esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Immersed Boundary-Lattice Boltzmann Coupling Scheme
### The LB Method for the Fluid Flow
#### 2.2.2 Introducing the External Force into the LB Model  
The external force term of the LB model plays a significant role in the accuracy and stability of simulations. The literature [18] reports that, when the IB material parameters are relatively large or the fluid viscosity is small, severe numerical fluctuations can be detected in the external force term between adjacent timesteps if an explicit method is applied to discretize the IB-LB coupling scheme. The numerical fluctuations may initialize numerical instability. Therefore, an averagely weighted iteration (AWI) procedure is proposed in this paper, which improves the stability of the IB-LB coupling scheme by modifying a special introducing approach of the external force term (proposed by [PERSON] and [PERSON] [21], and will be called [PERSON]'s approach hereafter). The characteristic of [PERSON]'s approach, where the force term is split into two parts (the present and the next time step force effects), is the basis of the AWI procedure. [PERSON]'s force term in Equation (16) is written as:  
\[\textbf{F}_{a}=\frac{1}{2}[g_{a}(x,t)+g_{a}(x+\textbf{e}_{a}\delta t,t+\delta t )] \tag{20}\]where \(g_{a}\) is defined as:  
\[g_{a}=\omega_{a}\{A+3\mathbf{B}\cdot[(\mathbf{e}_{a}-\mathbf{u})+3(\mathbf{e}_{a}\cdot\mathbf{u}) \mathbf{e}_{a}]\} \tag{21}\]  
Here, \(A\) is the source term in the continuity equation, and \(\mathbf{B}\) is the external force term in the momentum equations.  
In Equation (21), \(g_{a}(x+e_{a}\delta t,t+\delta t)\) is an unknown variable, which can generally be treated using two types of approach: the explicit method, where \(g_{a}(x+e_{a}\delta t,t+\delta t)\) is replaced by \(g_{a}(x+e_{a}\delta t,t)\) for simplification, and the implicit method (or iterative approach), where \(g_{a}(x+e_{a}\delta t,t+\delta t)\) is iteratively solved. To distinguish from the averagely weighted iteration (AVI) approach proposed in this paper, the traditional iterative approach is named the directly iterative (DI) approach.
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 2 Methods and Study Area
### Specific Process
These differences directly affect the physical properties, hydrological processes, vegetation cover, and the geological stability of slopes, thereby influencing the risk level of landslides. Therefore, when subdividing slope units, we applied the eight-direction aspect classification [35], dividing the slopes into subunits oriented towards different directions. The aspect division diagram is shown in Figure 3.  
The slope [36] gradient primarily affects the subdivision of slope units by directly influencing the processes of surface energy and material transfer, the soil mechanical  
Figure 3: Algorithm division diagram. state, the vegetation coverage capacity, and geological stability. The calculation formula is Equation (2):  
\[\delta=\arctan\left(\sqrt{\left(\frac{\partial z}{\partial x}\right)^{2}+\left( \frac{\partial z}{\partial y}\right)^{2}}\right)\times\frac{180}{\pi} \tag{2}\]  
where \(\frac{\partial z}{\partial x}\) represents the rate of elevation change in the x-direction, \(\frac{\partial z}{\partial y}\) represents the rate of elevation change in the y-direction, and \(\delta\) denotes the slope. Therefore, incorporating the slope gradient into the subdivision of slope units helps reveal the differential impact of variations in slope steepness on both the intrinsic properties and the external characteristics of slope units. This provides critical foundational information for refined geographic analysis and decision support. According to the \"Operating Guidelines for Forest Resource Planning and Design Survey in Yunnan Province\" [37], the slope gradients were classified as shown in Table 1.  
Curvature [38] plays a crucial role in slope unit division, accurately reflecting the terrain's curvature and local morphology, thereby affecting the accuracy of terrain representation. The calculation formula is Equation (3):  
\[k=\left(k_{plan}+k_{pro}\right)/2 \tag{3}\]  
where \(k\) denotes the average curvature; \(k_{plan}\) denotes the plane curvature; and \(k_{pro}\) denotes the section curvature. In combination with [PERSON]'s [39] and [PERSON] et al. 's [22] introduction to curvature, we can determine the convexity or concavity of each grid cell through the mean curvature (\(k\)). When \(k>0\), the grid cell is identified as convex, and, when \(k<0\), the grid cell is identified as concave. Convex grid cells are classified as ridge areas, whereas concave cells are classified as valley areas. The boundary between these two regions, where the mean curvature changes signs, is a flat area (\(k=0\)). Thus, the terrain is divided into three categories: ridges, valleys, and flat areas. Considering curvature allows for the scientific delineation of the convex, concave, and flat regions of slopes.
