esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In [26], a time domain analysis was presented to elaborate on how the rotor current and voltage can be coordinated to ride through severe grid faults under the capacity limiting effect of RSC. In [27], a short-circuit fault analysis of a grid-connected DFIG based on a wind turbine was proposed by considering the active crowbar circuit protection for ride-through capability and power quality improvement. Through the above literature review, it has been found that there is little research on analyzing and improving the frequency offset of the DFIG fault current while successfully completing the fault ride through of the DFIG. Therefore, this study first conducted a mathematical modeling of the DFIG and theoretically deduced the fault current expression of the DFIG. The accuracy of the theoretical fault current expression was verified through the fault current curve obtained through simulation. Through analysis, it can be found that during fault periods, the fault current of the DFIG exhibits a frequency offset phenomenon and can be affected by voltage dips in the crowbar circuit. Thus, during fault periods, a DVR-based SMES system was adopted to compensate for the fault voltage at the DFIG outlet, which can achieve the fault ride through of the DFIG and improve the frequency offset of the fault current by raising the fault voltage. The rest of this article is organized as follows. Section 2 introduces the working principle of the DFIG-based wind turbine system and the model of the DFIG and theoretically derived expression of the DFIG fault current and the verification of the accuracy of the theory through simulation. Section 3 introduces the principle of the DVR-based SMES system, the VSC converter control strategy in DVR, and the DC/DC converter control strategy of SMES. In Section 4, extensive simulation results are provided to verify the performance of the adopted control method. Finally, the conclusion is given in Section 5.
## 4 Experiment Results and Analysis
### Experimental Settings  
#### 4.2.1 Data Settings  
For EMDS-6, each type of EM is randomly grouped into training, validation, and test dataset with a ratio of 4:1, which means 32 images are applied for training (with 5-fold cross validation), and the last 8 images are applied for testing. Though the dataset is small for training a complex model, it can still achieve excellent detection performance by using transfer learning [94].
## 2 Materials and Methods
### Satellite Data and Processing  
Google Earth Engine (GEE) has been used as a tool to perform climate and hydrology and natural disaster analysis, and image processing, land use/land cover classification (LU/ LC), and urban planning [57; 58]. GEE consists of petabytes of science-ready datasets and is equipped with high performance computing that can be accessed through an application programming interface (API) that is available in JavaScript and Python [59]. Some notable applications of GEE are, for instance, the Global Mangrove Watch [10], global forest cover change [60], global surface water changes [61; 62], global shoreline  
Figure 4: Working steps of the processing and post-processing phase of the point clouds.  
changes [63], and continent level agricultural mapping [64]. To understand the mangrove extent development, we processed satellite imagery, such as Landsat 7/8 and Sentinel 1/2, in GEE with Python package geemap [65].
## 3 Materials and Methods
### Study Conditions and Measures
#### 3.5.3 Dependent Variables: Questionnaire  
In the course of the study, we essentially aimed to measure users' perception of visual realism and presence. To do so, we built upon several relevant questionnaires from the literature to create a study form with multiple Likert-type items. We provide an overview of this questionnaire in Table 1. We notably included questions from previous works investigating visual realism, comfort, and general preference [7; 12] (questions 1-3, 9, and 10). We also included questions related to our more specific question of viewing volume size (questions 4 and 5). Finally, to measure presence, we used the three-item SUS questionnaire from the literature [19] (questions 6-8), which was seen as more practical than longer forms given that participants answered from within VR. All questions were on 7-point scales, with short descriptions provided for the scales' extremes.  
Figure 6: User’s view in the “Museum” scene rendered with view-dependent effects and a view radius of 20 cm. From left to right, the user’s head has moved to \(x\ =\ 0\) cm, \(x\ =\ 10\) cm, \(x\ =\ 20\) cm, and \(x\ =\ 22.5\) cm. Notice how the view fades out (at \(x\ =\ 25\) cm, it would be entirely black), as well as how the reflection on the window moves in front of the items inside the glass case.
