esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, specimen size and aggregate size were considered as variables to study their effects on the compressive strength of RFC, and corresponding numerical experiments were established based on the RFPA method and the multiple path damage analysis method. The failure mode and compressive strength data of RFC were analyzed, and the conclusions are summarized as follows:  
1. RFC has a formally consistent compressive strength size effect law with SCC and rock, and a better result is obtained by fitting using the negative exponential function. Based on the fitting function, the characteristic sizes of RFC under different particle size types are given in this paper. There is no obvious law between the characteristic sizes and particle size types. The influence of specimen size on the failure mode of RFC is not significant.  
2. For RFC with the same proportion of aggregate, as the aggregate size decreases, the compressive strength increases, and this increasing trend gradually slows down. The compressive strength of RFC is positively correlated with the total energy released through acoustic emission. RFC with smaller aggregate sizes all exhibit inclined through-cracks after failure, which manifests as a macroscopic shear failure consisting of a large number of tensile damaged elements.
3. This paper analyzes the compressive strength data of RFC, and obtains the fitting surface function of specimen size and aggregate size against compressive strength. Based on the fitted surface, it is evident that the effect of specimen size on compressive strength is more pronounced than that of aggregate size.  
It should be noted that in this paper, the aggregate in the RFC is simplified as circular and arranged in simple cubic stacking, which is different from actual engineering situations. In practical engineering, the mechanical properties of RFC vary greatly due to the irregular shape of rock and the randomness of its spatial distribution. Based on this paper, further research can be conducted to investigate the impact of the coupling of these complex factors on the compressive strength of RFC.  
Conceptualization, X.L.; methodology, T.Y. and X.L.; software, T.Y.; validation, Y.Z. ([PERSON]), G.W. and Y.L.; formal analysis, X.L. and Y.Z. ([PERSON]); writing--original draft preparation, X.L.; writing--review and editing, X.L., L.Y. and Y.Z. ([PERSON]); visualization, H.Q.; project administration, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No.52069004, No.42107189), Guizhou Provincial Science and Technology Projects (QKHJC-ZK [2021] YB293), and the Guizhou Province Graduate Research Fund (YJSKYJ) [2021] 064).  
Not applicable.  
We are grateful to [PERSON] of Taiyuan University of Technology for his assistance in the experimental design and thesis writing of this study.  
The authors declare no conflict of interest.
# Fractional Order Controller Design for Wind Turbines
## 2 Materials and Methods
### Two-Mass Wind Turbine Model
[25] and was used to represent the aerodynamic torque \(T_{t}\) that was produced by the wind turbine model:  
\[T_{t}=\frac{P_{t}}{\omega_{t}}=\frac{\rho\pi R^{3}vC_{p}(\lambda,\beta)}{2 \lambda} \tag{5}\]  
Furthermore, the dynamic response of the rotor was interpreted using Equation (6), where \(J_{t}\) symbolizes the turbine side inertia at speed \(\omega_{t}\) using the torque \(T_{t}\) from Equation (5) and \(K_{t}\) is the wind turbine external damping coefficient:  
\[J_{t}\frac{d\omega_{t}}{dt}=T_{t}-T_{ls}-K_{t}\omega_{t} \tag{6}\]  
Equation (7) was employed to calculate the low-speed shaft torque \(T_{ls}\) using the low-speed shaft stiffness \(K_{ls}\), the damping coefficient \(B_{ls}\), the low-speed shaft \(\omega_{ls}\), the rotor side angular deviation \(\theta_{t}\) and the gearbox side angular deviation \(\theta_{ls}\):  
\[T_{ls}=K_{ls}(\theta_{t}-\theta_{ls})+B_{ls}(\omega_{t}-\omega_{ls}) \tag{7}\]  
The generator inertia \(J_{g}\) was calculated using the high-speed shaft torque \(T_{hs}\) and the electromagnetic torque \(T_{em}\), as can be seen in Equation (8), where \(K_{g}\) and \(\omega_{g}\) stand for the generator external damping coefficient and the generator speed, respectively:  
\[J_{g}\frac{d\omega_{g}}{dt}=T_{hs}-T_{em}-K_{g}\omega_{g} \tag{8}\]  
The mathematical equation that was used for \(T_{em}\) is shown in Equation (9), where \(K_{1}\) and \(K_{2}\) are electromagnetic torque coefficients:  
\[P_{t}=\frac{1}{2}\rho R^{2}C_{p}(\lambda,\beta)v^{3} \tag{1}\]0  
By assuming that the gearbox \(n_{g}\) was ideal, the transmission ratio could be expressed as:  
\[P_{t}=\frac{1}{2}\rho R^{2}C_{p}(\lambda,\beta)v^{3} \tag{1}\]1  
Finally, with the help of Equations (6)-(10), a block diagram of the wind turbine could be constructed (as shown in Figure 2) and a transfer function for the two-mass system was derived in the form of Equation (11):  
\[P_{t}=\frac{1}{2}\rho R^{2}C_{p}(\lambda,\beta)v^{3} \tag{1}\]2  
where all of the constant values that were used are presented in Table 1 to compare the results, similar to in [4]. Obviously, these values could be updated for any turbine under consideration. After obtaining the transfer function for the wind turbine, a control scheme (as shown in Figure 3) was proposed for implementation in MATLAB Simulink.
