esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Presentation of Components, Data Acquisition Methodology and the System
### Signal Filtration with a High-Pass Filter  
The EEG signal coming from 32 electrodes should be subjected to proper filtering. A high-pass filter with a sampling frequency of \(f_{s}=1000\) Hz and bandwidth of \(f_{p}=200\) Hz were used. A signal processed in this way may be subjected to further investigations. It is necessary in order to apply proper algorithms classifying given signals of bioelectric brain activity. Figure 4 shows the effects of high-pass filtration applied on the electroencephalographic signals coming from 32 electrodes of the headset. The plots presented in this way effectively facilitate the analysis of various states or mental orders. The example from Figure 4 shows the potentials evoked when a tested person was at rest.  
Literature contains many investigations concerning a widely understood classification of EEG signals. Numerous investigations concern, among others, the use of the analysis of time and spectral features, the analysis of brain waves (delta, theta, alpha and beta) or the investigations of a hierarchic structure based on quantum mechanics to implement feature extraction and the EEG signal classification [35,36]. Not a very large number of the investigations using Emotiv Epoc Flex Gels offers the possibility of suggesting new approaches to the issues connected with the analysis and effective classification of  
Figure 3: Signal analysis dashboard in EmotivPRO software.  
electroencephalographic signals. A large number of electrodes reading the measurements, which ensure an accurate potential reading, can offer an original solution. Details are presented in the next stages of work.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Network Phase Design  
Figure 1 shows the topology of the SS-MAC. Node S is a sink, nodes labeled 1 to 18 are CHs, and unmarked nodes are member nodes. Member nodes are used for raw data sensing, while sink and CHs are only used for data relay and aggregation. All nodes, including CHs, are randomly deployed in the sensing area, either remaining stationary or moving slowly. All CHs and sink construct a tree topology first, then each CH and its member nodes form a cluster, which is a hierarchical cluster topology that can be defined as a cluster tree topology. For a sparse WSN, limiting CHs to data forwarding and aggregating could only have a significant impact on protocol performance. For SS-MAC which mainly serves large-scale and dense networks, whether CHs can sense data has little impact on the network performance. In large-scale wireless sensor networks with cluster tree topology, the sink needs to collect and process a large number of data packets from nearby CHs. To reduce channel competition and traffic conflicts, member nodes cannot directly communicate with the sink in the topology of SS-MAC.  
Figure 2 illustrates the network phase design. The Initial Construction Phase (ICP) mainly completes the network initialization, including clock synchronization. This is followed by a fixed duration of data collection round (Round), which is the longest time unit of network operation. Each Round includes a Setup Phase (SP) and a Steady-State Phase (SSP). The network must rebuild the route in SP to prevent energy \"holes\" or link  
Figure 1: Cluster tree topology.  
interruptions in subsequent SSP caused by mobility or energy depletion of nodes, especially CHs, which act as routers in the network and whose link interruptions will significantly affect the network. In each Round, the first data collection from member nodes to CHs is completed in SP, and then the process is performed in each Session. The SSP consists of several Sessions of the same duration. The length of Round (i.e., the number of Sessions in a Round) is dynamically adjusted according to the average moving speed of the nodes in the network. The faster the nodes move, the shorter the length of the Round, and the more frequent the route rebuild.
# Thermal Conductivity and Rheology of Graphene Oxide Nanofluids and a Modified Prediction Model
## 3 Results and Discussion
### Rheological Properties of GO Nanofluids  
The viscosity of nanofluids is an important parameter to determine the performance of heat transfer fluids. As with conventional fluids, temperature is also a key independent variable which affect the viscosity of nanofluids. In our case, viscosity of deionized water is measured to be 1.034 mPa\(\cdot\)s, which is in good agreement with its theoretical value at 20 \({}^{\circ}\)C, and the relative deviation is less than 2.5%. It indicates that our test equipment is reliable. As is well known, deionized water exhibits Newtonian properties in the shear rate range.  
