esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Node Clustering for WMSNs
### Overlapping Areas between FoV of Multimedia Nodes
Figure 2 shows the case in which each side of each triangle intersects the perimeter of the other one in exactly two points \(\mathrm{V}_{\mathrm{i}}\,\mathrm{and}\,\mathrm{V}_{\mathrm{j}}\), becoming the segment \(\mathrm{V}_{\mathrm{i}}\mathrm{V}_{\mathrm{j}}\) one of the sides of the intersection  
\[\mathrm{x_{p_{1}}}=\mathrm{x_{A}}+\mathrm{R_{S}}\cdot\cos(\alpha) \tag{1}\]1 Examples of side intersection in only one point, for instance \(\mathrm{AP}_{1}\) intersect the perimeter of the other triangle in only \(\mathrm{V}_{1}\) in both (a) and (b). \[\mathrm{x_{p_{1}}}=\mathrm{x_{A}}+\mathrm{R_{S}}\cdot\cos(\alpha) \tag{1}\]2 Non-acceptable intersection points, each two anti-parallel lines have an intersection point but the point have to satisfy Equation (8) to be a vertex of the intersection polygon. polygon. However, there are other situations in which the intersection of one side of a triangle with the perimeter of another triangle occurs only at one point, resulting in that one of the vertices associated with that side lies within the second triangle and will become one of the vertices of the polygon (Figure 4a,b). In the third step, a decomposition approach is used for calculating the area of the overlapped polygon in a 2D-plane. Let a polygon (W) be defined by its ordered vertices V\({}_{i}\) = (x\({}_{i}\),y\({}_{i}\)) for i = 0,...,n with V\({}_{n}\) = V\({}_{0}\). Also, let P be a reference point in the 2D-plane. For each edge V\({}_{i}\)V\({}_{i+1}\) of the polygon W, form the triangle \(\Delta_{i}\) = PV\({}_{i}\)V\({}_{i+1}\). Then, the area of the polygon W is equal to the sum of the signed areas of all the triangles \(\Delta_{i}\) for I = 0,...,n - 1:  
\[\mathrm{x_{p_{1}}}=\mathrm{x_{A}}+\mathrm{R_{S}}\cdot\cos(\alpha) \tag{1}\]3  
\(\mathrm{A(\Delta_{i})}\) refers to the area of triangle \(\Delta_{i}\). Notice that, for a counter-clockwise oriented polygon, when the point P is on the left side of an edge V\({}_{i}\)V\({}_{i+1}\), the area of \(\Delta_{i}\) is positive; whereas, when P is on the right side of an edge V\({}_{i}\)V\({}_{i+1}\), the area \(\Delta_{i}\) is negative. If the polygon is oriented clockwise, then the signs are reversed. This computation gives a signed area for a polygon and similar to the signed area of a triangle, is positive when the vertices are oriented counter-clockwise around the polygon, and negative when oriented clockwise. We refer to [22] for a detailed description of the algorithm for calculating the area of a 2D polygon.
