esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.2 The Calculation of the RSEI  
This study employed the RSEI for dynamic monitoring and assessment of the ecological environment quality of the study area. This index couples four assessment indicators--greenness, wetness, heat, and dryness--which are expressed as follows [8]:  
Figure 3: Ecological environment assessment of the Qinling based on the EI of 2020 (revised based on the ecological environmental data from the 2020 Shaanxi Province Environment Status Bulletin [39]).  
\[\text{RSEI}=\text{f}(\text{NDVI},\text{WET},\text{LST},\text{NDBSI}) \tag{2}\]  
In this formula, WET represents the wetness index, NDVI represents the greenness index, LST denotes the heat index, and NDBSI stands for the dryness index. The formulas of these remote sensing indices mentioned above are listed in Table 1. Since the four indices were not dimensionally uniform, they were normalized to map their values to the [0, 1] interval. After normalization, Principal Component Analysis (PCA) was conducted. The first principal component (PC1) obtained through PCA was then normalized to yield the RSEI. Results closer to 1 indicated a better ecological environmental quality in the study area. All the aforementioned computational processes were implemented in GEE.
## 4 Discussion
### Limitations of This Study and Future Research Initiatives  
While our results demonstrate the relative performance of various LCUs and highlight the factors influencing polymerization outcomes, several limitations must be acknowledged to contextualize these findings and guide future research efforts. First, this study included a limited range of LCUs, specifically a halogen unit, a monowave LED, and two polywave LEDs [9; 43]. The results apply to the bulk-fill composite resin and LCUs tested. Moreover, this study relied on microhardness as an indirect measure of the degree of conversion (DC), which, while reliable, does not provide a direct quantification of polymerization. Direct methods like Fourier-transform infrared spectroscopy (FTIR) or Raman spectroscopy offer more precise insights into the DC at different depths and should be considered in future studies for a more accurate evaluation [39; 43]. Another limitation is the standardized curing times and irradiance levels used for each LCU in this study. Different combinations of these parameters, such as varying exposure times or irradiance levels, could significantly affect polymerization outcomes, particularly for composites with different photoinitiator systems or filler compositions and possessing a variety of shades and opacities [46; 53; 70; 71; 72].
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 2 Materials and Methods
### Algorithm  
#### 2.3.1 Random Forest  
RF is an accurate and computationally efficient technique capable of swiftly processing extensive datasets [25; 41]. It is an extension of Classification Tree Analysis (CTA), frequently employed in the analysis of remotely sensed data for generating land cover classifications.  
In each classification tree, the input data (the pixels) undergo a hierarchical partitioning into increasingly homogeneous groups corresponding to different land cover classes based on their relationships with a set of predictive variables (the spectral bands) [23; 42]. The model is then trained using the spectral band responses to produce an output map displaying potential land cover classes. Each decision tree node utilises a randomly selected subset of attributes from the original set, promoting diversity in the model. Moreover, the decision trees are constructed using random yet equally sized subsets of data, and the final class assignment for a pixel is determined by a majority vote [41].  
This method is simple to apply, as it has few parameters and is easy to interpret [43]. However, although the RF is robust to outliers and overfitting [25; 44], it is unstable and allows small changes in the input to produce very different decision trees.  
In this study, the classification was carried out for the Valencian Community area, using more than 1000 points for each year for training (Table 1). Due to the large amount of information to be analysed, all processing was performed via GEE. To implement the algorithm, we used the SmileRandomForest function, a Classifier package that handles supervised classification by traditional machine learning algorithms. GEE's RF classifier offers various customisation options, such as the number of decision trees to create, the size of the input subset per tree, and the maximum number of parent nodes for each tree. The algorithm was applied using 150 decision trees in order not to excessively increase the computational cost while maintaining good accuracy, as recommended by [PERSON] et al. [41]. Default values were maintained for the other parameters.  
Although some authors question the accuracy of the manual approach [45], compared to unsupervised and object-based methods, RF is equally accurate with Earth observation images at medium spatial resolution [46]. Furthermore, in our case, it would have been impossible to carry out a supervised segmentation as proposed by [PERSON] et al. [47] due to the heterogeneity of the landscape and the low spatial resolution of the pixels compared to the dimensions of the objects.