## 3 Results
### Data Organization in Representative Mission Example (PoC in CMSS): 1 st Meth
Furthermore, increased operational awareness, given the continuous integration of input data with relevant categorization (e.g., traffic conditions, route security), provides actors with a comprehensive understanding of the mission's current status. In addition, it offers risk mitigation by considering potential obstacles and seeking alternative information sources and therefore actors can foresee and mitigate risks, enhancing the mission's safety. Moreover, communication and coordination are optimized, since the structured approach streamlines communication between different actors, e.g., police, medical teams, ensuring better coordination and collaboration. The benefits of the data organization technique from the design engineer's perspective are as follows: Initially, systematic data management enables the system to categorize and to prioritize data, ensuring efficient handling and retrieval of mission-critical information. In addition, the system's design allows scalability and adaptability to different mission types and sizes by supporting various thematic categories and real-time updates. Furthermore, robust information processing, since the system is engineered to process a wide range of data sources, e.g., local news, traffic apps, ensuring comprehensive situational awareness. Last, but not least, this design achieves improved data accuracy and reliability, since the system's design enhances the accuracy and reliability of the data input by accessing information from appropriate channels and sources, e.g., police networks, medical records, etc. In summary, this data organization technique significantly enhances the mission's execution by providing actors with a clear, adaptable, and comprehensive operational situational awareness, while from an engineer's standpoint, the system is robust, flexible enough, and able to handle complex mission requirements effectively.
## 4 Results and Discussion
### Effect of H/C, O/C, ER, and T on LHV-Gas  
The heating value of syngas for internal combustion engines must be between the range of 5.02 to 7.47 MJ/Nm\({}^{3}\)[76] Hence, the calorific value has significant importance in the biomass gasification process. This value is determined by the gas composition of CO, CH\({}_{4}\), H\({}_{2}\), and C\({}_{6}\)H\({}_{6}\). Equation (53) indicates that changes in the LHV\({}_{\text{gas}}\) (MJ/Nm\({}^{3}\)) are significantly affected by variations of ER and temperature. Figure 8 depicts the response of LHV\({}_{\text{gas}}\) as a function of (a) ER and T, as well as (b) H/C and O/C ratios.  
Figure 7: Simulation of C\({}_{6}\)H\({}_{6}\) (g/Nm\({}^{3}\)) content in producer gas as a function of (**a**) ER and T at H/C = 1.494, O/C = 0.689, and N/C = 0.0198; (**b**) H/C and O/C at N/C = 0.0198, ER = 0.331, and T = 923 K.  