## 3 Results
### Simulation Results of the Hourly FTOP Model  
The NSGA-II algorithm was used to optimize the parameters of the hourly scale FTOP model, and the best parameters obtained are shown in Table 5. Table 6 describes the statistical indicators between the simulated and measured flows of the 22 floods. Although the performance of the FTOP model was worse than that of the NTOP model and the ITOP model in a specific index of a particular flood, the average of the statistical indicators of 22 floods showed that the performance of the FTOP model was better than the other two models. Regarding the average value of RDRE, the FTOP model reduced its value from 3.61% and 4.39% to 1.99%. The FTOP model did the same with the PDRE average, reducing its value from 17.04% and 30.51% to 14.06%. Regarding the average value of POTE, the FTOP model and NTOP model were \(-1.27\) and \(-1.32\), respectively, being similar but significantly better than the \(-2.18\) of the ITOP model. Similarly, in terms of the mean value of NSE, the FTOP model and NTOP model were 0.88 and 0.87, respectively, being similar but significantly better than the ITOP model (0.77). To sum up, the FTOP model performed slightly better than the NTOP model and considerably better than the ITOP model. Figure 6: Comparison of cumulative flow curves of three TOPMODEL models simulating different flow levels. The black (**a**–**c**), blue (**d**–**f**), and red (**g**–**i**) lines represent FTOP, NTOP, and ITOP, respectively. Figure 7 compares the simulated flood data of FTOP, NTOP, and ITOP models with the observed flood data. It can be seen intuitively from Figure 7 that the simulated peak flood discharge by the FTOP model of (a), (h), (m), (n), (s), and (t) graphs were significantly smaller than the observed peak flood discharge. Compared with the NTOP and ITOP models, the FTOP model clearly simulated the flooding process in the remaining flood events. Figure 8 depicts the distribution of the four statistical values of the FTOP model, the NTOP model, and the ITOP model. The closer the box of a specific model in the figure to the dotted line, the better the simulation effect of the model. In Figure 8a-c, the performance of the FTOP model was less different from that of the NTOP model, but it was significantly improved compared to the ITOP model. As shown in Figure 8d, the performance of the FTOP model was considerably enhanced compared to the NTOP model and the ITOP model.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 2 Related Work
### DDoS Defense against SDN  
[PERSON] et al. [24] proposed a two-level load balancing solution in SDN networks to increase survival time of a system during DDoS attack. Their main work is load balancing and in doing so they do not mitigate DDoS attack but they were able to increase the survival time by dividing the load.  
[PERSON] et al. [25] proposed a hard timeout mechanism to phase out fake flow table entries created by an attacker to clog switch-controller communication channel and overflow the TCAM memory of a switch. The timeout is explicitly applied to arbitrary infrequent flows for error or DDoS packets whose sole purpose is to overwhelm the capacity an OF-switch.  
StateSec [26] employs stateful SDN in the context of DDoS defense and delegate local processing to switches. Switches directly handle traffic monitoring of pertinent features (e.g., IP sourceand destination, port source and destination) by using stateful programming, thus reducing the computational burden on the controller. The precise results are then fed to an entropy-based algorithm for attack detection at the controller. This work in its current form is very basic without any extensive evaluations.
## 3 Results and Discussion
### Pressure and Vacuum Limits  
Figure 8 presents the maximum barometric pressure variation while pressuring and depressurizing the chamber. During 20 min of testing for the positive pressure, the barometric pressure increased by 19.63 hPa, starting from an initial value of 940.41 hPa. For the initial pressure of 940.36 hPa, barometric pressure decreased by 47.15 hPa while evaluating the negative pressure. Both positive and negative pressures are sufficient for the proposed entomology experiments, which require pressure variations of \(\pm\)15 hPa. The observed differences in pressure capacity while pressurizing and depressurizing the chamber are related to the operational characteristics of the air pump, as shown on the datasheet [25]. Higher values of pressure variation could be obtained by selecting an air pump of higher flow capacity. The control system (controller.py) has a safety routine, in which it continuously monitors the barometric pressure and turns off the air pump if the barometric pressure is beyond the stated safety limits, ranging from 850 to 1050 hPa in this application. In addition, if higher values of positive/negative pressure are required by the application, the air pump can be replaced by separate devices dedicated to pressurizing (e.g., air compressor) and depressurizing (e.g., vacuum pump) the chamber. Values beyond the range of 850 to 1050 hPa might be achieved by the apparatus but, as far as we understand, there are no practical reasons to run experiments beyond the mentioned range. In addition, beyond the stated limits the chamber should be redesigned and built to support the operating pressures.  
Figure 8: Limits of pressure (hPa) inside the chamber: (**a**) maximum positive pressure; (**b**) maximum vacuum. Lines indicate the maximum variation in barometric pressure that can be achieve while pressurizing and depressurizing the chamber in its current setup.  
Figure 7: Barometric chamber built for entomology experiments (INCT Semicochemicals in Agriculture): (**a**) chamber and electronic control system; (**b**) air pump and proportional solenoid valves.  
