esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Icing on power transmission lines is a serious threat to the security and stability of the power grid, and it is necessary to establish a forecasting model to make accurate predictions of icing thickness. In order to improve the forecasting accuracy with regard to icing thickness, this paper proposes a combination model based on a wavelet support vector machine (w-SVM) and a quantum fireworks algorithm (QFA) for prediction. First, this paper uses the wavelet kernel function to replace the Gaussian wavelet kernel function and improve the nonlinear mapping ability of the SVM. Second, the regular fireworks algorithm is improved by combining it with a quantum optimization algorithm to strengthen optimization performance. Lastly, the parameters of w-SVM are optimized using the QFA model, and the QFA-w-SVM icing thickness forecasting model is established. Through verification using real-world examples, the results show that the proposed method has a higher forecasting accuracy and the model is effective and feasible.  
Keywords:icing forecasting; support vector machine; fireworks algorithm +
Footnote †: journal: Applied Sciences
## 4 Evaluation
### Experimental Results
On new data (\"75/25\" testing), compared to PPM, the PPMN predicts over 10% more often on the \"Workers\" dataset and over 20% more often on the \"Trainees\" dataset, with a prediction rate of 91.18% and 77.23%, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**PPMN**} & \multicolumn{2}{c}{**Prediction Rate (\%)**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Coverage (\%)**} \\ \cline{2-7}  & **100/100** & **75/25** & **100/100** & **75/25** & **100/100** & **75/25** \\ \hline
1 & 97.68 & 77.23 & 78.89 & 57.69 & 77.06 & 44.55 \\
2 & 97.68 & 77.23 & 81 & 57.69 & 79.12 & 44.55 \\
3 & 97.68 & 77.23 & 81.27 & 57.69 & 79.38 & 44.55 \\
4 & 97.68 & 77.23 & 81.27 & 57.69 & 79.38 & 44.55 \\
5 & 97.68 & 77.23 & 81.27 & 57.69 & 79.38 & 44.55 \\
6 & 97.68 & 77.23 & 81.27 & 57.69 & 79.38 & 44.55 \\
7 & 97.68 & 77.23 & 81.27 & 57.69 & 79.38 & 44.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of the PPMN on the “Trainees” dataset. Figure 6: Prediction rate. Both the PPM and PPMN have a similar prediction accuracy across all datasets, with PPM being slightly higher (see Figure 7). On \"100/100 testing\", the Markov predictor has the highest prediction accuracy. Although LSTM has the highest prediction rate, it is the one that has the lowest accuracy across all the evaluation scenarios. The coverage measures the capacity of these prediction methods to model existing data and to adapt to new data. As Figure 8 depicts, PPMN is the best prediction method to model existing data and has a coverage of 44.55% on the \"Trainees\" dataset and 71.18% on the Workers dataset, considering the \"75/25\" testing method. Taking into account that the coverage is an important efficiency indicator, as it expresses the ratio of correct predictions, these results are remarkable. The combination of different order Markov predictors, as well as the exploration of the neighboring states, proved to be a good solution. The PPMN can easier find matching assembly patterns to provide next step prediction.
