esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Wind Turbine Rotor Results
#### 4.1.1 Axial Uniform Free-Stream Flow
In the root region, no corresponding correction model has been used to capture the effect of root vortices. For this reason, the axial induction factor there drops to zero. Back to the tip region, the same pattern holds for LL, but for a different reason. In this case, it is a result of the excessively strong vortices released from the tip. The exaggeration in tip vortices intensity, stems from the fact that the non-zero circulation of the last strip of the blade is followed by a zero circulation after that. In vortex methodologies, this sharp drop of circulation is directly translated to very high vorticity of the near wake filaments close to the tip. This is a fundamental difference between LL and AL, as the latter affects the flow through momentum exchange between fluid and blade. Another difference between the two models comes from the method of calculating the wake-induced velocity. In LL, the computation is performed directly on the control points of the strips through the application of Biot-Savart law. On the other hand, in this AL implementation the control point velocity is calculated based on a distance and volume weighted interpolation to the surrounding cells velocities using Radial Basis Functions [60]. For these reasons, the induced velocity predicted by the AL model tends to zero towards the tip along with blade normal force. A comparison of the predicted circulation distribution along the span is shown in Figure 21. AL effectively predicts a circulation distribution that decreases sharply close to the tip. However, this is not depicted in the axial induction factor the same way with LL (Figure 20). It is therefore shown that in AL, the axial induction factor is not directly related to circulation radial variation as in the case of LL, or to an engineering correction model as in the case of BEM. The increased values of circulation in the root vicinity of LL can be justified by the potential nature of LL that changes circulation proportionally  
Figure 18: Root twisting moment and tip torsion angle at 8 m/s axial wind speed. to the angle of attack. The higher circulation predicted by the LL model is related to the high values of the angle of attack at the root of the blade (see Figure 22) and the fact that the LL equation presumes a 2\(\pi\) slope for the lift variation although this is not valid for the thick non-aerodynamic sections of the root region. Then an a posteriori correction on loads is performed to account for the reduced (or even zero) lift of these sections and the higher drag. In BEM and AL analysis, the calculation of the induction and the loads are in tight connection with the local airfoil polars. To sum up, the main differences between the models in prediction of the above mentioned flow parameters are focused on the blade tip and root regions. Figure 19: Normal force radial distribution at 8 m/s axial wind speed. Averaging over the final revolution of the simulation. Figure 20: Axial induction factor radial distribution at 8 m/s axial wind speed. Averaging over the final revolution of the simulation.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c} \hline \hline  & \(h_{c}=0.3416-0.1255\times[00]-10\times[41]+0.8560\times[42]+1.0\times[43]-1.0\) \\  & \(\times\)\(\{4[4]-1.0\times[45]+1.0\times[46]-0.2071\times[47]-1.0\times[48]+1.0\times[49]\}\) \\ \multicolumn{2}{l}{**The normalized polynomial kernel**} & \(+1.0\times[10]+1.0\times[11]-0.3152\times[412]-1.0\times[43]+1.0\times[414]\) \\  & \(+0.4007\times[15]-10\times[416]-0.7025\times[417]+1.0\times[418]-0.7020 \times[419]+0.0656\times[420]\) \\ \hline  & \(h_{c}=0.4527\cdot 0.3017\times[40]-0.2619\times[41]-0.08917\times[42]+0.03710\times[43]- 0.24516\times[44]-0.05424\times[45]+0.2599\times[46]+0.0062\times[47]-\) \\
**The Pearson VII function-based universal (PUK) kernel** & \(0.0103\times[48]+0.3469\times[49]+0.1565\times[410]+0.1490\times[411]+0.3641 \times[412]-1.0278\times[416]\) \\  & \([412]-1.0278\times[417]+0.2997\times[414]+0.1489\times[415]-0.072\times[416]\) \\  & \(-0.2559\times[417]-0.236\times[418]-0.044\times[49]-1.06728\times[420]\) \\ \hline  & \(h_{c}=0.384-1.0\times[40]-1.0\times[41]-1.0\times[42]-0.2996\times[43]-1.0 \times[44]-1.0\times[45]+1.0\times[49]+1.0\times[49]+1.0\) \\
**The radial-based function (RBF) kernel** & \(h[4]-1.0\times[45]+1.0\times[46]+1.0\times[47]+0.0764\times[48]+1.0\times[49]+1.0\) \\  & \(\times\)\(\{100\times[41]+1.0\times[41]+1.0\times[412]+1.0\times[413]+1.0\times[414]+1.0\times[415]\}\) \\  & \(+0.2232\times[46]-1.0\times[47]-1.0\times[418]-1.0\times[419]-1.0\times[420]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Support vectors obtained in SVM regression for \(h_{c}\). Figure 11: Actual and Predicted data of \(h_{c}\) for radial basis function (RBF) kernel. indicates a high estimate. [PERSON] [8] and [PERSON] [9] have developed a predictive model using the ANN for \(h_{c}\) value in different systems. [PERSON] and others predicted the heat transfer of concentric tube heat exchanger value using ANN. They achieved root mean square error (RMSE) as 0.354 in predictive model. [PERSON] and others predicted the pool boiling heat transfer coefficient of alumina water-based nanofluids using ANN. They used 870 data for ANN. They found mean square error (MSE) as 4.17 in predictive model. [PERSON] [11] predicted the two phase boiling heat transfer coefficient using SVM. He found predictive value of RMSE as 0.581. In this study, the MSE and RMSE values of the predictive model for the heat transfer coefficient using the normalized polynomial kernel in SVM regression are 0.279 and 0.3351. A better predictive model was obtained than the studies given above. More efficient result may be obtained when parameters of SVM are optimized. Extreme learning machine which is one of the most recent machine learning methods may also be efficiently used for regression of convective heat transfer coefficient problem. Furthermore; when the number of data can be increased by using products with longer drying periods in this system, data mining methods may be utilized for the discovery of valuable knowledge automatically.
