esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Clustering via Adaptive Graph Convolution Using Heat Kernel
### Motivation  
A graph signal \(x\) can be linearly represented by the bases of the spectral domain, which are denoted as \(x=\alpha_{1}u_{1}+\alpha_{2}u_{2}+\ldots+\alpha_{N}u_{N}\), where \(\{\alpha_{i}\}_{i=1}^{N}\) is the coefficient of the eigenvector. The smoothness of a basis vector \(u_{q}\) corresponding to the graph smoothness is measured by the Laplacian-Beltrami operator \(\Omega(\cdot)\)[11], i.e.,  
\[\Omega\Big{(}u_{q}\Big{)}=\frac{1}{2}\sum_{(v_{i},v_{j})\in E}a_{ij}\|\frac{u_ {q}(i)}{\sqrt{d_{i}}}-\frac{u_{q}(j)}{\sqrt{d_{j}}}^{2}\|=u_{q}^{T}Lu_{q}= \lambda_{q}, \tag{7}\]  
where \(u_{q}(i)\) means the \(i^{th}\) element of the basis signal \(u_{q}\), \(d_{i}\) denotes the degree of node \(v_{i}\), and \(a_{ij}\) represents the connected weight between nodes \(v_{i}\) and \(v_{j}\). Equation (7) verifies that the basis signals associated with smaller eigenvalues (lower frequencies) are smoother concerning graph structures. Hence, to make the graph signal smoother, graph filters should highlight low-frequency signals by assigning larger weights to low-frequency basis signals, acting as a low-pass filter.  
Based on the above principle, we analyze the weakness of previous graph filters in Section 2.2. The frequency response function in ChebyNet [10] is \(g\big{(}\lambda_{q}\big{)}=\sum_{p=0}^{P-1}\lambda_{q}^{p}\), which assigns weight \(\lambda_{i}^{p}\) to \(u_{i}u_{i}^{T}\). Since \(\lambda_{q}\) is in ascending order, i.e., \(0\leq\lambda_{1}^{p}\leq\lambda_{2}^{p}\leq\ldots\leq\lambda_{N}^{p}\), ChebyNet [10] assigns higher importance to high-frequency basis signals and discounts graph smoothness. GCN [3] defines graph convolution based on the frequency function \(g\big{(}\lambda_{q}\big{)}=1-\lambda_{q}\)[12], which cannot perform better low-pass characteristics as \(g\big{(}\lambda_{q}\big{)}\) is negative for \(1<\lambda_{q}\leq 2\). The frequency response function in AGC [7] is \(g\big{(}\lambda_{q}\big{)}=1-\frac{1}{2}\lambda_{q}\), which exploits a linear low-pass filter to suppress the high-frequency signal; however, it might not highlight low-frequency signals adequately and cannot determine the appropriate neighborhood that reflects the relevant information of smoothness.
# A V2V Identity Authentication and Key Agreement Scheme Based on Identity-Based Cryptograph
## 5 Lightweight V2V Identity Authentication and Key Agreement Scheme
### V2V Secure Communication  
The user private information, such as vehicle location and driving habits, may be transmitted in V2V communication; user privacy information must be protected. Moreover, the information transmitted in V2V communication may be traffic conditions, traffic accidents, and other information related to traffic safety. Thus, it must be ensured the data transmitted in V2V communication will not be stolen, tampered, or forged, and the V2V communication is secure.  
V2V secure communication should minimize the amount of calculation, save computing resources, and reduce the impact on communication delay. Thus, the V2V communication uses a symmetric algorithm and uses the working key (_wk_) negotiated in the previous authentication process.  
The data encryption process is described as follows:  
(1) Firstly, the hash algorithm is used to calculate the hash value of the data, and the hash value is spliced behind the data.  
(2) Then, the symmetric encryption algorithm is used to encrypt the data and hash value and send the cipher data to the communication receiver.  
When the C-V2X equipment receives data from other equipment, it queries the working key (_wk_) with the equipment VID and decrypts the data using the working key. If there is not a working key between the equipment, authentication negotiation is required first.  