# May the Extensive Farming System of Small Ruminants Be Smart?
## 3 Results
### Wearable Sensors  
According to their physical relationships to the animals, PLF sensors are commonly categorized as wearable and non-wearable. Some sensors are applied directly on the individual animal and can provide information in real time or via data loggers downloading in key passages. The translation of data collected from sensors (movement, body position, temperature, etc.) into physiologic status form such as ovulation or lameness is relevant for farm management. In the case of extensive systems, these sensors provide useful information related to the grazing and resting behaviours of the flock. Wearable and \begin{table}
\begin{tabular}{c c c c c c} \hline
**Device** & **Position** & **Application** & **Technology** & **Recorded Parameters** & **References** \\ \hline Electronic animal identification & \begin{tabular}{c} Ear tag \\ Ruminal bolus \\ Injectable transponder \\ \end{tabular} & Sorting gate, feeder, mating & Radio frequency & Individual data & [4,5,24,4] \\ \hline GPS and localisation & Collar & Virtual fence, localisation, grazing & Satellite net-work & Location & [16,29,42,43,46,47] \\ \hline Sensors to measure physiological activity: Temperature & \begin{tabular}{c} Ear tag \\ Bolus \\ Injectable transponder \\ \end{tabular} & Health (fiver), stress, heat, drinking & Thermistor & Rectal, rumen, or vaginal temperature & [4,5,7,48,49,50] \\ \hline Sensors to measure physiological activity: pH & Bolus & Stress sensor data & Rumen pH & [37,51] \\ \hline Sensors to measure behaviour & \begin{tabular}{c} Ear tag \\ Bolus \\ Pedometer \\ Harness \\ \end{tabular} & Grazing behaviour, localisation & Triaxial piezoelectric, accelerometer, jav-movement recording system & Behaviour, feeding, rest, rumination, lameness, behaviour during & [5,32,42,44,53,54,55,56,57,58,59,60,61] \\ \hline Virtual fencing technology & \begin{tabular}{c} Collars with position, \\ acoustic and electrical stimuli \\ \end{tabular} & Collars with position, acoustic and electrical stimuli & Collars with position, acoustic and electrical stimuli & Coordinates and GPS or satellite signals or by a ground cable that communicates with the collars & livestock management for welfare monitoring, pasture management & [15,24,27,28,62,63,64] \\ \hline Network technologies & \begin{tabular}{c} Collars with \\ accelerometers and position sensors; \\ \end{tabular} & \begin{tabular}{c} Sensor nodes with data \\ storage and processing \\ capabilities, gateway, web \\ server, algorithm \\ \end{tabular} & \
\begin{tabular}{c} IoT, big data, and \\ machine learning \\ (ML) technologies \\ \end{tabular} &
\begin{tabular}{c} Iivestock management \\ for welfare monitoring, \\ and pasture \\ \end{tabular} & [52,65,66] \\ \hline \end{tabular}
\end{table} 
Table 1: Main types of wearable sensors.
## 3 Results and Discussion
### Burst Pressure Estimation Using Static and Explicit Dynamics Analyses  
The burst pressure of additively manufactured (AM) 316L stainless steel in the as-built condition was estimated using static structural and explicit dynamic analyses based on the equivalent von Mises stress theory. Figure 26 presents the equivalent von Mises stress distribution at an operating pressure of 25 MPa, obtained using the static structural analysis. The relationship between the equivalent von Mises stress and the corresponding pressure was derived from the static structural analysis and is shown in Figure 27. The burst pressure was determined as the pressure at which the equivalent von Mises stress reaches the ultimate tensile strength of the as-built 316L stainless steel. The results indicate that the estimated burst pressure is approximately 70 MPa.  
Using the explicit dynamic analysis, a realistic simulation of the burst test was conducted. The stress value just before the explosion was observed, and the corresponding pressure was recorded as the burst pressure. The equivalent von Mises stress distributions just before and just after the explosion are illustrated in Figure 28. The explicit dynamic analysis results indicate a burst pressure of 81 MPa, as shown in Figure 29. Additionally, it can be observed that the burst pressure corresponding to the ultimate tensile strength (UTS) of the material is approximately 76 MPa based on the explicit dynamics results. This value represents the onset of material failure as it reaches its UTS. Therefore, the results obtained from both the static structural and explicit dynamic modules are in good agreement.  
Figure 26: Equivalent (von Mises) stress results from the static structural analysis of AM 316L stainless steel in the as-built condition.  
Figure 27: Burst pressure vs. equivalent (von Mises) stress graph obtained from the static structural analysis of AM 316L stainless steel in the as-built condition.  
Figure 28: Equivalent (von Mises) stress results from the explicit dynamic analysis of AM 316L stainless steel in the as-built condition: (**a**) just before explosion and (**b**) just after explosion.  
Figure 29: Burst pressure vs. equivalent (von Mises) stress graph obtained from the explicit dynamic analysis of AM 316L stainless steel in the as-built condition.  
[PERSON] et al. [17] experimentally determined the burst pressure of AM 316L stainless steel to be 70 MPa, which aligns with the present findings, thereby confirming the validity of both the current results and the employed analysis modules.  
