esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Construction of Lexical Situation Type System

Table 3: Inter-group Kappa consistency test results for four annotators. As shown in the figure above, the distribution patterns of verbs based on their co-occurrence features with aspectual markers exhibit significant differences. Through semantic distance-based clustering analysis, the verbs are categorized into nine subclasses, which are further merged into three main categories from the bottom up. Observing the figure clockwise from the top, the first main category consists of three subclasses, which include verbs such as zhuo1 (catch), ben1 pao3 (run) and gui4 (kneel), representing typical physical actions. These verbs describe dynamic and durative situations, which we classify as activity situation types. The second main category also comprises three subclasses, including verbs such as tao3 yan4 (hate), ming2 bai2 (understand) and ren4 shi2 (recognize), which denote mental activities or attributive relations. While these verbs also exhibit a durative feature, unlike activity situation types, they are strongly static in nature, and we classify them as state situation types. The third main category similarly consists of three subclasses, containing verbs such as tong2 yi4 (agree), shi2 xian4 (achieve) and ti2 gaol (improve). These verbs all indicate changes in state and possess inherent endpoints, which we classify as transition situation types. CPCC is a metric used to evaluate the fitting degree between hierarchical clustering and the data. A CPCC value closer to 1 indicates a higher similarity between the distance matrix used in hierarchical clustering and the original matrix, signifying a better clustering performance. The CPCC value calculated for the hierarchical clustering shown in Figure 3 is 0.97, demonstrating that the clustering results are satisfactory. The above presents the automatically generated verb situation types based on objective semantic distances. The hierarchical clustering results suggest that the tripartite classification of situation types into activity, state and transition is a scientifically sound approach. In fact, many previous scholars, such as [PERSON] [49], [PERSON] [18], [PERSON] [50] and [PERSON] [19], have proposed similar tripartite classifications, and their categories largely correspond to the three situation types mentioned above. Building on these theoretical foundations, this study further validates the rationality of the tripartite classification of situation types through statistical methods. Figure 3: Hierarchical clustering dendrogram of verb situation types [48].
# Damage Identification in Warren Truss Bridges by Two Different Time-Frequency Algorithms
## 2 Models for Numerical Simulations
### Representation of the Damage  
When dealing with steel truss bridges, one of the most likely-to-happen damage consists of a partial or total degradation of the fastenings in correspondence of the connections of the bridge elements. [PERSON] et al. (2018) propose an experimental campaign on highway, in which a corrosion damage is reproduced by means of a fully severed diagonal element. The damage is caused artificially, but it is based on real observations performed on a Japanese truss bridge (i.e., Kisogawa bridge), according to [32; 35]. In [35] [PERSON] shows that corrosion phenomena can lead to the total inefficiency of the connection between diagonal elements and lower chords on a steel truss bridge. A similar defect was observed, again in Japan on a steel truss bridge, precisely on the Honjo Ohashi bridge [32]. Following  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Element**} & **Section Area** & **Torsion Constant** & **Principal Moment** & **Principal Moment** \\ \cline{2-5}  & **A (m2)** & **I\({}_{\mathbf{t}}\) (m4)** & **I\({}_{\mathbf{2}}\) (m4)** & **I\({}_{\mathbf{3}}\) (m4)** \\ \hline
1 & 0.0143 & \(1.54\times 10^{-6}\) & \(2.12\times 10^{-4}\) & \(1.8\times 10^{-4}\) \\
2 & 0.0148 & \(1.91\times 10^{-6}\) & \(2.14\times 10^{-4}\) & \(1.4\times 10^{-4}\) \\
3 & 0.00615 & \(9.2\times 10^{-7}\) & \(1.65\times 10^{-4}\) & \(4.1\times 10^{-5}\) \\
4 & 0.0013 & \(4.6\times 10^{-7}\) & \(8.2\times 10^{-5}\) & \(2.1\times 10^{-5}\) \\
5 & 0.0167 & \(6.6\times 10^{-7}\) & \(8.97\times 10^{-4}\) & \(1.9\times 10^{-5}\) \\
6 & 0.0048 & \(6.4\times 10^{-7}\) & \(2.3\times 10^{-4}\) & \(2.2\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Span dimensions, natural frequencies and geometrical properties of each element shown in Figure 1.  
\begin{table}
\begin{tabular}{l c} \hline \hline Span length & 21.42 (m) \\ \hline Height & 3.71 (m) \\ \hline Width & 4.5 (m) \\ \hline
1 st bending mode & 16 (Hz) \\ \hline
1 st torsional mode & 19.5 (Hz) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Span dimensions and natural frequencies.  
