esa-sceva/satcom-chunk-collection huggingface dataset:
# FCNN-SE: An Intrusion Detection Model Based on a Fusion CNN and Stacked Ensemble
## 5 Conclusions  
An intrusion detection model identifies malicious attacks by analyzing the characteristics of key nodes for network traffic and is an important part of the network security protection architecture. In this paper, an intrusion detection model based on the FCNN-SE is proposed. The proposed model can improve intrusion detection performance by combining heterogeneous base learners into classifiers and using the FCNN to extract feature information from traffic data. To measure the comprehensive performance of the proposed model, a comprehensive performance evaluation method based on the radar chart method is introduced to evaluate the experimental results. The in-depth experiments are conducted  
Figure 5: The representation map of the Barnes-Hut variation of t-SNE: (**a**) raw data; (**b**) 1 DCNN-processed data; (**c**) 2 DCNN-processed data; (**d**) FCNN-processed data.  
on the NSL-KDD dataset, and the experimental results demonstrated the FCNN-SE model is effective and can outperform the comparison models.  
The limitations of this paper are as follows:  
1. Only used machine learning-based methods are used as base learners, while the neural network-based methods are ignored. Because the structure of the neural network itself is very complex, and multiple complex neural networks are integrated to work together at the same time, the structure of the model will be too large, which will greatly increase the training time of the model.
2. The CNN is adopted as a base extractor without considering other feature extraction techniques. This paper has proved that CNN has good performance as a feature extractor, but the CNN designed in this paper is still very simple compared with mature neural networks such as GoogLeNet and ResNet, and cannot give full play to the powerful feature extraction ability of CNN.  
In future work, we could try to lightweight the neural network design, and simplify the neural network structure on the basis of maintaining the excellent performance of the neural network. After joining the ensemble model, it will not make the ensemble model too large. At the same time, using the currently popular neural networks such as GoogLeNet and ResNet, and using transfer learning technology to extract higher-quality data traffic features.  
Conceptualization, C.C. and Y.S.; methodology, C.C.; software, S.Y.; validation, Y.S.; formal analysis, C.C.; resources, X.X.; writing--original draft preparation, L.Z., Q.L. and L.Y.; writing--review and editing, C.C.; supervision, C.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Natural Science Foundation of China (grant number: No.61703426, No.61806219, No.61876189), Youth talent promotion plan of Shaanxi University Association for science and technology (grant number: No. 20190108) and Shaanxi innovation capability support plan (grant number: No.2020 KJXX-065).  
Not applicable.  
All data used in this paper can be obtained by contacting the authors of this study.  
The authors declare no conflict of interest.
## 3 Modeling Approaches
### Support Vector Machine (SVM)  
SVM is widely used supervised classification method in the field of analyzing landslide susceptibility [45]. The key to SVM is finding the hyperplane that separates the data into different classes. The initial data are converted into a feature space of higher dimensions, and an optimal hyperplane is then sought in this space. The effectiveness of the hyperplane is assessed using the margin, which is defined as the distance between the hyperplane and the nearest points from each class in the high-dimensional feature space. A larger margin represents a more suitable hyperplane.
