esa-sceva/satcom-chunk-collection huggingface dataset:
# Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network
## 4 Experimental Results
### Evaluation of Fault Diagnosis
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Layer** & **Kernel Size** \\ \hline \multirow{8}{*}{Feature extraction} & Convolution1 & (9, 1), channel = 6 \\  & Convolution2 & (1, 9), channel = 6 \\  & Max\_pooling1 & (2, 2) \\  & Convolution3 & (9, 1), channel = 6 \\  & Convolution4 & (1, 9), channel = 6 \\  & Max\_pooling2 & (2, 2) \\ \hline \multirow{4}{*}{Classification} & Fusion Method & \multirow{2}{*}{
\begin{tabular}{c} Fuzzy Layer \\ Output Layer \\ \end{tabular} } \\  & & Fuzzy Layer & \\ \cline{1-1}  & - & 91.62\% & 93.77\% & 92.43\% & 22,854 \\ \hline \multirow{4}{*}{CFNN} & - & 98.68\% & 98.8\% & 98.80\% & 3890 \\  & GAP & 95.92\% & 99.86\% & 97.68\% & 3578 \\  & GMP & 95.61\% & 99.75\% & 98.06\% & 3578 \\  & CAP & 91.82\% & 99.32\% & 96.17\% & 3674 \\  & CMP & 95.57\% & 99.58\% & 98.11\% & 3674 \\  & Network Mapping & 99.25\% & 99.67\% & 99.49\% & 6106 \\ \hline \multirow{4}{*}{Vector-CFNN} & - & 97.76\% & 99.61\% & 98.89\% & 1910 \\  & GAP & 99.81\% & 99.91\% & 99.84\% & 1214 \\ \cline{1-1}  & GMP & 98.73\% & 99.39\% & 99.11\% & 1214 \\ \cline{1-1}  & CAP & 99.52\% & 99.79\% & 99.68\% & 1502 \\ \cline{1-1}  & CMP & 99.63\% & 99.78\% & 99.71\% & 1502 \\ \cline{1-1}  & Network Mapping & 99.61 & 99.82 & 99.69\% & 5278 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The architecture of vector-CFNN. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Fusion Method** & **Lowest Accuracy** & **Highest Accuracy** & **Average Accuracy** & **Parameter** \\ \hline ANN & - & 71.77\% & 77.32\% & 75.78\% & 533,008 \\ \hline FNN & - & 82.38\% & 93.98\% & 88.76\% & 132,368 \\ \hline CNN & - & 91.62\% & 93.77\% & 92.43\% & 22,854 \\ \hline \multirow{4}{*}{CFNN} & - & 98.68\% & 98.8\% & 98.80\% & 3890 \\  & GAP & 95.92\% & 99.86\% & 97.68\% & 3578 \\ \cline{1-1}  & GMP & 95.61\% & 99.75\% & 98.06\% & 3578 \\ \cline{1-1}  & CAP & 91.82\% & 99.32\% & 96.17\% & 3674 \\ \cline{1-1}  & CMP & 95.57\% & 99.58\% & 98.11\% & 3674 \\ \cline{1-1}  & Network Mapping & 99.25\% & 99.67\% & 99.49\% & 6106 \\ \hline \multirow{4}{*}{Vector-CFNN} & - & 97.76\% & 99.61\% & 98.89\% & 1910 \\ \cline{1-1}  & GAP & 99.81\% & 99.91\% & 99.84\% & 1214 \\ \cline{1-1}  & GMP & 98.73\% & 99.39\% & 99.11\% & 1214 \\ \cline{1-1}  & CAP & 99.52\% & 99.79\% & 99.68\% & 1502 \\ \cline{1-1}  & CMP & 99.63\% & 99.78\% & 99.71\% & 1502 \\ \cline{1-1}  & Network Mapping & 99.61 & 99.82 & 99.69\% & 5278 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy of fault diagnosis and parameters used in each model. advantages of vector-CFNN are that few parameters are needed while retaining a high accuracy rate, and the fusion methods can greatly improve the network classification performance in diagnosing faults.