# Rethinking LEO Mega-Constellation Routing to Provide Fast Internet Access Services
## 4 Evaluation  
In this section, we provide the performance evaluation results of the software simulation, to evaluate the performance of our proposed algorithm. We implemented the constellation model and deployed the IFAR algorithm by STK (Satellite Tool Kit) and OPNET Modeler. OPNET Modeler is a network simulation software, which promotes the simulation process by triggering various discrete events. All simulations were run on a Windows machine, with a 2.9 Ghz Inter Core i7-10700 CPU and 16 GB of RAM. We take the average packet loss rate and average transmission delay under different loads as performance metrics. To verify the effectiveness of the IFAR algorithm, we compared it with OSPF (Open Shortest Path First), a routing algorithm commonly used in LANs (local area networks).
# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 2 Materials and Methods
### Simulation and Validation Platform  
Based on [92], the Gazebo platform was selected to execute the simulation experiments, as it has extensive documentation on its webpage. In addition, it is the most mentioned and used simulation platform in previous work [73; 74] that implemented path planning or CPP, used UAV sensors, or deployed several UAVs [92]. Gazebo also allows the modeling of commercial UAVs using the PX4 autopilot software [91], as shown in Figure 5. In addition, this figure shows one of the experiments conducted with the Typhoon UAV, flown over the CSUSL plant. The UAV sonar sensor, represented by blue lines, is also shown, as is the UAV camera, in the box at the upper right.  
The integration of Gazebo [92], PX4 [91], Python, and the CPP methods was implemented using ROS (Robot Operating System) as middleware [93]. This tool allows communication among nodes. The nodes are processes, and each node has a task associated with it, such as sending a Mavlink command to control the UAV trajectory and permitting reading messages from the UAV to discern its status in flight, using a simulation mode referred to as software in the loop (SITL). This simulator provides the ability to run different vehicles, such as a plane, copter, or rover, without a need for any microcontrollers or hardware [94]. In addition, two PX4 autopilot rotary wing UAVs, the 3 DR Iris and Typhoon UAV, were chosen because they have good maneuverability and are more suitable for small-scale CPP missions. Furthermore, these UAVs have been widely used in other research [67; 70; 95].  
The GCS software (QGroundControl) was selected to validate the CPP calculated for each UAV and PV plant obtained, because it is the most compatible tool with the PX4 autopilot. It is also recommended on the PX4 webpage. On other hand, another compatible tool, namely a GUI, was designed using Qt to modify and vary the parameters and to convert the way points from RoI pixels to geo-referenced points [96].
# Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions
## 4 Optimization Problem  
### Formulation  
_The problem with the optimal placement of wireless nodes can be posed as follows: given a sensor located at a fixed point on the head of a human body, find locations (g) for two other wireless sensors, which should be placed on the trunk or arms and on one leg for operation in the standing and sitting positions with twofold goals: minimize the on-body channel path loss represented by the objective function f\({}_{1}\)(g) (3) and simultaneously maximize the off-body gain represented by the objective function f\({}_{2}\)(g) (4), in the frequency band of 2.4 GHz._  
The following definitions will be used in the analysis:  
\(g\in\Re^{4}\): the design vector (i.e., the set of four cylindrical coordinates identifying the positions of the two wireless sensors: on the trunk and on one of the legs)  
\(\Omega_{g}\): the set of admissible positions  
B: the 2.4 GHz band  
\(k=1\): the standing posture index; \(k=2\): the sitting posture index  
\(\mathrm{G}_{1+2}\ =\ \mathrm{G}_{1}\cup\mathrm{G}_{2}\): the off-body directivity pattern (i.e., the maximum of individual radiation patterns G\({}_{1}\) and G\({}_{2}\) in a given direction).  
