esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Results with Dual-Polarimetric Data
#### 4.2.2 Enhancement Performance with C-Band Dual-Pol Data
As illustrated, higher intensity values corresponding to the ship pixels are slightly reduced (by approximately 3 dB) in the optimal channel compared to the VV polarization (these values remain very similar to those in the VH polarization). However, the sea clutter level is significantly reduced to around \(-40\) dB in the optimum channel, compared to \(-15\) dB and \(-25\) dB in the VV and VH channels, respectively. More details on the signal-to-clutter ratios are \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Processed Polarimetric** & & \multicolumn{3}{c}{**{HH,VV}**} & \multicolumn{3}{c}{**{HH, HV}**} & \multicolumn{3}{c}{**{VV,VH}**} \\
**Channels** & & & & & & & & \\ \hline Signal-to-Clutter & HH & VV & OPT & HH & HV & OPT & VV & VH & OPT \\ Ratio [dB] & 14.84 & 13.22 & 29.15 & 14.84 & 27.59 & 37.45 & 13.22 & 27.47 & 37.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Signal-to-clutter ratio between a ship and its surrounding sea area after applying the contrast enhancement method with different dual-pol combinations of channels using C-band Radarsat-2 data. provided in Table 6. As shown, the optimum channel exhibits an improvement of around 9 dB compared to the VH polarization and 13 dB compared to the VV polarization. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polarization** & **VV** & **VH** & **Optimum** \\ \hline Signal-to-Clutter & & & \\ Ratio [dB] & 22.21 & 26.13 & 34.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Signal-to-clutter ratio between the ship highlighted with the red rectangle in Figure 12 and its surrounding sea clutter for different polarimetric channels. Figure 9: Optimization results of dual-polarimetric [HH,VV] image acquired by Radarsat-2 over the city of Barcelona (Spain). Figures (**a**–**c**) represent the image intensity in logarithmic scale ranging from \(-35\) to \(0\) dB.
# Mechanical and Conductive Behavior of Graphite Filled Polysulfone-Based Composites
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.2 Tensile Test  
One of the fundamental mechanical tests, the tensile test, involves applying a controlled tensile force to a carefully prepared specimen in order to assess the strength of any material. The relationship between the load placed on the material and the deflection (or strain) that results in comparison to the load can be used to measure the tensile characteristics. The tensile test specimens were prepared according to the ISO standards and were shaped into rectangles using a hot press process. In a tensile test, the sample is stretched until it breaks. In this case, the strain rate must be low so that the result is not distorted. During the tensile test, the force and buckling of the specimen are measured.  
Figure 5 shows the results of tensile tests of the PSU-based composites filled with NG, AG and EG. Results for AG (Figure 5b) showed that increasing the filler content to 40 wt.% increased the tensile strength; it then started to decrease as the filler content increased above 50 wt.%. The maximum tensile strength of the PSU-based composite filled with 30 wt.% NG, 50 wt.% AG and 40 wt.% EG reached 16.22 MPa, 25.71 MPa and 16.48 MPa, respectively.  
In comparison to composite materials filled with NG and EG, AG's small particle size results in a material that is denser and has a higher tensile strength. Natural graphite sheets stacked on top of one another produce adhesion forces between the polymer and the graphite that are weaker than those between the polymer and industrial graphite.  
Figure 5: Tensile test curves of PSU based composites filled with 30 (1), 40 (2), 50 (3), 60 (4) and 70 (5) wt.% of NG (**a**), AG (**b**) and EG (**c**).  
The mechanical characteristics are significantly altered when different types of graphite filler are added in increasing amounts to the polymer matrix. Tensile and flexural strengths showed a similar pattern of behavior.  
Graphite and polysulfone have highly different mechanical characteristics. Graphite is a fairly brittle substance, while PSU is a polymer with a high degree of plasticity. This is the main reason why combining the two materials results in less plasticity and more stiffness and brittleness than the pure polymer's natural qualities. It seems that the mechanical properties of graphite are more likely to be adopted by composite materials than those of polysulfone.
## 4 Parametric Analysis
### Effects of the Unit Cell Thickness  
The results for the piezoelectric phononic crystal band structure changing with the thickness \(h\) are presented in Figure 12. The default thickness parameter set in the results  
Figure 11: The directionality of elastic wave attenuation.  
