esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 3 Pipeline Overview
### Octree Generation and Voxelization  
In our proposed pipeline, the first step uses the depth images and camera calibration data to project pixels into an octree of all potentially nonempty voxels of configurable voxel size. Instead of building the octree using bottom-up sorting [64], we build a pointer-based octree from top-down using a heuristic to determine whether a certain arbitrarily sized voxel is occupied. For each of the voxel corners of a potentially occupied child node, the position is projected into each depth camera's image space and compared with the depth value of the residing pixel. If any backprojected depth pixel of one of the eight corners matches up to a query radius threshold defined by the voxel size, it is deemed occupied. For efficiency, the last leaf occupancy query uses only a circumsphere for the voxel center instead of querying all eight corners. This method allows false positives and will generate false negatives if all eight corners of a voxel project into an empty pixel, which is not a significant issue in our testing.  
Since we use a pointer-based octree instead of a hash-map-based data structure, such as [PERSON] et al. [64], data locality is better enforced blockwise using stream compaction [65].  
Our approach allows mixing arbitrary voxel sizes. For demonstration purposes, we allow configuring two different low- and high-detail regions separated using an axis-aligned, user-defined bounding box. The subsequent processing steps for normal estimation and simplification also allow separate configurations for the different areas.
## 5 Conclusions  
In this paper, we propose a method for predicting the grazing incidence focusing mirror imaging quality based on accurate modelling of the surface shape accuracy for the whole assembly process. Based on the measurement data obtained before assembly, with the suspension gravity during assembly, and adhesive curing shrinkage force after assembly, the surface shape distribution error of the inner surface of the focusing mirror is reconstructed by using the NURBS surface, then a prediction for the angular resolution of imaging quality is performed. The results show the following:  
(1) The surface shape distribution error of the grazing incidence focusing mirror itself has the most significant impact on its imaging quality during the whole assembly process, and the angular resolution of the focused spot decreases by about 47\({}^{\prime\prime}\) compared with that of the ideal surface shape;  
(2) The difference in the angular resolution of the focused spot between the three-hanging-point and the six-hanging-point method under the influence of gravity is about 2.11\({}^{\prime\prime}\), and the six-hanging-point mode is more suitable for the grazing incidence focusing mirror with this diameter;  
(3) The adhesive curing causes a decrease in the angular resolution of the focused spot by about 4.2\({}^{\prime\prime}\).  
In contrast to previous imaging quality analyses based on ideal grazing incidence focusing models, the prediction method comprehensively considers the effect of changes in the surface shape distribution error on the imaging quality caused by different influencing factors for the whole assembly process of the grazing incidence focusing mirror. It can predict the angular resolution of the mirror with different surface shape accuracy before assembly, which can effectively support the optimisation of the precision assembly process for grazing incidence focusing mirrors.  
**Author Contributions:** Conceptualization, Z.Z. and Z.M.; methodology, E.L.; validation, T.S.; investigation, F.Z.; writing--original draft preparation, E.L.; writing--review and editing, C.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (project No. 42327802).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the corresponding authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Queue Spillover Management in a Connected Vehicle Environment
###### Abstract  
To alleviate the queue spillovers at intersections of urban roads during rush hours, a solution to the cross-spill problem based on vehicle networking technologies is proposed. This involves using connected vehicle technology, to realize the interactive information on vehicle and intersection signal control. The maximum control distance between intersections is determined by how vehicles are controlled and would travel in that connected environment. A method of calculating overflow tendency towards intersection queuing is also proposed, based on the maximum phase control distance. By this method, the intersection overflow is identified, and then the signal phases are re-optimized according to the requirements of different phases. Finally, overflow prevention control was also performed in this study. The VISSIM simulation results show that the method can better prevent the overflow of queues at intersections.  
intelligent transportation; connected vehicles; queue spillover; maximum queue length; traffic simulation +
Footnote †: journal: Future Internet
# Hybrid Active-Passive Reconfigurable Intelligent Surface for Cooperative Transmission Systems
###### Abstract  
Reconfigurable intelligent surfaces (RISs) are acknowledged as one of the key technologies for the next-generation communication systems due to their low cost, high-energy efficiency, and the ability to intelligently control the wireless propagation environment. In this paper, we present a hybrid active-passive reconfigurable intelligent surface (HAPR) for cooperative transmission system, where HAPR can intelligently change its operating mode according to the channel environment, eliminating the \"multiplicative fading\" effect of traditional passive RIS (P-RIS) and higher power consumption of active RIS (A-RIS), and combining the advantages of both to effectively improve the system performance. First, we investigate the ideal reflection coefficient of RIS reflecting elements (REs) under the condition of a limited power budget. Using the compound Simpson formula, the closed-form approximation expression for the system outage probability (OP) has been derived. Finally, Monte Carlo simulation is used to confirm the accuracy of the expression. The simulation results demonstrate that HAPR has a better performance than both A-RIS and P-RIS, which can achieve a lower OP.  
