esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
#### Quantitative Comparison of MFO with DFO and PSO Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Response**} & \multirow{2}{*}{**Statistics**} & \multicolumn{6}{c}{**PSO**} \\ \cline{3-11}  & & & **RMSE** & & & **MAE** & & & \(R^{2}\) & \\ \cline{3-11}  & & **Total** & **Training** & **Testing** & **Total** & **Training** & **Testing** & **Total** & **Training** & **Testing** \\ \hline \multirow{10}{*}{L} & Mean & 0.0658 & 0.0640 & 0.0726 & 0.0567 & 0.0549 & 0.0639 & 0.9802 & 0.9800 & 0.9816 \\  & StDev & 0.0009 & 0.0009 & 0.0009 & 0.0008 & 0.0007 & 0.0009 & 0.0107 & 0.0104 & 0.0122 \\  & Minimum & 0.0642 & 0.0626 & 0.0710 & 0.0553 & 0.0538 & 0.0625 & 0.9600 & 0.9621 & 0.9609 \\  & Q1 & 0.0654 & 0.0634 & 0.0721 & 0.0561 & 0.0542 & 0.0631 & 0.9721 & 0.9699 & 0.9727 \\  & Median & 0.0660 & 0.0639 & 0.0726 & 0.0568 & 0.0547 & 0.0640 & 0.9809 & 0.9810 & 0.9842 \\  & Q3 & 0.0665 & 0.0647 & 0.0732 & 0.0573 & 0.0555 & 0.0646 & 0.9898 & 0.9883 & 0.9903 \\  & Maximum & 0.0672 & 0.0656 & 0.0743 & 0.0579 & 0.0562 & 0.0653 & 0.9980 & 0.9972 & 0.9993 \\  & Range & 0.0030 & 0.0031 & 0.0033 & 0.0026 & 0.0024 & 0.0029 & 0.0380 & 0.0351 & 0.0384 \\ \hline \multirow{10}{*}{D} & Mean & 0.0660 & 0.0640 & 0.0729 & 0.0568 & 0.0552 & 0.0642 & 0.9818 & 0.9836 & 0.9779 \\  & StDev & 0.0009 & 0.0009 & 0.0010 & 0.0008 & 0.0008 & 0.0007 & 0.0118 & 0.0120 & 0.0109 \\  & Minimum & 0.0643 & 0.0627 & 0.0710 & 0.0554 & 0.0538 & 0.0628 & 0.9632 & 0.9612 & 0.9610 \\  & Q1 & 0.0653 & 0.0633 & 0.0720 & 0.0563 & 0.0545 & 0.0637 & 0.9723 & 0.9769 & 0.9693 \\  & Median & 0.0663 & 0.0638 & 0.0729 & 0.0568 & 0.0553 & 0.0643 & 0.9802 & 0.9851 & 0.9775 \\  & Q3 & 0.0668 & 0.0648 & 0.0737 & 0.0574 & 0.0560 & 0.0649 & 0.9931 & 0.9941 & 0.9846 \\  & Maximum & 0.0672 & 0.0654 & 0.0745 & 0.0580 & 0.0565 & 0.0655 & 0.9993 & 0.9996 & 0.9987 \\  & Range & 0.0030 & 0.0027 & 0.0035 & 0.0026 & 0.0027 & 0.0028 & 0.0361 & 0.0386 & 0.0377 \\ \hline \multirow{10}{*}{W} & Mean & 0.0642 & 0.0626 & 0.0713 & 0.0554 & 0.0541 & 0.0626 & 0.9826 & 0.9793 & 0.9811 \\  & StDev & 0.0008 & 0.0008 & 0.0001 & 0.0008 & 0.0008 & 0.0010 & 0.0134 & 0.0111 & 0.0105 \\ \cline{1-1}  & Minimum & 0.0629 & 0.0613 & 0.0696 & 0.0542 & 0.0528 & 0.0612 & 0.9612 & 0.9607 & 0.9603 \\ \cline{1-1}  & Q1 & 0.0635 & 0.0621 & 0.0703 & 0.0549 & 0.0534 & 0.0619 & 0.9698 & 0.9698 & 0.9773 \\ \cline{1-1}  & Median & 0.0641 & 0.0626 & 0.0711 & 0.0553 & 0.0541 & 0.0624 & 0.9860 & 0.9808 & 0.9813 \\ \cline{1-1}  & Q3 & 0.0647 & 0.0630 & 0.0722 & 0.0560 & 0.0547 & 0.0636 & 0.9932 & 0.9871 & 0.9879 \\ \cline{1-1}  & Maximum & 0.0656 & 0.0644 & 0.0730 & 0.0568 & 0.0554 & 0.0643 & 0.9998 & 0.9998 & 0.9996 \\ \cline{1-1}  & Range & 0.0027 & 0.0030 & 0.0034 & 0.0026 & 0.0026 & 0.0030 & 0.0386 & 0.0393 & 0.0393 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Statistical analysis of output obtained by the PSO algorithm. proposed method performed well for responses L in Figure 10a and for response W in Figure 10c as compared to response D in Figure 10b.