While existing research efforts aimed at enhancing the SCSO algorithm often concentrate on optimizing the global performance, as mentioned earlier, they frequently overlook the optimization of the sand cat population and the specific process of sand cat search and attack. In this paper, we propose a Hybrid Sand Cat Swarm Optimization (HSCSO) algorithm, which includes Logistic-Tent chaotic mapping and an adaptive lens opposition-based learning strategy, to optimize the initial sand cat population and improve the global performance of the algorithm, and inspired by the swarm intelligence scheme, an inertia-momentum strategy and an elite crossover pool are introduced in the search and attack phases to improve the search efficiency and convergence ability. The contributions of this work can be summarized as follows. (1) The proposal of the multimodal transport model: considering six aspects including transportation, transfer, quality damage, fuel consumption, carbon emissions, and time penalty costs, and introducing a road congestion index and a hybrid embedded time window, a green vehicle comprehensive multimodal transport model is constructed to better reflect the actual cost situation of multimodal transportation. (2) The improvement of the SCSO algorithm: The initial sand cats are optimized through Logistic-Tent chaotic mapping, and inspired by the swarm intelligence scheme, a momentum-bellicos strategy and an elite crossover pool are introduced to enhance the search performance. Moreover, an adaptive lens opposition-based learning strategy is introduced to enhance the global search capability. The test results indicated that the proposed algorithm is a powerful and effective approach for high-dimension complex optimization. (3) Taking vehicle logistics in east China as an example, the proposed method is successfully applied to multimodal transportation path planning, achieving a reduction in comprehensive costs. The effectiveness of the proposed method in green multimodal transportation path planning has been demonstrated. The remainder of this paper is structured as follows. In Section 2, the process of building this green vehicle comprehensive multimodal transport model is introduced. In Section 3, the basic sand cat swarm algorithm is introduced. In Section 4, the improved method of the HSCSO algorithm is described in detail. In Section 5, the algorithm is tested against the benchmark function. In Section 6, the proposed model and algorithm are applied to solve the practical multimodal vehicle transportation, and the results are briefly analyzed. In Section 7, the paper concludes with a summary.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR
### Improved Watermarking Scheme  
Although the basic watermarking scheme can be directly applied to 5G NR system, the watermark reduces the reliability of user-data transmission, since the watermark modifies the normal DMRS. The improved watermarking scheme is proposed according to the  
Figure 4: Demodulated phase of DMRS. (**a**) Phase of DMRS. (**b**) Constellation of DMRS.  
Figure 5: Basic watermark authentication scheme.  
characteristics of 5G NR system, where the robustness of the watermark is improved and the effect of the watermark on the user-data transmission is negligible.
## 3 Results and Discussion
### Exposure Assessment
#### 3.3.2 Health Effect #02  
As for 'health effect #2', all values were above the threshold considering five active substances. MOET values are displayed in Table 5.  
As it can be seen, the values confirm limited health effects of pesticides on hypertrophy, hyperplasia and neoplasia of parafollicular cells (C cells). This concurs with higher values of MOET for the 99 th percentile that covered toddlers, other children and adults in 10 EU countries ranging between 1740 and 5010 [17]. This EFSA study covered 14 active substances with proven 'health effect #2'. C-cell hyperplasia is a precursor for C-cell cancer of the thyroid gland which can remain unchanged for years as an indolent tumor, but can also become an aggressive variant with a high mortality rate [43].  
Figure 1: Estimated total margin of exposure (MOET) at average, 95 th and 99 th percentile for two recall methods: (**a**) FFQ recall; (**b**) 24 h recall. MRL—maximum residue level. LOD—limit of detection. Legend: “as is”—all samples; “1.0 MRL”—samples below MRL; “0.7 MRL”—only samples below 0.7 MRL; “zero”—below LOD.  
Figure 2: Pareto distribution of active substances (ASs) contributing to MOET values for the two recall periods and four scenarios. MRL—maximum residue level. LOD—limit of detection. Legend: “as is”—all samples; “1.0 MRL”—samples below MRL; “0.7 MRL”—only samples below 0.7 MRL; “zero”—below LOD.  
As mentioned above, MOET values of 100 or lower at the 99.9 th percentile are considered as thresholds [17]. Opposed to 'health effect #01' results for the worst-case scenario when values at 99 th percentile were below 100, for 'health effect #2', the 'Max' scenario at 99 th percentile was 828.5 for FFQ recall method and 611.5 for 24 h recall.
## 6 Numerical Tests and Discussion
### Verification of the Proposed Methods
#### 6.2.2 Validation of D-Vine Copulas  
Since the load at all buses is perfectly positively correlated, there are five correlated RVs in the test system, i.e., the wind speed at four sites and the total load of the system.  
We apply D-vine copulas to construct the correlation model based on historical data. Each bivariate copula can be chosen from Normal, Student t, Clayton, Gumbel, Frank and Joe copulas, whose parameters are shown in Table 3.  