# Dynamic Nonprehensile Manipulation of a Moving Object Using a Batting Primitive
## 3 Method
### Ball Recognition
#### 3.1.2 Circle Fitting  
Figure 3a shows the batting experiment environment captured using a stereo vision camera. Because the ceiling light creates a shadow on the lower part of the ball, the RGB value of the lower part of the ball changes. Figure 3b shows a binarized image with color segmentation applied to the image in Figure 3a. In the binary image of Figure 3b, the shape of the ball appears as a semicircle rather than a circle because the lower part of the color change is not properly color segmented. Incorrect color segmentation causes errors in the center position measurement of the ball. For example, if only the upper part of the ball is color-segmented, the position error in the vertical direction increases. Threshold adjustment is limited because the difference between the color value of the lower part of the ball and the color value of the upper part is large.  
To overcome the limitations of color segmentation, we additionally applied a circle fitting method that uses the geometric characteristics of a circular ball. Figure 3c shows only the edge of the semicircle shown in Figure 3b. By applying a circle fitting to the edge of the semicircle, we can estimate the original circle ball shape, as shown in Figure 3d. Figure 3e shows the center position of the ball before applying the circle fitting (line green point) and the center position of the ball after application (blue point). The positions of the lime green and blue points are (X, Y, Z) = (\(-\)0.0416, 1.3299, \(-\)0.0888) and (X, Y, Z) = (\(-\)0.0413, 1.3207, \(-\)0.0955), respectively. The true position of the ball is (X, Y, Z) = (\(-\)0.0414, 1.3187, \(-\)0.0985). Prior to the circle fitting method, the error in the z direction was 0.0097 m, which is approximately 20% of the diameter of the ball at 0.05 m. After the circle fitting method was applied, the center position of the ball moved by about 0.007 m further down the Z-axis, and the position error in the Z direction was reduced from 0.0097 m to 0.0030 m.  
Figure 3: Circle fitting process. (**a**) Batting experiment environment captured using a stereo vision camera, (**b**) A binary image with color segmentation, (**c**) The edge image of the semicircle, (**d**) An image with circle fitting applied to the edge of a semicircle, (**e**) Center position before (green) and after (blue) circle fitting.  
Figure 2: Color image (**left**) and binary image (**right**). Green circle of color image represents the position of the red ball.  
Figure 4: Red marker attached to the end-effector of the robotic arm to calculate the homogeneous matrix.  
Figure 5: Red marker positions measured from the vision sensor. The positions of the markers measured at various positions are used to accurately calculate a homogeneous matrix.
# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 4 Method and Data
### Data and Toolset  
The present work makes use of several different data sets. To be able to work with a complete and high-quality dataset, comprising all relevant measurements (pressure, temperature, humidity, etc.), the data of a 200 m high wind met mast was analyzed. The met mast is located at a complex, forested site at \"Rodeser Berg\" close to Kassel in central Germany. The availability of comprehensive meteorological data allows investigating the relevance of complete measurement data for the accuracy of the different KPIs. A detailed description of the location, as well as the instrumentation of the met mast, can be found in a publication by [PERSON] et al. [46]. Furthermore, data of the Wind Energy Information Data Pool (WInD-Pool) [41] was available where needed. The WInD-Pool includes data in the Hz resolution  
Figure 2: Approach of this paper to analyze sensitivities and uncertainties for selected KPIs and uncertainty causes.  
for a German offshore WF (5 MW WT) in the North Sea, which was needed for this work to evaluate the effect of different resolutions. Further details cannot be stated due to confidentiality reasons. Wherever possible, however, the data from ENGLEs open data WF \"La Haute Borne\" [47] was used to ensure the replicability of the results. The WF comprises four Senvion MM82 WT with a rated power of 2 MW each and is located in the department Meuse in northeast France. For each WT, operational data over approximately six years was available. All datasets were filtered for implausible values and stuck signals [48], for example, the plausible wind speed range is 0 m/s to 50 m/s.  
