esa-sceva/satcom-chunk-collection huggingface dataset:
# From Brown-Field to Future Industrial Networks, a Case Study
## 4 Brown-Field Traffic Analysis
### Performance Dynamics Characterization
The cyclic pattern in IT traffic is not as evident as in OT traffic, but similar patterns are observable in the smoothed pattern, Figure 7b. One reason for the higher level of fluctuations observed in IT traffic is the higher number of diverse impacting factors, such as the different number of protocols in specific time intervals. It is also notable that IT dynamics clearly show seasonality and trend, which, when removed, can provide more insight and discussion points to draw a more accurate conclusion. A closer look at the generated throughput dynamic patterns shows some level of similar dynamics in IT and OT behavior, Figure 6(c). This similarity can be reasoned with the observed shared traffic between OT and IT in some of the VLANs. As presented in the comparative analysis of volume and dynamic mixture, the assumption of exclusive traffic class and flows patterns based on the pre-defined VLANs does not hold for brown-field traffic, i.e., IT and OT communications are not necessarily isolated in VLANs. The IT-OT flows can potentially show their effect on IT dynamics by similar patterns repeated in longer time intervals. Finding the correlated patterns between IT and OT bandwidth dynamics can further assist in characterizing the IT-OT flows and their requirements in terms of communication intervals and the amount of transmitted data. This insight subsequently can provide a baseline for integrating traffics from new technologies or replaced network components.
# Recognition of Earthquake Surface Ruptures Using Deep Learning
## 3 Methods
### Training Methods of Models  
After building the model architecture, we set the model initialization method, activation function, loss function, and parameter update method. During the training process, the Xavier method was applied to initialize all the training values; downsampling was performed by max pooling for the pooling layer, and the ReLU activation function was used.  
In addition, the dropout method was used to reduce overfitting in the neural network. Each class of the SoftMax loss function can output a normalized probability in the last layer of the network. Therefore, we classified the classes by their probabilities and restored the classification results to the original image size through bilinear upsampling. We used the adaptive moment estimation (Adam) optimizer to train our network.  
For each pixel in the input image of the high-resolution UAV image, the output of the corresponding training model is expressed as the posterior probability that the pixel belongs to the crack. Our crack identification was a pixel binary classification task performed on high-resolution UAV images. Typically, the binary segmentation task for common images is to use a binary cross-entropy loss function, as shown in Equation (1), where \(N\) denotes the number of pixels in the input image. The term \(y_{i}\) represents the true binary labels and \(p_{i}\) represents the prediction probabilities computed from the SoftMax function.  
\[L=-\sum_{i=0}^{i=N}y_{i}log(p_{i})-\sum_{i=0}^{i=N}(1-y_{i})log(1-p_{i}) \tag{1}\]  
For most classification problems, the deep learning samples have a balanced distribution and apply regular cross-entropy loss functions. However, crack identification in high-resolution UAV images is a binary classification issue, and the number of cracks and non-cracks is highly unbalanced, i.e., there are significantly more non-crack samples than crack samples. Zero prediction is a good solution for loss function convergence in crack segmentation problems. Therefore, by applying the regular cross-entropy loss function, the network would quickly converge in the wrong direction and make many zero predictions. To solve the problem of unbalanced samples, we weighted the loss function and used the weighted cross-entropy loss [30] function, which can be expressed as:  
\[L_{W}=-\alpha\sum_{i=0}^{i=N}y_{i}log(p_{i})-(1-\alpha)\sum_{i=0}^{i=N}(1-y_{i })log(1-p_{i}) \tag{2}\]  
where \(\alpha\) is the ratio of non-cracks to the samples in the training data; the network model can converge accurately by adjusting the weights in the loss function.