The burst testing results further demonstrate that the additive manufacturing process significantly enhances the burst pressure of Type 1 pressure vessels compared to conventionally manufactured counterparts. This indicates that additive manufacturing can be effectively utilized to produce high-safety and durable pressure vessels.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 3 Gaussian Process Regression for Time Series
### Sparse Gaussian Process Regression  
From a conceptual viewpoint, Gaussian Process Regression (GPR) is versatile and elegant, with a simple conceptual structure and many desirable theoretical properties. In practice, however, GPR can only deal with regression tasks of moderate size, with at most a few thousand input data points, due to the cubic time complexity required for computing the posterior and the predictive posterior distributions. As a result, several sparse approximation methods have been proposed to make GPR practically applicable to settings with a medium to large number of input data points (see, e.g., [15; 17] and the references therein). These sparse GPR methods aim to represent the underlying Gaussian Process using a much smaller set of \(m\), with \(m\ll n\), inducing points, which can be learned so that they are highly informative about the actual posterior Gaussian Process. Sparse GPR methods achieve a time complexity of \(O(m^{2}n)\) and a space complexity of \(O(m^{2}+n)\) for approximating the posterior and the predictive posterior Gaussian Processes.  
A standard approach to optimizing the sparse Gaussian Process is by minimizing its Kullback-Leibler (KL) divergence to the actual (and possibly intractable) Gaussian Process. In general, optimal (with respect to the KL divergence) sparse Gaussian processes do not have a closed form (as, e.g., happens with the predictive Gaussian process in (13) and (14)). Then, Variational Inference (VI) can be used to approximate the actual posterior with a variational distribution.  
In this work, we use the _Variational Free Energy_ (VFE) framework (a.k.a. _Sparse Gaussian Process Regression_ (SGPR)), introduced by [PERSON] [16]. SGPR uses a small number of carefully selected inducing points, along with variational inference, to obtain a low-rank approximation (with respect to the KL divergence) to the actual Gaussian Process. In SGPR, the total number \(m\) of inducing points is chosen in advance so that the overall time and space complexity are acceptable. Their locations in time and their values are optimized so that more inducing points are located at time intervals where the time series exhibits a more complex behavior (see also Figure 2).  
Other approaches to sparse GPR include treating inducing point selection as a continuous optimization problem [32] and online approaches where the sparse Gaussian Process is iteratively trained by processing each input individually [33; 34]. We refer the interested reader to [17] for an elaborate treatment of Sparse Gaussian Process Regression.
## 3 Results
### Inferior Alveolar Nerve (IAN) Injury
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Pericoronitis (\%)** &
\begin{tabular}{c} **Without** \\ **Pericoronitis (\%)** \\ \end{tabular} & **Total (\%)** & \(p\) **Value** \\ \hline Horizontal impaction & 297 (6.8) & 4046 (93.2) & 4343 (100.0) & \\ Mesioangular impaction & 175 (5.7) & 2900 (94.3) & 3075 (100.0) & \\ Vertical impaction & 189 (8.0) & 2176 (92.0) & 2365 (100.0) & \\ Disatomgular impaction & 38 (24.1) & 120 (75.9) & 158 (100.0) & \(p\) \textless{} 0.001 \({}^{*}\) \\ Inverted impaction & 49 (17.5) & 231 (82.5) & 280 (100.0) & \\ Transverse impaction & 6 (6.7) & 83 (93.3) & 89 (100.0) & \\ Total & 754 (7.3) & 9556 (92.7) & 10,310 (100.0) & \\ \hline \hline \end{tabular} \({}^{*}\)Statistical significance \(p\) \textless{} 0.05, [PERSON]’s chi-square test.  
\end{table} 
Table 10: Association between impaction angulation and pericoronitis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gender** & **Numbness (\%)** & **Without Numbness (\%)** & **Total (\%)** & \(p\) **Value** \\ \hline Male & 10 (0.2) & 5374 (99.8) & 5384 (100.0) & \\ Female & 20 (0.4) & 4906 (99.6) & 4926 (100.0) & \(p\) = 0.038 \({}^{*}\) \\ Total & 30 (0.3) & 10,280 (99.7) & 10,310 (100.0) & \\ \hline \hline \end{tabular} \({}^{*}\)Statistical significance \(p\) \textless{} 0.05, [PERSON]’s chi-square test.  
