esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Algorithm
### Finger Detection  
#### 3.2.1 Segment Fingers  
We proposed a two-phase distance transformation method to segment fingers. Distance transformation (DT) of the Euclidean type calculates the Euclidean distance between a pixel and its nearest boundary in linear time [34]. In previous studies [29; 30], it was used to locate the palm centroid for drawing a circle with a proper radius to separate fingers and palm. Here, we discovered another of its property that it could retrain the shape of the palm by eliminating spindly fingers with a performance better than circle-based segmentation.  
In phase one, the edge of the hand mask is set as the boundary for the first DT. The resultant image is shown in Figure 4a, where whiter pixels represent a further distance to the boundary. The radius of the hand's maximum inner circle \(R_{0}\), which equals to the largest distance value in DT image, is used as a reference for following operations. Then, the region with a distance value larger than \(d_{in}\) is found and named as the palm kernel which would be used for the second DT. This process could also be comprehended as contracting the hand towards its inside to eliminate fingers while retaining the shape of the palm. The value of the contraction distance \(d_{in}\) was set as \(R_{0}/2\).  
Figure 3: **(a)** Original RGB image; **(b)** \(\mathrm{Y}\mathrm{C}_{\mathrm{b}}\mathrm{C}_{\mathrm{r}}\) images; **(c)** and the hand mask segmented by thresholding in \(\mathrm{Y}\mathrm{C}_{\mathrm{b}}\mathrm{C}_{\mathrm{r}}\) color space.  
In phase two, the palm kernel is utilized as the second boundary for DT, displayed in Figure 4b. After DT, the region with a distance value no greater than \(d_{in}\) is considered as the palm, which is similar to expanding the palm kernel to its original shape. Further, the regions with a value larger than \((d_{in}+d_{out})\) are considered as the finger regions, where \(d_{out}\) is set as \(R_{0}/7\) after experiment. The reason for setting this expansion distance \(d_{out}\) is twofold: (1) to exclude narrow regions that are not fingers, either caused by noises or a not fully bent finger; and (2) to more robustly estimate the region width to determine the number of fingers it contains. The results of several other gesture samples are shown in Figure 4c-f.
# Validation and Parametric Study of a Soil-Structure Interaction Nonlinear Simplified Model
## 4 Parametric Study of the Simplified Model
### Superstructure Size and Number of Stories  
The mass, stiffness and damping of the superstructure not only contributed to the overall mass, stiffness and damping matrix of the simplified model, but also determined the number of lumped-mass points in the structural system based on the number of stories in the superstructure. The dimensions of the superstructure dictated parameters such as its mass and stiffness. Therefore, structural systems formed by superstructures with different numbers of stories could be completely different. Two structural shapes were selected for the superstructure, with each floor having a span, depth and height of either 2 m or 3 m. The superstructure was then configured with 1, 3 and 6 stories to produce simplified models of the soil-structure interaction systems, with the corresponding conditions shown in Table 24.  
The frequencies of the structural system are presented in Table 24. When the dimensions of each story of the superstructure were 2 m \(\times\) 2 m \(\times\) 2 m, the first-order frequencies of the structural system for one story, three stories, and six stories were 25.168 Hz, 20.989 Hz, and 11.369 Hz, respectively. Similarly, when the dimensions of each layer of the superstructure were 3 m \(\times\) 3 m \(\times\) 3 m, the first-order frequencies of the structural system for one layer, three stories, and six stories were 22.819 Hz, 11.910 Hz, and 6.452 Hz, respectively. When the structural form of each layer remained unchanged, the relative differences between Condition 1 and Condition 3 were 121.37, between Condition 2 and Condition 3 were 84.61, between Condition 4 and Condition 6 were 253.68, and between Condition 5 and Condition 6 were 84.61. When the number of stories of the superstructure was six, the relative difference between Condition 3 and Condition 6 was 76.22. It was observed that both changes in the number of stories and the dimensions of the superstructure had a significant impact on the entire structural system, resulting in considerable differences among simplified models. This highlighted the importance of superstructure design in the seismic analysis of soil-structure interaction simplified models.
