esa-sceva/satcom-chunk-collection huggingface dataset:
# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
## 4 GIS Partial Discharge Defect Detection Algorithm  
### YOLOv5 Detection Process  
The GIS partial discharge defect detection model uses the YOLOv5 algorithm to extract feature maps of three sizes: 20 \(\times\) 20, 40 \(\times\) 40, and 80 \(\times\) 80, through a backbone feature extraction network. After the PANet up-sampling operation to achieve the fusion of deep and shallow information, the feature maps containing rich feature information in three different sizes are finally obtained. The basic process of GIS partial discharge defect detection using YOLOv5m in this paper is shown in Figure 8 and contains the following steps.  
Figure 8: YOLOv5 target detection flow chart.  
1. GIS partial discharge PRPD map is normalized to the prescribed input size of 640 \(\times\) 640 using adaptive images and input to a target detection network for image feature extraction.
2. The target detection image has meshed, the target border and the classification to which the target belongs are predicted, and then whether the specified threshold value is met is judged according to the confidence score ranking.
3. The predicted edges that meet the specified threshold are retained, and the boundary edges generated by the detection are filtered using non-maximum suppression to eliminate redundant edges.
4. After eliminating all the redundant borders and marking out all the bounding boxes, output the target bounding box, label the target type and confidence score.
## Appendix A  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{n}\) & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **x (\(\mathbf{m}\))** & **y (\(\mathbf{m}\))** & **Range (\(\mathbf{m}\))** \\ \hline
1 & 46.343386 & 3.434335 & 0 & 0 & 150 \\
2 & 46.346520 & 3.435697 & 100 & 350 & 90 \\
3 & 46.347632 & 3.437019 & 200 & 475 & 90 \\
4 & 46.347387 & 3.439093 & 360 & 450 & 110 \\
5 & 46.344879 & 3.437938 & 275 & 170 & 90 \\
6 & 46.348953 & 3.444582 & 780 & 630 & 90 \\
7 & 46.346051 & 3.446861 & 960 & 310 & 215 \\
8 & 46.349827 & 3.437973 & 270 & 720 & 80 \\
9 & 46.346187 & 3.442187 & 600 & 320 & 80 \\
10 & 46.345326 & 3.442819 & 650 & 225 & 70 \\
11 & 46.343771 & 3.440838 & 500 & 50 & 120 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Georeferenced and metric coordinates of 15 nodes to be visited with their respective radio communication ranges: benchmark used to test the algorithm in the Section 3.1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**n** & **x (m)** & **y (m)** & **Radius (m)** & **n** & **x (m)** & **y (m)** & **Radius (m)** \\ \hline
1 & 37 & 52 & 2.8 & 27 & 30 & 48 & 3.3 \\
2 & 49 & 49 & 3.3 & 28 & 43 & 67 & 3.0 \\
3 & 52 & 64 & 3.0 & 29 & 58 & 48 & 3.0 \\
4 & 20 & 26 & 2.7 & 30 & 58 & 27 & 3.6 \\
5 & 40 & 30 & 0.2 & 31 & 37 & 69 & 2.0 \\
6 & 21 & 47 & 3.0 & 32 & 38 & 46 & 3.2 \\
7 & 17 & 63 & 4.0 & 33 & 46 & 10 & 3.6 \\
8 & 31 & 62 & 3.7 & 34 & 61 & 33 & 3.0 \\
9 & 52 & 33 & 2.0 & 35 & 62 & 63 & 2.0 \\
10 & 51 & 21 & 2.0 & 36 & 63 & 69 & 2.6 \\
11 & 42 & 41 & 2.5 & 37 & 32 & 22 & 2.4 \\
12 & 31 & 32 & 2.7 & 38 & 45 & 35 & 3.0 \\
13 & 5 & 25 & 3.4 & 39 & 59 & 15 & 2.5 \\
14 & 12 & 42 & 2.8 & 40 & 5 & 6 & 3.4 \\
15 & 36 & 16 & 2.4 & 41 & 10 & 17 & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Benchmark of 50 nodes used to test the algorithm in the Section 3.2.
## 4 Results and Discussion  
### Characteristics of Coal Quality  
The No. 7 coal seam in the study area exhibits a vitrinite maximum reflectance (R\({}_{\rm{o,max}}\)) averaging 0.69%, classifying it as medium-metamorphic bituminous coal. Key coal quality parameters include an average moisture content (M\({}_{\rm{ad}}\)) of 1.28%, ash yield (A\({}_{\rm{d}}\)) of 12.49% (low ash), volatile matter (V\({}_{\rm{d}}\)af) of 38.48% (high volatile), fixed carbon (FC) of 53.59% (low fixed carbon), total sulfur (S\({}_{\rm{t,d}}\)) of 0.70% (low sulfur), and an air-dried high calorific value (Q\({}_{\rm{gr,ad}}\)) of 30.13 MJ/kg (high calorific value) (Table 1).  
