esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Physics Simulation Analyses of GMM-FBG Current Sensor  
The temperature drift of the Bragg-wavelength signal modulated by the GMM-FBG current sensor in the process of detecting the current can be attributed to three dominant mechanisms: (1) thermal strain directly bringing about the geometry changes of the ferrite magnetizer, GMM and air gap, which leads to distribution changes in the current-induced magnetic field in the GMM rod; (2) the variation of ambient temperature (device operating temperature) causes thermal strains in the GMM rod, accordingly altering the grating period and Bragg wavelength of FBG fixed to the GMM side surface; (3) the GMM magnetostrictive characteristics and FBG core refractive index are shifted by varying temperatures, while the infinitesimal change in ferrite permeability caused by temperature change can be ignored. Due to the actual condition that the thermal strain caused by the change in operation temperature in ambient range has little effect on the geometric structure of the GMM-FBG current sensor, the first temperature drift mechanism mentioned above can be ignored. Therefore, the finite element multi-physics simulations are carried out for investigating the latter two temperature drift mechanisms in the present study.
###### Abstract  
The existing thermal infrared (TIR) ship detection methods may suffer serious performance degradation in the situation of heavy sea clutter. To cope with this problem, a novel ship detection method based on morphological reconstruction and multi-feature analysis is proposed in this paper. Firstly, the TIR image is processed by opening- or closing-based gray-level morphological reconstruction (GMR) to smooth intricate background clutter while maintaining the intensity, shape, and contour features of ship target. Then, considering the intensity and contrast features, the fused saliency detection strategy including intensity foreground saliency map (IFSM) and brightness contrast saliency map (BCSM) is presented to highlight potential ship targets and suppress sea clutter. After that, an effective contour descriptor namely average eigenvalue measure of structure tensor (STAEM) is designed to characterize candidate ship targets, and the statistical shape knowledge is introduced to identify true ship targets from residual non-ship targets. Finally, the dual method is adopted to simultaneously detect both bright and dark ship targets in TIR image. Extensive experiments show that the proposed method outperforms the compared state-of-the-art methods, especially for infrared images with intricate sea clutter. Moreover, the proposed method can work stably for ship target with unknown brightness, variable quantities, sizes, and shapes.  
Keywords:thermal infrared (TIR) imaging; small ship target detection; sea clutter; gray-level morphological reconstruction; saliency detection; multi-feature analysis +
Footnote †: journal: Applied Sciences
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 6 Applications and Policy Implications
### Spatial Analysis and Risk Assessment
#### 6.2.2 Applied Risk Assessment and Hotspot Identification  
[PERSON] et al. [111] developed an integrated approach to blackspot identification, combining crash count and severity in a joint econometric model. Their weighted risk score methodology, which incorporates both frequency and severity predictions, demonstrates superior performance in identifying locations with high risk of severe injuries. This approach provides a more nuanced tool for prioritizing safety improvements, particularly in contexts where resources are limited.  
[PERSON] et al.'s [78] dynamic modeling approach demonstrated a 25-38% improvement in comparison with existing models for identifying 10-20% of high-crash roadsegments. This performance improvement has direct implications for resource allocation in safety improvement programs.  
[PERSON] et al.'s [79] comparison of traffic safety across French counties identified significant interactions between county and road type, indicating that differences in safety across counties depend on the road type and vice versa. This finding emphasizes the need for location-specific safety strategies rather than one-size-fits-all approaches.
