esa-sceva/satcom-chunk-collection huggingface dataset:
# Superconducting Phases in Neutron Star Cores
## 4 Phase Transitions with \(\mathbf{H}\)
### The Critical Field, \(H_{\text{c}}\)  
The two simplest solutions of the Euler-Lagrange Equations (16)-(18) are the Meissner (flux-free) state, which has \(|\psi_{\text{p}}|=|\psi_{\text{n}}|=1\) and \(\mathbf{B}=\mathbf{0}\), and the non-superconducting state, which has \(|\psi_{\text{p}}|=0\), \(|\psi_{\text{n}}|=\sqrt{1+\alpha/R^{2}}\) and a uniform magnetic flux density \(\mathbf{B}\) with \(|\mathbf{B}|=2\pi/a\). As discussed earlier, these two states can only be realized if the mutual repulsion/attraction \(g_{\text{pn}}\), and thus \(\alpha\), between the two condensates is sufficiently weak. Expressed in terms of the dimensionless parameter \(\alpha\), this implies the conditions \(\alpha^{2}<R^{2}\epsilon\) and \(\alpha>-R^{2}\). We will assume from here on that both of these are satisfied.  
To determine the thermodynamical critical field, \(H_{\text{c}}\), we equate the energy of the Meissner state with that of the non-superconducting state. By construction, the free-energy density (12) in the Meissner state is \(F=0\), and, hence, its mean Gibbs energy is \(\overline{G}=0\). For a type-I system, the transition to the non-superconducting state therefore occurs when the corresponding energy density \(\overline{G}\) becomes negative.  
Figure 5: Phase diagrams for the two-component Ginzburg–Landau superconductor, for different values of the Ginzburg–Landau parameter, \(\kappa\), and other parameters fixed to the same values as in Figure 2. Both figures are plotted in the same style as Figure 1. Shading of the respective regions is indicative of the magnetic flux distribution. In addition to the phases observed for the single-component case (we label the intermediate type-I phase as M/N), we also obtain inhomogeneous regimes where Meissner and fluxtube regions (M/F) as well as fluxtube and non-superconducting regions alternate. We refrain from labeling the latter to avoid overcrowding the plot. These are associated with the appearance of the critical fields \(H_{\text{c1}^{\prime}}\) and \(H_{\text{c2}^{\prime}}\), respectively.  
The purple, short-dashed curve in Figure 2 represents the free energy per unit length per quantum of flux in the non-superconducting state. From Equation (12), we obtain  
\[\mathcal{F}=\frac{a}{2}\left(1-\frac{\alpha^{2}}{\epsilon R^{2}}\right)+\frac{(2 \tau\kappa)^{2}}{a}\,. \tag{26}\]  
Substituting into Equation (25), we then find that the minimum mean Gibbs energy density, \(\overline{G}\), is achieved for \(a=2\pi/|\mathbf{H}|\), as expected, and that this minimum is  
\[\overline{G}=\frac{1}{2}\left(1-\frac{\alpha^{2}}{\epsilon R^{2}}\right)- \kappa^{2}|\mathbf{H}|^{2}\,. \tag{27}\]  
Thus, assuming that we have a type-I superconductor, there is a first-order transition between the Meissner and the non-superconducting state at  
\[|\mathbf{H}|=H_{\mathrm{c}}\equiv\frac{1}{\sqrt{2}\kappa}\sqrt{1-\frac{\alpha ^{2}}{\epsilon R^{2}}}\,. \tag{28}\]  
This defines the critical field, \(H_{\mathrm{c}}\), in agreement with previous studies [8; 37].
## 3 Methodology
### Object Extraction
These masks, including sparse and natural vegetation masks, forest masks, as well as slope land masks, were all excluded to minimize commission errors--for example, pixels whose annual maximum vegetation index value is less than 0.3 can be considered as sparse vegetation. Forest and slope land mask are generated from the Japan Aerospace Exploration Agency forest map [22] and Shuttle Radar Topography Mission (SRTM) elevation map [23], respectively. The unmasked non-edge pixels were clustered into objects based on the four-neighbor connectedness rule. The morphologic operation of closing was then performed to fill holes and smooth boundaries using a 3 \(\times\) 3 pixel crossing kernel. Figure 5 shows how the workflow is applied to extract objects in a case site. Figure 3: Edge detection based on multi-temporal images where the blue lines denote false edges and black dashed lines denote true edges.
