esa-sceva/satcom-chunk-collection huggingface dataset:
# MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
## 3 Experiment and Analysis
### Datasets
#### 3.1.1 Peking University PCB Defects Dataset  
The Peking University PCB dataset contains 1386 images, each exhibiting high clarity and detail. To better adapt the model to real-world industrial environments, the original dataset underwent several preprocessing operations. These operations include brightness correction, which involved randomly adjusting the image brightness to 0.5 to 1.5 times the original brightness to simulate varying lighting conditions in industrial settings; random noise addition, which employed Gaussian noise with a mean of 0 and a standard deviation between 0.05 and 0.15, as well as salt-and-pepper noise with a density of 0.05 to 0.2; rotational transformations conducted within the range of \(-\)30 to 30 degrees in 5-degree increments; and horizontal and vertical mirroring, which involved simple mirror flipping of the images. As a result of these processes, a total of 3000 images with a resolution of 1024 \(\times\) 1024 were generated. To ensure both effective training and robust generalization, a scientifically sound data splitting strategy was adopted: 2000 images were randomly selected as training samples, 500 images were used as a validation set for tuning hyperparameters, and the remaining 500 images were reserved as a test set for the final evaluation of model performance.
# Mobility Prediction of Mobile Wireless Nodes
## 7 Discussion and Analysis  
This section presents a discussion of the results of the implementation of ML classifiers using datasets generated by different mobility models. The datasets indicate node location as well as previous and current APs. Current APs were determined by choosing APs nearest to the mobile node until the target location was reached. This experiment was evaluated using a classifier accuracy performance metric, that is to say the score function in the Scikit-learn library.  
Figure 9 summarizes the total average accuracy of all six classifiers in all sixteen scenarios using four mobility models. As previously noted, the prediction accuracy in random mobility models (i.e., RW and RD) fluctuated when the number of APs increased, with accuracy values starting from 39% and never exceeding 81%. On the contrary, the patterns of the GM and RSSGM models were constant and had high ranges of accuracy. The accuracy of the GM model reached 89% with the LDA classifier and fell to 66% with the LR model. However, the last mobility model tested, i.e., RSSGM, achieved the highest accuracy, hitting 98% with the DT classifier, 94% with GNB, 92% with KNN, SVM, and LDA, and 80% with the LR algorithm.  
Figure 10 illustrates the mobility models' accuracy when the number of APs increased to indicate the classifier's behavior in a high-scale motility network. RSSGM and GM had high prediction accuracy in all scenarios, reaching 98% and 89%, respectively, whereas the RD and RW models had low accuracy at 39% and 58% on average and going to 79% and 82%, respectively. However, the DT model had an excellent level of prediction accuracy with different numbers of APs, especially when using the RSSGM model, where it was 99% accurate with five APs. The RD had insignificant values, especially with the LR classifier, where it had only 39% accuracy.  
To increase the confidentiality of the results, we reclassified the RSSGM model for all algorithms with a different number of steps to compare the classification behavior when the user had to move more or fewer steps than average. Therefore, we added two experiments (i.e., 2000 and 10,000 steps) over the previous scenarios, and then collected and compared obtained results with 5000 steps. The results show a positive correlation between step count and classification accuracy, based on expanding the learning and understanding between user movement and classifier when the number of trials increases. Moreover, all results of applying classifiers based on the RSSGM model are considered to have very high accuracy performance. Figure 11 shows the comparison between all scenarios.  
Figure 9: Total average accuracy.  
Figure 11: Average accuracy with a different number of Steps.  
Figure 10: Example of different mobility model patterns using different number of APs and ten centroids. (**a**) Classifier accuracy with 5 APs. (**b**) Classifier accuracy with 10 APs. (**c**) Classifier accuracy with 15 APs. (**d**) Classifier accuracy with 20 APs.
# Nonlinear Wave-Induced Uplift Force onto Pipelines Buried in Sloping Seabeds
## 5 Conclusions  
In this paper, the nonlinear wave-induced uplift force onto pipelines buried in sloping seabeds is studied using a two-dimensional numerical model. Compared with previous investigations, the sloping seabed is considered in this study. Based on the numerical results, the following conclusions can be drawn:  
1. The nonlinear wave-induced pore pressure distribution near pipelines buried in sloping seabeds is inclined, and the inclination direction of the pore pressure distribution corresponds to the seabed inclination.
