esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Matrices for Storing Model Data
#### 2.2.2 Structural Matrix  
In this study, the shortest path length information, the number of shortest paths, and reachability states are stored using three structural matrices: the shortest path length matrix (SPLM), the shortest path number matrix (SPNM), and the reachability matrix (RM). These matrices are used to characterize the topological relationships between nodes in the network. Their expressions are defined as follows:  
\[SPLM_{ij}^{(m)}=\begin{cases}\sum\limits_{a,b\in PV_{ij}^{(m)}}AM_{ab}^{(m)},\;|\;PV_{ij}^{(m)}\
eq\Phi\\ \hskip 142.26378 ptInf,\;|\;PV_{ij}^{(m)}=\Phi\end{cases}, \tag{4}\]\[SPNM_{ij}^{(m)}=\left\{\begin{array}{c}spnm_{ij}^{(m)},\ |\ PV_{ij}^{(m)} \
eq\Phi\\ 0,\ |\ PV_{ij}^{(m)}=\Phi^{{}^{\prime}},\end{array}\right. \tag{5}\]  
\[RM_{ij}^{(m)}=\left\{\begin{array}{c}1,\ |\ SPLM_{ij}^{(m)}\
eq Inf\\ 0,\ |\ SPLM_{ij}^{(m)}=Inf^{{}^{\prime}}\end{array}\right. \tag{6}\]  
where  
* \(AM_{ij}^{(m)}\): Adjacency matrix of \(RSCN^{(m)}\);
* \(PV_{ij}^{(m)}\): The set of nodes on the shortest path from node \(V_{i}^{(m)}\) to node \(V_{j}^{(m)}\);
* \(a,b\): Represent any nodes within the set \(PV_{ij}^{(m)}\);
* \(spnm_{ij}^{(m)}\): The number of shortest paths from node \(V_{i}^{(m)}\) to node \(V_{j}^{(m)}\);
* \(Inf\): Indicates an infinite path length, meaning no shortest path exists between node \(V_{i}^{(m)}\) and \(V_{j}^{(m)}\);
* \(\Phi\): The empty set, signifying no nodes exist along the shortest path.  
The SPLM and SPNM matrices, respectively, characterize the structural features of RSCN from the perspectives of shortest path lengths and the number of shortest paths between nodes. In contrast, the RM describes the network from the perspective of whether indirect connections exist between nodes.
## 2 Potential Application Areas of CR-WSNs  
CR-WSNs may have a wide range of application domains. Indeed, CR-WSN can be deployed anywhere in place of WSNs. Some examples of prospective areas where CR-WSNs can be deployed are as follows: facility management, machine surveillance and preventive maintenance, precision agriculture, medicine and health, logistics, object tracking, telemetries, intelligent roadside, security, actuation and maintenance of complex systems, monitoring of indoor and outdoor environments. This section discusses some of the potential areas where CR-WSNs can be deployed with examples.
# Code Similarity and Location-Awareness Automatic Program Repair
## 4 Empirical Study
### Experimental Result
We found that patch verification consumes a huge amount of time when repairing bugs. Figure 10 shows the time-consumption situation of SLARepair's patch verification on 43 repairable bugs. The horizontal axis represents the bug Id, the main vertical axis represents time (unit: minutes), and the secondary vertical axis represents the percentage of patch verification in the total repair time. It can be seen from Figure 10 that for most bug repairs, patch verification takes much longer than other runtimes, such as positioning and searching. Patch verification accounts for the smallest proportion (37.4%) of the total repair time and the largest (98.7%). SLARepair repairing a bug requires an average of 79.4% time consumption for patch verification. The reason for this phenomenon is that SLARepair needs to rely on the fitness function to guide the patch search process, and the calculation of the fitness function is determined by the code similarity and the number of passed test cases. The calculation of code similarity includes collecting the program identifier of the buggy program and the candidate patch program at each suspicious location and calculating the length of the longest common subsequence of the identifier, which does not require excessive computing resources. Calculating the number of test cases passed by the patch needs to drive the entire buggy program to execute all test cases, which requires a lot of computing resources. On the other hand, from Table 3, SLARepair needs to generate 29 candidate patches on average to repair a bug, which means that every time a bug is repaired, the entire bug program needs to be driven to execute thousands of test cases dozens of times. Therefore, patch verification consumes huge computing resources in the repair process. This is also an important problem in all current search-based automatic program repair methods. In summary, the repair ability of SLARepair under the three evaluation criteria is significantly better than the original GenProg repair method based on genetic programming. On the one hand, SLARepair's higher repair ability comes from code similarity, which effectively guides the patch search process. On the other hand, it comes from the reduction of the search space caused by location-awareness repair. In terms of the number of repairable faults, SLARepair is also superior to other repair methods. Figure 9: Compares the number of bug repairs on Defects4J between SLARepair and 4 repair methods. (**a**) Chart, (**b**) Lang, (**c**) Math, (**d**) Time.