\end{table} 
Table 11: Association between gender and numbness. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Age** & **Numbness (\%)** & **Without Numbness (\%)** & **Total (\%)** & \(p\) **Value** \\ \hline
10–19 & 0 (0.0) & 1879 (100.0) & 1879 (100.0) & \\
20–29 & 16 (0.3) & 6046 (99.7) & 6062 (100.0) & \\
30–39 & 6 (0.4) & 1387 (99.6) & 1393 (100.0) & \\
40–49 & 4 (0.6) & 616 (99.4) & 620 (100.0) & \\
50–59 & 4 (1.8) & 221 (98.2) & 225 (100.0) & \(p\) \textless{} 0.001 \({}^{*}\) \\
60–69 & 0 (0.0) & 98 (100.0) & 98 (100.0) & \\
70–79 & 0 (0.0) & 30 (100.0) & 30 (100.0) & \\
80 & 0 (0.0) & 3 (100.0) & 3 (100.0) & \\ Total & 30 (0.3) & 10,280 (99.7) & 1031 (100.0) & \\ \hline \hline \end{tabular} \({}^{*}\)Statistical significance \(p\) \textless{} 0.05, [PERSON]’s chi-square test.  
\end{table} 
Table 12: Association between age and numbness. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Proximal Caries** & **Without Proximal** & \\  & **of Second** & **Caries of Second** & **Total (\%)** & \(p\) **Value** \\  & **Molar (\%)** & **Molar (\%)** & & \\ \hline Horizontal impaction & 195 (4.5) & 4148 (95.5) & 4343 (100.0) & \\ Mesioangular impaction & 251 (8.2) & 2824 (91.8) & 3075 (100.0) & \\ Vertical impaction & 75 (3.2) & 2290 (96.8) & 2365 (100.0) & \\ Disatomgular impaction & 5 (3.2) & 153 (96.8) & 158 (100.0) & \(p\) \textless{} 0.001 \({}^{*}\) \\ Inverted impaction & 9 (3.2) & 271 (96.8) & 280 (100.0) & \\ Transverse impaction & 0 (0.0) & 89 (100.0) & 89 (100.0) & \\ Total & 535 (5.2) & 9775 (94.8) & 10,310 (100.0) & \\ \hline \hline \end{tabular} \({}^{*}\)Statistical significance \(p\) \textless{} 0.05, [PERSON]’s chi-square test.  
\end{table} 
Table 9: Association between impaction angulation and proximal caries of the second molar.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Carthamus tinctorius L
#### 3.1.1 Anti-Inflammatory Properties  
Inflammation is associated with the pathogenesis of many diseases. According to the current literature, substances derived from safflower, such as hydroxyasafflor yellow A, kaempfer, and hyperoside, exhibit anti-inflammatory effects in different organs and  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Potential Pro-Health** & _Carthamus tinctorius_**L.** & _Camelina sativa_**L.** & _Nigella sativa_**L.** \\ \hline Anti-inflammatory & + & + & + \\ Antioxidant & + & + & + \\ Antibacterial & + & + & + \\ Antilobesity & + & + & + \\ Anticancer & + & + & + \\ Cardioprotective & + & + & + \\ Hepatoprotective & + & + & + \\ Immunomodulatory & & & + \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the potential pro-health properties of _Carthamus tinctorius_ L., _Camelina sativa_ L., and _Nigella sativa_ L. based on the analyzed data. The symbol “+” indicates specific properties noted for a given plant based on analyzed literature.  
tissues [8]. However, this issue is still intensively researched. In various studies regarding the anti-inflammatory properties of safflower, researchers' attention is attracted to various parts of the plant (flowers, leaves, and seeds). In a study by [PERSON] et al., the ethanol extract of safflower leaves was used to test its anti-inflammatory potential. The results showed that the ethanol extract of _C. tinctorius_ leaves protects the lipopolysaccharide-induced HaCaT (human keratinocyte cell line) cells by inhibiting the expression of inducible nitric oxide synthase (iNOS), as well as nitric oxide (NO), interleukin-6 (IL-6), and interleukin-1 beta (IL-18) production. These results indicate that safflower leaves are a potential candidate to prevent inflammation-related diseases [11]. The results of research conducted by [PERSON] et al. [12] showed that polyacetylene glucosides isolated from the florets of safflower significantly inhibited LPS-induced NO production in RAW 264.7 cells (macrophage cell line) in a dose-dependent manner. Four compounds derived from safflower seeds, acacetin, cosmosinin, N-feruloyl serotonin, and N-(p-coumaroyl) serotonin, restrained formation of LPS-stimulated NO and cytokines that cause or enhance inflammatory responses [13].