## 5 Conclusions  
In this study, a series of three-dimensional numerical analyses were carried out, to simulate construction of a pair of paralleled twin tunnels in a reinforced soil layer. The influence of the following tunnel excavation on the preceding tunnel when adopting different reinforcement strategies was revealed. On the basis of the numerical results, it is possible to draw the following conclusions:  
1. The surface settlement profile due to the excavation of the twin tunnels is asymmetric. The reinforcement strategies of the LR and TR can reduce the surface ground settlement and preceding tunnel deformation more effectively than the strategies of the RR or CR. When \(t=0.30\)\(D\), the LR and TR can reduce the settlement by more than 90%, the maximum horizontal displacement of the preceding tunnel by more than 73%, and the maximum vertical displacement of the preceding tunnel by more than 63%.
2. After the completion of the excavation of the following tunnel, with an increase in \(t\), the shape of the preceding tunnel lining gradually changes from a standing \"duck egg\" to an oblique \"duck egg\". Among all strategies, the TR is the most effective at controlling the development of convergence. When \(t=0.3\)\(D\), this approach can reduce the horizontal convergence by 98.4% and vertical convergence by 56.7% when compared with the case of \(t=0.0\)\(D\). The LR shows a similar effect, which results in a reduction of 86.5% horizontally and 54% vertically.
3. With an increase in \(t\), the bending moments at the tunnel vault and tunnel intrados gradually decrease, while the bending moments at the right and left haunch gradually increase. When \(t=0.30\)\(D\), the LR and TR can reduce the maximum bending moment at 90\({}^{\circ}\) by more than 43%.
4. In light of the numerical observations, the reinforcement of the soil layer around the following tunnel (i.e., LR) is an appropriate strategy, considering both engineering safety and cost savings.  
Conceptualization, C.H., K.Z. and D.S.; methodology, B.D., D.L. and Q.S.; data curation, C.H. and B.D.; data analysis, K.Z. and Q.S.; writing--original draft preparation, Q.S.; writing--review and editing, D.S.; supervision, C.H.; project administration, K.Z.; funding acquisition, D.S. and C.H. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by the National Natural Science Foundation of China (Grant No. 52090081), the Natural Science Foundation of Shenzhen (Grant No. JCYJ20210324094607020), Key Research and Development Project of Guangdong Province (Grant No. 2019B111105001), and Scientific Research Project of China Railway 15 th Bureau (Grant No. CR15 CG-ZH-HQMZSD-2021-00001(KJ)).
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 2 SDT Concept Development and Methods Used
### Data Acquisition and Processing  
SDT uses sensors installed onboard to collect data, such as heading, vessel speed, GPS coordinates, azimuth angles, propulsion power, engine speed of rotation, engine load, vibrations, and structural strains, depending on the purpose. However, sensor logs, or raw data, could not usually be immediately useful for a DT, i.e., it might be necessary to perform data processing to extract physical meaning from it, as noted in [1].  
Therefore, data acquisition and data processing could be identified as the second step of the process. The data acquisition method and the purpose of the model are ones that would determine the choice of data processing method. For instance, stress data collected from optical sensors and stress data collected from strain gauges require different methods to extract useful information from the data.  
For data processing purposes, various methods have been used, such as filtering outliers, as in [8; 13], filtering noisy data, as in [18], interpolation and calculation of wave spectra, as in [16], data fusion, as in [10; 12], calculating root-mean-square (RMS) values from raw data, as in [13; 20], convex optimisation, as in [21], skewness and kurtosis analysis, as in [13], and Fourier transformation, as in [26].
