esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
In the first instances of customers, scenario 1 obtained average savings of more than 30%, and the other instances were reduced by half of these savings; the advantage of this scenario is that drones are able to ship any product, and we verified that the use of drones is cheaper than the traditional approach and one can have logistic advantages in package delivery. The second-best scenario is the 6, where savings were identified by contemplating few clients of more than 20%. As we increased the number of customers, the savings were decreased by half but without much difference between the last instances; in the instance of four customers, the mixed modern approach achieved good results. The best third scenario is 4; we can see in the graph that the last instances have almost the same level of savings, achieving a better result than scenario 2; this is because this scenario, when experiencing with 20, 30 and 40 clients, has less participation to use drones and therefore, the level of savings is low. According to the experimentation, it is concluded that greater savings are obtained from small packages; it should be noted that most of the packages that the university needs are of that size, and when taking into account mixed packages, the savings are reduced by almost 50%; it was shown that the use of drones can have savings in any scenario. The use of drones offers logistical advantages in the distribution process, according to the scenarios, savings are obtained in several examples; it is important to understand that the traditional approach needs improvement. In the experiments,CO\({}_{2}\) was also analyzed in the routes, INECC [31] describe the truck used in the experiments generates 255 g/km of carbon dioxide, a comparison of the mileage saved when using drones was made, the comparison is presented in Figure 5.  
Figure 4: Total savings graph by scenario. Starting from instances of big customers, a route of 1143.1311 km was obtained, while using drones decreased to 871.982 km, where the difference lies in 271.1491 km; the use of UAVs allowed the km traveled by the truck to be reduced; analyzing this factor, the CO\({}_{2}\) that was saved was of 69.143 g per km. We conclude that there are considerable savings when using Unmanned Aerial Vehicles, there are different percentages of savings according to the size of the packages.
# Spectrum Based Power Management for Congested IoT Networks
## 5 Test Results and Analysis  
After implementing our method in real environment, these were the 4 significant results of our experiment: (1) interference and channel management for all IoT devices from FREDEVPLA is simple and easy (2) successfully measured how much power consumed in connected and sleep status of IoT device (3) during the lifetime of an IoT device most of its power is consumed in sleep mode and (4) power consumption of IoT device minimized significantly. We executed 20 successive tests using 20 IoT sensors. During each experiment, each IoT sensor would send around 100 data packets. All the information displayed below is the randomly selected tests to send 100 packets per sensor.
# Plant Growth Regulators Improve the Production of Volatile Organic Compounds in Two Rose Varieties
###### Abstract  
The study focused on the influence of the plant growth regulators (PGRs) benzyladenine (BA) and naphthalene acetic acid (NAA) on the production of volatile organic compounds (VOCs) from the flowers of two modern rose varieties, Hybrid Tea and Floribunda. Thirty-six plants of Hybrid Tea and Floribunda were tested. Benzyladenine and naphthalene acetic acid were applied at 0, 100 and 200 mg/L to both rose varieties. Gas chromatography, coupled with flame ionization detection and mass spectrometry, was used to analyze and identify the volatile organic compounds from the flowers. A three-phase fiber 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane was used to capture VOCs, at 2, 4 and 8 weeks, and 4 weeks was selected as it had the highest peak area. In total, 81 and 76 VOCs were detected after treatment of both rose varieties with BA and NAA, respectively. In addition, 20 compounds, which had significant differences between different treatments, were identified from both rose varieties. The majority of VOCs were extracted after the application of 200 mg (BA and NAA) /L of formulation, and four important compounds, cis-muroda-4(141)5-diene, y-candinence, y-muroplane and premyl acetate, increased significantly compared to the controls. These compounds are commercially important aroma chemicals. This study used the rapid and solvent-free SPME method to show that BA and NAA treatments can result in significant VOC production in the flowers of two rose varieties, enhancing the aromatic value of the flowers. This method has the potential to be applied to other valuable aromatic floricultural plant species.  
benzyladenine; naphthalene acetic acid; Hybrid Tea; Floribunda; HS-SPME; GC-MS 16 November 2018; Accepted: 29 January 2019; Published: 31 January 2019
## 3 Results and Discussion  
### Initial Characterization of the Adsorbent  
Figure 1 presents the textural characterization for the activated carbon used in the present study. The specific surface area and pore distribution of the materials were determined through N\({}_{2}\) adsorption/desorption. Subsequently, the specific values obtained from this characterization step are shown.  
