esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Acquisition and Preprocessing
### Dataset Acquisition

Table 1: PCHE flow channel dataset information under four working conditions. Figure 2: Schematic diagram of sample division. similar; however, their specific value ranges differ. The numerical range of the heat transfer coefficient for the straight flow channel is relatively narrow, whereas that for the hexagonal flow channel is relatively broad. Specifically, the heat transfer coefficient for the straight flow channel is primarily concentrated between 0 and 8000, while for the hexagonal flow channels, it is mainly concentrated between 0 and 5000.
# Fire Behavior of U-shaped Steel Beams Filled with Demolished Concrete Lumps and Fresh Concrete
## 4 Numerical Study
### Parametric Studies  
The validated numerical models are employed to investigate the effects of the thickness of fire insulation (\(t\)), load ratio (\(n\)), and longitudinal reinforcement ratio on the fire resistance of the U-shaped steel beams filled with DCLs and FC. The geometric dimensions of the U-shaped steel and concrete slab, material properties, and boundary conditions of the beams are the same as those of Specimen B3 and remain unchanged in the parametric studies.  
Variations of the beam's fire resistance with the thickness of fire insulation, load ratio, and longitudinal reinforcement ratio are shown in Figures 28-30, respectively. It can be seen from these figures that:  
1. The beam's fire resistance almost increases linearly with the increasing of the thickness of fire insulation. When the insulation thickness rises from 10 mm to 14 mm (18 mm), the fire endurance enhances by about 43% (87%).
2. Increasing the load ratio decreases the beam's fire resistance noticeably. Comparing with the fire endurance related to the load ratio of 0.4, the fire endurances pertaining to the load ratios of 0.5, 0.6, and 0.7 decrease by about 12%, 24% and 35%, respectively.  
Figure 27: Effect of interface thermal resistance on mid-span deflection.  
Figure 26: Effect of interface thermal resistance on temperature development: (**a**) Specimen B4 and (**b**) Specimen B5.  
3. A significant rise in the beam's fire resistance can be achieved by increasing the longitudinal reinforcement ratio. For example, at the load ratio of 0.5, raising the longitudinal reinforcement ratio from 0% to 0.4% (0.7%, 1.0%) increases the fire endurance by about 22% (36%, 58%).  
Figure 28: Variation of fire resistance with thickness of fire insulation (without longitudinal reinforcements).  
Figure 30: Variation of fire resistance with a longitudinal reinforcement ratio (fire insulation thickness = 10 mm).  
Figure 29: Variation of fire resistance with a load ratio (without longitudinal reinforcements).
# Numerical Investigation of Slurry Fracturing during Shield Tunneling under a Reservoir
## 4 Model Setup and Analysis of the Project
### Slurry Fracturing Pressure of Different Sections  
According to the design characteristics of the tunnel line, four typical cross-sections of the tunnels are selected for modeling and calculation. Figure 18 illustrates the slurry pressure during the formation fracturing process of the four control sections, respectively. The maximum peak value of the pressure curve is the formation fracturing initiation pressure. With the parameters provided in Table 3, it can be observed that the water depth difference of the four sections is small. The initial slurry fracturing pressure of the formation increases with the increase of burial depth. With the increase of buried shield tunnel depth, the maximum length of the slurry fracturing in the formation will also increase. At the same time, the amount of slurry injected will increase with the growth of fracture, which is reflected in the pressure fluctuation curves of different lengths in Figure 18.  
When the slurry pressure at each section reaches the peak of the pressure curve, the slurry fracturing begins to extend continuously. In order to prevent slurry from fracturing and extending to slurry blow-out, it is better to take the peak value of the slurry pressure curve as the maximum value of shield slurry support pressure. Due to the error in the control of the shield support pressure, the support pressure of the section at the shallow depth of the tunnel is small and it is more difficult to control. In addition, the slurry fracturing of the section with smaller buried depth spreads faster, and the construction control is more difficult.  
Figure 17: Variation of slurry pressure with slurry injection volume (EK4 + 820).
