esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nutritional Properties of Honey Corresponding to Its General and Specific Chemical Composition
### Specific Chemical Composition on Honey  
#### 2.2.1 Phenolic Compounds  
Phenolic compounds are bioactive substances that can be found in honey, and it has been demonstrated that they play an essential role in honey's antioxidative, anti-inflammatory and antibacterial properties, among others [73,81], thus possessing beneficial effects for human health. These compounds are secondarily metabolised by plants in order to gain protection against biotic and abiotic stress and oxidative damage and are subsequently transferred into honey via nectar. Therefore, their composition is dependent mainly on the botanical source, the geographic origin, climate conditions, season [82] or even the floral preference of each bee species at the time of collecting food (nectar or exudate) [83]. However, there are some other external factors which they are subjected to that can have a considerable influence on the composition of these bioactive compounds, with the analytical methods used for their quantification, particularly high-performance liquid chromatography (HPLC), being the most relevant [42].  
Phenolic compounds can be classified into different classes according to their chemical structure: phenolic acids, including phenolic esters, and flavonoids [84]. The most important phenolic acids found in honey are hydroxybenzoic acids (gallic acid, benzoic acid, vanillic acid) and hydroxycinnamic acids (caffeic acid, p-cumaric acid, ferulic acid and cinnamic acid), each one having a different chemical structure [85].  
In addition to the phenolic acids found in honey, flavonoids can be sub-divided into other groups depending on their structural complexity. In this regard, we can discuss flavonoids (such as myricitin, galangin, quercetin, rutin and kaempfer), flavones (such as chrysin, tectochrysin, luteolin and apigenin), flavanols (such as cate-chin), flavanones (such as hesperetin, naringenin, pinochemrin and pinobanksin), isoflav, anthocyanins and chalcones [53].  
Various authors have studied the profile of phenolic compounds in honey. [PERSON] et al. (2020b) [86] analysed the total phenolic content (TPC) of various Romanian honey samples: Raspberry, Mint, Thyme, Rape, Sunflower and Polyfloral, and noticed that the TPC varied between 18.91 mg GAE\(\cdot\)100 g\({}^{-1}\) for Thyme honey, and 23.71 mg GAE\(\cdot\)100 g\({}^{-1}\) for Mint honey. Some phenolic compounds were described as possible tool markers classification and authentication for different honey types, monofloral honey in particular [87]. Among them, gallic acid (361.81 ug 100 g\({}^{-1}\)), was reported as the most frequent phenolic compound found in mononforlal honey [88]. Moreover, studies showed that quercetin could be attributed for sunflower honey [61], while other research [27] revealed that quercetin is related to Romanian Rape honey. Conversely, rutein was observed in greatest proportion in Chinese Acacia honey (3825 ug/100 g\({}^{-1}\)) [89].
## 6 Validation of Method and Comparison of Computational Efficiency
By refining grids of the model in Figure 5, the total computational times of the proposed method and Newmark [31] method are compared in Figure 7. The model is discretized into approximately 1000, 2000, 3000, 4000, and 5000 nodes, respectively. Comparison of the computation time of the two algorithms show that when the model has a  
Figure 5: Numerical model for 2D saturated porous media. Figure 6: Dynamic responses of the 2D model for saturated porous media. relatively small number of nodes, the implicit Newmark method leads to much less computation time than the proposed method, because it is unconditionally stable and allows a relatively large time step. However, as the number of nodes increases to approximately 3000, the total computation time of the two methods is basically similar, and the computational efficiency of the proposed method is significantly improved. When the number of nodes increases to approximately 4000, the computational efficiency of the proposed explicit method is far higher than the implicit Newmark method, and such an advantage becomes more significant as the number of nodes continues to increase.