## 3 System Model
### Energy Transfer Model  
The MCGV is able to charge multiple nodes in one cell at the same time. For instance, the energy transfer efficiency \(\eta_{i}\) of node \(v_{i}\) is obtained using Equation (1) [27].  
\[\eta_{i}=\frac{\tau}{\left(d_{i,c_{m}}+\beta\right)^{2}}\times 100\%,i\in c_{m}, \tag{1}\]  
where \(\tau=\frac{G_{\text{S}}G_{\text{R}}\beta}{L_{p}}\Big{(}\frac{\varphi}{4\pi} \Big{)}^{2}\), \(G_{\text{s}}\) denotes the source antenna gain, \(G_{R}\) is the receive antenna gain, \(\delta\) can be referred to as rectifier efficiency, \(\varphi\) represents the wavelength, \(L_{P}\) means polarization loss, and \(\beta\) represents the parameter used to change the Friis free space equation. \(d_{i,c_{m}}\) symbolizes the distance between the node \(v_{i}\) and its cell center. The charging efficiency decreases with the increase in distance.
## 3 Methodology
### EFA-Exploratory Factor Analysis  
Exploratory Factor Analysis (EFA) and then Confirmatory Factor Analysis (CFA) are the two most common approaches to doing factor analysis. In this study, CFA was used to assess how different variables in hypotheses or theories fit together structurally. However, EFA was used to collect data on inter-factor connections and to distill many variables down to a manageable number of important frameworks [36; 41]. SPSS has a feature for doing this. Data collection led to the development of the main multivariate analytic techniques such as EFA for investigating the fundamental building blocks of the IOT barriers [6; 42]. Measurement variables were tested for one-dimensionality and reliability to see whether construct validity had been appropriately evaluated [85; 86]. Principal Component Analysis (PCA) was chosen over Principal Axis Factoring (PAF), Image Factoring, Maximum Probability, and Alpha Factoring, because it is more accurate and has a broader theoretical foundation [29; 43; 45]. In contrast, principal component analysis (PCA) is advised if exploratory factor analysis (EFA) yielded only preliminary results despite the lack of a preexisting theory or model. [PERSON] et al. (2020) and [PERSON] et al. (2020) argued that exploratory factor analysis is most often performed using principal components analysis (PCA), which is the default option in many statistical software programs. Instead of using more labor-intensive approaches such as Oblimin or Promax, this study opted for the more equitable Varimax Rotation [31; 32]. When it comes to factor elucidation, whether basic or complicated, Varimax stands out as a wonderful global approach [87; 88]. The sample size may be considered representative if it falls within a certain acceptable range. Factor analysis was found to be appropriate in this investigation due to the 25 variables and sufficient sample size. This study's sample size and methods are consistent with those found in the literature.  
Figure 1: Research flowchart.
## 3 Results and Discussions
### Equivalent Circuit Model  
An equivalent circuit model (ECM) of the FSS unit cell is derived using an advanced design system (ADS) to further evaluate its performance obtained using a full-wave simulator. Figure 6 reveals the lumped element formation, and Figure 6 shows the derived lumped circuit model of the proposed FSS. The unit cell consists of two independent elements. The outer element is modelled as a series LC resonator represented by \(L_{1}\), \(C_{1}\), and \(C_{2}\), whereas the inner element is modelled as a combination of series and parallel LC resonators with the lumped elements \(C_{3}\), \(C_{4}\), \(L_{2}\), and \(L_{3}\), respectively. The metallic/traces are modelled as inductances, while the apertures/slots are modelled as the inter- and intra-element capacitances, and the dielectric substrate is modelled as a transmission line. The characteristic impedance (\(Z_{d}\)) of the supporting dielectric can be calculated as \(Z_{d}=Z_{0}/\sqrt{\varepsilon_{r}}\), where \(Z_{0}=377\)\(\Omega\) is the characteristic impedance of free space on both sides of the FSS, and \(\varepsilon_{r}\) is the dielectric constant of the slab. Moreover, the surface impedance of the outer element (CMSL) for the lower band is expressed in Equation (6).  
\[Z_{CMSL-FSS}=j\omega L_{1}+\frac{1}{j\omega C_{1}}+\frac{1}{j\omega C_{2}}, \tag{6}\]  
where \(\frac{1}{j\omega\omega_{1}}\), and \(\frac{1}{j\omega\omega_{2}}\) are the reactances associated with \(L_{1}\), \(C_{1}\), and \(C_{2}\), respectively. By simplifying Equation (6), we obtain  
\[Z_{CMSL-FSS}=\frac{(j\omega L_{1})\big{(}\omega^{2}C_{1}C_{2}\big{)}-(j\omega) (C_{1}+C_{2})}{\omega^{2}C_{1}C_{2}}, \tag{7}\]  
Similarly, the surface impedance of the MJCL-FSS for the higher band can be calculated using the same procedure as in Equations (6) and (7).  