Figure 1: The scheme of the open Warren truss bridge considered in the paper.  
these researchers, two structural defects are considered in this paper, affecting, respectively, the connection of a diagonal member of the side wall (Section 2.2.1), and the deck in correspondence of the cross-girder connection with the lower chord (Section 2.2.2). The finite element schematization adopted following the main geometry of the real structure enables location of the damage in correspondence with the considered nodal connection.
# Evaluating the Capture Efficiency of Microchannel Plates Through Photoelectron Detection
## 2 Materials and Methods  
The MCPs (25 mm in diameter) tested in this study were recently manufactured by China Building Materials via a refined hydrogen reduction process: glass rods and tubes were melted and drawn into single fibers, assembled into rod-tube assemblies with precisely aligned fibers, and then formed into composite filaments through secondary sorting and polishing. Chemical etching selectively removed the glass core to create hollow microchannels, followed by hydrogen reduction at elevated temperatures to eliminate impurities and enhance conductivity. A critical precision polishing step after slicing optimizedchannel wall verticality and surface flatness. Finally, metal electrodes were deposited to complete the MCPs with high-open-area ratio and uniform channel. Three types of MCPs with varying channel geometries were fabricated and tested, including a circular MCP, a tapered circular MCP, and a hexagonal MCP. In particular, hexagonal MCP features a novel end-face structure comprising hexagonal conical holes with inner cylindrical extensions in a hexagonal close-packing arrangement, replacing the conventional circular packing array. Such geometry may reduce the edge discharge risks associated with expanded apertures.  
We built a simple setup to assess the CE of MCPs. As shown in Figure 1, it mainly consists of a UV lamp (Heraeus, Model: PKS 106, Hanau, Germany), a zinc plate (50 mm \(\times\) 30 mm), and an MCP detector. The centerline of the zinc plate and UV light was set at an angle of about 60\({}^{\circ}\), and the incident surface of the MCP was positioned parallel to the zinc plate, with a gap of approximately 20 mm. In the experiments, photons (10.6 eV) emitted by the UV lamp irradiated the zinc plate, causing the emission of photoelectrons. Voltages of approximately \(-\)800 V and \(-\)100 V were applied to the incident and output sides of the MCP, respectively. Moreover, a voltage of around \(-\)1200 V was applied to the zinc plate to form a potential difference between the MCP and the zinc plate (marked as V\({}_{\text{M-Z}}\)), which could promote the emission of photoelectrons and guide them to the MCP. The entire system is housed in a vacuum chamber with a pressure of 1 \(\times\) 10\({}^{-5}\) mbar.
# Wildfires in the Siberian Arctic
## 4 Discussion
### Changes in Wildfire Dynamics for the Siberian Arctic
In a synergistic fashion, warming conditions themselves are reported to increase the frequency of lightning strikes within the circumboreal Arctic (e.g., [15]). Research on lightning strikes in the continental USA projects that an increase in air temperature by 1 \({}^{\circ}\)C leads to an increase in lightning frequency of \(\sim\)12% [13]. For Alaska and northern Canada, lightning ignition was estimated to increase by 90-230% by the end of this century [15]. Increased wildfire ignition by lightning was also reported for closed-canopy forests in North America [14]. Although such estimates have not been made for the Siberian Arctic, similar increases in the frequency of lightning strikes are likely. In addition to combustion of aboveground vegetation, fire from lightning and other sources may smolder and overwinter in the carbon-rich taiga and tundra soils, to emerge in the subsequent spring as described for Alaska, the Northwest Territories in Canada, and northeast Siberia [48; 49].
## 7 Conclusions  
This study proposes the EAND-LPRM model to address the issue of low accuracy in license plate recognition tasks under low-quality datasets and complex scenarios. The proposed network integrates the advantages of convolutional neural networks, self-attention, and connectionist temporal classification. Particularly, the introduction of self-attention mechanism captures global dependencies among characters, thereby enhancing the model's understanding of license plate character sequences. Connectionist temporal classification, by learning the correspondence between characters, eliminates the need for additional alignment information, allowing the model to directly recognize character sequences of different lengths from images. Additionally, we propose an encoder-decoder architecture that, through multiple convolutional layers and residual blocks, captures more contextual information and global relationships in noisy and blurry images without sacrificing accuracy. Experimental evaluations using the CMPD dataset demonstrate that EAND-LPRM outperforms previous models, proving its effectiveness in various scenarios. This suggests that the EAND-LPRM method provides a better solution for license plate recognition tasks in real-world traffic scenarios, which is significant for the future of license plate recognition technology.  