## 3 Experimental Setup and Results
### Datasets
#### 3.1.2 Davidson Dataset  
Davidson dataset, which contains 24,783 tweets, is the largest dataset for hate speech collected from Twitter [8]. The tweets in the dataset are labeled as 'Hate', 'Offensive', and 'Neither'. We combined the tweets with labels 'Hateful' and 'Offensive' to be 'Hateful Tweet' and the tweets with 'Neither' label to become 'Non-hateful Tweet' according to the label combinations used by [PERSON] et al. [30]. The 'Hateful Tweet' in this dataset are the tweets that attack a woman, Table 3 presents the distribution of the Hateful and Non-hateful Tweets across the training, development, and test sets of the Davidson dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset Name** & **Link** \\ \hline HatEval & [[http://hatespeech.di.unito.it/hateval.html](http://hatespeech.di.unito.it/hateval.html)]([http://hatespeech.di.unito.it/hateval.html](http://hatespeech.di.unito.it/hateval.html)) (accessed on 30 June 2021) \\ \hline [PERSON] & [[https://data.world/thomasradavidson/hate-speech-and-offensive-language](https://data.world/thomasradavidson/hate-speech-and-offensive-language)]([https://data.world/thomasradavidson/hate-speech-and-offensive-language](https://data.world/thomasradavidson/hate-speech-and-offensive-language)) \\ \hline COVID-HATE & [[http://claws.cc.gatech.edu/covid/](http://claws.cc.gatech.edu/covid/)]([http://claws.cc.gatech.edu/covid/](http://claws.cc.gatech.edu/covid/))\#dataset \\ \hline ZeerakW & [[https://github.com/ZeerakW/hatespeech/blob/master/NAACL](https://github.com/ZeerakW/hatespeech/blob/master/NAACL)]([https://github.com/ZeerakW/hatespeech/blob/master/NAACL](https://github.com/ZeerakW/hatespeech/blob/master/NAACL))\_SRW\_2016.csv \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets used in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Non-Hateful Tweet** & **Hateful Tweet** & **Total** \\  & **(0)** & **(1)** & **Total** \\ \hline Train & 5217 (58\%) & 3783 (42\%) & 9000 \\ Development & 573 (57\%) & 427 (43\%) & 1000 \\ Test & 1740 (58\%) & 1260 (42\%) & 3000 \\ Total & 7530 (58\%) & 5470 (42\%) & 13,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: HatEval dataset distribution of Non-hateful and Hateful tweets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Non-Hateful Tweet** & **Hateful Tweet** & **Total** \\  & **(0)** & **(1)** & **Total** \\ \hline Train & 3010 (17\%) & 14,833 (83\%) & 17,843 \\ Development & 327 (17\%) & 1656 (83\%) & 1983 \\ Test & 826 (17\%) & 4131 (83\%) & 4957 \\ Total & 4163 (17\%) & 20,620 (83\%) & 24,783 \\ \hline \hline \end{tabular}
\end{table}
Table 3: [PERSON] dataset distribution of Non-hateful and Hateful tweets.
## 2 An Overview of Line Simplification Algorithms
### Line Simplification Algorithms and Error Measures
Deviation of the discretized polyline to the original shape depends on the chosen method and can be quantified measuring the error, taking into account both the number and position of the reduced points. Two commonly employed methods for error calculation are distance-based measures and area-based measures. The positional error sum, a distance-based measure, quantifies positional errors by calculating the distance from the removed points on the original curve to the simplified polyline. Summing the positional errors of every original point results in the positional error sum measure [32], offering a metric for comparing different line-discretization algorithms. To better understand the global deviation of the simplified polyline from the original, the areal difference measure can be employed [32]. By connecting the first and last points of both the original and discretized polylines, an area is formed. The final error measure is obtained by quantifying both polyline areas and calculating the absolute difference between these areas. Figure 4 illustrates the method for determining the areal difference measure on a polyline similar to the shape of the nonlinear part of a steel stress-strain curve. These error measures provide valuable insights into the deviation from the original material curve and can serve as an effective stopping criterion when employing simplification algorithms.
## 2 Experimental Methodology
### Data Analysis  
The measured pressure variation with time (with frequency of 10 kHz) can visually illustrate the pressure oscillation conditions. In addition, the spectral analysis of these pressure signals was conducted using the Fast Fourier Transform (FFT) to identify the dominant frequency (\(f_{\text{M}}\)) and its amplitude of sound pressure for a quantitative analysis.  
Figure 1: Schematic of the experimental setup.  
Here, the root-mean-square pressure (\(P_{\text{RMS}}\), Pa) was used to characterize the oscillation amplitude, as given by Equation (1), and the pressure signals collected at P\({}_{5}\) (combustion chamber bottom) were selected for representing the oscillation behavior of system.  
\[P_{\text{RMS}}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}{p_{i}}^{2}} \tag{1}\]  
where \(N\) is the number of samples and \(p_{i}\) is the measured pressure.  
Alternatively, the sound pressure level (SPL, dB) was also used for a broader amplitude comparison, as given in Equation (2).  
\[\text{SPL}=20\text{lg}\frac{P_{\text{RMS}}/\sqrt{2}}{p_{\text{ref}}} \tag{2}\]  
where \(p_{\text{ref}}\) is the reference value (2 \(\times\) 10\({}^{-5}\) Pa, here).  