# Evaluation of Ride Comfort under Vortex-Induced Vibration of Long-Span Bridge
## 3 Ride Comfort Criteria
### Calculation of Motion Sickness Incidence  
The primary factor influencing motion sickness is the acceleration experienced in three directions: vertical, nodding, and rolling. Consequently, when analyzing motion sickness for drivers in vehicles, the considered accelerations include \(a_{ss}\), \(a_{sf}\), \(a_{gb}\), \(a_{xx}\), and \(a_{ry}\). Similarly to the calculation method for the OVTV, the ISO 2631-1 standard [14] employs a specific frequency weighting function (\(W_{f}\)) to account for the influence of varying vibration frequencies on human motion sickness. This function applies frequency modulation to the accelerations measured in the aforementioned five directions. According to the ISO 2631-1 standard [14], it assumes that different positions and directions exert an equivalent effect on human motion sickness. Hence, the weighting coefficient assigned to each axial vibration component is set at 1.  
\[MSDV_{ij}=\left\{\int\limits_{0}^{T}\left[a_{\omega-ij}(t)\right]^{2}dt\right\} ^{1/2}=RMS_{ij}T^{1/2}, \tag{36}\]  
\[MSDV_{T}=\left\{\sum(MSDV_{ij})^{2}\right\}^{1/2}, \tag{37}\]  
where \(MSDV\) denotes the motion sickness dose value, and \(MSDV_{T}\) represents the total motion sickness dose value considering the contributions of different axial accelerations.  
\[MSI(\%)=K_{m}MSDV_{T}, \tag{38}\]  
where the subscript \(K_{m}\) represents a constant, and for a group consisting of adult males and females, the ISO 2631-1 standard [14] recommends using the value 1/3. It is important to note that the resulting \(MSI\) is expressed as a percentage. The limitation of \(MSI\) is that there is no clear comfort limit standard, and it can only be analyzed qualitatively.  
Motion sickness depends on various factors, including driver tolerance, individual differences (such as gender and age), and the duration of vibration exposure. According to the ISO 2631-1 standard [14], individual sensitivity to low-frequency oscillations varies significantly, with women being more susceptible than men. Individuals are generally susceptible to motion sickness, but the incidence of symptoms decreases with age. Longer exposure to vibration increases the likelihood of experiencing motion sickness.
## 3 Results and Discussion
### Morphology
Hackle marks (comb-like microcracks) due to shear fractures appeared prominently in the resin with numerous CNTs. As shown in Figure 9(b), the baseline specimen surface without hackle marks was extremely smooth. In contrast, there were extensive shear hackle marks for the buckypaper-interleaved specimen, as shown in Figure 9(c). The hackle marks were created due to crack deflection under a shear load because of the presence of CNTs. Figure 10: _Cont._  
The critical strain energy release rates in mode II (Figures 5 and 7) were significantly higher than those in mode I. This was because the fracturing process zone in the mode II fracture was longer than that for the mode I fracture. Using a cohesive zone model, [PERSON] et al. observed that the length of the mode II fracture process zone was approximately six times that of the mode I fracture process zone [31]. The longer process zone was accompanied by extensive hackle marks due to shear fractures containing numerous CNTs, which led to the toughening of CFRP composites [32]. In addition, CNTs increased the fracture toughness of the matrix in CFRP at the shear loading condition rather than at the opening loading condition. In the case of WN, the warp and weft created a matrix-rich area. In the matrix-rich region, CNTs resisted matrix cracking via crack bridging. Moreover, the initiation and propagation of cracks required more of a load (Figure 10f). Therefore, interleaving buckypaper into CFRP enhanced the mode II fracture toughness. Figure 10: SEM images of the fracture surface of the mode II test specimens: (**a**) UD baseline (**left**) and UD interleaved with buckypaper (**right**), (**b**) UD baseline, (**c**) UD interleaved with buckypaper, (**d**) WN baseline (**left**) and WN interleaved with buckypaper (**right**), (**e**) WN baseline resin-rich area, and (**f**) WN buckypaper-interleaved resin-rich area.
