esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evolutionary Algorithms
#### 2.2.1 fugeR  
The first framework named _fugeR_[24] delivers solutions to develop fuzzy logic (FL) models by applying evolutionary algorithms fundamentals. Fuzzy logic was introduced by [PERSON] [25], who claimed intermediate values between absolute true and absolute false, so called many-valued logic. The _fugeR_ uses a genetic algorithm to evolve a population of the fuzzy logic models. In general, solution development starts by generating a random population of fuzzy systems, test them with available data, and sort them based on the goodness of fit metric. The following generations are created as decedents of the best performing models by applying cross-over function and random mutations. At the end of the process, the fuzzy system with the best performance is returned. The _fugeR_ framework delivers enclosed functions to develop FL models using EA. However, to make model development process efficient and in line with the adopted standards, a wrapper software was created [18]. The system was built using Bash and R languages and works under Linux OS. It allows generating computational tasks with grid-like generated parameters including: learn and test dataset files, factors pass to _fugeR.run()_ function for model development and scheme of models creation and assessment. Such approach allows testing developed models according to k-fold cross-validation standards and returns model performance as structured reports. Moreover, parallelization of the computational tasks speeds up the model development process and allows to find global problem solution. The general pipeline presenting the process of model development and testing process using the software is shown in Figure 1. During computational experiments, both model architectures and optimization settingswere tuned. The maximum number of rules differed from 10 to 50 with 1 to 20 variables within every fuzzy rule. The number of fuzzy membership functions (singletons) varied from 5 to 15. The learning strategy was controlled by population size (from 50 to 500) and generations number (from 100 to 500).
## 2 System Model  
It is observed from the scenarios described in [PERSON] et al. (2016); [PERSON] et al. (2016) that satellites can be provided on a secondary basis, and the primary services can be either terrestrial or satellite-based. The architecture and system model of CSS for LEO mobile satellite services is established as depicted in Figure 1. We consider the CR network consisted of K SUs (i.e., earth stations or sensors) and one LEO satellite (i.e., FC), which are taken as secondary system. Besides, the primary system is composed of PU licensed in the incumbent satellite services or terrestrial networks operating in the same spectrum, such as 3G mobile telephony, LTE, WiMax and WiFi. We define two types of links that we call sensing link (i.e., from PU to SU) and reporting link (i.e., from SU to FC). In this system model, there are broadly three steps: first, each SU implements local sensing to independently detect the presence or absence of PU via sensing link; second, SU reports the detection result to FC through reporting link; and third, FC combines the results with hard counting fusion rule [PERSON] et al. (2017); [PERSON] et al. (2017) to make a global decision about the state of primary system and then reports back to SU. Only when the primary system is detected to be idle can the SU use the licensed frequency for transmission. Once any of PU activity is found, the secondary system must fleetly vacate the spectrum to avoid interference to PU. We assume that all the SUs in the coverage of satellite can sense the PU. The signals received at SU may come from terrestrial or satellite-based primary users spreading over a wide band. The employed CS-based approach has been developed to detect PU using sub-Nyquist rate samples to reduce the burden on the ADC in wideband spectrum sensing (WSS). In the compressed settings, the problem of PU detection can be formulated as a testing of two hypotheses: one is Hypothesis \(\mathcal{H}_{0}\) when PU is absent, the other is hypothesis \(\mathcal{H}_{1}\) when PU is present. The problem now is converted to distinguish between \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\) in the following way:  
\[ \begin{split}\mathcal{H}_{0}&:\,y=\Phi n\\ \mathcal{H}_{1}&:\,y=\Phi(\textit{ls}+n\,)\end{split}  \tag{1}\]  
where \(y\) is an \(M\times 1\) compressive-sampled received signal. \(\Phi\) is a compressive measurement matrix, i.e., \(\Phi\in\mathbb{R}^{M\times N},M\leq N\), which can be generated by Gaussian random matrices, Bernoulli random matrices, database-friendly matrices and random orthoprojection to \(\mathbb{R}^{M}\). \(M\) and \(N\) are positive integer that depict size of the vector and matrix. \(s\) is an \(N\times 1\) primary signal vector, and \(s\in\mathbb{R}^{N}\) with power \(\sigma_{s}^{2}=\|s\|_{2}^{2}\), \(n\) is Additive White Gaussian Noise (AWGN) which is assumed as independent and identically distributed (i.i.d.) random process with zero mean and variance \(\sigma_{n}^{2}\), i.e., \(n\sim N(0,\sigma_{n}^{2}I_{N})\), \(h\) is the channel gain between SU and PU.