Figure 4a-e shows the dynamic viscosity changes of nanofluids with different mass concentrations in the temperature range of 25 \({}^{\circ}\)C to 45 \({}^{\circ}\)C when the shear rate is changing from 0 to 100/s. It can be seen that the dynamic viscosity of the nanofluid almost keeps a constant value within the concentration range at a certain temperature. This means that in our experiments, GO nanofluid has Newtonian fluid characteristics. Figure 4f displays the relationship between temperature and dynamic viscosity. As can be seen in this Figure, the viscosity of nanofluids decreased with the increase in temperature. It is known that the interaction between particles and molecules decreases when temperature is increased, which in turn results in a decreased viscosity, as also has been observed for many other kinds of nanofluids [17]. At the same time, it is also noted that with the increase in mass concentration viscosity is also increased. In general, with the increase in nanoparticle concentration, particles would be easier to agglomerate, as a result, the frictional force between particles would be increased in the shear flow, which leads to an increased viscosity [18].  
Figure 3: (**a**) Zeta potential of 0.002% GO nanofluid and (**b**) particle size distribution intensity curve of 0.002% GO nanofluid.
## 5 Experimental Analysis
### Experimental Results and Analysis  
#### 5.4.1 Distribution of Solid Feed in Pipes  
When the conveying wind speed is less than 18 m/s, as shown in Figure 8a, due to the low wind speed, there are more collisions between particles, and the friction resistance between particles and the pipe wall is relatively high. It can be obviously observed that most solid feed is transported forward along the lower pipe wall. With the continuous feeding of the hopper and the increase in the conveying distance, the front solid feed has insufficient power, and the speed gradually decreases until eventually it stops and builds up. As shown in Figure 8b, the transported solid feed collided with the accumulation, most of which stayed at the bottom of the pipeline and became part of the accumulation, and the rest continued to be transported along the pipeline after passing over the accumulation, resulting in low transportation efficiency and much residue in the pipeline. The relationship between accumulation position and conveying wind speed is shown in Table 3. In order to avoid accumulation, the maximum feeding amount corresponding to different conveying wind speeds is measured, as shown in Table 4. During the experiment, only the feeding amount corresponding to a 15.5 m/s conveying wind speed needed to be controlled.  
When the conveying wind speed reaches 20.6 m/s and above, the solid feed transportation speed is accelerated, the deposited feed in the conveying pipeline is reduced and the suspended amount is increased. The solid feed almost becomes a suspended flow, and there is no residue in the transportation pipeline, which can be completed well.
## 3 Control Strategy Development and Optimization
### Meta-Heuristic Optimization of the PID Controller
#### 3.2.2 PSO-PI Controller  
PSO is a meta-heuristic technique inspired by the behavior of social animals such as birds. It is based on the population attributed to [PERSON] and [PERSON] in 1995 [65], which was meant to simulate social behavior. The PSO is inspired by the foraging strategy seen in bird flocks in which individuals are guided by their knowledge, momentum and collective wisdom to move towards the most favorable food sources [29]. The PSO technique emulates this behavior by representing each solution as a particle with global and personal positions and inertia [29]. Therefore, each particle has certain features, i.e., velocity, position, and objective, which strive to attain convergence toward global optimum through a series of iterations [29]. In PSO algorithm, each particle searches constantly for the best position in the multidimensional space. Therefore, the flying particle in PSO algorithm is changing its position on the basis of its experience with the position of the neighboring particles, keeping its previous position and its neighboring particles. During this process, the individual particles have undergone a change in position influenced by the flying particles' experiences and that of their neighbors [65]. Therefore, the velocity and position for the particles are updated as expressed in [65] as follows:  
\[\begin{cases}V_{i}^{k+1}=w\cdot V_{i}^{k}+C_{1}\cdot\text{rad}_{1}\cdot(P^{ \text{lbest}}-P_{i}^{k})+C_{2}\cdot\text{rad}_{2}\cdot(P^{\text{gbest}}-P_{i}^ {k})\\ P_{i}^{k+1}=P_{i}^{k}+V_{i}^{k+1}\end{cases} \tag{25}\]  
where \(P_{i}^{k}\), \(V_{i}^{k}\), \(C_{1}\), \(C_{2}\), \(w\), \(P^{lbest}\), \(P^{gbest}\) are particle's position, velocity, acceleration coefficients, inertia weight, local best position, global best position, and \(rand_{1}\) and \(rand_{2}\) are the  
Figure 17: GA-PID Control Structure adapted from [63; 64].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Selection Strategy & Random \\ Fitness Performance & Proportional \\ Generations & 40 \\ Population Size & 100 \\ Number of Variables & 3 \\ Lower Bound [LB] & [0 0 0] \\ Upper Bound [UB] & [2000 2000 2000] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Genetic Algorithm Parameters.  