Although the model performs well in license plate recognition tasks in complex traffic environments, it has one drawback: it does not directly recognize double-layer license plates but adopts a strategy of concatenating them before recognition. Therefore, the features extracted by the model may become more complex and inaccurate. To further explore the effectiveness of EAND-LPRM, we plan to extend the transformer model structure to consider the recognition of double-layer license plates in complex scenarios.  
Conceptualization, X.D. and S.C.; methodology, Z.L.; software, S.C.; validation, Q.N., Z.L. and S.C.; formal analysis, X.D.; data curation, S.C.; writing--original draft preparation, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was fund by the Research Program of Xiamen University Technology, grant number XPDKT20029, Natural Science Foundation of Fujian Province under Grant 2023J011427 and Natural Science Foundation of Xiamen under Grant 3502Z20227067.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Results
Examples of the observed changes in the friction coefficient value over time are illustrated in Figure 2. For these two cases, significant  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Annealing Time,** & **Pressure Force,** & **Testing Time,** & **Mass Loss,** & **Hardness,** & **Friction** & **Wear Depth** \\
**h** & **N** & **s** & **g** & **HV10** & **Coefficient** & **\(\mathbf{\mu}\mathbf{\mathrm{m}}\)** \\ \hline \multirow{6}{*}{4} & \multirow{6}{*}{100} & \multirow{6}{*}{2000} & 0.0020 & 286 \(\pm\) 2 & 0.669 \(\pm\) 0.012 & 24.3 \(\pm\) 1.0 \\ \cline{3-6}  & & & 0.0016 & 286 \(\pm\) 4 & 0.595 \(\pm\) 0.008 & 25.4 \(\pm\) \(-\)0.9 \\ \cline{3-6}  & & & 0.0030 & 284 \(\pm\) 3 & 0.663 \(\pm\) 0.010 & 44.1 \(\pm\) 1.2 \\ \cline{3-6}  & & & 0.0046 & 284 \(\pm\) 5 & 0.548 \(\pm\) 0.008 & 42.8 \(\pm\) 1.4 \\ \cline{3-6}  & & & 0.0035 & 283 \(\pm\) 2 & 0.676 \(\pm\) 0.014 & 54.6 \(\pm\) 2.0 \\ \cline{3-6}  & & & 0.0022 & 289 \(\pm\) 5 & 0.552 \(\pm\) 0.016 & 54.4 \(\pm\) 1.8 \\ \hline \multirow{6}{*}{8} & \multirow{6}{*}{100} & \multirow{6}{*}{2000} & 0.0022 & 298 \(\pm\) 5 & 0.614 \(\pm\) 0.020 & 29.0 \(\pm\) 1.6 \\ \cline{3-6}  & & & 0.0016 & 292 \(\pm\) 8 & 0.714 \(\pm\) 0.015 & 28.9 \(\pm\) 1.8 \\ \cline{3-6}  & & & 0.0013 & 314 \(\pm\) 4 & 0.638 \(\pm\) 0.008 & 39.2 \(\pm\) 2.0 \\ \cline{3-6}  & & & 0.0020 & 317 \(\pm\) 8 & 0.622 \(\pm\) 0.010 & 38.7 \(\pm\) 2.2 \\ \cline{3-6}  & & & 0.0018 & 294 \(\pm\) 4 & 0.715 \(\pm\) 0.020 & 45.8 \(\pm\) 2.0 \\ \cline{3-6}  & & & 0.0017 & 285 \(\pm\) 6 & 0.716 \(\pm\) 0.022 & 44.4 \(\pm\) 1.6 \\ \hline \multirow{6}{*}{12} & \multirow{6}{*}{100} & \multirow{6}{*}{2000} & 0.0024 & 308 \(\pm\) 9 & 0.749 \(\pm\) 0.015 & 40.7 \(\pm\) 1.6 \\ \cline{3-6}  & & & 0.0025 & 302 \(\pm\) 6 & 0.796 \(\pm\) 0.018 & 40.5 \(\pm\) 1.2 \\ \cline{3-6}  & & & 4000 & 0.0025 & 288 \(\pm\) 4 & 0.677 \(\pm\) 0.008 & 31.5 \(\pm\) 1.0 \\ \cline{3-6}  & & & 0.0023 & 298 \(\pm\) 6 & 0.690 \(\pm\) 0.010 & 31.5 \(\pm\) 1.5 \\ \cline{3-6}  & &differences in the value of the friction coefficient could be observed. The mean value of the coefficient of friction was determined from values ranging from approximately 500 s to approximately 2000 s of test time. The error of the data presented in Table 1 was assessed as the error of the mean. The low error value is related to the large number of tested values for each of the tested variants. It can be observed that the course of variation of the friction coefficient values is strongly heterogeneous. The significant variations in the instantaneous friction coefficient values can be attributed to the strong interaction between the surface of the test material and the surface of the counterexample. Significant differences between the character of the curve for each variant were not observed. The findings from the analysis of variance (ANOVA) for mass loss, friction coefficient, and wear depth are presented in Table 3.