# A Review of Document Image Enhancement Based on Document Degradation Problem
## 5 Conclusions  
In recent years, many network models have been proposed to solve various degradation problems in document image enhancement. This article discusses six kinds of image enhancement classification problems, such as shadow removal under uneven illumination, BEDSR-Net, CANet, etc., that are proposed to solve this problem. For example, DeblurGAN, Blur2 sharp, etc., are also offered to solve the image recovery problem for blurred text. These methods can handle specific types of degradation problems very well. However, document image enhancement tasks still have some issues. For example, because of the small number of open-source datasets, many methods use GAN models to generate data, which leads to the generation of data that need to be sufficiently considered. It is not enough to be used in a wide variety of factual scenarios, and even though the models are effective for such data, the generalization ability of the models is not sufficient. Many of the current methods are based on machine learning methods for processing, but machine learning methods require data to be trained for learning. Therefore, sufficient datasets are also needed. Sufficient datasets can better refine the data and optimize the results. In the future, models trained with suitable datasets will be better able to handle document image enhancement problems.  
Conceptualization, Y.Z. and R.Z.; methodology, Y.Z.; software, Z.Y.; validation, Y.Z., J.H. and J.S.; formal analysis, Y.Z.; investigation, Z.Y.; resources, J.H.; data curation, J.S.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; visualization, R.Z.; supervision, S.Z.; project administration, S.Z.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Fujian Province grant number 2020J01295. And The APC was funded by Natural Science Foundation of Fujian Province.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 2 Analysis of Safety Distances for Overhead Line Conductors Under Ice-Covered Conditions
### Safety Distance Analysis of Overhead Lines  
When ice is abruptly removed from a transmission line, the tensioned line relaxes, causing the conductor to experience vertical displacement due to elastic potential energy and subsequently to oscillate. This alters the spacing between the conductor and ground lines. To assess the risk of phase-to-ground discharge post-de-icing, it is essential to calculate the minimum safe distance between the conductor and ground lines.  
In triangular and longitudinal arrangements, the conductor and ground wire are positioned at an angle, not in the same vertical plane. However, during the de-icing jump, the conductor-ground line spacing decreases. When analyzing parallel transmission lines (ignoring crossing lines), the minimum clearance between phases and ground occurs when the conductors and ground wire are on the same horizontal plane. In Figure 2, \(S_{1}\) represents the original conductor-ground line distance before de-icing, and \(S_{2}\) denotes the significantly reduced spacing after de-icing.  
By extracting the spatial coordinates of each point on the ground wires at different moments, the minimum distance between the ground wires can be calculated using the Pythagorean theorem. The expression for the minimum distance between ground wires is as follows:  
\[S_{2}=\sqrt{{S_{3}}^{2}+{S_{4}}^{2}} \tag{1}\]
# Fuzzy Decision Algorithm for Health Impact Assessment in a 5G Environment
## 5 Fuzzy Algorithm for the Assessment of 5G's impact on Health  
Researchers working on health and environmental issues often deal with vague concepts. The fuzzy algorithm (Figure 7) is the logical link between the conceptual pathway (Figure 2) and the fuzzy system (Figure 8) used for fuzzy modeling. Since the 5G network covers several RF bands, the algorithm for assessing the impact of the 5G network on health (Figure 7) consists of two parts. The first (left) part of the algorithm refers to RF frequencies up to 30 GHz and presents thermal effects analysis using ICNIRP 2020. The second (central and right) part of the algorithm refers to the analysis of the frequency range from 30 GHz to 100 GHz (the frequency range marked in gray in Figure 1).  
Figure 6: Electromagnetic spectrum with the zoom of frequency ranges from 300 MHz to 300 GHz [22; 23].  
sets make use of linguistic variables to express uncertain or vague reasoning. They are expressed by membership functions that assign a degree of membership to a fuzzy set. Some commonly used fuzzy logic equations include:  
1. [PERSON]'s fuzzy inference method is used to map input variables to output variables using a set of fuzzy rules. The output is obtained by taking the weighted average of the rule outputs.