To evaluate the goodness-of-fit of D-vine copulas, we use the statistic recommended by [47], which is based on Cramer-Von Mises (CvM) criterion and empirical copula process (ECP). We entitle it ECP-CvM, which indicates the difference between the fitted model and the empirical model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SNMM** & **GMM** & **Weibull** & **KDE** \\ \hline BUS 1 & 33.45 & 93.65 & 9907.10 & 213.53 \\ BUS 2 & 20.15 & 39.17 & 421.69 & 94.90 \\ BUS 13 & 27.73 & 62.53 & 1452.37 & 84.06 \\ BUS 21 & 51.42 & 67.22 & 6611.95 & 123.53 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chi-square values of three models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Copula** & **Types** & **Para1** & **Para2** & **AIC** & **BIC** \\ \hline \(c_{12}\) & [PERSON] & \(1.27\) & \(0\) & \(-621.86\) & \(-614.79\) \\ \(c_{23}\) & [PERSON] & \(1.47\) & \(0\) & \(-1462.92\) & \(-1455.85\) \\ \(c_{34}\) & [PERSON] & \(1.17\) & \(0\) & \(-291.79\) & \(-284.71\) \\ \(c_{45}\) & [PERSON] & \(-0.03\) & \(0\) & \(-5.15\) & \(1.93\) \\ \(c_{13|2}\) & [PERSON] & \(1.85\) & \(0\) & \(-3132.12\) & \(-3125.05\) \\ \(c_{24|3}\) & [PERSON] & \(1.34\) & \(0\) & \(-1666.12\) & \(-1659.05\) \\ \(c_{35|4}\) & [PERSON] & \(1.02\) & \(0\) & \(-8.93\) & \(-1.85\) \\ \(c_{14|23}\) & Student t & \(-0.15\) & \(13.07\) & \(-263.15\) & \(-249.00\) \\ \(c_{25|34}\) & Normal & \(-0.05\) & \(0\) & \(-20.21\) & \(-13.14\) \\ \(c_{15|234}\) & Student t & \(0.00\) & \(26.16\) & \(-9.38\) & \(-2.30\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the constructed D-vine copulas.  
Figure 6: Fitted PDFs of the wind speed at Mazongshan.  
The ECP-CvMs of D-vine copulas, C-vine copula [27], Normal copula and t copula are calculated and shown in Table 4.  
According to the results in Table 4, D-vine copulas exhibit superiority over Normal and t copulas due to a much smaller ECP-CvM value. It performs much better than any individual multivariate copula model, so we can adopt it for reliability assessment of power systems.
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 3 Design and Optimization
### Optimization of Micro VCA
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Values** & **Step** \\ \hline Magnet radius & [mm] & \(0.1\leq\text{r}_{\text{m}}\leq 1.1\) & 0.1 \\ Magnet height & [mm] & \(0.05\leq\text{b}_{\text{m}}\leq 1\) & 0.05 \\ Coil inner diameter & [mm] & \(0.2\leq\text{d}_{\text{c-in}}\leq 1\) & 0.2 \\ Coil outer diameter & [mm] & \(0.4\leq\text{d}_{\text{c-out}}\leq 2.2\) & 0.2 \\ Coil height & [mm] & \(0.1\leq\text{h}_{\text{c}}\leq 1\) & 0.1 \\ Air gap & [\(\upmu\)m] & \(50\leq\text{h}_{\text{g}}\leq 100\) & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters for micro VCAs to be optimized. Figure 3: The electromagnetic force of VCA is a function of current for different magnetization directions of the permanent magnet. Figure 5a shows electromagnetic force and efficiency as a function of current before and after optimization. The obtained results indicate that the electromagnetic force and efficiency are increased after optimization. The output electromagnetic force of the VCA with optimized structural parameters is increased by a factor of 10%. The results in Figure 5b confirm that the efficiency is also increased obviously. The efficiency of the optimized VCA is up to 0.032 N/W\({}^{1/2}\). Figure 4: Force and efficiency as a function of VCA’s structural parameters. (**a**) Permanent magnet radius. (**b**) Permanent magnet height. (**c**) Coil inner diameter. (**d**) Coil outer diameter. (**e**) Coil height. (**f**) Air gap.
