esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Data Collection
#### 4.1.2 Experiment Scenarios for Data Gathering  
This subsection provides a concise overview of the approach employed to specify urban scenarios using the SUMO simulation framework. Within SUMO, a range of tools was utilized to define the urban components, including obstacles such as buildings. Figure 3 illustrates the process of generating urban scenarios in SUMO.  
In our study, it was crucial to accurately represent the geometric structure of the network, considering urban elements such as buildings. To accomplish this, we leveraged the capabilities of the NETCONVERT and POLYCONVERT tools provided by the SUMO traffic simulator.  
The NETCONVERT tool facilitated the conversion of files (e.g., OSM) containing information about the geometric structure of road networks from diverse sources into a format compatible with SUMO. Specifically, for our research, we imported the specifications of the geometric structure of the road network from the OpenStreetMap platform [34].  
The POLYCONVERT tool was employed to transform the geometric forms (i.e., buildings) obtained from various sources into a visualizable representation within the graphical interface of SUMO.  
After defining the urban model, the next step is to develop a script in NS-3 that builds upon this model.  
Figure 2: Experimental environment.
## 2 Design Parameters of the Metamaterial and Simulation Setup
### Parametric Studies
#### 2.2.4 The Effect of Field Propagation Direction  
A change in transmission coefficient (S\({}_{21}\)) is observed with varying electric field and magnetic field direction. Figure 6 demonstrates the simulation setup for changing the field propagation. Initially, electric field (Ex) propagates along the X direction, and the magnetic field (Hy) is applied to the Y direction. The simulation result shows quad-band resonance peaks at 2.35 GHz, 7.72 GHz, 9.23 GHz and 10.68 GHz. If the fields are interchanged with each other, the simulated results show two resonance peaks at 4.58 GHz and 8.38 GHz. Figure 7 illustrates the simulated results for propagation in the ExHy and HxEy directions.  
Figure 4: Transmission coefficients (\(S_{21}\)) for different substrate dielectric materials.  
Figure 5: Transmission coefficients (\(S_{21}\)) for different sizes of unit cell substrate.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Data and Evidence Mapping
#### 4.4.1 Decentralized Privacy-Preserving Access for Low-Emission Zones [23]  
A decentralized access-control system for LEZ is introduced. It ensures privacy preservation and fraud detection through group signature schemes and blockchain technology. The proposed system addresses the limitations of centralized LEZ schemes by decentralizing access verification and payment processes. Its key contributions are as follows:  
* Decentralized Architecture: Utilizes blockchain to eliminate centralized third-party control over LEZ access and payment processes, transferring trust to a decentralized network of nodes.
* Privacy Preservation through Group Signatures: Ensures anonymity and unlinkability for honest users by leveraging group signatures, where access data are signed on behalf of vehicle emission category groups without disclosing individual identities.
* Revocable Anonymity for Fraud Control: Honest users retain complete anonymity, but fraudulent users who violate protocol can have their identities disclosed through group signature revocation.
* Integration with Smart Contracts: Automates payment and verification processes using Ethereum smart contracts, which calculate and transfer access fees in LEZ tokens, ensuring a transparent and tamper-proof system.
* Efficiency and Scalability: Designed to operate efficiently with minimal computational overhead, leveraging lightweight cryptographic operations suitable for real-time LEZ scenarios.  
Blockchain and privacy-enhancing technologies may be integrated into dynamic LEZs to adapt to real-time traffic and emission conditions. By ensuring scalability, transparency, and privacy, it aligns closely with the goals of adaptive LEZ management.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 3 Neural Network (NN) Based Elasticity
### Representative Numerical Examples
#### 3.2.1 Compression Test of a Plate  
The first model problem is the uniaxial compression test of a rectangular plate. The geometric setup and the loading conditions of the specimen are depicted in Figure 6a. The plate is fixed at the bottom, and a prescribed displacement with an amplitude of \(u=-0.04\) mm is imposed at the top surface of the plate with \(L=1\) mm. The geometric domain of the structure is discretized by 400 triangular T1 elements, leading to 231 nodes.  