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 3 Preliminaries
### K-Means Clustering  
K-means clustering is one of the most popular unsupervised learning methods. The main goal of K-means is to group similar data instances together and find patterns in the given datasets. To achieve this goal, K-means defines number of clusters (\(K\)) and then groups the similar elements into these clusters. It starts by selecting the centroids, which are the starting points of the clusters. In the next step it assigns the instances to the closest centroid and then updates the positions of the centroids iteratively until the centroids are stabilized or the predefined maximum number of iterations is reached.  
Given a dataset of \(n\) instances \(S=\{x_{1},\ldots,x_{n}\}\in R^{d}\), and an integer number \(K\), K-means algorithm aims to find \(C=\{c_{1},\ldots,c_{K}\}\), the set of centroids with respect to the following error function:  
\[E(C)=\sum_{x\in S}\underset{i=1,\ldots,K}{\min}\left\|x-c_{i}\right\|^{2} \tag{1}\]  
As mentioned before, K-means assigns instances to one of the specified clusters according to the similarity between them. To measure the similarity, it usually uses the Euclidean distance between the instance and the centroids.
## 2 Related Works  
### Power Line Detection  
The existing image-based methods for power line detection can be divided into traditional and deep-learning-based methods, as listed in Table 1. Traditional methods have focused on low-level local features, such as gradient, luminance, texture, and other prior information. Power lines are assumed to be straight lines or polynomial curves with the lowest intensity in the image and parallel to each other. [PERSON] et al. [20] adopted Radon transform to extract line segments, and then connected the segments into the whole line using the grouping method and the Kalman filter. [PERSON] et al. [21] proposed a knowledge-based power line detection method using the Pulse Coupled Neural Network (PCNN) to remove background noise from the images. [PERSON] et al. [22] proposed an adaptive thresholding approach, Hough transforms and the Fuzzy C-Means (FCM) clustering algorithm for power line detection, removing spurious lines using the properties of power lines. [PERSON] et al. [23] proposed a method called Circle-Based Search (CBS) for detecting power lines by searching for lines between two opposite points. [PERSON] et al. [24] proposed a sequential local-to-global power line detection method based on a graph-cut model. However, the limitations of these methods are still obvious when applied to a real environment. For instance, manually tuning dozens of parameters makes it difficult to achieve the optimal result for each image during the inspection. Thus, when the parameters are fixed, the methods tend to produce more false positives and negatives on a dataset.  
Deep learning-based methods have a strong ability to learn multiscale features and perceive global information, and they can produce high-level representations of objects in natural images. State-of-the-art CNN-based edge detectors, such as Holistically Nested Edge Detection [25], DeepContour [26] and DeepEdge [27], can be applied to produce very-high-quality edge maps. Then, the edge maps can be used by traditional straight-line detection methods (e.g., Hough transform). [PERSON] et al. [28] developed an accurate power line detection method using convolutional and structured features, improving the detection accuracy. [PERSON] et al. [29] treated power line detection as a semantic segmentation task, adopting an expansive convolutional network to develop a power line detection framework. Semantic segmentation using CNN is a highly accurate method. CNNs are robust to change in illumination and scenarios, reducing the chances of false positives and negatives. However, it is well known that CNN, and particularly segmentation networks (e.g., SegNet [30] or DeepLab [31]), usually have a high computational cost. This fact is crucial for FPLIR due to payload limitations and the need for real-time detection. The STDC-Seg [32; 33; 34] is used to address this problem, as it is able to provide real-time semantic segmentation with low-computing cost and high accuracy.