## Materials and Methods
### Endolysin Activity Spectrum  
The lytic activity of the two endolysins was tested using a turbidity reduction assay [9] which consists in measuring the decrease in OD\({}_{595}\) caused by the enzymatic activity. Briefly, an O/N bacterial culture was diluted 100-fold in fresh medium and incubated until mid-log phase (3 to 4 h). After centrifugation (6000\(\times\)_g_, 5 min) the pellet was washed with PBS and then resuspended in 50 mM Tris-HCl pH 8 (PlyB221) or pH 7 (PlyP32) to reach an OD\({}_{595}\) between 0.8-1. From this bacterial suspension, 180 \(\upmu\)L were transferred to each well of a 96-well plate and mixed with phage endolysins at given concentrations. The plate was incubated at 30 \({}^{\circ}\)C in a Multiskan(tm) FC Microplate spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) where the OD\({}_{595}\) was monitored every min for 30 min. Wells containing bacterial suspensions and the protein buffer served as control which was subtracted from the experimental data to obtain the final results, expressed as inhibition percentages. The dose response experiment was performed on _B. cereus_ ATCC 10,987 using endolysin concentrations ranging from 3 to 100 \(\upmu\)g/mL. For the activity spectrum assays, 100 \(\upmu\)g/mL of endolysin were tested on strains described in Table 3.
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 4 Delay Analysis for V2X Communications
### LTE-A Delay  
Cellular networks have multiple components that contribute to the total system delay, including the control plane, core network, and user plane delay. These components are analyzed in [27] for V2X applications. In the rest of this subsection, the latency components for both the control and user plane are analyzed.
## 3 Stability Analysis under Weak Grid
### Frequency Response of LCL Filter under Grid Impedance Change
As shown in Equations (6) and (7), six gains are needed to be tuned in the direct grid current controller: the proportional gain at the fundamental frequency \(K_{p1}\), and five resonant gains at the fundamental and selected harmonic frequencies \(K_{r1}\), \(K_{r5}\), \(K_{r7}\), \(K_{r11}\), and \(K_{r13}\). The controller gains are selected in the stiff grid condition. Under weak grid, the closed-loop pole locations designed in the stiff grid move from the designed locations as \(L_{g}\) is increased. Figure 6 shows the locations of the closed-loop poles for the direct grid current controller [13] under distorted grid and grid impedance variation as \(L_{g}\) is increased from stiff grid to weak grid. The chosen LCL filter parameters produce the resonance frequencies higher than the critical frequency. To suppress the current harmonic distortion caused by distorted grid, the PR controllers are incorporated in the orders of 5 th, 7 th, 11 th, and 13 th in the stationary frame. In spite of large variation of \(L_{g}\), most of the closed-loop system poles remain in the stable region as is shown in Figure 6. On the other hand, the poles of the PR controllers in 11 th and 13 th orders move toward the stability boundary of the unit circle as  