For comparison, the computer-controlled barometric chamber designed by [24] employed compressed air and a vacuum pump as the air source to change the barometric pressure and their system was designed to operate in the range from 950 to 1050 hPa.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 2 Materials and Methods
### SRR Design at 3.5 GHz  
The designed SRR with its LC equivalent at 3.5 GHz is represented in Figure 7. Its dimensions are indicated in Table 1. The LC resonating equivalent circuit of this SRR is precisely tuned at 3.50 GHz, with inductance L = 13.4987 nH and capacitance C = 153.043 ff.
## 2 Materials and Methods
### CNTs Bonded to Metal as a Working Electrode  
Electrochemical characterization and electrical conductivity measurements of the chemically bonded CNTs to Cu metal were performed on electrodes assembled as shown in Figure S2. A single HD-CNT within a film (\(\sim\)70 \(\upmu\)m in diameter and \(\sim\)40 \(\upmu\)m thick) with open-ended CNTs was connected to a metal and employed as a working electrode. To investigate the stability and electrochemical behavior of the fabricated electrodes, cyclic voltammetry measurements of [Ru(NH\({}_{3}\))\({}_{6}\)]Cl\({}_{3}\) with 0.5 M KCl as a supporting electrolyte were performed in the potential window from 0.1 to \(-\)0.5 V. Stripping voltammetry is an effective technique for heavy metal detection with various kinds of electrodes. Therefore, stripping analysis was performed for Pb\({}^{2+}\) detection in the potential window of \(-\)1.2-0 V, with CNT-Cu chemically bonded electrodes as the working electrode. Commercially available Pb(NO\({}_{3}\))\({}_{2}\) in a 2% HNO\({}_{3}\) solution was used to prepare the Pb\({}^{2+}\) solutions in a 0.1 M acetate buffer (pH 4.3) at concentrations of 20-50 ppb, and calibration curves were constructed based on the current intensities recorded for the Pb\({}^{2+}\) solutions. The following optimized square wave anodic stripping voltammetry (SWASV) parameters were used: frequency, 15 Hz; amplitude, 0.025 V; deposition potential, \(-\)1.2 V; potential step, 0.004 V; and deposition time, 120 s. The stripping peaks were used to quantify the Pb concentration.  
Figure 2: Proposed mechanism for the chemical bond formation between metals and open-ended CNTs. (**A**) In situ generation and attachment of an amine functional group to a Cu surface and subsequent bonding to a carboxylic acid functional group at the open end of a CNT. (**B**) Electrochemical attachment of ethylenediamine to a Pt surface and subsequent bonding of the amine end to the carboxylic acid at the open ends of the CNTs.
## 3 Methodology  
In this section, the in-depth architecture of the power-aware ant colony optimization (ACO) routing protocol (P-BIOSARP) is presented. P-BIOSARP is an extension of the enhanced E-BIOSARP, which includes an encryption mechanism. In E-BIOSARP, as discussed in Section 2, the parameters include link quality (\(\alpha\)), delay (\(\beta\)), and remaining energy (\(\delta\)) for deciding the most optimal node based on the probability (\(\mathcal{P}\)) function, as in Equation (1), among the neighboring nodes to forward the data packet to so that it can reach the destination. On top of the optimal value calculation, the encryption is enabled to secure the packet information with a mathematical encryption function. \[\mathcal{P}_{cn}(\varepsilon)=\frac{[\alpha_{cn}(\varepsilon)]^{\texttt{inv}} \cdot[\beta_{cn}(\varepsilon)]^{\texttt{w}\beta}\cdot[\delta_{cn}(\varepsilon )]^{\texttt{w}\gamma}}{\sum_{\xi\,\varepsilon\,cn}^{\infty}[\alpha_{cn}( \varepsilon)]^{\texttt{inv}}\cdot[\beta_{cn}(\varepsilon)]^{\texttt{w}\beta} \cdot[\delta_{cn}(\varepsilon)]^{\texttt{w}\gamma}} \tag{1}\]  
where \(\mathcal{P}_{cn}(\varepsilon)\) is the calculated probability of the neighboring node ID \(\varepsilon\) for data packets that need to be transferred from current node \(c\) to the neighboring node \(n\), \(w\) is the weight of each parameter, \(w\) is the link-quality weight, \(w\beta\) is the delay weight, and \(w\gamma\) is the
# Numerical Design and Laboratory Testing of Encapsulated PCM Panels for PCM-Air Heat Exchangers
## 2 Methods
### Data Reduction
#### 2.2.2 Laboratory Test  
Similarly to the numerical design, the heat transfer in the laboratory test was analyzed in terms of Nusselt number. The temperature surface was averaged and the average convection heat transfer coefficient was calculated by Equation (9):  
\[\mathbf{h}=\frac{\mathbf{Q}_{conv}}{A\left(\widetilde{T}_{s}-T_{b}\right)} \tag{9}\]  
where \(\mathbf{A}\) is the panel surface area, \(T_{in}\) and \(T_{out}\) is the air inlet and outlet temperature acquired by thermocouples.  