Figure 10: (**a**) Band structure in the MT direction; (**b**) Attenuation constant in the MT direction.  
and discussion section is 16.67 mm, which is shown by the black dotted line in Figure 12. Four bandgaps appear at the position of the black dotted line, which is completely consistent with the numerical calculation results presented in Figure 3.  
With the increase of the thickness of the piezoelectric phononic crystal, the lower boundary of the first gap basically does not change, whereas its upper boundary gradually rises, increasing the width of the first bandgap. For the second bandgap, the upper and lower boundaries rise with the increase of the thickness, but the upper boundary rises faster than the lower boundary, leading to the continuous increase of the bandgap width.  
Similar to the second bandgap, the upper and lower boundaries of the third bandgap also climb with the increase of the thickness, but their growth speed is the same, so the width of the third bandgap does not change very much. However, when the thickness increases to 25 mm, the rising speed of the upper boundary slows down, but the rising speed of the lower boundary remains the same, which eventually leads to the complete disappearance of the third bandgap. The variation of the fourth bandgap with the thickness is only slightly different from that of the third bandgap. To be precise, when the thickness is 33.33 mm, the fourth bandgap is opened again, but it steadily disappears as the thickness further increases.
## 2 Related Work
### Local Strategy  
[PERSON] et al. [6] proposed a distributed datagram routing algorithm tailored for LEO satellite networks. The algorithm considers the congestion status when selecting the next satellite node to optimize congestion handling and reduce latency. Simultaneously, it ensures data transmission efficiency and success rates in the event of node failures. [PERSON] et al. [7] proposed the traffic-light-based intelligent routing (TLR) algorithm, which utilizes traffic light indicators to sense the congestion states of the current and next-hop nodes. Through a combination of pre-planning and real-time adjustments, TLR aims to derive an approximately optimal transmission path. [PERSON] et al. [8] studied the network coding-based multipath cooperative routing (NCMCR) algorithm, where data streams can dynamically and collaboratively transmit across multiple paths. [PERSON] and [PERSON] et al. [9] proposed a traffic return routing algorithm based on segmented routing. This algorithm dynamically divides the network into light-load and heavy-load zones. The light-load zone employs a pre-balanced shortest path algorithm, while the heavy-load zone utilizes a minimum congestion index path algorithm. This approach effectively enhances the load performance of a LEO satellite network. [PERSON] and [PERSON] [10] addressed the issue of cascading congestion in traffic regions due to the lack of a global view in distributed load balancing routing schemes. They proposed a load balancing routing scheme based on hybrid traffic diversion, where the forwarding path is determined based on prior information acquisition and real-time congestion awareness. This approach aims to alleviate cascading congestion and achieve efficient routing transmission.
## 2 Results
### Effect of Plasma-Treated Water on the Synthesis of Gamma-Decalatone and other Volatile Metabolites
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multicolumn{4}{c}{**Type of Medium**} \\ \cline{2-5}  & **Control** & **PWTN** & **PWTA** & **Nantes PW** \\ \hline \multicolumn{5}{c}{**Percentage Content (\%)**} \\ \hline
**Higher alcohols** & 15.05 \({}^{c}\)\(\pm\) 0.23 & 8.92 \({}^{b}\)\(\pm\) 0.97 & 7.33 \({}^{a}\)\(\pm\) 0.21 & 7.53 \({}^{a}\)\(\pm\) 0.69 \\ \hline
3-methyl-butan-1-ol & & & & \\ \hline \(n\)-heptanol & & & & \\ \hline oct-1-en-3-ol & & & & \\ \hline
2-phenylethanol & & & & \\ \hline
**Aldehydes** & 39.59 \({}^{a}\)\(\pm\) 0.86 & 44.47 \({}^{b}\)\(\pm\) 3.15 & 41.33 \({}^{a}\)\(\pm\) 0.77 & 40.28 \({}^{a}\)\(\pm\) 3.53 \\ \hline hexanal & & & & \\ \hline heptanal & & & & \\ \hline benzaldehyde & & & & \\ \hline phenylacetaldehyde & & & & \\ \hline
**Ketones** & 7.17 \({}^{a}\)\(\pm\) 0.18 & 7.54 \({}^{a}\)\(\pm\) 0.54 & 7.21 \({}^{a}\)\(\pm\) 0.11 & 7.03 \({}^{a}\)\(\pm\) 0.12 \\ \hline
2-octanone & & & & \\ \hline
3-methylene-2- & & & & \\ pentanone & & & & \\ \hline