2. [PERSON]'s fuzzy inference method represents the output as a linear combination of the inputs. Its ability to handle complex systems with both continuous and discrete inputs makes it a popular choice for applications ranging from control systems to data analysis.
3. Fuzzy clustering is used to group items based on similarity using a fuzzy membership function.
4. Fuzzy decision trees are used to represent decision-making processes based on fuzzy rules.
5. Fuzzy neural networks are used to learn patterns and relationships in data using fuzzy logic.  
Given the conflicting opinions on the benefits and negative impacts of the 5G network, this paper presents the fuzzy modeling of several scenarios based on RFs' impact on health. The paper presents a few fuzzy models based on fuzzy decision theory and fuzzy logic systems (FLS) [24]. There are a few types of fuzzy sets [25; 26].  
The measurement methodologies used for developing decision-making algorithms are affected by different uncertainties. Many of them could be processed using Fuzzy logic systems type-1 (FLS-T1) which has clear and precise membership functions. Application of FLS-T1 may be insufficient in the following cases: different expert opinions, the impact of the unknown EMF sources, spatial allocation of measurement points, the proximity influence of objects and obstacles, the weather conditions, etc. In such cases, fuzzy logic systems type-2 (FLS-T2) is used. FLS-T2 is characterized by a fuzzy membership function, whose values also belong to the interval [0, 1]. These functions are three-dimensional and include a footprint of uncertainty.
# Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots
## 4 Discussion
### Different Numbers of Robots  
In swarm foraging, the total number of robots plays an important role in determining the foraging performance. To test how the number of robots affects the swarm foraging, we ran experiments for a swarm with \(N_{r}=30\) and keeping the other parameters the same as in the comparison experiments.  
As can be seen in Figure 14, the DRTM (\(V_{f}\) = 3.17) maintains a number of food tokens that is closer to the desired level compared to the ARTM (\(V_{f}\) = 4.94). The total number of food tokens collected by the DRTM is 229.44 and the net food is 19.99; the corresponding values for the ARTM are 236.40 and 15.77, respectively. The ARTM (\(E_{e}\) = 6.67%) consumes much more energy than the DRTM does (\(E_{e}\) = 8.72%), and so the DRTM gives rise to more efficient foraging.  
In Figure 15, the total number of OAEs for the ARTM (\(M_{T}\)= 1612.26) is clearly higher than it is for the DRTM (\(M_{T}\) = 1203.32). Compared with the ARTM (\(M_{V}\) = 53.74), the DRTM (\(M_{V}\) = 40.11) has a greater advantage regarding traffic congestion.  
Figure 14: Net number of food tokens in nest (\(N_{f}\) = 20, \(E_{a}\) = 10, \(N_{r}\) = 30, \(L\) = 4).  
Figure 13: Number of OAEs (\(N_{f}\) = 20, \(E_{a}\) = 20, \(N_{r}\) = 20, \(L\) = 4).  
On the one hand, the net number of food tokens approaching the desired level takes less time than in the comparison experiments because many robots forage simultaneously. On the other hand, too many robots foraging simultaneously not only produces many more OAEs but also decreases the probability of finding food tokens because they are limited in number. As can be seen from the experimental data, the energy efficiency, the ADF, and the average number of OAEs all deteriorated. Therefore, increasing the number of foraging robots increases the swarm performance up to a point, after which the performance starts to decrease influenced by the negative effects of interference among the robots.
## 3 Results
### Tracking Performance Test 1  
When the BSM1 wastewater treatment system is operating, due to the dynamic changes in the flow rate and composition of the influent, one need to adjust the oxygen transfer rate (\(K_{L}\)a\({}_{5}\)) in the fifth tank in real-time to maintain the dissolved oxygen concentration in the appropriate range to ensure the effluent water quality meets the discharge standards. Therefore, how to control the DO concentration around the set point during the process is the aim of control algorithm that is being employed. The tracking performance is one of the deterministic criteria to evaluate whether an algorithm can be applied to an actual wastewater treatment system control.  
Generally, the effluent water quality is the best when the DO concentration in the aeration tank is kept between 1-3 mg/L. Therefore, the DO concentration is setup to 2 mg/L in this simulation. The simulation results of last seven days are taken to evaluate the performance of the controller, which is shown in Figure 6.  