As shown in Figure 1, the samples do not display a steep slope, indicating that adsorption does not significantly increase at high values of P/P\({}_{0}\). Additionally, there is a trend toward stabilization of adsorption at these values. All curves exhibited distinct paths for adsorption and desorption, which is characteristic of hysteresis [48]. According to [PERSON] and [PERSON] [49], the presence of hysteresis suggests a tendency toward mesopore formation.  
The specific surface area of the granular activated carbon used in this study was estimated at 582.60 m\({}^{2}\)/g, comparable to other commercial activated carbons reported in previous studies [36; 37; 46]. In terms of surface area, volume, and mean pore diameter, the sample exhibited values of 348.67 m\({}^{2}\)/g, 0.13 cm\({}^{3}\)/g, and 4.83 A, respectively, indicating that it is suitable for microporous materials [48]. According to the classification by the International Union of Pure and Applied Chemistry (IUPAC), the isotherm curves displayed behavior characteristic of type I isotherms, which indicates micropore adsorption. Based on the empirical classification of hysteresis curves, the observed hysteresis can be classified as type H4, typical of materials with ordered and uniform pores, often found in complex materials that contain both micropores and mesopores.  
The XRD profile of the adsorbent before the adsorption of pollutants is illustrated in Figure 2. Two peaks of graphite carbon diffraction can be observed in the reflections at angles of 20 near 25\({}^{\circ}\) and 45\({}^{\circ}\), indicating that an amorphous phase is formed, and the activated carbons are ordered by the stacking of micrographs [50].  
Figure 3 shows the FTIR spectrum of the activated carbon before adsorption process, in terms of percentage of transmittance (%T) and wave number (cm\({}^{-1}\)).  
From Figure 3 it is possible to observe peaks between the 2300 and 2000 cm\({}^{-1}\) bands and also small changes between the 1700 and 1500 cm\({}^{-1}\) lengths. The 2360 and 1645 cm\({}^{-1}\) peaks are indicative of carboxylic structures (C=O), while the 1629 cm-1 band is related to the presence of C=C in the aromatic rings, and 1558 cm\({}^{-1}\) corresponds to the elongation of the C=O bonds, present in the structure of the solid. The absence of significant peaks  
Figure 1: N\({}_{2}\) adsorption/desorption isotherms for the activated carbon.  
at 2800-3000 cm\({}^{-1}\) may indicate little presence of aliphatic residues or adsorbed organic compounds, which is expected before the adsorption process [48].
## 3 Application to the Case Study
### LASA Equilibrium Solution
To obtain the tangential and the normal components of the horizontal force per unit length that is transmitted through the steps to the wall, Equations (12) and (13) can be written as a function of \(\alpha\):  
\[p_{ni}(\alpha)=\rho_{i}(\alpha)S_{i}(\alpha), \tag{18}\]  
\[p_{t,tot}=\sum_{i}p_{it}(8\pi), \tag{19}\]  
The corresponding plots are reported in Figure 6d. The maximum values of the normal and tangential components, evaluated at the end of the stair, are: \(p_{n,tot}=113.0\) kN\(/\)m and \(p_{t,tot}=5.1\) kN\(/\)m, and the value of the modulus of their resultant is \(p_{tot}=113.1\) kN\(/\)m.  