## 5 Calculation of Outage Probability Using (5)  
In this section, the use of the derived outage probability expression (5) is demonstrated using various scenarios as follows. Scenario 1: Let the fading envelopes of the two links take Rayleigh and generalized Gamma PDF values.The generalized Gamma distribution generalizes frequently used models for multipath, shadowing, and composite fading, containing Nakagami, Rayleigh, and Weibull distributions as special cases and a log-normal distribution as a limiting case [17]. The Rayleigh-generalized Gamma fading scenario models a scenario in which one link, represented by Rayleigh fading, is subjected to intense scattering with no line of sight of transmission and the other hop, represented by generalized Gamma fading, can experience either multipath fading, shadowing, or composite fading at any time. The Rayleigh fading model also applies to the propagation of reflected and refracted pathsthrough the troposphere and ionosphere and to ship-to-ship radio links. Using the CDF expressions from Table 2 in (5), the outage probability for scenario 1 can be calculated as  
\[p_{out}(\gamma_{th})=1-\exp\left(\frac{-\gamma_{th}}{\hat{\gamma}}\right)\frac{ \Gamma\left(m,\beta^{p}\gamma_{th}^{p}\right)}{\Gamma(m)} \tag{6}\]  
Scenario 2: For this case, let the two links take Nakagami-m PDF and Weibull PDF respectively. Nakagami-m is a versatile generalized multipath fading distribution that is capable of modeling fading scenarios that are more severe than Rayleigh fading (\(m<1\)). Nakagami-m contains Rayleigh, One-sided Gaussian as special cases and AWGN as a limiting case [21] and it can be easily matched to equivalent Rice distribution [20]. Weibull fading has proven to produce a good fit to experimental data in multipath fading and contains Rayleigh fading as a special case. Weibull can also model fading scenarios worse than Rayleigh fading for \(c<2\)[8]. This scenario models multipath fading in both links. Utilizing the expressions for CDF from Table 2 in (5), the outage probability for scenario 2 can be calculated as  
\[p_{out}(\gamma_{th})=1-\frac{\Gamma\left(m,\frac{m}{\hat{\gamma}}\gamma_{th} \right)}{\Gamma(m)}\exp\left(-\frac{\gamma_{th}}{\hat{\gamma}}\Gamma\left(1+ \frac{2}{c}\right)^{\frac{5}{2}}\right) \tag{7}\]
## 3 Influence of Physical Properties of NS on Communication, Navigation, and TT&C Integration
### NS Rainfall Attenuation
According to Equation (4) and combined with the simulation results in Figure 3, it can be seen that the magnitude of raindrop loss depends on the signal frequency and the elevation angle of the beam, because different values of the antenna elevation angle determine the length of the oblique path when the radio wave passes through the rainy area; the larger the elevation angle is, the shorter the length of the oblique path passing through the rainy area will be, and the smaller the rainfall attenuation will be. Conversely, the smaller the elevation angle is, the greater the rainfall attenuation will be. If the NS vehicle operates at a low elevation angle of \(5^{\circ}\), most of the propagation path will be in the rain clouds; for example, under the condition of moderate rain at a frequency of 30 GHz, the losses caused by passing through a 100 km rainy area can reach 225 dB and 185 dB under horizontal and vertical polarization conditions, respectively. Figure 3: _Cont._
## 3 Reduced Mass Flow Fitting
### Twin-Entry Turbine  
In twin-entry turbines, the induction flow path from the turbine inlet to the leading edge of the rotor blades is divided into shroud and hub branch, as shown in Figure 9. Due to the assumption of two individual turbines, the blade height has been shared between two branches by splitting the actual value in half (as in the current case it is symmetrical turbine). Subsequently, the stator area variation for two branches can be obtained as shown in Equation (12). In the case of asymmetric inlets, the proper fraction of blade height would be assigned for each turbine inlet.  
\[A_{3,i}^{TE\ zoom}=\pi\cdot D_{3}^{TE}\cdot\left(\frac{Bh^{TE}}{2}\right) \tag{12}\]  
For calculating the rotor outlet geometrical area (\(A_{4,i}^{TE\ zoom}\)) in the case of twin-entry turbines, it is more relevant to consider the rotor outlet area for each branch in the view of two turbine approach modeling. For this purpose, the area estimated using Equation (A4) is shared equally between shroud and hub branch, as shown in Equation (13). By this, it is ensured that the mean-line (\(D_{4m}\)) divides the blade-to-blade duct into shroud and hub branches, as shown in Figure 9. Once the geometrical area of each branch is known, it is possible to estimate the rotor outlet mean diameters of shroud (\(D_{4m,Sh}\)) and hub (\(D_{4m,H}\)) branch using Equations (14) and (15) based on Figure 9  
\[A_{4,i}^{TE\ zoom}=\frac{A_{4}^{\textrm{geom}}}{2} \tag{13}\]  
\[\frac{A_{4,Sh}^{TE\ zoom}}{2}=\pi\cdot\left(\frac{D_{4}^{2}-D_{4m,Sh}^{2}}{4}\right) \tag{14}\]  
\[\frac{A_{4,H}^{TE\ zoom}}{2}=\pi\cdot\left(\frac{D_{4m,H}^{2}-D_{nut}^{2}}{4}\right) \tag{15}\]  
Figure 8: Geometrical description of vaneless turbine.  