The data decryption process is described as follows:  
(1) Firstly, the symmetric encryption algorithm is used to decrypt the cipher data and get the plain data.  
(2) Secondly, to obtain the data and hash value from the plain data, the hash algorithm is used to calculate the hash value of the data and compares whether the calculated hash value is same with the decrypted hash value. If they are same, the decryption is successful. Otherwise, it discards the data.
## 3 Results
### Environmental-Hazard Testing of the Air-Cooled Slag Aggregates  
The air-cooled slag aggregates and concrete made with them satisfied all of the criteria of the Korean waste elution test (Table 12). The concentrations of seven elements in the eluate, including Pb and Cu, were below the maximum allowable levels. The air-cooled slag aggregates yielded undetectable amounts of Pb, As, Hg, Cd, and Cr\({}^{+6}\). Copper and CN concentrations were 0.0012 and 0.0025 mg/L, respectively, which easily satisfied the maximum allowable limits of 3 and 1 mg/L, respectively. Concrete containing the air-cooled slags yielded undetectable levels of As, Hg, Cd, and Cr\({}^{+6}\), and only 0.020, 0.004, and 0.003 mg/L of Pb, Cu, and CN, respectively, which satisfied the maximum allowable limits of 3, 3, and 1 mg/L. The measurable level of Pb extracted from the concrete containing the air-cooled slags was attributed to Pb contained in the other materials used in preparing the cement; i.e., the water and admixture. Conventional concrete made with natural aggregates yielded undetectable levels of all species except Cu and CN, which were detected at 0.0014 and 0.003 mg/L, respectively. Nevertheless, these levels easily satisfied the maximum allowable amounts. The elution volumes were slightly greater for concrete made with the air-cooled slag aggregates, but were well within the regulatory limits for all of the analyzed species. This testing established that both the air-cooled slag aggregates and concrete made with them met the environmental hazards requirements. Hence, air-cooled slag aggregates are suitable for use as concrete aggregate from the perspective of resource recycling. The results of testing according to the Japanese JIS K 0058 standard [25] are given in Table 13. The air-cooled slag aggregates gave the following results for Cd, As, Se, F, and B: 0.0923, 0.3, 3.633, 107.17, and 68.61 mg/kg, respectively, and which satisfied the respective maximum allowable limits value of all the test items. Pb, Cr\({}^{+6}\) and Hg were not detected. The air-cooled slag aggregates yielded elution volumes of 1.287, 1.330, not detected, 0.333, 1.203, 143.937, and 35.69 mg/kg for Cd, Pb, Cr\({}^{+6}\), As, Hg, Se, F, and B, respectively. Although Pb and Hg were detected, their elution volumes were within the normal range, and their detectable amounts were attributed to the cement admixture. The elution volumes of heavy metals from conventional concrete made with natural aggregates were 1.65, 10.033, not detected, 1.227, 0.013, 3.967, 189.647, and 36.53 mg/kg for Cd, Pb, Cr\({}^{+6}\), As, Hg, Se, F, and B, respectively. The elution volume of the concrete made with air-cooled slag aggregates was less than that of the concrete made with the natural aggregate.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 4 Frequency Domain Equalization
\begin{split}\overline{\mathbf{x}}_{v}^{(n)}{}^{\prime}& =IFFT\Big{\{}\overline{\mathbf{x}}_{v}^{(n)}\Big{\}};\overline{\mathbf{y} }_{v}^{(n)}=IFFT\Big{\{}\overline{\mathbf{y}}_{v}^{(n)}\Big{\}},\\ \overline{\mathbf{x}}_{v}^{(n)}&=E_{N+1}^{2N}\Big{(} \overline{\mathbf{x}}_{v}^{(n)}{}^{\prime}\Big{)};\overline{\mathbf{y}}_{v}^{(n)}=E_{ N+1}^{2N}\Big{(}\overline{\mathbf{y}}_{v}^{(n)}{}^{\prime}\Big{)}.\end{split}  \tag{21}\]  
As explained before, the last block--i.e., the last N samples--of the signal should be extracted. This operation is denoted by the operator \(E_{N+1}^{2N}\). The equalized signals are compared to the expected signals according to the described error functions to obtain the error coefficients: \(\mathbf{C}_{1v}^{(n)}\) and \(\mathbf{C}_{2v}^{(n)}\). Since our equalizers are based on unfolded signals of two samples per symbol, the update coefficients of the non-optimum sampling point are filled with zeros. Figure 3 illustrates the 2 \(\times\) 2 equalization performed in FD. Figure 3: 2 \(\times\) 2 frequency domain (FD) equalizer by showing the most relevant steps. Figure 2: Graphical illustration of the overlap-save technique.