# Anomaly Detection in Microservice-Based Systems
## 2 Data and Materials
### Neural Networks  
Multi-layer Perceptron (MLP) neural networks are widely employed in anomaly detection tasks, mainly when dealing with small datasets that exhibit non-linear relationships [53]. In the context of monitoring data in a microservices system, where complex relationships may exist between different metrics, MLPs offer a suitable modeling approach. MLPs are known for their flexibility and ability to capture intricate patterns in data [54], making them well-suited for handling the diverse and interconnected nature of monitoring data in microservice architectures.  
The structure of an MLP consists of an input layer, one or more hidden layers, and an output layer [55]. Each layer comprises multiple artificial neurons or nodes that process incoming data and pass it through a series of weighted connections. Non-linear activation functions, such as the sigmoid or rectified linear unit (ReLU), are typically applied to introduce non-linearity and enable the network to capture complex relationships. During  
Figure 3: Representation of the pairwise relationships of the service-level anomaly dataset.  
training, the network learns to adjust the weights of the connections through backpropagation, iteratively refining its ability to map input data to the desired output.  
Monitoring data in a microservices system often suffers from incompleteness or errors due to various factors. MLPs can handle such noisy data gracefully, thanks to their inherent ability to generalize and extract meaningful patterns from imperfect information [56; 57]. By learning from the available data, MLPs can identify abnormal patterns and flag them as potential anomalies, even in the presence of noise.  
In the context of anomaly detection tasks, MLP neural networks have been extensively employed to detect deviations from normal behavior in various domains. Researchers and practitioners have successfully applied MLPs to monitor system logs [58], network traffic [59; 60], sensor data [61; 62], and other sources of information to identify anomalies. By training MLPs on historical data that encompasses normal behavior, the networks learn to recognize patterns and predict expected outcomes. During the testing phase, when presented with unseen data, MLPs can compare the observed behavior against what they have learned and detect anomalies that deviate significantly from the expected patterns. This ability to learn from historical data and generalize to new instances makes MLPs valuable tools for anomaly detection in microservices monitoring systems.  
MLP neural networks offer a powerful approach to anomaly detection in microservices monitoring. Their flexible architecture, capable of capturing non-linear relationships, makes them suitable for handling complex interdependencies within monitoring data. Additionally, their robustness to noisy data enables them to operate effectively in real-world scenarios where data quality may be compromised. By leveraging MLPs, anomaly detection systems can uncover hidden anomalies and contribute to microservices architectures' overall stability and reliability.
## 4 Properties of MoT
### Radionuclides  
Before discharged and used as a building material, the radionuclides in MoT should be assessed. According to GB 6566-2010 [18], the specific activity of Ra\({}^{226}\), Th\({}^{232}\) and K\({}^{40}\) in the material should not exceed the limits depending on the different usage. In this study, MoT was used as the main material of building applications. Therefore, the internal exposure index \(I_{Ra}\) and external exposure index \(I_{y}\) of MoT should be derived and meet the requirements as follows:  
\[I_{Ra}=C_{Ra}/200\leq 1.0 \tag{1}\]  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Material** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **CaO** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** & **TiO\({}_{2}\)** & **P\({}_{2}\)O\({}_{5}\)** & **MnO** & **SO\({}_{3}\)** & **Ignition** \\ \hline MoT in Shaanxi Province [17] & 72.38 & 3.88 & 9.19 & 1.08 & 2.25 & 0.27 & 1.93 & 1.06 & 0.13 & 0.22 & 5.00 & 2.56 \\ \hline MoT in Henan Province [16] & 56.23 & 9.12 & 3.48 & 3.34 & 9.78 & 1.77 & 1.06 & 5.89 & 0.65 & – & 0.13 & 1.10 \\ \hline MoT in Zhejiang Province [5] & 42.86 & 5.13 & 18.98 & 2.57 & 24.14 & 0.35 & 0.23 & 0.46 & 0.06 & 1.82 & 0.92 & 2.48 \\ \hline Natural sand & 71.34 & 8.89 & 5.73 & 1.16 & 5.49 & 1.56 & 1.66 & – & – & – & 0.19 & 3.98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of MoT and natural sand (mass fraction)/%.  