Figure 8a illustrates that the highest \(\text{LHV}_{\text{gas}}\) could be obtained at low ER and high T, under constant O/C, H/C, and N/C. The highest \(\text{LHV}_{\text{gas}}\) could also be obtained at high O/C and H/C ratios under constant N/C, ER, and T (Figure 8b). Therefore, selecting biomass with high H/C and O/C ratios is important to get high \(\text{LHV}_{\text{gas}}\) during gasification by maintaining low ER and high T during the process. Biomass with a high H/C ratio also leads to more production of H\({}_{2}\) and CH\({}_{4}\), whereas a high O/C ratio promotes more production of CO and CO\({}_{2}\). The simulation depicted in Figure 8 is in line with other experimental studies. Gasification of palm kernel oil biomass at 850-950 \({}^{\circ}\)C. increases LHV from 4.02 to 4.65 MJ/Nm\({}^{3}\), compared to the literature. \(\text{LHV}_{\text{gas}}\) rises from 4.49 to 5.15 MJ/Nm\({}_{3}\) at 720-802 \({}^{\circ}\)C [44]. In rice husk pellet gasification trials at 700-850 \({}^{\circ}\)C, \(\text{LHV}_{\text{gas}}\) rose from 4.19 to 5.86 MJ/Nm\({}^{3}\)[56]. Gasification with ER 0.20 and \(\text{LHV}_{\text{gas}}\) 3.21 MJ/Nm\({}^{3}\)[70]. The ER biomass MSW fluidized bed reactors between 0.2 and 0.4, \(\text{LHV}_{\text{gas}}\) reduced MJ/Nm\({}^{3}\) from 12.40 to 11.23 [31]. RDF gasification at 0.2-0.6, \(\text{LHV}_{\text{gas}}\) lowers from 12.77 to 12.50 MJ/Nm\({}^{3}\). \(\text{LHV}_{\text{gas}}\) declines around 0.14-0.3 ER. From 700 to 850 \({}^{\circ}\)C, \(\text{LHV}_{\text{gas}}\) climbs from 5.76 to 6.32 MJ/Nm\({}^{3}\)[77]\(\text{LHV}_{\text{gas}}\) decreases as ER grows [78] Other empirical correlations corroborate that \(\text{LHV}_{\text{gas}}\) rises as temperature rises, ER drops, H/C and O/C rise, and \(\text{LHV}_{\text{gas}}\) rise. [61, 62]. Many empirical correlations and experimental studies reveal that when temperatures rise, ER drops, H/C and O/C ratios rise, and the heating value rises.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 8 Directions for Research
### Security Reference Architecture  
Security reference architectures (SRAs) play a crucial role in providing a structured approach to securing WoT environments. An SRA includes security and threat patterns, which are essential for developing comprehensive security strategies [37; 38; 87]. Research in this area can focus on creating SRAs that address specific WoT use cases, such as smart cities or industrial automation. These architectures can guide the implementation of security controls, ensuring that all potential threats are mitigated effectively. Moreover, SRAs can be continuously updated to reflect new security challenges and technological advancements, providing a dynamic framework for WoT security.
## 2 Optimization Methodology  
An optimization process can take very different objective functions into account or even consider more than one function at the same time. Due to the reduced workspace of parallel manipulators, a common optimization objective is to obtain the biggest possible workspace. We apply two optimization methods: the first one finds the geometric parameters for the biggest useful workspace, and the second one finds the geometric parameters (GP) combination that results in the lowest power consumption for a given trajectory. For both the methods, we have to define the ranges of the geometric parameters we want to optimize and a step size to discretize them. Then, we obtain the finite number of geometric parameters combinations to be checked.  
Both optimization methods calculate the useful workspace for each of the geometric parameters combinations. The useful workspace is the one that we obtain after we consider all the constraints on the joints and actuators. Additionally, the useful workspace has to be free of singularities so that we can ensure that the manipulator can follow a trajectory inside that workspace without crossing any singular position. Not only is the size of the useful workspace very important but also its regularity; a big useful workspace that is very irregular is not practical.  
For the first method, we propose considering the best geometric parameters combination as the one that results in a useful workspace containing the biggest desired geometry object.  
The second method uses the set of geometric parameter combinations for which the geometric object is not the biggest but is big enough. This set of combinations is the one we optimize in order to obtain the lowest power consumption during a desired trajectory. In this way, we ensure that the result also has a big and regular enough workspace. In this optimization process, we first have to define the trajectory for which we want to optimize the manipulator. Note that different trajectories may result in different results. We solve the kinematic and dynamic problems for each geometric parameters combinations we want to study. We only consider the combinations for which the requirements of the joints and specifications of the actuators and motors are fulfilled. We then obtain the power consumption along the desired trajectory. The best parameter combination is the one that results in the lowest power consumption. Figure 1 shows the basic steps to follow in the optimization process.
## 2 Material and Methods
### Case Definition  
Considering the problem presented here, insulators' measurements using ultrasound signals were performed inside a salt spray chamber in the Regional University of Blumenau's high voltage laboratory. The objective was to simulate different operating conditions of the insulators, based on parameters such as contamination, humidity and the presence of mechanical defects: (1) insulator without perforation and without contaminants; (2) insulator without perforation and with contaminants; (3) insulator without perforation and with contaminants (with added salt); (4) perforated insulator without contaminants; (5) perforated wet insulator; (6) perforated contaminated insulator.  
