esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 3 Methodology
### Active Learning  
Active learning is a subfield of machine learning and artificial intelligence. It falls under the category of semi-supervised machine learning, where a learning model can interactively request information from the user or another information source to obtain desired outputs at new datapoints [26]. In the statistics literature, it is sometimes known as \"query learning\" or \"optimal experimental design\" [38]. Active learning encompasses various problem scenarios in which a machine learning model can ask queries, such as membership query synthesis, stream-based selective sampling, and pool-based active learning. In the context of rumor detection, as discussed earlier, it is often possible to gather a substantial volume of unlabeled data, aligning with the common scenario in pool-based active learning.  
Figure 2 illustrates the typical workflow cycle of active learning in pool-based scenarios. The raw dataset for rumor detection contains a small portion of labeled data and a large amount of unlabeled data, designated \(D_{u}\). The labeled dataset is divided into an initialized training dataset \(D_{l}\) and a test dataset \(D_{t}\) based on a certain proportion. Let \((x,y)\) be an instance in the raw dataset, where \(x\) is a d-dimensional feature vector and \(y\) is its corresponding label. A machine learning model, denoted as \(P_{\theta}\), begins with the small labeled training dataset \(D_{l}\) and undergoes standard supervised training to establish an initial model. This initial model is then evaluated on the unlabeled dataset \(D_{u}\), with \(\hat{y}\) representing the predicted label for instance \(x\).  
A query strategy is employed to compute a measurement criterion with \(\hat{y}\), which is used to select one or a few of instances from \(D_{u}\). The selected unlabeled instances are typically informative or representative samples, and are referred to as query instances. The query instances are then sent to an oracle for labeling. When the query instances have been labeled by querying the oracle, they are added to the training dataset \(D_{l}\). The machine learning model \(P_{\theta}\) is then retrained with the updated labeled dataset \(D_{l}\) and tested on the test dataset \(D_{t}\) to evaluate its current performance. This process is repeated until the model achieves satisfactory performance on \(D_{t}\) or until specific preset conditions are met.  
The primary objective of active learning is to maximise the model's effectiveness by minimizing the number of samples that require manual labelling. The main challenge lies in identifying informative or representative query instances that facilitate the rapid convergence of model training.
## 5 Conclusions  
The transport of gas molecules through NG and GO membranes, taking into account the influence of the surface processes on graphene sheets, was studied theoretically. The combined effect of the features of molecule scattering by graphene sheets and surface diffusion of adsorbed molecules on these sheets on the transport of gas molecules through GO membranes was analyzed. It was shown that some experimental results given in [12] can be explained on the basis of the one-pathway model for mass transfer in GO membranes (diffusion of gas through the slit-like pores of the membranes). In particular, it was shown that the presence in the system of CO\({}_{2}\) molecules can decrease the permeance of H\({}_{2}\) molecules that corresponds to the experimental results given in the aforementioned paper. It was shown that resonance radiation affecting parameters such as the sticking coefficient and adsorption time of gas molecules can be used for the management of mass transfer through NG and GO membranes. The presented results can be used in the intensification and optimization of processes such as the separation of gas mixtures and natural gas purification using graphene-based membranes. Of course, much more future measurements where we can consider these and possibly other models need to be performed, but that is out of the scope of the present manuscript.  
[PERSON] is the author of the modeling and contributed in writing of the article; [PERSON], supervised this work and contributed in writing of the article, [PERSON] was the author of the idea to do this research and contributed in writing of the article; [PERSON] financed this work from her project and contributed in writing of the article. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Czech Science Foundation grant No. 17-05421S, and by EU structural funding in the frame of Operational Programme Research, Development and Education, project No. CZ.02.1.01./0.0/0.0/17_049/0008419 \"COOPERATION\".  
The authors declare no conflict of interest.  
**Nomenclature**  
\(D_{\text{Kn}}\) diffusion coefficient in classic theory of Knudsen diffusion  
\(D_{\text{s}}\) coefficient of surface diffusion  
\(E_{\text{a}}\) adsorption energy  
\(E_{\text{s}}\) activation energy of surface diffusion  
\(E_{\text{t}}\) activation energy of molecule translocation  
\(E_{\text{d}}\) energy barrier of activated Knudsen diffusion  
\(d\) diameter of two dimensional pores of porous graphene membrane  
\(d_{\text{c}}\) diameter of cylindrical pore  
\(d_{\text{m}}\) kinetic diameter of molecule  
\(F_{\text{H}}\) permeance for H\({}_{2}\)  
\(f\) probability of diffuse scattering of gas molecules  
\(f_{\text{sp}}\) probability of elastic (which is assumed to be specular) reflection  
\(H\) width of slit-like pore  
\(J\) flux density of molecules passing through slit-like channel (pore)  
\(j\) flux density of molecules passing through porous graphene
# Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.1 Experimental Results on the CWRU Dataset  
Figure 6a-d presents the confusion matrix of bearing faults in the CWRU dataset using the proposed method.  