## 3 Results and Discussion
### Thermogravimetric Analysis
Measured conversion rates over time of pellet samples with different water contents for the reduction and oxidation reactions during the 2 nd, 5 th, and 6 th or 9 th cycles at 800 \({}^{\circ}\)C. The conversion rate of \(\alpha_{red}\) and \(\alpha_{ox}\) = 0.8 are marked with the dashed horizontal line in each diagram. The reaction kinetics of samples with a water content of equal or less then 20 wt.-\(\%\) (wc\({}_{1}\)) strongly decreased within six redox cycles. In contrast, the samples with higher water contents (wc\({}_{2}\) = 26–27 wt.-\(\%\), w\({}_{3}\) = 33 wt.-\(\%\)) could achieve a faster conversion rate during even nine redox cycles. The best results could be obtain with w\({}_{3}\) samples. Samples with a similar water content (wc\({}_{1}\), w\({}_{2}\), and w\({}_{3}\)) showed almost the same reaction behavior during the measured cycles, except for the ninth reduction cycle of sample P10. increases from the second to the sixth cycle by 40% during the reduction reaction and 90% during the oxidation reaction. In contrast, sample P3 showed a higher cycle stability, with \(t_{red6-P3}\approx 20\) min and \(t_{ox6-P3}\approx 13\) min necessary to reach \(\alpha=0.8\) during the reduction and oxidation reactions, respectively. This is equivalent to a decrement of 10% in the reduction and 30% in the oxidation kinetics between the second and the sixth cycle. Between the second and the ninth cycle, the aging effect becomes more obvious as the decrement in the reduction kinetics amounts to 50% (\(t_{red9-P3}\approx 27\) min), while the oxidation kinetics are reduced by 80% (\(t_{ox9-P3}\approx 17\) min). In comparison, sample P6, with the medium water content of 27 wt-%, reaches \(\alpha=0.8\) in about 23 min during the 9 th reduction and 16 min during the 9 th oxidation. Compared with the second redox cycle, a decrement in the kinetic performance of 50% for the reduction and 90% for the oxidation reaction can be determined. Indeed, sample P9, with the highest water content (33 wt.-%), showed the highest reduction and oxidation kinetics and cycle stability (cf. \(t_{red2-P9}\approx 14\) min, \(t_{red9-P9}\approx 18\) min; \(t_{ox2-P9}\approx 8\) min, \(t_{ox9-P9}\approx 14\) min). This implies an increase in the reaction time of 20% and 60% regarding the second and ninth reduction and oxidation cycles, respectively. The rather large difference in the performance of sample P3 with 21 wt.-% compared to sample P2 with 20 wt.-% of water content indicates that the water content might have a dominant effect on the reaction kinetics and the cycle stability as the key performance indicators (KPIs). However, other factors, such as the sample structure, may also influence both KPIs. Accordingly, further investigations regarding the influence of the micro structure of the investigated samples on the reaction kinetics and cycle stability were carried out. The results of theses investigations are documented and discussed in the following section.