2. The nonlinear wave-induced uplift force onto pipelines buried in sloping seabeds is positively correlated with wave height and period, but negatively correlated with water depth.
3. The nonlinear wave-induced uplift force onto pipelines has the trend of first positive and then negative correlation with soil permeability, while it keeps negative correlation with Poisson's ratio and Young's modulus.
4. A large burial depth can reduce the nonlinear wave-induced uplift force onto pipelines, while a large pipeline diameter can enhance it due to the \"shielding effect\" of the structure.
5. Large slope angles can enhance the nonlinear wave-induced uplift force onto pipelines, and the shallower the water depth, the more likely this enhancement effect is to weaken.  
Conceptualization, L.D.; methodology, L.D. and B.Z.; software, L.S. and B.Z.; validation, M.F.; formal analysis, B.Z.; investigation, L.D.; resources, L.D.; data curation, L.D.; writing--original draft preparation, B.Z. and L.S.; writing--review and editing, L.D.; visualization, M.F.; supervision, D.W.; project administration, D.W.; funding acquisition, L.D. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the financial support from the National Natural Science Foundation of China (52108268), Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education (SLK2021B03), Chongqing Natural Science Foundation (cstc2021 jcyj-msxmX1123) and Postdoctoral Project of Chongqing Human Resources and Social Security Bureau (2010010005785982). The authors declare no conflict of interest.  
Figure 20: Comparison of wave-caused uplift force applied to the pipeline between the new formula and numerical model.
## 2 Materials and Methods  
### Design of MO Isolator  
As shown in Figure 1, our MO isolator (Shenzhen University, Shenzhen, Guangdong, China) was formed by inserting two rectangular gyromagnetic ferrite sheets (red) into an ultra-wideband and high efficiency PCW (white). The PCW includes two triangle lattice photonic crystals (TLPCs) formed by Al\({}_{2}\)O\({}_{3}\) dielectric post arrays. The dielectric post arrays fixed with high accuracy by the upper (transparent) and under (blue) aluminous panels in order to guarantee the TLPCs' PBG characteristic. The two ferrite sheets are clung onto the inside of the upper and under panels, respectively, which keep a distance of \(d\) from the boundary of the PCW. Four flange interfaces were designed at each side of the PCW to connect with the testing device.  
Figure 1: Magneto-optical (MO) isolator based on the photonic crystals waveguide (PCW) and gyromagnetic ferrites.
## 3 Results
### Influence of GCPs Distribution  
The experimental results of evaluating the influence of the GCP distribution are shown in Figure 9. The box diagram represents the difference between the elevation of checkpoints and the elevation of the DSM extraction point. In the layout of one GCP, four tests (excluding K15) showed low variability, in which K10 was near the center of the region and the error was the smallest. In the experiments with different distributions of four GCPs, compared with the results of using K1, 6, 8, and 15, the error of using K2, 5, 9, and 12 was significantly reduced. The difference is that the distance between K2, 5, 9, and 12 decreased.  
Figure 8: M3C2 distance between the point cloud obtained by using different numbers of GCPs as control points and the reference point cloud (obtained by using all 16 GCPS). (**a–d**) Zero-, 3-, 6-, and 9-GCP and Gaussian distribution. Solid black dots indicate the location of GCPs. The vertical red line in the figure are zero error lines.  
Figure 9: Boxplots represent vertical errors obtained by seven layout schemes divided by the number of GCPs (the horizontal line represents the median, the lower part of the hinge represents the 25 th percentile, and the upper part represents the 75 th percentile). The whiskers represent 1.5\(\times\) IQR (interquartile range) in both directions. The black squares and circles represent the mean and outliers, respectively. Refer to the schematic diagram of different GCP distribution in Figure 4. 1 and k1 in 1-k1 represent the number and code of control points respectively, and others are similar.  