## 3 Research on the Formation and Evolution of Discontinuous Microfractures
### Microscopic Discontinuous Fractures  
For the entire continental crust, faults can be considered as microfractures, and the study of en echelon faults can provide a cross-scale reference for understanding microscopic discontinuous fractures. However, the generation, evolution, and mechanical mechanisms of micro-discontinuous fractures still lack a comprehensive explanation. The current consensus is that the formation of discontinuous fractures is influenced by the heterogeneity of shale mechanical strength and complex stress fields, including factors such as porosity, fractures, discontinuity planes, and mineral composition [17; 35]. However, these viewpoints lack quantitative evidence from in situ loading experiments.  
There has been significant research on the connection forms of discontinuous fractures, and researchers have made good progress in summarizing them [50; 51; 52]. The research mainly focuses on the connection process of pre-existing fractures within rocks, including tension fracture connection, shear-driven tensile fracture connection, shear fracture connection, mixed-mode fracture connection, and non-direct connection [53; 54; 55]. Most of this research on the connection forms of discontinuous fractures is based on observations of fractured microscale rock samples, with limited research on in situ fracture propagation at the microscopic scale. Therefore, further investigations are needed to study and validate the connection forms of microscale discontinuous fractures.  
Microscopic discontinuous fractures exhibit hierarchical distributions, and researchers have summarized the hierarchical distribution characteristics of microfractures through in situ scanning electron microscope tests (Figure 4) [17; 34; 56]. The spatial distribution of microfractures follows a power law distribution and exhibits self-affinity and hierarchical structure. Based on the evolution of micro-damage and the progressive process of fracturing, laws for multiscale fracture propagation have been proposed, considering the geometric evolution characteristics of fractures in a multiscale damage power law model [27; 57]. Currently, the hierarchical distribution characteristics of discontinuous fractures are mostly statistically significant, and hypotheses and numerical simulations are used to explain their  
Figure 3: Macroscopic discontinuous fractures. (**a**) Aerial photographs of discontinuous en echelon faults. Credit: US Geological Survey. (**b**) Discontinuous fractures on the road surface [47]. (**c**) Discontinuous fractures forming during incipient stages of faulting [48]. (**d**) En echelon vein array in claystone [49].  
formation mechanisms [15; 35; 58]. Further in situ observation experiments are required to validate these conclusions.
# Piecewise Function Hysteretic Model for Cold-Formed Steel Shear Walls with Reinforced End Studs
## 3 Hysteretic Model of CFS Shear Walls with Reinforced End Studs
### Model Verification
A comparison of the cumulative energy dissipation between the calculated results and test results for each specimen is shown in Figure 12. It can be observed that during each level of horizontal load, the calculated results of the cumulative energy dissipation for 140-type walls (specimens W140-1 and W140-2) are very close to their test results; for 89-type walls, the relative errors \(\varepsilon\) of the cumulative energy dissipation between the calculated results and the test results are small in the initiation of loading and increase constantly with growing load (see Figure 12a,c). It also can be concluded from Table 3 that the relative error \(\kappa_{3}\) of the total energy dissipation between calculated results and test results for 140-type walls is within 10%, which is obviously lower than that of 89-type walls with a maximum value of 28%. The reason is that the energy dissipating mechanism of a CFS shear wall  
Figure 11: Hysteretic curves of test results and the proposed hysteretic model. (**a**) Specim W89-1; (**b**) Specim W89-2; (**c**) Specim W89-3; (**d**) Specim W140-1; (**e**) Specim W140-2. depends on the deformation of screw connections; due to a larger stiffness of the reinforced end studs in a 140-type wall, the lateral of deformation of the wall was effectively mitigated, and the deformation of the screw connections in the fields of the wall were allowed to get full development, reaping the benefit of a steady growth of the cumulative energy dissipation, therefore, the energy dissipation displayed minor differences between the calculated results and the test results during the loading process; whereas, for a 89-type wall with reinforced end studs that had lower stiffness, besides steel frame buckling, the addition of a supplementary torsional deformation, that--except for shear deformations in screw connections, which were caused by the in-plane rotation of wallboards--was involved in energy dissipation, improved the energy dissipating capacity of the wall during the later stage of loading; however, the proposed hysteretic model cannot consider both steel frame buckling and the supplementary torsional deformation into the energy dissipation of the wall, and the calculated results are thereby obviously lower than the test results. It should be noted that, in the end of the loading process, due to a larger aspect ratio (\(H/L=2.5\)), there was no obvious damage in specimen W89-2 except screws in the end studs being sheared off on a single side of the wall [22]. The wall's energy dissipating capacity was significantly reduced by the insufficient deformation of screw connections (see Figure 12a). Consequently, for such walls with larger aspect ratio, the energy dissipating capacity should not be considered.
