esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this research, the nonlinear dynamic response of a CC-RCC combined dam structure under near-fault pulse seismic records and non-pulse records with consideration of the incidence angle of seismic P waves is investigated. The selected seismic records have the same PGA of 0.2 g. Different from the uniform excitation input method, this study takes the dam-foundation system into account. Combining with the viscous-spring artificial boundary, the wave input method is used. The relative horizontal displacements between the dam crest and foundation, and damage of the gravity dam under P wave incidence angles of 0\({}^{\circ}\) (vertical), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\) are analyzed. Conclusions are as follows.  
(1) When the seismic incident angle is the same, the destruction of the dam to the near-fault pulse ground motions is significantly larger than that to the non-pulse motions. In addition, the relative horizontal displacement of the dam crest increases with the ratio of PGV/PGA in the entire trend.  
(2) When the seismic incident angle increases, the nonlinear behavior of the dam increases first, and then decreases, and generally reaches the maximum when the incidence angle is 60\({}^{\circ}\) and 75\({}^{\circ}\).  
(3) In some cases, the damage of the gravity dam subjected to non-pulse records with considering the small incident angle of the P wave is more severe than that subjected to pulse records with considering the large incident angle of the P wave.  
Figure 15: Tensile damage on inflection point D of the dam.  
(4) The worst damage of the dam body occurs at the interface between II-CC and RCC, and the inflection point of the dam. The interface of different materials is a weak link, and the spatial variation of its damage is very different subjected to obliquely incident seismic records.  
The response of combined dam structure under near-fault ground motions is complicated. Due to the limitation of space, this paper only discusses the oblique incidence of the P wave. In practical engineering, SV wave and SH wave should be considered comprehensively.  
[PERSON] did the finite element simulation and wrote the paper; [PERSON] provided the idea and edited the manuscript; [PERSON] gave professional seismic guidance of hydraulic structures; [PERSON] gave professional guidance on finite element simulation; [PERSON] collected the engineering data in this paper; [PERSON] gave professional seismic guidance of hydraulic structures. 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 (grant no. 51879185), the Tianjin Natural Science Foundation for Distinguished Young Scientists of China (grant no. 17 JCJQJC44000) and the Fund of National Dam Safety Research Center (grant no. CX2019B02).  
The authors declare no conflict of interest.
# Underwater 2D Image Acquisition Using Sequential Striping Illumination
## 1 Introduction
Using the image formation model for light striping, a simple algorithm is developed to obtain the 3D reconstruction of the scene in the presence of strong scattering. For calibration, the 3D world coordinates of a few points on planes are measured a priori, and the parameters describing the optical properties of the medium are estimated by fitting observations to the model. Once the attenuation coefficient is evaluated and the 3D surface is reconstructed, the clear-air appearance of the scene can be computed for each object intersection strip and all the intersection strips geometrically stitch together to generate the 2D image. The algorithm relies on the evaluation of the attenuation coefficient and the preset calibration, which required extra in situ measurements underwater. The image acquisition using structured light has been given less attention until now. It is clear that the existing methods mentioned above work for the removal of back scattered light but still neglect the forward scattering effect. In their methods, image acquisition commonly utilizes the stitching approach, i.e., simply stacking the sequential frames to output the 2D image. Although stitching operation is straightforward, it fails to tackle the forward scattering effect in image acquisition, as stated in Section 2. Figure 1: Typical structured lighting technique. In this paper, we present a new approach to image acquisition using a structured lighting source and a CCD camera. Significantly, both forward scattering and back scattering are dealt within the image acquisition. Using the striping illumination in a scanning mode, sequential frame images are collected by the CCD camera. Instead of deploying an opto-electric coupled aperture sensor in the SLL system, we apply a virtual aperture to limit the field of view in each frame image, so as to separate the back scattered light from the reflected light of the object. The virtual aperture is specified by a masking operation, having the same mechanism as the SSL system [9] but computationally implemented rather than physically implemented. Different to the stitching approach commonly used in previous work for image formation, a method of image recovery is proposed. Sequential frame images are modeled on the convolution integration of the illumination function and the albedo of the object surface, with which each frame image is processed by integration operation. Thus, the albedo of the object can be recovered by deconvolution. The advantage of the proposed algorithm is that the forward scattering effect is fully eliminated through the integration operation. Theoretically, the integration operation enables us to collect the forward scattered light generated during the transmission from the object to the camera, enhancing the total imaging energy of the object.