# Numerical Study on Hydrodynamic Performance of Bionic Caudal Fin
## 3 Results and Discussion
### Influence of the Spanwise Flexibility  
In nature, caudal fin is usually flexible and the flexibility plays an important role in the motion of fish. So the flexibility should be considered in the modeling of caudal fin. However, the flexibility problem is complicated and the modeling of the problem is very difficult. In addition, the flexibility in nature is not controllable. Thus, the paper applies an imposed motion to the original motion model. In this way the influences of flexibility can be investigated more intuitively and the flexibility motion model can also easily adopted in engineering applications. Therefore, the spanwise flexibility is introduced in the model. Here, the phase angle \(\gamma\) is adopted to describe spanwise flexibility. At certain chordwise positions, the phase angle \(\gamma\) describes the phase difference of pitch motion between edge point and center point of the caudal fin. Here, the angle of attack \(\alpha(t)\) of the edge points can be written as:  
\[\alpha(t)=\alpha_{max}\sin(2\pi ft-\gamma) \tag{15}\]  
At any time, the coordinates of edge point and center point can be obtained. Using different curve functions, these three points can determine a curve and the coordinates of any point along the spanwise direction can be calculated through curve functions. Figure 16 shows the shape of fin along the spanwise direction with different phase angle \(\gamma\).  
Figure 14: Performance curves _versus_ Strouhal number: (**a**) mean thrust coefficient; (**b**) propulsive efficiency.  
Figure 15: Vortex pattern in the wake: (**a**) \(St=0.3\); (**b**) \(St=0.5\).  
The performance curves _versus_ phase angle are shown in Figure 17. In this paper, elliptic curve, hyperbolic curve, and parabolic curve are adopted in simulations. It can be seen that the case with the elliptic curve can achieve higher propulsive efficiency. Furthermore, the fin can get larger thrust force with high propulsive efficiency when the phase angle keeps a positive value, which is consistent with fish in nature.
# An Economical and Precise Cooling Model and Its Application in a Single-Cylinder Diesel Engine
## 2 Model of the Cooling System
### Model of the Radiator  
This subsection describes the mathematical model of the radiator based on experimental data or calculations [30; 31]. The heat dissipation potential \(Q_{c}\) of a radiator depends on the volumetric flow rate of air \(q_{a}\), the volumetric flow rate of water \(q_{w}\), and the temperature difference \(\Delta T\) between the air and water measured at the inlet of the radiator. One can divide every variable into several intervals and measure the heat exchange of the radiator for each combination. The orthogonal experimental design method is used to reduce the number of experiments. The measured values of the three variables are used to fit a continuous polynomial function. Assuming that heat exchange will not occur when \(\Delta T=0\) or \(q_{w}=0\), we have  
\[Q_{c} = \Delta T\cdot q_{w}(a+b\;\Delta T+c\;q_{w}+d\;q_{a}+e\;\Delta T^{2}+ \tag{3}\] \[f\;q_{w}^{2}+g\;q_{a}^{2}+h\;q_{w}\Delta T+i\;q_{a}\;\Delta T+j\; q_{w}q_{a})\] \[\equiv Q_{c}(\Delta T,q_{w},q_{a}),\]  
where these coefficients are \(a=0.188,b=-3.02\times 10^{-3},c=-2.91\times 10^{-2},d=0.128\), \(e=3.61\times 10^{-5},f=2.33\times 10^{-3},g=1.73\times 10^{-2},h=-7.00\times 10^{-5},i=-1.77\times 10^{-3}\), \(j=-1.78\times 10^{-3}\); the fitting error is in the range of [\(-4.7\%\), \(4.8\%\)]. The solution to the mathematical model described in Section 'Model solutions' indicates that a simple form of \(Q_{c}\), such as in Equation (3), reduces the amount of time used in the numerical calculations; however, this process is not necessary. One can interpolate the experimental data to obtain greater accuracy.  
Since there is resistance from the conductor and radiator, the pressure is lowered when water and air pass through the cooling system. The values of the decrease in pressure, \(\Delta P_{w}\) and \(\Delta P_{a}\) depend on the volumetric flow rate of air and water and are measured in the experiments. \(\Delta P_{w}\) and \(\Delta P_{a}\) are fit as a function of \(q_{w}\), \(q_{a}\), respectively,  
\[\Delta P_{w} = 0.37\times q_{w}-1.4\times q_{w}^{2}+2.1\times q_{w}^{3}, \tag{4}\] \[\Delta P_{a} = 836\times q_{a}-999\times q_{a}^{2}+537\times q_{a}^{3}. \tag{5}\]  
The experimental data and the fitted functions are shown in Figures 4 and 5, respectively, where in the subsequent graphics discrete points stand for experimental data and segments stand for interpolation.  