## 3 Results
The plots of Figure 1 show that the tracking differentiators with different gains effectively smooth the angles of the rhombus of the primitive trajectories (45), (46), set with different values of velocities of motion. At the same time, the tracking differentiator (32), (44), set with a velocity limit of 1.9 m/s (1.9 \(<\) 2, 1.9 \(<\) 2, 5), provides a larger (compared to the tracking differentiator (32), (43)) tracking error \(e_{1}(t)=z_{1}(t)-\chi_{1}(t)\) both at singular points and in the steady state. Thus, the quality of angle smoothing deteriorates if the maximum velocity of the primitive trajectory exceeds the maximum velocity \(Y_{1}\) assumed at the setting of the tracking differentiator. The plots of Figures 3 and 4 show that the first derivatives of the smoothed trajectories \(z_{21}(t),z_{22}(t)\) in all cases do not exceed the design velocity constraints. The second derivatives of \(z_{31}(t),z_{32}(t)\) are constrained (Figures 5 and 6). It can be concluded that the tracking differentiator provides good performance and provides velocity and acceleration constraints for tracking different trajectories. By reducing the velocity near singular points at the planning stage, the smoothing quality of primitive trajectories and their derivatives can be controlled. In the second experiment, the dimensions of the wheeled robot \"SRX 1\" were adopted to model the safety corridor: 765 \(\times\) 1370 mm (width \(\times\) length). Figure 7 shows plots of the output variables \((z_{11}(t),z_{12}(t))\) of the differentiator (32), (44), tracking primitive trajectory (46), and the trajectories of the platform angular points (42). As can be seen from Figure 7, when changing the direction of motion, the trajectories of the angular points of the right and left sides change places, but this does not change the shape of the dimensional trajectory. Figure 8 shows the results of smoothing (Figure 8a) and generating a dimensional trajectory (Figure 8b) for a more complex route specified by the following set of 3D points:  
\[ \begin{array}{l}(x_{i}[m],y_{i}[m],t_{i}[s]):(0,0,0);(2,3,3);(4,3,6);(4,5,9) ;\\ (5,2,12);(6,3,15);(8,3,18);(8,1,21);(10,4,24).\end{array}  \tag{47}\]  
Based on these data, a non-smooth 3D trajectory \(\chi_{1}(t)=(\chi_{11}(t),\chi_{12}(t))^{\text{T}}\) (39) was generated for simulation. It is also presented in Figure 8a. Figure 7: Trajectory considering the dimensions of the wheeled platform.
## 4 Numerical Simulation of Surface Deformation Induced by Backfill Mining
### Simulation Results and Analysis  
According to the simulation results for surface deformation above the backfill panel, 3-d mapping software was adopted to process the simulation data, thus obtaining 3-d surfaces representing the surface subsidence after different creep durations (Figure 11).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Applied Stress/MPa**} & \multicolumn{6}{c}{**Model Parameter**} \\ \cline{2-7}  & \(\mathbf{K_{e}/MPa}\) & \(\mathbf{G_{e}/MPa}\) & \(\mathbf{\eta_{v}^{e}/MPa\cdot min}\) & \(\mathbf{\alpha}\) & \(\mathbf{G_{ev}/MPa}\) & \(\mathbf{\eta_{ev}^{\beta}/MPa\cdot min}\) & \(\mathbf{\beta}\) \\ \hline \(18\) & \(108.83\) & \(23.32\) & \(97\),\(853.49\) & \(0.38\) & \(315.01\) & \(3651.44\) & \(0.61\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values of the constitutive model of gangue backfill materials.  
Figure 10: Numerical model for surface deformation induced by backfill mining.  
According to the analyses of Figures 11 and 12:
* With the increase in the creep duration, the maximum surface subsidence gradually increases, while the increase amplitude constantly decreases. The surface subsidence reaches the maximum at the central area of the goal of the backfill panel.
* When the creep durations are 30, 60, 90, 180, 270, and 360 d, the maximum surface subsidence are 46.18, 78.04, 94.76, 144.11, 166.31, and 171.82 mm, respectively. Once the creep duration exceeds 270 d, the rate of surface subsidence decreases and finally tends to stabilize.  
Figure 11: 3-d surfaces for surface subsidence after different creep durations: (**a**) 30 d, (**b**) 60 d, (**c**) 90 d, (**d**) 180 d, (**e**) 270 d, (**f**) 360 d.  
Based on definitions of surface deformation indices (subsidence, horizontal movement, inclination, curvature, and horizontal deformation), the simulation scheme with the creep duration of 360 d was used to process monitoring data from measuring lines 1 and 2 (Figure 13).  
Figure 12: Change in maximum surface subsidence.  
Figure 13: _Cont._  
According to the analysis of Figure 13, the maximum values of surface subsidence, horizontal movement, inclination, curvature, and horizontal deformation are 171.82 mm, 56.38 mm, 0.38 mm/m, 0.002 mm/m\({}^{2}\), and 0.29 mm/m, respectively. Therein, the horizontal compressive deformation and horizontal tensile deformation are separately \(-\)0.29 and 0.18 mm/m. The maximum surface subsidence and horizontal deformation both satisfy the fortification criterion of surface buildings above the backfill panel.