Figure 6: (**a**) Projected stair and line geometry for the spiral stair of Nisida; Plots of (**b**) thrust \(S_{i}(\alpha)\), (**c**) normal force \(N_{i}(\alpha)\), (**d**) tangential \(p_{it}(\alpha)\) and normal \(p_{ni}(\alpha)\) components of the stress. Referring to Figure 7, the stresses in the wall and in the bottom tread at the end of the stair are checked using Equations (21) and (22):  
\[\sigma_{w,max}=p_{tot}L_{sw}\frac{1}{L_{sw}b_{m}}. \tag{20}\]  
\[\sigma_{step,max}=N_{max}\frac{1}{L_{step,j}b_{m}}. \tag{21}\]  
Their values are \(\sigma_{w,max}=0.54\) MPa and \(\sigma_{step,max}=1.72\) MPa, respectively. If we consider that both the stair and the wall are made of tuff with an average crushing limits of 20 MPa [37] and an admissible stress value of \(\sigma_{adm}=20/3\) MPa, by using  
\[\chi_{wall}=\frac{\sigma_{adm}}{\sigma_{w,max}},\quad\chi_{step}=\frac{\sigma _{adm}}{\sigma_{step,max}}, \tag{22}\]  
we have a loading capacity ratio for the wall and for the step respectively of \(\chi_{wall}=12.3\) and \(\chi_{step}=3.9\).
## 3 Results
### Linear Conservation Laws
#### 3.1.2 System Case  
**Problem 2**.: _In Equation (19), let \(\underline{U}\) consists two components and each \(\underline{A}\) be a \(2\times 2\) matrix:_  
\[\underline{U}(\vec{x},t)=\begin{bmatrix}U_{1}(x,y,z,t)\\ U_{2}(x,y,z,t)\end{bmatrix},\qquad\underline{A}^{x}=\begin{bmatrix}6&-2\\ -2&6\end{bmatrix},\qquad\underline{A}^{y}=\underline{A}^{z}=\begin{bmatrix}0 &0\\ 0&0\end{bmatrix},\]  
_The following boundary conditions_  
\[\underline{U}(x=0,y,z,t)=\begin{bmatrix}0\\ 0\end{bmatrix}=:\underline{U}_{\mathrm{L}},\qquad\underline{U}(x=4,y,z,t)= \begin{bmatrix}12\\ -4\end{bmatrix}=:\underline{U}_{\mathrm{R}},\]  
_and the initial condition_  
\[\underline{U}(x,y,z,t=0)=\underline{U}_{\mathrm{R}}\]  
_are applied._  
To solve this problem analytically, we first obtain the eigenvalue decomposition of \(\underline{A}^{x}\), which is  
\[\underbrace{\begin{bmatrix}6&-2\\ -2&6\end{bmatrix}}_{\underline{A}^{x}}=\underbrace{\begin{bmatrix}1&1\\ -1&1\end{bmatrix}}_{\underline{R}^{x}}\underbrace{\begin{bmatrix}8\\ 4\end{bmatrix}}_{\underline{A}^{x}}\underbrace{\begin{bmatrix}1/2&-1/2\\ 1/2&1/2\end{bmatrix}}_{\underline{(R}^{x})^{-1}}\]  
By introducing the characteristic variable \(\underline{V}:=(\underline{R}^{x})^{-1}\underline{U}\), which means  
\[\begin{bmatrix}V_{1}\\ V_{2}\end{bmatrix}:=\begin{bmatrix}1/2&-1/2\\ 1/2&1/2\end{bmatrix}\begin{bmatrix}U_{1}\\ U_{2}\end{bmatrix}=\frac{1}{2}\begin{bmatrix}U_{1}-U_{2}\\ U_{1}+U_{2}\end{bmatrix},\]  
the boundary conditions become  
\[\underline{V}_{\mathrm{L}}=(\underline{R}^{x})^{-1}\underline{U}_{\mathrm{L}}= \begin{bmatrix}0\\ 0\end{bmatrix},\qquad\underline{V}_{\mathrm{R}}=(\underline{R}^{x})^{-1} \underline{U}_{\mathrm{R}}=\begin{bmatrix}8\\ 4\end{bmatrix},\]  
and the system can be decoupled:  
\[\begin{bmatrix}\partial_{t}+8\partial_{x}\\ \partial_{t}+4\partial_{x}\end{bmatrix}\begin{bmatrix}V_{1}\\ V_{2}\end{bmatrix}=\begin{bmatrix}0\\ 0\end{bmatrix}.\]  
We can then solve these two scalar problems independently, which gives  
\[V_{1}(x,y,z,t)=\begin{cases}0&x<8t\\ 8&x>8t\end{cases},\qquad V_{2}(x,y,z,t)=\begin{cases}0&x<4t\\ 4&x>4t\end{cases}.\]  
The solution of Problem 2 can be obtained by \(\underline{U}=\underline{R}^{x}\underline{V}\), which gives  
\[\underline{U}(\vec{x},t)=\begin{bmatrix}V_{2}+V_{1}\\ V_{2}-V_{1}\end{bmatrix}=\begin{cases}\underline{U}_{\mathrm{L}}&x/t<4\\ \underline{
