esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application of CFD-Based Digital Twins
### Application of the Proposed Digital Twin for Filling Procedures
#### 5.1.1 Data Analysis--Filling Procedures  
The Digital Twin representing the filling procedures was refined from the hydraulic scenarios defined in Table A1. From these scenarios, pressure patterns were identified according to the presence or absence of air valves, air valve discharge diameter, and initial air pocket size. Figure 9 presents a sample of the real-time measurements carried out using the big data platform, which aids in analysing the pressure patterns according to the input data for filling procedures.  
Figure 9 compares the pressure evolution in the different test scenarios. The results indicate that the most critical peak pressure occurs in the hydraulic filling conditions associated with the Type II scenario, specifically in Test No. 11, where a pressure peak exceeding 120 m-H\({}_{2}\)O occurs within the first second. This behaviour suggests a rapid transition of flow conditions, likely influenced by the use of the D040 air valve, which does not effectively regulate the air expulsion rate, leading to sudden pressure surges. In contrast, the Type I scenario (Test No. 6), where no air valve is used, exhibits a more  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scenario** & **Geometry** & **Mesh** & **Turbulence Model** & **Algorithm of Solution** \\ \hline Filling & 3D & Unstructured & SST \(k-\omega\) & PISO \\ \hline Empyting & 2D & Structured & SST \(k-\omega\) & PISO \\ \hline \end{tabular}
\end{table}
Table 6: Modelling conditions for the application of Digital Twins through CFD modelling.  
gradual pressure response, although with prolonged oscillations due to the trapped air and its compressibility effects.  
In addition, differences in the pressure oscillation patterns are observed between the different scenarios. In the Type I and V scenarios, pressure oscillations are more pronounced and persist for extended periods, as the trapped air pocket is compressed without controlled expulsion. This situation is also evident in the Type III and VI scenario, where the S050 air valve is used but still allows considerable pressure oscillations, albeit slightly less extreme than in Types I and V.  
The influence of initial air pocket size is also significant. A different pressure evolution is observed in Type V-VII, where the air pocket is increased by 1.5 m while maintaining the same conditions as Types I-IV. The increased air volume acts as a form of damping, altering the transient response. However, pressure fluctuations persist for extended periods without an air regulation mechanism. In contrast, when air valves are incorporated (Types VI and VII), pressure peaks are reduced, and the system stabilizes rapidly.
# Experimental and Numerical Study on Dynamics of Two Footbridges with Different Shapes of Girders
## 2 Materials and Methods
### Eigenvectors and Eigenvalues  
The Lanczos method was chosen to extract the pairs of eigenvectors and eigenvalues from the FEM model in both cases. Due to the geometry of the structures, all designated modes of vibration were marked with symbols and a number, which depended on the type of dominant vibration mode. The symbols Vd, Ld, and Td were used for vertical, lateral, torsional eigenvectors of the deck respectively, and Va and La for the vertical or lateral mode of the arch or steel girder. The symbol is connected with the ratio of the maximum amplitude in the specified direction and normalized with respect to the largest of the components. They also refer directly to the structural element of the footbridges. It indicates that most of the mode shapes are not pure, but they also have other components, which are generally smaller.
# Durability of Fabric-Reinforced Cementitious Matrix (FRCM) Composites: A Review
## 3 Conditioning Environments  
The best way to evaluate the long-term behavior of materials is to expose them to the actual environment. However, this procedure requires several years and would hinder the use of the material until its conclusion. In addition, the differences in the weathering conditions from one place to another and the variability of the exposures make this procedure difficult to be standardized. Indeed, non-repeatability of the data and excessive duration of actual environmental exposures led to the development of accelerated laboratory tests. Accelerated ageing was adopted in an attempt to mimic actual environments and to predict the long-term behavior of the material [37].  
Currently, the US Acceptance Criteria AC 434 (2018) [23] and Italian Initial Type Testing CSLLPP (2019) [24] are the only guidelines available regarding test methods for FRCM composites. These guidelines recommend test methods to evaluate the durability of the FRCM including freeze-thaw cycles, ageing in high-moisture condition (hygrothermal environment), concentrated solutions (chloride and sulfate solutions and alkaline environments), and even exposition to fuel (not covered in this paper). Since these guidelines are quite recent, different test methods were adopted in the past by some researchers [14; 22; 26; 36; 38] and the results obtained could not be easily compared due to the differences in ageing temperature, number of cycles, and solution type. In this section, the results found in the literature are discusses and critically compared in an attempt to gain general indications on the long-term performance of FRCM and TRM composites.
# Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes
## 3 Results
### Data Feature Visualization  
#### 3.3.1 Sample Features  
Some first order mode shape samples are shown in Figure 7. There are 14 curves of different damage scenarios in Figure 7 and these curves have different colors; but they are so similar that they look like only one curve. Some first order mode curvature difference samples are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Prediction Accuracies**} \\ \hline \multirow{2}{*}{**Type**} & \multicolumn{8}{c}{**Damage Degree**} \\ \cline{2-9}  & **0\%** & **4\%** & **6\%** & **10\%** & **30\%** & **50\%** & **70\%** & **90\%** \\ \hline Front lower rod & & 57  
Figure 7 shows that, the first order mode shapes of various damage scenarios are almost the same, they are not distinguishable for naked eyes; on the other hand, the mode curvature differences show evident fluctuations in the damage locations (Figure 8).  
As for the input data having distinguishable features, such as the mode curvature differences, the CNN can locate the damaged rods using the shallow learning rather than using the deep learning, as shown in Figure 6, the prediction accuracy is no longer improved when the layer number increases. While if the input data have no obvious fluctuations at the damage locations, such as the first order mode shapes, it requires deep learning to achieve a higher prediction accuracy, as shown in Figure 5, the prediction accuracy is gradually improved as the layer number increases.
## 3 Overview of Video Delivery in 4G Mobile Cellular Networks
### Core Network Support for Mobile Video Delivery over 4G LTE
Overall, providing eMBMS services requires significant CN upgrade, which, together with lack of killer applications [41], contributes for slow deployment of eMBMS at mobile operators.
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 3 Location Estimation Based on the Manhattan Distance
### Properties of the Minimizer
#### 3.3.1 Example 1  
As shown in Figure 1, the black circles stand for the demand points and black hollow squares represent the optimal facility locations. In Example 1 (see Figure 1a), the optimal facility location is located at the second demand point due to its coordinate values and weights, which are given by  
\[\widetilde{u} = \text{weighted median}\ (x_{1},\ x_{2},\ x_{3})=3 \tag{25}\] \[\widetilde{v} = \text{weighted median}\ (y_{1},\ y_{2},\ y_{3})=3 \tag{26}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**Example 1**} & \multicolumn{3}{c}{**Example 2**} & \multicolumn{3}{c}{**Example 3**} & \multicolumn{3}{c}{**Example 4**} \\  & \(\mathbf{x_{i}}\) & \(\mathbf{y_{i}}\) & \(\mathbf{\omega_{i}}\) & \(\mathbf{x_{i}}\) & \(\mathbf{y_{i}}\) & \(\mathbf{\omega_{i}}\) & \(\mathbf{x_{i}}\) & \(\mathbf{y_{i}}\) & \(\mathbf{\omega_{i}}\) & \(\mathbf{x_{i}}\) & \(\mathbf{y_{i}}\) & \(\mathbf{\omega_{i}}\) \\ \hline \(i=1\) & 1 & 2 & 0.1 & 1 & 2 & 0.1 & 1 & 6 & 0.1 & 1 & 6 & 0.1 \\ \(i=2\) & 3 & 3 & 0.5 & 3 & 3 & 0.4 & 3 & 2 & 0.5 & 3 & 2 & 0.4 \\ \(i=3\) & 5 & 6 & 0.4 & 5 & 6 & 0.5 & 5 & 3 & 0.4 & 5 & 3 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of data in four examples.  
Figure 1: Candidate locations for the facility. **(a)** Example 1; **(b)** Example 2; **(c)** Example 3; **(d)** Example 4.