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 1 Introduction  
China's inland waterway transportation is developing rapidly. By 2025, the navigable mileage of the nation's inland waterways is projected to reach approximately 133,000 km, of which about 18,600 km will be high-grade waterways, thereby increasing the proportion of high-grade waterways to 14%. With the growth of transportation volume [1], an inevitable rise in ship collision accidents is anticipated, with the majority caused by human error [2]. In particular, at the confluence areas of inland waterways, environmental complexity and uncertainty in ship maneuvering make crews more prone to misjudging collision avoidance actions [3]. The development of autonomous ship navigation technology offers novel solutions and technical support for enhancing inland navigation safety. By advancing ship autonomy, it can assist crews in identifying potential collision risks as early as possible and determining the optimal timing for collision avoidance actions as well as feasiblemaneuvering schemes, thereby reducing the impact of human factors in inland waterway collisions [4].  
Therefore, the primary objective of this research is to develop an autonomous navigation system tailored to typical confluence areas of inland river main channels and tributaries. This system aims to alert crews to collision risks and support them in promptly implementing effective collision avoidance measures, thereby preventing ships from entering hazardous situations or suffering collisions due to vigilance lapses or situational misjudgments. The significance of this research lies in its potential to enhance the safety and efficiency of inland waterway navigation, particularly in challenging tributary environments. By leveraging intelligent technologies, the proposed methods seek to reduce reliance on human intervention, mitigate collision risks, and optimize ship operations. This study not only advances autonomous navigation systems but also provides practical solutions for the sustainable development of inland waterway transportation.
# The Electromagnetic Shielding of Optoelectronic Devices by Mesh Structures
## 2 Theoretical Methods of Calculation  
To evaluate the effectiveness of shielding from electromagnetic radiation, the attenuation could be expressed as follows:  
\[K(dB)=-10*\lg(P_{i}/P_{t}), \tag{1}\]  
where \(P_{i}\) and \(P_{t}\) are the powers of incident and transmitted electromagnetic radiations, respectively. They are defined by a reflection loss and absorption loss and depend on the frequency of electromagnetic radiation [12].  
One-mode calculation can be used to describe the shielding properties of a mesh structure via the simplification of a full set of Floquet modes [13]. Based on this simplification of the analytical theory, formulas for the transmission and reflection coefficients of the mesh structure were obtained. However, this model can only described a free-standing mesh structure with a specific configuration. The configuration types described in the model are shown in Figure 1. This condition restricts the use of the model.  
The second model used for modeling the shielding properties was described in detail in the monograph by [PERSON] [14]. In this model, a simple equation was used to evaluate the approximate attenuation:  
\[K(dB)=-20*\lg(\frac{2h}{\lambda}*\ln(\frac{h}{2\pi a})), \tag{2}\]  
where \(h\) is the side length of the square cell, \(\lambda\) is the radiation wavelength, and \(a\) is the effective transverse conductor size:  
\[a=\frac{d}{2}*(0.5+0.68*(\frac{t}{d})^{3/2}), \tag{3}\]  
where \(d\) is the conductor width and \(t\) is the conductor thickness.  
The limitations of the applicability of this model can be determined. This is the condition under which the decimal logarithm expression is equal to 1. Hence, the expression for the cut-off wavelength can be defined as follows:  
\[\lambda_{b}=2h*\ln(\frac{h}{2\pi a}). \tag{4}\]  
The wavelength range of the model's applicability includes wavelengths that exceed \(\lambda_{b}\). The second theoretical approach is further named the Kontorovich calculation.  
For a benchmark calculation of mesh structure transmission, a numerical calculation was carried out by the COMSOL Multiphysics software. The calculation was performed using the finite element method for the full set of Floquet modes in a wavelength range from 200 um to 12 cm. In this method, a minimum size of a grid element was much smaller than the wavelength.
## 2 Materials, Experimental and Simulation Setting
### Calculation Methods  
The information about the aromatic carbon skeleton in coal can be determined by the ratio of bridge carbons to surrounding carbons (\(X_{\text{BP}}\)). The bridge-to-surrounding carbon ratio (\(X_{\text{BP}}\)) can be calculated using the formula [24], as shown in Equation (1).  
\[X_{BP}=\frac{f_{a}^{B}}{f_{a}^{H}+f_{a}^{P}+f_{a}^{S}} \tag{1}\]  
where \(f_{a}^{B},f_{a}^{H},f_{a}^{P},\) and \(f_{a}^{S}\) represent the fractional contributions of bridge aromatic carbons, protonated aromatic carbons, oxygen-substituted aromatic carbons, and hydrocarbon-substituted aromatic carbons, respectively, to the total number of carbon atoms in the coal structure.  