# Optimization of Uplift Piles for a Base Plate Considering Local Anti-Floating Stability
## 2 Optimization Method  
### Problem Description  
Figure 2 shows the force analysis of the base plate. Water buoyancy and earth pressures are _Pw_ and _Ps_, respectively. The pulling force provided by each uplift pile is _Fpi_. The column loads of the superstructure is _F\({}_{N}\)_. The base plate will generate differential deformation under multiple forces. The deformation degree of the base plate can be measured by its total strain energy \(C\). Strain energy is the energy stored due to deformation, and it is potential energy that reflects the degree of deformation. From the aspect of improving the local anti-floating stability of the base plate, the optimization goal is as follows: under the constraint conditions, strain energy \(C\) is as small as possible by changing the design parameters of the uplift piles. Therefore, strain energy \(C\) can be used as the optimization objective function in the BESO method and expressed as follows:  
\[C=\int\mathbf{f}^{\text{int}}\mathbf{d}\mathbf{u} \tag{1}\]  
where \(\text{f}^{\text{int}}\) is the internal force vector acting on the base plate element, and \(\mathbf{u}\) is the displacement vector of the base plate element.  
The BESO process in this study is based on the following assumptions. The soil is homogeneous and isotropic. Liquid-solid coupling is not considered. The pile is assumed to be a rigid body. The frictional resistance on the pile side varies linearly with the length of the pile.  
Figure 2: Force analysis of a base plate.
# Development of Wearable Textile MIMO Antenna for Sub-6 GHz Band New Radio 5G Applications
## 6 Comparison of Existing Systems and Proposed Antenna  
Table 3 provides a comparison between various parameters of different antennae, including size, frequency range, gain, and isolation, including the proposed system and existing systems.  
Regarding the existing MIMO antennas for portable wireless applications with specific outcomes, each is different from the others in terms of its size or operating frequency range. If we focus on one particular antenna parameter, then we might not even obtain ideal values for other antenna parameters. There is an existing MIMO antenna that has dimensions of 40 \(\times\) 70 mm and the gain achieved by this antenna is 4.4 dB in the sub-6 GHz band from 4.4 GHz to 8 GHz. Here, we propose a MIMO antenna with dimensions of 40 \(\times\) 50 mm\({}^{2}\), and we achieved 6.74 dB of gain, which is greater than the average gain and provides better return loss, at around \(-\)45 dB, and better directivity. With regard to antenna size, this antenna is smaller, providing improved results within its target frequency range of approximately 4.35 GHz to 7 GHz, making it well suited to 5G applications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{\begin{tabular}{c} **Dimensions** \\ **(mm\({}^{2}\))** \\ \end{tabular} } & \begin{tabular}{c} **Dielectric** \\ **Permititivity** \\ **(\(\epsilon_{R}\))** \\ \end{tabular} & \begin{tabular}{c} **Operating** \\ **Frequency (GHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Peak** \\ **Gain** \\ **(dB)** \\ \end{tabular} &
\begin{tabular}{c} **Isolation** \\ **(dB)** \\ \end{tabular} \\ \hline
[2] & 40 \(\times\) 70 & 1.6 & 2.4–8.0 & 4.4 &
\begin{tabular}{c} Min 22 \\ Max. 53 \\ \end{tabular} \\
[8] & 30 \(\times\) 30 & 10.2 & 2.37–2.52 & - & \textgreater{}20 \\
[9] & 38.1 \(\times\) 38.1 & 4.4 & 2.3–2.8 & 2.79 & 12 \\
[10] & 92.3 \(\times\) 101.6 & 1.3 & 5.147–5.863 & 5.8 & 20 \\  & & & 3.3–3.6 & 35 \\
[11] & 40 \(\times\) 40 & 1.7 & 4.5–5 & - & \textgreater{}17 \\  & & & 3.6–7 (simulated) & \textgreater{}12 \\ In this paper & 40 \(\times\) 50 & 1.6 & 4.35–7 (measured) & 6.5 & \textgreater{}28.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of existing systems and the proposed antenna.
