esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Optimization
### Hydrogen Plasma  
The simulation of hydrogen gas was also carried out at a stage height of 118 mm, a gas pressure of 70 Torr, and a microwave power of 600 W. The reactions and collision formulas of hydrogen are listed in Table 2. The results are shown in Figure 8, and the electric field intensity observed is \(1.6\times 10^{6}\) V/m, which is a very strong electric field concentrated at the tip of the antenna. The corresponding plasma density reaches \(1.2\times 10^{20}\) m\({}^{-3}\), concentrating at the tip of the antenna. The electron temperature is measured at 10 eV. The simulated hydrogen plasma is consistent with the actual experiment, as discussed below.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Formulas**} \\ \hline
1 & \(\mathrm{e+H\to e+H}\) \\
2 & \(\mathrm{e+H\to e+H^{*}}\) \\
3 & \(\mathrm{e+H^{*}\to e+H}\) \\
4 & \(\mathrm{e+H\to 2e+H^{+}}\) \\
5 & \(\mathrm{H^{*}+H^{*}\to e+H+H^{+}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Collisions and chemical reactions of hydrogen (H) used in the MPJCVD model.  
Figure 8: _Cont._  
Figure 7: Electron density of the argon plasma as a function of microwave power level for gas pressures of (**a**) 5 Torr, (**b**) 35 Torr, (**c**) 70 Torr, and (**d**) 90 Torr.
# Practical Methodology for a Three-Dimensional-Printed Hybrid Desalination System
## 2 Materials and Methods
### Three-Dimensional Printting  
For the manufacturing of the reactor components, the CAD design was converted into a coordinate file format for the printer, known as Standard Triangle Language (.STL). For the end plates and FD, the material must have high mechanical strength, as the parts will be subjected to compression and high chemical resistance to withstand varying pH levels. The end plates and FD were produced using the Fortus(r) 450 mc 3D production system (Stratasys Ltd., Edina, MN, USA). Acrylonitrile Styrene Acrylate (ASA), an engineering plastic (Stratasys Ltd.), was selected for its high mechanical strength and chemical resistance. The ASA material is designed to withstand substantial pressures, with a compression limit of 75.4 MPa in the XZ orientation and 188 MPa in the ZX orientation, according to the manufacturer's specifications. Assembly of the reactors is performed using a torque of 6 Nm to ensure a secure fit without over-compressing the components. Excessive compression is not recommended, as it may lead to the deformation of internal parts, rupture of membranes, or even constriction of the channels supplying the reactor. Although the material can tolerate significant pressure, maintaining a controlled pressure level is essential to preserve the integrity and functionality of the reactor structure, thus avoiding potential issues associated with high-pressure desalination processes.  
The printing process was configured using an Insight slicer, which converts STL files into CMB files (a format containing the print path and settings for 3D printers). Both the end plates and FD were printed solid (100% infill) to prevent electrolyte permeation, using a nozzle with a layer height of 0.1778 mm for high-dimensional precision. For the gaskets, a flexible yet durable material is required. Thermoplastic polyurethane (TPU) is a good choice for printing gaskets due to its combination of elasticity, abrasion resistance, and chemical durability. Various TPU formulations are determined by their Shore hardness, which affects the material's rigidity and flexibility. In 3D printing, a lower Shore hardness indicates more flexibility, while a higher Shore hardness results in greater rigidity. TPU 95A was selected, and 1 mm thick gaskets were printed using an Ultimaker 2 Extended+ printer (Ultimaker, Utrecht, The Netherlands). The process was configured in a Cura slicer, utilizing a nozzle with a 0.6 mm layer height. The fabrication of two reactors (RED and ED) took a total of 20 h and 50 min, using a material volume of 606.97 cm\({}^{3}\). The total printing cost for both reactors, including the FD, gaskets, and end plates, was $232.46. Figure 4 shows the 3D-printed components used in the reactor assembly. In Figure 4a, a gasket printed in TPU 95A and an FD printed in ASA are displayed; Figure 4b shows the reactor assembled.  
Figure 4: Three-dimensional-printed components used in the reactor fabrication: (**a**) gasket printed in TPU 95A and FP printed in ASA; (**b**) assembled reactor.