\(L_{g}\) is increased. Eventually, when \(L_{g}\) is increased larger than 7 mH, the system operation becomes unstable. This fact indicates that this control method is very poor to mitigate the current harmonics in the presence of distorted grid and grid impedance variation. To investigate the stability of this control scheme under different LCL filter selections, Figures 7 and 8 show the location of the closed-loop poles with different \(C_{f}\) values of 10 \(\mu\)F, and 30 \(\mu\)F, respectively, which produce the resonance frequencies around the critical frequency, and lower than the critical frequency. Similar to Figure 6, the resonant controllers are incorporated in the orders of 5 th, 7 th, 11 th, and 13 th in the stationary frame, and \(L_{g}\) is varied from 0 to 10 mH. As the resonance frequency is selected in low frequency region, the system is more vulnerable to the uncertainty in \(L_{g}\) as shown in these two figures. In Figure 7, one of the resonant controller poles in 11 th and 13 th leaves the stability boundary before \(L_{g}\) reaches 2 mH. In low resonance frequency region of the LCL filter in Figure 8, the resonant controller poles in 11 th and 13 th always remain outside of the stability region, which indicates that this control scheme fails to stabilize the system in the presence of the uncertainty of grid impedance under distorted grid.
## 3 Results
### Radiation Dose of the Patient
#### 3.2.2 Lateral and Abdominal Peak Skin Dose  
In contrast to the ED of the patient, the localized radiation dose at the lateral abdomen of the patient was significantly higher during surgeries with C-arm fluoroscopy, compared to iCT (Figure 7 (left)). The median lateral PSD of the patient was 70.35 (49.06-96.49) mGy with iCT and 154.15 (96.60-235.14) mGy with C-arm fluoroscopy. Thus, the lateral PSD of the patients decreased approximately two times when iCT was used instead of C-arm fluoroscopy. However, the abdominal PSD was lower with both imaging devices and did not differ significantly between surgeries with iCT and C-arm fluoroscopy (Figure 7 (right)). The median abdominal PSD of the patient ranged between 44.524 (37.973-59.103) mGy with iCT and 46.347 (19.972-65.721) mGy with C-arm fluoroscopy.  
Figure 6: [PERSON]’s correlation between BMI and the effective dose of the patient. Correlation analysis of the BMI and the ED of patients who had an MI-PLIF surgery assisted by iCT (\(n\) = 24) (R\({}^{2}\) = 0.192, \(p\) < 0.05) or C-arm fluoroscopy (\(n\) = 37) (R\({}^{2}\) = 0.005, \(p\) > 0.05). The lines delineate a 95% confidence interval per subgroup.  
Figure 7: Peak skin dose (PSD) of the patient during an MI-PLIF procedure. (**left**) The lateral peak skin dose of the patient (mGy) compared between surgeries assisted by iCT (\(n\) = 24) or C-arm fluoroscopy (\(n\) = 27). (**right**) The abdominal peak skin dose of the patient (mGy) compared between surgeries assisted by iCT (\(n\) = 24) or C-arm fluoroscopy (\(n\) = 28). Extreme values are marked as “\(\bigstar\)” and outliers are marked as “\(\circ\)” on the boxplot. Significance (\(p\) < 0.01) is indicated with “**” above the two boxplots on (**left**) and non-significance is indicate with “ns” above the two boxplots on (**right**).
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 2 Sensors and Monitoring Technologies
### Temperature Sensors
Although IC sensors have a narrower temperature measurement range (\(-\)40-150 \({}^{\circ}\)C) and moderate response times (0.5-100 s), these devices have reasonably good accuracy (\(\pm\)0.5-1 \({}^{\circ}\)C), excellent linearity, simple circuitry and small packaging (as small as 0.8 mm \(\times\) 0.8 mm), low cost ($1-$15) and no need for any calibration process (as these sensors are calibrated during production testing), features which make IC sensors highly suitable for monitoring and control purposes within buildings.