hybrid RIS; outage probability; _Nakagami-m_ distribution; cooperative transmission +
Footnote †: journal: Applied Sciences  
1
## 2 Materials and Methods  
### Synthesis of Silicalite-1 Zeolites  
Silicalite-1 samples were prepared by hydrothermal synthesis in fluoride medium using tetraethylorthosilicate (TEOS, 99%, Merck, Darmstadt, Germany) as the silica source, aqueous solutions of tetrapropylammonium hydroxide (TPAOH, 20%, Aldrich, Darmstadt, Germany) and/or tetrapropylphosphonium hydroxide (TPPOH) and HF (48%, Aldrich). TPPOH is not commercially available and was prepared by alkylation of tripropylphosphine (97%, Aldrich) with 1-bromopropane (99%, Aldrich) in anhydrous acetonitrile (99%, Alfa-Aesar, Darmstadt, Germany) as solvent, in N\({}_{2}\) atmosphere at room temperature for two days. The tetrapropylphosphonium bromide product was dissolved in Milli-Q water and exchanged to the hydroxide form in batch overnight using an anionic exchange resin (Amberlite IRN-78, Supelco, Darmstadt, Germany) to get the corresponding hydroxide (TPPOH).  
The synthesis procedure consisted of the hydrolysis of TEOS in the aqueous solutions of TPAOH, TPPOH, or mixtures of them in different ratios, and stirring the solutions until complete evaporation of the ethanol produced in the hydrolysis together with the required amount of water to get the final water content. Finally, the HF solution was added and the mixture was homogenized. The general chemical composition of the synthesis gels was the following:1 SiO2:0.4 OSDAOH:0.4 HF:15 H2O  
The gels were transferred into Teflon-lined stainless steel autoclaves and the crystallization was carried out at 175 \({}^{\circ}\)C under tumbling conditions (60 rpm) for times comprised between 7 and 12 days. The particular conditions for the samples prepared are summarized in Table 1, where the ratio of each OSDA is specified together with the synthesis time. In addition, the nomenclature of the samples is given in Table 1, where TPA-MFI and TPP-MFI stand for silicalite-1 zeolites prepared using a unique OSDA source, i.e., TPAOH and TPPOH, respectively. On the other hand, silicalite-1 samples prepared using the _dual-template_ method are named as TPX-MFI\({}_{\text{xx}}\), with xx being the molar percentage of TPP in the mixture of TPP/(TPP + TPA) incorporated in the zeolite.
## 2 Numerical Methods
### Grid and Numerical Technique  
The numerical investigation presented was performed by using the commercial software Fluent (12.0, ANSYS Inc. Pittsburgh, PA, USA). The Reynold-stress model (RSM) is used for turbulence closure with enhanced wall treatment, since the previous work about corner separation has shown that the RSM model provided better correlation with the experimental data at a \(0^{\circ}\) incidence angle [32]. One blade passage is simulated and a periodic boundary condition is set on both sides of the flow passage. A structured O4H mesh topology is generated by using the commercial grid generation software Autogrid 5 in the investigation. Figure 2a shows the representative computational grid of the cascade, and the mesh is duplicated along the spanwise direction into 91 planes (half span) to obtain 3D mesh. The value of \(y^{+}\) adjacent to the wall is less than 1.0. Figure 2b shows the velocity distribution at 10% spanwise at the blade trailing edge for different number of grid at a \(0^{\circ}\) incidence angle. Results show little difference when the grid number reaches 1.7 millon, so case 2 with the total number of about 1.7 million grid points for the half-span of computational domain is selected for the simulation. The inlet plane is located at \(2c\) upstream of the blade leading edge imposed with the same inlet velocity profile which has been configured to fit the experimental boundary layer. The outlet plane is located at \(2c\) downstream of the blade trailing edge and the experimental downstream area traverses were carried out at about 50% of the axial chord from the trailing edge. Since only half of the blade span is simulated to reduce the computational cost, the midspan is set to be symmetric and the pitchwise boundaries are connected by the periodic condition. Both the blade and the endwall are set to be non-slip adiabatic walls.  