## 6 Assessment of the ELM\(-\)SVM Model
### Symmetric, Non\(-\)Congruent, and Rat\(-\)Independent Hysteresis  
We make advantage of the findings from tests that [PERSON] et al. [31] carried out on an exterior beam\(-\)column joint of a multi\(-\)story concrete frame. They reported \(x\) = drift ratio versus \(y\) = the lateral load of the frame. As shown in Figure 5, the hysteretic behavior is non\(-\)congruent; thus, most Prandtl and Preisach\(-\)type models are not appropriate. Even if the hysteresis loops exhibit same extremum values as the damage index is increased, the loops become non\(-\)congruent. In addition, the hysteresis loops in this example are rate\(-\)independent and non\(-\)Masing. Since the PNN model can only learn congruent Masing hysteresis behaviors [22], they are not helpful in learning non-congruent hysteresis behaviors. The generalized Prandtl neural network (GPNN) was introduced by [PERSON] et al. [22] to recognize deteriorating hysteretic behavior (GPNN). They also used their new proposal in this case. It is possible to train non\(-\)congruent and non\(-\)Masing hysteretic behavior using GPNN. However, it has no universal ability to precisely learn these behaviors because GPNN is a linear combination of different DS neurons. It lacks a nonlinear mapping between DS neurons in the hidden layer and output layer. Therefore, the ELM\(-\)SVM model is superior to GPNN as it uses LS\(-\)SVM for nonlinear mapping. The ELM\(-\)SVM model has been utilized to learn this hysteretic behavior. Figure 4 shows the architecture of the ELM\(-\)SVM model used in this example. As mentioned in the previous section, the number of the DS neurons \(n\) and the value of \(\beta_{max}\) should be appropriately chosen. For this purpose, the grid search method has been utilized, and its results have been depicted in Figure 6. Root mean square error (RMSE) has been considered as a performance measure for comparing different models. Figure 6 shows that the model with 20 DS neurons and \(\beta_{max}=100\) outperforms the others. In Figure 5 hysteresis obtained from the ELM\(-\)SVM has been compared with experimental data, and it can be seen that there are in excellent agreement. For the purpose of showing the efficiency of the ELM\(-\)SVM model, the comparison between the ELM\(-\)SVM response and the other model is helpful. Figure 7a,b depict a comparison between the absolute error value of ELM\(-\)SVM model and GPNN model [22], respectively. It is obvious that ELM\(-\)SVM outperforms GPNN for this experimental data.
# Research on O2O Takeout Delivery Route Optimization Problem Based on Improved Genetic Algorithm
## 5 Experimental Results and Analysis
### Design and Analysis of Experiments
#### 5.3.6 Comparative Experiments on the Weights of Platform Costs and Rider Earnings  
With the lower bound of average customer satisfaction fixed at 80% and the number of riders set to 10, comparative experiments were conducted by varying the platform costs weight \(\omega_{1}\) and the average rider earnings weight \(\omega_{2}\) in steps of 0.1. The experimental results are shown in Figure 6. The results indicate that decision-makers' preferences for balancing platform costs and average rider earnings have a significant impact on the results. On the one hand, as the platform costs weight increases, the platform costs decrease continuously; however, when the weight exceeds 0.7, the rate of decrease slows down. On the other hand, as the average rider earnings weight increases, the average rider earnings also increase accordingly; but, when the weight exceeds 0.7, the rate of increase also slows  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of** & **Delivery** & **Penalty** & **Start-up** & **Platform** & **Average Rider** & **Average** \\
**Riders** & **Cost (CNY)** & **Cost (CNY)** & **Cost (CNY)** & **Costs (CNY)** & **Earnings** & **Customer** \\  & & & & & & **(CNY)** & **Satisfaction** \\ \hline
6 & 117.89 & 0 & 60 & 177.89 & 21.13 & 84\% \\
8 & 121.71 & 0 & 80 & 201.71 & 22.26 & 88\% \\
10 & 123.46 & 0 & 100 & 223.46 & 23.70 & 93\% \\
12 & 130.95 & 0 & 120 & 250.95 & 23.81 & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experimental results for different numbers of riders.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Average** & **Delivery Cost** & **Penalty Cost** & **Start-up Cost** & **Platform Costs** & **Average Rider** \\
**Customer** & **(CNY)** & **(CNY)** & **(CNY)** & **(CNY)** & **(CNY)** \\ \hline
60\% & 116.67 & 3 & 100 & 219.67 & 17.20 \\
70\% & 122.52 & 0 & 100 & 222.52 & 19.42 \\
80\% & 123.46 & 0 & 100 & 223.46 & 23.70 \\
90\% & 130.87 & 0 & 100 & 230.87 & 24.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experimental results for different levels of satisfaction.  
down. Therefore, for decision-makers, in the case of adequate customer satisfaction, if the focus is more on platform costs, the platform costs weight can be set to 0.7; if more attention is paid to average rider earnings, the average rider earnings weight can be set to 0.7; and, if both platform costs and average rider earnings are considered in a balance manner, the weights for both can be set to 0.5, respectively.