**Fatty acids** & 15.43 \({}^{b}\)\(\pm\) 0.14 & 11.82 \({}^{a}\)\(\pm\) 0.57 & 12.31 \({}^{a}\)\(\pm\) 0.22 & 11.38 \({}^{a}\)\(\pm\) 0.09 \\ \hline
2-methyl-butanoic acid & & & & \\ \hline
3-methyl-butanoic acid & & & & \\ \hline
2-hydroxyoctanoic acid & & & & \\ \hline
4-hydroxydecanoic acid & & & & \\ \hline
3,4-dihydroxy-decanoic acid & & & & \\ \hline
**Lactones** & 13.01 \({}^{a}\)\(\pm\) 0.44 & 14.89 \({}^{a}\)\(\pm\) 0.52 & 22.03 \({}^{b}\)\(\pm\) 0.87 & 24.55 \({}^{b}\)\(\pm\) 0.36 \\ \hline gamma-butyrolactone & & & & \\ \hline gamma-nonalactone & & & & \\ \hline
3-hydroxy-gamma- & & & & \\ decalactone & & & & \\ \hline dec-2-en-4-olide & & & & \\ \hline dec-3-en-4-olide & & & & \\ \hline
**Others** & 9.75 \({}^{a}\)\(\pm\) 0.77 & 12.36 \({}^{b}\)\(\pm\) 1.98 & 9.79 \({}^{a}\)\(\pm\) 0.92 & 9.23 \({}^{a}\)\(\pm\) 1.07 \\ \hline
2,2-dimethylhexane & & & & \\ \hline
2,4-dimethylhexane & & & & \\ \hline
2,5-dimethylhexane & & & & \\ \hline
3-ethylpentane & & & & \\ \hline
2-acrylate & & & & \\ \hline
3-methyl-undec-5-ene & & & & \\ \hline
1-dodecene & & & & \\ \hline Mean values followed by the same letter are not significantly different according to HSD Tukey (\(p\leq\) 0.05). Difference in colors presented indicate differences in content of compounds in the samples: yellow (<3%), blue (3–5%), red (\(>\)5%). &  
\end{table} 
Table 3: Volatile compounds (percentage content) detected in biotransformation media (after 72 h) based on water treated with low-temperature, low-pressure glow plasma in contact with air (PWTA), nitrogen (PWTN) or Nantes plasma water with heightened singlet oxygen level.
# Data-Driven Social Security Event Prediction: Principles, Methods, and Trends
## 3 Research on Data-Driven Predicting of Social Security Events
### Technical Methods  
#### 3.2.1 Classification and Regression  
This type of method is the most commonly used method in predicting social security events, with the most related research. The primary idea is to utilize the concept of feature engineering to find discriminative features in event data, transforming the prediction task into a classic data classification problem. Subsequently, datasets are constructed, classification models are trained, and new test data are used to predict the probability of event occurrences.  
[PERSON] et al. [26], inspired by [PERSON]'s quote \"History never repeats itself but it rhymes\", approached event prediction from a causal perspective. They built classifiers to infer the likelihood of future outcomes from specific event sequences and predicted the cholera outbreak event in Bangladesh. In reference [7], logistic regression models were employed for social security event prediction and combined with the least absolute shrinkage and selection operator to select sparse feature sets to predict the probability of civil unrest events in different countries. [PERSON] et al. [15] explored the structured statistical features of cascading diffusion graphs of social network information dissemination during events like protests and unrest. They utilized these features to classify current Twitter user information through logistic regression to predict event occurrences, retrospectively forecasting events that occurred in Brazil in June 2013. [PERSON] et al. [27] used logistic regression models and Lasso to select a sparse feature set from various datasets to predict the probability of civil unrest events in multiple Latin American countries. In this process, [PERSON] used diverse heterogeneous data sources for event classification feature extraction, including features from ICEWS and GDELT, Tor anonymous access traffic logs, and economic databases such as exchange rates, enabling the selection of optimized features from a wider range of candidate features. [PERSON] et al. [28] utilized logistic regression to establish the correlation between mobilization-related media information and protest events, applying fitted models to estimate the probability of protest events during a specific period. [PERSON] et al. [21; 22; 23] conducted hierarchical feature learning based on multi-source spatiotemporal data and utilized group LASSO regression analysis to predict the occurrence probability of conflict events in multiple Latin American countries. [PERSON] et al. [24] studied social security event prediction using the \"Protest Participation Theory\" proposed in the field of political science, combined with SVM support vector machine models, providing the probability of protest events occurring in Cairo within a specific time window.