Under sustained-oscillation conditions, a standing wave was formed within the whole system, and the mode shape is similar to a quarter wave within the combustion chamber. Consequently, the acoustic resonant frequency of combustion chamber (\(f\)) can be estimated using Equation (3), which can be used to analyze the system oscillation behavior. Here, the wave mode shape within combustion chamber is not perfectly a quarter wave, and thus\(f\) could differ slightly from the true oscillation frequency.  
\[f=\frac{1}{4\times\frac{1}{i}h_{i}/c_{\text{mix},i}} \tag{3}\]  
where \(h_{i}\) and \(c_{\text{mix},i}\) are the height and acoustic speed of the mixture gas of the \(i\)th section of the combustion chamber (divided into nine sections along height, as shown in Figure 1), respectively, and the outlet correction [27] is included in \(h_{9}\).  
The measured temperatures were used together with properties of mixture gas to estimate the \(c_{\text{mix},i}\), as shown in Equation (4).  
\[c_{\text{mix},i}=\sqrt{\text{R}\frac{\gamma_{\text{mix},i}T_{i}}{M_{\text{mix }}}} \tag{4}\]  
where \(M_{\text{mix}}\) is the molar mass of mixture (ignore the change in combustion chamber), \(\gamma_{\text{mix},i}\) is the adiabatic exponent of the mixture in the \(i\)th section, and \(T_{i}\) is the temperature measured from the \(i\)th thermocouple.  
In addition, the acoustic wavelength (\(\lambda_{\text{P}}\)) within the premixed chamber can be calculated by the ratio of \(c_{\text{mix}}\) within the premixed chamber to the oscillation frequency (\(f\) or \(f_{\text{M}}\)). This \(\lambda_{\text{P}}\) is crucial for determining the pressure mode shape before the combustion chamber inlet, and thus influences the oscillation behavior through the Rayleigh criterion [24; 25], as is also discussed in Section 4.1.
# Vibration Energy Harvesting from Raindrops Impacts: Experimental Tests and Interpretative Models
## 7 Conclusions  
The aim of this research was twofold: the confirmation of the validity of the harvester with a spoon by means of tests with actual rain; and the development of an interpretative mathematical model that is able to explain the improvement in performance due to the presence of the water layer. The outdoor tests carried out with actual rain confirmed the effectiveness of the harvester with a spoon. A drop making an impact at the center of the filled spoon is able to generate a voltage about four times larger than the one generated by the same impact on a dry spoon. Outdoor tests showed larger dispersions in terms of of the damping ratio and peak-to-peak voltage. These phenomena were due to the variability of the impact position, which is inherent in actual raindrop impacts, and were replicated by means of specific indoor tests. In order to develop a mathematical model of the impact of raindrop on the liquid surface of the spoon, a correlation between the generated voltage and the various phases of the impact (crown formation, crown collapse, sloshing) was made by means of video analysis. The complex fluid motion generated by the raindrop impact was modeled considering the uni-dimensional motion of two concentric cylinders that oscillate after the impact. The inner cylinder represents the cavity formation, whereas the outer cylinder represents the crown generation. The parameters of this oscillator were determined from experimental videos by means of an optimization method. The application of the linear momentum equation to the spoon with oscillating liquid made it possible to predict the force on the harvester and the generated voltage. The comparison between simulated results and experimental results in the time and frequency domains showed good agreement. The error in frequency was about 1.7%, whereas the error in peak-to-peak voltage was about 6%.  
Conceptualization, I.P., A.D., E.M., M.B. and G.R.; methodology, I.P., A.D., E.M., M.B. and G.R.; software, I.P.; validation, E.M., M.B. and G.R.; formal analysis, I.P. and A.D.; investigation, I.P., A.D., E.M., M.B. and G.R.; writing--original draft preparation I.P., E.M. and M.B.; writing--review and editing, A.D. and G.R.; funding acquisition, A.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by University of Padova -DII, programme BIRD 2019, grant DORI_SID19_01 \"Vibration energy harvesting from raindrop\". P.I. [PERSON].  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Influence of Plasma Gas Temperature on Bactericidal Effect  
Disinfection using plasma, such as direct treatment and with plasma-activated liquids, has been reported [20,21]. In this study, we treated bacterial suspensions with temperature-controlled plasma, and evaluated their bactericidal effects. _Staphylococcus aureus_ ATCC6538, the target of disinfection, was transferred to Luria-Broth agar medium on the day before the experiment, and incubated overnight at 36 \({}^{\circ}\)C. The bacterial suspensions were prepared by suspending _Staphylococcus aureus_ in the DPBS solution to 10\({}^{8}\) CFU/mL. Carbon dioxide and nitrogen at 3 L/min (standard) were used as the plasma gases. The plasma gas temperature was controlled at 30, 45, 60, 75, and 90 \({}^{\circ}\)C, at 2 mm from the plasma outlet. The bacterial suspension (500 \(\upmu\)L) was dispensed into a 24-well plate (MS-8024R, Sumitomo Bakelite Co.,  
Figure 1: Overview of the plasma treatment.  