# A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
## 3 Tessellation-Based Algorithms  
Tessellation-based algorithms have their origins in representing the inner surfaces of three-dimensional objects. For the first time, [PERSON] and [PERSON] introduced a tessellation-based method in [20], which aims to display the inner surfaces and functions of a human brain. This requires the outer surface, the subcortex, to be removed. To do this, a volume, the so-called minimum distance field, is first determined, where each point within the volume is assigned the minimum distance to the nearest point on the cortical surface as a value. The thresholding of the volume is then performed so that a set of surfaces with a constant minimum distance to the cortex is created. Then, the surface elements generated by thresholding are divided into connected components so that the desired inner surfaces are separated from the surfaces equidistant from the brain. The authors use a modified marching cubes algorithm for thresholding. Since the marching cubes algorithm can lead to ambiguous situations that can lead to topologically unconnected surfaces, each cube is divided into five tetrahedra and each tetrahedron through which the surface passes is then polygonized. As previously mentioned, the basic idea of building cubes of eight pixels each over two neighboring slices is therefore initially based on the marching cubes algorithm. In [20], the authors show that, apart from possible holes at the edges of the sampled field, the method produces topologically correct, closed surfaces that can then be subjected to both quantitative analysis and rendering. In [21], [PERSON] describes an algorithm for the polygonization of implicit surfaces. In the method, an initial cube is first centered on a surface point and then a new cube is generated on each face that contains corners with opposite polarity. If the entire surface contains cubes, the surface in each cube is approximated by one or two polygons. To do this, the cube is divided into five tetrahedra, which are then polygonized. The polygonization is based on a table that has an entry for every possible configuration. In contrast to the 256 entries with possible configurations in the marching cubes algorithm, there are only 16 entries when using tetrahedra. Each tetrahedral configuration produces either nothing, a triangle or a quadrilateral, i.e., two triangles, as shown in Figure 5.  
In [22], [PERSON] and [PERSON] present an approach, based on the algorithm of [PERSON] and [PERSON], for the fast and efficient extraction and visualization of surfaces defined as isosurfaces in three-dimensional interpolated data. The surface is extracted as a collection of closed triangles, where each triangle is an oriente
## 3 Data Collection and Observation Quality Assessment
### Satellite Availability and PDOP  
The number of available satellites can reflect the positioning performance directly. In addition, PDOP, which represents the strength of the geometry structure between the user and observed satellites, can also be adopted to show the positioning accuracy. Theoretically, more available satellites or a smaller PDOP would mean a higher positioning accuracy. Usually, a PDOP value less than 3 is considered an ideal GNSS observing condition. Otherwise, a larger PDOP value could mean a lower positioning accuracy. Shown in Figure 9 are the number of GPS+BDS satellites and the corresponding PDOP of MI8 in the static and dynamic experiments, respectively. The number of GPS+BDS satellites observed by MI8 is about 16-22 in the static experiment, while the number of GPS+BDS satellites was approximately 13-17 in the dynamic experiment. The PDOP value of MI8  
Figure 8: Correlation of C/N\({}_{0}\), satellite elevation angle, and multipath error for G15 and C19 in the dynamic experiment.  
Figure 7: Correlation of C/N\({}_{0}\), satellite elevation angle, and multipath error for G22 and C35 in the static experiment.  
is between 1.0 and 1.4 in the static experiment, and that in the dynamic experiment is between 1.2 and 1.6. According to Figure 9, the PDOP values fluctuated frequently during the dynamic experiment, which is due to the frequent re-tracking of the GNSS satellite.
# Positioning Information Privacy in Intelligent Transportation Systems:
## 5 Related Security and Privacy Threats  
In this section, we list the attacks explicitly dangerous for location data exchange with another nodes. Note, we further elaborate not only on the attacks explicitly related to the exchange of the data between the vehicle and infrastructure node.  
Attackers are mainly focusing on two main sides while penetrating ITS systems. First, to get unauthorized access based on weaknesses in social engineering and/or physical protection of infrastructure elements and onboard controllers. Second, to exploit the design of the security protocols and communications mainly in terms of message modification or replaying previously received messages [95]. Most of the attacks today are solved with Trusted Platform Module (TPM)-based protection, identity-based cryptography, and short-lived key certificates.
## 3 Results and Discussion
### Development of the LFIA of MG  
As working, sample, and adsorption membranes, a nitrocellulose CNPC-SS12 membrane with a 15 \(\upmu\)m pore size, a GFB-R4 membrane, and AP045 membranes were used, which proved to be effective in the analysis of meat extracts described in our previous study [34]. To form the control zone of the test strip, RAMI at a concentration of 0.5 mg mL\({}^{-1}\) was applied. To determine the optimal working combination of the immunoreagents, test strips of different compositions were prepared. Thus, antibodies of 4E2 cc and 7C3 clones were immobilized in the analytical zones of the strips, while 4E2 cc, 7C3, 1B4, MyoA6, MyoD4, MyoE4, MyoF5, and MyoH9 clones labeled by AuNPs were applied onto fiberglass membranes. The results presented in Table 2 indicate that the maximum colony intensity is observed when using the 7C3/AuNPs--MyoA6 pair. It was shown that the maximum assay sensitivity and the highest analytical signal amplitude were reached at a concentration for 7C3 clone of 2.0 mg mL\({}^{-1}\). A further increase in the amount of 7C3 antibodies immobilized in the analytical zone did not significantly affect the signal intensity. The AuNPs--MyoA6 conjugate was applied onto a fiberglass membrane at a concentration that corresponded to OD520 = 8.0 with 16 \(\upmu\)L cm\({}^{-1}\) loading, which ensured an intense coloration of the analytical band and the absence of nonspecific signal. The LFIA duration was 15 min because this time was shown to be sufficient to complete the movement of labeled antibodies along the test strip. Although the formation of colored bands was  
Figure 2: Flocculation curve for the MyoA6 clone of monoclonal antibodies. The flocculation point and a concentration of antibodies selected for the conjugation are indicated by arrows (5.2 and 8 \(\upmu\)g mL\({}^{-1}\), respectively).  
observed within 5-10 min after the immersion of the strip into the tested sample, this time was not enough to obtain highly reproducible quantitative results.  