## 4 Optimal Mapping In Dima
### Solution of Multi-Objective Optimization
#### 4.2.3 Lexicographic Optimization
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Paper** & **Objective** & **Contrains** & **Solution** & **Contribution** \\ \hline
[34] & Minimize mass and OIC individually constraints & Segregation, device location, power constraints & CPLEX & Present binary program formulation of single objective optimization for DIMA as early work \\ \hline
[48] & Maximize spare time of processors & Memory capacity, maximum number of allocated tasks & ILP solver & Solve task mapping and scheduling problem for IMA architecture \\ \hline
[28] & Minimize execution time of functions & AFDX delay & ILP solver & Propose an algorithm for task assignment with respect to AFDX delay \\ \hline
[35] & Minimize development cost & Segregation & Tabu search-based algorithm & Propose a metaheuristic algorithm for single objective optimization on cost \\ \hline
[49] & Minimize the total cost of the system & Device resource & Particle swarm optimization & Propose a metaheuristic algorithm for single objective optimization on effectiveness \\ \hline
[54] & Minimize the maximum bandwidth utilization; maximize the route availability; minimize the number of nodes; minimize the number of edges & Segregation, resource bandwidth consumption & SAT4 solver & Compare performance between SAT4j and lp\_solve \\ \hline
[49] & Minimize the failure probability of CPMs and RDCs & Device resource & Weighted sum method and particle swarm optimization & Propose to use weighted sum method and decompose the problem into CPM discipline and RDC discipline for multi-objective optimization \\ \hline
[33] & Minimize mass, SSC, OIC and IPC cooling capacity & Segregation, slot resource, cooling capacity & CPLEX \& Pareto optimization & Present solution to multi-objective optimization for DMA as early work \\ \hline
[9] & Minimize mass and OIC & Device resource and segregation & CPLEX \& GUROBI & Consider quadratic cost functions for objectives \\ \hline
[61] & Minimize mass and OIC and link & Segregation, switch capacity (signal and link) & CPLEX \& GUROBI \& Pareto & Provide solution to network topology optimization with multiple objectives \\ \hline
[62] & Minimize mass and SSC & Device resource and segregation & Two-phase multiobjective local search & Propose a two-stage approach to solving the multi-objective optimization \\ \hline
[64] & Minimize the CPM burden, mass and SSC cooling resource and segregation & Not resource, mass resource, cooling resource and segregation & Lexicographic optimization & Compare the performance between DIMA and IMA optimization with multiple objectives \\ \hline \end{tabular}
\end{table} 
Table 2: A list of previous work on Distributed Integrated Modular Avionics (DIMA) optimization is summarized based on their objectives, constraints, solution and contribution.
## 3 Finite-Time ANFTSMC Design
### Proof of Stability
\begin{array}{ll}\dot{V}_{A}&\leq-\sqrt{2}\rho_{1}\Big{(}\frac{1}{2}s_{1}^{2} \Big{)}^{1/2}-\sqrt{2}\rho_{3}\Big{(}\frac{1}{2}s_{3}^{2}\Big{)}^{1/2}-\sqrt{2 }\rho_{5}\Big{(}\frac{1}{2}s_{5}^{2}\Big{)}^{1/2}-\sqrt{2\mu_{i1}/\gamma_{i1} \beta_{1}b_{2}}\rho_{i1}\Big{(}\sum\limits_{i=0}^{2}\frac{\gamma_{i1}\beta_{1} b_{2}}{2\mu_{i1}}\bar{a}_{i1}^{2}\Big{)}^{1/2}\\ &-\sqrt{2\mu_{i3}/\gamma_{i3}\beta_{3}b_{4}}\rho_{3}\Big{(}\sum\limits_{i=0}^{2 }\frac{\gamma_{i3}\beta_{1}b_{4}}{2\mu_{i3}}\bar{a}_{i3}^{2}\Big{)}^{1/2}-\sqrt {2\mu_{i5}/\gamma_{i5}\beta_{5}b_{6}}\rho_{i5}\Big{(}\sum\limits_{i=0}^{2}\frac {\gamma_{i5}\beta_{5}b_{4}}{2\mu_{i5}}\bar{a}_{i5}^{2}\Big{)}^{1/2}\\ &\leq\rho_{\Theta}V_{A}^{1/2}\\ &\text{where}\quad\rho_{\Theta}=min\Big{\{}\sqrt{2}\rho_{1},\sqrt{2}\rho_{3},\sqrt{2}\rho_{5},\sqrt{2\mu_{i1}/\gamma_{i1}\beta_{1}b_{2}}\rho_{i1},\sqrt{2 \mu_{i3}/\gamma_{i3}\beta_{3}b_{4}}\rho_{i3},\\ &\sqrt{2\mu_{i5}/\gamma_{i5}\beta_{5}b_{6}}\rho_{i5}\Big{\}}.\ \Box\end{array}  \tag{40}\]  