# Antibacterial Ceramics for Dental Applications
## 1 Introduction
Ag\({}^{+}\) ions are known for their toxicity to bacteria, viruses, fungi, and various other organisms, while they have little or no toxicity to human cells. Ag ions can damage the cell membrane, leading to a disruption of the cell functions or intracellular biomolecules and the induction of oxidative stress. The generated ROS result in the formation of free radicals and more extensive cell damage. Charged Ag ions can react with the negatively charged bacterial surface, affecting the zeta potential of the bacterial cell and increasing membrane permeability. Ag\({}^{+}\) ions have a high affinity for sulfur groups on the cell membrane and thus inhibit respiratory chains, electron transfer, protein secretion, and lipid biosynthesis, leading to bacteria death [16; 24]. Metal-doped bioceramics offer many advantages over other antibacterial methods. They are non-toxic, improve the mechanical properties of the biogas such as hardness and wear resistance, and provide long-term implant stability, which is very important for the success of the implant. The most commonly used glass-ceramics and bioceramics for dental applications are listed in Table 1.  
This review demonstrates the importance of developing a new generation of antibacterial biocercamics for dental applications by presenting the current state of the art in this field. A comprehensive understanding of antibacterial mechanisms and strategies can help  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Materials** & **Crystalline Microstructure** & **Manufacturing Technique** & **Mechanical Properties** & **Clinical Applications** & **Ref. ** \\ \hline \multicolumn{6}{c}{Glass-ceramics} \\ \hline  & & & \(\sigma\): 350–450 MPa & & \\  & Morphology: & & Hv: 4–6.5 GPa & & \\  & needle-like & & KIC: 0.8–1.5 MPa & & \\  & crystals & & E: 70 GPa & & \\  & (-70 vol\(\%\)) & & & \\  & length 3–6 \(\mu\)m & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multicolumn{6}{c}{Crowns in the anterior and posterior regions,} \\  & Morphology: & & \(\sigma\): 444 \(\pm\) 39 MPa & & \\  & homogeneous fine & & Hv: 6.5 \(\pm\) 0.5 GPa & & \\   silicate & Li\({}_{2}\)SiO\({}_{3}\) crystals & & & \\  & & & & \\  & & & &  
researchers develop innovative solutions to prevent implant infections and improve the lives of patients.
## 3 Results and Discussion
### Transport of Small Molecules through FCNT  
For comparison purposes, we also simulated the transport of CO\({}_{2}\) and urea through FCNT, as shown in Figure 8. Though the hydroxyl groups at the entrance of FCNT improved its hydrophilicity, the interior of FCNT was still hydrophobic. Therefore, the inner FCNT was gradually occupied by CO\({}_{2}\) molecules (see Figure 8A,B). The number of water molecules decreased from 25 to 2, correspondingly, that of CO\({}_{2}\) molecules increased from initial 0 to 11 at \(t\) = 50 ns. Surprisingly, it became more difficult for urea to enter the FCNT. During the whole simulation, only one or two urea molecules appeared in the FCNT. On the other hand, the number of water in Figure 8C,D fluctuated more dramatically than that in Figure 7C,D. This was because the hydroxyl groups at the entrance of FCNT enhanced the interactions with water molecules.  
Figure 7: The transport properties of (7,7) PCNT in the lipid bilayer. (**A**,**B**) Snapshots at \(t\) = 50 ns, only half of the tubes are shown to highlight CO\({}_{2}\) and urea molecules in the PCNT hollow channels. (**C**,**D**) Number of water, CO\({}_{2}\), and urea within PCNT as a function of simulation time.
# Effect of Surfactant on Water Content of Phosphogypsum
## 3 Results and Discussion
### Effect of Surfactant on Filtrate Rate of Phosphogypsum  
The addition of surfactant not only decreased the surface tension and contact angle, and changed the wetting characteristics of the phosphate rock, but also obtained large size of phosphogypsum. The results shown in Table 6 display the effect of surfactant on the filtrate rate of phosphogypsum. The filtrate rate was 905.80 kg-m\({}^{-2}\)-h\({}^{-1}\) without surfactant. The addition of surfactant could affect the filtrate rate significantly. The rate increased 13% (with SDS), 20% (with SDBS), 16.7% (with PDMS), 20.86% (with SDS+SDBS), 24.3% (with SR+SDBS) and 17.41% (PDMS+SDBS), respectively.  