To analyze the diffusion capability of H\({}_{2}\) on the surface of lignite, the diffusion coefficient \(D\) is calculated. The specific calculation methods [25; 26] are given in Equations (2) and (3).  
\[MSD=\frac{1}{N}\sum_{i=1}^{N}[r_{i}(t)-r_{i}(0)]^{2} \tag{2}\]  
\[D=\lim_{t\rightarrow\infty}\biggl{(}\frac{MSD}{6t}\biggr{)}=\frac{1}{6}K_{MSD} \tag{3}\]where \(MSD\) denotes mean square displacement; \(N\) denotes the total number of diffusing molecules; \(r_{i}(t)\) and \(r_{i}(0)\) are the position vectors of the \(i\)th molecule at time \(t\) at time \(t=0\), respectively; and \(K_{MSD}\) denotes the slope of the \(MSD\) curve.
###### Abstract  
Surface roughness is a critical factor in restorative dentistry, as it influences both the aesthetic and functional outcomes of dental materials. The choice of abrasive powders used during the air polishing of dental restorations can significantly impact the surface roughness, potentially affecting the longevity and performance of the restoration. This study aimed to compare the effects of three different air-polishing powders--sodium bicarbonate, erythroid, and glycine--on the surface roughness of a 3M Filek(tm) Supreme XTE (3M St. Paul), Minnesota 55144-1000) United States composite resin and a Fuji(tm) IX glass ionomer cement. This study also examined how different application times (5 and 10 s) influenced these outcomes. **Materials and Methods:** An in vitro study was conducted using standardized blocks of 3M Filek(tm) Supreme XTE composite resin and Fuji(tm) IX glass ionomer cement. Each material was treated with sodium bicarbonate, erythroid, and glycine at two different exposure times (5 s and 10 s) using a professional air-polishing device. Surface roughness (Ra) was measured before and after the treatments using a contact profilometer. The data were statistically analyzed using ANOVA and [PERSON]'s post hoc test to compare the mean roughness values among the different groups, with a significance level set at \(p<0.05\). **Results:** The results showed significant differences in surface roughness between the materials, powders, and application times. The composite resin exhibited lower roughness and variability compared with the Fuji(tm) IX, indicating a more uniform and predictable surface. Sodium bicarbonate produced the highest roughness values, particularly after 10 s of application, while glycine resulted in the lowest roughness with greater variability. Erythritol produced intermediate roughness with moderate variability. For the Fuji(tm) IX, sodium bicarbonate caused the greatest variability in roughness, while glycine produced the smoothest surfaces. The ANOVA results confirmed significant differences in mean roughness among the abrasive groups, with [PERSON]'s post hoc test showing that glycine had significantly lower roughness than sodium bicarbonate, and erythroid had higher roughness than glycine but was lower than sodium bicarbonate. **Conclusions:** This study demonstrated that the choice of air-polishing powder and application time significantly affected the surface roughness of both 3M Filek(tm) Supreme XTE composite resin and Fuji(tm) IX glass ionomer cement. Glycine consistently produced the smoothest surfaces, making it a preferable option for clinical scenarios where minimal surface roughness is desired. Sodium bicarbonate, while effective in cleaning, resulted in rougher surfaces, particularly after longer exposure times. These findings suggest that careful selection of polishing powders can enhance the esthetic and functional outcomes of dental restorations, contributing to their longevity.  
surface roughness; air polishing; abrasives; polishing powders +
Footnote †: journal:
# Estimating Significant Wave Height from SAR with Long Integration Times
## 2 Estimating Significant Wave Height from SAR with Long Integration Times  
In this section, a method of estimating significant wave height from SAR with long integration times is proposed, which is designated for the SAR single-look complex (SLC) data. The flow chart of the proposed method is shown in Figure 1. As can be seen, the method is divided into six parts: pre-processing, azimuth cutoff calculation, ocean wave refocusing, scanning distortion calibration, wavelength and propagation direction calculation of \(n\)th ocean wave and significant wave height estimation. The above six parts will be detailed below.