It can be seen from the above analysis that the finite-time stability of the attitude ring is guaranteed according to Lemma 1. In order to prove the global closed-loop stability of the system, the Lyapunov functions of the coaxial HAUV system are determined using the following equation:  
\[V=V_{P}+V_{A} \tag{41}\]  
According to the proof process of Theorems 1 and 2 above, it can be obtained as follows:  
\[\dot{V}=V_{P}+\dot{V}_{A}\leq-\rho_{P}V_{P}^{1/2}-\rho_{\Theta}V_{R}^{1/2}\leq -\rho V^{1/2} \tag{42}\]  
where \(\rho=min\{\rho_{\Theta},\rho_{P}\}\).\(\Box\)  
Finally, the global finite-time stability of rotation and shift-tracking errors is proven using the Lyapunov method. The above proof process is complete.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
###### Abstract  
In order to address a problem in the research related to the low stability and communication efficiency issues in the generation of optical communication constellation network topology, there is a critical component for sensing the interaction among satellites. This paper makes a novel contribution by proposing a multi-constraint optimization algorithm for optical communication constellation network topology generation. The proposed method significantly improves the existing systems by considering multiple attributes that influence the establishment of inter-satellite links and reducing the impact of subjective factors. This unique approach involves calculating the entropy weight of each attribute using the information entropy method based on the degree of change in each indicator. Subsequently, the weights of the indicators are corrected to obtain the objective weight of each attribute. The comprehensive weight of the link, computed based on the initial link attribute values and weights, serves as the decision basis for link selection, thereby forming the satellite network topology. Upon evaluation, the proposed method has shown remarkable superiority over the compared schemes in terms of communication efficiency and stability.  
optical communication; constellation network topology; inter-satellite link; topology generation
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 6 Conclusions  
Based on the chirp technique widely applied in the fields of communication, radar, and indoor navigation, we have proposed a high-accuracy, low-ambiguity, low-power consumption signal modulation method named ChPN. The signal model, ACF, and PSD have been presented, and the signal generation and detection methods have been discussed. The O-ChPN is also discussed. The theoretical performance of ChPN signals are analyzed, the simulation is also carried out to evaluate the signal performance.  
The signals are analyzed in three situations: (1) the ChPN signals with the same MLB and different \(p\) and \(q\), such as ChPN(5,1), ChPN(4,2) and ChPN(3,3); (2) the different signals with the same MLB, such as ChPN(5,1) and BOC(5,1); (3) the ChPN signals with different MLBs, such as ChPN(5,1)  
Figure 16: False locking probability caused by side-peaks for various signals.  
Figure 17: False locking probability with different near-to-far signal amplitude ratio. The dash and the solid lines are drawn with the C/N0 of 40 dB and 45 dB, respectively.  
and ChPN(10,1). By analyzing the ACF, PSD, LBTE, tracking error, MPEE, AWME and ACF peaks ambiguity characteristics of ChPN and BOC signals, we have found that: (1) for the first situation, ChPN(5,1) has the best tracking precision and multipath resistance, while ChPN(3,3) has the smallest MSPR and FLP; (2) for the second situation, BOC(5,1) has a better tracking precision and LBTEs, while ChPN(5,1) provides better overall resistance to multipath effects and has smaller MSPR and FLP; (3) for the third situation, ChPN(10,1) has better tracking precision, better multipath resistance, while ChPN(5,1) has smaller FLP. O-ChPN can mitigate the near-far effect, although it may decrease the transmitter amount. Last but not least, ChPN receivers can be implemented by minor modification of GNSS receivers owing to their similar signal structures. In summary, ChPN is a feasible and promising signal modulation method for PRN code ranging systems.  