Figure 9: Geometrical description of twin-entry turbine (T#ITE).  
The geometrical areas and diameters estimated from Equations (12) to (15) are used in corresponding effective equivalent nozzle area (\(A^{TE}_{eff,i}\)) of each branch while fitting the model. It is worth highlighting that Equation (8) also depends on the apparent efficiency of each branch. For that, the apparent efficiency values obtained by applying Equation (5) to each branch are used.
# A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection
## 4 Experiment and Results
### Experiment and Analysis
#### 4.3.4 Kyoto 2006+ Test Experiment  
This section presents the detection performances of different models for the attack and normal samples in the Kyoto 2006+ dataset of which the dataset in 2015 is used. In the training phase, the numbers of normal and attack samples are 10,000 and 10,000, respectively, while in the testing phase, the numbers of normal and attack samples are 2000 and 2000, respectively.  
In Table 11, the selected features of the different feature selection methods, which are used to detect attacks, are summarized.  
Table 12 shows the results of the confusion matrices with the normal and attack data in different models. It can be seen that the FP of the RPFMI + SVM model is only bigger than those of the CIMI [22] and FMIFS [33] models. The ACC, DR, FPR and PR of various models are presented. The ACC of the RPFMI algorithm (i.e., 97.749%) is highest (Figure 13). The DR of the RPFMI algorithm is higher than the MIFS-U [21], CIMI [22], MMIFS [32] and FMIFS [33] algorithms (Figure 14) and its FPR is only larger than the CIMI [22] and FMIFS [33] algorithms (Figure 15). In terms of the PR, the RPFMI algorithm ranks third among all the other methods (Figure 16). As a consequence, the ability of the proposed RPFMI algorithm to enhance the ACC and decrease the FPR has been verified.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Number of Features** & **Selected Features** \\ \hline MIFS (\(\beta=0.3\)) [19] & 14 & 16, 4, 17, 14, 20, 15, 12, 7, 13, 8, 2, 11, 6, 19 \\ MIFS (\(\beta=0.7\)) [19] & 14 & 16, 4, 17, 14, 20, 15, 12, 7, 13, 8, 2, 11, 6, 19 \\ mRMR [20] & 7 & 16, 17, 4, 14, 20, 19, 2 \\ MIFS-U (\(\beta=0.3\)) [21] & 14 & 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 \\ MIFS-U (\(\beta=0.7\)) [21] & 14 & 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 \\ CIMI [22] & 4 & 16, 1, 19, 17 \\ MMIFS [32] & 7 & 16, 17, 4, 19, 14, 2, 3 \\ FMIFS [33] & 3 & 16, 17, 4 \\ RPFMI & 6 & 16, 17, 4, 14, 19, 2 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Features selected by different feature selection algorithms.  
Figure 12: Precision Rate on the R2L test data.  
Figure 14: Detection Rate on the Kyoto 2006+ test data.  
Figure 13: Accuracy on the Kyoto 2006+ test data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **FP** & **FN** & **TN** & **TP** \\ \hline All features & 871.2 & 1.64 & 1128.8 & 1998.36 \\ MIFS (\(\beta=0.3\)) [19] & 124.46 & 33.58 & 1875.54 & 1966.42 \\ MIFS (\(\beta=0.7\)) [19] & 124.46 & 33.58 & 1875.54 & 1966.42 \\ mRMR [20] & 78.6 & 42.09 & 1921.4 & 1957.91 \\ MIFS \(-\) U (\(\beta=0.3\)) [21] & 662.22 & 212.42 & 1337.78 & 1758.58 \\ MIFS \(-\) U (\(\beta=0.7\)) [21] & 662.22 & 212.42 & 1337.78 & 1758.58 \\ CIMI [22] & 20.69 & 77.45 & 1979.31 & 1922.55 \\ MMIFS [32] & 38.25 & 56.09 & 1961.75 & 1943.91 \\ FMIFS [33] & 0 & 1989 & 2000 & 11 \\ RPFMI & 35.75 & 54.31 & 1964.25 & 1945.69 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Confusion matrices in different models using the Kyoto 2006+ test data.