The load-deflection curve is depicted in Figure 6b. Next, the stresses computed with the neural network based model is compared with that of [PERSON]'s model. It can be observed from the contour plot that FFNN predicts the stresses very accurately as shown in Figure 7. This verifies the accuracy of the proposed neural network approach for elasticity problems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Name** & **Value** & **Unit** \\ \hline
1. & Number of samples & \(10^{4}\) & – \\
2. & Training duration & 888 & s \\
3. & Training performance & \(2.2\times 10^{-16}\) & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Neural network model specifications—small strain elasticity.  
Figure 5: Mean squared error (MSE) of neural network model with two hidden layers.
## 3 Method
### LH Algorithm
#### 3.2.3 Complexity Estimation  
Among all three benchmark methods, the A-star algorithm with Manhattan distance almost always has the best computational complexity for an HDS system; therefore, we compare the computational complexity of our method with the A-star algorithm.  
1. Time complexity  
We define \(d\) as the length of the shortest path from the start node to the destination, and \(b\) is the branching factor, the maximum nodes of successors for any given state. By using the two designs of main track priority and 'jumping' mechanisms on sub-tracks, the value of \(b\) can be divided into two parts: (1) \(b_{1}=4\) for nodes on the main track, where the Manhattan distance is adopted for the HDS system; and (2) \(b_{2}=1\) on sub-tracks.  
Case One: best case--find the destination directly without any obstacles  
The time complexity can be expressed as  
\[T_{\text{LH}}^{\text{best}}=\mathcal{O}\Big{(}4^{d_{1}}+d_{2}\Big{)}, \tag{2}\]  
where \(d_{1}\) is the number of nodes along the main track and \(d_{2}\) is the number of nodes in the first sub-track until destination arrival.  
Case Two: worst case--find the destination after traversing all of the nodes in the map  
The time complexity can be expressed as  
\[T_{\text{LH}}^{\text{worst}}=\mathcal{O}\Big{(}4^{d_{1}}+d_{1}\times d_{2} \Big{)}. \tag{3}\]  
Under the best case scenario, the time complexity of A-star is \(\mathcal{O}\Big{(}4^{d_{1}+d_{2}}\Big{)}\), which is always larger than our best case time complexity of \(\mathcal{O}\Big{(}4^{d_{1}}+d_{2}\Big{)}\). Under the worst case scenario, the time complexity of A-star is \(\mathcal{O}\Big{(}4^{d_{1}\times d_{2}}\Big{)}\), which is also larger than \(\mathcal{O}\Big{(}4^{d_{1}}+d_{1}\times d_{2}\Big{)}\), our time complexity.  
Therefore, the time complexity of our algorithm performs better than the A-star algorithm. From Equations (2) and (3), one can also tell that the larger the map of an HDSsystem, the better the comparison result. This is displayed in both the numerical and case studies.  
(2) Space complexity  
Not all traversed nodes during path planning need to be kept in memory. We reuse the nodes along the main track and reuse the majority of the nodes along the sub-tracks. In this way, data that are kept in memory are bounded by the map's size, \(\mathcal{O}(d_{1}\times d_{2})\). On the contrary, the worst space complexity in A-star is \(\mathcal{O}\left(4^{d_{1}\times d_{2}}\right)\), which is much larger than our value. In a word, we have better computational complexity than the benchmark algorithms.
## 4 Results and Analysis
### Identification of Signal App Traffic  
To identify the activities associated with the Signal app, we dumped the network traffic from the targeted Android device. As the captured traffic was encrypted it could not be decrypted without the cryptographic key. Therefore, we extensively performed and captured the various activities to uncover traffic patterns against the respective activities. User A is a target device on which multiple activities are performed. For better understanding, we analyzed and classified target User A (device) in the capacity of the sender, caller, and receiver, and its respective activities on the app are as follows:  
1. Accessing/opening the Signal app;
2. Target device (User A) typing patterns;
3. User B typing patterns;
4. Call initiated by User A (caller);
5. Call received by User A (callee);
6. Media messages;
7. Video calls.
## 3 Results and Discussion
### Causality Analysis
#### 3.1.2 [PERSON]'s Test of Co-Integration  
The presence of at least one co-integrating equation is one of the pre-conditions for developing a VECM model. This implies that at least two of the time series variables used in this study needed to be integrated among each other [42]. The results of [PERSON]'s test of co-integration for model 1 and model 2 are presented in Tables 5 and 6, respectively. Results show the presence of at least one co-integrating equation in the model at 0.05 level. This justifies the development of a VECM for this study instead of a vector autoregression (VAR) model.  