## 3 Results and Discussion
### Pull-Off Evaluation Method  
To confirm the adhesive performance of an SPRG according to the substrate surface humidity ratio, the specimens were tested using the adhesive performance test method for the construction of waterproofing materials, as specified in KS F 3211:2015, with modifications [17]. The test substrate was prepared using a metal mold with inside dimensions of approximately 210 mm \(\times\) 70 mm and a thickness of approximately 20 mm, adjusted according to the method specified in KS L ISO 679. The substrate was cured for 24 h at a temperature of (20 \(\pm\) 3) \({}^{\circ}\)C and a humidity of 80% or more, and then removed from the mold and allowed to age for six days at (20 \(\pm\) 2) \({}^{\circ}\)C in water, followed by a further seven days of conditioning in the standard state. The bottom surface of the substrate was sufficiently ground with a 150-grit abrasive paper according to KS L 6003, and then the surface humidity ratio was measured. After applying the SPRG to the substrate, three dollies were placed on each of SPRG test specimen. Any adhesive protruding from the side during the attachment process was removed, and the four sides of the attachment were cut using a grinder cutter until reaching the substrate. The adhesive performance test was conducted by fixing the test specimen and dolly to a tensile testing machine and measuring  
Figure 3: Specim humidity ratio conditioning: (**a**) substrate submerged in water for predetermined period of time; and (**b**) humidity ratio measurement.  
the maximum load by applying tension at a vertical speed of 2 mm/min to the sample surface. The calculation was performed according to Equation (1). The test specimens and test status are shown in Figure 4.  
\[T_{f}=\frac{T_{n}}{A} \tag{1}\]  
where;  
\(T_{f}=\) Adhesive Strength \(\left(\mathrm{N/mm^{2}}\right)\)  
\(T_{n}=\) Maximum Load \(\left(\mathrm{N}\right)\)  
A = Specim cross-section area (mm\({}^{2}\))
## 3 Results and Discussion
### 3.6. Dough Rheological Properties during Heating
#### 3.6.4 Starch Retrogradation  
The regression equation obtained for C5 torque, which represents starch retrogradation during the cooling period of the Mixolab, is given by the Equation (15).  
\[\text{C5}=2.54+0.0559\cdot\text{X}_{1}-0.4681\cdot\text{X}_{2}\ +0.0297\cdot\text{X}_{1}\cdot\text{X}_{2}-0.0791 \cdot\text{X}_{1}^{2}\ +0.0730\cdot\text{X}_{2}^{2} \tag{15}\]  
The ANOVA results show that the quadratic model fitted for C5 torque was well adjusted to the experimental data (\(R^{2}\) = 0.98).  
The maximum consistency during starch retrogradation (C5) was significantly (\(p\) < 0.0001) affected by the particle sizes and different levels of QF added to wheat flour. It was found that there was a significant (\(p\) < 0.05) positive effect of increasing particle size and a significant (\(p\) < 0.0001) negative effect of QF level added to wheat flour on C5 torque. The response surface graph for the combined effect of particle size and level of QF added to wheat flour on C5 is given in Figure 5a. As the level of QF increased and the particle size decreased, the C5 torque decreased. This indicates that starch retrogradation depends on the starch proportions of amylose and amylopectin [79], amylose recrystallizing faster than amylopectin. The decreases of C5 torque suggested that particle size composition influenced \(\alpha\)-amylase activity in quinoa-wheat composite flours, a high activity causing a lowering of C5 value.  