A calibration curve obtained under optimized conditions of the LFIA of MG using a 7C3/AuNPs--MyoA6 pair is shown in Figure 3. The developed test system is characterized by a MG detection limit of 5 ng mL-1. The working range of detectable concentrations is 14-210 ng mL-1. Relative standard deviation (RSD) of the measured signal intensity was not more than 12%.
## 3 Ground-Truth Data and Methods for Satellite-Sunphotometer Comparison
### Methods for Aerosol Model Evaluation and Aerosol Properties Analysis
This method is actually using the aerosol size and scattering properties to classify the aerosol type, which has been studied by [PERSON] et al. [35]. After classifying the CE318 aerosol type, the comparisons of aerosol type between the VIIRS_EDR and CE318 and between the VIIRS_ML and CE318 are done to show the accuracy rate of aerosol model type used in the VIIRS_EDR and VIIRS_ML. If the VIIRS aerosol model type is same as that of CE318, the aerosol model type used in the VIIRS is deemed as accurate. The accuracy rate is defined as the ratio of the number of accurate to the number of all daily matchups. The accuracy rate reflects the applicability of aerosol model type used in the VIIRS algorithms. We also conduct the SSA comparison of different modes (fine, coarse and bi-mode) between the VIIRS_EDR and CE318 retrieval. We compute the SSA for all aerosol modes at 440 nm in the VIIRS_EDR by inputting the aerosol parameters (refractive indices, size parameters and volume concentrations of each mode) into Mie scattering calculation [36]. The reason for using 440 nm is that the aerosol model properties in the VIIRS_EDR algorithm are mostly referred at 440 nm [6]. The input aerosol parameters of the VIIRS_EDR at each site are obtained by extracting the aerosol model type over the site pixel and calculated according to the Table 2 in reference [6]. Although \(\text{SSA}_{440\text{nm}}\) is available from CE318 inversion, for consistency we also use the Mie code to compute the \(\text{SSA}_{440\text{nm}}\) based on the aerosol optical properties inversed from CE318 sky radiances. We have compared the SSAs between the CE318 inversion and the Mie scattering calculation and the result shows that the bias of the two SSA values is very low (less than 0.01). That is because the CE318 inversion also uses Mie scattering calculation to obtain SSA. The resultant \(\text{SSA}_{440\text{nm}}\) of CE318 is compared with that of the VIIRS_EDR \(\text{SSA}_{440\text{nm}}\). Since the CE318 sky-radiance inversion product is only reliable for AOD\({}_{440\text{nm}}>0.4\), we also choose the aerosol properties of the VIIRS_EDR when AOD\({}_{550\text{nm}}>0.25\)[23].
## 3 Results and Discussion  
### Phenolic Content of the Extracts  
To date, a significant amount of effort has been placed to examine the phenolic content of extracts obtained solely by ultrasound treatment of plant mixtures and lesser effort to study the effect of PEF treatment. In addition, studies focusing on the combination of the two aforementioned techniques are insufficient and in short supply [25; 28]. The results obtained from PEF pretreated and non-pretreated samples subjected to ultrasound extraction are summarized in Table 1. In accordance with the results, the use of PEF without the aid of ultrasonication does not lead to notable changes in the polyphenolic content of the extracts (the recorded increase in the TPC was \(<\)4% and was not found to be statistically significant). This minuscule increase in the TPC can possibly be attributed to the fact that even though the cell membranes are partially damaged, the phenolic compounds cannot, yet, be released [25]. On the contrary, when PEF was used as a pretreatment step, prior to the ultrasonication extraction step, a notable increase in the polyphenolic contained in the extract was recorded. More specifically, when 1:1 \(v/v\) methanol:water was used as a solvent, during the PEF pretreatment step, a 17% increase in the polyphenolic content of the extract (after US extraction) was recorded, compared with the non-pretreated sample. Similarly, when water was used as a solvent during the PEF pretreatment step, the respective extract contained 35% more polyphenols, compared with the non-pretreated sample with PEF extract.  