Figure 2: Structure diagram of neural network model.  
Similarly, the No. 8 coal seam records a vitrinite R\({}_{\text{o,max}}\) of 0.72%, also indicative of medium-metamorphic bituminous coal. Its average M\({}_{\text{ad}}\), A\({}_{\text{d}}\), V\({}_{\text{d}af}\), FC, S\({}_{\text{t,d}}\), and Q\({}_{\text{gr,ad}}\) values are 1.13%, 11.06%, 37.93%, 54.98%, 0.93%, and 30.44 MJ/kg, respectively, categorizing it as low ash, low fixed carbon, high volatile, low sulfur, and high calorific value coal (Table 1).  
In summary, both the No. 7 and No. 8 coal seams of the Shanxi Formation are characterized as medium-metamorphic bituminous coals with low sulfur, low ash, low fixed carbon, high volatile matter, and high calorific value.  
Correlation analysis of moisture, ash content, volatile matter, fixed carbon, total sulfur, and calorific value in the Shanxi Formation coals from the Kongzhuang and Xuzhuang mines of northern Jiangsu reveals distinct relationships. Ash yield demonstrates a significant negative correlation with fixed carbon content and calorific value. Fixed carbon content, in turn, shows strong correlations with volatile matter yield and calorific value. Total sulfur content exhibits a significant positive correlation with volatile matter yield but only weak associations with ash yield and volatile matter (Figure 3). Meanwhile, the fixed carbon, calorific value, and volatile matter in coal quality can further predict coal grades [41].
## 4 EAT Segmentation Methods via Pericardium Delineation
### Machine Learning-Based Methods  
Machine learning can make the problem of finding the pericardium more complex and increase the data requirements but can lead to better and much faster results in terms of processing time. However, these results come at the cost of having to engineer and select appropriate features for training a model. For instance, [PERSON] et al. (2016) uses the mean values, variance, and derivatives of the pixel intensities of a region around a voxel as features to train a random forest classifier. The classifier classifies image pixels into one of three classes: just inside the pericardium, just outside the pericardium, and background. Another important drawback of machine learning is that it requires heavy preprocessing of the input images. [PERSON] et al. (2016) use feature-based multi-atlas registration to initialize a pericardium localization before the image is processed by the machine learning model.  
Similarly, [PERSON] et al. (2016) segment the paracardial and pericardial adipose tissues using a different machine learning algorithm ([PERSON] et al., 2016) as a preprocessing step. Then, to segment an elliptical pericardium, the authors use a genetic algorithm to fit an ellipse to the edges between paracardial and epicardial tissue. The algorithm can successfully find an ellipse that delineates EAT and paracardial adipose tissue and that closely follows the pericardium contour. Note that the method requires an already segmented image, and is mainly aimed to be used as a performance optimization for other EAT segmentation methods that delineate the pericardium. By fitting the pericardium ellipse on a few slices of the CT scan, they can interpret the pericardium contour on the rest of the slices, thus saving processing time.
# Stabilization of Underactuated Surface Vessels: A Continuous Fractional Power Control Method
## 3 Main Results
### Fast Convergence Control Law  
To improve the convergence rate both near and far from the origin, we give the following continuous control law for stabilization of the USV,  
\[\begin{cases}\begin{aligned} \alpha_{1}&=-\kappa_{1}sign(\theta_{2})| \theta_{2}|^{\phi_{1}}\theta_{6}-\kappa_{2}\theta_{5},\\ \omega_{2}&=-\kappa_{3}\theta_{3}-\kappa_{4}\theta_{6}+ \lambda|\theta_{2}|^{\phi_{2}}\sin t,\end{aligned}\end{cases} \tag{36}\]  
where \(\kappa_{1}>0\), \(\kappa_{2}>0\), \(\kappa_{3}>0\), \(\kappa_{4}>0\) and \(\lambda\
eq 0\) are constants to be chosen, and  
\[\begin{cases}\phi_{1}=0.5(1+\xi_{1})+0.5(1-\xi_{1})\cdot sign(\mid\theta_{2} \mid-1),\\ \phi_{2}=0.5(1+\xi_{2})+0.5(1-\xi_{2})\cdot sign(\mid\theta_{2}\mid-1),\end{cases} \tag{37}\]  
with \(\xi_{1}>0\) and \(\xi_{2}>0\) are such that \(\xi_{1}+\xi_{2}\leq 1\).  
