esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
Similarly, the normalized switching frequency level of both existing and proposed modulation schemes was validated with respect to varying modulation index, as shown in Figure 14. This analysis showed that the SVPWM and PWM techniques had a constant normalized switching frequency. When compared to the other models, the proposed technique provided an improved switching frequency by efficiently controlling the switching pulses. Figure 14: Analysis of normalized switching frequency. Figure 8:0 Analysis of THD. Figure 15 compares the existing and proposed techniques based on the measure of switching frequency distortion product with respect to varying modulating index. The obtained results show that the distortion production of the proposed DiSDM technique was efficiently reduced at all modulation indexes when compared to the other techniques. It proves the overall effectiveness and efficiency of the proposed mechanism with reduced error values and harmonics. Consequently, Figure 16 shows the switching loss of both existing and proposed techniques under varying modulation indexes. Typically, the controlling mechanism has the key responsibility for reducing both the switching and conduction losses by efficiently triggering the pulses for actuating the switching devices. Hence, the loss factor highly depends on the operating functions and characteristics of the generated pulses. From the analysis, it was evident that the switching loss of the proposed DiSDM technique was effectively reduced when compared to the other modeling schemes. Because the dithering model was utilized for suppressing the noise contents in the proposed mechanism, this helped to prevent the switching losses. Figure 8:1 Analysis of switching loss. Figure 8:2 Analysis of switching frequency distortion. Figure 17 and Table 1 compare the harmonic spread factor of both existing and proposed controlling models with respect to the voltage and current measures. Typically, the harmonic spread factor is mainly used for determining the quality of signals, which is estimated as follows:  
Figure 8:3  
where \(\mathrm{H_{SF}}\) represents the harmonic spread factor, \(\mathrm{S_{E}}\) is the statistical expectation value, \(\mathrm{H_{n}}\) is the total number of harmonics, \(\mathrm{M_{H}}\) denotes the magnitude of harmonics, and \(\mathrm{A_{H}}\) is the average value of harmonics. From the evaluation, it was identified that the proposed DiSDM scheme provided a reduced harmonics spread factor in both voltage and current, when compared to the other models.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Location-Based Routing Schemes
#### EECAR-AC  
ECAR-AC [24] is an event-driven cluster-based approach for routing in UWSNs. In this scheme, routing is carried out with the help of agents implemented in sensor nodes, AUVs, surface gateways, and underwater gateways (Figure 4). The scheme has two phases: (1) route discovery phase, and (2) route maintenance phase. Route discovery deals with the selection of relay nodes from the source to a surface gateway. The selection of relay nodes is based on link quality, depth, residual energy levels, hop count, and queue size. Data transmission is initiated as soon as the paths are set up. The route maintenance phase starts after paths are set up, and data transmission has started. This phase ensures the availability of alternate paths from the source to surface gateways in the case of link breakages or node malfunction. If the network gets partitioned i.e., alternate paths through regular relay nodes are not available, AUVs are employed to act as relays. Route maintenance improves the chances of successful delivery; however, it involves multiple transmissions of control packets, which increases communication overhead. The proposed scheme achieves reliability, load balancing, and end-to-end delay by the inclusion of link quality, residual energy, and hop count, respectively, as the next hop selection criteria.