Ltd., Tokyo, Japan), and the plasma was treated from 16.5 mm above the liquid surface. Subsequently, 100 uL of the bacterial suspension was collected and diluted, and the number of surviving bacteria was evaluated using the colony counting method. For comparison, the bacterial suspension was treated with carbon dioxide and nitrogen gas at 90 \({}^{\circ}\)C, and the number of surviving bacteria was evaluated. In this case, temperature control using a chiller could not achieve 90 \({}^{\circ}\)C gas without generating plasma. Therefore, the plasma jet body was directly heated using a ribbon heater, and the gas at 90 \({}^{\circ}\)C was generated by adjusting the voltage applied to the heater.  
To quantitatively evaluate the bactericidal effect, we used the bactericidal activity (\(BA\)) value, which can be calculated using Equation (1) [12]:  
\[BA=\frac{\log_{10}(N1/N2)}{T2-T1} \tag{1}\]  
The \(BA\) value indicates the number of bacteria inactivated per minute. It has been shown that plasma disinfection takes time after treatment, until the disinfection effect is achieved [22]. In this study, we evaluated the disinfection effect by calculating the \(BA\) value from the time when the number of surviving bacteria decreased by more than one log reduction from the initial number to the time just before the number of bacteria decreased below the detection limit. Here, \(T1\) is the time at the measurement point just before the number of surviving bacteria decreased by more than one digit from the initial number, and \(T2\) is the time at the measuring point just before the number of surviving bacteria fell below the detection limit. The numbers of surviving bacteria at \(T1\) and \(T2\) are denoted as \(N1\) and \(N2\), respectively.
## 4 Results and Discussion
### Descriptive Analysis (EDA)
#### 4.1.4 Temporal Analysis  
To complement the above analysis, a temporal assessment of trips throughout the day was performed, considering both public transport demand and waiting times. This approach facilitates the identification of critical periods.  
* Number of trips vs. time of day (Figure 4a): Peaks occur at 07:00 (99,734 trips) and, more notably, at 18:00 (121,363 trips), aligning with typical work/school rush hours. Inadequate service frequency during these intervals can significantly increase waiting times.
* Average waiting time vs. time of day (Figure 4b): \(\circ\) From 00:00 to 05:00, waiting times are high (exceeding 17 min at 03:00), likely due to low operational frequency. \(\circ\) Between 06:00 and 10:00, waits are below the overall average (7.20 min), with the most efficient period around 08:00 (approximately 6 min). \(\circ\) After 18:00, waiting times gradually rise again, reaching 9.79 min at 23:00--possibly due to reduced nighttime services.  
It is worth noting that Figure 4b represents the average waiting time for all transport modes; during peak hours, this value remains close to the daily mean because the higher scheduled frequencies of high-capacity services--such as Metro Valparaiso, which operates on segregated infrastructure--offset surface congestion, even though some user groups still experience longer waits.  
These patterns highlight the importance of adjusting Urban Bus and Taxi Colectivo operations according to demand, particularly during peak and nighttime periods, to decrease waiting times.  
Figure 3: Scatter plots of travel and demographic variables vs. waiting time.