# Influence of Metal Fear Tooth Geometry on Load and Wear within Metal-Polymer Fear Pairs
## 6 Derivation of Functional Relationships and Evaluation of the Transferability  
Based on the findings, functional relationships are derived (Figure 11). Changing the tooth geometry by reducing the tip and root radii of the pinion leads to an increased active flank of the pinion. This enlarges the overlap within the pairing.  
Due to the greater overlap, the line load and the local stress of the tooth flank are decreased. The locally reduced load leads to locally declined wear of the tooth flanks of the metal pinion and polymer wheel. The adaptation of the tooth geometry thus leads to an improved wear behavior and an increased service life of the pairing.  
Figure 11: Functional relationships between gear radii, overlap, load and wear.  
Figure 10: Combined load and wear of the varied tooth geometries.  
To ensure the generality of the derived functional relationships, the transferability to a steel-polymer pairing is evaluated. Figure 12 shows the wear and surface topography when varying the tooth geometry for the reference material (AlMgSi1 T6) and the steel material 16 MnCr5.  
The reduction in wear determined for the reference material on the basis of the wear volume and the alteration of the surface topography is also found for the steel-polymer combination. The total wear volume of the pairing is reduced from 353.8 \(\pm\) 18.7 \(\upmu\)m\({}^{3}\) when using the steel pinion with maximum radii to 271.2 \(\pm\) 25.4 \(\upmu\)m\({}^{3}\) for the reference geometry and 228.4 \(\pm\) 25.2 \(\upmu\)m\({}^{3}\) for the minimum radii. Due to potential failure of the pairing because of tooth fracture of the polymer wheel with progressive wear, this directly affects the achievable service life [18].  
It should be noted that within the steel-polymer pairing, wear predominantly occurs on the polymer wheel due to adhesion [13]. No significant wear occurs on the steel pinion, which is not affected by the change in tooth geometry. At the maximal determined local load of 25.8 N/mm, there is no abrasive wear of the steel surface due to the high hardness (HV 450). Only a smoothing of the surface due to the reduction of roughness peaks occurs. In contrast to that, significant wear occurs at the aluminum pinion with a reduced hardness (HV 120).
## 4 Experimental Results and Discussion
### Influence of the Key Parameters of the VFPEH on Its Output Performance
#### 4.1.2 Load Resistance  
For different wind velocities, this paper tested the output power of VFPEH-30 at different load resistances, extracted the experimental data, and plotted the curves of the output power of VFPEH-30 with the load resistance at different wind velocities, and the curves of the power of VFPEH-30 with the wind velocity at different impedances, as shown in Figure 4a,b. The wind velocities of the experiment are 5.1 m/s, 8.0 m/s, 10.0 m/s, 11.7 m/s and 12.7 m/s, respectively, and the test range of matching resistance R was 1 k\(\Omega\)-500 k\(\Omega\).  
It can be seen from Figure 4a that under different wind velocities, the influence of the \(R\) on the output power of VFPEH-30 is consistent and nonlinear. With the same load resistance, the output power of VFPEH-30 increases and then decreases with the increase in wind velocity. When the load resistance \(R\) starts to increase from 1 k\(\Omega\), the output power of VFPEH-30 increases rapidly, reaches the maximum value between 130 k\(\Omega\)-150 k\(\Omega\), and then decreases with the increase in \(R\). Therefore, the matching load resistance of VFPEH-30 is between 130 k\(\Omega\)-150 k\(\Omega\). When the wind velocity is 5.1 m/s, the output power of VFPEH-30 changes little with the increase in \(R\). Therefore, the load resistance has little influence on the output power of the VFPEH-30 at low wind velocity. Figure 4b shows that when the \(R\) changes within the range of 1 k\(\Omega\)-150 k\(\Omega\), the output power of VFPEH-30 under the same load resistance changes nonlinearly with the wind velocity, increases first and then decreases, and the change trend is basically the same, and the maximum output power has a certain wind velocity range. When the load resistance is 130 k\(\Omega\), 140 k\(\Omega\), and 150 k\(\Omega\), the maximum output power of VFPEH-30 is large. It can be seen from the local enlarged view in the figure that when the \(R\) is 140 k\(\Omega\), the output power reaches the maximum, so the best matching resistance is 140 k\(\Omega\).  