The performance of bearing fault classification is shown in Table 1, with the classification accuracy reaching 99.58% for the 1 st hp and 100.00% for the other three sets. The average accuracy is 99.90%, the average sensitivity is 99.90%, and the average specificity is 99.97%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Acc (\%)** & **Sen (\%)** & **Spe (\%)** \\ \hline
0 hp & 100.00 & 100.00 & 100.00 \\
1 hp & 99.58 & 99.58 & 99.86 \\
2 hp & 100.00 & 100.00 & 100.00 \\
3 hp & 100.00 & 100.00 & 100.00 \\ average & 99.90 & 99.90 & 99.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of fault detection results in CWRU.
## 2 Methods
### System Functioning Test  
Attributes of the finished product, such as the maximum allowable load, stability, durability, and noise, were determined through tests. The maximum allowable load was measured according to the standard that can withstand four times the maximum user weight of 100 kg. First, a balance was placed on one side of the step plate, and a load of 400 kg was applied for 5 min. To evaluate stability, a subject (with a height and weight of 175 cm and 100 kg) boarded the contraption and operated it at a speed of 60 beats per minute, during which the inclination of the instrument was evaluated. within 10\({}^{\circ}\) forward and backward and 5\({}^{\circ}\) in the lateral direction. To evaluate durability, a load of 50 kg was applied to each of the two-step plates, and 100,000 cycles were performed at a rate of 60 cycles per minute. After this, the load was removed and the contraption was examined for any damages. To determine the noise generated, a decibel noise meter was used to measure the noises generated at distances of one meter from the front, rear, left, and right sides of the MGTR during its regular operation.
## 6 Conclusions and Future Work  
Network traffic is becoming more complex due to the increase in the amount of data transferred between WSN nodes resulting from increased usage. It is important to reduce power consumption and improve data protection in these networks, especially in order to prevent DoS attacks. In this paper, we modified the LEACH clustering protocol to improve its performance by adding various factors such as WSN node residual power, distance between WSN nodes, and the distance between the candidate CHs and the AP. Moreover, we analysed the performance of various feature selection techniques along with different machine learning algorithms to improve DoS detection in the WSN-DS dataset. The feature selection techniques used were WC, SPO, HS, and GA, and with each feature selection technique different machine learning algorithms such as DT, DL, KNN, NB, and SVM were used. Performance accuracy metrics were used to evaluate each algorithm. The best technique for feature selection was WC as its average performance accuracy was 2%, 5%, 3%, and 3% higher than that of PSO, SA, HS, and GA, respectively. The best machine learning algorithm results when used with WC were displayed by the DT and DL algorithms, which had the highest accuracy of 100% and the lowest number of features (Expanded Energy). The rest of machine learning algorithms achieved accuracy performances of SVM 99%, KNN 99%, and NB 81% with different numbers of WSN_DS features distributed between 12 and 15. The Friedman and Iman-Davenport statistical tests were used to select which of the two highest-performing machine learning algorithms (DT or DL) was most appropriate. The WC+DT had the lowest score of 5.449, hence WC + DT was selected as the best DoS detection technique.  
Furthermore, Cooja simulator software was also used to obtain WSN lifetime. The simulation environment was managed by either CH_Rotations, a modified LEACH protocol, or the LEACH standard protocol. CH_Rotations improved the WSN lifetime by 30% compared to the standard LEACH routing protocol. The WC + DT technique consumed 5% of the total WSN lifetime compared to the WC + DT-free scenario. In future, we plan to collect a new WSN dataset from the 6 LoWPAN protocol and add new features such as packet size per stream, dropped packets per stream, flow change ratio, and packet change ratio.  