For the handling, filtering, and manipulation of the data as well as the calculation of KPIs, the Python Open-Source Tool for Wind Farm Operational Performance Analysis (OpenOA) [49; 50] was used. OpenOA is developed by the National Renewable Energy Laboratory (NREL) and extended according to the requirements of this work. OpenOA is available under an open source license as Python source code on Github [51]. Due to the public availability of the ENGIE Open-Data-Dataset and OpenOA, the results can be replicated.
# An Initial Load-Based Green Software Defined Network +
## 6 Performance Analysis
### Performance Metrics  
Three parameters are used in the evaluation of ICWGA performance:  
* \(\eta_{e}\): the percentage of links that be turned into sleep mode. For simplicity, we assume that the energy consumption of each link in the network is the same. Thus, the percentage of links that are in sleep mode can represent the energy saving ratio.
* \(\rho_{l}\): the average bandwidth utilization of all the active links. Since the aim of the green methods is to use the fewest links to route the traffic, the bandwidth utilization of the active links will be improved. Average bandwidth utilization can be calculated by: \[\rho_{l}=\sum_{\forall i,j\in E_{v}}(u_{ij}/c_{ij})/\left|E_{v}\right|,\] (8) where \(\left|E_{v}\right|\) denotes the number of links in the virtual topology.
* \(\delta_{p}\) and \(\delta_{h}\): the increased delay of the paths. For energy saving, some traffic will not be routed in the shortest paths. The delay of these paths will be increased. The impact on delay of ICWGA is observed from two aspects, the increased ratio of propagation delay denoted as \(\delta_{p}\), and the increased number of hops of a path denoted as \(\delta_{h}\). The number of hops is used to evaluate the other delays caused by the switches.
## 3 Results and discussion  
### Wind Tunnel Test  
#### 3.1.1 Nozzle Flow Rate  
The flow test result (Table 3) showed that under the corresponding spray pressure, the actual measured spray flow rates of most nozzles per unit time were close to the standard flow reference value. Only CP04-1 and CP04-3 nozzle had a large deviation from the standard flow reference value of 0.47 L/min and 0.32 L/min, respectively. It was speculated that this nozzle orifice has become enlarged due to a normal wear condition over time.
## 2 Synchronous ZETA Converter
### Control via Ramp Compensation  
Now, we add a feedback loop to the system which allows us to obtain the desired output value \(V_{ref}\). To do so, we implement a ramp compensation which is schematized in the feedback loop of Figure 1. The ramp compensation \(I_{ramp}\) is formed by a ramp periodic signal of period \(T\) that is subtracted at the output of a PI controller. The PI control operates over the output voltage and its aim is to reject disturbances while eliminating the steady state error and controlling the transient state. Notice that the PI controller corresponds to an external control loop of the voltage, as:  
\[I_{ramp}=k_{V}(V_{ref}-v_{2})+k_{int}x_{5}-\frac{A}{T}\mod(t,T), \tag{5}\]  
where \(k_{V}\) and \(k_{int}\) are the gains of the PI control and \(A\) is the ramp slope. At the beginning of the cycle, the main switch is set to the ON position (\(u=1\)). The main switch is then set to the OFF position (\(u=0\)) when \(I_{ramp}\) is equal to the current in the first coil \(i_{1}\) (internal loop), or equivalently when:  
\[i_{1}=I_{ramp}. \tag{6}\]  
After this, the switch remains OFF until the beginning of the next cycle, completing the design of the controller. The switching condition (6) can be rewritten in the form \(h_{r}(\mathbf{x},t)=0\) with  
\[h_{r}(\mathbf{x},t)=k_{V}(V_{ref}-v_{2})+k_{int}x_{5}-\frac{A}{T}\mod(t,T)-i_{ 1}. \tag{7}\]  
Figure 2 shows an example of the ramp controlled sZETA converter. In Figure 2A, we display the evolution of \(I_{ramp}\) and \(i_{1}\), during two cycles in the steady state. It is possible to see that, when both functions are equal, the control signal changes from \(u=1\) to \(u=0\) (Figure 2B). Figure 2C shows the evolution of the voltages \(v_{1}\) and \(v_{2}\) and depicts how, after a short transient, the output voltage (red line) asymptotically converges to the reference value \(V_{ref}\) (dashed black line). Throughout the paper, we make use of the values of the parameters shown in Table 1 unless otherwise stated.  