## 2 Materials and Methods  
### Application of the AOP-helpFinder  
#### 2.1.1 Development of the Dictionaries of Stressors and Events to Be Screened  
In order to explore the associations between events (e.g., \"metabolic health outcomes\") and stressors of interest (i.e., PFAS), the AOP-helpFinder tool needs two input dictionaries that will be automatically screened in order to identify the co-occurrence in abstracts between at least one event and a stressor.  
The stressor dictionary contained a list of 59 PFAS that was created by experts. The majority of the PFAS were selected in accordance with the HBM4 EU Scoping Document [25], and the list was additionally complimented by the judgment of experts.  
The second dictionary, including events related to metabolism, i.e., MIEs, KEs, and AOs, was subsequently generated. This dictionary contained two distinct lists of events: one using data from the AOP-wiki database and a second one using medical subject headings (MeSH) knowledge. To define the AOP-wiki event list, we went through the downloaded full list of events from the AOP-wiki database (as of March 2020) and selected 17 AOPs of interest linked to metabolism. These selected AOPs were mostly related to lipid metabolism and liver steatosis, which are related to metabolic outcomes. Moreover, one AOP for obesity and two AOPs for hypertension were added after a manual search. From this, a first list containing a selection of 154 events extracted from the AOP-wiki database ([[https://aopwiki.org](https://aopwiki.org)]([https://aopwiki.org](https://aopwiki.org)), accessed on 27 October 2020) was created. To select these events, all event types (MIE, KE, and AO) were considered, and only the ones of interest (i.e., related to metabolic outcomes) were kept. The second list included 19 MeSH specifically related to metabolic syndrome.
# A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
## 3 Original Data
### The Road Network  
The raw input OSM roads employed in this study can be downloaded from the OSM website ([[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org))). Firstly, we exported the spatial data of all types of roads in the osm file format using an enveloping rectangle [43.1974, -95.1108, 43.1043, and 95.201] of the study area. Secondly, we applied the osm2 pgrouting tool to select the roads for cars and built the road network in the PostGreSQL/PostGIS database. Thirdly, we used the ST_Intersects function provided by the PostGIS extension to clip the roads within the Spencer city boundary, which can be downloaded from OSM Boundaries 4.6.4 ([[https://wambachers-osm.website/boundaries/](https://wambachers-osm.website/boundaries/)]([https://wambachers-osm.website/boundaries/](https://wambachers-osm.website/boundaries/))). A total of 2081 road segments for cars and 1456 junctions were successfully selected, as shown in Figure 6.  
Note that intersections should be extracted from road junctions since junctions are not always intersections. A total of 1065 intersections in Spencer city were selected using the intersection extraction algorithm (discussed in Section 2.1), as shown in Figure 7.  
Figure 6: Distribution of the road network in Spencer city.
## 4 Southern Cluster's BUC, Products, and KPIs
### Relationhip with other Use Cases  
With the help of several System Use Cases (SUCs), which will be developed and implemented as part of the OneNet project, the F-channel application itself will show how valuable and superior it is to other, current tools and applications of a similar nature. The following system use cases show the direct connection.  
4.1. _Subc 1_: Process for Storm and Icing-Related Preventive Maintenance in Local Microgrids, DSO Grids, and TSO Grids  
The DGs, the OHLs of micro-grids and HEDNO, and the predictive maintenance and protection with better predictions of storms and icing are the scope of 1. Its objectives are:  
* Predictive congestion management to keep the functioning of the power system safe and stable.
* System functioning that is economical.
* Early notification of a risky power system regime.  
The severe weather conditions that may trip power lines or induce DG outages, leading to brownouts or blackouts in the region we are interested will be recognized during this SUC. The following are the primary anticipated advantages and features for this specific use case:  
* Better Islanded operation on the DSO and TSO sides.
* More adequate systems on both sides.  
The key performance indicators are presented in Table 7.  