\[T_{b}=(T_{out}+T_{in})/\mathbf{2} \tag{10}\]and \(\widetilde{T}_{\mathbf{s}}\) is the surface temperature average of all thermocouples and is given by equation 11:  
\[\widetilde{T}_{\mathbf{s}}=\sum_{i=\mathbf{1}}^{N}T_{\mathbf{s}i}/\mathbf{N} \tag{11}\]  
where \(\mathbf{N}\) is the number of thermocouples and \(Q_{conv}\) is the heat transfer between panels and air given by:  
\[Q_{air}=Q_{conv}=\dot{m}_{air}\Big{(}c_{p_{air}_{(out)}}T_{out}-c_{p_{air}_{( in)}}T_{in}\Big{)} \tag{12}\]  
where \(\dot{m}_{air}\) is the air mass flow rate in \(\frac{kg}{kg}\); \(c_{pair}\)in \(\frac{1}{kgK}\).  
With the heat of convection evaluated, it is possible to estimate the averaged Nusselt number (Equation (2)) and the Thermal Enhancement Factor (Equation (6)).  
To estimate \(Nu_{o}\) and \(\Delta p_{\omega}\), the Dittus-Boelter (Equation (13)) correlation was used to validate the Nusselt number results, which is:  
\[\mathbf{Nu_{DB}=0.023\;Re_{D}^{A/5}Pr^{n}} \tag{13}\]  
where \(n=0.4\) for the fluid being heated and \(n=0.3\) when the fluid is being cooled. This procedure was used by [PERSON] [16] in a similar experiment.  
To evaluate the energy stored and released, Equations (12) and (13) were used to evaluate the PCM-air heat exchanger performance.  
\[Q_{c}=\int_{0}^{t}\dot{m}_{air}\;c_{p,air}\;(T_{out}-T_{in})_{air}\;d\mathbf{t} \tag{14}\]  
\[Q_{h}=\int_{0}^{t}\dot{m}_{air}\;c_{p,air}\;(T_{out}-T_{in})_{air}\;d\mathbf{t} \tag{15}\]
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology
### Automated Measurements: The Radiometric Calibration Test Site (RadCaTS)
#### 2.2.6 Surface Reflectance Measurements at the RadCaTS
* Continue this process until the difference between the two values converge to being within the predetermined value. (Note: the wavelength regions used for this comparison are as follows: 400 nm to 1200 nm, 1500 nm to 1700 nm, and 2000 nm to 2250 nm. These spectral regions are chosen in order to avoid absorption regions in the atmosphere.) * At this point, the hyperspectral surface BRF has been determined for the given time and date of interest.