## 3 Results and Discussion  
### Structure  
Room temperature X-ray diffraction (XRD) patterns are shown in Figure 3. The new phase La\({}_{2}\)ScZnO\({}_{5.5}\) has the same symmetry of the lattice as the orthorhombic perovskite LaScO\({}_{3}\) with the space group _Pnma_. Moreover, no additional superstructure lines were observed in the PXRD patterns, indicating a random distribution of cations in the B-sublattice without any ordering. All doped phases--La\({}_{2}\)Sc\({}_{0.9}\)Mg\({}_{0.1}\)ZnO\({}_{5.45}\), La\({}_{2}\)Sc\({}_{0.9}\)Ca\({}_{0.1}\)ZnO\({}_{5.45}\), and La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\)--also crystallized in orthorhombic symmetry. The lattice parameters are shown in Table 1.  
The change in the lattice parameters correlates with the difference in the radii of the host ions and dopants. After doping with smaller ions, the unit cell parameters slightly decreased for La\({}_{2}\)Sc\({}_{0.9}\)Mg\({}_{0.1}\)ZnO\({}_{5.45}\) and La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\) (rLa\({}^{3+}\) = 1.36 A, rCa\({}^{2+}\) = 1.34 A; rSc\({}^{3+}\) = 0.745 A, rMg\({}^{2+}\) = 0.72 A [51] for corresponding coordination numbers). At the same time, for the composition, the La\({}_{2}\)Sc\({}_{0.9}\)Ca\({}_{0.1}\)ZnO\({}_{5.45}\) Sc\({}^{3+}\) ion was replaced by a significantly larger ion Ca\({}^{2+}\) (r = 1.00 A), which led to the expansion of the lattice. This is also confirmed by the shifts of the diffraction peaks; a shift to smaller angles rep  
Figure 3: (**a**) The XRD patterns of new phase La\({}_{2}\)ScZnO\({}_{5.5}\) (1) and doped phases La\({}_{2}\)Sc\({}_{0.9}\)Mg\({}_{0.1}\)ZnO\({}_{5.45}\) (2), La\({}_{2}\)Sc\({}_{0.9}\)Ca\({}_{0.1}\)ZnO\({}_{5.45}\) (3), La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\) (4); (**b**) shifts of the diffraction peak (111) for investigated phases.  
Figure 2: The scheme of investigations of perovskite La\({}_{2}\)ScZnO\({}_{5.5}\) and doped samples.  
the cell, and a shift towards larger angles corresponds to the contraction of the unit cell. Figure 3b shows this dependence using the diffraction peak as an example (111).  
The lattice-free volume for the doped samples increased in the sequence La\({}_{2}\)ScZnO\({}_{5.5}\)\(-\)La\({}_{2}\)Sc\({}_{0.9}\)Mg\({}_{0.1}\)ZnO\({}_{5.45}\)\(-\)La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\)\(-\)La\({}_{2}\)Sc\({}_{0.9}\)Ca\({}_{0.1}\)ZnO\({}_{5.45}\) (Table 1).
## 5 Experimental Results  
### Simulation Model  
To validate the effectiveness of the proposed hybrid LCC-VSC DC transmission frequency control strategy, a simulation test system was constructed using the electromagnetic transient simulation software PSCAD/EMTDC v4.6.2, as illustrated in Figure 1.  
The detailed parameters of the generator units are provided in reference [26]. The rated value of the DC voltage is 400 kV, with a permissible fluctuation range of \(\pm\)5%. The LCC station adopts fixed active power control with a rated operating power of 350 MW, while theVSC2 and VSC3 stations use adaptive DC voltage droop control, with rated operating powers of 500 MW and 150 MW, respectively. The capacitance of the converter stations is set to C = 2000 uF. The distances from the LCC station and VSC3 station to the VSC2 station are both 200 km, connected by a DC cable with an impedance of 0.005 \(\Omega\)/km. In Area 1, the wind farm has a rated capacity of 500 MVA, and generators G1, G2, G3, and G4 each have a rated capacity of 900 MVA, equipped with speed governor devices. However, only the generator in Area 1, G1, is equipped with AGC. The active power load is 900 MW in Area 1 and 1800 MW in Area 2.