Starting from a feasible solution \(g_{0}\) in \(\Omega_{g}\), the following f\({}_{1}\) objective is to be maximized:  
\[\mathrm{f}_{1}(g)\ =\ \underset{k}{\inf}[\mathrm{S}^{(k)}(g)]\ \,\ k\ =\ 1,2\ \,\ g\in\Omega_{g} \tag{3}\]  
where  
\[\mathrm{S}^{(k)}\ =\ \underset{f}{\inf}\Big{[}\mathrm{S}^{(k)}_{21}(f),\ \mathrm{S}^{(k)}_{31}(f),\ \mathrm{S}^{(k)}_{32}(f)\Big{]};\ \ k\ =\ 1,2;\ \ \mathrm{f}\ =\ (2400+l\cdot 0.440)[\mathrm{MHz}];\ \ l\in[0,226]\]  
and, simultaneously, the following f\({}_{2}\) objective is to be maximized:  
\[\mathrm{f}_{2}(g)\ =\ \underset{k}{\inf}\Big{|}\mathrm{G}^{(k)}_{1}(g)\cup \mathrm{G}^{(k)}_{2}(g)\Big{|}\ \,\ \ k\ =\ 1,2\ \,\ g\in\Omega_{g} \tag{4}\]  
The values of the scattering matrix elements S\({}_{21}\)(f), S\({}_{31}\)(f), and S\({}_{32}\)(f) are calculated within the frequency band of interest 2.4-2.5 GHz with an interval of 440 kHz--i.e., for 227 values of f. These values are elements in the Fourier Transform of the time-domain response to wideband excitation obtained from a single run of the FDTD analysis. From Equations (1) and (2), it appears that our optimization problem is characterized by four design criteria, i.e., the path loss S and gain G\({}_{1+2}\) for each posture \(k=1,2\), depending on the set of four design variables. Maximizing f\({}_{1}\) and f\({}_{2}\) over \(k\) gives rise to a min-max formulation. It is, therefore, possible to reduce the number of independent objective functions to just the (f\({}_{1}\),f\({}_{2}\)) pair. A bi-objective optimization problem is thus formulated:  
_Starting from a guess solution, find the values of a four-dimensional design vector (coordinates of two wireless nodes on the user's body) yielding a Pareto-optimal solution, i.e., a compromise point trading off the on-body path loss and the off-body radiation pattern._
# A Review on Potential Antimutagenic Plants of Saudi Arabia
## 7 Which Are the Medicinal Plants of Saudi Arabia That Might Demonstrate Anti-Mutagenesis?
### Conyza dioscordis  
_Conyza dioscordis_ ([PERSON] 1832) is a wild plant that grows in several places in the Middle Eastern regions, including Saudi Arabia. The plant belongs to the Asteraceae family and is frequently used as a folk medicine. The phyto-ingredients identified are phenols, flavonoids, glucosides, alkaloids, sterols, triterpenes, beta-sitosterol, alpha-amyrin, etc. [94]. Screening for therapeutic activity indicated that the plant's extract showed promising effects in treating rheumatic pain, epilepsy, colic pain, ulcers, colds, diarrhoea, diabetes, and microbial infections. Antioxidant studies indicated that extracts of the plant reduced ROS-mediated changes and were attributed to the presence of components such as gallic acid, quercetin and kemperol. The in vitro DPPH free radical scavenging method was employed to determine the antioxidant property [94; 95].
# Molecular Basis of Irisin Regulating the Effects of Exercise on Insulin Resistance
## 4 Signaling Pathways Mediating the Link between Exercise, Irisin, and Insulin Sensitivity
### Mitochondrial Action and Interaction between Exercise, Irisin, and Insulin Resistance  
Exercise has a positive impact on insulin action in part because it improves mitochondrial function. Exercise intervention increased mitochondrial function in BAT of rats, promoting the release of proteins that regulate mitochondrial biogenesis and fission while decreasing the level of markers that modulate mitochondrial fusion [90]. As a result, circulating insulin levels fell while insulin sensitivity rose in BAT [90]. Mitochondrial dysfunction can result in a buildup of secondary products of fat metabolism that in turn activate protein kinase C (PKC) and protein phosphatase A2 (PPA2) [91; 92]. In addition, IRS tyrosine phosphorylation is impaired, and AKT is dephosphorylated, eventually weakening the physiological function of insulin [93]. Moreover, mitochondrial dysfunction is the main source of free radicals and oxidative stress, which ultimately lead to IR [94; 95].  
