esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Wind Tunnel Tests
### Aerodynamic Characteristics of Pipeline Girder with Ice Accretion  
Aerodynamic force coefficients of section with various ice classes are shown in Figure 25. Because of the asymmetry of the section, the aerodynamics force coefficients were not symmetric between \(\alpha=-12^{\circ}\) to \(12^{\circ}\). Aerodynamic force coefficients were significantly affected by ice class. \(C_{D}\) increased with increasing ice class, especially for the C3 ice class, which can be ascribed to the increase in the windward area after icing. Aerodynamic force coefficients increased after the pipeline with icicles. \(C_{L}\) and \(C_{M}\) increased after the guardrail with ice, especially the \(C_{M}\). \(C_{L}\) and \(C_{M}\) were slightly affected by the ice on guardrail and grate plate. The influence of \(\alpha\) on \(C_{D}\) for C1 ice class was smaller than that for C2-C4 ice class. \(C_{L}\) decreased, and then increased with increasing \(\alpha\) for ice-free section, while \(C_{L}\) decreased with increasing \(\alpha\) for ice-accreted section. For C0 and C1 ice class, \(C_{M}\) decreased with increasing \(\alpha\) in the range of \(\alpha=-12^{\circ}\) to \(-3^{\circ}\), and then was almost invariant in the range of \(\alpha=-4^{\circ}\) to \(12^{\circ}\). \(C_{M}\) decreased with increasing \(\alpha\) for C2 ice class. Meanwhile, \(C_{M}\) decreased, then increased, and finally decreased with increasing \(\alpha\) for C3 and C4 ice class.  
Figure 24: Pipeline girder sectional models: (**a**) ice-free, (**b**) crescent-shaped ice for C4, (**c**) D-shaped ice for C1, and (**d**) sector-shaped ice for C4.  
Figure 25: _Cont._Aerodynamic force coefficients of section with various ice types for C4 ice class were shown in Figure 26. Ice accretion had a negligible effect on the variation of the aerodynamic force coefficients with \(\alpha\). \(C_{L}\) of the ice-free model gradually increased with increasing \(\alpha\) in the range of \(\alpha=-12^{\circ}\) to \(-2^{\circ}\) and then gradually decreased with increasing \(\alpha\) in the range of \(\alpha=-2^{\circ}\) to \(12^{\circ}\). \(C_{L}\) for the pipeline with D-shaped ice was almost invariant in the range of \(\alpha=-12^{\circ}\) to \(-3^{\circ}\) and then gradually decreased with increasing \(\alpha\). The \(C_{L}\) values for pipelines with crescent- and sector-shaped ice gradually decreased with increasing \(\alpha\). The \(C_{L}\) and \(C_{M}\) values for the pipeline with D-shaped ice were larger than those with crescent- and sector-shaped ice. In conclusion, the aerodynamic force coefficients of the iced sections were much higher than those of the ice-free one, which may be ascribed to the large windward area and the asymmetry of the ice-accreted section.  
Figure 25: Aerodynamic force coefficients of section with various ice classes (\(U=15.5\) m/s): (**a**) pipeline with crescent-shaped ice, (**b**) pipeline with D-shaped ice, (**c**) pipeline with sector-shaped ice.
## 3 Results and Discussion
### Analysis of Observation Well Water Level Decline Patterns  
Four observation wells (Well 2020-Taiguan2, Well 2022-Taiguan3, Well 2020-Taiguan4, and Well 2022-Taiguan5) were selected to monitor the variation in water levels during the drainage process. Among them, the water levels in Well 2020-Taiguan4 and Well 2020-Taiguan2 gradually decreased during drainage, with the rate of decline slowing over time (Figure 9a,c). However, in Well 2022-Taiguan3, the water level significantly declined between June 2022 and January 2023, followed by a slight recovery after January 2023 (Figure 9b). This phenomenon occurred because deep coal seam mining disturbed the limestone surrounding Well 2022-Taiguan3, inadvertently connecting the observation well to a karst water channel located farther from the drainage well. Within the radius of influence, areas farther from the drainage well typically exhibited higher water levels, leading to the observed water level recovery in Well 2022-Taiguan3. Similarly, the water level in te Well 2022-Taiguan5 gradually decreased from June 2022 to October 2022 during the drainage process, albeit at a slower rate. However, starting from October 2022, the rate of water level decline significantly accelerated (Figure 9d). This acceleration was also attributed to the connection established between Well 2022-Taiguan5 and the drainage well through karst water channels during deep coal seam mining.