# Spatiotemporal Image Fusion in Remote Sensing
## 3 Synthesis: Challenges and Opportunities
### Important Data Pre-processing Issues to be Considered when Fusing Spatiotemporal Images  
When developing new spatiotemporal image fusion methods, the following issues need to be considered [46; 88]:  
* **Spectral responses of input images have to be unified:** Reconstruction and unmixing spatiotemporal image fusion methods assume that input images have similar spectral information. Therefore, their application is limited, given that the sensors might have different wavelength. When blending information from different remote sensing data sources, we have to spectrally normalize the input sensors to common wavebands [70]. According to [PERSON] et al. [127], the absence of similar wavelength has a low impact on the fusion results when physically-based reflectance methods are used for blending surface reflectance of the input images. Machine learning based spatiotemporal image fusion methods, on the other hand, are less sensitive to similarity between spectral responses of the input images.
* **Co-registration of multi-source input images:** Multi-source images alignment is a very important issue to be considered when fusing them. For example, reported misalignments between Landsat and Sentinel-2 by several pixels need to be carefully addressed when fusing the two input images [125]. Further investigation in the development of automatic solutions for images alignments is highly required [70].
* **Atmospheric corrections:** Radiometric consistency of the multi-source images to be fused might vary because of the presence of clouds and haze, or because of the differences in the illumination and acquisition angles [88]. Therefore, input images have to be radiometrically corrected before fusing them [70] using one of the available existing radiometric corrections techniques such as MODerate spectral resolution TRANSmittance code (MODTRAN) [128]. These techniques can be grouped into two categories, namely absolute and relative techniques. Absolute techniques require information on the sensor spectral profile for sensor calibration and corrections of images for atmospheric effects [129]. Relative radiometric techniques involve either the selection of landscape elements whose reflectance remain constant over time [130; 131] or normalization using regression [132; 133].
## 2 Optimization Models
### Wind Turbine Model and Wind Farm Cost Model  
The real wind farm model studied in this paper is an offshore wind farm that is situated in Northern Europe. As reported in reference [24], the Bonus 1 MW wind turbine is installed in the wind farm, and the wind turbine properties are shown in Table 1 (\(P\) is the individual wind turbine power output and \(U\) is the incoming wind speed magnitude).  
There are mainly two types of objective functions for the study of wind farm optimization in the literature, which are the total power output [25; 26] and the cost of energy [27; 28]. In this paper, [PERSON]'s cost model is introduced for the optimization study and it incorporates a variety of elements that are associated with real wind farm development, such as the market cost, labor cost, etc. [29; 30]. The cost model is given by [31]:  
\[cost=-0.1539\times P_{\rm r}-0.001\times N+2\times P_{\rm r}\times N+0.2504 \tag{8}\]  
where the cost unit is in million dollars. \(P_{\rm r}\) is the wind turbine nominal power and \(N\) is the wind turbine number. With this model, it can be seen that the wind farm cost is associated with two factors: the wind turbine number and wind turbine rated power. Notice that this cost model is a general expression that describes the real wind farm cost and it is not customized for the current wind farm due to a lack of relevant local-market information. However, since the main focus of this paper is to compare the effectiveness of different wind farm design methods, while the influence of cost model on the comparative study outcomes should be trivial, the utilization of the cost model for the current optimization study should be justified.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Rated power & 1000 kW \\ Rotor diameter & 54 m \\ Hub height & 60 m \\ Cut-in wind speed & 3 m/s \\ Rated wind speed & 15 m/s \\ Cut-out wind speed & 25 m/s \\ Turbine power & \(P=0.2963U^{3}\) kW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wind turbine model properties installed in the real offshore wind farm.