The quadratic regression model obtained from the ANOVA for the difference between C5 and C4 torques (C5-4) (Equation (16)) was found to be significant (\(p\) < 0.0001) and the \(R^{2}\) value of 0.97 confirmed the adequacy of the model.  
\[\text{C5}-4=0.8712+0.0480\cdot\text{X}_{1}-0.2295\cdot\text{X}_{2}\ +0.0427\cdot\text{X}_{1}\cdot\text{X}_{2}-0.0424\cdot\text{X}_{1}^{2}\ +0.0656\cdot\text{X}_{2}^{2} \tag{16}\]  
Based on the obtained results, it was found that there was a significant effect of factors, particle size and QF level added to wheat flour (\(p\) < 0.005 and \(p\) < 0.0001), and of their interaction (\(p\) < 0.05), on the C5-4. All the formulations had a lower value for C5-4 torque compared with the control sample.  
Figure 5: Response surface graph for combined effects of quinoa flour particle size and level added in refined wheat flour on Mixolab parameters during starch retrogradation: (**a**) C5 torque (C5) and (**b**) difference between torques C5 and C4 (C5–4).  
The response surface graph for combined effect of factors on C5-4 is given in Figure 5b. As the particle size decreased and QF addition increased, the C5-4 decreased, the lowest value (0.56 N\(\cdot\)m) being achieved at 20% QF of small particle size which substituted the wheat flour. This decrease may be attributed to the quinoa flour addition to wheat flour limiting starch retrogradation and preserving bread freshness depending on particle size.
## 4 Results and Discussion
### Effect of Lightweight Particles on the Sound Absorption Performance of Composite Porous Materials  
Figure 8 shows the sound absorption coefficient curves for porous materials with varying amounts of zeolite added under five different densities, all prepared using fibers with a diameter of 5 \(\upmu\)m. Among them, Figure 8f summarizes the NRC for the various samples (the specific values are provided in Table S4). At lower densities (60 kg/m\({}^{3}\) and 80 kg/m\({}^{3}\)), the addition of zeolite particles reduces the sound absorption performance of the porous materials, with the performance degradation becoming more pronounced as the zeolite content increases. Although the introduction of zeolite helps to form a favorable median pore size, the limited number of fibers at low densities combined with the substitution effect of zeolite prevents the formation of an effective space structure for sound absorption within the material. As a result, while sound waves may penetrate the material, they are less effectively dissipated, leading to reduced sound absorption performance. Furthermore, as the amount of zeolite increases, excessive zeolite particles tend to agglomerate (Figure 5c from Section 4.1), blocking the pores of the porous material and impairing its acoustic performance.  
On the contrary, for higher densities (\(\geq\)100 kg/m\({}^{3}\)), a small amount of zeolite substitution can enhance the sound absorption performance of the material. The improvement of sound absorption performance can be attributed to two main factors. On one hand, compared to low-density porous materials, higher-density materials contain a greater number  
Figure 8: Sound absorption coefficient and NRC of composite materials under different densities: (a) 60 kg/m\({}^{3}\); (b) 80 kg/m\({}^{3}\); (c) 100 kg/m\({}^{3}\); (d) 120 kg/m\({}^{3}\); (e) 140 kg/m\({}^{3}\); (f) NRC.  
of fibers. This means that the substitution of fibers with a small amount of zeolite does not significantly reduce the structural complexity of the material. Instead, the addition of zeolite particles modifies the pore size distribution, creating an optimal pore size. The synergistic effect between the particles and fibers results in a significant pressure diffusion effect [48], thereby enhancing sound absorption performance. On the other hand, the addition of zeolite introduces two distinct interfaces within the porous material, due to the difference in acoustic impedance between these two interfaces, and sound waves undergo strong reflection and refraction as they transition from one interface to another. This process increases the dissipation of sound energy within the material, further improving its acoustic performance.