# Bayesian Survival Analysis Model for Girth Weld Failure Prediction
## 3 Survival Probability and Cases
Therefore, the mechanical properties of the weld should be strictly controlled by monitoring weld defects. Figure 5 shows the predicted survival function of the girth weld under the influence of several different factors, where:  
Num1 indicates that only the Case1 factor is operating,  
Num2 indicates that the Case1 and Case4 factors are operating,  
Num3 indicates that the Case3, Case4, and Case5 factors are operating,  
Num4 indicates that the Case1, Case2, Case3, and Case4 factors are operating,  
Num5 indicates that the Case1, Case2, Case3, Case4, and Case5 are operating,  
Num6 indicates that other factors than Case6 are operating,  
Num7 indicates that all factors are operating. As the failure factor increases, the occupancy rate also increases, and the survival probability of the girth weld is significantly reduced. So, the more the factors, the more fragile the girth weld. Figure 4: Survival function of the girth weld under the influence of a single factor. The algorithm was also used to verify the failure of a girth weld of a high-pressure, API X80 steel natural-gas pipeline. The pipeline was commissioned in 2013, the first girth-weld cracking accident occurred in 2017, and the second cracking accident in 2018. Failure analysis showed that the failure-causing factors included external force caused by soil movement, changes in wall thickness, elbow joint, and weld impact toughness. That means these failure factors are those of Cases 2, 4, and 5. According to the survival function model described above, a weld with similar failure factors had a survival probability of 0.120 in 2017 and of 0.109 in 2018. If the accident in 2017 is included to the sample, and the impact factor of Equation 7 is recalculated, the survival probability of the girth weld in 2018 changes to 0.104, which is not much different from the original calculation result. Therefore, the number of samples is sufficient for effectively predicting the cracking of the girth weld. This study also analyzed the other girth welds in this pipeline, and the statistical results showed that only two weld failures corresponded Cases 2, 4, and 5 at the same time; also, Case 4, was not represented by other weld failures. So, two girth-weld accidents occurred since the hydrotest. The pipeline operating company should take measures to prevent additional external loads from pressing on the pipe. The failure factors of girth welds are relatively complex and difficult to quantify, and traditional risk assessment determine the possible risk factors on the basis of general knowledge and limited experience. This paper proposes a new model to improve the tools of girth-weld failure prediction. With this model, it is easy to determine the failure trend in time and the most critical factors and combination of factors for girth welds of a pipeline.
# Dual-Band MIMO Antenna for n79 and sub-7 GHz Smartphone Applications
## 3 Results and Discussion
### MIMO Performance Parameters
#### 3.3.3 Mean Effective Gain (MEG)  
The MEG (mean effective gain) is a significant measure to judge the MIMO diversity since it considers every antenna part separately in the multipath channel.  
\[\text{MEG}_{i}=\frac{1}{2}\biggl{[}1-\sum_{j=1}^{N}|S_{ij}|^{2}\biggr{]} \tag{3}\]  
This formula takes into consideration the power lost through the reflection and coupling effects of the antenna array from [28]. The MEG values are shown in Figure 11. The plot suggests that the simulated and measured MEG are almost the same and are less than \(-6\) dB. The low MEG value is healthy for the system, as it indicates low losses and good gains in the  
Figure 9: Envelope correlation coefficient of proposed ten-port MIMO antennas.  
multipath system. Further, a plot showing the ratio of MEG values from antenna 1 to antenna 2 is also presented. As seen in the findings above, both the ratios simulated and the ratios measured are substantially less than the 1 dB threshold and well below 3 dB. This low ratio provides strong support for the uniformity in the performance of the antenna elements, an aspect that is essential for good MIMO diversity. These MEG results show the effectiveness of the proposed MIMO antenna design to achieve a good diversity gain. The low MEG values and the small MEG ratio of the different antenna elements suggest that the antenna elements are harmoniously providing an efficient and reliable signal-capturing mechanism that forms the basis of effective MIMO communication.  
Figure 11: Mean effective gain of proposed ten-port MIMO antennas.  