## 2 Materials and Methods
### Mechanical Testing, Contact Angle, and Thermal Behavior  
The mechanical properties of the electrospun fibers were investigated by means of uniaxial tensile test at room temperature (5960 Dual Column Tabletop Testing System, Instron(r), Darmstadt, Germany) by fixing electrospun samples in a paper frame, following the protocol reported in [9]. Five samples for each electrospun mat were tested. Furthermore, contact angle measurements were performed by releasing 3 \(\mu\)L of distilled H\({}_{2}\)O (Kruss DSA30, Hamburg, Germany) to assess the wettability of electrospun fibers. Thermogravimetric analysis and differential thermal analysis (TG/DTA) (Netzsch Differential Thermal Analyzer STA 429 CD, NETZSCH-Geratebau GmbH, Selb, Germany) were employed to ascertain the incorporation of bioactive glass particle considering the residual amount of mass after PCL decomposition. Thermal decomposition analysis was executed in static air atmosphere on 30 mg of samples heated from 25 \({}^{\circ}\)C to 800 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. The tests were performed in static air atmosphere.
# Study on Influencing Factors of Helicopter Brownout Evolution Based on CFD-DEM
## 3 Verification of Calculation Method
### Lynx Rotor  
In this experiment, the inflow velocity is 2.5 m/s and the tip Mach number is 0.60. The geometric parameters are shown in Table 1. The blade plane reference diagram is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Airfoil** & **Disk Radius (mm)** & **Chord Length (mm)** \\ \hline NPL9615 & 1105 & 180 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric parameters.  
Figure 1: Calculation process.  
Five groups of data are selected for comparative verification. The lift values obtained by simulation are shown in Table 2. The values are compared with the experimental results through the form of the lift coefficient, as shown in Figure 3.  
From the above figures, the rotor lift is positively correlated with the collective pitch degree. Comparing the simulation and experimental results, the data error of the lift coefficient is less than 8% under different working conditions. The validation of the rotor lift coefficient in different cases shows that the grid and turbulence model used in this paper are reliable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Hover Height** & **Pitch (°)** & **Lift (N)** \\ \hline
1 & 0.84R & 13 & 2871.36 \\
2 & 0.64R & 14 & 3486.65 \\
3 & 0.52R & 15 & 3933.99 \\
4 & 0.72R & 16 & 4078.84 \\
5 & 0.78R & 17 & 4340.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 3: Comparison of lift coefficient results.  
Figure 2: Lynx tail rotor characteristics.  
are almost consistent but errors exist at the leading edge of the airfoil, which ranges from 15% to 20%. When the Mach number reaches a certain value, the position of the maximum pressure coefficient of the airfoil will move backward from the leading edge of the airfoil.
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 2 Problem Formulation and Tracking Strategy
### Kalman Filter Algorithm  
In this paper, the Kalman filter algorithm is used to estimate and predict the position of the target in real time, and the target state is constantly updated during a pursuit. The dynamic equation and measurement equation of the discrete-time system of the Kalman filter can be expressed as follows:  
\[\left\{\begin{array}{l}\tilde{\mathbf{x}}_{k}^{-}=F\tilde{\mathbf{x}}_{k-1}^{-}+ \mu\mathbf{\eta}\\ \dot{\mathbf{Z}}_{k}=H\mathbf{X}_{k}+\mathbf{\omega}\end{  
value represents a more accurate prediction of the state. The initial value only affects the initial convergence speed, and the value of \(\mathbf{P}\) will be updated with the iteration of the Kalman filter. Set initial value \(\mathbf{P}_{0}=0\).  