# Effects of Climate Conditions before Harvest Date on Edamame Metabolome
## 2 Results
### Correlation Analysis between Climate Conditions and Edamame Metabolome  
Correlation analysis was performed using six years of meteorological and metabolomic data (Figure 3). We found correlations between the metabolites and between climate conditions and metabolite amounts. Amounts of some amino acids in edamame were correlated with the average temperature and sunshine duration. A strong correlation was observed between lysine (Lys) and the average temperature, whereas inverse correlations with the sunshine duration and average temperature were observed for some organic acids and nucleotides. There was no clear correlation among rainfall, average wind speed, temperature difference, and the amount of each compound.
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 5 Experimental Evaluation
### Effectiveness and Robustness of the AEs in Novelty Detection
#### 5.1.4 Lesson Learned
From the industrial perspective, the proposed approach gives the opportunity to automatically assign a label to the acquired signals, indicating the correct machinery operating condition, regardless of the dataset available during the model training phase. In this way, the secrecy of the implemented machinery setting is kept, and only the change from one condition to another, both know and novel, is recorded in the dataset to facilitate the subsequent activities of fault detection and diagnosis. In addition, results show that AEs outperform baseline approaches in performing a simultaneous analysis at a system-level and component-level. Indeed, although it decreased with respect to the previous experiment, a higher F1 score is obtained, even if both nominal conditions corresponding to the system setting and the anomalous condition corresponding to the motor fault, are considered during model training.
## 4 Results and Discussion
### Most Suitable Feature Selection (FS) Method  
Different FS methods were evaluated for allergenic tree species mapping using PS imagery. Table 6 shows OA and Kappa values calculated for classification scenario 3 (i.e., multitemporal PS imagery with VI and GLCM). As an outcome of the FS approach described in Section 3.2., MDA, MDG, and VSURF selected 28, 30, and 20 most important features for tree species mapping, respectively. Out of the feature selection methods evaluated, VSURF selected the smallest number of input variables. The best classification results were achieved with RF and XGB when using this VSURF subset. VSURF method has already confirmed its high performance in choosing an optimal subset of features for classification [65]. Furthermore, FS methods are not suitable for MLP classifiers, since large training datasets may avoid the overfitting problem [86; 87].  
Although the tested FS methods did not increase the classification results (Table 6) compared with the CS3 results (Section 4.1), individual input features from eight-band PS imagery can be analyzed for allergenic tree mapping. Since the VSURF method produces a list of the most pertinent input features [55], Figure 3 evaluates the number of input features grouped by the source (i.e., PS spectral band, vegetation indices, or GLCM texture feature). As expected, VI is the most represented group of input features for mapping allergenic tree species, whereas PS bands and GLCM texture features are included in a final model with five features each. In urban green areas, simple VI, which combines visible and NIR bands, has significantly improved tree detection sensitivity [36]. Out of ten VI input features, EVI and GNDVI were the most frequent. Furthermore, this research used multitemporal PS imagery acquired from the new generation of the sensor system (i.e., PSB.SD). Out of four newly added spectral bands, red-edge and coastal blue showed to be the most indicative input features. From GLCM texture features, the Variance was the most indicative input feature in a final model. As mentioned in the research by [PERSON] [47], variance is commonly associated with visual edges of land-cover patches. Finally, imagery acquired in April, the peak pollination month [11], proved to be the most indicative date for mapping allergenic trees.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 3 Methodology  
As previously mentioned, this paper is subsequent work to the previous eye-tracking experiment at BT and Ulster University [11; 16], in which a group of participants used BT Player while their eye movements were monitored by an eye tracker. The dataflow of the eye gaze movement is integrated with other information, such as the BT Player screens, by the Tobii Studio (version 1803, Windows) software package. For the duration of the experiment, participants were instructed to complete several activities connected to TV viewing. This study focuses largely on 2 activities, namely, how TV customers acquire content and how their eyes stay on the Content Discovery page for the first few minutes throughout this activity. Besides, since Markov models have been chosen to model the eye-tracking data, this section discusses how the calculations and techniques can be implemented. As with most forms of data analysis, certain date manipulation needs to take place to achieve the required format before the Markov analysis can begin.