Figure 10: Total active reflection coefficient of proposed ten-port MIMO antennas.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 2 Literature Review
The model could be optimized through data mining to improve its prediction accuracy [18]. Moreover, customer segmentation is often combined with customer churn prediction for greater management effectiveness [22]. By comparing the accuracy of telecom customer churn prediction models constructed using different data mining methods, we can measure which data mining method is best [23]. In addition to accuracy, there are other metrics for measuring the performance of customer churn prediction models, such as the understandability and intuitiveness of the model [24]. [PERSON] et al. [25] established a telecom customer churn prediction model with good understandability and intuitiveness using the GP-AdaBoost method. There are two well-known data mining methods with outstanding prediction accuracy and understandability. One of them is the decision trees (DT) method and the other one is logistic regression. However, both methods have shortcomings: it is difficult for DT to deal with the linear relations of variables, and it is hard for logistic regression to handle the interaction impacts of variables. Thus, the logit leaf model (LLM) method performs better in classifying data. Compared with DT or logistic regression, LLM has shown better performance and understandability [26]. [PERSON] et al. [27] stated that the current churn prediction methods mainly use structured rather than unstructured data to conduct analyses. Moreover, unstructured data and telephone communication voice content are innovatively used to build customer churn prediction models. Machine learning (ML) and deep learning (DL) are suitable for customer loss prediction. An optimized synthetic minority oversampling method named the ISMOTE-OWELM model was used to improve accuracy in customer churn prediction [28].
# Determining the Quality of a Dataset in Clustering Terms
## 2 Materials and Methods
### Methodology
#### 2.2.12 Computational Complexity  
Firstly, AutoEmbedder was trained on the training dataset. Then, to encode categorical variables, only the embedding and encoder parts of AutoEmbedder were used.  
If there were \(p\) categorical features given, and each of them had \(k_{i}\) levels (\(i=1,2,\ldots,p\)), then the embedding layer generated \(k_{i}+1\) values for each layer, resulting in a vector of size \(\sum_{1}^{p}(k_{i}+1)\) for each of the \(n\) samples in the dataset. The encoder consisted of dense layers with a ReLU activation function. If there were \(l\) layers with \(l_{i}\) (\(i=1,2,\ldots,l\)) nodes each, the complexity of calculating the prediction for one sample in each dense layer would be \(O(l_{i}\times l_{i-1})\), where \(l_{0}\) would be the size of the input--in the presented case, it was \(\sum_{1}^{p}(k_{i}+1)\) because of the embedding layer put before the encoder.  
Standard PCA involves \(O(min(p,n)^{3})\) operations, where \(n\) is the number of samples and \(p\) is the number of features for each sample [34]. In our case, since the number of samples was much higher than the number of features, the time to complete the PCA algorithm was \(O(n^{3})\). The complexity of computing the similarity matrix was \(O(n^{2})\). The clustering algorithms used had the following complexities:  
* The Louvain algorithm is believed to run in \(O(m)\) time, where \(m\) is the number of edges in the graph [35],
* In the greedy modularity algorithm, in each step, the pair of nodes/communities that, joined, increase modularity the most, become part of the same community, and then the modularity is calculated for the full network. This step requires \(O(m)\) calculations. After merging communities, the adjacency matrix of the network is updated in a worst-case scenario in \(O(n)\) time. Each merging event is repeated \(n-1\) times. Hence, the overall complexity is \(O((m+n)n)\), or \(O(n^{2})\) in a sparse graph [36],
* The label propagation algorithm has \(O(n)\) computational complexity [37],
* The DBSCAN algorithm scans the whole dataset only one time and needs to calculate the distance of any pair of objects in the dataset. Hence, the worst possible computational complexity of the whole algorithm is \(O(n^{2})\)[38]. In our case, the distances did  
Figure 2: Proposed methodology of clustering data containing categorical variables.  
not need to be computed, because we took distances as one minus the value from the similarity matrix.  
From the above analysis, it can be seen that the worst possible computational complexity occurs with the PCA algorithm, where it amounted to \(O(n^{3})\). Therefore, we could conclude that the worst possible complexity for the proposed methodology was \(O(n^{3})\).