Figure 2: Grid independence test: (**a**) view of the computation domain and RANS mesh; and (**b**) velocity distribution at 10% of the span at the trailing edge.  
The simple algorithm is used for the pressure-velocity coupling and the second-order upwind scheme is used for the spatial discretization of the equations. Enhanced wall treatment, which is a near-wall modeling method that combined a two-layer model with enhanced wall functions, was used to represent the turbulent flow in the near-wall region.
# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 2 Methodology  
According to the pinhole camera model and stereo geometric projection, monocular depth estimation using a self-supervised learning method captures the relationships within image sequences and constructs supervised information through geometric constraints between adjacent frames. The proposed method is grounded in the principle of re-projection, with the supervisory information derived from the re-projection error, which can reveal the relationship between depth and the pose transformation of adjacent frames. Consequently, the proposed depth estimation method is a multi-task learning model that integrates both a depth estimation network and a pose estimation network.
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 4 Case Study: Stabilization of a Smart Grid
### Addition of Stabilization
#### 4.2.2 Adding Stabilization for \(\delta_{r_{2}}\)  
At the beginning of Algorithm 1, \(R\) is initialized with \(S\). In the first iteration of loop on Lines 4-14, \(R_{p}\) is the set of states outside \(S\) that can reach a state in \(S\) with only one program transition. A program transition cannot change the value of any sensor. In addition, according to \(\delta_{r_{2}}\), it cannot change the status of both switches simultaneously. Therefore, state predicate \(R_{p}\) is the union of state predicates \(R_{p_{1}}\) to \(R_{p_{6}}\) as follows (\(\oplus\) denotes the xor operation):\(R_{p_{1}}=(V_{1}+V_{1}\leq V_{G})\wedge(w_{1}\oplus w_{2})\)  
\(R_{p_{2}}=(V_{1}\leq V_{G}\wedge V_{2}>V_{G})\wedge(w_{1}\oplus\
eg w_{2})\)  
\(R_{p_{3}}=(V_{1}>V_{G}\wedge V_{2}\leq V_{G})\wedge(\
eg w_{1}\oplus w_{2})\)  
\(R_{p_{4}}=(V_{1}>V_{G}\wedge V_{2}>V_{G})\wedge(\
eg w_{1}\oplus\
eg w_{2})\)  
\(R_{p_{5}}=(V_{1}+V_{2}>V_{G}\wedge V_{1}\leq V_{G}\wedge V_{2}\leq V_{G}\wedge V _{1}\leq V_{2})\wedge(\
eg w_{1}\oplus w_{2})\)  
\(R_{p_{6}}=(V_{1}+V_{2}>V_{G}\wedge V_{1}\leq V_{G}\wedge V_{2}\leq V_{G}\wedge V _{1}>V_{2})\wedge(w_{1}\oplus\
eg w_{2})\)  
Similarly, \(\
eg(R\cup R_{p})\) includes every state that is outside \(S\) and more than one step are needed to reach a state in \(S\). Therefore, state predicate \(\
eg(R\cup R_{p})\) is the union of state predicates \(R^{\prime}_{p_{1}}\) to \(R^{\prime}_{p_{6}}\) as follows:  
\(R^{\prime}_{p_{1}}=(V_{1}+V_{1}\leq V_{G})\wedge(\
eg w_{1}\wedge\
eg w_{2})\)  
\(R^{\prime}_{p_{2}}=(V_{1}\leq V_{G}\wedge V_{2}>V_{G})\wedge(\
eg w_{1}\wedge w _{2})\)  
\(R^{\prime}_{p_{3}}=(V_{1}>V_{G}\wedge V_{2}\leq V_{G})\wedge(w_{1}\wedge\
eg w _{2})\)  
\(R^{\prime}_{p_{4}}=(V_{1}>V_{G}\wedge V_{2}>V_{G})\wedge(w_{1}\wedge w_{2})\)  
\(R^{\prime}_{p_{5}}=(V_{1}+V_{2}>V_{G}\wedge V_{1}\leq V_{G}\wedge V_{2}\leq V _{G}\wedge V_{1}\leq V_{2})\wedge(w_{1}\wedge\
eg w_{2})\)  
\(R^{\prime}_{p_{6}}=(V_{1}+V_{2}>V_{G}\wedge V_{1}\leq V_{G}\wedge V_{2}\leq V _{G}\wedge V_{1}>V_{2})\wedge(\
eg w_{1}\wedge w_{2})\)  
Now, observe that from any state in \(R_{p}\), it is possible to reach a state in \(\
eg(R\cup R_{p})\) by an environment transition. Therefore, no state is added to \(R\) in the first iteration and loop on Lines 4-14 terminate in the first iteration. Since all the states outside \(S\) remain in \(\
eg R\), the algorithm declares that there is no solution to the addition problem. Therefore, according to the completeness of Algorithm 1, there does not exist a 2-stabilizing program for the smart grid described in this section when the set of program restriction is \(\delta_{r_{2}}\).