Contamination is a common problem found in insulators installed outdoors [12]. Insulators that are installed in agricultural regions, where there are unpaved streets, can have organic contaminants and dust [36]. Insulators close to coastal regions generally have higher surface conductivity due to the accumulation of saline on their surface [37]. Insulators installed near large urban centers can have artificial contaminants, from industrial and pollution caused by car traffic [38].  
As the surface becomes more contaminated, there is a greater chance of developing a failure. Failures can be intermittent, during different climatic conditions, in which it is necessary to replace the damaged components in a corrective way [39]. Over time, surface contamination can generate discharges that can cause tracking and generate perforations that make it necessary to replace the component [40]. Various types of contamination can increase the conductivity in the vicinity of the insulator, making it more vulnerable to discharges [41; 42; 43].
# Generation of Data-Driven Expected Energy Models for Photovoltaic Systems
## 3 Results and Discussion
### High-Capacity Systems  
For high-capacity systems, a significant (almost uniform) difference is found in both the \(\log RMSE\) and \(R^{2}\) values between the AIT and the IEC reference models (Figure 5(a),b). Across the sites, the AIT model improves the goodness of fit by 0.42 in \(R^{2}\) (IEC: 0.501; AIT: 0.93) and 1.16 in \(\log RMSE\) (IEC: 6.99; AIT: 5.83). Generally, there are very few systems for which the IEC model performs better than the trained models (Table 3). The percent error (\(\delta\)), on average, of the AIT model (3.65) is significantly lower than the IEC model (20.86) for high-capacity systems. An evaluation of percent error shows that the AIT generally  
Figure 5: High-capacity (**a**,**b**) and low-capacity (**c**,**d**) site-level model evaluations with test data. Models with lighter colors (i.e., low values for \(\log RMSE\) and values closer to 1 for adjusted \(R^{2}\)) indicate better performance.  
performs well (i.e., \(\delta\approx 0\) and thinner standard deviation bars) for most irradiance levels, except at the two extremes (i.e., \(<\)200 and \(>\)1100 \(\frac{\mathrm{W}}{\mathrm{m}^{2}}\)) (Figure 7).  
The difference in performance relative to the IEC model is especially pronounced for larger system sizes (Figure 6a,b). Although the AIT model appears to show a small improvement over the partner-provided values (Figure 6a,b), a \(t\)-test concluded that the distributions of both the \(\log RMSE\) (\(p\)-value: 0.78) and \(R^{2}\) (\(p\)-value: 0.48) are not significantly different.  
Figure 6: Model evaluation using \(\log RMSE\) and \(R^{2}\) metrics for high-capacity systems (**a**) and low-capacity systems (**b**). Data points reflect site-level summaries of associated test data while dotted lines reflect best line fits to support visual pattern identification. The \(R^{2}\) metric was used for this analysis (vs. adjusted \(R^{2}\)) since partner-provided model architectures are unknown. The ’additive interaction’ regression model (in red) is comparable to the partner-provided proprietary values (green) and consistently performs better than the IEC standard (blue), especially at higher capacity values.
## 3 Results and Discussions
### Adsorption Thermodynamics  
The influence of temperature on the adsorption of U(VI) by PVKAP is shown in Figure 4b. The adsorption capacity of PVAP for U(VI) increases with the increased temperature, which indicates that high temperature promotes the capture of U(VI) by PVKAP. The thermodynamic parameters of the adsorption process (\(\Delta H\)), entropy change (\(\Delta S\)), and Gibbs free energy change (\(\Delta G\)) are listed in Table 3, and the details of the calculation Equations and methods are explained in the Supplementary Materials [47; 48]. As shown in Table 3, \(\Delta G<0\) indicates that the capturing of U(VI) by PVKAP is a spontaneous process. Furthermore, the absolute value of \(\Delta G\) increases with the increased temperature of the reaction system, which reveals that high temperature promotes the more efficiently capturing of U(VI) on PVKAP. In addition, \(\Delta H>0\) reveals that the process of capturing U(VI) by PVKAP is an endothermic reaction; namely, increasing the temperature can increase the degree of reaction. This is also in accordance with the results of the adsorption isotherm experiment. Furthermore, \(\Delta S>0\) reveals that the process is an entropy-driven adsorption process, which means the degree of freedom of the solid-liquid interface is increased after the capturing of U(VI) by PVKAP. Therefore, the efficient capturing of U(VI) by PVKAP is a spontaneous endothermic process.