A few words must be said about the values of the gains chosen. On the one hand, \(k_{V}\) was chosen to be relatively small to avoid oscillatory behavior during transient state. Moreover, \(k_{int}\) has a rather large value to guarantee fast convergence to the steady state and small steady error. Finally, the most critical parameter is the ramp compensation, i.e., the slope \(A\), which is tuned in such a way to guarantee stable operation in a set of points chosen in the range of parameters that we use in this paper. To do so, we make use of the stability criteria that we describe in the following section.  
Figure 1: Schematic representation of the synchronous ZETA converter controlled via ramp compensation.
## 2 Related Work
By removing dimensions from correlation and information gain [22], ANN is applied to the KDD99 dataset for intrusion detection and produced better results in terms of accuracy. The authors used PCA and multivariate CA to detect DDoS attacks in real-time. [PERSON] et al. [23] designed an approach using an autoencoder for intrusion detection on a current dataset CICIDS2017. The scientists developed a memetic algorithm for aberrant traffic identification and tested it on the NSLKDD and KDD-CUP 99 datasets, two well-known deep learning models [13]. In order to create an efficient framework for intrusion detection, feature augmentation has been combined with SVM [24]. This has produced reliable results in terms of false alarm rates. Researchers have utilized multilevel intrusion detection for this purpose [25]. For intrusion detection, a unique deep learning model has been designed to increase accuracy [26]. Increasing network connectivity and the incorporation of terrestrial networks into satellite networks provide new security concerns and difficulties. One of the most frequent assaults that affect satellite-terrestrial integrated networks is DDoS, which slows down service. For identifying DDoS in satellite and terrestrial networks, many research works have been carried out. An adaptive strategy based on Q-learning and a jamming detection system were proposed by [PERSON] et al. [27]. It has been suggested to use machine learning models to monitor traffic linked to socket programming [28]. It has been built to identify DDoS attacks dynamically using fuzzy logic [29]. These research projects aim to develop appropriate methods for intrusion detection in satellite and terrestrial networks. Table 1 presents a comparative analysis of the research papers that have been addressed.
# Influence of Dielectric Plate Parameters on the Reflection Coefficient of a Planar Aperture Antenna
###### Abstract  
In this article, an aperture antenna excited by a waveguide with a circular cross-section and covered with a dielectric plate was analyzed via simulation calculation and verified via measurements. The influence of the geometrical and electromagnetic parameters of the dielectric plate on the reflection coefficient S\({}_{11}\) of the antenna opening was systematically analyzed. The geometrical parameters taken in this analysis are the thickness d and the width/length h\({}_{1}\)/h\({}_{2}\) of the dielectric plate. The electromagnetic parameters used in this analysis are the real and the imaginary part of permittivity (\(\epsilon_{r}\), tan \(\delta\)) and the electrical conductivity of the dielectric plate (\(\sigma\)). The simulation calculation and analysis included other structural and electromagnetic parameters of the dielectric plate (density of the radome material, relative permeability, and magnetic loss tangent (\(\rho\), \(\mu_{r}\), and tan \(\delta_{\mu}\), respectively)), but the results show that in the range of real values of these parameters for the materials used for the dielectric plate, they had no significant influence on the reflection coefficient. The results show that impedance-matched antennas with very low values of the reflection coefficients S\({}_{11}\) at the resonant frequency can be realized by changing the geometrical and electromagnetic parameters of the dielectric plate material. The results are presented for a circular aperture antenna on a planar grounded plane with a dielectric plate on the opening, and the achieved lowest values of the S\({}_{11}\) parameter were \(-\)45.17 dB (simulated) and \(-\)43.93 dB (measured) at the frequencies of 1.7820 GHz and 1.7550 GHz, respectively. The estimated values of the dielectric plate parameters in this case are thickness d = 11.08 mm (0.67 \(\lambda\)); width \(\times\) length of grounded plane and dielectric plate h\({}_{1}\)\(\times\) h\({}_{2}\) = 423 \(\times\) 450 mm\({}^{2}\) (2.51 \(\times\) 2.67 \(\lambda\)); relative permittivity 2.5, tan \(\delta\) = 0.09, \(\mu_{r}\) = 1, tan \(\delta_{\mu}\) = 0.00, \(\rho\) = 1200 kg\(\cdot\)m\({}^{-3}\); and electrical conductivity \(\sigma\) = 0 S/m. The simulation calculation results were verified by measuring the reflection coefficient S\({}_{11}\) on the created laboratory model of the aperture antenna with the dielectric plate and showed a very good match.  