Figure 6: Comparison results of the tracking performance (dry weather): (**a**) DO concentration in the fifth tank; (**b**) Dynamic changes of the manipulated variable \(K_{L}\)a\({}_{5}\); (**c**) Dynamic adaptive adjustments of the three parameters \(kp\), \(k\), \(kd\) of RBFNNPID algorithm.
## 3 System Modeling  
In this section we describe our system architecture and the training process. We start by presenting the notations used through out the remainder of this paper in Table 1.  
Figure 1: Relationship between deep learning and the rest of AI.  
We model the system as a graph \(G=(P\cup L\cup C,E)\), where:  
* \(C\) is the central controller. The central controller can be played by the big health facility within a defined catchment area.
* \(L=\{l_{1},l_{2},\cdots,l_{M}\}\) is the set of local hospitals under the control of \(C\). Given that \(M\) is the number of hospitals under consideration, we define the following; Let \(p_{m}(t)\) be the number of patients at the \(m\)th hospital at time \(t\). Then: \[P(t)=\{p_{1}(t),p_{2}(t),\cdots,p_{M}(t)\},\] (1) is the set of patients over the set of hospitals at time \(t\).  
Note that, here, \(M\) is the total number of hospitals located in a particular region with each hospital serving a population in its catchment area. If we let \(R\) be the average number of patients in the catchment area of \(L\), i.e., the total catchment area covering the \(M\) hospitals, then \(|L|=M\times R\). \(R\) is also time variant, since the population of a particular area varies with time. Each local hospital maintains a local database of patient information.  
Define \(E\) to represent the edges in a graph \(G\). Let an edge \(e\in E\) in the graph represent the route connecting the two nearest hospitals. Then the weight, \(w(e)\), represents the obstacles that will impact a patient's ability to get from one facility to another. These obstacles include distance, traffic congestion, transport cost and the physical terrain. Here we consider the shortest possible road connecting two facilities. In Figure 2, the arrows indicate information flow between the central controller and each of the facilities in its domain and the solid circular line defines a catchment area while the dotted circular line defines a network of health facilities under the control of the central controller. From now on we abbreviate central controller as Cc.  
\begin{table}
\begin{tabular}{c l} \hline \hline _Symbol_ & _Meaning_ \\ \hline \(G\) & A graph-a network of health facilities \\ \hline \(E\) & Edges of a graph \(G\) \\ \hline \(P\) & A set of patients \\ \hline \(L\) & A set of hospitals in a particular region \\ \hline \(M\) & Total number of hospitals in a particular region \\ \hline \(R\) & Average number of patients in the catchment area of hospital \(L\) \\ \hline \(w_{ij}\) & Weight of link between \(i\) and \(j\) \\ \hline \(b_{i}\) & Bias of unit \(i\) \\ \hline \(\xi\) & Learning rate \\ \hline \(f(\cdot)\) & Activation function \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.
## 3 Results and Discussion
### Amplitude Demodulation Analysis  
An amplitude demodulation analysis was also performed for all the configurations tested. First, the results without modulating frequencies are shown, then the results with one modulating frequency and, finally, the ones with two modulating frequencies.  
Figure 6: _FRF_ between the sensors in the rotating frame and the PZT for the excitation in the band of 10–11 kHz. The blue line is the “in air” configuration, and the black line is the “in water” configuration. Rotating case (\(f_{t}\) = 4.1 Hz).
# RGCLN: Relational Graph Convolutional Ladder-Shaped Networks for Signed Network Clustering
## 3 The Proposed Model
### Objective Function and Discussions
#### 3.3.1 Global Size Constraints  
A fundamental advantage of deep learning is its flexibility, but extremes can occur if left unconstrained, especially in clustering problems where extreme divisions may occur. To circumvent this overfitting situation, similar to that in images, this chapter explores an algorithm for constrained clustering. Some previous work has explored uniform distributions, which are only applicable to specific clusters. Here, a more general constraint is described.  