# PTMD Control on a Benchmark TV Tower under Earthquake and Wind Load Excitations
## 6 Vibration Control with PTMD
### Wind Load Excitation Cases
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Resposes** & **Uncontrolled** & **TMD Controlled** & **PTMD Controlled** \\ \hline \multirow{4}{*}{Mast top (_long axis_)} & Peak displacement (m) & 1.128 & 0.742 & 0.736 \\  & RMS displacement (m) & 0.376 & 0.251 & 0.251 \\  & Peak acceleration (m/s\({}^{2}\)) & 5.445 & 3.913 & 3.857 \\  & RMS acceleration (m/s\({}^{2}\)) & 1.713 & 1.155 & 1.145 \\ \hline \multirow{4}{*}{Tower top (_long axis_)} & Peak displacement (m) & 0.081 & 0.064 & 0.064 \\  & RMS displacement (m) & 0.028 & 0.022 & 0.022 \\  & Peak acceleration (m/s\({}^{2}\)) & 0.070 & 0.065 & 0.065 \\  & RMS acceleration (m/s\({}^{2}\)) & 0.023 & 0.018 & 0.018 \\ \hline \multirow{4}{*}{Mast top (_short axis_)} & Peak displacement (m) & 0.726 & 0.491 & 0.487 \\  & RMS displacement (m) & 0.271 & 0.164 & 0.164 \\ \cline{1-1}  & Peak acceleration (m/s\({}^{2}\)) & 3.558 & 2.662 & 2.623 \\ \cline{1-1}  & RMS acceleration (m/s\({}^{2}\)) & 1.289 & 0.773 & 0.766 \\ \hline \multirow{4}{*}{Tower top (_short axis_)} & Peak displacement (m) & 0.039 & 0.033 & 0.033 \\ \cline{1-1}  & RMS displacement (m) & 0.012 & 0.010 & 0.010 \\ \cline{1-1}  & Peak acceleration (m/s\({}^{2}\)) & 0.031 & 0.037 & 0.037 \\ \cline{1-1}  & RMS acceleration (m/s\({}^{2}\)) & 0.011 & 0.012 & 0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of peak and RMS responses under Typhoon Nanmade excitation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Resposes** & **Uncontrolled** & **TMD Controlled** & **PTMD Controlled** \\ \hline \multirow{4}{*}{Mast top (_long axis_)} & Peak displacement (m) & 0.802 & 0.637 & 0.636 \\  & RMS displacement (m) & 0.278 & 0.238 & 0.238 \\  & Peak acceleration (m/s\({}^{2}\)) & 3.138 & 2.375 & 2.346 \\  & RMS acceleration (m/s\({}^{2}\)) & 0.987 & 0.788 & 0.781 \\ \hline \multirow{4}{*}{Tower top (_long axis_)} & Peak displacement (m) & 0.126 & 0.092 & 0.092 \\  & RMS displacement (m) & 0.044 & 0.034 & 0.034 \\  & Peak acceleration (m/s\({}^{2}\)) & 0.065 & 0.053 & 0.053 \\  & RMS acceleration (m/s\({}^{2}\)) & 0.023 & 0.018 & 0.018 \\ \hline \multirow{4}{*}{Mast top (_short axis_)} & Peak displacement (m) & 0.502 & 0.397 & 0.396 \\  & RMS displacement (m) & 0.190 & 0.150 & 0.150 \\ \cline{1-1}  & Peak acceleration (m/s\({}^{2}\)) & 2.288 & 1.558 & 1.543 \\ \cline{1-1}  & RMS acceleration (m/s\({}^{2}\)) & 0.758 & 0.526 & 0.522 \\ \hline \multirow{4}{*}{Tower top (_short axis_)} & Peak displacement (m) & 0.036 & 0.032 & 0.032 \\ \cline{1-1}  & RMS displacement (m) & 0.014 & 0.012 & 0.012 \\ \cline{1-1}  & Peak acceleration (m/s\({}^{2}\)) & 0.019 & 0.022 & 0.022 \\ \cline{1-1}  & RMS acceleration (m/s\({}^{2}\)) & 0.008 & 0.008 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of peak and RMS responses under Typhoon Haima excitation. Figure 10: _Cont._  
Figure 9: Generated wind speed on top of Canton Tower.