## 2 Background
### Petri Net Theory  
In the field of system theory and applications, Petri nets are widely adopted as a formal tool for simulating and analyzing complex systems involving concurrent operations and resource allocation. As an effective system modeling and analysis tool, Petri nets enhance the descriptive capability of system operations, facilitating a deeper understanding of their complexity and interactions. The core of Petri nets lies in their graphical representation, utilizing places (representing states) and transitions (representing changes) to depict the dynamic behavior of the system. Each place can store a certain number of tokens, and the distribution of these tokens reflects the system's state. Through transitions, tokens move between places, thereby simulating system behavior and resource flow. Petri nets possess powerful analytical capabilities, enabling the identification of potential issues within the system and assisting in the design of more efficient and reliable systems. Through visualization and formalization, Petri nets provide system designers with intuitive understanding tools that aid in identifying bottlenecks, reducing costs, and improving efficiency. In modern systems engineering, Petri nets play an indispensable role, offering robust theoretical support and practical guidance for the design and optimization of complex systems.
## 2 Materials and Methods
### Data Pre-Processing and Feature Extraction
#### 2.2.6 Train/Test Split  
The resulting dataset was divided into two segments: (a) a training portion, which includes the examples used to train the model, and (b) a testing portion, which contains the examples used to assess the model's performance. Given the extensive data collected during the experimentation phase, an 80/20 split was chosen for the training and testing datasets, similarly to [24]. This split was carried out at the subject level to ensure that the model's performance could be evaluated on the unique movement characteristics of unseen subjects. For testing, data from four randomly selected subjects were used to have the trained model predict their recorded activities. The remaining data from the sixteen subjects were reserved for training the ML algorithm.
# Workload Assessment of Operators: Correlation Between NASA-TLX and Pupillary Responses
## 4 Results
### Correlation Between NASA-TLX Scores and Pupil Size  
Using the whole dataset, we conducted a correlation analysis to explore the relationships among original mean pupils, adjusted pupil size, and NASA-TLX scores. The results, summarized in a correlation matrix (Figure 3), included Pearson correlation coefficients and scatter plots for visual interpretation. Notably, a moderate positive correlation was found between the original mean pupil and adjusted pupil size (r = 0.513, \(p<0.001\)), indicating that the adjusted pupil sizes were highly associated with the original mean pupil. A weaker but statistically significant positive correlation was observed between the original mean pupil and the NASA-TLX scores (r = 0.312, \(p<0.001\)), suggesting that the increased workload was moderately associated with the larger pupil sizes. In contrast, the correlation between the adjusted pupil size and the NASA-TLX scores was weak and not statistically significant (r = 0.103, \(p=0.11\)). Although not statistically significant, the scatterplots showed positive linear trends between the adjusted pupil size and the NASA-TLX scores.
# Deep Learning-Based Portable Image Analysis System for Real-Time Detection of _Vespa velutina_
## 3 Results
### Performance Evaluation of YOLOv5s Based on Detection Range  
As beekeepers typically place two or three beehives close together, we conducted tests to determine the detection range of the system. For performance evaluation, image data with one, two, or three beehives within the detection range were used. The performance levels of YOLOv5s based on the detection range at the confidence threshold of \(\geq\)0.600 and IoU of \(\geq\)0.500 are as follows (Table 1; Figure 6).  