Recent evidence indicates that irisin seems to be involved in the benefits of exercise affecting the biological function of mitochondria. In a study, after four weeks of continuous training in mice (at 75% VO2 max), the levels of both PGC-1\(\alpha\) and FNDC5 increased significantly and the serum irisin expression was also higher than that of sedentary mice [96]. With the elevated expression of PGC-1\(\alpha\)/FNDC5/irisin, mitochondrial fission and mitophagy markers increased, which help to improve mitochondria function [96]. Moreover, upregulation of irisin levels improved insulin intolerance in T2 DM mice, and reduced serum insulin and glucose levels [97]. Therefore, irisin can reduce IR by enhancing the function of mitochondria. After the mice were injected with irisin (0.5 \(\upmu\)g/day) for fourteen days, the phosphorylation levels of p38/MAPK and ERK in adipocytes increased, and the expression of UCP1 also elevated significantly. UCP1 can cause a browning of WAT and an increase in mitochondrial density [43]. Given that irisin is a strong stimulator for promoting mitochondrial biogenesis and insulin physiological action [53; 98], it might be an essential regulator in the process of exercise improving IR via upregulating mitochondrial function. Indeed, [PERSON] et al. [99] reported that after eight weeks of resistance exercise, the soleus muscle expression of FNDC5 in the mice increased significantly, and UCP1 expression in the adipose tissue under the skin enhanced markedly, which subsequently increased mitochondria function and benefited insulin action.
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 5 Experiments
### Evaluating Image Quality
#### 5.2.2 Ssim  
The structural similarity index method (\(SSIM\)) is another no-reference quality metric, similar to \(PSNR\), but it incorporates aspects of human visual perception. An \(SSIM\) model images distortion by assessing changes in structural information [46]. It evaluates luminance, contrast, and structural similarity, measuring the correlation between the original and processed images. The \(SSIM\) value ranges from 0 to 1, with higher values indicating better image quality. The \(SSIM\) is computed as follows:  
\[SSIM(I_{Gr},I_{th})=\frac{\left(2\mu_{I_{Gr}}\mu_{I_{th}}+C1\right)\left(2\sigma _{I_{Gr}I_{th}}+C2\right)}{\left(\mu_{I_{Gr}}^{2}+\mu_{I_{th}}^{2}+C1\right) \left(\sigma_{I_{Gr}}^{2}+\sigma_{I_{th}}^{2}+C1\right)} \tag{25}\]  
\[\sigma_{I_{0}I_{Gr}}=\frac{1}{N-1}\sum_{i=1}^{N}\left(I_{Gr_{i}}+\mu_{I_{Gr}} \right)\left(I_{th_{i}}+\mu_{I_{th}}\right) \tag{26}\]  
where \(\mu_{I_{Gr}}\) and \(\mu_{I_{th}}\) are the mean value of the original and the umbralized image, respectively, and for each image, the values of \(\sigma_{I_{Gr}}\) and \(\sigma_{I_{th}}\) correspond to the standard deviation. C1 and C2 are constants used to avoid the instability when \(\mu_{I_{Gr}}^{2}+\mu_{I_{th}}^{2}\approx 0\); experimentally, in [PERSON] et al. [48]), both values are C1 = C2 = 0.065.