In this paper, the default maximum cluster size will not exceed half of the number of nodes, and the maximum number of clusters will be penalized if it exceeds this maximum limit. This definition is consistent with the distribution law of most clusters and can effectively improve the robustness and reliability of the model. The size constraint is divided into hard and soft constraints; the former means that the model must satisfy this constraint to get clustering results, while the soft constraint is not satisfied. Soft constraints are usually much better than hard constraints, so soft constraints are used here. Let the total number of clusters be \(k\), the total number of training instances be \(n\), the ratio of the desired maximum number of cluster nodes to the total number of nodes be \(up\), and \(q_{ic}\) can be considered as the degree of instance \(i\) belongs to cluster \(c\); then the global size constraint loss function is defined as:  
\[\mathcal{L}_{\text{SC}}=\min\left[\sum_{c\in\{1,\dots,k\}}\cdot\left(\max \left(\sum_{i=1}^{n}q_{ic}/\mathbf{n}\right)-up\right)\right],up=0.5 \tag{2}\]  
The loss function in this paper operates by minimizing the difference between the desired maximum number of clusters and the actual maximum number of clusters. Considering a more specific case, the constraint defines that the relevant hyperparameters can be self-adjusting. The upper limit defined as half of the total number of nodes is the best assumption based on the real situation of the experimental part of the dataset, which is also consistent with the cluster distribution pattern of most real-world or generative networks associated with signed networks. The value of \(up\) can be adjusted to achieve the best constraint based on real-world conditions.
## 3 Experimental Results and Analysis
### Analysis of Parameters  
In the M-SSL model, the performance of was affected by some critical parameters, such as window size and temperature. In the experiment, \(\tau\) denotes temperature, which was set to 0.01, 0.025, 0.05, 0.075, and 0.1, respectively. Similarly, the sliding window size was set as 12, 15, 17, 19, and 21. The OA of the classification results is recorded on the three datasets of Chapter 2.2 with different parameters. With the above effective parameters, from Figures 9-11, the experimental results derived mainly from MAAE and MVAE are significantly better than from them alone. The value of \(\tau\) only affects the features of the supervised learning models, so features of MVAE and MAAE have no relation with \(\tau\). The learning performance of the supervised models in experiment is influenced by \(\tau\). From Figure 9, when \(\tau\) is 0.01, the best results were obtained for tree classification with the M-SSL model.  
Figure 8: Tree classification maps of models for dataset (3).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{3}{c}{**Supervised Model**} & \multicolumn{3}{c}{**Unsupervised Model**} \\ \cline{2-7}  & **LDA** & **1D-CNN** & **S-CNN** & **CUFL** & **AAE** & **VAE** & **M-SSL** \\ \hline Birch & 72.71 & 47.42 & 64.19 & 72.43 & 72.14 & 70.07 & 79.34 \\ Larch & 77.83 & 55.53 & 65.97 & 73.16 & 71.98 & 76.69 & 80.33 \\ Spruce & 74.47 & 48.89 & 57.88 & 72.46 & 75.78 & 74.12 & 76.84 \\ Mongolia & 43.83 & 37.93 & 60.17 & 71.33 & 70.72 & 72.73 & 79.92 \\ Willow & 53.14 & 69.29 & 72.18 & 75.81 & 76.60 & 75.38 & 76.29 \\ Poplar & 50.43 & 77.45 & 75.69 & 78.47 & 65.37 & 64.06 & 75.83 \\ OA (\%) & 60.49 & 55.43 & 67.21 & 74.56 & 70.71 & 69.11 & 76.54 \\ AA (\%) & 60.01 & 62.60 & 63.61 & 74.52 & 68.61 & 67.80 & 74.06 \\ Kappa (\%) & 55.34 & 54.69 & 62.75 & 73.68 & 65.89 & 63.17 & 73.65 \\ \hline \end{tabular}
\end{table}
Table 13: The classification accuracy of models for dataset (3).  
Figure 7: Tree classification maps of models for dataset (2).  
In addition, the performance of MVAE and MAAE for feature learning is improved with increased window size, which may be one of the parameters related to the results, whereas changing the parameter almost has few impacts on the learning of contrastive features. More valuable information could be obtained from a multisource dataset. Contrastively, the increase in spatial information is of little help to the experiment. The results conclude that the multisource self-supervised model is robust, and the parameter is insensitive.  
Figure 11: OA of M-SSL for \(\tau\) with a different size of window.  
Figure 10: OA of MVAE for \(\tau\) with different window sizes.  
Figure 9: OA of MAAE for \(\tau\) with a different size of window; \(\tau\) denotes the temperature of Equation (8).