## 2 Materials and Methods
### Design of the 90\({}^{\circ}\) Quadrature Branch Line Coupler  
The quadrature branch line coupler (BLC) is a symmetric coupler and has a 3 dB coupling factor with phase shift of 90\({}^{\circ}\) between the output ports [60]. The 90-degree BLCs consist of two input ports and two output ports, with a phase difference of 90 degrees be-tween the output ports. Each BLC is constructed by utilizing two horizontal and two vertical microstrip lines. These lines have a length of \(\lambda_{g}/4\) and are characterized by respective characteristic impedances of \(Z_{0}/\sqrt{2}\) and \(Z_{0}\). The geometry of the branch line coupler is shown in Figure 3a. The BLC was designed for 5.5 GHz using the similar parameters of the substrate. The dimensions of the branch line coupler were determined using Equations (1)-(4) at 5.5 GHz [61]:  
\[Z_{0}=\left\{\begin{array}{cc}\frac{60}{\varepsilon_{eff}}\,ln\left(8\frac{ h}{W}+\frac{1}{8}\frac{W}{W}\right)&\frac{W}{h}\leq 1\\ \frac{120}{\varepsilon_{eff}}&\\ \frac{W}{W}+1.393+0.667\,ln\left(\frac{W}{h}+1.44\right)&\frac{W}{h}\geq 1 \end{array}\right. \tag{1}\]  
\[ZW=\left\{\begin{array}{cc}\frac{8\,ln\,\varepsilon^{h}}{\varepsilon^{2h}} &\\ \frac{2h}{\pi\left(B-1-ln\left(2B-1+\frac{\varepsilon_{eff}}{2\varepsilon_{r}} \left(ln\left(B-1\right)+0.39-\frac{0.61}{\varepsilon_{r}}\right)\right) \right)}&\frac{W}{h}\leq 2\\ \frac{W}{h}\geq 2\end{array}\right\} \tag{2}\]  
\[A=\frac{Z_{0}}{60}\sqrt{\frac{\varepsilon_{r}+1}{2}}+\frac{\varepsilon_{r}-1} {\varepsilon_{r}+1}(0.23+\frac{0.11}{\varepsilon_{r}}) \tag{3}\]  
\[\varepsilon_{eff}=\frac{\varepsilon_{R}+1}{2}+\frac{\varepsilon_{R}-1}{2} \left[\frac{1}{\sqrt{1+12\left(\frac{h}{W}\right)}}\right] \tag{4}\]  
where \(W\) is the width of the line, \(h\) is the height of the substrate, \(\varepsilon_{r}\) is the dielectric constant of the substrate, \(Z_{0}\) is the input impedance of the port, and \(\varepsilon_{eff}\) is the effective dielectric constant. The length of the line (\(\lambda_{g}\)) is calculated using Equation (5):  
\[\lambda_{g}=\lambda\,\surd\in_{eff} \tag{5}\]  
where \(\lambda\) the wavelength in the free space, Figure 3b is illustrates the proposed BLC.  
Figure 3: The 90\({}^{\circ}\) hybrid branch line coupler’s (**a**) geometric layout and (**b**) proposed layout.
# ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management
## 4 Performance Evaluations
In Figure 10, the average task failure rate is shown with respect to the mobile user preference levels. That is, the simulation was performed using the mobile user preference levels that adjust the frequency of mobility based on the time the mobile user dwells in the access MECS. We set the mobile user preference level as 1 (low) and 3 (high) and the results are given in Figure 10a,b, respectively. Figure 10a shows the average task failure rate when the movement of mobile user frequency is low. Since the proposed scheme manages access MECS as the concept of Zone, it is possible to provide the low average task failure even when the mobile user moves to the inside or outside of the Zone. In other words, if the mobile user is staying in a specific access MECS for a long time, the mobile user can receive the task result with a low failure rate because there is enough time to receive the task result being performed from the access MECS that the mobile user was previously located. On the other hand, Figure 10b shows the average task failure rate when the movement of mobile user frequency is high. If the mobile user frequently moves, the mobile user frequently requests the task migration from the previously located access MECS and moves to other access MECS. Therefore, the failure rate of the high-mobile frequency is much higher than that of the low-mobile frequency. When the mobile user frequently moves, the time for the mobile user to stay in a particular access MECS is relatively small. That is, in this case, since the mobile user frequently requests the task migration from the previously located access MECS and moves to the other access MECS, the failure rate is increased as compared with the case where the mobile frequency is low. In other words, since the proposed scheme manages access MECS as the concept of Zone, mobile user can retransmit task migration request to access MECS, which the mobile user was previously located. It is possible to provide continuous service to mobile users even if mobile user moves frequently. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline User Mobility Model & Nomadic Mobility Model \\ Number of Mobile User & 50–400 \\ Number of Mobile User Preference Level & 3 \\ Probability of Selecting a Mobile User Preference Level & equal (1/3) \\ Number of access MECS per Place & 1 \\ VM Processor Speed (MIPS) per access MECS & 1000 \\ Average Task Size (MI) & 2000 \\ WLAN Bandwidth (Mbps) & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 9: Average service time for various task sizes: (**a**) 500 MI task size; and (**b**) 3000 MI task size.
## 1 Introduction
The proposed multicast technique was evaluated through simulations and compared with the progressively adapted sub-tree in dynamic mesh (PAST-DM) and the modified secure zone routing protocol (MSZRP). The simulation results showed that the ZSM is excellent regardless of the node movement speed, the number of sources, the number of group members, and the size of the network. In addition, ZSM showed excellent results by keeping the routing load low in the presented scenario compared to PAST-DM and MSZRP. The structure of this paper is as follows. Related work is presented in Section 2. Section 3 describes the zone-based secure multicast technique proposed in this paper. In Section 4, the performance of the proposed technique through experiments is confirmed. The paper is concluded in Section 5, where future directions are discussed as well.
###### Abstract
Additionally, liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-QTOF) has been used to identify authenticity markers in black seed, pumpkin seed, evening primrose, hemp, and milk thistle oils [20]. Other methods include differential scanning calorimetry for detecting high oleic sunflower oil in extra virgin olive oil [21], non-thermal plasma for evaluating olive oil adulteration [22], and dielectric spectroscopy for identifying sesame oil adulteration [23]. Conventional methods have traditionally been applied for edible oil authentication and determination of important quality parameters, such as fatty acid composition, free fatty acids, peroxide value, iodine value, and p-anisidine value. However, these techniques also suffer from significant disadvantages, such as requiring exhaustive sample preparation, use of complex and expensive equipment, and evaluation of each parameter individually, making them impractical for industrial applications. In contrast, vibrational spectroscopy has emerged as a highly promising alternative due to its numerous advantages, such as being cost-effective, user-friendly, requiring minimal or no sample preparation, being minimally destructive to samples, needing only small sample volumes, and providing structural information that serves as a molecular fingerprint of the sample [24; 25]. When combined with multivariate chemometric data analysis, vibrational spectroscopy enables the reliable and complete profiling of edible oils, thereby allowing for the simultaneous assessment of several quality parameters. Several studies have focused on the authentication of poppy seed oil, such as that conducted by [PERSON] et al., in which SPME-GC-MS was used to determine sunflower oil adulteration in poppy seed oil. [PERSON] et al. 's method using R-pinene as a marker compound successfully estimated the adulteration of sunflower oil in poppy seed oil within the range of 5% to 40%. Their work was able to identify even small amounts of sunflower oil mixed with poppy seed oil [6]. This present work aimed to provide a wide representation of the market despite the challenges in sourcing an extensive number of commercial poppy seed oil samples. Notably, the use of FT-IR spectroscopy in commercial poppy seed oil has not been explored to date. Compared to other presented methodologies, FT-IR spectroscopy represents a non-destructive and fast approach that, once enhanced, can provide an authentic means for quality control in the industry. The aim of the present work was to test the performance of a non-invasive FT-IR spectroscopic technique for the authentication of commercial poppy seed oil, which could support quality control activities by producers and regulatory agencies.