# Antibacterial Effects of _Paeonia lactiflora_ Extract on Oral Microcosm Biofilms
## 4 Discussion
Second, this study did not include a detailed characterization of the microbiome in the saliva samples. While microbial composition varies between individuals, our primary focus was on the analysis of ecological changes induced by PL extract in oral biofilms. Future studies could complement this approach by incorporating microbial profiling to provide additional context for the observed ecological dynamics. Third, this study did not specifically evaluate the effects of PL extract on microorganisms associated with oral health. While this study assessed bacterial viability and aciduric bacteria as key indicators, further investigation of mechanisms by which PL extract influences both beneficial and pathogenic microorganisms is necessary to gain a more comprehensive understanding of its potential to maintain microbial balance and support oral health. Fourth, while this study demonstrated that PL extract effectively inhibits oral biofilm maturation, it did not specifically analyze how the active compounds in the extract, such as polyphenols and flavonoids, affect the structural and compositional changes within mature oral biofilms. Future studies should include advanced analytical methods, such as metagenomic or proteomic profiling, to assess the direct impact of PL extract on the compositional dynamics of mature oral biofilms. This approach could enhance our understanding of its therapeutic potential in terms of targeting pathogenic biofilm communities while preserving beneficial microbial populations. Finally, this study employed a 2-day mature oral microcosm biofilm model. The microbial composition of oral biofilms changes as they mature, and their resistance to antimicrobial agents increases with maturation [58]. Therefore, future studies should evaluate the antimicrobial effects of PL extract on oral microcosm biofilms matured over a longer duration. To the best of our knowledge, this is the first study to evaluate the antibacterial effects of PL extract using an oral microcosm biofilm model that reflects the actual oral environment. Most studies have assessed the effects of PL extracts using single-species biofilm models [40; 49; 59]. However, according to the integrated hypothesis of dental caries and periodontal diseases, oral diseases may arise from not only specific bacteria but also the disruption of dynamic stability between the oral microbiota and its surrounding environment [60]. Using an oral microcosm biofilm model that incorporates human saliva, we analyzed the pathogenic changes that occur in response to ecological shifts in oral biofilms. These findings could provide an important starting point for understanding the potential of PL extracts to influence the balance of the oral ecosystem. In this context, the integration of PL extract into oral care products such as toothpaste or mouthwash can potentially benefit not only high-risk populations vulnerable to oral diseases (e.g., dental caries and periodontal conditions) but also the broader population by supporting the maintenance of overall oral health.
## 5 Results and Discussion
### Relationship between Area Density and COSF  
To conduct these tests, the number of slip specimens with the zigzag pattern attached to the underside of the weight-bearing frame was varied from 1 to 4 while ensuring that the PV panel was not scratched. The area density was calculated using the formula \(\rho=m/A\), where \(\rho\) represents the area density (g\(\cdot\)mm\({}^{-2}\)), \(m\) is the mass of the weight-bearing frame (1600 g), and \(A\) is the contact area corresponding to the number of slip specimens used (900 mm\({}^{2}\) for each slip specimen). The relationship between the area density (0.44 g\(\cdot\)mm\({}^{-2}\), 0.59 g\(\cdot\)mm\({}^{-2}\), 0.89 g\(\cdot\)mm\({}^{-2}\), and 1.78 g\(\cdot\)mm\({}^{-2}\)) and the COSF is depicted in Figure 12. It is evident that the COSFs showed an increasing trend as the area density decreased. It is worth noting that there were substantial differences in the COSF between the dry and wet working conditions. For instance, when the area density was 1.78 g\(\cdot\)mm\({}^{-2}\), the COSFs were 0.4 for wet and 0.59 for dry. Conversely, when the area density was 0.44 g\(\cdot\)mm\({}^{-2}\), the COSFs were 0.58 for wet and 0.8 for dry.  
Figure 11: Relationship between outside pattern of slip specimens and COSF.  
Figure 10: Outsole patterns of track grooves exposed to slip resistance test apparatus.  
Figure 13 illustrates the correlation between area density, outside pattern (flat--F, rectangular--R, hexagons--H, and zigzag--Z), and the COSF under wet (W) and dry (D) working conditions. The vertical axis represents the outside patterns, while the horizontal axis represents the area density. This graph can be used to determine the suitable outside pattern for a given area density, ensuring the safe operation of the PV cleaning robot equipped with the belt-based track and specific geometric grooves in either wet or dry conditions. For example, it is not recommended to use track grooves with a flat pattern in wet conditions, regardless of the area density, due to a low COSF range (\(<\)0.68). On the other hand, the track grooves with hexagon or zigzag patterns at low area densities (e.g., 0.44 g-mm\({}^{-2}\)) can provide the necessary safety for the PV cleaning robot to operate in wet conditions.  
In summary, the results of the experiment may show that the unevenness of COSF in all cases is caused by the different sizes of specimens that were used for each scenario.  
Figure 12: Relationship between area density and COSF.  
Figure 13: Correlation of area density, outside patterns, and COSF.  
However, the objective of this research was to evaluate the effectiveness of the proposed design of the testing apparatus; hence, it can be acceptable. Moreover, deep research on the several impacts of coated timing belts in preventing slipping for PV cleaning robots will be the focus of future work.