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 4 Ballistocardiography
Despite these problems, a very powerful algorithm for individual heart rate detection was proposed by [PERSON] et al. [73]. The application of BCG sensing for driver monitoring has a lot of similarity with BCG monitoring in a chair. Generally, in this position, the craniocaudal BCG signal component is measured, while the  
Figure 5: Potential BCG sensor locations: (**a**) measuring cardiac activity on a weighing scale (craniocaudal component of the BCG momentum); (**b**) measuring both cardiac and respiratory activiy in bed (dorsoventral component of the BCG momentum). dorsoventral signal component is much more difficult to measure as there may not always a proper mechanical coupling between the torso and the backrest, especially for thoracic sensor locations. Figure 6 sketches the options. Early attempts to integrate BCG sensors into car seats started about 10 years ago. In 2008 and again in 2011, the placement of BCG sensors in the seating area of a SMARTTM co-driver seat (DAIMLER AG, Mercedesstrasse 137, D-70327 Stuttgart, Germany) was successfully demonstrated [42; 74]. In this demonstrator, a quasi-piezoelectric ferroelectric EMFiTM mat (Emfit Ltd., Kontisentie 8, FI-40800 Vaajakoski, Finland) was employed as a BCG sensor. However, data supplied in [74] indicates that motor vibrations may make BCG monitoring in a running car difficult. In order to fight driver fatigue (which is said to account for 20-35% of serious accidents), the HARKEN (HARKEN: Heart and respiration in-car embedded non intrusive sensors, funded by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. : 286265) project proposed to monitor both cardiac and respiratory activity by means of smart materials embedded in the seat cover and the safety belt of the car [75]. However, no further results on these BCG principles were published. In 2015, the Faurecia Group (Faurecia S.A., Nanterre, France), a large automotive supplier, presented a car seat called _Active WellnessTM_[76], which claims to detect the traveler's drowsiness or stress level and then takes countermeasures to relieve the driver from these conditions [77]. The underlying sensing concept employs strain-gauge BCG sensors in the seating to detect cardiac and respiratory activity of drivers and/or occupants. Very recently, in 2018, [PERSON] and [PERSON] presented heart and respiratory rate estimation obtained with a BCG sensor in a Ford Mustang passenger seat [78]. As a sensor, the authors proposed using a \"fluid filled bladder connected to a solid-state pressure transducer\". In their study, data from eleven volunteers were collected. Under these controlled laboratory conditions, the authors were able to extract both vital signs with decent accuracy.
## 4 Results and Discussion
### Group Identification  
The results for a few frames for the group identification model are shown in Figure 8. An example from the UTI dataset and Oxford towncenter datasets is shown here. The frames with the persons detected are shown on the left and the group activity matrices showing the group characteristics are shown on the right. If two people are of the same group, the group activity matrix element corresponding to the row and column of the IDs of these two persons is shown in yellow, and otherwise, it is shown in blue. The people of the same group are also joined by a white line in the original frame to show this.
## 2 Mathematical Model Descriptions  
The nonlinear equations of the Cardan shaft linked with a single Hooke's joint were derived based on several assumptions. Figure 1 represents the modelled Cardan shaft system composed of drive and driven shafts connected by a single Cardan joint. The sub-assembly in Figure 1 is the assembly in a deformed frame. The model at its end was developed based on a simple Jeffcott's approach with the following assumptions: (1) each shaft is uniform, balanced, and symmetric across its axis; (2) the power transmission system is only loaded torsionally, and the lateral oscillations are significant enough to be considered; (3) the joint mass is negligible comparing to the shaft-disc mass and is conceived as ideal, without any clearance, depreciation, and friction; (4) the input angular velocity is inconstant; (5) the U parts attached to the middle shaft ends are in phase; (6) the intermediate shaft is available in most vehicles; however, the choice not to consider it was based on the lower operating angle between the two offset shafts. Due to the nonlinear vibrations of the driveline in both torsional and lateral directions, the joint angle was considered not constant during operation. The system considered was characterised by the system's kinetic energy. The two discs were of masses \(M_{1}\) and \(M_{2}\) and carry eccentric masses, \(m_{u1}\) and \(m_{u2}\), respectively. We assumed that \(m_{u1}\) and \(m_{u2}\) remained in the mid-span of the rotor disc during vibration. Gyroscopic effects due to discs' spinning were neglected. The flexural stiffness of the shaft was considered relatively small compared to bearings.  
Self-aligning bearings were used to ensure that the bearing took up the bending mode shape of the shafts at the supports. Transfer of vibration from the rest of the chassis to the Cardan shaft was negligibly small in comparison with the torsional vibration of the Cardan shaft itself. This assumption allowed us to ignore and self-align bearing moments due to the mode of the shaft. Only linear viscous damping effects of the bearings were considered.  
A Hooke's joint connects the two shafts, and each behaves as an Euler-Bernoulli beam represented by the first mode. Therefore, the model is valid below the second lateral critical speed. The coordinate frame used to develop the model is shown in Figure 1. The system's degree of freedom was lumped at the centres of the inertias, \(J_{M}\), \(J_{D1}\), and \(J_{D2}\). \(J_{M}\) and \(J_{D2}\) were coupled by a light torsional spring while avoiding the risk of mass matrix singularity of the equation of motion. Figure 1, shows the orientation of the masses on the two shafts viewed in the mid-span planes perpendicular to each shaft axis. It was considered that, at a time \(t\), the centres of the discs are at the points \(x_{1},y_{1}\) and \(x_{2},y_{2}\), i.e., the rotating coordinates attached to the respective centres of the discs.