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 3 Background  
The distribution of secret keys over insecure networks plays a pivotal role in guaranteeing secure communications. This problem was first addressed in 1976 by the celebrated Diffie-Hellman algorithm, which considered the challenge of secret key agreement by relying on the classically difficult discrete logarithm problem. Unfortunately, this problem can, nowadays, be easily solved using quantum computers by leveraging [PERSON]'s algorithm [15].
# Strategic Resource Planning for Sustainable Biogas Integration in Hybrid Renewable Energy Systems
## 2 Materials and Methods
### Biomass Logistics and Transportation
The total volume of biomass inside the reactor must not exceed the reactor's maximum capacity to ensure efficient and stable biogas production. \(V_{Min}\leq V_{Storage}\leq V_{Max}\): This ensures that the biogas storage volume remains within the defined limits, between the minimum and maximum capacities. This maintains a buffer of stored gas for emergencies while preventing overflow from exceeding the maximum capacity. In this study, the optimization problem is classified as a Mixed-Integer Nonlinear Programming (MINLP) model due to its distinct characteristics. The discrete nature of the problem arises from the use of fixed truck capacities (5, 10, 15, 20, and 25 tons), where the logistics of biomass transportation are discretized based on these specific truck sizes. Furthermore, the biogas production process follows an exponential decay pattern, introducing nonlinearity into the model. As a result, we are dealing with a MINLP problem where both the discrete logistics decisions and the nonlinear dynamics of biogas generation must be considered. Given the complexity of the search space and the large number of potential scenarios, stochastic optimization methods such as genetic algorithms or particle swarm optimization may not be reliable or guaranteed to find the optimal solution. While these methods are effective in some contexts, they can struggle with the precision required for problems like this, where discrete and nonlinear elements are intertwined. To address this, numerical and computational methods, like those provided by the Gurobi optimizer, are preferred. Gurobi efficiently solves the MINLP by optimizing the balance between transportation costs, biomass input, and biogas production to meet the energy demand while minimizing total costs. By leveraging Gurobi's capabilities, the model accounts for the truck capacities' discrete nature and biogas generation's nonlinearity, resulting in a robust and accurate solution. This approach ensures that the optimization process is reliable and practical, meeting field-based constraints such as fixed truck sizes and the nonlinear production curve of biogas over time.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 4 Results and Discussion
### Model Validation  
To gain confidence in the simulation outcomes, the simulation models must be validated to ensure that the results are valid with statistical accuracy. All the results presented in this paper are obtained with a 95% confidence interval and \(\leq\)5% statistical errors. We validate the simulation model in the following ways. We first ensure that the OMNeT++ simulation parameters and configuration are working for N = 2 nodes. We also check the log files to ensure that there are no runtime errors. Second, all the assumptions, representations, and relationships are checked. Finally, the model is validated in terms of how it reacts to its inputs and the model's output behaviors [19]. We also validate our simulation model using the following methods.  
* Animation--simulating the average speed yielded information about the types of messages being sent across the network--contents, their sources, and destinations.
* Parameter variability and sensitivity analysis--systematically varying the simulation parameters and analyzing the impact that they have on the outputs (results).
* Face validity--seeking advice and opinions from experts to see whether a model concept is acceptable and reasonable to use for simulation.  
The adopted validation techniques not only help us in developing and building a network model that is appropriate and near accurate but also improve the quality of the simulation results, which makes analysis and evaluations of results more meaningful.