## 1 Introduction
On the other hand, with the development of spaceborne communication and GNSS augmentation technologies, the next-generation LEO constellations with large numbers of satellites might tend to integrate the functions of satellite communication, satellite navigation, and satellite remote sensing in one system. Nowadays, LEO satellite constellations, e.g., Iridium, Spire, Starlink, China's Hongyan, etc., have been designed and implemented [40; 44], which can provide more LEO satellites and navigation signal opportunities for GNSS RO atmospheric detection. Large LEO constellation and multi-GNSS RO mission design and detection capability evaluation should consider the requirements of the GCM and GNWP applications. However, the traditional GNSS RO event descriptions and statistical approaches such as the global distribution of RO event locations and histograms of RO event numbers in specific latitude and longitude bins are too simple to quantitatively describe and analyze the GNSS RO spatiotemporal coverage and uniformity information. In this paper, according to the requirements of the GCM and GNWP applications, GNSS RO event spatial and temporal distribution analysis approaches were proposed by defining the global coverage fraction (GCF) and uniformity coverage index (UCI), in which an equal-area projection method [59] was involved. Then, based on real navigation satellites' and designed LEO satellites' (constellations) orbit data, a simulation study was conducted to investigate the variation characteristics of the multi-GNSS RO event number, global coverage, and uniformity. Under the existing multi-GNSS conditions, the following scientific questions were analyzed: \(\copyright\) to what extent can the multi-GNSS integration improve the number of RO events and their global coverage fraction for a single polar-orbit LEO satellite? \(\copyright\) An RO constellation with how many polar-orbit LEO satellites can achieve the Spire/GPS RO system detection capability and thus can meet the GNWP's basic requirements in terms of the RO event number, timeliness, and global coverage fraction? The structure of this paper is as follows: Section 2 introduces the GNSS RO event simulation and their detection capability assessment approaches, the satellite orbit datasets, as well as the experimental programs used in this study; Section 3 presents the key results of this study including the effects of the multi-GNSS integration on the single polar-orbit LEO satellite RO detection capability and the RO detection capabilities of the LEO constellations; Section 4 discusses the impacts of the orbital types on the GNSS RO event global distribution characteristics; finally, the main conclusions of this study are provided in Section 5.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 5 Optical Wireless Communication Standards  
VLC is the most promising technology for communications. Due to LED lighting's rapid development, the cost is rapidly falling; however, some of the challenges that must be addressed are listed as follows:  
1. Integration of VLC system with existing communication standards.
2. Ambient light source interference problem.
3. VLC should properly consider mobility issues such as handover.
4. Specification of a forward error correction plan to improve communication system performance.  
As the number of VLC devices increases, there will be interference between different VLC devices. The Electronic Information Technology Industry Association developed the standard 802.15.7, which is a standard established by the IEEE for the physical layer and the MAC layer [123]. Objectives of this standard are:  
1. Establish access to hundreds of terahertz frequency bands.
2. Establish anti-electromagnetic interference capability.
3. Communication of additional services that supplement the current visible light equipment.
4. VLC communication that specifies a forward error correction (FEC) scheme, modulation form, and transmission rate.
5. Channel access mechanism, as visibility support also describes channel access, and contention access period (CAP) and contention-free period (CFP).
6. Physical layer specifications, such as optical mapping, TX-RX, RX-TX turnaround time, flicker, and dimming relief. IEEE 802.15.7 is new product development standard. Three different types of devices used by VLC are vehicles, mobile equipment, and infrastructure, as shown in Table 5[125].