\[Z_{MJCL-FSS}= j\omega L_{2}+\frac{1}{j\omega C_{3}}+\frac{1}{j\omega C_{4}}, \tag{8}\]  
Figure 5: Surface current distribution plot of the FSS unit cell (\(\theta=0^{\circ}\)) for TE mode (a) at 7.9 GHz and (b) at 11.9 GHz.  
\[Z_{MJCL-FSS}=\frac{1-\omega^{2}L_{2}C_{3}}{j\omega C_{3}}+\frac{j\omega L_{3}}{1- \omega^{2}L_{3}C_{4}}, \tag{9}\]  
Multiple simulations are performed in the ADS to obtain the optimized dual-bandstop response of the FSS at the desired frequencies. Figure 6c shows the SE curves of several simulations for various lumped element values. The optimized values of the lumped parameters are tabulated in Table 3. In addition, Figure 6d shows the comparison of the ECM and full-wave simulations. It is observed that the circuit simulations are in good agreement with the full-wave EM simulations.
## 1 Introduction
Therefore, it is a feasible research topic to analyze the performance of annular concrete structures under the joint constraints of stress and temperature. Given the need for accurate assessment of structural strain under the joint constraints of stress and temperature, FBG sensors are ideal measurement tools due to their wide measurement range, low transmission loss, high accuracy, and stable operation under different temperature conditions [10; 11; 12; 13; 14]. In recent years, [PERSON] et al. [15] used FBG sensors to characterize the thermal strain of cement-based materials at low temperatures in real time, and the results showed that higher water content and the addition of fine aggregates significantly increased the thermal strain of cement-based materials at low temperatures and their freeze-thaw cycles. [PERSON] et al. [16] used FBG strain sensors to measure the stress strain and total strain of concrete structures, extract the temperature strain of concrete structures, and calculate the thermal expansion coefficient of concrete structuresunder different working conditions. [PERSON] et al. [17] proposed an experiment consisting of a cantilever beam and a uniform fiber grating sensor that simultaneously measured non-uniform strain and temperature. The effect of FBG length on Bragg wavelength shift and FWHM change is discussed. The experimental results agree well with the simulation results, and the error is less than 6.5%. These studies show that the FBG sensor has a high application potential in the temperature and stress measurement of concrete structures, so the FBG strain sensor is used in this study for structural strain and temperature monitoring. In this paper, according to the special environmental conditions in the cold area, a detection model was established to measure the structural strain by combining the structural mechanics model and FBG detection technology, and a numerical simulation model was established to reflect the mechanical response of annular concrete structures. Through the performance test of the structure, the bearing capacity range of the structure is determined. Gradient cooling experiments were then conducted under different stress constraints. The total strain of the structure under stress constraint was measured, and the thermal strain data is extracted and evaluated statistically. The nonlinear thermal strain response of the structure is discussed and analyzed. The novelty of this method lies in its focus on the annular shape of concrete, combines structural mechanics analysis with FBG detection technology to build a new detection model. By comparing and analyzing simulation and experimental data, this method accurately evaluates the complex thermodynamic response of the annular concrete structure under stress and temperature constraints.
# Optical Design and Stray Light Analysis of Underwater Spectral Radiometer
## 5 Experiment and Results
### Radiometric Calibration  
The underwater spectrometer is calibrated in the laboratory by the standard irradiation lamp calibration method. The standard lamp used is a 1000 W quartz halogen tungsten lamp, whose spectral range covers the measurement band of the underwater spectrometer and whose spectral irradiance can be traced to the national high-temperature blackbody radiation reference source. The irradiance calibration using standard lamps is shown in Figure 17. The standard irradiance lamp uses the standard constant-current power supply to provide the calibration light source according to the measurement specification, and the underwater spectrometer is measured at the standard distance. The distance measurement uses the horizontal laser rangefinder, which not only ensures collimation but also provides a distance measurement accuracy better than 1 mm. An anti-stray stop wrapped in black cloth is placed between the spectrometer and the calibration light source, and the experiment is completed in the optical darkroom to achieve the suppression of space stray light and reduce the error introduced by it to the calibration.  