random number between 0 and 1 [65]. However, the parameters of the PID (PI in this case) controller were tuned using the PSO algorithm since it can be find the optimal solutions to a nonlinear and complicated system [65]. Therefore, Table 9 presents the simplified parameters of the PSO used based on the ITAE objective function to get the optimal control parameters. Figure 18 shows the flowchart of the PSO-PI strategies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of Particles & 100 \\ Number of Iterations & 300 \\ Number of Variables & 2 \\ Lower Bound [LB] & 0 \\ Upper Bound [UB] & 6000 \\ Cognitive Coefficient (c) & 1.9 \\ Inertia Weight (w) & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 9: PSO Parameters.  
Figure 18: PSO-PI Flowchart [66].
## 2 Materials and Methods
### Dark Fermentation Assays  
The DF assays were carried out in a 2.1 L lab-scale continuous-stirred tank reactor (CSTR) (BIOFLO 1000, New Brunswick Scientific/Eppendorf UK Ltd., Stevenage, UK), equipped with control systems for temperature (thermocouple from New Brunswick Scientific/Eppendorf UK Ltd., Stevenage, UK), pH (epoxy electrode with temperature compensation from Hanna Instruments HI8711E), and redox potential (epoxy redox electrode with a reference solution from Thermo Scientific, Orion 97-78). The stirring system was constituted by a shaft equipped with two blade systems, which was kept at a low speed (10 rpm) to prevent the occurrence of major stress in the bacteria consortium.  
The DF assays were carried out under mesophilic conditions (37 \(\pm\) 1 \({}^{\circ}\)C) and an average pH of 5.0. The electrical conductivity was measured in the anaerobic effluent of the bioreactor using a conductivity meter (Orion Star A215, Thermo Scientific, Thermo Fisher Scientific Inc., Indonesia). The biogas produced was stored in two acrylic cylindrical columns with maximum volumes of 3437 and 3288 mL. These columns were filled up with acidified deionized water (pH = 2.0) to reduce the solubilization of CO2 in the water. The experimental setup is shown in Figure 1.  
According to [PERSON] [19], an HRT between 2 and 6 d is appropriate for the DF of organic food wastes in completely mixed bioreactors. As such, the HRT of 4, 5, and 6 days were selected for the DF experimental assays. These assays were coded as HRT4, HRT5, and HRT6, respectively.  
Throughout this work, the gas produced in the bioreactor is named \"biogas\" being mainly composed of H2 and CO2. The term \"biogas\" is more common for methanogenic bioreactors, in which the gas produced is composed of CH4 and CO2. In the absence of a better designation, the term \"biogas\" was adopted in this work, even if no CH4 should be expected in the gas produced.  
The bioreactor was inoculated with 700 mL of anaerobic sludge that was obtained from the effluent of the anaerobic tank of the aforementioned full-scale AD plant. The reason for using such high inoculum volume was to avoid a long lag phase due to biomass growth. The chemical characterization of the inoculum is shown in Table 2.  
Figure 1: Experimental setup: (**1**) stirring system with an external engine and shaft; (**2**) feeding tube (influent); (**3**) outflow tube (effluent); (**4**) biogas outflow line; (**5**) temperature controlling system; (**6**) redox potential module; (7) pH and conductivity meter; (**8**) HCl pumping system; (**9**) influent pump; and (**10**) water columns for biogas storage.  
After inoculation, the bioreactor was fed daily according to an impulse-feed mode with adequate volumes of OFMSW to accomplish the desired HRTs of 4, 5, and 6 days.  
Figure 2 summarizes the experimental procedure followed in this work.