## 4 Methodology
### Mobile Interface Development  
The mobile interface comprises two primary components: a frontend mobile application developed using React Native and a backend server built on Node.js. The React Native application offers an intuitive and user-friendly interface for users to record and send voice messages. A single recording button facilitates the capture of audio, which is then transmitted to the backend server for processing.  
The backend server leverages the Multer library for efficient file handling, receiving and temporarily storing the audio recordings sent from the mobile application. The audio files are converted and stored with specific parameters to ensure compatibility and quality for subsequent analysis. These parameters include the following:  
* File Format: Conversion to WAV format to maintain compatibility with audio processing tools.
* Audio Codec: Utilization of the pcm_s16 le codec to ensure high-fidelity, lossless audio encoding.
* Sampling Rate: Standardization of the sampling rate to 16,000 Hz.
* Audio Bitrate: Setting the bitrate to 256 kbps for superior audio quality.
* Audio Channels: Processing audio in mono to focus on voice analysis and reduce file size.  
These specifications are carefully selected to match the original recording characteristics, ensuring accurate and consistent analysis.
## 2 Materials and Methods
### Extraction of the Genomic DNA, PCR Amplification, and Illumina MiSeq Sequencing  
The _Daqu_ samples' genomic DNA was extracted using a PowerSoil DNA Isolation Kit (Mo-Bio, Carlsbad, CA, USA) according to the manufacturer's instructions, and then electrophoresis was performed on a 1% agarose gel, which checked the DNA quality. The bacteria were identified via PCR amplification of the v3-v4 region of the 16S rRNA gene, using the primers 338F (5\({}^{\prime}\)-ACTCCTACGGGAGGCAGC-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GGACTACHVGG GTWTCTAAT-3\({}^{\prime}\)) [30] and the internal transcribed spacer (ITS1) regions of the fungal rRNA genes were amplified using the primers ITS1F (5\({}^{\prime}\)-CTTGGTCATTTAGAGGAAGTA-3\({}^{\prime}\)) and ITS1R (5\({}^{\prime}\)-GCTGGTTCTTCATCGAT GC-3\({}^{\prime}\)) [30]. The PCR products were sequenced on an Illumina MiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, CA, USA), according to the standard protocols by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).
# Network Analysis to Identify the Risk of Epidemic Spreading
## 2 Materials and Methods
### Epidemic Spreading Based on the SIR Model  
For the epidemic spreading simulations, we applied an SIR model to the generated scale-free network. The classical SIR model can be expressed by the following nonlinear differential equations [21]:  
\[\frac{dS}{dt}=-\beta SI, \tag{1}\]  
Figure 2: Degree distribution of the scale-free network. We analyzed the degree distribution of the network based on the number of nodes (\(N\)) and mean degree (\(k\)).  
\[\frac{dI}{dt}=\beta SI-\gamma I, \tag{2}\] \[\frac{dR}{dt}=\gamma I,\] (3) \[S+I+R=N, \tag{4}\]  
where \(S,I,\) and \(R\) represent susceptible, infected, and recovered compartments, respectively, in the whole population. \(S\) represents people who have not been infected yet but can be infected in future. \(I\) represents infected people who can spread the epidemic to susceptible people through physical contact. \(R\) denotes people who have recovered or died from the epidemic and who no longer participate in the epidemic spreading process. The sum of the \(S,I,\) and \(R\) values represents the whole population size \(N\). Epidemics have two parameters in the SIR model, transmission rate (\(\beta\)) and recovery rate (\(\gamma\)), which arise from the basic reproduction number \(R_{0}\) (Figure 1B). The basic reproduction number is the number of infections caused by one infective node [31; 32; 33]. If the \(R_{0}\) is more than 1, the infection can spread in a population, whereas if \(R_{0}\) is less than 1, the infection cannot spread. We express the basic reproduction number as \(R_{0}=\beta/\gamma\), where \(\beta\) represents the spreading rate of epidemics between infective nodes and adjacent susceptible nodes and \(\gamma\) represents the probability of recovery from infection [34]. We mainly used \(\gamma_{d}\), which is the reciprocal of \(\gamma\) and reflects the time interval between infection and recovery.