Figure 1: Microscopic structure of MoT [13]. (**a**) tailing sand; (**b**) MoT (+0.16 mm); (**c**) MoT enlarged 40 times (+0.16 mm); (**d**) MoT enlarged 100 times (+0.16 mm).  
\[I_{\gamma}=C_{Ra}/370+C_{Th}/260+C_{K}/4200\leq 1.0 \tag{2}\]  
where \(C_{Ra}\), \(C_{Th}\) and \(C_{K}\) stand for the specific activity of Ra\({}^{226}\), Th\({}^{232}\) and K\({}^{40}\) in MoT respectively.  
Table 2 shows the radionuclide assessment of the MoT in Zhejiang and Shaanxi province respectively [5; 19]. It can be seen that the internal exposure index \(I_{\text{Ra}}\) and external exposure index \(I_{\gamma}\) of the MoT in these two provinces both meet the requirements for using as structural materials. Due to the different chemical composition as mentioned above, the radionuclide indexes of the MoT also vary from the mineral resources.
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 4 Some Special Cases
### Gamma-Gamma Distribution Channels  
The Gamma-Gamma distribution is derived by letting \(\rho=1\) and \(\Omega^{\prime}=1\) in the M distribution (2). Therefore, the PDF of the Gamma-Gamma distribution is written as  
\[f_{H_{n}}(h)=\frac{2(\alpha_{n}\beta_{n})\frac{\alpha_{n}+\beta_{n}}{2}}{\Gamma( \alpha_{n})\Gamma(\beta_{n})}h^{\frac{\alpha_{n}+\beta_{n}}{2}-1}K_{\alpha_{n }-\beta_{n}}\left(2\sqrt{\alpha_{n}\beta_{n}\mu}\right),\ h\geq 0 \tag{43}\]  
With this channel, the lower bound of SOP in (16) becomes  
\[P_{\text{SOP}}^{\text{L}}= 1-\frac{(1+\gamma_{\text{th}})^{\frac{\alpha_{n}+\beta_{n}}{4}}}{ \Gamma(\alpha_{\text{B}})\Gamma(\beta_{\text{B}})\Gamma(\alpha_{\text{E}}) \Gamma(\beta_{\text{E}})}\bigg{(}\frac{\alpha_{\text{B}}\beta_{\text{B}}\alpha _{\text{B}}}{\alpha_{\text{E}}\beta_{\text{E}}\alpha_{\text{E}}}\bigg{)}^{ \frac{\alpha_{n}+\beta_{n}}{2}}G_{3,3}^{3}\bigg{[}\frac{\alpha_{\text{B}} \beta_{\text{B}}\alpha_{\text{B}}}{\alpha_{\text{E}}\beta_{\text{E}}\alpha_{ \text{E}}}\bigg{]}^{\frac{\alpha_{n}+\beta_{n}}{2}}G_{3,3}^{3}\bigg{[}\frac{ \alpha_{\text{B}}\beta_{\text{B}}\alpha_{\text{B}}\sqrt{1+\gamma_{\text{th}} }}{\alpha_{\text{E}}\beta_{\text{E}}\alpha_{\text{E}}}\bigg{]}^{\frac{2-2\alpha _{\text{E}}-\alpha_{\text{E}}-\beta_{\text{B}}}{2}}\frac{2-2\beta_{\text{E}}- \alpha_{\text{B}}-\beta_{\text{B}}}{2}}{\frac{4\alpha_{\text{B}}-\beta_{ \text{B}}}{2}}\frac{2-2\beta_{\text{E}}-\alpha_{\text{B}}-\beta_{\text{B}}}{2 }\bigg{]} \tag{44}\]  
Moreover, the probability of SPSC in (20) reduces to  