Figure 4: Influence of key parameters on the output power of the VFPEH-30: (**a**) Curves of the output power with load under different wind velocities; (**b**) Curves of output power with the wind velocity under different loads.
## 2 System Arrangement
### Formulation of System Arrangement  
Different from monitoring communication, data is periodically generated from substations and it is an end-to-end information exchange. In such a case, there is no load imbalance issue or bottleneck issue [17] near substations. However, an optimal arrangement of the wireless sensor node for differential protection needs to be formulated considering the survivability of sensor nodes in natural disasters while satisfying the connectivity of communication networks. It is obvious that the more nodes are deployed the better. Link specific reliability can be acquired at the cost of higher complexity. Thus, there is a tradeoff between cost and reliability. In this section, an optimal arrangement formulating the placement problem is proposed while respecting all constrains.
## 5 Conclusions  
A general strategy has been presented for the fabrication of 3D graphite/ceramic composite parts with a combination of low thermal conductivity and high strength. The mixture powder was modified by adding silicon powder and expanded graphite for the printing of preforms via SLS. The addition of 24% silicon powder resulted in an increase of thermal conductivity and compressive strength by 61.70% and 28.57%, respectively. The amount of 1% EG caused a decrease of thermal conductivity and compressive strength by 25.62% and 16.82%, respectively. The porous structure in preforms could be filled by phenolic resin in the process of vacuum pressure infiltration. The conversion of phenolic resin into glass carbon in the subsequent carbonization process improved the strength and thermal conductivity. The silica sol solution was infiltrated after carbonization, and the silicon carbide reinforcing phase was generated during high-temperature sintering, thereby significantly improving the strength and thermal conductivity of composite parts.  
The fabrication scheme developed in this paper allowed the manufacture of thermal-conductivity-tunable 3D graphite/ceramic composite parts and realized the possibility of fabricating 3D graphite/  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Density** & **Compressive Strength** & **Thermal Conductivity** & **Operating Temperature** & **Repeated use Times** & **Production Costs** \\ \hline
3D graphite/ceramic composite mold & 1.05 g\(\cdot\)cm\({}^{-3}\) & 10.92 MPa & 1.88 W\(\cdot\)m\({}^{-1}\)K\({}^{-1}\) & \textgreater{}1550 \({}^{\circ}\)C & 28 & 6500\%/t \\ \hline Water glass sand mold & 1.11 g\(\cdot\)cm\({}^{-3}\) & 13.04 MPa & 1.94 W\(\cdot\)m\({}^{-1}\)K\({}^{-1}\) & \textgreater{}1550 \({}^{\circ}\)C & 36 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of 3D graphite/ceramic composite mold and water glass sand mold.  
Figure 14: Casting defects of (**a**) 3D graphite/ceramic composite mold; (**b**) Water glass sand mold.  
Figure 13: 3D graphite/ceramic composite mold.  
certainic composite parts with low thermal conductivity but high mechanical properties that met the demand for the potential of many engineer fields.  
This paper was accomplished based on collaborative work of the authors. [PERSON] performed the experiments, analyzed the data, interpreted the experimental results, and wrote the paper. [PERSON], [PERSON] and [PERSON] contributed to design and perform the experiments. [PERSON] and [PERSON] supervised the entire research progress. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.51575313), the Major Technological Innovation Project of Hubei Science and Technology Department (No.2019 AAA164), Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance (No.2018 KJX07) and Open Research Fund for the State Key Laboratory of Mechanical Manufacturing System Engineering, Xi'an Jiaotong University (No.sklms2018001).  
The authors declare no conflict of interest.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Interfacing Protocols
#### 3.3.2 IEEE 1609 WAVE  
This protocol includes network and resource management supporting multi-channel operation with network security and administration. IEEE 1609 comprises four standards: IEEE 1609.1 for application management, IEEE 1609.2 for safety and security mechanisms, IEEE 1609.3 WAVE management and networking services, and IEEE 1609.4 for multi-channel operations with logical connections and controls of each layer.