All authors contributed to this manuscript. Conceptualization, R.A., R.W., T.A.-A. and W.A.-A.; investigation, R.A., Q.B., R.W. and T.A.-A.; data duration, R.A., R.W. and W.A.-A.; writing--original draft, R.A., T.A.-A. and Q.B.; visualization, R.A.; supervision, R.A.; writing--review & editing, R.A., R.W. and Q.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 5 Numerical Results
### Probability of SPSC Results  
Figure 6 shows the probability of SPSC versus \(E(\gamma_{\rm B})\) with different \(E(\gamma_{\rm E})\) when \((\alpha_{\rm B},\beta_{\rm B})=(\alpha_{\rm E},\beta_{\rm E})=(2,2)\). In this figure, the value of SPSC decreases with the increase of \(E(\gamma_{\rm B})\). However, the value of the probability of SPSC increases with the increase of \(E(\gamma_{\rm E})\). This shows the impacts of the average strength of the main channel and the average strength of the main channel on system performance. Moreover, it can be seen that the theoretical results match the simulation results very well, and thus the derived exact expression of the probability of the SPSC is very accurate to evaluate the system performance.  
Figure 7 shows the probability of SPSC versus \(E(\gamma_{\rm B})\) with different \((\alpha_{\rm E},\beta_{\rm E})\) when \((\alpha_{\rm B},\beta_{\rm B})=(2,2)\). Obviously, with the increase of \(E(\gamma_{\rm B})\), the probability of SPSC decreases. The conclusion is the same as that in Figure 6. Once again, it can be observed that small differences appear between the theoretical results and the simulation results. Therefore, the exact expression of the probability of SPSC can be used to evaluate the system performance without time-intensive simulations. Moreover, it can be seen that when \(E(\gamma_{\rm B})\) is small, the probability of SPSC increases with the increase of \((\alpha_{\rm E},\beta_{\rm E})\). However, the trends of curves changes when \(E(\gamma_{\rm B})\) is large, i.e., the probability of SPSC decreases with the increase of \((\alpha_{\rm E},\beta_{\rm E})\).  
Table 3 shows the accuracy of the SOP for different \(E(\gamma_{\rm B})\) when \((\alpha_{\rm B},\beta_{\rm B})=(2,2)\) and \((\alpha_{\rm E},\beta_{\rm E})=(1,1)\). It can be seen that the discrepancies between the simulation and theoretical results are small enough in most cases. Therefore, the derived closed-form expression of the probability of SPSC is accuracy to evaluate the system performance.  
In Figure 8, the probabilities of the SPSC over different fading channels are also provided. Specifically, the probabilities of the SPSC over the K, exponential, and Gamma-Gamma fading channels are obtained by (37), (41) and (45), respectively. Moreover, the probability of the SPSC over the M-distributed fading channel (i.e., (20)) with special parameters are also provided as shown in Table 1. Similar to Figure 5, the results over the M-distributed fading channel with special parameters match the results of the other fading channels very well. Therefore, the derived probability of SPSC over the M-distributed fading channel can be used as a unified expression for many well-known fading channels.
# Deep Machine Learning for Path Length Characterization Using Acoustic Diffraction
## 2 Materials and Methods
### Preprocessing
#### 2.2.1 Convolutional Neural Network  
As convolutional neural networks are designed to process images, the preprocessing of the data for this type of model begins with converting the received waveforms into an image. In our previous work on using machine learning for acoustic diffraction patterns, we introduced the concept of Short Distance Fourier Transforms (SDFTs) [9]. These SDFTs represent the change in the waveforms over both frequency and distance in the form of an image so that the data can be parsed by a CNN. Each SDFT, in this case, is created by taking the fast Fourier transform (FFT) of each of the 17 waveforms collected at each lateral location for a single trial. For example, the first trial has data collected in 2.5 mm increments at locations from \(-1\) to 3 cm. Once all FFTs are taken, an FFT for each location is selected randomly from the set of 5 waveforms collected for each location in each Trial. Each of these FFTs are then stacked into an image as a column of the SDFT image in order from \(-1\) to 3 cm. These images are saved as greyscale images and stored in directories that correspond to the data type (amplitude or phase), hole diameter, driving frequency, and thickness of the disc. To enable easy use with a machine learning algorithm, the file names and important metadata, such as the experiment, data type, hole diameter, trial number, acoustic path length, and disc thickness are stored in a CSV file so that any value can be pulled out to use as a training label for the images. Examples of the SDFTs are included in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Frequency** & **Wavelength (mm)** & **Height (mm)** & **Path Length (Rad)** \\ \hline
1 MHz & 0.33 & 0.68 & 12.95 \\ \hline
1 MHz & 0.33 & 1.32 & 25.13 \\ \hline
1 MHz & 0.33 & 6.46 & 123.0 \\ \hline
5 MHz & 0.066 & 0.68 & 64.74 \\ \hline
5 MHz & 0.066 & 1.32 & 125.66 \\ \hline
5 MHz & 0.066 & 6.46 & 614.99 \\ \hline \end{tabular}
\end{table}
Table 4: Values for derivation of the 6 acoustic path lengths.