## 2 The Principle and Element Design of the CPFRA
### Geometry and Performance of the Metasurface-Based Element on the Primary Reflector Surface
#### 2.3.2 Analysis of the Transmissive Linear-to-Circular Polarization Converter  
The simulated reflective and transmissive results are shown in Figure 8a. The symbol \(\left\lvert\,r_{vv}\,\right\rvert\) represents the reflection coefficient of the linearly polarized incident wave along the \(v\)-polarization direction. \(\left\lvert\,t_{yy}\,\right\rvert\) indicates the amplitude of the transmitted wave along the \(y\)-axis component when the incident wave is linearly polarized along the \(v\)-polarization direction. \(\left\lvert\,t_{xy}\,\right\rvert\) represents the amplitude of the transmitted wave along the \(x\)-axis component when the incident wave is linearly polarized along the \(v\)-polarization direction. The simulation results show that when the incident wave is incident perpendicularly, the linearly polarized EW in the \(x\)-direction is reflected back and cannot pass through the polarization converter. The linearly polarized incident waves along the \(y\)-axis direction can pass through this polarization converter, and within the frequency band of 23-29 GHz, the amplitudes of the two orthogonal components of the transmitted waves are approximately equal, and the phase difference is \(-90^{\circ}\pm 15^{\circ}\). Figure 8b shows the transmission amplitude and phase difference responses of the linear-to-circular polarization converter under the oblique incidence condition. The results indicate that the proposed transmissive linear-to-circular polarization converter still has good polarization conversion performance under oblique incidence and is suitable for the design of broadband low-profile circularly polarized RAs.  
Figure 7: Geometry and performances of the transmissive linear-to-circular polarization converter on the sub-reflector surface: (**a**) 3D view, (**b**) top view, and (**c**) top view of the second layer of the render line; (**d**) top view of the grid.
# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 1 Introduction
Landslide susceptibility analysis not only analyzes the cause of landslide occurrence, but also estimates the susceptibility of future occurrence, so it can be effectively used to prevent landslides in advance [15]. Accordingly, research on the susceptibility of landslides has been actively conducted by researchers. Since a geographic information system (GIS) provides integrated analysis of various spatial data, it is useful to effectively analyze complex interrelationships between landslides and landslide-related variables. For this reason, there have been many attempts to explain the spatial pattern with various models based on GIS, in order to analyze the susceptibility of landslide occurrences in recent years. Most of the information for landslide prevention can come from modeling efforts using predictive models. Traditionally, to evaluate the susceptibility of landslide occurrence, simple, statistics-based frequency ratio (FR) [16; 17; 18] and logistic regression (LR) [19; 20; 21] were proposed and used as a representative prediction model. In recent years, various attempts have been made to produce machine learning (ML) models, such as support vector machines [22; 23], artificial neural networks (ANNs) [24; 25; 26], ensembles [27; 28], or hybrid models [29; 30] including neuro-fuzzy [31; 32] or metaheuristic ones [33; 34]. Several spatial modeling studies were conducted on areas where landslides could occur in various regions by using these models with GIS [35; 36]. In recent years, deep learning (DL), one of the ML technologies, has been receiving a lot of attention since it shows reliable results similar to or better than the existing ML methods [37]. In particular, it has been shown that model architectures, such as deep neural networks (DNNs), which are widespread, can significantly improve the quality of results [38]. In the area of landslide research, DL algorithms have mostly been used for landslide detection [39; 40; 41; 42]. Additionally, among various DL methods, convolutional neural networks (CNNs) can identify expressions with extreme variability through convolution and pooling layers [43]. As a result, CNNs can play an important role in susceptibility analysis, as they can remove tedious functional engineering steps and extract useful information directly from the original data. Although various techniques have been used, there is no academic consensus on which technique is most effective. Therefore, it is important to improve the accuracy of landslide susceptibility analysis by conducting comparative studies using various techniques, or by integrating multiple models [44; 45]. Therefore, in this study, we tried to build this effort by using and comparing DL methodologies to map the susceptibility of landslides in the Mt. Umyeon area of Seoul, Korea. To this end, DNN, kernel-based DNN, and CNN models were applied among the DL models. This study proposes the application of DNNs for estimating landslide susceptibility, and compares their predictive ability with other models of ML techniques.