## 3 Results and Analysis
### IR and AE Spatial Distribution of Different Coal Impact Types  
The spatial distribution of the AE locations and the infrared thermal image of coal failure are shown in Figure 4. The infrared thermal image is the difference between real-time IRT data and initial first-second data.  
Figure 3: Time series characteristics of IR and AE of different coal impact types.  
Figure 4: Spatial distribution characteristics of IR and AE of different coal impact types.  
As shown in Figure 4a, at the initial loading stage of 0.2\(\sigma_{\text{max}}\) for no-impact coal, the AE localization points are relatively scattered, and the IRT distribution is uniform. As the stress increases, at 0.5\(\sigma_{\text{max}}\)-0.85\(\sigma_{\text{max}}\), the IRT rises, and the AE localization points gradually increase, but the distribution is still scattered. At 0.95 \(\sigma_{\text{max}}\) and \(\sigma_{\text{max}}\), the AE localization points continue to increase, and the IRT of the coal body further rises, but there is no high-temperature concentration area. As shown in Figure 4b, weak-impact coal exhibits a small number of AE localization points at 0.2\(\sigma_{\text{max}}\)-0.5\(\sigma_{\text{max}}\), and the IRT distribution is uniform. As the stress increases, the AE localization points and IRT gradually increase. When reaching 0.95\(\sigma_{\text{max}}\), the AE localization points become dense and the infrared high-temperature area becomes concentrated. At \(\sigma_{\text{max}}\), the coal body becomes unstable and fails, resulting in a dense appearance of numerous AE localization points. As shown in Figure 4c, strong-impact coal only produces a small number of AE localization points before 0.7\(\sigma_{\text{max}}\), and the IRT distribution is uniform without significant changes. After 0.85\(\sigma_{\text{max}}\), the number of AE localization points and IRT rapidly increase, and the high-temperature area becomes concentrated. At \(\sigma_{\text{max}}\), the strong-impact coal undergoes impact failure, with a large number of AE localization points and infrared high-temperature concentration areas appearing in the middle of the coal body.  
Comparing different types of coal impact, it can be seen that AE location and infrared thermal image evolution have good consistency. The infrared high-temperature concentration area, the dense area of AE positioning points, and the location of the coal damage correspond to each other (Figure 4b,c), but there are significant differences among the different types of coal impact. The distribution of the AE localization points of the no-impact coal is scattered, and the infrared thermal image has no high-temperature concentration area. The weak-impact coal AE localization points and infrared high-temperature areas gradually become concentrated. The strong-impact coal AE localization points mainly occur before the coal failure, and the AE localization points and infrared high-temperature areas are most concentrated.
## 3 Preliminaries
### Mathematical Tools
#### 3.3.1 Chinese Remainder Theorem  
The Chinese remainder theorem (CRT) is widely used in authentication schemes for VANETs [41]. In addition, CRT is an essential tool for proving theorems in number theory, which shows that, once the Euclidean division remainders of an integer n are known, then the remainder of n is uniquely determined under pairwise coprime divisors [42; 43].  
Consider \(k_{1}\), \(k_{2}\), \(k_{3}\),..., \(k_{n}\) to be the positive integers pairwise prime, and consider \(K_{i}^{-}\) to be the modular multiplicative inverse of \(K_{i}\) mod \(k_{i}\). Hence, it satisfies Equation (1) as follows, where \(i=1,2,3\ldots n\).  
\[K_{i}K_{i}^{-}\equiv 1(mod\ k_{i}) \tag{1}\]  
Consider \(a_{1},a_{2},a_{3},...,a_{n}\) to be a specified \(n\) positive integers. Hence, CRT shows that the congruence pair has a unique solution mod \(\zeta_{g}\)=\(k_{1}\)\(k_{2}\).\(k_{i}=\prod_{i=1}^{n}(sk_{i})\), as the following equation.  
\[\begin{split}& X\equiv a_{1}\ mod\ k_{1}\\ & X\equiv a_{2}\ mod\ k_{2}\\ &\ldots\ldots\ldots,\\ & X\equiv a_{n}\ mod\ k_{n}\end{split} \tag{2}\]  
The solution is obtained by the key server using the following equation, where \(\beta_{i}=\frac{\zeta_{g}}{k_{i}}\) and \(\beta_{i}\gamma_{i}\equiv 1\) mod \(k_{i}\).  
\[X=a_{1}+a_{2}+\ldots+a_{n}(mod\ \zeta_{g})=\sum_{i=1}^{n}a_{i}\beta_{i} \gamma_{i}(mod\ \zeta_{g}) \tag{3}\]
## 2 Data and Methods  
### Study Area  
Zhejiang Province, located on the southeast coast of China, is famous for its rich Marine resources and developed aquaculture industry [13]. This study focuses on the cage aquaculture area of the large yellow croaker along the coast of Zhejiang Province, which is characterized by a unique marine environment (numerous bays, zigzag coastlines and complex continental shelves, rich marine resources, diverse water types, and wide sea area) (Figure 2a) and climatic conditions (located in the transitional zone between subtropical and temperate zones, with obvious seasonal alternations and mild and humid climate). It is an ideal place for aquaculture. In addition, Zhejiang Province has a long tradition of marine fisheries and rich experience and technology in aquaculture, which also helps develop marine aquaculture in Zhejiang Province [14].  