# Study of the Algorithm for Wind Shear Detection with Lidar Based on Shear Intensity Factor
## 3 Test of the Improved Algorithm Based on Numerical Simulation
### Comparison and Analysis of the Results from the Three Algorithms under the Four Wind Winds  
To better analyze the performance of the three algorithms, this paper has listed the number of wind shear alerts of different levels under different wind fields (Table 2). The results show that when the main wind shear intensity is at \"Strong\" and \"Severe\" levels, algorithm WSDSF releases the most alerts for \"Strong\" and \"Severe\" wind shear, which is due to the improved ramp length extension and contraction strategy. For example, under the downdraft wind field, algorithm WSDSF releases 18 alerts for \"Severe\" wind shear and 2 alerts for \"Strong\" wind shear. However, the double ramp detection algorithm releases 4 alerts for \"Severe\" wind shear and 2 alerts for \"Strong\" wind shear, while algorithm WSDHP only releases 2 alerts for \"Severe\" wind shear. When the main wind shear intensity is at \"Mild\" and \"Moderate\" levels, algorithm WSDSF still releases the most alerts for \"Mild\" wind shear, but the double ramp detection algorithm releases more alerts of \"Moderate\" and \"Strong\" wind shear than algorithms WSDHP and WSDSF (see the results under the frontal wind field and turbulent wind field).  
Figure 13: The results of wind shear detection under the background of the turbulent wind field.
## 3 Model Definition
### Elastic Support Stiffness  
Computing the stiffness for the elastic support varies according to the outer shape of the submerged structure, the stiffness of the floating structure, and its mass while the ferry is loaded in water:  
\[W_{F}=W_{w} \tag{3}\]  
Figure 7: The four cases of loading. Case (1) centered load (no eccentricity); Case (2) transversely eccentric from the center (0.5 m); Case (3) transversely eccentric from the center (1 m); Case (4) transversely eccentric from the center (1.5 m).  
where \(W_{F}\) denotes the weight of the ferry and \(W_{w}\) denotes the weight of the displaced water. The own weight of the simulated ferry (\(W_{F}\)) is calculated using ANSYS software, while \(W_{w}\) equals the following:  
\[W_{w}=B\times L\times D_{s}\times\
ot{V}_{w} \tag{4}\]  
where \(B\) denotes the ferry breadth without connections and equals 9.6 m, \(L\) denotes the ferry length without connections and equals 21 m, \(D_{s}\) denotes the submerged depth of the ferry in water, and \(\
ot{V}_{w}\) is the density of water with value equals (9.81 kN/m\({}^{3}\)). By substituting in Equation (7), \(D_{s}\) is obtained. The stiffness (\(k\)) is calculated as follows:  
\[W_{w}=k\times B\times L\times D_{s} \tag{5}\]  
By substituting in Equation (8), we obtain the following:  
\[k=\
ot{V}_{w} \tag{6}\]  
The value of the elastic support stiffness is found to be equal 9.81 kN/m\({}^{3}\) for the considered model. Generally, calculating the stiffness value for any floating structure is obtained by plotting three graphs as discussed by [PERSON] [10] in order to achieve the exact draft (submerged depth). As affecting load dynamically moves along the ferry, the general equation of motion introduced by [PERSON] [37] governs the model computations as follows:  
\[F(t)=m\;\ddot{u}(t)+c\;\dot{u}(t)+k\;u(t) \tag{7}\]  
where \(F(t)\) denotes the dynamic force at time \(t\), (\(m\)) denotes the mass of the structure, \(\ddot{u}(t)\) denotes the acceleration of the structure at time \(t\), (\(c\)) denotes the damping coefficient, \(\dot{u}(t)\) denotes the velocity of the structure at time \(t\), (\(k\)) denotes the stiffness of the structure, and \(u(t)\) denotes the displacement of the structure at time \(t\).