# Analysis of Pressure Effect on Three-Dimensional Flame Surface Density Estimation
## 4 Modeling and Approximations of \(\langle\cos\phi\rangle_{s}\)
### Analysis of the Fluctuations of Normal Vectors  
Based on the analysis of a vector by [PERSON] et al. [28], the local flame front normal vector is composed by two components, the mean and fluctuation, which can be written as  
\[n_{i}=\langle n_{i}\rangle_{s}+m_{i}\quad\text{with}\quad\langle m_{i}\rangle_ {s}=0. \tag{13}\]  
Since \(\mathbf{n}\) and \(\mathbf{n}^{\text{2D}}\) are the unit vector, their norms are unity, which will give \(\langle|\mathbf{n}|\rangle_{\mathbf{s}}=1\) and \(\langle|\mathbf{n}^{\text{2D}}|\rangle_{\mathbf{s}}=1\). Written with the index notation, it will give  
\[\langle n_{i}n_{i}\rangle_{s}=1 \tag{14}\]  
Equation (14) is satisfied for both 2D and 3D, i.e., \(i=1,2\) for 2D and \(i=1,2,3\) for 3D. Substituting Equations (6) and (13) into Equation (14) was accompanied by with additional assumptions, discussed in [13; 15], which is listed in Appendix A, it results  
\[\langle\cos\phi\rangle_{s}^{2}+\langle m_{z}m_{z}\rangle_{s}=1 \tag{15}\]  
\(m_{z}\) denotes the fluctuating component of \(n_{z}\). Equation (15) establishes a relationship between the surface-averaged cosine value and the fluctuating component on the transverse direction that is not accessible directly from the measurement plane. If \(\langle m_{z}m_{z}\rangle_{s}\) is assumed to be identical to the fluctuating component in other two directions, i.e., \(\langle m_{z}m_{z}\rangle_{s}\approx\langle m_{x}^{2D}m_{x}^{2D}\rangle_{s}\) or \(\langle m_{z}m_{z}\rangle_{s}\approx\langle m_{y}^{2D}m_{y}^{2D}\rangle_{s}\), then we have  
\[\Sigma=\sqrt{1+\langle m_{x}^{2D}m_{x}^{2D}\rangle_{s}}\Sigma^{2D} \tag{16}\]or  
\[\Sigma=\sqrt{1+\langle m_{y}^{2D}m_{y}^{2D}\rangle_{s}}\Sigma^{2D}. \tag{17}\]  
The models of Equations (10), (12), (16) and (17) are designated as Method1 to Method4, which will be referred to as M1, M2, M3 and M4 in the later sections.
## 4 Frequency Response Function (FRF)  
### Transition from Time to Frequency Domain  
As it is known, dynamic problems can be solved in the time and frequency domain. In the time domain, all quantities are displayed depending on the time \(t\) (s) and, in the frequency domain, depending on the angular frequency \(\omega\) (rad/s) or the time frequency \(f\) (Hz). The dependence between the angular and time frequency is given by the relation:  
\[\omega=2\pi\cdot f,\ f=\frac{\omega}{2\pi}. \tag{8}\]  
Figure 3: Natural modes of the bridge.  
For the transition from the time to the frequency domain, it is most advantageous to use some integral transformations, for example, Fourier, Laplace, Hilbert, or other transformations [8]. The Fast Fourier Transform (FFT, or Discrete Fourier Transform, DFT [8]) is most often used in numerical calculations. After the transformation from the time to the frequency domain, the problem can be solved in the frequency domain (this simplifies the mathematical apparatus, as the differential equations change to algebraic equations), and the results can be transformed back into the time domain. Alternatively, it is possible to analyze dependencies directly dependent on frequency, such as frequency spectra, frequency response functions, or coherence functions.
## 3 Sensitivity of the Intraglottal and Vocal Fold Contact Pressures to Sensor Position
### Results  
#### Validation of Synthetic Vocal Fold Model Kinematics  
The kinematics of the hemi-VF arrangement were first investigated using the silicone VF model with a cover layer of \(E_{c}=0.95\) kPa, which had an onset pressure of 1.6 kPa. The VF had a MPC = 0.75 mm, which corresponded to MPP = 1.45 kPa. Eight images from HSV acquisitions were extracted at a subglottal pressure of \(p_{\text{sub}}=2.15\) kPa over the open phase of the glottal cycle, and are shown sequentially in Figure 12 (see also, Supplementary Material). The mean flow rate was \(Q=409\) mL/s, and the fundamental frequency was \(f_{o}=168\) Hz. The primary point of excursion occurred about the midline, which is common in synthetic VF investigations with symmetric anterior-posterior boundary conditions [33]. A clear convergent-divergent motion is evident, as noted in Figure 12e-g, and is demarcated by the solid and dashed lines that indicate the inferior and superior VF margins, respectively.  