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 4 Challenges of Predictive Maintenance
### Financial and Organizational Limits  
For-profit companies inevitably consider the expected costs of any new investment. Predictive maintenance efforts, such as sensor installation, information retrieval, model preparation and maintenance, and maintenance activities generate costs for the companies in which predictive maintenance methods are introduced. These costs can vary depending on multiple factors, such as the type and complexity of equipment and corresponding sensors, the cost of consulting, installation, and knowledge extraction, and whether the necessary expertise can be found internally or externally [49]. One method of assessing whether the introduction of predictive maintenance can be beneficial is to create a projected return on investment (ROI) [50]. The projection of the predictive ROI should consider the value of predictive maintenance results, the payback time, and the described costs. The financial rationale for the use and applicability of predictive maintenance also depends on the size and type of business in which it is introduced [51].
# Adaptive Segmentation of Streaming Sensor Data on Edge Devices
## 3 Problem Formulation  
Consider a stream of sensor data points, \(S_{D}=(D_{0},D_{1},D_{2},\dots)\), that arrive (or are accessed) sequentially, and describe an underlying signal \(f(q)\), \(q\in\mathbb{R}\) (note that in the subsequent formulae the \(q\) stands for any independent variable, typically it will refer to time (\(t\))), where:  
\[D_{k}=(q_{k},f(q_{k}))=(q_{k},y_{k}). \tag{1}\]  
This stream in a general case is \"noisy\", i.e.,  
\[f(q_{k})=g(q_{k})+\epsilon_{k},\;k=0,1,2\dots \tag{2}\]  
where \(g(\cdot)\) is the _true signal_ and \(\epsilon\sim\mathcal{N}(\mu,\sigma^{2})\), i.e., it is Gaussian noise. This model is shown in Figure 1.  
Problem statementGiven a stream of data points \(S_{D}=(D_{0},D_{1},D_{2},\dots)\), where \(D_{k}=(q_{k},y_{k})\) with \(y_{k}=g(q_{k})+\epsilon_{k},\;k=0,1,2\dots\), find the \(C^{2}\)-continuous cubic spline whose segments correspond--in the space generated by the user-defined segment length adaptation strategy (\(\delta\))--to the optimal segmentation of \(S_{D}\), with regard to the reconstruction of the original signal (\(g\)).  
**Remark 1**.: _The adaptation strategy, \(\delta\), usually depends on the target platform capabilities (e.g., the available memory), and on the required accuracy of the solution. In specific cases, it can be very sophisticated, e.g., Machine Learning (ML)-based._
# Advancements and Research Trends in Microgrids Cybersecurity
###### Abstract  
Microgrids are growing in importance in the Smart Grid paradigm for power systems. Microgrid security is becoming crucial since these systems increasingly rely on information and communication technologies. Many technologies have been proposed in the last few years for the protection of industrial control systems, ranging from cryptography, network security, security monitoring systems, and innovative control strategies resilient to cyber-attacks. Still, electrical systems and microgrids present their own peculiarities, and some effort has to be put forth to apply cyber-protection technologies in the electrical sector. In the present work, we discuss the latest advancements and research trends in the field of microgrid cybersecurity in a tutorial form.  
s mart microgrids; cybersecurity; software defined networking (SDN); intrusion detection systems (IDS); anomaly detection; resiliency +
Footnote †: journal: Applied Sciences  
15 July 20219  
to defeat. To detect is a fundamental step of the defense-in-depth paradigm, which tackles the problem of recognizing malicious activities as rapidly as possible before or after the attacker has gained access to the system. Once the attack has been detected, the system should be able to react against the attacker, delaying the activities of the attacker, and allowing to take proper countermeasures to defeat the attack and recover from possible damages. The technologies and techniques that will be discussed in this paper address these issues.  