## 4 Application to Power Electronics
### Modelling of the Aircraft Power Channel  
Each diode is characterized by a resistance that assumes a high value when activated, and a low value otherwise. The generic methods illustrated in Figure 5 are employed within this model, encompassing the _TimeDerivatives_, _Initialize_, _Output_, and _Update_ methods. The incidence matrix of the electrical circuit is constructed to facilitate the computation of the state matrices (\(A\), \(B\), \(C\)), as delineated in Appendix A. The _Initialize_ method executes the calculation of these state matrices, incorporating the optimization inputs. Meanwhile, the _TimeDerivatives_ and _Output_ methods derive the state and output vectors, utilizing the state representation articulated in Equation (6).  
\[\left\{\begin{array}{c}\frac{\mathrm{d}X}{\mathrm{d}t}=\mathrm{A(I)}* \mathrm{X}+\mathrm{B(I)}*\mathrm{U(t)}\\ \mathrm{Y}=\mathrm{C(I)}*\mathrm{X}\end{array}\right. \tag{6}\]  
Here:  
* \(A\), \(B\), \(C\) are state matrices depending on the optimization inputs, I;
* \(\mathbf{U}\) is the AC three-phase voltage source vector.  
The _Update_ method delineates the events that may prompt a reconfiguration of the model; these events are inherently unpredictable, corresponding to instances when the currents through or the voltages across the diodes reach zero, effectively signifying a change in sign, and, thus, altering their states. The implementation of the aircraft power channel model within the NoLOAD environment is depicted in Figure 13.  
The state representation of the aircraft electrical power channel comprises 10 state variables and 25 output variables, as detailed in Appendix A. The program code associated with the overarching sizing model encompasses the following approximate components:  
* 600 lines of code, inclusive of the dynamic component's description;
* 200 intermediate equations that model the electrical parameters derived from the geometric parameters;
* the equations governing the optimization outputs in relation to the optimization inputs (for instance, the total mass of the application and the Routh Criteria).
## 3 Experiments  
### Simulation  
A simulation was used to analyze the performance of the maximum likelihood estimator and verify the derived asymptotic variance in Equation (12). The simulation used a signal with a bandwidth of 50 Hz and a period length of 1 s. In total, 10,000 Monte Carlo simulations with Gaussian white noise were performed to obtain the mean square errors (MSE) under different SNRs and observation lengths of 2 s, 4 s, and 6 s, where \(N=2\), 4, and 6.  
The results are presented in Figure 2, together with the asymptotic estimation variance, which fitted well the simulated MSEs at high SNR. This demonstrated that the proposed asymptotic estimation variance could be used to describe the performance of the maximum likelihood estimator with adequate accuracy. In addition, the asymptotic variance of the signals with longer length decreased significantly. Under the same SNR, the variance of the 4 s signal was 1/10 of that of the 2 s signal, and the variance of the 6 s signal was only 1/35 of that of the 2 s signal.
## 1 Introduction  
Optical fiber interferometer sensors have the advantages of small size, compactness, and high sensitivity, which is why they are widely used in petroleum, chemical industries, and physical and biomedicine sensing [1; 2; 3; 4; 5]. There are various types of optical fiber interferometers, including the fiber Fabry-Perot interferometer, the Mach-Zehnder interferometer (MZI), and the Michelson and Sagnac interferometer [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Among these sensor structures, the optical fiber MZI sensor has attracted considerable research interest and been applied for various parameter measurements, such as temperature, strain, bending, liquid level, and refractive index (RI), due to its flexibility in structure design [16; 17; 18; 19; 20; 21; 22; 23]. A single mode-multimode-single mode (SMS) fiber structure is a typical fiber inlineMZI configuration, where the multimode fiber (MMF) section could be a multimode fiber [24], photonic crystal fiber [25], thin-core fiber [26; 27; 28], no-core fiber, and twin/multiple core fiber [29; 30; 31; 32; 33]. These structures are based on the fusion splicing between two single mode fibers (SMFs) and a multimode fiber section. Once the MMF section is given, very limited flexible design of the sensor can be provided.  