Tables 7 and 8 present the co-integrating coefficients of various independent or explanatory variables for model 1 and model 2, respectively. The sign of a coefficient shows the relationship between an independent variable and the corresponding dependent variable. Results presented in Table 7 indicate that CH4 emissions generated from municipal solid wastes in Saudi Arabia were positively correlated with GDP per capita and urban population. This implies that emissions from municipal solid waste increase with an increase in GDP per capita and urban population. The relationship running from FDI inflows and literacy rate to municipal solid waste emissions was negative, which suggested that an increase in FDI inflows and the literacy rate was likely to reduce CH4 emissions from municipal solid waste and vice versa. The R-squared value of model 1 was 0.86 which meant that the independent variables used in the model explained the variation of the dependent variable very well. Regarding model 2, CH4 emissions generated from industrial solid wastes in Saudi Arabia had a positive relationship with GDP per capita, urban population, and FDI inflows. However, the relationship between the unemployment rate and industrial solid waste emissions was negative, meaning that a decrease in the unemployment rate is likely to increase CH4 emissions from industrial solid wastes. An R-squared value of 0.90 suggested that the independent variables used in model 2 mostly present the variations of the dependent variable.
## 5 Conclusions  
The use of a time series of VIs with different sensitivities to biophysical attributes of savannas in an ensemble approach improved the CCDC-detection of disturbance when compared to the detection using single VIs. Considering the specific objectives of the work, we concluded that:(a) The disturbance caused by clearing and fire produced significant reflectance modifications in the NIR and SWIR-1,2 Landsat bands that explained the better performance of NBR2, NBR, and MSI to detect such events.  
(b) The ensemble VI approach with CCDC had an accuracy of 71.2 \(\pm\) 4.0% for detecting total disturbance (savanna clearing plus fire), reducing the omission errors when compared to the use of single VIs in the analysis. The accuracy was generally higher for detecting clearing rather than for mapping burned areas, but the opposite was observed with respect to the recorded date of the occurrence of disturbance.  
(c) Savanna clearing predominated in the transition from the rainy to the dry season (April to July), while most events of fire disturbance occurred between August and October toward the end of the dry season.  
Finally, the results highlight the importance of data-driven approaches for generating early warning alerts of disturbance in the _Cerrado_ to support programs of monitoring of savannas.  
Conceptualization, A.A.d.S., L.S.G. and T.S.K.; writing--original draft preparation, A.A.d.S. and L.S.G.; writing--review and editing, T.S.K. and C.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) (Financial Code 001) and Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [grant numbers 301486/2017-4 and 303360/2019-4].  
The authors declare no conflict of interest.
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 4 Experimental Results and Discussion
### Parameters Selection of SVR  
In the context of SVR, the estimation and generalization performances were impacted by the hyperparameter pair \((C,g)\), where the penalty factor is denoted by \(C\), and the RBF kernel function is represented by \(g\). The higher the trade-off factor \(C\), the more prone to overfitting the model becomes. The larger the \(g\), the fewer the support vectors, and vice versa, noting that this number significantly influences the speed of training and prediction processes. The fitness function is an effective measure for optimizing the SVR parameter settings to achieve the best prediction accuracy and generalization ability. First, we employed 10-fold cross-validation to train the model parameters and comprehensively assess the model's performance. Then, the grid search method was used to search a twice enlarged/reduced range to obtain the optimized kernel function parameter range \((C,g)\). Finally, the MMPSO algorithm was used to optimize \((C,g)\), where the actual measured values were obtained for each body dimension, and the residual sequence was constructed based on the KRR of the RBF kernel, based on input information. To evaluate the effectiveness of the MMPSO method of enhancing the hyper-parameters of the SVR model, we selected the maximum number of iterations for the MMPSO-SVR (i.e., 1000), a population size of 50, two dimensions, and initial ranges of the variables \(C\) and \(g\) as \([10,1000]\) and \([1\times 10^{-4},1]\), respectively.  
To analyze the influences of different training proportions, we performed another comparative test taking crotch height and knee circumference parameters as examples, as depicted in Figure 5A,B. When the size of the training sample exceeded 40, the \(e_{RMSE}\) and \(e_{MAE}\) values of the GRFN hybrid model were stable, demonstrating that it had satisfactory generalizability and performed better than SOTA models on small-batch sample prediction tasks.  