Figure 1: Framework of the Study. This diagram illustrates the overall structure and organization of the manuscript. It outlines the key sections of the paper, including Introduction, Data and Methods, Results, Discussion, and Conclusion. The arrows indicate the logical flow and interconnection between the sections. The abbreviations in the figure are as follows: CMES (Copernicus Marine Environment Monitoring Service), GEBCO (General Bathymetric Chart of the Oceans), SPMC (Suspended Particulate Matter Concentration), RTM (Radiative Transfer Model), LYC-A (Large Yellow Croaker Aquaculture Area), and SSM-LYC (Site Selection Method for Large Yellow Croaker).  
The economy of Zhejiang Province is developing rapidly, and the aquaculture industry plays an important role in the local economy. As a fish with high economic value, the large yellow croaker cage aquaculture area, an important aquaculture base in the province [15], not only plays a significant role in promoting local economic development but also plays a non-negligible role in promoting biodiversity protection and maintaining ecological balance [16]. As a kind of seafood with high economic value, the development of the large yellow croaker aquaculture industry has led to the prosperity of related industrial chains, created a lot of employment opportunities for local communities, and increased the income level of fishermen. In addition, through scientific aquaculture management and technological innovation, the aquaculture industry in the region also helps to reduce dependence on wild fishery resources and promote the sustainable use of fishery resources, which is of great significance for the protection of marine ecosystems and the realization of the sustainable development of fisheries.
## 6 Mechanisms for PMS Activation by Copper-Based Catalysts
### Non-Radical Pathways
Experiments, such as quenching tests and scavenger degradation tests, ruled out the involvement of \({}^{1}O_{2}\). Ultimately, the dominant mechanism was identified as the electron transfer pathway in the SACu@NBC/PMS system via the anodizing analogy method that combined the quantitative structure-activity relationship. Moreover, dynamic fitting combined with density functional theory calculations revealed that the isolated atomic Cu in carbon supports acted as the active sites, narrowing the band gap and enhancing the electron transfer capacity. The catalyst-oxidation efficacy of SACu@NBC/PMS was effectively evidenced in an ultrafiltration device, indicating the potential of the electron transfer pathway in the SACu@NBC/PMS system aimed at elevated salinity effluent remediation. PMS systems based AOPs are increasingly focusing on reformation due to the advantageous characteristics of non-radical pathways. These mechanisms can address certain constraints associated with radical activation, such as short half-lives. While the non-radical pathway does not primarily depend on vigorous oxidation to target pollutants (except for \({}^{1}O_{2}\)), it does play a crucial role in facilitating redox reactions. This is achieved either by forming a complex with strong oxidation or through electron transfer within the reaction system, ultimately leading to the elimination of contaminants. It is crucial to emphasize that the non-radical pathway is not a singular process; rather, it may encompass two or more pathways. This complexity is essential to consider when examining the activation mechanism of PMS. In addition to their capability to facilitate both radical and non-radical degradation pathways, Cu-based catalysts offer significant synergistic benefits through their dual pathway activation. Many Cu-based catalysts can activate PMS through both pathways simultaneously, resulting in enhanced degradation efficiency. This synergy allows different types of pollutants to be more effectively treated, as one pathway may be more suitable than the other for specific contaminants. For instance, CuFe\({}_{2}\)O\({}_{4}\) catalysts are particularly versatile, participating in the generation of ROS via the radical pathway and the production of \({}^{1}O_{2}\) through the non-radical pathway, making them adaptable for various environmental applications. Moreover, the catalytic activity of Cu-based catalysts can be finely tuned to favor one pathway over the other based on the specific requirements of the application. By adjusting parameters such as catalyst composition, surface modifications, and operating conditions like pH and temperature, it is possible to optimize the catalytic process. This not only maximizes pollutant degradation but also helps minimize secondary pollution and reduce energy consumption. These synergistic roles highlight the versatility and effectiveness of Cu-based catalysts, further establishing them as key components in advanced environmental remediation technologies.