In this paper, a novel in-line fiber MZI sensor based on a micro taper structure was proposed and investigated in both theoretical and experimental studies. The structure is based on a single SMF, where two micro tapers are created with a short SMF sandwiched between the two micro tapers. The first micro taper's purpose is to excite multiple modes transmitting within both the core and the cladding of the sandwiched SMF, and the second micro taper is to collect these multiple modes into the output SMF. Since the micro taper can be created using a common commercial fusion splicer, the structure of the micro taper can be changed simply by controlling the arc-discharge parameters, such as arc-discharged time, power, and stepped length, resulting in different coupling coefficients from the input SMF to the sandwiched SMF and, thus, a flexible design of the sensor structure. To demonstrate the application of the micro taper design, a high-temperature measurement (up to 950 \({}^{\circ}\)C) was studied experimentally. In addition, to demonstrate the practical application of the sensor, a probe-type fiber MZI was studied for high temperature measurement by cutting the sandwiched SMF.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 3 Methodology
### Vibration-Data Acquisition Technique
#### 3.2.2 Vibration Feature Extraction  
Since frequency domain decomposition (FDD) theory was first described by [PERSON] et al. [20], the method has become popular due to its robustness and efficiency. The FDD method decomposes the vibration response into a series of independent single-degree-of-freedom systems. This is achieved by applying the SVD to the cross-spectral density (CSD) matrix [20; 21], which outputs the natural frequencies and mode shapes of the system.  
Figure 3: Vibration measurements for a caisson-foundation system: (**a**) accelerometer setup and impact load; (**b**) Concatenated time-history response.  
Figure 2: Vibration monitoring setup on a submerged caisson-foundation system.  
There are four main steps in the FDD method. In the first step, the CSD matrix is computed from the acceleration responses collected from \(n\) sensors on the structure. The general form of the CSD matrix \(S_{yy}(\omega)\) is as follows:  
\[S_{yy}(\omega)=\begin{bmatrix}S_{11}(\omega)&S_{12}(\omega)&\dots&S_{1n}(\omega )\\ S_{21}(\omega)&S_{22}(\omega)&\dots&S_{2n}(\omega)\\ \vdots&\vdots&\ddots&\vdots\\ S_{n1}(\omega)&S_{n2}(\omega)&\dots&S_{nn}(\omega)\end{bmatrix} \tag{1}\]  
In the second step, the CSD matrix is decomposed into unitary matrices (i.e., \(U(\omega)\), \(V(\omega)\)), and diagonal matrix \(\sum(\omega)\)) using the SVD. Due to the symmetricity of \(S_{yy}(\omega)\), the unitary matrices \(U(\omega)\) and \(U(\omega)\) are identical. The diagonal matrix \(\sum(\omega)=diag\{\sigma_{n}(\omega)\}\) contains singular values \(\sigma_{n}(\omega)\) in descending order, with the first value, \(\sigma_{1}(\omega)\), representing the highest energy of the dynamic system. The new form of the CSD matrix \(S_{yy}(\omega)\) is as follows:  
\[S_{yy}(\omega)=U(\omega)^{T}\,\sum(\omega)\,V(\omega) \tag{2}\]  
In the third step, natural frequencies are identified by selecting peak frequencies \(\omega_{p}\) on the singular value chart. The final step involves extracting mode shapes from the first column vector of \(U(\omega_{p})\) at these peak frequencies.  
Considered as a linear structural system with light damping, the caisson system exhibits sharp resonance peaks in the SVD chart [22]. These peaks contain critical structural information, making them more significant compared to other regions of the frequency spectrum. To fully exploit the informational content within the SVD responses, a min-max normalization in a logarithmic scale technique is employed as follows [13]:  
\[x^{\prime}=\frac{\log(x)-\log\bigl{(}10^{-6}\bigr{)}}{\log(10^{-1})-\log(10^{- 6})} \tag{3}\]  
where the range from \(10^{-6}\) to \(10^{-1}\) for normalization is empirically chosen to suit the specific characteristics of the data used for deep learning.