In image data with one beehive, 2-3 _V. velutina_ objects per hive appeared, resulting in more labels and, consequently, a higher TP rate compared to images with two or three beehives. Additionally, the shorter the distance between the camera and the beehive, the larger the size of the _V. velutina_ objects included in the image data, resulting in a higher detection rate. The detection accuracy for three hives was 82.5%, which is lower than those of one and two beehives (96.7 and 93.8%, respectively). This trade-off indicates that accuracy increases as the distance decreases and vice versa. However, _V. velutina_ could be detected even when monitoring three beehives, with both precision and recall exceeding 90.0% and the mAP_0.5 and F1 score exceeding 0.900. Another advantage is that the detection range becomes wider as the distance increases. Therefore, we decided to monitor three beehives with a larger detection range. Figure 7 shows the detection results for image data containing one, two, or three beehives.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Beehive** & **Distance** & **TP \({}^{*}\)** & **FP \({}^{*}\)** & **FN \({}^{*}\)** & **TN \({}^{*}\)** & **Accuracy \({}^{*}\)** & **Precision \({}^{*}\)** & **Recall \({}^{*}\)** & **F1 Score** & **mAP\_0.5** \\ \hline
1 & 40 cm & 1017 & 10 & 24 & 0 & 96.7\% & 99.0\% & 97.6\% & 0.982 & 0.988 \\
2 & 55 cm & 288 & 4 & 15 & 0 & 93.8\% & 98.6\% & 95.0\% & 0.967 & 0.973 \\
3 & 90 cm & 189 & 19 & 21 & 0 & 82.5\% & 90.8\% & 90.0\% & 0.903 & 0.916 \\ \hline \hline \end{tabular} \({}^{*}\) Confidence threshold \(\geq\) 0.600 and IoU (Intersection of Union) \(\geq\) 0.500, TP - True Positive, FP - False Positive, FN - False Negative, TN - True Negative.  
\end{table}
Table 1: Results of performance evaluation based on detection range.  
Figure 6: Comparison of performances based on detection range: (**a**) Precision, recall, and accuracy; (**b**) mAP_0.5 and F1 score.
## 2 Materials and Methods  
### Determination of Acoustic Emission Parameters  
The pressure machines used for the acoustic emission (AE) tests on rocks under uniaxial compression are INSTRON 1346 electro-hydraulic servo-controlled rigid material testing machines, while those for AE tests on rocks under indirect tensile stress are INSTRON 1342 electro-hydraulic servo-controlled rigid material testing machines. The AE instrument used to measure AE parameters during rock loading is the PCI-2 multi-channel AE testing system produced by the Physical Acoustics Corporation (PAC) in the United States. The AE sensors are R\(\delta\)x resonant high-sensitivity sensors. The AE instrument and sensors are shown in Figure 1. The rock samples used in this study were collected from the vicinity of the 307 stope at the \(-\)70 m level with the sample F1-1. The samples are cuboid in shape, with dimensions of 53.65 mm \(\times\) 52.30 mm \(\times\) 99.50 mm, a weight of 849 g, and a density of 3.04 g/cm\({}^{3}\). The samples exhibit good integrity and show slight weathering on the surface, indicating that their physical properties have not been significantly affected by environmental factors. The samples were stored at the stope for approximately 1-2 years prior to testing, during which they were not subjected to significant mechanical damage or chemical erosion. The number of samples collected meets the requirements for specimen processing, ensuring the reliability and representativeness of the experimental data. As shown in Figure 2, Vaseline was used for coupling between the samples and the AE sensors, and electrical tape was used for fixation. The samples were continuously loaded under uniaxial compression until complete failure, with displacement control and a loading rate of 0.005 mm/s. The AE monitoring instrument had a sampling frequency of 1 MHz and a threshold value of 40 dB, and the AE sensors had a resonant frequency of 150 kHz. The changes in AE events, energy, load, duration, and other parameters during the failure process of the samples were recorded.  
As shown in Figure 3, the curve illustrates that the intensity of acoustic emission (AE) activity has good consistency with the trend of load variation.  
In acoustic emission (AE) research, it is common to randomly select variables for analysis without exploring the correlation between each variable. Factor analysis is a multivariate statistical method that starts with studying the internal correlation and dependence among variables, and it reduces a number of complexly related variables into a few comprehensive factors. The basic idea of factor analysis is to classify observed variables by grouping those with high correlation, i.e., closely related variables, into the same category, while variables in different categories have low correlation. Each category of variables thus represents a basic structure, known as a common factor. The goal of factor analysis in the context of the
## 3 New Compact Models
### The Reduced Model
#### 3.2.1 Selecting the Precedence Constraints involving a Zero-Cost Path  
Zero-cost paths in \(G_{0}\) that start from \(t\) and end in \(s\) for some \((s,t)\in R\) can be retrieved by running the procedure described in Algorithm 1.  