Figure 3: Map of target temperature of the water jacket in the DK32 engine.
# Artificial Neural Network Assisted Error Correction for MLC NAND Flash Memory
## 4 Experiment Results  
### Training  
Throughout all experiments, we used a rate-0.9 (4544, 4096) QC-LDPC code and the BP decoding algorithm. The experimental platform is implemented in Matlab. The channel parameters, which are used to generate the training dataset, are shown in Table 2. Since the parasitic coupling capacitances of CCI are invariable in a flash memory ship, without loss of generality, we set the cell-to-cell coupling strength factor to be \(s=1\). According to the raw bit error rate (RBER), we generate the dataset at \(PE=\{3000,4000,5000\}\) and divide the dataset into two parts: error and correct bits, which are to be corrected, e.g., the cell-(\(N,M\)) in Figure 7. In total, the sizes of the training and validation data are 336,000 and 84,000, respectively. According to the performance of neural network versus the different numbers of hidden layer node, shown in Figure 8, the basic neural network structure is set to be \(\{10,3,2\}\), meaning that there are 10 nodes in the input layer, 3 nodes in the hidden layer and 2 nodes in the output layer.
# Reactance Regulation Using Coils with Perpendicular Magnetic Field in the Tubular Core design
## 3 Materials and Methods
### The Analytical Background for the Calculation of Magnetic Coupling  
This section presents the analytical background for the calculation of magnetic coupling. A preliminary analysis of magnetic coupling was discussed in our previous work [33]. Figure 3 demonstrates the equivalent circuit of the proposed MA. The equivalent first harmonic of current is employed for the analytical description. Despite having additional harmonics in the current, this method gives enough accuracy for the magnetic coupling assessment discussed in this work. Generally, the standard Kirchhoff equations can be applied for the analytical analysis.  
The equivalent circuit is expressed as the following set of equations with phasor variables and parameters:  
\[\dot{V}_{1}-\dot{I}_{1}(R_{1}+jX_{1})+\dot{j}_{2}X_{m}=0 \tag{2}\]  
\[-\dot{I}_{2}(R_{2}+R_{L}+jX_{2})+\dot{j}_{1}X_{m}=0 \tag{3}\]  
where \(X_{1}=\omega L_{1}\), \(X_{2}=\omega L_{2}\), \(X_{m}=\omega L_{m}\). The dot over symbols represents the phasor variables. The inductive coupling (\(K_{\textit{coupling}}\)) is estimated using \(X_{1}\), \(X_{2}\), and \(X_{m}\) as follows [38]:  
\[K_{\textit{coupling}}=\frac{X_{m}}{\sqrt{X_{1}\cdot X_{2}}} \tag{4}\]  
Now, the important question is how to measure all inductances (\(X_{1}\), \(X_{2}\), and \(X_{m}\)). In our previous work [33], we suggested that the open-circuit test can be applied to assess magnetic coupling. For this purpose, Equations (2) and (3) are rearranged for the open-circuit case (\(I_{2}\) = 0) as:  
\[\left\{\begin{array}{c}\dot{V}_{1}-\dot{I}_{1}(R_{1}+jX_{1})=0\\ -\dot{V}_{2}+\dot{j}_{1}X_{m}=0\end{array}\right. \tag{5}\]  
And after simplification:  
\[X_{m}=\frac{\dot{V}_{2}}{j\dot{V}_{1}}(R_{1}+jX_{1}) \tag{6}\]  
Figure 3: Equivalent circuit of two coupled magnetic coils [AC: controllable voltage source; \(\omega\): frequency of voltage source; \(R_{1}\), \(R_{2}\): equivalent resistances of coupled windings; \(L_{\sigma 1}\), \(L_{\sigma 2}\): inductivities of magnetic leakage paths in both coils; \(L_{\sigma m}\): mutual inductance between windings; A/V: tester for voltage and current measurements].  
Since the magnitude of \(X_{m}\) is the real number the angle \(\varphi_{\text{T}}=0\) it provides the following expression for the estimation of \(X_{m}\) through the open-circuit test:  
\[X_{m}=\frac{V_{2}}{V_{1}}\sqrt{R_{1}^{2}+X_{1}^{2}}=\frac{V_{2}}{V_{1}}Z_{1} \tag{7}\]