# Antioxidant Activity of _Panax ginseng_ to Regulate ROS in Various Chronic Diseases
## 6 _P. ginseng_ in ROS-Mediated Diseases
### Antioxidant Activities of P. ginseng in Cancer  
According to growing data, ROS are implicated in multiple steps of tumorigenesis from the initiation of the tumor to metastasis [139]. Excessive amounts of ROS in the cell or the defective antioxidant defense mechanisms quicken the cellular damage and initiate carcinogenesis [140]. It has been reported that the cancer cell initiates more ROS than their counterpart [141]. ROS play a dual role in cancer. First, the overabundance of ROS instigates autophagy, apoptosis, and cell cycle arrest signals [142; 143]. Second, ROS can influence the initiation, growth, and transmission of cancer via the activation of the signaling pathways which affect cell proliferation, survivability, angiogenesis, and metastasis [139]. In general, when ROS are low to moderate, they may contribute to the initiation of a tumor, and a high amount of ROS causes massive cell damage and death, typically at the early stages of tumor formation [144]. So, it may be possible to destroy cancer cells specifically by raising oxidative stress through exogenous ROS production without significantly harming normal cells [145]. _P. ginseng_ and its derivatives, ginsenosides, might be a promising supplemental therapy for cancer patients. According to growing researches, ginsenosides have a lot of potential to offer multiple strategies for treating different cancers (Table 2). Therefore, it might be difficult for cancer cells to develop resistance against ginsenosides. Additionally, ginsenosides are desirable candidates for new therapeutics due to their ability to destroy tumor cells. In general, ginsenosides can produce ROS in a variety of cancer cells which can enhance autophagy, apoptosis, parapptosis, and decrease cell proliferation in vivo and in vitro [139]. However, according to current research, by comparing between healthy and cancer cell lines (CCD841 and HT-29), ref. [146] confirmed that _P. ginseng_ berry extract exhibited prebiotic activity against colorectal cancer by increasing ROS production without affecting the normal cells. It has also been newly proposed that ginsenoside Rh1 from _P. ginseng_ is a potential compound to prevent and treat lung cancer through the regulation of metastasis and apoptosis [147]. _P. ginseng_ extract suppresses ROS production by increasing the antioxidant biomarker GSH, and reduces the inflammatory biomarkerTNF-\(\alpha\) and apoptotic biomarker caspase-3 to prevent doxorubicin-induced cardiovascular disease [148].
## 2 Materials and Methods
### Validation of Microsoft Kinect Azure Camera with Xsens MVN and OptiTrack Motion Capture Systems  
In the current study, the Kinect Azure camera accuracy and reliability was compared to a gold standard motion capture system, OptiTrac, and a high accuracy inertial measurement unit, MVN Xsens. The Xsens MVN inertial motion capture system is an easy to use, completely portable system, which is based on state-of-the-art miniature inertial sensors and wireless communication solutions combined with advanced sensor fusion algorithms, incorporating synchronized video data. Instant graphical output is provided, including joint angles [31]. OptiTrack is a widely used motion capture system and considered as one of the most accurate [23; 24]. These two systems can be regarded as gold standard marker-based systems; they are installed in movement analysis laboratories for the purposes of gait analysis, musculoskeletal research and animation.  
Five (Female = 2; Age = 33.6 \(\pm\) 9.4 years) healthy and physically active participants were involved in the comparative examinations. On the first testing day the Xsens body packs were fixed on the participants' extremities as they stood in front of the Kinect Azure camera. Only lower leg functions were examined, with markers placed on the pelvis, calves, thighs and feet (Figure 1). Participants executed 10 single-leg squats on each side while the Kinect camera and Xsens were recording at the same time. On the second testing day the participants were examined in an OptiTrack movement laboratory. They wore reflective markers on their pelvis, knees and ankles (Figure 1). In both tests, during the examinations, the knee medial-lateral and pelvis vertical movements were recorded with each pair of motion capture systems simultaneously. Recorded data streams were synchronized and resampled to 30 Hz frequency. Data from the instruments were compared frame to frame, and means of differences were calculated from absolute differences in cm.