# Numerical Simulation of Flow Field of Submerged Angular Cavitation Nozzle
## 2 Numerical Calculation Method
### Mathematical Model of Angular Cavitation Nozzle  
The mathematical models of the angular cavitation nozzle include the multiphase flow mixture model, cavitation model, and RNG \(k\)-\(\epsilon\) turbulence model [25], which must satisfy the momentum conservation equation, continuity equation, and relevant model equations.
## 3 PSS Signal Detection at UE
### Detection Algorithm of PSS Signals Using Generalized Likelihood Ratio Test (GLRT)  
Using the GLRT, the cell search problem can be stated as a binary hypothesis problem in which we know either \(H_{0}\) or \(H_{1}\). Specifically, for each delay value \(\tau\), \(H_{0}=d(t)\) corresponds to \"no signal detected\" at the UEs, hence there is only noise, and \(H_{1}=z(t)\) is the hypothesis that signals are present at the receiver, where \(z(t)\) is described in Equation (1). The GLRT is then used to decide between the two hypotheses as,  
\[\Lambda(\mathbf{\tau})=\frac{max_{\theta_{1}}p(\mathbf{Z}_{\tau}|H_{1},\mathbf{\theta})}{ max_{\theta_{0}}p(\mathbf{Z}_{\tau}|H_{0},\mathbf{\theta})}\begin{array}{c}H_{1}\\ \gtrless\\ H_{0}\end{array}t^{\prime} \tag{4}\]  
where \(\mathbf{\theta}=\left(\sigma^{2},\alpha_{x,t,t},\alpha_{rx,t}\right)\) is the vector of unknown parameters, \(p(\mathbf{Z}_{\tau}|H_{i},\mathbf{\theta})\) is the probability density of the received signal \(\mathbf{Z}_{\tau}\) under each hypothesis \(H_{0}\) or \(H_{1}\), and \(t^{\prime}\) is the threshold value as reference. The problem in (4) can be solved statistically by using the maximum likelihood estimators, in which the values of \(\mathbf{\theta}\) are estimated to maximize the value of probability density function under each hypothesis. Further details on this method can be found in [31; 37; 38; 39].  
By using the matched-filtering method, the likelihood ratio in Equation (4) can be evaluated via a correlation computation. As the details can be found in [21], we only show the important formulas used to calculate the likelihood ratio between the two hypotheses. With the received signal at the UE as shown in Equation (1), let \(v_{l}(\tau)\) be the normalized correlation of the received signal \(z(t)\) with the transmitted PSS signal \(p_{l}(t)\). Then,  
\[v_{l}(\tau)=\frac{1}{||p_{l}(t)||}\int p_{l}^{*}(t)z(t-\tau)dt \tag{5}\]  
The likelihood ratio or test statistics \(\Lambda(\mathbf{\tau})\) in Equation (4) is then equivalent to the test form.  
\[\Lambda(\tau)=\frac{||\mathbf{v}(\tau)||^{2}}{E(\tau)}\begin{array}{c}H_{1}\\ \gtrless\\ H_{0}\end{array}t^{\prime} \tag{6}\]  
where \(t^{\prime}\) is the threshold value, \(\mathbf{v}(\tau)\) is a vector as defined below  
\[\mathbf{v}(\tau)=\begin{bmatrix}v_{1}(\tau),\;v_{2}(\tau),\dots,v_{N_{slot}}(\tau )\end{bmatrix} \tag{7}\]  
and \(E(\tau)\) is the total received energy over \(N_{slot}\) time slots defined as:  
\[E(\tau)=\sum_{k=1}^{N_{slot}}\int||z(t-\tau)||^{2}dt \tag{8}\]