4.2. _Subc2_: Process for Managing Outages in the IPTO Grid, the HEDNO Grid, and the Local Micro-Grid  
The focus of 2 is on the outage management of the IPTO/HEDNO grid and the local microgrid that accounts for better forecasts and forecasting effectiveness from integrated AI and NWPs with higher geographical resolution. Its objectives are:  
* Predictive congestion management to keep the functioning of the power system safe and stable.
* System functioning that is economical.
* Early notification of a dangerous power system regime.
* The following are the main advantages and features for this specific use case:
* Increased IPTO and HEDNO system adequacy
* Improved IPTO and HEDNO side Islanded operation The KPIs are the same with 1 presented in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Reference to Mentioned Use Case Objectives** \\ \hline \hline \multirow{6}{*}{Rate of contingency identification} & \(\bullet\) & Improved energy forecasts and power system state forecasts \\  & \(\bullet\) & Using predictive congestion management to keep the functioning of the power system safe and stable \\  & \(\bullet\) & Early notification of risky regimes in the power system \\ \hline \multirow{6}{*}{Early notification of a dangerous power system regimes} & \(\bullet\) & More accurate predictions of the power’s system state \\  & \(\bullet\) & Predictive congestion control for ensuring safe and reliable power system operation \\ \cline{1-1}  & \(\bullet\) & Early notice of potentially dangerous power system regimes \\ \hline \hline \end{tabular}
\end{table}
Table 7: Key performance indicators for SUC 1.
# An Improved Genetic Algorithm for Solving the Semi-Soft Clustered Vehicle Routing Problem
## 3 Problem Description and Mathematical Modeling  
### Problem Description  
The SemiSoftCluVRP can be described as follows: Let \(G=(V,E)\) be a complete undirected graph, where \(V=\{0,1,\cdots,n\}\) is the set of vertices, and \(E=\{(i,j)|i,j\in V,i\
eq j\}\) is the set of edges. Vertex 0 represents the depot, and vertices \(1,2,\cdots,n\) are customer, denoted by \(N=\{1,2,\cdots,n\}\). Each customer \(i\in N\) is associated with a demand \(q_{i}>0\). The vertex set \(V\) is divided into \(m+1\) non-empty and mutually disjoint subsets \(V_{0},V_{1},V_{2},\cdots,V_{m}\), where \(V_{0}=\{0\}\). Each subset is called a cluster. Let \(R=\{V_{1},V_{2},\cdots,V_{m}\}\). \(R\) is further divided into two disjoint subsets \(R_{1}\) and \(R_{2}\), where \(R_{1}\) represents the set of hard clusters, and \(R_{2}\) represents the set of soft clusters. Each edge \((i,j)\in E\) is associated with a distance \(d_{ij}>0\). There are \(K\) identical vehicles available at the depot, each with a capacity of \(Q\).  
The requirement of the problem is to design a route for each vehicle such that each customer is visited exactly once, subject to the following constraints: (1) the total demand of customers assigned to a vehicle cannot exceed its capacity \(Q\); (2) each vehicle's route must start and end at the depot; (3) customers within each cluster \(V_{i}\in R\) must be servedby the same vehicle. Meanwhile, if \(V_{i}\) is a hard cluster, the service for its customers is not allowed to be interrupted, while if \(V_{i}\) is a soft cluster, there is no such restriction for its customers. The objective of the problem is to minimize the total distance traveled by all vehicles.  
Figure 1 provides a schematic diagram of feasible solutions for the CluVRP, SoftCluVRP, and SemiSoftCluVRP, where the number of customers is 16, and all the customers are divided into five clusters: \(V_{1}\), \(V_{2}\), \(V_{3}\), \(V_{4}\), and \(V_{5}\). Furthermore, in the SemiSoftCluVRP, \(V_{1}\), \(V_{2}\), and \(V_{3}\) are hard clusters, while \(V_{4}\) and \(V_{5}\) are soft clusters. In the figure, to more intuitively distinguish between hard clusters and soft clusters, we use solid-line rectangular boxes to represent hard clusters and dashed-line rectangular boxes to represent soft clusters.