reflection coefficient; aperture antenna; dielectric plate; permittivity; loss factor; permeability; magnetic loss factor; electric conductivity; circular waveguide +
Footnote †: journal: Applied Sciences
## 3 Results
### Functionality Tests
#### 3.2.2 Migration Potential  
Migratory potential of isolated cells was determined in order to investigate the impact of general disease and of HA (Figure 3B). Following culture for 48 h, the migratory potential values of hMSCs showed a statistically significant difference for cells grown in normal medium (control) as compared to HA-containing medium: 0.7016 (\(\pm\)0.0095) (control), 0.86 (\(\pm\)0.0356) (HA) (\(p\) = 0.012). No significant differences were recorded for the other two cell lines cultivated in normal medium in comparison to HA-containing medium: SScgMSCs--0.723 (\(\pm\)0.034) (control), 0.764 (\(\pm\)0.0342) (HA) (\(p\) = 0.215); SScgtMSCs--0.6723 (\(\pm\)0.0241) (control), 0.716 (\(\pm\)0.0335) (HA) (\(p\) = 0.175).  
No significant differences in migration potential (\(p\) = 0.115) were recorded when the three MSC types cultured in control medium were compared (hMSCs vs. SScgMSCs \(p\) = 0.572; hMSCs vs. SScgtMSCs, \(p\) = 0.572; SScgMSCs, \(p\) = 0.376; SScgMSCs vs. SScgtMSCs, \(p\) = 0.101), whereas significant differences in migration potential between the cell lines grown in HA-containing medium have been recorded (\(p\) = 0.05). HA significantly stimulated the migration of hMSCs in comparison to SScgMSCs (\(p\) = 0.033) and SScgtMSCs (\(p\) = 0.005), whereas no differences in migration potential values were observed when SScgMSCs and SScgtMSCs were compared (\(p\) = 0.231).