Please note that the above control law gives a controller with \(\phi_{1}\) and \(\phi_{2}\) being the parameters, which take values in accordance with the magnitude of \(\theta_{2}\), namely  
\[\begin{cases}\phi_{1}=1,\ \phi_{2}=1&|\theta_{2}|\geq 1,\\ \phi_{1}=\xi_{1},\phi_{2}=\xi_{2}&|\theta_{2}|<1.\end{cases} \tag{38}\]  
should satisfy that \(\xi_{1}+\xi_{2}<1\). Specifically, when \(|\theta_{2}|>1\), \(\phi_{1}=1\), \(\phi_{2}=1\). When the system state enters the region of \(|\phi_{2}|<1\), \(\phi_{1}=\xi_{1},\phi_{2}=\xi_{2}\). By such a continuous control law, a satisfactory convergence rate can be guaranteed.  
In what follows, we show that the control law in (36) and (37) is continuous when \(\theta_{2}\) approaches \(1\) and \(-1\) from both sides. Around \(\theta_{2}=1\), we have  
\[\begin{cases}\lim_{\theta_{2}\to 1^{-}}\omega_{1}=-\kappa_{1}\theta_{6}- \kappa_{2}\theta_{5}=\lim_{\theta_{2}\to 1^{+}}\omega_{1},\\ \lim_{\theta_{2}\to 1^{-}}\omega_{2}=-\kappa_{3}\theta_{3}-\kappa_{4} \theta_{6}+\lambda\sin t=\lim_{\theta_{2}\to 1^{+}}\omega_{2},\end{cases} \tag{39}\]  
which means that \(\omega_{1}\) and \(\omega_{2}\) are continuous around \(\theta_{2}=1\). Similar arguments apply to \(\theta_{2}=-1\). Therefore, the control law is continuous at \(|\theta_{2}|=1\).  
**Remark 3**: _Compared with continuous method in [10; 11; 12; 14; 16], the presented control method has a faster convergence rate. Although [6; 7; 8] gave a discontinuous control method to increase the convergence rate, the control law here is superior as it is a continuous control method._
## 4 Numerical Simulation Analysis
### Discussion  
Experimental and numerical results show that the shear resistance of non-persistent joints before rock bridge failure is obviously higher than that after rock bridge failure. Before rock bridge failure, the joint ends and the crack extension are areas of stress concentration. This phenomenon is more pronounced with the increase of joint aperture, joint persistency, and normal stress. Therefore, in engineering construction, priority should be given to strengthening these areas before rock bridge failure to improve the shear resistance of rock mass.  
In this study, rock-like material specimens were used in the indoor tests, and numerical model parameters were calibrated based on the rock-like material. Although the selected rock-like material has properties close to those of natural rock, some composition and mechanical properties may differ. Therefore, in future research, non-persistent joint specimens will be prepared by processing natural rock, and the shear behavior of non-persistent joints in different rock types will be the focus of further research.
## 4 Results and Discussions  
As an experimental study, we reconstructed the cloud-free Sentinel-2 image time-series with 67 images of the study period from 26 April to 10 October 2018 using the proposed methods and data described in Sections 2 and 3. For the results and discussions, without losing generality, we employed six typical cloud contaminated images from the reconstructed time-series to illustrate the effectiveness of the methods. Figure 6 shows the false color composition of the six images. Three of these images (16 May, 15 June and 5 October 2018) were cloud contaminated, two of them were mainly contaminated by haze (30 July and 21 August 2018), and one (3 October 2018) was contaminated by mixed thin clouds and thick clouds. We select these six images as examples for two reasons. The first was that the total contaminated pixels should be less than 50%. This was required by the method to make sure that the difference comparison was reliable for an effective result. The second was that they should include images contaminated by both cloud and haze for some challenged cases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Band Name**} & \multicolumn{4}{c}{**Sentinel-2A * MSI**} & \multicolumn{4}{c}{**MODIS (MCD43A4)**} \\ \cline{2-7}  & **Band ID** & **Bandwidth** & **Spatial** & **Band ID** & **Bandwidth** & **Spatial** \\  & **(nm)** & **Resolution (m)** & & **(nm)** & **Resolution (m)** \\ \hline Blue & b2 & 439–533 & 10 & b3 & 459–479 & 500 \\ Green & b3 & 538–583 & 10 & b4 & 545–565 & 500 \\ Red & b4 & 646–684 & 10 & b1 & 620–670 & 500 \\ Near-infrared (NIR) & b8A & 837–881 & 20 & b2 & 841–876 & 500 \\ Shortwave infrared (SWR1) & b11 & 1539–1682 & 20 & b6 & 1628–1652 & 500 \\ Shortwave infrared (SWR2) & b12 & 2078–2320 & 20 & b7 & 2105–2155 & 500 \\ \hline \hline \end{tabular}
* Sentinel-2B MSI has the similar bandwidths.  