## 5 Conclusions  
Due to the production-process characteristics of the downward slicing and filling method, there are many vertical distribution weak filling surfaces in the roof of the filling body in the mining approach. Indoor tests and numerical simulation were adopted, and numerical simulation comparisons and analysis are carried out on different upper- and lower-layered mining approach layouts considering filling weak surfaces. The following conclusions are drawn.  
1. The mechanical strength of filling weak surface is much lower than that of surrounding filling body;
2. The shear failure process of filling weak surfaces can be divided into two stages: strain-softening stage and strain-hardening stage. With an increase in normal load, the peak shear stress of a filled weak surface rises, and the strain migrates back to the peak shear stress;
3. Comparing the numerical simulation results of various schemes, it can be seen that the upper- and lower-layered mining method is the best one when the arrangement is vertical; thus, it is advised that the real engineering mining route arrangement be as similar to the vertical layout as possible. After the mining approach excavation, the mechanical condition of the roof is most similar to the design without covering the weak surface.  
This research combined together the direct shear test of weak surfaces of a filling body in the chamber and the stability of filling roofs under different layered mining approaches, which was numerically simulated and analyzed by means of a medium interface unit in FLAC\({}^{\text{3D}}\). The optimal layered layout scheme of upper and lower layers is determined,which can provide a theoretical basis for mines adopting the downward horizontal layered cemented filling method.  
Conceptualization, K.Z.; methodology, N.L. and W.W.; software, N.L. and W.W.; validation, P.Z.; formal analysis, P.Z. and W.W.; investigation, P.Z. and W.W.; resources, C.G. and H.L.; data curation, L.X.; writing--original draft preparation, N.L.; writing--review and editing, P.Z. and C.G.; visualization, L.X.; supervision, K.Z.; project administration, P.Z.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Primary Research and Development Plan of Jiangxi Province, grant number 20212 BBG71009; the Jiangxi Postdoctoral scientific research project, grant number 2020K739; and the Scientific and Technological Research Projects of the Education Department of Jiangxi Province, grant number GJJ2200824.  
Not applicable.  
The authors would like to acknowledge the Jiangxi University of Science and Technology.  
The authors declare no conflict of interest.
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 4 Experiments and Results
### Evaluation Metrics  
In order to assess the performance of skin disease image segmentation models in clinical settings, it is common practice to use several standard metrics for evaluation. In this experiment, we chose to use five metrics: accuracy (_Acc_), Dice similarity score (_Dice_), specificity (_Spec_), sensitivity (_Sens_), and mean intersection over union (_mIoU_).  
Specifically, _Acc_ represents the proportion of pixels correctly classified in the segmentation result to the total pixels, _Dice_ represents the degree of overlap between the predicted mask and the ground truth mask, _Spec_ represents the fraction of pixels classified as negative samples among all negative samples, _Sens_ represents the fraction of pixels classified as positive samples among all positive samples, and _mIoU_ calculates the intercept ratio between the predicted result and the ground truth result.  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
\[Dice=\frac{2 TP}{2 TP+FP+FN} \tag{10}\]  
\[Spec=\frac{TN}{TN+FP} \tag{11}\]  
\[Sens=\frac{TP}{TP+FN} \tag{12}\]  
\[mIoU=\frac{TP}{TP+FP+FN} \tag{13}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Images** & **Size** & **Train** & **Valid** & **Test** & **Task** \\ \hline ISIC2017 & 2000 & Variable & - & - & 300 & Binary Segmentation \\ ISIC2018 & 2596 & Variable & 1800 & 260 & 536 & Binary Segmentation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the datasets.  
where _TP_, _FP_, _TN_, and _FN_ represent true positive, false positive, true negative, and false negative. By calculating these five metrics for the predicted masks and ground truth masks of each case in the real dataset and comparing them with other popular models, we can evaluate the performance of the improved skin disease image segmentation model and select the best model for practical applications.