## 2 Methodology
### Interpolation Model
#### 2.1.2 LSM  
LSM is based on the coefficient representing the trend of the corrections, and the coefficients are calculated by least-squares that is performed on data that are generated by the reference network. Taking the first-order form as an example, the mathematical model is as follows [15]:  
\[A_{i}=\alpha_{1}\Delta X_{i,u}+\alpha_{2}\Delta Y_{i,u}+\alpha_{3} \tag{2}\]  
where \(A_{i}\) represents the atmospheric delay, \(i\) represents the station; \(\Delta X_{i,u}=X_{i}-X_{u}\), \(\Delta Y_{i,u}=Y_{i}-Y_{u}\) and \(X\), \(Y\) are the plane coordinates of the station; and \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) represent model coefficients. LSM can choose whether to add an elevation factor, and the fitting order can be selected in first-order or second-order forms to adapt to different application environments. The interpolation coefficient is related to the position and atmospheric delay of the reference stations.
# Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales
## 3 System Optimization
### Objective Function  
This paper comprehensively considers renewable generation uncertainty, power system security, and the carbon emissions of fossil generation, and then optimizes the hybrid energy storage installation capacity with the goal of minimum generation cost.  
\[\text{min}F_{m}=C_{W}\times E_{W}+C_{PV}\ \times E_{PV}+C_{d}\times E_{d} \tag{15}\]  
where \(E_{W}\), \(E_{PV}\), and \(E_{d}\) are the electrical energy generated by wind power, a PV system, and diesel generators, respectively. \(C_{W}\), \(C_{PV}\), and \(C_{d}\) represent the levelized cost of energy (LCOE) of a wind turbine system, PV system, and diesel generator system. It is worth noting that the LCOE considers various factors such as the depreciation of the system fixed assets, the operating costs of the project, the maintenance costs, and so on. Therefore, the LCOE can be used to represent the total costs of generating per-unit electricity (kWh) of different energy sources. Equation (16) is the mathematical expression of LCOE:  
\[LCOE=\left(\sum_{i=0}^{n}\left(I_{i}+M_{i}+F_{i}\right)/\left(1+s\right)^{i} \right)/\left(\sum_{i=0}^{n}\left(E_{i}\right)/\left(1+s\right)^{i}\right) \tag{16}\]  
where \(I_{i}\) is the investment expenditures in the year \(i\), \(M_{i}\) is the operations and maintenance expenditures in \(i\), and \(F_{i}\) and \(E_{i}\) are the fuel expenditures and electricity generation in \(i\), respectively. \(s\) represents the discount rate, and \(n\) is the expected plant lifetime.
# Magnetic Resonance-Electrical Properties Tomography by Directly Solving Maxwell's Curl Equations
## 2 Theory and Method
### Simulation Study  
The proposed EPT method was first evaluated using a simple phantom. The phantom was composed of an inner compartment with a diameter of 15 mm and an outer compartment with a diameter of 40 mm. The height of the inner and outer compartments was 50 mm. The relative permittivity for the inner and outer compartments was 78.5, and the conductivities were 0.43 S/m and 0.78 S/m, respectively. After the phantom evaluation, the head of the realistic human model [PERSON], from the virtual family [32], was used to study the proposed EPT method. The initial model resolution of 1 mm\({}^{3}\) was re-meshed as 2 mm\({}^{3}\), as a trade-off between accuracy and computational efficiency of the forward solver. The electrical properties of the brain tissues at 298 MHz (the operating frequency of 7T MRI) were determined based on the reports from Reference [33]. The head model was placed at the center of the coil (Figure 2). To assess the noise robustness of the proposed method, white Gaussian noise (GWN) was added to the simulated \(B_{1}^{+}\) field data to achieve signal-to-noise ratios (SNR) between 15 and 55 dB [34].