In a previous study, a similar approach was employed to examine whether PEF pretreatment can increase the TPC content of rosmary and thymre extracts, obtained by ultrasound-assisted extraction [25]. In accordance with the authors, the use of PEF as a pretreatment step was found to be beneficial in both cases. However, the PEF pretreatment was found to increase the TPC of thymre extracts, compared with rosmary extracts. In another study, the PEF pretreatment increased by nearly 15% of the TPC content of almond extract (obtained by US-assisted extraction), compared with PEF non-pretreated extract [28]. Therefore, in accordance with our results, it can be concluded that not only PEF can be used as a pre-treatment step to maximize the extraction yield of polyphenols, but the solvent used during the PEF treatment step, is also of high importance to obtain the optimum extract.
## 2 Related Work
### Expert Knowledge-Driven Complexity Evaluation Methods  
Expert knowledge-driven complexity evaluation models quantify scenario complexity using expert-defined rules and experience-based indicators and have been widely applied in autonomous driving test scenarios in recent years. The Analytic Hierarchy Process (AHP) [27] is commonly used to construct hierarchical complexity evaluation models, integrating expert scoring with information entropy theory to reduce subjectivity and enhance the objectivity and applicability of complexity quantification. For example, [PERSON] et al. [28] proposed a model combining AHP with an improved Delphi method, integrating expert scores with information entropy for complexity evaluation in autonomous driving scenarios, demonstrating high objectivity. [PERSON] et al. [29] developed an AHP-expert evaluation model that distinguishes between fundamental environmental complexity and additional environmental complexity to provide both objective and subjective assessments for autonomous driving systems.  
In addition, to address the complexity of dynamic environments, [PERSON] et al. [30] combined grey relational analysis with an improved gravitational model to develop a quantification model suitable for evaluating complexity in urban road scenarios for autonomous vehicles. [PERSON] et al. [31] used information entropy and gravitational models to assess the dynamic interaction complexity between vehicles, particularly suited for complexity quantification in car-following scenarios. On the other hand, [PERSON] et al. [32] applied the spherical fuzzy-analytic network process (SF-ANP) method, utilizing a multi-layer decomposition model to hierarchically assess the risk and complexity of elements within autonomous driving test scenarios. [PERSON] et al. [33], based on Operational Design Conditions (ODC) and AHP, introduced driver status, vehicle status, and network information into complexity quantification for the first time, enhancing the model's adaptability to real testing scenarios.  
Although AHP offers a clear structure and strong operability advantages in expert knowledge-driven complexity evaluation, it relies on expert-defined weights, making subjectivity difficult to avoid. Additionally, its hierarchical structure struggles to capture subtle changes in dynamic traffic environments, limiting its generalizability in complex, dynamic scenarios.
## 3 Results and Discussion  
### Various Sizes of Mountain Bike Tires  
For decades, mountain bikes were equipped with so-called 26-inch wheels. This was in reference to the approximate outer tire diameter, consisting of a rim with approximately a 22-inch (559 mm) outer diameter and a tire that is about 2 inches tall at top and bottom. The so-called \"29 er\" wheel and tire size, with a 24.5-inch (622 mm) diameter rim (equal to that of \"700c\" road bike wheels) gained popularity in the early 2000s on the basis of its purported ability to roll over obstacles with greater ease. However, some riders still yearned for the quick handling of smaller diameter wheels. After significant tooling investments from the bicycle industry, the so-called 27.5-inch or \"650b\" wheel size, with about a 23-inch (584 mm) rim diameter, offered what some thought was the optimal compromise.  
As wheel diameters ebbed and flowed, pioneers within the industry have also explored various tire widths. Fat bike tires, which are generally 3.8 inches (96.5 mm) or wider, grew out of a desire to ride on soft snow and sand. The \"plus tire\", 2.6 inches (66 mm) to roughly 3 inches (76 mm) wide, split that difference, allegedly trading some of the original 29 er's outright speed for more tire volume and confidence-inspiring traction. Now \"29 plus\" tires take an extreme to the extreme in terms of both diameter and width, and are a growing segment in both enduro mountain bike and bike packing applications.  
As tire sizes have evolved, new rim widths have been adapted to follow suit. In the past, 18 or 19 mm inner width rims, similar to those used on past road racing bikes, were the norm. Over the past decade, rims gradually grew in width, chasing various trade-offs and trends in wheel stiffness, tire stiffness, tire profile, weight, and strength. For this study, \"modern\" rim widths appropriate to each tire size were selected on the basis of realistic use case and/or market benchmarking within that segment. Inner rim width will be referred to in millimeters where appropriate.  
Similarly, each of the above tire and rim combinations have their own performance trade-offs in terms of \"nominal\" inflation pressure for a given rider weight, bike fitment, and terrain. Nominal inflation pressures used for each tire size in the following study are based on the author's experience with these tire setups for summer off-road trail riding.  
For the remainder of the paper, the \(29\times 2.3\)\" knobby tire on 25 mm inner rim width at 25 psi inflation pressure is considered the baseline to which data of the other tire configurations are compared.