Figure 11: Schematic drawing of the hemi-vocal fold orientation and the Wheatstone Bridge circuit utilized for locating the point of contact.  
A kymogram was extracted along the midline of the VF model, and is plotted in Figure 13 to highlight additional features of the oscillation. Again, a clear phase shift is identified between the superior and inferior VF edge, as denoted by the superimposed lines in the plot. The open quotient (\(OQ\)), defined as \(OQ=T_{\text{open}}/T_{\text{cycle}}\)[44] was found to be \(OQ\simeq 0.74\), where \(T_{\text{open}}\) and \(T_{\text{close}}\) are defined in Figure 13. This value is well within the physiological range that spans \(\sim\)0.3-0.9, with a mean of \(\sim\)0.6 [45; 46; 47], and is a significant advancement over prior modeling efforts that have reported values that are at the upper limit of the range of \(OQ\)[33; 48]. In addition, the VF model presented herein produced a speed quotient, defined as \(SQ=T^{+}/T^{-}\), of \(SQ\simeq 1.6\); \(T^{+}\) and \(T^{-}\) are defined in Figure 13. Again, this is an important distinction of the current model, as prior synthetic modeling efforts typically find \(SQ\) values that are less than 1.0 [33], although physiological values range from \(1.0\leq SQ\leq 3.0\)[46; 49]. Finally, the maximum medial-lateral displacement was measured as 0.74 mm, which is within the physiological range of reported values [50].  
Figure 12: Extraction of eight instances in time, labeled sequentially as (**a**–**h**), throughout one cycle of the synthetic VF oscillation at a subglottal pressure of \(p_{\text{sub}}=2.15\) kPa (see also, Supplementary Video). The location of the superior (\(--\)) and inferior (\(-\)) VF edges are identified.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Quantitative (Holistic) Approach
#### 4.2.1 General Resilience (GR)  
One of the most promising metrics presents an approach aimed at developing a mathematical model capable of examining the functionality of smart ICIs infrastructures. The primary objective is to generate a time-dependent method for estimating infrastructure resilience and utilizing the results for comparison purposes [42]. The outcomes of this method provide detailed analysis for decision makers, supporting improvements in infrastructure resilience; for example, estimating the priority of each phase of an infrastructure (i.e., the recovery phase) enables the more effective application of resilience strategies in that phase.  
Measuring resilience is insufficient when analyzing an individual infrastructure's capability. As such, a unique model has been proposed that integrates various resilience capabilities (i.e., absorptive, adaptive, and restorative capability) across multiple phases (i.e., initial steady, disruptive, recovery, and new steady phase). This distinctive method can assess infrastructure performance over time based on system performance. To manage the progress of the unified resilience method, the representation of all infrastructures in Figure 6 is examined across four phases. Different approaches are used for each phase to reflect system performance, as summarized in Table 1. Subsequently, all values are combined into a single measurement of performance (MOP) value. This method, called General Resilience (GR), is represented as follows:  
\[GR=f(R,RAPI_{DP},RAPI_{RP},TAPL,RA)=R*(\frac{RAPI_{RP}}{RAPI_{DP}})*(TAPL)^{-1}*RA \tag{3}\]  
As a case study for their model, the Swiss electric power supply system (EPSS) was selected as a representative application to demonstrate the utility of the suggested quantitative method. The case study will be discussed in greater detail in the next section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **Time Scope** & **Capabilities** & **Measurements** \\ \multicolumn{4}{c}{**Measurements**} \\ \hline Original Steady phase & \(t<t_{D}\) & Susceptibility & Susceptibility \\ \hline Disruptive Phase & \(t_{D}\leq t<t_{D}\) & Absorptive Capability & R \\  & & \(RAPI_{DP}\) \\  & & \(PL_{DP}\) \\ \hline Recovery Phase & \(t_{R}\leq t<t_{NS}\) & Adaptive Capability Restorative & \(RAPI_{RP}\) \\  & & \(PL_{RP}\) \\ \hline New Steady Phase & \(t\geq t_{NS}\) & Recovery Capability & \(RA\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of different resilience phases [42].  
Figure 6: Illustration of essential resilience capabilities [42].