In addition, ArcGIS was used for differential calculations. The difference was assessed by subtracting the DSM produced using a uniform distribution of five GCPs and the DSM produced using the optimal distribution of 16 GCPs; the differential DSM resolution was 0.2 m (Figure 10a). The results show that when the distance from the nearest GCP increases, the DSM local vertical difference tends to increase. There are many scattered points in Figure 10b, indicating that the local DSM accuracy is not solely determined by the distance to the nearest GCP; therefore, a nonlinear curve is used for fitting.
## 4 Results  
### CSWGAN Evaluation  
We extensively evaluated the SR performance from different networks trained on both the simulation dataset and GF_Sen dataset.  
From Table 3, it can be seen that the network accuracy is generally higher when trained on the GF_Sen dataset compared to the simulation dataset, and the CSWGAN achieved the best results. Specifically, on the GF_Sen dataset, compared with the relatively excellent ESRGAN, the CSWGAN was competitive due to the combination of spatial-frequency-domain features, with an average improvement of 4.877 in the PSNR value and 0.181 in the SSIM value. This proved that our approach of restoring high-frequency information in images using a cascaded network structure is highly effective. By taking a comprehensive consideration of the results in Table 3 and Figure 5, we can find that the CSWGAN can produce acceptable super-resolution results compared to classical networks, especially in terms of texture structure. Finally, the CSWGAN combined the advantages of both the SGAN and WGAN, and the edge contour of the recovered image was clearer, the aliasing effect was effectively improved, and the overall tone of the image was closer to the HR image.  
In addition, due to the characteristics of remote sensing images, compared with small-sized image blocks, they are more likely to cover a larger range of scenes. Therefore, this study compared the reconstruction results of several classic networks in the GF_Sen and USC-SIPI datasets, under larger and even larger scales of scenes, as shown in Figures 6 and 7. It can be seen that the reconstruction results of the CSWGAN outperform other methods in both high-frequency information and visual perception.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Trained on Simulated Dataset**} & \multicolumn{3}{c}{**Trained on GF\_Sen Dataset**} \\  & **PSNR** & **SSIM** & **SAM** & **RMSE** & **PSNR** & **SSIM** & **SAM** & **RMSE** \\ \hline Bicubic & & & & 18.863 & 0.593 & 0.301 & 30.039 \\ SRCNN & 17.975 & 0.506 & 0.284 & 28.506 & 18.191 & 0.512 & 0.316 & 30.553 \\ ESPCN & 18.006 & 0.612 & 0.302 & 29.864 & 18.266 & 0.582 & 0.303 & 29.864 \\ ESRT & 19.092 & 0.461 & 0.219 & 24.484 & 20.287 & 0.669 & 0.191 & 24.484 \\ SRGAN & 18.654 & 0.611 & 0.385 & 36.675 & 19.217 & 0.639 & 0.372 & 36.168 \\ ESRGAN & 18.975 & 0.621 & 0.286 & 28.948 & 19.458 & 0.647 & 0.338 & 33.060 \\ CSWGAN & 23.350 & 0.853 & 0.192 & 17.781 & 24.335 & 0.828 & 0.178 & 15.356 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quality evaluation index results of different SR methods.  
Figure 5: Conf.  
Figure 5: Reconstruction results of different methods.