## 2 Materials and Methods
### Mesh Sensitivity Study  
The numerical mesh quality may have significant impact on the results of numerical calculations, thus a mesh sensitivity study is required to support the choice of numerical mesh [46]. In case of this study, the numerical domain was discretised in space using a non-structural tetrahedral mesh. The final mesh was chosen after a series of numerical analyses, in which the mesh was refined until no further change in the results of numerical was observed.  
The mesh was densified near the upper and lower leak holes, where intensive flow phenomena could be expected. The mesh size at the leakage was set as 5 mm (a smaller mesh of 2 mm was also investigated). The interior of the test chamber meshed with three different element sizes--50 mm, 100 mm and 200 mm. The intermediate mesh size between the smallest and the largest elements is determined by the mesh growth function, which is the relative difference in size between neighbouring elements. For this project, two mesh growth functions were investigated--1.15 and 1.20. However, it must be noted some best practice guidelines recommend values up to 1.3 [26]. The study was  
Figure 5: Changes in oxygen concentration (v/v) on the probe No. 1, indicating the end of the retention time during experimental tests and computational fluid dynamics (CFD) simulations, fire-extinguishing gas Ar 50% v/v and N\({}_{2}\) 50% v/v.  
Figure 6: Changes in oxygen concentration (v/v) on the probe No. 5, indicating the end of the retention time during experimental tests and CFD simulations, fire-extinguishing gas Ar 0% v/v and N\({}_{2}\) 100% v/v.  
performed for Scenario No. 1 (50% Ar 50% N\({}_{2}\) gas mixture), with RANS k-\(\varepsilon\) Realizable solver, and consisted of 6 numerical simulations.  
The results are presented in Figure 7. The mesh with larger growth rate function (filled symbols) yielded lower gas concentrations, which means it underestimated mixing. For a mesh with 1.15\(\times\) growth rate, minuscule differences were noticed. Additional improvement in the mesh by changing the mesh size at the leakage to 2 mm at the leakage did not significantly alter the results. Balancing the solution quality and the computational cost, the final mesh chosen for numerical experiments had the size of 5 mm at the leakages and 50 mm in the domain, with a 1.15 growth rate function. For this function, the expected results of gas concentration were in a 10% range from the experimental measurements, for gas mixture No. 1.  
The mesh size for the surrounding domain was chosen as 300 mm with a 1.15 mesh growth function. The coarser mesh was chosen for the exterior, as no relevant parameters were measured in this space. An overview of the mesh is shown in Figure 8.  
Figure 8: A general overview of the mesh used in the numerical experiment.  
Figure 7: Results of mesh sensitivity study for Reynolds-Averaged Navier-Stokes (RANS) k-\(\varepsilon\) (Realizable) turbulence model and IG 55 mixture (50% Ar, 50% N\({}_{2}\))—oxygen concentration (v/v) at lowest measuring element in the experiment and numerical study.
## 1 Introduction
By dynamically integrating these layers, the MMtQHU provides a comprehensive approach to modeling human uncertainty. It stands out for its adaptability, allowing the model to be applied to various real-world scenarios, such as 5G network management and autonomous vehicle deployment. Structuring the model around boundary objects (BOs) facilitates interdisciplinary collaboration, enabling stakeholders from different fields, such as engineering, social sciences, and urban planning, to understand and work together on complex problems. BOs serve as shared references that maintain consistency across disciplines while allowing for the flexibility needed to incorporate domain-specific knowledge. Building upon the MMtQHU framework, this study aims to achieve three main objectives: (i) to evaluate the conceptual human uncertainty model using a more robust dataset specific to Ingolstadt; (ii) to identify how drivers' reactions to various urban characteristics, considered as factors of human uncertainty, affect resource allocation in 5G networks; (iii) to identify possible methods to enhance human uncertainty management in CPSS through the integration of detailed urban data and advanced simulation techniques. The remainder of the paper is structured as follows: Section 2 reviews the related works, providing an overview of existing literature on uncertainty modeling in CPSS and highlighting the gaps addressed by this study. Section 3 establishes the conceptual framework of the research. Section 4 provides a detailed explanation of the simulation framework parameters, including the configurations and methodology employed. Section 5 offers an in-depth discussion of the simulation framework, presenting the configurations and methodology. The results are presented in Section 6, followed by a thorough discussion of their implications in Section 7. Finally, Section 8 concludes the paper with a summary of the key findings and recommendations for future research directions.