\[P_{\text{PSPC}}=1-\frac{1}{\Gamma(\alpha_{\text{B}})\Gamma(\beta_{\text{B}}) \Gamma(\alpha_{\text{E}})\Gamma(\beta_{\text{E}})}\bigg{(}\frac{\alpha_{\text {B}}\beta_{\text{B}}\alpha_{\text{B}}}{\alpha_{\text{E}}\beta_{\text{E}}\alpha _{\text{E}}}\bigg{)}^{\frac{\alpha_{n}+\beta_{n}}{2}}G_{3,3}^{3}\bigg{[}\frac{ \alpha_{\text{B}}\beta_{\text{B}}\alpha_{\text{B}}}{\alpha_{\text{E}}\beta_ {\text{E}}\alpha_{\text{E}}}\bigg{]}^{\frac{2-2\alpha_{\text{E}}-\alpha_{ \text{E}}-\beta_{\text{B}}}{2}}\frac{2-2\beta_{\text{E}}-\alpha_{\text{E}}- \beta_{\text{B}}}{2}-\frac{2-\alpha_{\text{E}}-\beta_{\text{B}}}{2}\bigg{]} \tag{45}\]  
Furthermore, the lower bound of ESC in (32) becomes  
\[C_{\text{Eng}}^{\text{L}} =\Bigg{\{}\frac{1}{4\pi\Gamma(\alpha_{\text{B}})\Gamma(\beta_{ \text{B}})}\bigg{(}\frac{\alpha_{\text{B}}\beta_{\text{B}}\alpha_{\text{B}}}{ \sqrt{P}}\bigg{)}^{\frac{\alpha_{n}+\beta_{n}}{2}}G_{2,6}^{6,1}\bigg{[}\frac{ \alpha_{\text{B}}\beta_{\text{B}}\alpha_{\text{B}}}{16P}\bigg{]}^{\frac{\alpha _{n}-\beta_{n}}{4}},\frac{\alpha_{\text{B}}-\beta_{\text{B}}+\alpha_{\text{B} }}{4},\frac{\alpha_{\text{B}}-\alpha_{\text{B}}}{6}\frac{1-\alpha_{\text{B}} \alpha_{\text{B}}}{4},-\frac{\alpha_{\text{B}}+\beta_{\text{B}}}{4}-\frac{ \alpha_{\text{B}}+\beta_{\text{B}}}{4}\bigg{]} \tag{46}\]
## 3 Results  
### Maize Yield and NUE  
The combined application of urea and FA solution had a positive effect on maize grain yield and the yield components (Table 2). Compared to U treatment, the yield and aboveground dry biomass of maize in the U+FA treatment were significantly increased by 8.31% and 5.80%, respectively, in 2017 and by 16.90% and 15.84%, respectively, in 2018. Meanwhile, the rows per ear in the U+FA treatment was significantly higher by 18.90% in 2017. The NUE in the U+FA treatment was higher by 17.09 and 24.31 percentage points in 2017 and 2018, respectively. The results showed that combing urea and FA solution not only increased maize grain yield but also improved NUE as compared with the application of urea only.
# A Compact Monopole Wideband Antenna Based on DGS
## 2 Antenna Design Methodology and Optimization Process
### Parametric Analysis
#### 2.5.4 Effect of Thickness of L-Slot Horizontal (T)  
Varying T at 8, 11, and 14 mm indicated that T = 11 mm yielded the best performance. The simulated S\({}_{11}\) for various T values is shown in Figure 7d, while other values resulted in reduced bandwidth.
# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 1 Introduction
* We compare the algorithms used for building the models by evaluating them using specific metrics such as structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) to measure their performance. Furthermore, a visual comparison between the predicted spectral responses and the ground truths is performed to validate the effectiveness of the proposed methods. This approach allows us to identify the best-performing models and optimize techniques for approximating the spectral response of 8-band one-shot MSFA cameras. The rest of our manuscript is organized as follows: Section 2 deals with the state of the art regarding the spectral response of multispectral cameras. Section 3 details the materials and methods used in our approach. Section 4 presents the experiments carried out and the results obtained. Section 5 provides a discussion of these results. Section 6 addresses the threats to validity. Finally, Section 7 presents our conclusion.
# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
## 5 Analysis on _p-y_ Curve  
### Applicability Analysis of API Specification p-y Curve Method  
The _p-y_ curve method is widely used in the analysis of lateral deformation of traditional PF and is adopted by the American Petroleum Institute (API). However, the design theory given by the API specification is based on a PF with a diameter less than 1.5 m, which is not suitable for large-diameter piles. In this subsection, the applicability of the API specification _p-y_ curve to the large-diameter PF was studied by establishing a finite element numerical model with diameters of 2, 4 and 6 m and a thickness to diameter ratio of 1%. According to the empirical calculation, the piles with diameters of 2, 4 and 6 m are rigid-flexible piles, but the pile with a diameter of 2 m is close to the flexible pile. As is shown in Figure 13a, the _p-y_ curve obtained according to the API specification is compared with the _p-y_ curve obtained by the ABAQUS. At shallow depths, the lateral ultimate soil resistance of the soil from the API specification curve is somewhat different from the ABAQUS finite element simulation for the large-diameter pile with a diameter of 2 m. Taking a depth of 2.25 m as an example, the result of ABAQUS is nearly 1.5 times that of the API specification. For the diameter of 4 m, the result is shown in Figure 13b. For the large-diameter pile with a diameter of 6 m, the API specification seriously underestimates the lateral ultimate soil resistance of the soil at a depth of 1 \(d\), but overestimates the lateral ultimate soil resistance of the soil at a shallow depth. As shown in Figure 13c, in the rising part of the curve, when the displacement is small, the curves of the same depth are well fitted, and the result is close. When the displacement is small and the soil is shallow, it is thus obtained that the curve recommended by the API specification still has certain usability in the PF with the pile's diameter as 6 m.  
In summary, the _p-y_ curve of the API specification for the large-diameter PF is close to the flexible pile, and the lateral ultimate soil resistance is underestimated in each layer of soil in relation to real behavior. For rigid or rigid-flexible large-diameter PF, there is an overestimate of lateral ultimate soil resistance in shallow soils; however, it is seriously underestimated in the deep soil. Thus, it can be concluded that the API specification is too conservative.  
Figure 12: The subgrade reaction force distribution of PF and P–BF. (**a**) The subgrade reaction distribution. (**b**) The subgrade reaction distribution. (**c**) The subgrade reaction distribution of PF of P–BF; \(D/h=18\) m/12 m \(D/h=24\) m/6 m. (**d**) The subgrade reaction distribution. (**e**) The subgrade reaction distribution. (**f**) The subgrade reaction distribution of P–BF; \(D/h=24\) m/12 m of P–BF, \(D/h=24\) m/18 m of P–BF, \(D/h=30\) m/12 m.  
Figure 13: _Cont._
## 4 Remote Sensing Platforms Used in Peatland Research
### Airborne-Based Remote Sensing
#### 4.2.3. Peatland Carbon Stock Assessment  
Accurate estimation of carbon stocks is essential for assessing peatlands' climate change mitigation potential [41; 101; 102]. Airborne-based remote sensing, combined with LiDAR technology, has been employed to estimate above-ground biomass and carbon stocks in peatland ecosystems. Studies by [PERSON] et al. (2022) presented methods that rely on high-quality airborne radiometric data and prior knowledge of peatland extent, which offer a valuable approach for international peatland mapping projects, enabling the identification of previously unrecognised peat areas and updates to boundary locations [41].  
This has significant implications for carbon stock assessment, land management decisions, and rehabilitation projects. It is more suitable for large areas with comprehensive datasets exhibiting clear statistical relationships. The findings can serve as a basis for conducting more targeted, region-specific investigations or integrating them into broader peatland mapping initiatives.