## 2 Extraction Methods
### Non-Conventional Extraction  
The major challenges of conventional solvent extraction include longer extraction time, the demand for costly and high purity solvent, evaporation of a large amount of solvent, low extraction selectivity, and the thermal decomposition of thermolabile substances [29]. To overcome these limitations, new and promising extraction techniques have been introduced. These techniques are considered non-conventional extraction techniques. Some of the promising techniques include ultrasound-assisted extraction, enzyme-assisted extraction, microwave-assisted extraction, and pulsed electric field assisted extraction [30]. Those techniques reduce solvent consumption and shorten the extraction time, while the extraction yields of anthocyanins are similar to or even higher than those procured with conventional methods. However, no single method is considered as standard for extracting bioactive phytochemicals. Due to the involvement of multiple factors in the non-conventional extraction method, factorial designs and response surface methodologies (RSM) are often used to optimize the extraction conditions. The anthocyanins extracted using RSM are mainly affected by solvent concentration, extraction time, and solvent/solid ratio [31]. The studies that used non-conventional extraction methods to extract anthocyanins from blackcurrants are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Condition** & **Efficiency 1** & **Reference** \\ \hline Microwave-assisted & pH 2, 10 min extraction time, 700 W & \(\uparrow\) 20\% & [32] \\  & microwave power & \(\uparrow\) 20\% & [32] \\ \hline Ultrasonic-assisted & 40 kHz frequency, 10 min extraction time, 70\% amplitude & \(\uparrow\) 20\% from freeze-dried samples, \(\rightarrow\) from frozen or over-dried samples & [33] \\ \hline Ultrasonic-assisted & 45 kHz frequency, 15 min extraction time, 90 \({}^{\circ}\)C & \(\rightarrow\) & [34] \\ \hline Ultrasonic-assisted & Ultrasonic bath, 40 min extraction time & \(\rightarrow\) & [35] \\ \hline Enzyme-assisted & \
\begin{tabular}{c} Pectinolytic enzyme preparations, \\ 30 min treatment, 60 \({}^{\circ}\)C \\ \end{tabular} & \(\rightarrow\) or \(\uparrow\) & [36] \\ \hline Enzyme-assisted &
\begin{tabular}{c} Pectinolytic enzyme preparations or \\ protease, 10 min treatment, 100 \({}^{\circ}\)C \\ \end{tabular} & \(\downarrow\) or \(\rightarrow\) & [37] \\ \hline Pulsed electrical field & 1318 V/cm and 315 pulses & \(\uparrow\) 6\% & [38] \\ \hline \hline \end{tabular}  
* Compared to the conventional extraction method performed in each study. \(\uparrow\) Indicates increased extraction yield. \(\rightarrow\) Indicates no change of extraction yield. \(\downarrow\) Indicates decreased extraction yield.  
\end{table}
Table 1: Non-conventional extraction methods of blackcurrant anthocyanin.
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 5 Experiment and Evaluation
### Dataset and Evaluation Matrix
#### 5.1.2 Evaluation Matrix  
The segmentation results from our proposed scheme are compared with ground truth images, which are segmented manually. The proposed scheme aims to classify transmission line icing pixels on ROI as true positive, false negative, or false positive, as represented by accuracy, precision, and recall. The system testing process is conducted using the confusion matrix. The confusion matrix can be interpreted as a tool that analyzes whether the classifier is good at recognizing tuples from different classes. The values of True Positive (TP) and True-Negative (TN) provide information when the classifier in classifying data is true, while False Positive (FP) and False-Negative (FN) provide information when the classifier is incorrect in classifying data. Table 2 is the confusion matrix used to determine TP, TN, FP, and FN. After obtaining the TP, TN, FP, and FN values, accuracy, precision, recall, and specificity can be calculated using Equations (31)-(34).  
Accuracy describes how often the model is classifying correctly. The accuracy can be calculated using Equation (31).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}. \tag{31}\]  
Precision describes the accuracy between the requested data and the predicted results provided by the model. To calculate precision, use Equation (32).  
\[Precision=\frac{TP}{TP+FP}. \tag{32}\]  
Recall, or sensitivity, describes the success of the model in retrieving information. Equation (33) can be utilized to compute recall.  
\[Recall=\frac{TP}{TP+FN} \tag{33}\]  
Specificity is used to measure the percentage of correctly identified negative data. To calculate specificity, use Equation (34).  
\[Specificity=\frac{TN}{TN+FN}. \tag{34}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Ground Truth**} \\ \hline  & & Transmission Line & Not Transmission Line \\ \hline \multirow{3}{*}{**Our Scheme**} & \multirow{3}{*}{Transmission line} & True Positive & True Negative \\  & & (TP) & (TN) \\ \cline{2-3}  & & & \\ \cline{2-3}  & & False Positive & False Negative \\ \cline{2-3}  & & (FP) & (FN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
Figure 19: A simulated ice scene image.
## Materials and Methods
### Constant Stress-Rate Test  
Rock strength generally increases as the applied stress rate increases. The constant stress-rate test utilizes this theory to relate the fracture strength and applied stress rate, and hence calculate subcritical crack growth parameters.  
The generalized stress intensity factor (\(K\)) is represented as:  
\[K=Y\sigma\sqrt{a} \tag{3}\]  
where \(Y\) is a geometric factor related to the crack geometry, \(\sigma\) is applied stress, and \(a\) is crack length.  