## 4 Results and Discussion
### Tracking Performance under Large-Load Disturbance  
The output voltage response of the closed-loop power converter during abrupt changes in load resistance is depicted in Figure 5. As shown in Figure 5a, the load profile changes abruptly from 75 \(\Omega\) to 350 \(\Omega\), 20 \(\Omega\), and then returns to 75 \(\Omega\). It can be seen that the output voltage, \(v_{O}\), tracks the reference signal, \(v_{R}\), accurately and rejects the load disturbances. Figure 5b shows an enlarged view of the closed-loop power converter response when the load changes from 350 \(\Omega\) to 20 \(\Omega\) at \(t=4\) ms. The output voltage, \(v_{O}\), exhibits a percentage undershoot of 5.8% and then returns to the reference trajectory within 15 \(\upmu\)s. Notably, the proposed control circuit exhibits robust tracking performance and fast transient response with a low percentage undershoot.  
Figure 3: The Simscape Electrical model of the closed-loop PWM dc-ac buck converter.  
It should be noted that the simplified DI-SMC method provides a wide operating range in the presence of large load variations. This is the advantage of utilizing the nonlinear control method, whereas the linear control techniques are only valid around a local operating point, which does not guarantee the stability of the power converter during large changes in load resistance. Hence, the simplified double-integral sliding-mode control method provides a robust alternative for linear controllers, such as classical PID and integral-lead controllers.  
Figure 4: The steady-state waveforms of the input voltage, \(v_{l}\), output voltage, \(v_{O}\), reference voltage, \(v_{R}\), and inductor current, \(i_{L}\) in CCM.  
Figure 5: (**a**) The tracking performance of the proposed control circuit under load disturbance. (**b**) Enlarged view of the transient response when the load changes from 350 \(\Omega\) to 20 \(\Omega\) at \(t=4\) ms.
# Fragility Analysis of Transmission Towers Subjected to Downburst Winds
## 3 Typical Engineering Case Study
### Uncertain Nonlinear Capacity Behavior Analysis  
Based on whether to consider the uncertainties of the structural model, the collapse analysis can be divided into two categories: i.e., the uncertain collapse analysis and deterministic collapse analysis. The latter cannot fully consider the influence of the uncertainty of the selected parameters on the performance of the tower. On the contrary, the uncertain collapse analysis can completely evaluate the collapse state of the transmission tower under the downburst. In addition, the wind attack angle is also an important influence factor on the collapse result. The influence of different wind attack angles on the initial failure members requires deep investigation.  
In this study, both the deterministic collapse analysis and uncertain collapse analysis of the 220 KV transmission tower under different cases of wind attack angles are carried out through the nonlinear buckling analysis method. For the uncertain collapse analysis, the parameters of the uncertain structural model introduced in Section 3.1 will be employed, where 20 samples are generated. Figures 13-15 include the stress nephogram of initial buckling state at 0\({}^{\circ}\) direction angle, the position, and the corresponding probability of the initial buckling member at each direction angle of the deterministic model and uncertain model. In the figure, \"0\({}^{\circ}\), 100%\" indicates that the failure probability of the corresponding member at the 0\({}^{\circ}\) direction angle is 100%. It can be found that most of the initial buckling members are located near the first section of the transmission tower. The initial failure member is the main member, indicating that the main failure mode of the transmission tower belongs to bending failure. The main member is a compression bending component, which is the main component of the wind resistance of the transmission tower, while the diagonal member is an axial compression component, which is an auxiliary component of the wind resistance of the transmission tower. The instability of the diagonal member will not cause the collapse of the tower structure--it will only accelerate the instability of the main member. Considering the structural uncertainty is helpful to comprehensively  
Figure 12: Capacity curve of one structural sample under different wind attack angles.  
analyze the failure path of transmission towers. Therefore, uncertain collapse analysis is particularly important to determine the location and probability of all potential buckling failure members.