Figure 4: Critical difference under Friedman test, statistical rank.
## 3 Small-Signal Admittance Model in dq-Domain
Based on Figure 4, the voltage and current in the controller frame and plant frame under small-signal perturbations can be derived as  
\[\left[ \begin{array}{c}\Delta i^{c}_{d}\\ \Delta i^{c}_{q}\end{array} \right]=\left[ \begin{array}{c}\Delta i^{g}_{d}\\ \Delta i^{g}_{q}\end{array} \right]+\left[ \begin{array}{cc}0&G_{pl}\overline {l}_{q}\\ 0&-G_{pl}\overline{l}_{d}\end{array} \right]\left[ \begin{array}{c}\Delta V^{g}_ {d}\\ \Delta V^{g}_{q}\end{array} \right]\ \left[ \begin{array}{c}\Delta V^{c}_{d}\\ \Delta V^{c}_{q}\end{array} \right]=\left[ \begin{array}{c}\Delta V^{g}_{d}\\ \Delta V^{g}_{q}\end{array} \right]+\left[ \begin{array}{cc}0&G_{pl}\overline {V}_{q}\\ 0&-G_{pll}\overline{V}_{d}\end{array} \right]\left[ \begin{array}{c}\Delta V^{g}_ {d}\\ \Delta V^{g}_{q}\end{array} \right] \tag{5}\]  
Figure 4: Synchronous reference frame phase-locked loop (SRF-PLL), (**a**) small signal model of PLL, (**b**) controller (\(f_{dq}{}^{c}\)) and system (\(f_{dq}{}^{g}\)) frames under small-signal perturbations because of the PLL dynamics. where \(\overline{I_{d}}\), \(\overline{I_{q}}\), \(\overline{V_{d}}\), \(\overline{V_{q}}\), \(\overline{V_{d}}^{inv}\) and \(\overline{V_{q}}^{inv}\) are the operating point values of the converter. \(\Delta\theta\) can be derived as  
\[\Delta\theta=G_{pll}\Delta V_{q}^{g}=\frac{G_{PI}^{PLL}}{V_{nom}+G_{PI}^{PLL} \overline{V_{d}}}\Delta V_{q}^{g}\quad G_{PI}^{PLL}=K_{p}^{pll}+\frac{K_{i}^{pll }}{s} \tag{6}\]  
Using (1), (2), (5) and (6) and some simplifications, the small signal admittance matrix can be obtained by  
\[ \begin{split} Y_{dq}=\left[\begin{array}{cc}y_{dd}(s)&y_{dq}(s )\\ y_{qd}(s)&y_{qq}(s)\end{array}\right]=-\left[\begin{array}{cc}\Delta V_{d}^ {g}\\ \Delta V_{q}^{g}\end{array}\right]^{-1}\times\left[\begin{array}{cc}\Delta \tilde{s}_{d}^{g}\\ \Delta\tilde{s}_{q}^{g}\end{array}\right]=-\left[\begin{array}{cc}sL_{f}+G _{cc}G_{d}&-\omega_{0}L_{f}\\ +\omega_{0}L_{f}&sL_{f}+G_{cc}G_{d}\end{array}\right]^{-1}\\ \times\left[\begin{array}{cc}G_{f}G_{d}-1&-G_{cc}G_{d}G_{pll}\overline{I_{q }}+G_{f}G_{d}G_{pll}\overline{V_{q}}-G_{pll}G_{d}\overline{V_{q}}^{inv}\\ 0&G_{cc}G_{d}G_{pl}\overline{I_{d}}+G_{f}G_{d}-G_{f}G_{d}G_{pll}\overline{V_{d }}+G_{pll}G_{d}\overline{V_{d}}^{inv}-1\end{array}\right]\end{split}  \tag{7}\]
## 3 NTNs for IIoT Applications  
In IIoT applications, the effective wireless connectivity is not only requisite for the data transmission between the multiple nodes but also represents a key factor to ensure the safety of personnel or citizens in remote locations, especially in cases with environmental hazards or in emergency situations. As previously mentioned, the IIoT can exploit both 3 GPP cellular-based and non-3 GPP license-free terrestrial infrastructures. The former allows for large-scale device deployments aided by the mobile network operators and supports widely adopted wireless standards. However, a huge number of base stations is indispensable for global radio coverage. In addition, the terrestrial communication infrastructure is sensitive to natural disasters, e.g., earthquakes and floods. To further extend the IIoT connectivity into remote and industrialized areas and enhance the reliability and resiliency of services, the aerospace infrastructure should be leveraged. NTNs are capable of simultaneously interconnecting a massive number of devices that struggle for connectivity, while ensuring the successful management of data-intensive applications, redundant connections at critical sites, low latency, and enhanced capacity. In addition, NTNs represent an ideal fit for supervisory control and data acquisition (SCADA), since they ensure the delivery of wireless services in challenging environments with highly mobile and dispersed nodes.  