## 4 Results and Analysis
### CPU Load  
The CPU load at the edge node (RSU) was monitored only in the experimental setup across different communication distances: 30 m, 50 m, 70 m, and 80 m (see Figure 6). The recorded percentages represent the proportion of CPU load overtime during CAM transmission.  
At 30 m, the CPU load demonstrates a relatively stable pattern. The initial utilization hovers around 7.14%, indicating a moderate load on the edge node. There is a noticeable increase as CAM transmission commences, peaking at around 13.09%. The CPU load then gradually decreases, reaching 10.89% as transmission stops.  
When the distance was increased to 50 m, the CPU load exhibited a more varied trend. Utilization started at approximately 10.89% and rose significantly during CAM transmission, with peaks reaching 17.75%. A decline in CPU load was observed after transmission cessation, stabilizing at around 10.43%.  
At 70 m, the CPU load demonstrates further variability. The initial CPU load was around 12.66%, escalating during CAM transmission and reaching peaks of approximately 18.53%. The post-transmission period saw a decline in CPU load, stabilizing at around 13.41%.  
The 80 m distance scenario presented distinct CPU load patterns. The initial CPU load was around 13.11%, escalating during CAM transmission, with peaks reaching 20.02%. After transmission concluded, the CPU load gradually decreased, reaching 13.11% and eventually stabilizing at the initial values.
## 2 Principles
### Line-Structured Light 3D Measurement Model
Generally, Equations (2) and (4) constitute the line-structured light 3D measurement model, which is used to calculate \(\mathbf{P}_{c}\) in the CCS from \(\mathbf{P}_{uv}\) in the CPCS. This model contains several undetermined parameters: the parameters in \(\mathbf{A}\), the radial distortion coefficients in \(\mathbf{K}\), the tangential distortion coefficients in \(\mathbf{G}\), and the coefficients in \(\mathbf{\Pi}_{c}\). Among these parameters, \(\mathbf{A}\), \(\mathbf{K}\), and \(\mathbf{G}\) are the intrinsic parameters of the camera, which are not affected by the calibration procedure. To obtain accurate camera parameters, several advanced calibration approaches have been developed. Herein, the intrinsic parameters of the camera are estimated by [PERSON]'s method [8]. Furthermore, a MATLAB toolbox developed by [PERSON] [20] can be directly utilized for their determination. Nevertheless, \(\mathbf{\Pi}_{c}\) is changed easily because it is related to the device position and orientation. Therefore, the proper calibration of the light plane \(\Pi\) (i.e., the acquisition of \(\mathbf{\Pi}_{c}\)) is the focus of the present research study, which is described in detail in the following sections.
## 4 Heat Dissipation Performance Simulation and Analysis
### Simulation System Design  
The data simulation process of this study first required the use of a graphics editor to draw and design different cold plate configurations and use the FEM simulation software to design cold plate grids and set parameters such as material characteristics, environment temperature, and boundary conditions (including heat transfer efficiency formed by cooling fans, cooling fins, and thermoelectric cooling chips).  
The lower cold plate of the general heat dissipation system is heated by a 48 W fixed heat source (simulating the heat source generated from the operation of a single T/R Module). Heat travels up the heat pipes to the upper cold plate to perform thermal convection, besides heat dissipation through natural convection and the heat expelled by the TEC attached at the cooling end.  
The critical components encompassed in simulation analysis include the upper/middle/lower heat-transfer panels, heat pipes, and cooling system. Heat pipes 9 to 12 utilized their fast thermal equalization and super thermal conductivity to transfer the massive amount of heat generated by the T/R module to the active cooling system to quickly expel heat. Using different heat pipe configuration designs in the limited space of the upper heat transfer panel, 3D models were utilized to conduct data simulations with the finite element method. Stable state analysis was used to differentiate the heat dissipation benefits of different heat pipe configurations.