## 2 Materials and Methods
### Swirel Mechanism Design  
To determine the relative motions associated with the swirel mechanism, the analysis of the linear motion was determined with mathematical expressions presented in Table 2. The design was to accommodate a human weight of 120 kg, which would allow for an extensive range of patient weights. As the mechanism demonstrates, this weight acts vertically like a vertical load. There are two swivel plates in the mechanism: the upper one, where the patient sits, and the lower plate, which is the base. The upper plate slides and rotates over the lower plate to transfer the patient between platforms. This shows that the mechanism is to be analyzed dynamically, considering the parameters involved in its movement. The centrifugal force, F\({}_{\text{c}}\), is the force that appears to act on an object moving in a circular path. It is directed away from the center around which the object is moving. This force plays a significant role in determining the experience of passengers in a vehicle, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Materials Properties**} & \multirow{2}{*}{**Density** & **Young’s** & **Ultimate** & **Elongation** & **Poisson** & **Tensile Yield** & **Fatigue** & **Shear** \\  & & **Modulus** & **Tensile** & **at Break** & **Ratio** & **Strength (MPa)** & **Strength (MPa)** & **Modulus** \\  & (GPa) & & **Strength (MPa)** & **(\%)** & **Ratio** & **Strength (MPa)** & **(GPa)** \\ \hline SS 316L & 8.0 & 193 & 515 & 60 & 0.3 & 205 & 146.45 & 193 \\ Al6061 & 2.7 & 68.9 & 310 & 13 & 0.33 & 276 & 96.5 & 26 \\ Ti6 Al4V & 4.43 & 114 & 951 & 14 & 0.342 & 882 & 510 & 44 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mechanical and failure properties of SS 316L, AA 6061, and Ti6 Al4V. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Expression** & \\ \hline Linear Displacement (S) & S = ut + (\(\
icefrac{{1}}{{2}}\))at2 & a = 0.0116 ms\({}^{-1}\) \\ Linear velocity (v) & v = u + at \\ \(\
icefrac{{1}}{{2}}\) = u2 + 2 as & v = 0.058 ms\({}^{-1}\) \\ Angular Displacement (\(\theta\)) & \(\theta=\theta_{0}+\omega\text{t}+(1/2)\alpha\text{t}^{2}\) \\ Angular Velocity (\(\omega\)) & \(\omega=\omega_{0}+\alpha\text{t}\) & \(\omega_{0}=1.028\) rad\({}^{-1}\) \\ Angular Acceleration (\(\alpha\)) & \(\alpha=(\omega-\omega_{0})/\text{t}\) & \(\alpha=0.0596\) rad\({}^{-2}\) \\ Linear Displacement (d) at a & d = r\(\theta\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of the basic equations of motion used for motion analysis. especially those seated on mechanisms such as the swivel car seat when the vehicle turns. When a car takes a curve, passengers inside feel a force pushing them toward the outer side of the turn. This force is particularly noteworthy for swivel car seat users, as they need to counteract this force to stay in position. The visual representation of this force is the arrows for force direction on the swivel seat, especially during left and right curves, as shown in Figure 1. The forces that are exerted on the seat are the force of gravity, normal force, frictional force, shear forces, and torsional forces.
## 5 Numerical Results
### The Impact of Macro- and Micro-Mobilities  
The critical components accounted for in the proposed framework are micro- and macro-mobilities of UE. Specifically, different values of micro-mobility correspond to different typical applications, as demonstrated in [6]. On the other hand, different types of users, e.g., pedestrians, cyclists, and drivers, are characterized by different macro-mobility speeds. We now proceed to evaluate the impact of these two factors on the mean spectral efficiency.  
We start with the impact of the micro-mobility on the mean spectral efficiency demonstrated in Figure 10 for different types of applications and initial positions of UE, mean displacement due to a micro-mobility of 2 \({}^{\circ}\)/s corresponding to the video application and 4 \({}^{\circ}\)/s representing VR application \({}^{\circ}\)/s (the corresponding diffusion constants are \(D_{\gamma}=0.05,0.1\)), \(y_{0}=0\), mean displacement due to a macro-mobility of 0.8 m/s (\(D=100\)), compartment size \(L=100\) m, and BS antenna size of 15 \(\times\) 15 elements. By analyzing the presented results, we observe that the initial location of UE affects the quantitative values of the time-dependent spectral efficiency only. Additionally, as we also see, there is no principal difference between applications having different micro-mobilities, as curves corresponding to them exhibit nearly similar behavior. The variations for the VR applications are larger though. Thus, we conclude that the presence of micro-mobility induces local fluctuations whose magnitude and rate depend on the interplay between the UE antenna array and micro-mobility speed.  