The spectral irradiance response coefficient \(R(\lambda)\) can be obtained by calibrating the quantitative relationship between the output value and the incident spectral irradiance of the instrument at different wavelengths. The corresponding expression is shown in Formula (25):  
\[R(\lambda)=\frac{S(\lambda)}{E(\lambda)} \tag{25}\]  
\(E(\lambda)\) represents the spectral irradiance of the standard lamp at the response wavelength, and \(S(\lambda)\) is the normalized function of the detector output signal. The spectral responsiveness curve obtained according to the above formula is shown in Figure 18.  
\begin{table}
\begin{tabular}{c c} \hline
**WAVE/nm** & **FWHM/nm** \\ \hline
399.902 & 2.458 \\
449.918 & 2.688 \\
499.862 & 2.891 \\
549.795 & 2.805 \\
599.634 & 2.823 \\
649.584 & 2.670 \\
699.513 & 2.432 \\
751.032 & 2.362 \\
801.181 & 2.217 \\
851.428 & 2.272 \\
901.501 & 2.422 \\
951.597 & 2.869 \\ \hline \end{tabular}
\end{table}
Table 4: Spectrometer calibration wavelength and spectral resolution.  
Figure 17: Schematic diagram of spectral irradiance calibration for standard lamp.
# Easy, Fast, and Accurate Method of 3-Dimensional Mirror Plane Creation for Actual Clinical Users
## 1 Introduction  
Surgeons might have a difficult time when they encounter patients who require comprehensive and reconstructive surgeries due to anatomical asymmetry or large facial defects. [1; 2; 3]. For this reason, there is a rise in interest in establishment of the facial mid-plane with accurate measurement of asymmetry deviation in the field of oral and maxillofacial surgery [4]. Many techniques have been introduced in the last few decades to help improve the precision of evaluation and pre-operative surgical planning; computer tomography, three-dimensional (3D) scanning, CAD/CAM, image processing software, tissue engineering, and even bio-printing [5]. Among these advances, medical images from the CT database and commercially available medical image software allow us to construct 3D facial models of the actual patients. In addition, useful engineering algorithms, such as iterative closest point (ICP) could extend the utilization of currently growing resources in the medical field by improving the accuracy and rate of convergence. From this technological advance, virtually mirrored 3D images of normal anatomic structures can provide an ideal image of desired structure for reconstruction of the defects or correction of the asymmetries [6; 7]. By using these mirror images, surgeons can obtain a lot of useful information for treatment plans, such as volume, form, location, and dimension measures of the affected sides [8].  
Despite the many advantages of surgical simulation using mirror images, until now, mirror planes have been mostly produced using facial bone landmarks. It is easy to make mirror planes using landmarks, but it is difficult to set the exact landmarks. For this reason, an accurate mirror imageshould be created through additional manual manipulation. This manual manipulation process is time-consuming and requires familiarity with dealing with medical CAD software. Therefore, we would like to introduce a method to create an easy, fast, and accurate mirror plane.
# A UAV Coverage Path Planning Method Based on a Diameter-Height Model for Mountainous Terrain
## 3 UAV Coverage Path Planning Method Based on the DH Model
### Coverage Path Planning Method Based on the DH Model  
In order to improve the coverage quality of mountainous terrain, it is necessary to adopt different coverage path patterns. With regard to the surface regions of the DH model in the 3D map, an equal-interval spiral ascent CPP method is adopted. For the sub-regions located in the flat area, an equal-interval circular CPP method is carried out. Assuming that the coverage width of the detector attached to the UAVs has a value of \(L\), graphically, the schematic diagram of these parameters about the different coverage path patterns is shown in Figure 4.