## 2 Materials and Methods
### Evaluation Model and Data Processing
#### 2.4.3 Entropy Weighting  
Entropy weighting can provide an objective substitution method for avoiding subjectivity in weight setting. The entropy weighting process is divided into several steps as discussed below.  
Step 1: Construct the following judgement matrix:  
\[X=\left(x_{ij}\right)_{m\times n}(i=1,2,\cdots,m;j=1,2,\cdots,n)=\left|\begin{array} []{cccc}x_{1,1}&x_{1,2}&\cdots&x_{1,n}\\ x_{2,1}&x_{2,2}&\cdots&x_{2,n}\\ \vdots&\vdots&\vdots&\vdots\\ x_{m-1,1}&x_{m-1,2}&\cdots&x_{m-1,n}\\ x_{m,1}&x_{m,2}&\cdots&x_{m,n}\\ \end{array}\right| \tag{10}\]  
where \(m\) is the quantity of evaluation target subjects and \(n\) is the quantity of evaluating indices.  
Step 2: Distinguish the maximum and minimum values from the basic value of the index after standardizing this index. Formulas (11) and (12) were used to calculate the positive and negative indexes, respectively.  
\[\text{Negative index}:\ y_{ij}=\frac{x_{\max}-x_{ij}}{x_{\max}-x_{\min}} \tag{11}\]  
where \(y_{ij}\) and \(x_{ij}\) are the standardized data and basic data of the index, respectively, and \(x_{\max}\) and \(x_{\min}\) are the maximum and minimum raw values of the index, respectively.  
Step 3: Calculate information utility (\(h_{j}\)) as follows:  
\[h_{j}=1+\frac{\sum\limits_{i=1}^{n}Y_{ij}\ln Y_{ij}}{\ln n} \tag{12}\]  
\[Y_{ij}=\frac{y_{ij}}{\sum\limits_{i=1}^{n}y_{ij}},i=1,2\cdots,n \tag{13}\]  
Step 4: Calculate the weight of the \(j\)-th index as follows:  
\[w_{j}=\frac{h_{j}}{\sum\limits_{j=1}^{n}h_{j}} \tag{14}\]  
Step 5: Reset the indicator data. The data is standardized as \(y_{ij}\), (\(j=1,2...\), \(n\)), and \(y^{\prime}_{ij}\) is calculated as  
\[y^{\prime}_{ij}=\left\{\begin{array}{ll}0&\text{The $j$-th indicator is missing}\\ y_{ij}&\text{The $j$-th indicator is complete}\\ \end{array}\right. \tag{15}\]
## 3 Case Study
### Selection of Rake Angle (\(\alpha\)) and Land Width (\(a\))  
For a chip to break spontaneously (see Figure 1a), it is necessary that it curls sufficiently. For that purpose, on this first stage of the design, FE simulations trials were performed to obtain the best combination of rake angle (\(\alpha\)) and land width (\(a\)) to optimize chip curling without using a chip breaker. These two parameters should not be analyzed independently, as the behavior of the chip during formation near the cutting edge depends on their combination.  
Three values of rake angle (\(\alpha\)) were proposed: 10\({}^{\circ}\), 20\({}^{\circ}\) and 30\({}^{\circ}\). It is assumed that higher rake angles lead to lower cutting forces, so the selection of a positive rake angles [38, 39] is recommended. However, there are some drawbacks for excessive rake angles. The key issue is that the higher the rake angle is, the weaker the cutting edge becomes. This fact introduces higher stresses on the cutting insert and leads to higher tool wear. This is because with higher rake angles, the contact area between the tool and the  
\begin{table}
\begin{tabular}{c c} \hline
**Workpiece Materials** & **Al 6082** \\ \hline Depth of cut (Ap) & 1 mm \\ \hline Cutting Velocity (Vc) & 100 m/min \\ \hline Feed rate (f) & 0.1 mm/rev \\ \hline \end{tabular}
\end{table}
Table 1: Cutting conditions.  
