggestion [15, 16, 17]. One-stage methods simultaneously complete the classification and localization tasks, making them more efficient than two-stage methods. Meanwhile, two-stage methods refine anchors several times, generating more accurate results than the former, but the training process is more complex and time-consuming [18]. These algorithms utilize the topmost layer of a CNN to recognize objects at multiple scales, which imposes a great burden for a single layer.  
Due to the large variations in object size in UAV images, building feature pyramids based on image pyramids is a basic solution. By featuring each level of an image pyramid,online hard example mining (OHEM) produces a multi-scale feature representation in which all levels have rich semantic features, including the high-resolution ones [19]. However, in real applications, this approach is impractical because of its long inference time [20]. The SSD is one of the first attempts employing ConvNets to produce a feature pyramid, which achieves faster detection than other mainstream object detection methods [2]. Recently, numerous studies have optimized feature pyramid networks, allowing them to outperform other methods in the field of target detection based on UAV images [21; 22].  
In order to mitigate the interference caused by complex backgrounds in UAV images, attention mechanisms are widely used in detection tasks. One notable application of attention mechanisms in UAV target detection is in the development of transformer-based models, such as the foreground enhancement attention Swin transformer network (FEA-Swin), which integrates contextual information into the Swin transformer backbone to improve the accuracy of dense object detection in UAV images [23]. Speeded up robust features (SURFs) is a proposed architecture for feature selection using foveated images that is guided by visual attention tasks and that reduces the processing time required to perform these tasks [24]. The UAV-YOLOv8 model incorporates an attention mechanism called BiFormer to optimize the backbone network, enhancing the model's focus on critical information and improving the detection of small objects in UAV aerial photography scenarios [25]. Similarly, the weather-domain transfer-based attention YOLO model employs an attention mechanism to improve the detection and classification of insulator defects in UAV images, demonstrating the versatility of attention mechanisms in various detection tasks [26].  
Overall, the integration of attention mechanisms in UAV target detection models has enhanced detection accuracy and efficiency across various applications and environments.