Figure 5: Effect of contact angle on fractal dimension.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Fractal Dimension** & **Mean Deviation** \\ \hline No Surfactant & 1.2753 & 0.0128 \\ SDBS & 1.2240 & 0.0132 \\ SDS & 1.2750 & 0.0127 \\ SR & 1.2754 & 0.0137 \\ PDMS & 1.2447 & 0.0131 \\ SDBS+SDS & 1.1931 & 0.0121 \\ SDBS+SR & 1.0249 & 0.0112 \\ SDBS+PDMS & 1.2354 & 0.0128 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of surfactant on the fractal dimension of phosphogypsum.  
The addition of SDBS, SDS, PDMS, SDBS+SDS, SDBS+SR, or SDBS+PDMS could increase the interfacial energy and hinder the formation of crystal nucleus, avoiding the formation of fine grain [19]. The addition of surfactant also increased the growth rate of crystal and the filtrate rate.
# The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios
## 2 Literature Review
### Learning Analytics and Educational Data Mining to Enhance the Teaching/Learning Process
Nevertheless, the addition of tools and resources should be carefully considered in order to analyse their effectiveness and the impact on the learning process. The 2020 NMC Horizon Report [28] includes analytics for student success and adaptive learning technologies as two of its six emerging technologies and practices practitioners believe will have a significant impact on postsecondary teaching and learning. When focusing on the impact of learning resources such as educational videos through a virtual learning environment, as aforementioned, learning analytics (LA) [29], as well as educational data mining (EDM) [30], become useful areas to analyse data collected of/from learners' interaction with a video in any virtual learning environment (VLE). However, LA and EDM focus on the use of large data sets, as can be seen in [PERSON] and [PERSON] [30], leaving behind small data sets collected to suggest enhancements by providing meaning insights to single courses with low ratio of learners, that we call small scale scenarios. As previously stated, we use \"small scale scenarios\" to refer to videos with collected data from less than one thousand playbacks. Multimedia Learning Theory (MLT) suggests several possible applications of LA data to improve learning [31]. Some interesting findings in the field include those by [PERSON] et al. [32] who reported a relationship between clusters of interactions such as those we analyse (pausing, skipping, etc.) and perceived difficulty. The interaction between worked-out examples as a teaching and learning tool and self-explanations by students when learning from those examples are of particular interest ([PERSON] et al., 1998). Worked-out examples consist of a problem formulation, solution steps and the final solution itself. Research has shown that learning from such examples is of major importance for the initial acquisition of cognitive skills in well-structured domains such as mathematics, physics and programming [33]. According to the research in the field [34], spontaneously generating explanations to oneself as one studies worked-out examples from a text is a process that promotes skill acquisition, even though it is neither the direct encoding of instruction nor the compilation of an encoded skill. Self-explanation is a domain general constructive activity that engages students in active learning and ensures that learners attend to the material in a meaningful way while effectively monitoring their evolving understanding. Several key cognitive mechanisms are involved in this process, including generating inferences to fill in missing information, integrating information within the study materials, integrating new information with prior knowledge, and monitoring and repairing faulty knowledge. Thus, self-explaining is a cognitively demanding but deeply constructive activity. For a review of the effectiveness of self-explanation and its use as a trainable learning strategy, see [35].
## 2 Related Work
### Deep Learning Methods
In scenarios with complex noise, occluded targets, or severe background interference, researchers often leverage attention mechanisms or prior knowledge to enhance the network's focus on defect regions. [PERSON] et al. [35] proposed PKAMNet, combining a prior knowledge transfer model with a coordinate attention module to achieve precise detection of high-voltage transmission line insulator faults; this approach can similarly be adapted to railway component detection to improve attention allocation and discrimination capabilities in complex environments. [PERSON] et al. [36] introduced image enhancement and BiFPN feature fusion in an improved YOLOX, further reducing missed and false detections through the NAM attention mechanism, demonstrating high detection stability across multiple experimental batches. To provide more accurate descriptions of defect morphology or edge contours, some research has shifted towards instance segmentation or semantic segmentation networks. [PERSON] et al. [37] utilized Mask R-CNN to perform multi-scale fusion of track surface defects and enhanced the region proposal network, improving defect localization and segmentation accuracy. This end-to-end instance segmentation approach is particularly advantageous for detecting curved cracks or irregular delamination. Another approach for detecting small or irregular targets involves further improving SSD or adding specific regularization and feature fusion, as demonstrated by [PERSON] et al. [38] in surface defect detection of rare-earth magnetic materials, offering valuable insights for track defect detection. Even prior to the large-scale application of deep learning, [PERSON] et al. [39], [PERSON] et al. [40], and [PERSON] et al. [41] explored track defect detection with limited samples, weak labels, and 3D stereo data, focusing on low-rank sparse representation, unsupervised saliency detection, and row-level annotations, respectively. Their common conclusion is that, even with constrained data, appropriate network structure optimization and feature fusion can still achieve high accuracy and robust detection performance. While existing studies have demonstrated high detection accuracy and feasibility across various scenarios, there remain several challenges. Firstly, most deep models have a high dependency on the scale and diversity of data annotations, and they lack robustness in weakly labeled or data-limited environments. Moreover, when confronted with complex distributions, diverse defect types, and significant noise on track surfaces, maintaining high accuracy while ensuring real-time performance remains a major hurdle. To address these shortcomings, the PerMSCA-YOLO model proposed in this study introduces perceptual loss in conjunction with multi-scale feature extraction and attention mechanisms, enhancing deep feature alignment, thereby enabling more efficient and accurate defect recognition in resource-constrained and complex track defect detection tasks.