## 3 Results
### Tensile Property Analysis
#### 3.3.2 Effects of Aging Process on Tensile Properties  
Table 5 shows the tensile properties of the three Al-Mg-Si alloy thin-walled profiles in different aging states. With different aging processes and treatments, the tensile properties of the three profiles differ significantly. After near-peak-aging processing, both the yield and tensile strengths are at the maximum, and the elongation during fracture is at the minimum. Compared with profiles in the near-peak-aged state, the tensile strength of the 6 xxx-1(1.6 Mg/Si), 6 xxx-2(1.33 Mg/Si), and 6 xxx-3(1.14 Mg/Si) profiles in the under-aged state decreased by 11.4, 3.3, and 5.6%, respectively; the yield strengths decreased by 14.3, 3.7, and 15%, respectively; and elongation after fracture increased by 37.8, 42.7, and 18.8%, respectively. The tensile strengths of the 6 xxx-1(1.6 Mg/Si), 6 xxx-2(1.33 Mg/Si), and 6 xxx-3(1.14 Mg/Si) profiles in the over-aged state decreased by 5.9, 13.6, and 5.6%, respectively; the yield strengths decreased by 7.1, 14.9, and 5.5%, respectively; and the elongation after fracture increased by 31.5, 35.4, and 22.3%, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Profile Number** & **Yield Strength (MPa)** & **Tensile Strength (MPa)** & **Elongation (\%)** \\ \hline
6 xxx-1(1.6 Mg/Si) & 294 & 305 & 11.1 \\
6 xxx-2(1.33 Mg/Si) & 355 & 367 & 8.2 \\
6 xxx-3(1.14 Mg/Si) & 273 & 288 & 11.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tensile properties of the three Al-Mg-Si alloy thin-walled profiles (at near-peak-aged state).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Profile Number** & **Aging Process** & **Yield Strength (MPa)** & **Tensile Strength (MPa)** & **Elongation (\%)** \\ \hline
6 xxx-1 & 175 °C/10 h & 252 & 270 & 15.3 \\
6 xxx-1 & 175 °C/16 h & 294 & 305 & 11.1 \\  & 205 °C/6 h & 273 & 287 & 14.6 \\ \hline
6 xxx-2 & 175 °C/10 h & 342 & 355 & 11.7 \\  & 205 °C/6 h & 302 & 317 & 11.1 \\ \hline
6 xxx-3 & 175 °C/10 h & 232 & 272 & 13.3 \\
6 xxx-3 & 175 °C/10 h & 273 & 288 & 11.2 \\  & 205 °C/6 h & 258 & 272 & 13.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tensile properties of the three Al-Mg-Si alloy thin-walled profiles with different aging states.
## 4 Results and Discussion
### Mixture Testing Results and Discussion  
#### 4.2.1 Stiffness--Dynamic Modulus  
The average dynamic modulus master curves for different mixtures are shown in Figure 9. Similar to complex modulus of binders, all asphalt mixtures with modified asphalt binders have higher dynamic modulus than the virgin mixture. Generally, the mixtures with EMAA modified binder, including EMAA and EMAA/SBR modification, are stiffer than the other mixtures. For 4% EMAA and 4%EMAA/3%SBR mixtures, the master curves are nearly the same at low frequencies/high temperatures, while the dynamic modulus of 4%EMAA/3%SBR mixture is greater than 4% EMAA mixture at high frequencies/low temperatures. Therefore, 4%EMAA/3%SBR mixture mostly exhibit higher stiffness than 4% EMAA mixture. However, it should be noted that there is no significant difference between these two mixtures. It also can be seen from the master curves of the virgin and 3% SBR mixtures that the dynamic modulus are almost the same at low frequencies/high temperatures, but not for high frequencies/low temperatures.
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 2 Background and Related Work
### Machine Learning in Cyberbullying Detection
#### 2.2.7 Support Vector Machine Classifier  
Support Vector Machine (SVM) is a supervised machine learning classifier widely utilized in text classification [61]. SVM turns the original feature space into a user-defined kernel-based higher-dimensional space and then seeks support vectors for optimizing the distance (margin) between two categories. SVM originally approximates a hyperplane separating the two categories. SVM accordingly selects samples from both categories, which are nearest to the hyperplane, referred to as support vectors [109].  