## 2 Materials and Methods
### Classification of Slope Properties
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Attribute**} & \multirow{2}{*}{**Subattribute**} & \multicolumn{4}{c}{**Grade of Stability**} \\ \cline{3-6}  & & **Stable (I)** & **Mostly Stable** & **Less Stable** & **Instable (IV)** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Rock mass \\ characteristics \\ \end{tabular} } & Lithological & Hard and intact & Less hard and less intact rock & Less soft and less fractured rock (6–8) & Soft and fractured rock (8–10) \\ \cline{2-6}  & \multirow{2}{*}{Slope structure} & Homogenized & Blocky structure & Bedding structure (6–8) & Fractured and Loose structure (8–10) \\ \cline{2-6}  & & \multirow{2}{*}{\begin{tabular}{c} Weathering \\ degree \\ \end{tabular} } & Unweathered & Slightly & Highly & \begin{tabular}{c} Weighted \\ weathered \\ \end{tabular} & \begin{tabular}{c} Completed \\ weathered \\ (8–10) \\ \end{tabular} \\ \hline \multirow{2}{*}{\begin{tabular}{c} Topographic features \\ \end{tabular} } & Mean inclination & \(<\)10\({}^{\circ}\) & 10–30\({}^{\circ}\) & 30–60\({}^{\circ}\) & \(>\)60\({}^{\circ}\) \\ \cline{2-6}  & Slope height & \(<\)20m & 20–50 m & 50–100 m & 100–150 m \\ \cline{2-6}  & Plant cover ratio & High (\(>\)60\%) & \begin{tabular}{c} Moderate \\ (30–60\%) \\ \end{tabular} & Low (10–30\%) & Bare (\(<\)10\%) \\ \hline \multirow{4}{*}{\begin{tabular}{c} Hydrometeorology \\ \end{tabular} } & Mean annual & \multirow{2}{*}{\
\begin{tabular}{c} Precipitation \\ \end{tabular} } & \(<\)500 mm & 500–1000 mm & 1000–1500 mm & \(>\)1500 mm \\ \cline{2-6}  & Human activity & Very weak (\(<\)3) & Weak (3–6) & Strong (6–8) &
\begin{tabular}{c} Very strong \\ (8–10) \\ \end{tabular} \\ \cline{2-6}  & Seismic intensity & 0–3 & 3–7 & 7–8 & 8–12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Normalized value of subattributes of slopes. When applying the neutrosophic Dice similarity measure to the collected data, the weight for each attribute and subattribute should be assigned as shown in Equation (12). The appropriate value of weight depends on the characteristics of all collected data and may be determined in a trial-and-error manner. To investigate the influence of weight on the assessment performance, all collected slopes were randomly divided into two groups (A and B), ten for each one. Group A was used to determine the weight vector and group B was used to validate the performance of the developed method. We selected three samples, five samples, six samples, eight samples, and ten samples from group A to calculate a set of weight vectors, respectively. These weight vectors were employed to assess the stability of slopes in group B. Because slopes in group B did not participate in the determination of weight, such a procedure could effectively appraise whether reasonable weight vectors exist in the developed method. The minimum sample number required to calculate a reasonable set of weight vectors can be determined.
# CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords
## 5 Conclusions  
This study proposed a CNN-based MFA system that uses biometric data and password text and is characterized by its mobility and data security. When three factors were used in the experiments, the eight-layer CNN exhibited an accuracy of 99.8%, while the four-layer MMCNN had an accuracy of 99.6%. The MMCNN has a lightweight structure, making it suitable for use in mobile devices, and the proposed SPT algorithm creates a secure image from a text password. Using the FAR and imposter images, the effect of each factor on CNN training in the MFA system was experimentally measured. The three factors proposed in this paper were compared according to the obtained FAR values when using one-factor and two-factor imposter images, which revealed that the three factors were learned with a uniform amount of information.  
While the previously proposed MobileNet and ShuffleNet are large-capacity CNN structures which can distinguish 1000 classes, the proposed system can be used as an application for groups with a small number of people (about 100) with a high level of security. Being applicable to mobile devices in particular, small, independent devices can be used even in systems where access to the Internet cloud is restricted, due to the risk of hacking. Future work will aim to further reduce the number of parameters in the models and extend the modality to multiple factors in order to increase the mobility and safety of usage.  
This research was funded by the 2022 Korean Bible University Academic Research Fund.  
Not applicable.  
The source of the data set is available on GitHub. Available online: [[https://github.com/hjinob/CNN-Based-Multi-Factor-Authentication-System-for-Mobile-Devices](https://github.com/hjinob/CNN-Based-Multi-Factor-Authentication-System-for-Mobile-Devices)]([https://github.com/hjinob/CNN-Based-Multi-Factor-Authentication-System-for-Mobile-Devices](https://github.com/hjinob/CNN-Based-Multi-Factor-Authentication-System-for-Mobile-Devices)) (accessed on 20 April 2024).  
The author declares no conflicts of interest.