Figure 3: A rhombic 80\({}^{\circ}\) turning insert: (**a**) The actual insert and (**b**) side view of the geometry of the chamfer. (Dimensions in mm).  
Figure 2: Geometric parameters of the chip breaker: Land width (\(a\)), tool-chip contact length (L), rake angle (\(\alpha\)), radius of curvature \({}^{\circledR}\) and groove width (W).  
chip is reduced, so the equivalent application-point of the normal force moves towards the cutting edge. Furthermore, this interface between tool and chip is the region of the maximum contact temperature and shifting this towards the cutting edge, shortens the life of the tool, according to [PERSON] [40]. Thus, a rake angle over 30\({}^{\circ}\) would be inappropriate, as it would excessively weaken the tool edge.  
Four values for the land width (a) were proposed according to the feed rate: Non-existent land width, a = 0.00 mm; a = 0.05 mm, equal to 50% of the feed rate; land width equal to the feed rate, a = 0.1 mm; and a = 0.15 mm, 1.5 times the feed rate.  
The results obtained from the FE simulation are shown in Table 2 and Figure 4. The best solution corresponds to a rake angle of \(\alpha\) = 20\({}^{\circ}\) and a land width a = 0.05 mm, that is 50% of the feed rate. This combination gives the lowest radius of curvature of the generated chip without using chip breaker, as well as considerably lowering the cutting forces and temperatures. Consequently, this was the design selected for the chip breaker's edge in the first stage.
## 3 Results
### EMG Activity  
The sEMG\({}_{\rm{RMS}}\) activities in the leg muscles did not show significant differences among the ten contact phases in either group (\(p\) > 0.05; Figure 6); however, in PwMS, the confidence intervals were wider than those of the controls. Activation of the vastus lateralis was significantly higher in PwMS than in HCs, even when they walked at higher speeds (PwMS vs. HC-1, \(p\) < 0.001, ES = 1.90; PwMS vs. HC-3, \(p\) < 0.001, ES = 1.84). Similarly, biceps femoris muscle activation was higher in PwMS than in HC-1 (\(p\) < 0.05, ES = 1.30) but not when PwMS were compared to HC-3 (\(p\) < 0.001; ES = \(-\)1.84). In contrast, PwMS showed less activation in the tibialis anterior when compared to HC-1 (\(p\) < 0.001; ES = \(-\)1.76) and to HC-3 (\(p\) < 0.001; ES = \(-\)1.84). A similar pattern showed the lateral gastrocnemius muscles in PwMS versus HC-1 (\(p\) < 0.001, ES = \(-\)1.70) and versus HC-3 (\(p\) < 0.001; ES = \(-\)1.85). By increasing the speed from 1 km/h to 3 km/h, HCs increased their activation in the vastus lateralis (HC-1 vs. HC-3, \(p\) < 0.05, ES = \(-\)1.91), biceps femoris (HC-1 vs. HC-3, \(p\) < 0.001, ES = \(-\)1.87) and lateral gastrocnemius muscles (HC-3 vs. HC-1, \(p\) < 0.05, ES = \(-\)1.58). In contrast, the tibialis anterior did not increase its activation at higher speeds (HC-1 vs. HC-3, \(p\) > 0.05; ES = \(-\)0.47).  
The coefficients of variation within and between subjects (CV, %) are reported for both groups (persons with multiple sclerosis-PwMS and healthy controls when walking at 1 km/h-HC-1 and at 3 km/h-HC-3). The CV within subjects was calculated in each leg separately: in the weakest (W) and strongest legs (S) for PwMS and in the right (R) and left legs (L) for HC-1 and HC-3. The CV between subjects was calculated in both legs: in the weakest-strongest (W-S) for PwMS and in the right-left (R-L) for HCs.  
Figure 5: Angle-angle plots of HC-3 (speed = 3 km/h). (**A,B**) are representative examples of one participant; (**C,D**) are the mean values of all participants in the HC group. The green lines indicate the phases between foot strike (FS) and maximal knee flexion during the stance phase (MKF-C).  
Figure 6: Average sEMGrms (SD) values of leg muscles (vastus lateralis, biceps femoris, tibialis anterior and lateral gastrocnemius) recorded in 8 participants for the weakest lower limb in PwMS (1 km/h) and for the dominant limb in HCs (1 km/h and 3 km/h). The significant differences between the two groups are reported in the text boxes within the graphs.