\end{table}
Table 1: Sentinel-2A MSI bands and the corresponding MODIS (MCD43A4) bands for image fusion.
## 3 Results and Discussion
### GE Synthesis Using FAMEs from Different Origins  
After optimization of the reaction conditions, GEs were synthesized using FAMEs derived from SCOs produced by _C. echinulata_, _U. isabellina_ and _N. gaditana_ and from an EPA concentrate (Table 3). The GE synthesis when EPA-FAMEs were used as substrate was excellent (i.e., 99% conversion), followed by _N. gaditana_-FAMEs, _U. isabellina_-FAMEs and _C. echinulata_-FAMEs (i.e., 86%, 85% and 80%, respectively) (Figure 6). The structures of the obtained GEs were confirmed on the basis of their FTIR spectra, in which the appearance of the broad and due to the hydroxyl groups of glucose was observed (Figure 7 and Figures S1-S5). According to the data presented above, we can conclude that the synthesis of GEs accomplished in this work was successful, while possible large-scale applications, using SCOs instead of traditional sources of PUFAs, will not interfere with the food supply chain.  
Figure 5: FTIR of olive oil FAMEs and their glucose esters (for details, see the text in Section 3.3).  
Figure 6: Reaction course over the time of conversion of FAMEs derived from different origins to glucose esters.
## 4 Discussion
### Feature Variables Selection, Model Construction, and Variable Contribution to the Model
#### 4.3.2 Comparison with This Study's Other Models  
The RF model outperforms the other models tested in this study. Specific performance metrics can be seen in Figures 10 and 11.  
(1) Data Distribution: Stratified sampling was applied to ensure that both the training and test sets maintained consistent distributions, representing a broad range of salinity samples. Thus, the differences in model performance are unlikely to be attributed to data distribution.  
(2) Feature Importance: Both RF and BPNN models automatically select important features. On January 26, the RF model achieved an R\({}^{2}\) of 0.55 and an RMSE of 3.4, whereas BPNN reached an R\({}^{2}\) of 0.46 and an RMSE of 3.72. These results were significantly better than those of PLSR, which had an R\({}^{2}\) of 0.25 and an RMSE of 4.36.  
(3) Hyperparameter Settings: Hyperparameter tuning for the RF model is relatively simple, requiring only adjustments to the number of trees and leaf nodes. Future work will streamline this process by keeping the leaf node setting at its default value. In contrast, BPNN requires more complex hyperparameter tuning (e.g., learning rate, optimizer like Adam, number of layers), and is sensitive to these settings. Overfitting or underfitting is common during training, which may explain why the BPNN model performed poorly on August 26, with an R\({}^{2}\) of only 0.3.  
(4) Model Characteristics: PLSR is a linear model that assumes linear relationships between variables, which limits its performance in handling nonlinear problems. In August, the linear correlation between salinity and spectral data was stronger than in January, resulting in a higher R\({}^{2}\) of 0.33 for PLSR in August compared to 0.25 in January. However, this study found that soil salinity inversion is not a simple linear problem. Both BPNN and RF, as nonlinear models, performed better in handling this complexity. Therefore, in January, RF achieved an R\({}^{2}\) of 0.55 and an RMSE of 3.4, while BPNN had an R\({}^{2}\) of 0.46 and RMSE of 3.72, outperforming PLSR (R\({}^{2}\) = 0.25, RMSE = 4.36).