SVM seeks to efficiently distinguish the two categories (e.g., positive and negative). If the dataset is separable by nonlinear boundaries, specific kernels are implemented in the SVM to turn the function space appropriately. Soft margin is utilized to prevent overfitting by giving less weighting to classification errors along the decision boundaries for a dataset that is not easily separable [101]. In this research, we utilize SVM with a linear kernel for the basis function. Figure 2 shows the SVM classifier implementation for a dataset with two features and two categories where all samples for the training are depicted as circles or stars. Support vectors (referred to as stars) are for each of the two categories from the training samples, meaning that they are nearest to the hyperplane among the other training samples. Two results of the training were misclassified because they were on the wrong side of the hyperplane.  
Therefore, SVM was used to construct cyberbullying prediction models in [104] and found to be effective and efficient. However, the work in [61] reported that the accuracy decreased when the data size increased, suggesting that SVM may not be ideal for dealing with frequent language ambiguities typical of cyberbullying.  
Figure 2: Implementation of Support Vector Machine (SVM) classifier [102].
## 3 EMF-Aware Routing Framework for WMNs
### EMF-Aware Routing Algorithm
#### 3.2.3 The Algorithm That Addresses the Minimization Problem  
There exist various algorithmic solutions for the minimum cost flow problem. Since we are more interested in the network model, rather than in the particular technique to solve it, we make use of a classical solution, namely the Cycle Canceling Algorithm (CCA). This algorithm exploits the optimality property, which establishes that a feasible solution \(x^{*}\) is optimal if, and only if, the residual graph \(G(x^{*})\) has no negative cycles [31]. The basic operation of the algorithm is described in Algorithm 1, where \(r_{ij}\) represents the residual capacity of the edge \((i,j)\).  
The aforementioned algorithm was originally introduced for networks having one single (source, destination) pair. In order to handle more realistic scenarios, where a number of nodes (gateways) might provide access to external networks, we propose adding two additional virtual nodes. A overall virtual source is connected to the actual traffic sources, while the real gateways are attached to a virtual destination that receives all the traffic. We fix the capacity of the links between the virtual and the real sources to one unit, ensuring that the sources are not generating more than one traffic flow. Besides, there is no upper limit for the amount of traffic on the link between gateways and the virtual destination. This way, although we could consider an arbitrary number of sources and gateways, the algorithm just needs to solve a problem with one single (source, destination) pair.  
```
0:\(G(\mathcal{N},\mathcal{E})\)
1: Establish possible one flow \(x\)
2:while\(G(x)\) contains a negative cycle do
3: Identify one negative cycle \(W\)
4:\(\delta:=\min\{r_{ij}:(i,j)\in W\}\)
5: Increment \(\delta\) flow units in the flow \(W\) and update \(G(x)\)
6:endwhile
```  
**Algorithm 1**Cycle Canceling Algorithm.  
It is worth pointing out that, according to the graph transformation presented in Section 3.2.1, the cost of the virtual edges that are added (the ones connecting the ingress and the egress virtual nodes) should not be proportional to the flow, since these edges are meant to model the exposure accumulated at the current real node level, up to now. It follows that, we can define the cost \(c_{ij}\) as follows:  
\[c_{ij}=\begin{cases}\alpha\cdot P_{ij}&\text{for original edges}\\ \frac{\mathcal{X}_{i}}{\sum_{j(x_{i})\in E}x_{in}}&\text{for new (virtual) edges}\end{cases} \tag{7}\]  
where \(P_{ij}\) is the transmission power, used by node \(i\) to reach node \(j\), \(\alpha\) is a constant factor, \(\mathcal{X}_{i}\) represents the accumulated exposure of node \(i\), and \(x_{in}\) is the amount of flow traversing the link \((i,n)\). In order to implement this, we heuristically modify the cost of the virtual links during the algorithm execution according to Equation (7), so that they are always constant. Although this could break the isotonicity of the corresponding graph, the obtained results empirically show that this approach was indeed valid.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 5 Numerical Example  
* Arrival rate (\(\lambda\)) = 6 customers/hour
* Service rate (\(\mu\)) = 5 customers/hour  
These values are fuzzified using triangular membership functions, and their degrees of membership are shown below (Table 7):