```
1: Compute the shortest path \(SP_{ij}\) for each pair of vertices \(i,j\) of \(G_{0}\)
2:\(V_{0}^{R}=\emptyset\)
3:for all\(t\in V_{0}\)do
4:\(V_{0}(t)=\emptyset\)
5:for all\(s\in V_{0}:(s,t)\in R\)do
6:if\(C(SP_{ij})=0\)then
7:\(V_{0}(t)=V_{0}(t)\cup\{s\}\)
8:endif
9:endfor
10:if\(V_{0}(t)\
eq\emptyset\)then
11:\(V_{0}^{R}=V_{0}^{R}\cup\{t\}\)
12:endif
13:endfor
```  
**Algorithm 1** Retrieve the Relevant Zero-Cost Paths from an Instance  
Line 1 can be implemented by running the algorithm of Floyd-Warshall [43] to retrieve the shortest path between each pair of vertices of a graph. The algorithm has a computational complexity of \(O(n^{3})\). Lines 2-13 scan the results to populate the sets \(V_{0}(t)\), containing vertices involved in relevant zero-cost paths for each vertex \(t\in V_{0}\), and the set \(V_{0}^{R}\), with a total computational complexity of \(O(n^{2})\). Therefore, the overall computational complexity of the procedure remains polynomial, in the order of \(O(n^{3})\). This guarantees negligible computation times for the graphs considered in the study and in real applications of the problem, for which \(n\) does not exceed 700.
## 2 Basic Method Principles
### Particle Swarm Optimization  
The PSO algorithm is an iterative optimization tool, and has advantages such as global optimization and a fast convergence speed [31]. In PSO, the solution to the problem is considered particles in the search space. Each particle represents a potential solution, and its fitness value is determined by an objective function (e.g., the mean square error between permeability coefficient sample values and predicted values, as shown in Equation (9)). The velocity of a particle determines its direction and distance of movement. The particle positions are dynamically adjusted based on the movement experience of the particle and that of other particles to achieve optimization in the feasible space.  
\[fitness=\frac{1}{n}\frac{1}{m}\ \sum_{i=1}^{n}\sum_{j=1}^{m}\Big{(}k_{ij}-k_{ij}^{ *}\Big{)} \tag{9}\]  
In the equations, \(k_{ij}\) and \(k_{ij}^{*}\) are the permeability coefficient sample value and predicted value for the \(i^{\text{th}}\) monitoring point and \(j^{\text{th}}\) sample, respectively; \(n\) is the number of monitoring points; \(m\) is the number of samples.  
PSO first generates a set of initial particles and assigns random values to the initial positions and velocities of the particles. By computing the fitness value of the particles, individual and global best positions are determined. Then, an iterative approach is used to seek the optimal solution. During the iteration process, the positions and velocities of particles are updated according to Equations (10) and (11), which involve tracking both individual and global best values. The individual best value is the best solution found by each particle in each search and denoted as the individual best \(P_{id}\). The global best value is the best solution found by all particles in the swarm in each search and denoted as the global best \(P_{gd}\) until convergence is achieved or the maximum iteration steps are reached.  
\[V_{id}^{t+1}=\omega V_{id}^{t}+c_{1}r_{1}\big{(}P_{id}^{t}-X_{id}^{t}\big{)}+c_{ 2}r_{2}\Big{(}P_{gd}^{t}-X_{id}^{t}\big{)} \tag{10}\]  
\[X_{id}^{t+1}=X_{id}^{t}+V_{id}^{t+1} \tag{11}\]  
where \(\omega\) is the inertia weight; \(d\) = 1,2,..,\(D\), \(D\) denotes the dimensions of the search space; \(i\) = 1,2,.., \(N\), \(N\) represents the particles in the swarm; \(t\) is the current iteration number; \(V_{id}\) is the velocity of the \(i^{\text{th}}\) particle; \(c_{1}\) and \(c_{2}\) are the acceleration coefficients with values of (0,2); \(r_{1}\) and \(r_{2}\) are random numbers between (0,1); \(V_{id}^{t}\) and \(V_{id}^{t+1}\) are the current and updated velocities of the \(i^{\text{th}}\) particle, respectively; \(X_{id}^{t}\) and \(X_{id}^{t+1}\) are the current and updated positions of the \(i^{\text{th}}\) particle, respectively.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 4 An Overview of Deep Learning Techniques
### UNet  
The name UNet is derived from the symmetric shape of the architecture [23], as shown Figure 8, and it is one of the evolved forms of CNNs. UNet has been widely used for medical video segmentation to detect polyps, blood clots, etc. Two characteristics of UNet make it a suitable choice for video data. Firstly, it performs well even when limited training data are available. Secondly, since it does not have any fully connected layers, there is no limitation on the size of the input. As medical videos are rich in content, having no input size restrictions assures effective ROI segmentation.