## 3 Results and Discussion
### SEM Analyses  
To investigate the effect of the marinating temperature of Atlantic herring on the microstructure of freeze-dried ISP extracts, SEM (200\(\times\)) micrographs were determined. No cryoprotectants, which might modify the growth of the ice crystals during the freezing process, thus forming a large or small number of small ice crystals, were added to the ISP extracts before the freeze-drying. All the samples were freeze-dried in the same conditions, meaning that all differences that were visible on the SEM micrographs were caused by the marinating temperature, and not by the freeze-drying process. The results of scanning electron microscopy showed that the ISP extracts from meat contained oblong microstructures similar to myofibrils and showed the effect of the marinating temperature on differences in the three-dimensional microstructure. As shown in Figure 3a, the myofibrils ISP-extracted from the marinades at 2 \({}^{\circ}\)C were tightly connected, and arranged in parallel, and the spaces between the myofibrils that were observed indicated a well-organized structure of the herring muscle. The SEM image of the ISP extract from the marinades-7 \({}^{\circ}\)C demonstrated shorter myofibrils, which formed small aggregates of fibers that began to appear, indicating that the well-organized structure of the muscle tissue was destroyed (Figure 3b). The results of SEM of ISP extract from the marinades at 12 \({}^{\circ}\)C showed a loss of myofibril structure, visibly dense aggregates, and a low population of small cavities, unevenly distributed (Figure 3c). It is tempting to suggest that a lower ripening temperature decreased cathepsins activity, which to a lesser degree destroyed the myofibrils' structure, that can be extracted from meat to water, despite the precipitation process. The ISP technique does not obtain 100% efficiency in protein precipitation, and short myofibrils treated by proteases probably precipitated at a lower degree. The SEM analysis of the ISP extracts from the brine samples indicated that neither myofibril structure was observed. The globular particles and their aggregates (Figure 3d-f) visible on SEM micrographs of the brine samples were larger than the myofibrils visible on the micrographs of the meat samples, suggesting that these globular particles were NaCl particles. The globular proteins extracted from the myofibrils (cut or dissolved) would have been lower than the fibers they were extracted from. Additionally, seven days of marinating in pH 4.3, close to the isoelectric point of herring proteins, probably precipitated most of the proteins diffused to the brine.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 5 Analysis of Influencing Factors
### Stratigraphic Loss Rate  
Taking the original strata loss rate \(\eta\) as a variable, the strata loss rate is set as 0.5 \(\eta\), 0.75 \(\eta\), 1.25 \(\eta\), and 1.5 \(\eta\), respectively. Subsequently, results of the settlement deformation can be obtained for different layer loss rates, as shown in Figure 14a. The settlement  
Figure 12: The existing tunnel’s settlement for various additional tunnel depths: Settlement overall (**a**); settlement maximum (**b**).  
Figure 13: Settlement of the existing tunnel for different new tunnel diameters: (**a**) Overall settlement; (**b**) maximum settlement.  
gradually increases as the strata loss rate increases. The term stratum loss rate denotes the proportion of volume reduction of the strata during the construction process. It can be deduced that a greater loss rate will result in a greater additional load acting on the existing tunnel. As demonstrated in Figure 14b, a positive correlation exists between the rate of strata loss and the maximum settlement value. With the increase of the strata loss rate, the maximum settlement value increases gradually. It is evident that when the loss rate is altered from 0.5 \(\eta\) to 1.5 \(\eta\), the maximum settlement value of the existing tunnel is observed to increase from 1.71 mm to 5.13 mm. This shows that the rate of soil loss has a significant effect on the maximum settlement of the existing tunnel.
## 6 Result and Discussions
### Discussions  
When comparing with other models, such as LSTM and the ARABERT preprocessing with LSTM model, Markov models showed better results on our Arabic corpus. Markov chain models give better results when working on sequential data. Markov models are prominent in anticipating the word based on the previous context because of their capacity to recognize local dependencies and patterns within the sequences. Markov chains are an effective way to capture short-term dependencies where the word that follows is heavily dependent on the word that comes before and best suited for our Arabic data in the next-word prediction. Refs. [3,21] show the next-word prediction of the Urdu language with 52.4% accuracy for LSTM and 73.7% accuracy with BERT. Ref. [25] presented the next-word prediction in English, and Ref. [13] provided the next-word prediction in Ukrainian. The detailed version is shown in the comparison Table 9 given below.  
In the next-character prediction, ARABERT + LSTM outperforms other models, as it combines the advantages of the long-term dependency capabilities of LSTM with the pre-trained Arabic model ARABERT. LSTM helps us capture contextual information and character relationships. ARABERT offers a solid foundation for comprehending the subtleties of the Arabic language. The combination makes it possible for the model to efficiently identify the pattern in the text, which enhances the prediction and boosts the overall performance in character prediction.  
Implication  
One important area of natural language processing (NLP) with several advantages is Arabic next-word prediction. It improves machine translation, speech recognition, and text input techniques by correctly anticipating the following word. Additionally, this technology helps us better comprehend the morphological and dialectal variances of the Arabic language. It is a useful tool for both people and corporations, with practical uses ranging from information retrieval to sentiment analysis.