## 3 Results and Discussion
### Quantitative Analysis of Identified Metabolites Using HPLC
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Compd.**} & \multicolumn{6}{c}{**Phenolic Contents, (mg/g)****a**} \\ \cline{2-7}  & **0\% b** & **10\%** & **30\%** & **50\%** & **70\%** & **90\%** & **100\%** \\ \hline
**1** & 9.6 \(\pm\) 1.4 & 8.40 \(\pm\) 1.2 & 6.48 \(\pm\) 1.4 & 4.13 \(\pm\) 0.9 & 3.70 \(\pm\) 0.5 & 3.41 \(\pm\) 0.9 & 2.77 \(\pm\) 0.6 \\
**2** & 0.98 \(\pm\) 0.05 & 2.83 \(\pm\) 0.5 & 2.93 \(\pm\) 0.3 & 3.04 \(\pm\) 0.2 & 5.90 \(\pm\) 0.6 & 3.51 \(\pm\) 0.3 & 3.26 \(\pm\) 0.4 \\
**3** & 0.24 \(\pm\) 0.02 & 11.3 \(\pm\) 1.2 & 14.64 \(\pm\) 2.5 & 142.4 \(\pm\) 4.4 & 98.3 \(\pm\) 12.1 & 113 \(\pm\) 16.4 & 12.56 \(\pm\) 1.9 \\
**4** & 0.19 \(\pm\) 0.01 & 22.9 \(\pm\) 2.5 & 20.50 \(\pm\) 0.9 & 21.24 \(\pm\) 3.0 & 21.72 \(\pm\) 3.9 & 19.70 \(\pm\) 1.2 & 21.55 \(\pm\) 2.9 \\
**5** & 5.26 \(\pm\) 0.4 & 8.28 \(\pm\) 2.9 & 9.64 \(\pm\) 1.7 & 9.90 \(\pm\) 0.5 & 14.87 \(\pm\) 2.8 & 7.21 \(\pm\) 0.8 & 9.72 \(\pm\) 1.1 \\
**6** & 0.24 \(\pm\) 0.03 & 0.40 \(\pm\) 0.04 & 1.05 \(\pm\) 0.2 & 1.00 \(\pm\) 0.07 & 1.32 \(\pm\) 0.5 & 1.45 \(\pm\) 0.05 & 0.94 \(\pm\) 0.06 \\
**7** & 0.63 \(\pm\) 0.12 & 1.01 \(\pm\) 0.2 & 2.12 \(\pm\) 0.8 & 1.15 \(\pm\) 0.04 & 1.13 \(\pm\) 0.1 & 1.47 \(\pm\) 0.04 & 1.57 \(\pm\) 0.08 \\
**8** & 0.11 \(\pm\) 0.05 & 0.20 \(\pm\) 0.06 & 0.32 \(\pm\) 0.05 & 0.53 \(\pm\) 0.08 & 0.52 \(\pm\) 0.06 & 0.29 \(\pm\) 0.04 & 0.41 \(\pm\) 0.02 \\ \hline \hline \end{tabular} All the experiments were triplicates. a The values are expressed as mg of each compound equivalents per g of dry weight as mean (\(n=3\)). b The percentage represents the ethanol ratio of _E. globulus_ leaf extract solvent.  
\end{table} 
Table 4: Quantitative analysis of major metabolites from extracts using HPLC. Figure 3: HPLC chromatogram of _E. globulus_ leaves under different extract conditions using 0%, 10%, 30%, 50%, 70%, 90%, and 100% ethanol.