[PERSON], [PERSON], [PERSON] and [PERSON] discussed the structure of this paper. [PERSON] derived the theoretical model of PSD and ACF, bring out the numerical and simulation results. [PERSON] brought some suggestions for Section 3. [PERSON] gave some suggestions for Section 2. [PERSON] checked the simulation and numerical results.  
This research was funded by China Scholarship Council (CSC) grant number 201706020026, Natural Science Foundation of Beijing grant number 4172021 and Beijing Municipal Education Commission grant number KM201711232010.  
We are grateful to the anonymous reviewers.  
The authors declare no conflict of interest.
###### Abstract  
In the context of significant climate change, monitoring inclination, water levels, and temperatures in public buildings and surrounding environments is sensible. This paper presents a pair of fiber Bragg grating (FBG) subsidence sensor systems designed to simultaneously measure tilt and water levels and explore the system's potential to detect temperature variations. The configuration of the FBG subsidence sensor is intentionally skewed to enhance measurement sensitivity. The system is capable of concurrently detecting a 0.5 cm variation in water level and a 0.424\({}^{\circ}\) change in tilt, with tilt measurements spanning from \(-1.696^{\circ}\) to \(1.696^{\circ}\). Furthermore, the measurement system can be integrated with free-space optics (FSO), which is anticipated to address the challenges associated with installing fiber optic cables. Consequently, the proposed innovative FBG sensor system can measure multiple parameters using fewer sensors, thereby improving sensing capacity and cost-efficiency.  
fiber Bragg grating; inclination sensing; water level sensing; free-space optics Article
## 3 Proposed Methodology
### Indexing Strategy Based on Quadtree  
#### 3.2.1 Structure of Quadtree  
We employ the quadtree data structure proposed by [PERSON] et al. [24] for our indexing and compression tasks due to its efficient hierarchical partitioning of spatial data, which allows for rapid querying and compression. Additionally, the quadtree's hierarchical nature facilitates multi-resolution compression, which is a key aspect of our method of spatial object indexing.  
Among the various tree structures available, the quadtree emphasizes uniform spatial partitioning, making it versatile for a wide range of scenarios. While other tree structures, such as R-tree or KD-tree, may optimize for specific types of queries or certain query ranges, the quadtree's uniform partitioning ensures consistent performance across diverse datasets. However, the use of a quadtree does come with potential trade-offs. One of the primary disadvantages is the increased storage size compared to linear data structures, especially when dealing with highly fragmented data. The hierarchical nodes can lead to overhead, impacting storage efficiency.  
The fundamental concept of the quadtree involves recursively partitioning geographic space into hierarchical tree structures. This entails dividing regions of known extent into four equally sized areas or quadrants until the tree reaches a specific depth or halts further subdivision based on certain criteria.  
Within the quadtree, each node corresponds to a region, encompassing a portion of the indexed space, with the root node covering the entire area. Each node comprises compressed data within the spatial extent, alongside associated metrics such as the peak signal-to-noise ratio (PSNR) and the maximum error inherent to the data. Nodes at the same level of the quadtree exhibit equal compression ratios, similar PSNR values, and closely aligned maximum error information. Deeper layers of the tree correspond to reduced compression ratios, elevated PSNR values, and diminished maximum error. In particular, the root node encapsulates the entirety of the spatial data, boasting the maximum compression ratio and the highest PSNR value in the tree. An example of the structure of a quadtree is shown in Figure 2.  
The structure of a quadtree is formally described as follows: Let \(S=\{R_{1},R_{2},\ldots R_{N}\}\) be a collection of \(N\) quadtrees on the plane. Each quadtree contains eleven attributes: the x-coordinate of the bottom left corner \(lx\), the y-coordinate of the bottom left corner \(ly\), the x-coordinate of the top right corner \(ry\), the left upper child node of the current node \(top\_left\_child\), the right upper child node of the current node \(top\_right\_child\), the left lower child node of the current node \(bottom\_left\_child\), the right lower child node of the current node \(bottom\_right\_child\), and the compression data, mean absolute error, and peak signal-to-noise ratio in the region represented by the bottom left and top right corners.  
Figure 2: An example of quadtree.