# Digital Gamification to Foster Attitudes Toward Science in Early Childhood Teacher Education
## 1 Introduction
### Study Aim and Research Questions  
The present study investigates the effects of combining structural and narrative gamification on PECTs' motivation and attitudes toward science. Structural gamification was implemented through the digital platform FantasyClass, while narrative gamification was embedded in a coherent storyline that unfolded across all course activities. By applying a case study approach, this research focuses on a single cohort enrolled in an early childhood education program. This design enables a detailed exploration of how gamification strategies function within a specific context, providing insights that may be applicable to similar settings, rather than aiming for wide generalization.  
In this context, the study builds upon previous research by combining structural and narrative gamification within a semester-long course, fully implemented through a digital platform. While prior studies have often examined short-term or isolated gamified activities, this intervention systematically embedded narrative elements and game mechanics across all instructional tasks. This cohesive and immersive approach provides the context for investigating how gamification may influence PECTs' motivation and attitudes toward science.  
The dual objective of the study is to understand how the integration of these two forms of gamification influences PECTs' attitudes toward science, and to identify which specific gamification elements are perceived as most effective in fostering motivation. Accordingly, the following research questions were formulated to guide the investigation:  
* RQ1: To what extent does the integration of structural and narrative gamification influence PECTs' motivation and attitudes toward science?
* RQ2: Which specific gamification elements are perceived by PECTs as the most motivating?
## 3 Fuzzy Set Theory-Based Risk Assessment System for Navigational Obstacles
### System Development
#### 3.5.4 Risk Assessment System  
The system for evaluating the risk of navigational obstacles is based on a fuzzy inference system and primarily utilizes fuzzy sets and rules. The operation of this system is illustrated in Figure 13.  
First, in the fuzzification step, the input values \(Risk\ index_{c}\) and \(Risk\ index_{p}\), which are provided as crisp values, are received, and each value is assessed to determine the degree to which they belong to the appropriate fuzzy set. In the second step involving the rule evaluation, these fuzzy inputs are applied to 24 rules, and the evaluation results for all the rules are numerically represented. This value is then applied to the MF on the output side of the rule. Subsequently, the fuzzy sets corresponding to the outputs of all rules are integrated. Finally, the output is calculated by synthesizing the results of all the rules, as shown in (12). This process involves defuzzification using the \"centroid\" defuzzification method.  
\[Risk\ index_{comprehensive}=\frac{\sum_{i=1}^{24}\mu Rule_{i}\Big{(}Risk\ index_{comprehensive}\Big{)}\times Risk\ index_{comprehensive_{i}}}{\sum_{i=1}^{24}\mu Rule_{i}(Risk\ index_{comprehensive}\Big{)}} \tag{12}\]  
Figure 12: Visualization of a risk assessment system for navigational obstacles.  
Figure 13: Risk assessment process for navigational obstacles.  
Here, \(Risk\ index_{comprehensive}\) is a value within the output range selected according to each rule. Each rule calculates the result for a given input, and all the results are then synthesized to calculate the final output value.
## 4 Discussion
Figure 4 shows that the multilayer perceptron (MLP) trained via the adaptive opposition-based slime mould algorithm (AOSMA) yields predictions that are significantly closer to actual values compared to other models. Notably, at index numbers 1, 15, 17, and 28, AOSMA-MLP demonstrates remarkable accuracy, predicting values that closely mirror the true RTs. Figure 4: Experimental and predictive values of ANN, ALO-MLP, WOA-MLP, SMA-MLP, and AOSMA-MLP in hydrogeochemical and RT data. Further insight is provided by scatter plots depicted in Figure 5. This graphical representation reinforces the accuracy of AOSMA-MLP, showcasing its predictions in close alignment with real RT values. The scatter plots vividly illustrate the precision of AOSMA-MLP, underscoring its superior performance in forecasting RTs based on the hydrogeochemical characteristics of geothermal resources. The ability to accurately predict RTs from the hydrogeochemical properties of geothermal resources signifies a substantial advancement. This approach mitigates the drawbacks associated with traditional geological studies, which are often time-consuming, expensive, and reliant on specialized equipment. Consequently, the financial burden on geothermal energy projects is lessened, offering a considerable advantage to investors. Moreover, this predictive capability enhances flexibility in feasibility assessments for engineers and experts engaged in geothermal energy projects. The proposed models not only aid in determining the potential applications of geothermal waters by preemptively identifying RTs but also serve as a decision-support system, enabling the selection of the most suitable options for exploiting geothermal resources.