Let us now investigate the impact of macro-mobility shown in Figure 11 for \(y_{0}=0\), \(x_{0}=20\), mean displacement due to macro-mobility varying between 0.8-2 m/s, mean displacement due to micro-mobility of 2 \({}^{\circ}\)/s, compartment size \(L=100\) m, and BS antenna array of 15 \(\times\) 15 elements. Specifically, in Figure 11a, we show the time-dependent spectral efficiency, while in Figure 11b--the mean distance to the BS serving as a supporting illustration. By observing the presented results, we see that the impact of macro-mobility mainly manifests itself in different rates to the average spectral efficiency in a given deployment dimension. Specifically, the relatively fast mobility at 2 m/s, corresponding to the diffusion constant of \(D_{M}=D_{x}=D_{y}=200\), is characterized by a faster decline in the time-varying spectral efficiency as compared to the low speed of 0.8 m/s, corresponding to a diffusion constant of \(D_{M}=D_{x}=D_{y}=25\). This is explained by different mean time-dependent distances to the BS depicted in Figure 11b. For larger deployments, this difference can be substantially higher.  
Figure 10: Spectral efficiency as a function of micro-mobility.
## 3 Experimental Data and Evaluation of Results
### Evaluation of Experimental Results  
The experimental results of the synthetic signals were evaluated using the signal-noise ratio (SNR) and correlation coefficient. Let \(s_{f},s_{m}\), and \(n\) represent the FECG, MECG, and noise signals, respectively. \(s\) represents the mixed signals in the abdomen, and \(\hat{s}_{f}\) represents the estimation of \(s_{f}\), that is, the FECG extraction result. Suppose that \(s_{f}\), \(s_{m}\), and \(n\) are independent and that the abdomen mixed ECG signal is the superposition of the three signals:  
\[s=s_{m}+s_{f}+n. \tag{19}\]  
If \(s_{f}\) is taken as the signal of concern, then both \(s_{m}\) and \(n\) are considered noise. Therefore, the SNR of the mixed signal \(s\) (i.e., input SNR) is defined as  
\[SNR_{in}=10\log_{10}\left(\frac{P_{s_{f}}}{P_{(s_{m}+n)}}\right) \tag{20}\]  
where \(P_{s_{f}}=\frac{1}{N}\sum\limits_{n=1}^{N}s_{f}^{2}\) and \(P_{(s_{m}+n)}=\frac{1}{N}\sum\limits_{n=1}^{N}(s_{m}+n)^{2}\) are the energy of the FECG and the energy of the MECG plus noise.  
For the successfully extracted FECG, the SNR of \(\hat{s}_{f}\) (i.e., output SNR) is defined as  
\[SNR_{out}=10\log_{10}\left(\frac{P_{s_{f}}}{P_{(\hat{s}_{f}-s_{f})}}\right) \tag{21}\]  
where \(P_{(\hat{s}_{f}-s_{f})}=\frac{1}{N}\sum\limits_{n=1}^{N}\left(\hat{s}_{f}-s_{ f}\right)^{2}\).  
The extraction performance was evaluated by determining SNR improvement, which is defined as the output SNR minus the input SNR:  
\[SNR_{improve}=SNR_{out}-SNR_{in}. \tag{22}\]  
The correlation coefficient \(C_{R}\) was used to evaluate the similarity between the extracted FECG and the original FECG signal. It is important to evaluate the extraction quality of the FECG. \(C_{R}\) was calculated using Formula (23):  
\[C_{R}=\frac{\sum\limits_{n=1}^{N}\hat{s}_{f}(n)s_{f}(n)}{\sqrt{\sum\limits_{n= 1}^{N}\left(\hat{s}_{f}(n)\right)^{2}\sum\limits_{n=1}^{N}\left(\hat{s}_{f}(n )\right)^{2}}}. \tag{23}\]