The motion equation of the escaping target object can be expressed as follows:  
\[\left\{\begin{array}{l}x_{k}\ \ =x_{k-1}+v_{k-1}^{x}T\\ v_{k-1}^{x}=v_{k-1}^{x}+\alpha_{k-1}^{x}T\\ y_{k}\ \ =y_{k-1}+v_{k-1}^{y}T\\ v_{k-1}^{y}=v_{k-1}^{y}+\alpha_{k-1}^{y}T\end{array}\right. \tag{10}\]  
where \(x_{k-1}^{x}\), \(v_{k-1}^{x}\), and \(\alpha_{k-1}^{x}\) are the position, velocity, and acceleration of the target in the \(x\)-axis direction at time \(t_{k-1}\), and \(y_{k-1}^{y}\), \(v_{k-1}^{y}\), and \(\alpha_{k-1}^{y}\) are the position, velocity, and acceleration of the target in the \(y\)-axis direction at time \(t_{k-1}\). \(T\) is the time interval between two adjacent sampling points. When the sampling frequency is approximately 10 Hz, the value of \(T\) can be taken as 0.1 s. The above formula in matrix form can be written as follows:  
\[\left[\begin{array}{c}x_{k}\\ v_{k}^{x}\\ y_{k}\\ v_{k}^{y}\end{array}\right]\ =\ \begin{bmatrix}1&T&0&0\\ 0&1&0&0\\ 0&0&1&T\\ 0&0&0&1\end{bmatrix}\left[\begin{array}{c}x_{k-1}\\ v_{k-1}^{x}\\ y_{k-1}^{y}\\ v_{k-1}^{y}\end{array}\right]\ +\ \begin{bmatrix}0.57^{2}&0\\ T&0\\ 0&0.57^{2}\\ 0&T\end{bmatrix}\left[\begin{array}{c}\alpha_{k-1}^{x}\\ \alpha_{k-1}^{x}\\ \alpha_{k-1}^{y}\\ \alpha_{k-1}^{y}\end{array}\right] \tag{11}\]  
The dynamic noise vector of the system is as follows:  
\[\mathbf{\eta}=\left[0.5\alpha_{k-1}^{x}T^{2},\alpha_{k-1}^{x}T,0.5\alpha_{k-1}^{y} T^{2},\alpha_{k-1}^{y}T\right]^{T} \tag{12}\]  
After the above system state equation and observation equation are established, the Kalman filter equation can be used to continuously predict the position of the target at the next time.  
\[\mathbf{H}=\begin{bmatrix}1&0&0&0\\ 0&0&1&0\end{bmatrix} \tag{13}\]
## 3 Improved Firefly Algorithm-Wavelet Support Vector Machine
### Wavelet Support Vector Machine (WSVM)
The model combing wavelet technology with the SVM is superior to the traditional SVM in terms of generalization ability and accuracy in classification [38; 39]. In wavelet analysis, a function or signal is approximated or expressed by a family of functions generated by dilations and translations of a function called the mother wavelet. The principle of wavelet support vector machine is to replace the kernel function of SVM with the mother wavelet function. As a kind of multidimensional wavelet function, the wavelet kernel can be expressed as the product of one-dimensional function. In this paper, the Morlet wavelet function is selected as the mother wavelet. Then, the wavelet kernel function of SVM is denoted by:  
\[K\left(\mathbf{x},\mathbf{x}^{{}^{\prime}}\right)=\prod\limits_{i=1}^{N}\text{cos} \left(1.75\frac{(\mathbf{x}_{i}-\mathbf{x}^{\prime}{}_{i})}{\tau}\right)\text{exp} \left(-\frac{\left\|\mathbf{x}_{i}-\mathbf{x}^{\prime}{}_{i}\right\|^{2}}{2\tau^{2}}\right) \tag{20}\]  
From the above contents, it becomes obvious that the effectiveness of WSVM model is highly dependent on its kernel parameters, including the penalty factor \(C\) and the dilation parameter \(\tau\). Different kernel parameters will produce different recognition results for the same problem. The excessively small values of them will result in under-fitting, whereas the disproportionately large values will lead to over-fitting phenomenon. Therefore, the firefly algorithm is implemented to obtain the optimal parameters of WSVM in the following subsection.