Figure 2: Key design requirements of critical IIoT, enabling efficient and innovative services across a wide range of industry verticals.  
The successful operation of space-air-ground integrated networks (SAGINs) that provide connectivity for the IIoT envisages the synergetic and seamless integration of heterogeneous wireless networks with distinct capabilities [33]. From a wireless communications engineering standpoint, the heterogeneity of the aforementioned networks corresponds to highly variable service requirements with respect to key metrics, such as the E2E communication delay and the data traffic. Since reliable communications are a prerequisite for critical IIoT applications, the choice of appropriate types of satellites and aerial platforms, such as those described below, heavily depends on the size, altitude, mobility issues, autonomy level, and specific application needs. Figure 3 demonstrates a SAGIN consisting of heterogeneous communication infrastructures and multiple network segments in space, air, and ground for cost-effective, flexible, and large-scale IIoT applications and services, e.g., Earth observation, navigation, and telecommunications. In this envisioned and promising SAGIN-based paradigm, satellite-to-ground (S2G), satellite-to-air (S2A), air-to-air (A2A), air-to-ground (A2G), and ground-based radio links exist, whereas typical limitations posed by single network segments are effectually resolved.
## 1 Introduction
### The Concept and Basis of Development of the Methodology  
If there are not sufficient data for the adequate characterization of the activity of suspicious fault or faults, the evaluation of permanent surface displacement hazard can be performed based on the magnitude-distance disaggregation of seismic hazard. Thus, the logic-tree modeling of seismogenic sources and the results of the probabilistic seismic hazard evaluation could be the basis of PGD hazard analysis, instead of the logic-tree modelling of fault activity as it is required by the probabilistic fault displacement hazard analysis. Therefore, the concept for the evaluation of the fault displacement hazard for the specific conditions of the study site is based on three existing and widely accepted methods:  
Figure 2: Young geological deformations (after [PERSON] et al., 2020 [13]) and recent earthquakes (1995–2020) in the vicinity of the Paks NPP site.  
* Probabilistic seismic hazard assessment method (see, [PERSON] R-CRISIS v20 [15]). It should be noted, a state-of-the-art PSHA exists for the site. The first PSHA has been performed for the site in 1995 [16] and regularly updated every ten years in the frame of periodic safety reviews of the Paks (see, e.g., in [17]) and post-Fukushima stress-test and very recently for the site licence of the new plant (see in [14]).
* Earthquake based probabilistic fault displacement hazard evaluation method, see [4] and [PERSON] et al. [5] since this method is based on the characterisation of seismogenic sources/ faults similar to the PSHA.
* Strike-slip displacement evaluation method of [PERSON] et al. [18] (see also, [PERSON] and [PERSON] [19] and [PERSON] [20]).  
The methods above and their applications are published in several papers. Therefore, the methods are considered as well known.
## 3 Artificial Intelligence Techniques for 6G Networks
### Channel Estimation  
Large intelligent surfaces, ultra-massive multiple-input multiple-output (MIMO), visible-light communication, terahertz communication, and other emerging technologies will enable 6G radio access in order to meet the stringent requirements of smart city applications in terms of high data rate (Tbps), low latency (order of 0.1-1 ms), and high reliability (order of 10-9). Due to the increased complexity of radio communication channels brought about by these technologies, standard mathematical methods for efficient channel estimates will become more difficult to use. The information is transferred using a wireless communication channel that attenuates phase changes and adds noise to the data [19].