## 1 Introduction
Based on findings from studies conducted under the aforementioned yaw situations and the stress-strain properties of wind turbine blades, researchers aiming to optimize wind power have considered (1) tracking the maximum power by controlling the rotor speed and yaw rotation of twin wind turbines [40]; (2) an individual pitch control system designed to reduce fatigue damage to key components [41; 42]; and (3) novel yaw control strategies [43; 44; 45]. In summary, yaw and blade stress in wind turbines have been extensively studied. Most of the existing analysis and design methods have considered only output power, fatigue load characteristics, and structural stress damage to wind turbines at a fixed angle. However, the dynamic variation of these characteristics during the yaw process is seldom researched. While some research on the dynamic yaw process additionally emphasizes the aeroelasticity of wind turbines, the blade structure has not been further examined. It is challenging to simulate the stress distribution of wind turbine blades based on dynamic yaw because wind has periodic, complicated, and varied features. Therefore, a self-developed horizontal axis wind turbine was selected in this study to analyze the real-time dynamic stress characteristics of wind turbine blades subjected to periodic dynamic yaw, combined with the method of one-way fluid-solid coupling numerical simulation. The results can provide a theoretical basis for the design of wind turbine blades and for yaw control strategies.
# A Novel QoS Provisioning Algorithm for Optimal Multicast Routing in WMNs
###### Abstract  
The problem of optimal multicast routing in Wireless Mess Networks (WMNs) with Quality-of-Service (QoS) provisioning, which is Non-Deterministic Polynomial (NP)-complete, is studied in this paper. The existing algorithms are not very efficient or effective. In order to find an approximation optimal solution for WMNs in feasible time from source to the set of destination nodes, combining the previous deterministic algorithm with the well-known Minimum Path Cost Heuristic (MPH) algorithm, a novel multicast heuristic approximation (NMHA) algorithm with QoS provisioning is proposed in this paper to deal with it. The theoretical validations for the proposed algorithm are presented to show its performance and efficiency. After that, the random static networks with different destination nodes are evaluated. Simulations in these networks show that the proposed algorithm can achieve the approximate optimal solution with the approximation factor of \(2(1+\varepsilon)(1-1/q)\) and the time complexity of O(\(qnm^{2}\)x\({}^{\mathrm{K}-1}\)).  
Keywords:multicast routing; Wireless Mess Networks; QoS provisioning; approximation algorithm +
Footnote †: journal:
## 5 Probability of Data Error in IEEE 802.15.4
### Chip Error Rate to Data Symbol Error Rate  
As discussed previously, in the 2.4 GHz PHY of IEEE 802.15.4, data symbols are mapped to the chips after the demodulation. Because of the symmetry in the data symbol set used by the 802.15.4 2.4 GHz PHY layer, the error probability of the data symbol, \(S_{0}\), is equal to the symbol error probability, \(P_{d}\)[31]:  
\[P_{d}=P(e|S_{0})=P(e|S_{i})\;\;\text{for}\;i=1,\ldots,15 \tag{35}\]  
Hence, with no loss of generality to obtain an analytical expression for \(P_{d}\), we can find the union upper bound of \(P(e|S_{0})\) expressed as:  
\[P(e|S_{0})\leq\sum_{i=1}^{15}P(S_{i}|S_{0}) \tag{36}\]  
where \(P(S_{i}|S_{0})\) represents the probability of deciding for the symbol, \(S_{i}\), once symbol \(S_{0}\) has been transmitted. Each probability in the summation of Equation (36) can be formulated as a function of Hamming distance, \(h_{i}\), between \(S_{0}\) and \(S_{i}\), and the chip error rate, \(P_{c}\), as:  
\[P(S_{i}|S_{0})=P(h_{i},P_{c})=\begin{cases}\frac{1}{2}\binom{h_{i}}{h_{i}/2}P _{c}^{h_{i}/2}(1-P_{c})^{h_{i}/2}+\sum_{i=\frac{h_{i}+2}{2}}^{h_{i}}\binom{h_{ i}}{i}P_{c}^{\;i}(1-P_{c})^{h_{i}-i}&\text{if}\;h_{i}\;\text{is even}\\ \sum_{i=\frac{h_{i}+1}{2}}^{h_{i}}\binom{h_{i}}{i}P_{c}^{\;i}(1-P_{c})^{h_{i}-i }&\text{if}\;h_{i}\;\text{is odd}\end{cases} \tag{37}\]  
By substituting Equation (37) in Equation (36), we obtain an upper bound that can be applied to any symmetric symbol set. The bound will be used in the next sub-sections to obtain expressions for the data symbol error probability of both coherent and non-coherent detection.  
Figure 6: Non-coherent chip error rate.