Using Equations (2) and (3) together with the mathematical expansion, the relation between the fracture strength and loading rate is explained as follows [27]:  
\[\log\sigma_{f}=\frac{1}{n+1}\log\dot{\sigma}+\log D \tag{4}\]  
where:  
\[\log D=\frac{1}{n+1}\log\!\left(\!\frac{2K_{c}^{2}(n+1)}{AY^{2}(n-2)}\sigma_{ i}^{n-2}\!\right) \tag{5}\]  
where \(\sigma_{f}\) is fracture strength, \(\dot{\sigma}\) is stress rate, and \(D\) is another subcritical crack growth parameter, which is a function of the fracture toughness (\(K_{c}\)), inert strength (\(\sigma_{i}\)), geometric factor (\(Y\)) and subcritical crack growth parameters (\(n\) and \(A\)).  
The inert strength can be determined by using high-speed stress rates or an inert environment.  
In this study, two kinds of stress rates, 0.02 MPa/s and 50 MPa/s, were employed to determine the subcritical crack growth parameter \(n\). In each loading rate, two to three compression tests were performed using four different pre-existing flaws geometries.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flaw Inclination Angle** & **30\({}^{\circ}\)** & **45\({}^{\circ}\)** & **60\({}^{\circ}\)** \\ \hline Strength & 28.1 MPa & 31.0 MPa & 34.7 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average dynamic compressive strength with respect to the initial flaw inclination angle.
# Multi-Winner Election Control via Social Influence: Hardness and Algorithms for Restricted Cases
## 2 Background
### Independent Cascade Model  
Consider a graph \(G=(V,E)\) with a weight \(b_{u,v}\in[0,1]\) on each edge \((u,v)\in E\). The same as LTM, all nodes are inactive, and at the first step the seed nodes \(S\subseteq V\) become active. Let us define \(S_{i}\) as the nodes that were inactive at step \(i-1\) and became active at step \(i\), then \(S_{1}=S\). At each step \(i>1\), each node \(v\in S_{i-1}\) will try to activate its outgoing neighbors with the probability of the edge between them. In other words, consider \(N_{v}^{o}\) as the set of outgoing neighbors of node \(v\); for each \(u\in N_{v}^{o}\), node \(v\) tries to activate \(u\) with the probability \(b_{v,u}\). If \(v\) has multiple outgoing neighbors, it tries to activate them in an arbitrary order. Note that a node becomes active once, let us say at step \(k\), and try to activate its outgoing neighbors exactly once, at step \(k+1\).  
[PERSON] et al. [7] considered the IM under ICM. They showed that the greedy algorithm works for this model, too. They also demonstrated that it is \(NP\)-hard to approximate the problem within any factor better than \(1-\frac{1}{\epsilon}\).
## 3 Results  
### Classification  
Supervised classification for the 2021 wet season in the Karimnagar district was performed using the maximum likelihood parametric rule in the ERDAS interface (Figure 4). Rice was the predominant crop throughout the study area, while other crops included cotton and pulses grown under the catchment area, within the southern part of the district.  
The total land area used to cultivate rice in the Karimnagar district depicted in the classified map (Figure 4) was 1.43 Lakh ha, compared with the 1.47 Lakh ha reported by the government statistics of Telangana [4]. According to the classified map, the most extensive area was occupied by rice followed by other LULC (0.43 Lakh ha). Water bodies and built-up occupied 0.1 Lakh ha each, while other crops and orchards comprised 0.055 Lakh ha and 0.021 L ha, respectively. An overall accuracy of 93.04% was observed with a kappa coefficient of 0.87 (Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline
**Classified Data** & **Producer’s Accuracy (\%)** & **User’s Accuracy (\%)** \\ \hline Rice & 95.96 & 96.94 \\ \hline Another crop & 85.71 & 85.71 \\ \hline Waterbody & 100 & 100 \\ \hline Orchards & 87.50 & 87.50 \\ \hline Built-up & 85.71 & 100 \\ \hline Other LULC & 83.33 & 71.43 \\ \hline Overall Accuracy & & 93.04\% \\ \hline Kappa Coefficient & & 0.87 \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy assessment of the Karimnagar district.