## Empirical Analysis
### Reliability and Validity Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Construct** & **No. of Items** & **Factor Loading** & **CR** & **AVE** & **Cronbach’s \(\alpha\)** \\ \hline \multirow{6}{*}{Perceived value} & \multirow{3}{*}{Utilitarian value} & \multirow{3}{*}{3} & 0.928 & \multirow{3}{*}{0.848} & \multirow{3}{*}{0.642} & \multirow{3}{*}{0.778} \\ \cline{4-4} \cline{6-6}  & & 0.645 & & & \\ \cline{3-6}  & & & 0.939 & & & \\ \cline{3-6}  & \multirow{3}{*}{Hedonic value} & \multirow{3}{*}{3} & 0.887 & \multirow{3}{*}{0.856} & \multirow{3}{*}{0.815} & \multirow{3}{*}{0.854} \\ \cline{3-6}  & & 0.608 & & & \\ \cline{3-6}  & \multirow{3}{*}{Social value} & \multirow{3}{*}{4} & 0.627 & \multirow{3}{*}{0.915} & \multirow{3}{*}{0.639} & \multirow{3}{*}{0.864} \\ \cline{3-6}  & & 0.824 & & & & \\ \cline{3-6}  & & 0.835 & & & & \\ \hline \multirow{6}{*}{Customer satisfaction} & \multirow{3}{*}{4} & 0.844 & \multirow{3}{*}{0.961} & \multirow{3}{*}{0.861} & \multirow{3}{*}{0.913} \\ \cline{3-6}  & & 0.908 & & & & \\ \cline{3-6}  & & 0.855 & & & & \\ \hline \multirow{6}{*}{Trust} & \multirow{3}{*}{4} & 0.800 & \multirow{3}{*}{0.929} & \multirow{3}{*}{0.766} & \multirow{3}{*}{0.872} \\ \cline{3-6}  & & 0.807 & & & \\ \cline{3-6}  & & 0.783 & & & & \\ \cline{3-6}  & & 0.780 & & & & \\ \hline \multirow{6}{*}{Loyalty} & \multirow{3}{*}{4} & 0.950 & \multirow{3}{*}{0.932} & \multirow{3}{*}{0.782} & \multirow{3}{*}{0.886} \\ \cline{3-6}  & & 0.937 & & & & \\ \cline{3-6}  & & 0.650 & & & & \\ \cline{3-6}  & & 0.701 & & & \\ \hline \multirow{6}{*}{Loyalty} & \multirow{3}{*}{4} & 0.9050 & \multirow{3}{*}{0.932} & \multirow{3}{*}{0.782} & \multirow{3}{*}{0.886} \\ \cline{3-6}  & & 0.937 & & & & \\ \cline{3-6}  & & 0.650 & & & \\ \cline{3-6}  & & 0.701 & & & \\ \hline \multirow{6}{*}{Loyalty} & \multirow{3}{*}{4} & 0.901 & \multirow{3}{*}{0.782} & \multirow{3}{*}{0.886} \\ \cline{3-6}  & & 0.900 & & & \\ \cline{3-6}  & & 0.900 & & & \\ \cline{3-6}  & & 0.900 & & & \\ \cline{3-6}  & & 0.900 & & & \\ \cline{3-6}  & & 0.900 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Reliability and convergent validity. 0.05. Although AGFI fell slightly below the 0.90 cutoff, the proposed model was still deemed suitable. The results of discriminant validity (Table 3) further confirmed the fulfillment of the criteria, as the square root AVE of each construct exceeded the correlation coefficients between the constructs, thus solidifying the evidence for discriminant validity [68]. In summary, these outcomes collectively established this study's adherence to rigorous reliability and validity standards.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 5 Results and Discussion
### Wafer Warpage under Different Processing Conditions
#### 5.3.2 The Influence of Diamond Wire Diameter on Wafer Warpage  
Reducing kerf loss can increase the number of sapphire wafers produced per unit size, and the thinning of diamond wire diameter has become a development trend by setting the diamond wire diameter as 0.2 mm, 0.22 mm, 0.24 mm, 0.26 mm, and 0.28 mm, respectively. Figure 14 shows the relationship between different wire diameters and wafer warpage. The influence of diamond wire diameter on wafer warpage is mainly reflected in the finite element calculation model by changing the kerf width d. In this simulation calculation, the wire saw speed is 1200 m/min, the feed rate is 0.1 mm/min, and the wafer thickness is 500 \(\upmu\)m.  
It can be seen that as the diameter of the diamond wire decreases, the wafer warpage gradually increases. This is because as the diamond wire diameter decreases, the sawing kerf becomes narrower, the heat dissipation effect deteriorates, and the amount of heat entering the wafer through thermal conduction increases. However, the heat dissipation ability of the wafer weakens, and the temperature in the sawing area increases. Therefore, the warpage of wafers cut by diamond wires of different diameters is not consistent.