Many papers have analyzed the vulnerabilities of smart grids and, specifically, of microgrids. This paper aims to discuss the main recent advancements and new research trends in the field of cybersecurity of smart microgrids, and the applications in this field of innovative technologies, such as Software Defined Networking, new approaches for intrusion and anomaly detection, and resilient control strategies. The paper is structured as follows. Section 2 discusses the state-of-the-art in the field of cybersecurity of smart microgrids. Section 3 analyzes techniques that can be applied to microgrid communication protocols and highlights possible vulnerabilities and countermeasures, with a particular focus on the role of IEC 62351 to secure microgrids. The rest of the paper focuses on technologies that can provide great benefits to the security of microgrids but that are still a research field. Section 4 discusses the application of the Software-Defined Networking (SDN) paradigm to enhance microgrid resilience and cybersecurity, while Section 5 considers the implementation of Intrusion Detection System (IDS) techniques on electrical devices with a special attention to Host-IDS (HIDS). Section 6 focuses on physics-based anomaly detection algorithms that can be applied to power systems. The main works on control strategies that can be implemented in distributed energy resources in order to improve the resilience to cyber-attacks and the availability of the whole system are summarized in Section 7. Conclusions are drawn in Section 8.
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## 4 Simulation Results  
Monte Carlo simulation was carried out. The results are average out on 500 simulations for ATPSN and JAMANC algorithms, where optimization is computationally extensive. The step size is taken as two devices for the ATPSN. The step size for MANC and JAMANC algorithm is five devices and five packets. Small intervals provide better mean estimates leads to a higher confidence interval. However, 5000 iterations are used for the MANC algorithm to cater for all possible combinations of packet missing. First, the ATPSN algorithm is compared, followed by the JAMANC algorithm, and a comparative study is carried with the MANC standalone (extended NCMI algorithm proposed in [5]) and ATPSN standalone algorithm [25]. Moreover, mode analysis is done. The performance of coding is compared with the upper and lower bounds derived earlier. As per International Mobile Telecommunications 2020 (IMT-2020) guidelines [30], the significant key performance indicators (KPIs)  
Figure 5: Workflow: Cooperative admission with network coding (NC).  
for the 5G network is capacity, energy efficiency, and fair usage policy. The time required to complete the packet completion is a critical factor in analyzing the network coding [5]. Earlier packet completion time leads to better performance of the network. The standard KPIs are used to analyze the strength of our proposed algorithms.
# A Study of Discriminatory Speech Classification Based on Improved Smote and SVM-RF
## 2 Related Work
### Discrimatory Speech Classification  
Discriminatory speech classification belongs to the text classification problem in natural language processing [12]. In recent years, some researchers have used machine learning methods to classify discriminatory speech. [PERSON] et al. employed an N-gram feature engineering approach to extract features from a pre-written dictionary of hate words and then fed the feature vectors into an SVM classifier, resulting in a final F-score of 0.67 [13]. [PERSON] et al. used a supervised machine learning approach to classify racist text datasets. To convert raw text into numeric vectors, the authors used binary feature extraction techniques and bag-of-words (BOW) feature representation techniques. This resulted in an accuracy of 87% [14]. [PERSON] et al. used machine learning methods to detect abusive language in online user content, and the authors adopted character-level N-gram feature representation techniques to represent features and feed them into an SVM classifier for processing, which obtained an F-measure of 77%, according to their research results [15]. [PERSON] et al. used supervised classification methods to classify hate speech in social media, and the authors used character-level, word-level N-gram, and word Skip-gram methods to obtain features, which were then fed into an SVM classifier, and the experimental results showed the highest accuracy of 78% [16]. Combined with dictionary-based methods, bag-of-words methods, and N-gram methods, SVM has shown good performance in text classification tasks. [PERSON] et al. aimed to investigate the application of machine learning techniques in sentiment analysis and conducted experiments with SVM and RF, which showed that both have considerable accuracy and are superior among machine learning algorithms [17]. [PERSON] et al. used the standard hate speech dataset to compare their classification effects on three feature engineering techniques and eight machine learning algorithms [18]. In the experimental results of these studies, SVM and RF performed well, and SVM achieved the best performance, possibly because it could effectively deal with high-dimensional data by using hyperplanes and deal with nonlinear problems by using its kernel function. RF performs slightly worse than SVM, but higher than other algorithms.