## 2 Materials and Methods  
### Simulation Framework  
Wave propagation in an elastic medium can be accurately and efficiently described using a finite-difference approach, which was the method followed in this study. Physical phenomena, such as mode conversion and frequency dependent scattering from grain boundaries, are included automatically. To calculate wave propagation, the elastodynamic equations are used, where the stresses and the particle velocities in the medium are described. Three sets of equations are required: the equation of motion, the strain-displacement equation, and the constitutive relation [38; 39]. Note that the steel grains are modelled as anisotropic elastic media, thereby ignoring intrinsic damping mechanisms.  
The numerical scheme that is used for the finite difference framework is based on a rotated staggered grid (RSG). Staggered grids are often used in finite-difference frameworks, which allows differences to be taken in a straight-forward way (parallel to one of the principal axes). However, they can lead to numerical artifacts in case of anisotropy, where differences over boundaries are not taken into account properly. In that case, an RSG provides better results. [PERSON] [39] applied the RSG to velocity-stress formulations of the elasto-dynamic wave equations. The advantage is that components are now located only at edges and in the center of an elementary cell, and not on the sides anymore.  
A schematic process of the finite-difference simulation framework is depicted in Figure 1, from the source data (geometry, phase, and material properties; top left), via the individual grain (single crystal) elasticity constants to a 3D grid usable in the finite-difference simulation. The output of the simulation is the modelled measurement response of the microstructure to a specific excitation.
## 4 Experiments and Results
### Results Analysis
#### 4.3.6 Attack Detection Time  
Attack detection time refers to the time taken by the controller to identify if there is an attacker node present in the network, and it is calculated by subtracting the time for traffic to start from the time it takes to detect an attack. In this study, the proposed solution, SLF-RPL, is compared with the PCC-RPL in terms of attack detection time. The results showed that the SLF-RPL outperformed the PCC-RPL due to its veracity, and the graph in Figure 15 illustrates that the SLF-RPL had a lower attack detection time, resulting in a longer network lifetime as the number of nodes increased, as illustrated in Figure 17.  
Figure 16: Average Residual Energy at the Node Level.  
Figure 15: Residual Energy at the Network Level.
## 3 Fundamental Concepts
### Smart City Scenario  
Many systems may be deployed in Smart City scenarios, with different particularities. However, for the proposed approach, we are particularly concerned with the perception of a Smart City as any generic system composed of one or more sensors. Actually, any kind of sensors may be considered (e.g., temperature, pollution, UV radiation, audio, image), since the only required information for the priority assignment system is the location of the sensors (managed by the WSN [43; 44]). In such way, a Smart City may be represented as a 2D map where a set of \(S\) sensors are previously positioned, and two sensors are never positioned in the same location.  
Therefore, a Smart City \(SC\), sizing \(SC_{m}\times SC_{n}\) meters, and assuming \((0,0)\) as the origins at the upper left corner of the rectangle created by \(SC\), is defined as the considered scenario to be optimized. In that scenario, \(S\) sensors will be deployed, belonging to one or more independent WSN. Each sensor \(s\in S\) is located at (\(s.x\), \(s.y\)) position, assuming that the dimensions of the sensors are too tiny to be considered when compared to the size of the city, and that \(0\leq s.x\leq SC_{m}\) and \(0\leq s.y\leq SC_{n}\). It is also assumed that all sensors are fixed and never move, and that they have sufficient processing power to execute the protocols and procedures defined in the proposed approach. The monitoring nature of the sensors are irrelevant in this work, but such information could be considered in future optimizations, employing some strategies based on the type of data gathered by sensors [42].  
Finally, it is assumed that all sensors are connected and that they can be reached from outside the sensor network. As sensors may get offline due to energy depletion (of theirs or others required relaying nodes), the list of sensors of the city \(SC\) may be updated along the time, but how they communicate and what routing protocols are employed are out of the scope of this work. Actually, as the final goal of the proposed approach is to assign priorities, which are in the last instance assigned on the application logical layer, the employed MAC and routing protocols to compose the ad hoc backbone of WSN are not necessarily an issue to be concerned.