esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Vehicular Communications: Core Concepts and Challenges
### Disconnected Areas of Vehicular Networks  
As the prevalence of connected vehicles continues to expand, there is a growing recognition of the essential role that infrastructure, particularly RSUs, plays in ensuring the reliability and trustworthiness of vehicular communication systems [34]. RSUs are pivotal in verifying message authenticity and confirming the eligibility of the sender, contributing significantly to the overall integrity of VANETs [35].  
However, the communication landscape becomes markedly challenging in geographical areas characterized by tunnels, mountainous terrain, and remote locations. In such regions, satellite and GSM communications, although theoretically available, suffer from inconsistencies in availability and quality of service. In addition, the strategic deployment of RSUs becomes a critical and intricate challenge. The absence or inadequate deployment of RSUs in these disconnected areas poses a significant risk to the efficiency and reliability of VANETs. This, in turn, compromises the overall service capability for a substantial number of vehicles operating in these challenging environments.  
This underscores the urgent need to address connectivity challenges in areas where RSU deployment is insufficient or suboptimal. Enhancing the robustness of VANETs  
Figure 3: Challenges of SCMS in Disconnected Vehicular Network.  
in disconnected or challenging terrains requires innovative solutions to overcome the limitations imposed by the sparse distribution of RSUs. Addressing these challenges is paramount to ensuring the trust and reliability of communication systems in diverse geographical settings, thereby maximizing the potential benefits of connected vehicles.
## 3 CFD Modelling as Digital Twin
### CFD Models for Filling and Emptying Processes
#### 3.3.1 Turbulence Equations
After the standard model, the Realisable variant was the second most used in studying two-phase flow scenarios in water pipelines (3 over 11). Another model used to simulate the effects of turbulence in two-phase transient flow is the SST \(k\)-\(\omega\) turbulence model, where \(\omega\) is the dissipation frequency, representing the dissipation rate of turbulent kinetic energy per unit energy. It is used to capture turbulence behaviour near the walls, where velocity gradients are largest [49; 50; 51]. The SST \(k\)-\(\omega\) is \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Main Characteristics** \\ \hline Standard \(k-\epsilon\) & Standard model using transport equations for the turbulent kinetic energy (\(k\)) and the dissipation rate (\(\epsilon\)) [43; 45]. \\ \hline Realisable \(k-\epsilon\) & Modified model that includes physical constraints in the solutions, ensuring that turbulence behaves more realistically [46]. \\ \hline RNG \(k-\epsilon\) & Includes an additional term that considers the curvature of the flow, improving the representation in non-isotropic flows [47]. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of \(k-\epsilon\) turbulence models. a combination of the techniques for capturing vorticity and far-wall mixing processes offered by the model \(k\)-\(e\), as well as capturing the logarithmic viscous layer and turbulence dissipation frequency near pipe walls, which the standard \(k\)-\(\omega\) model provides [52]. This model fits well with adverse pressure gradients, can predict fluid separation, and is easily adaptable to flow regime changes [53]. \[\frac{D(\rho k)}{Dt}=S_{k}-\beta^{*}\rho k\omega+\
abla\cdot(\rho D_{k}\
abla k )-\frac{2}{3}\rho k(\
abla\cdot\mathbf{u})+\rho G \tag{10}\]  
\[\frac{D(\rho\omega)}{Dt}=\
abla\cdot(\rho D_{\omega}\
abla\omega)+\frac{\rho \gamma G}{v_{t}}-\frac{2}{3}\rho\gamma\omega(\
abla\cdot\mathbf{u})-\rho \beta\omega^{2}+S_{\omega}+\rho(1-F_{1})CD_{k\omega} \tag{11}\]  
The SST k-\(\omega\) turbulence model has been used in some studies to represent the turbulence conditions of emptying processes in water pipelines, which are usually slow compared to filling processes. This turbulence model is favourable for representing two-phase transient flows adequately. Authors such as [PERSON] et al. [29] and [PERSON] et al. [6; 36; 54] applied these turbulence models to simulate emptying processes with trapped air pockets in simple and branched pipes thus ensuring reliable and adequate results in their respective studies. On the other hand, [PERSON] et al. [35] developed a 3D CFD model using the SST \(k-\omega\) model to represent filling processes in a horizontal pipeline, and [PERSON] et al. [55; 56] applied this turbulence model to simulate rapid filling processes using an air valve (see all references in Table 4). To illustrate the differences between the application of turbulence models \(k-e\) and the SST \(k-\omega\), the comparative analysis of a case study is carried out. The analysis is
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 2 Material and Methods
### Evaluation Approach of ZED-F9P  
The evaluation approach of the GNSS module ZED-F9P includes two aspects, namely, the positional quality and investigation of its potential for displacement detection. Firstly, static observations are carried out to analyze the reliability of GNSS data and to assess the quality of multi-frequency, low-cost instrument positioning. Secondly, controlled movements of the low-cost antenna are imposed to analyze the minimum level of displacement that may be detected with low-cost GNSS instruments.
###### Abstract  
The study of the mechanical properties of moderately consolidated sandstones is crucial for engineering safety assessments. As an effective research tool, the discrete element method (DEM) encounters challenges during the modeling phase, such as a large number of micromechanical parameters, low modeling efficiency, and unclear coupling mechanisms among multiple parameters. To address these issues, this paper proposes a calibration method for the micromechanical parameters of DEM models for moderately consolidated sandstones. By integrating orthogonal experimental design with a multivariate analysis of variance, the influence of micromechanical parameters on macroscopic mechanical properties is quantified, and a parameter prediction model is constructed using an intelligent dynamic regression selection mechanism, significantly improving the efficiency and accuracy of micromechanical parameter calibration. The results show that the macroscopic elastic modulus \(E\) is primarily controlled by the effective modulus (\(\overline{E}\)), stiffness ratio (\(k\)), and particle size ratio (\(R_{max}\)/\(R_{min}\)), following a linear relationship. The influence of the particle size ratio decreases significantly once it exceeds a threshold value. The macroscopic uniaxial compressive strength (\(UCS\)) is dominated by cohesion (\(\overline{c}\)) and tensile strength (\(\overline{o}_{c}\)), exhibiting a polynomial relationship, where a stronger synergistic effect is generated when both parameters are at higher levels. [PERSON]'s ratio (\(\mu\)) is significantly correlated only with the stiffness ratio (\(k\)), following a logarithmic relationship. An iterative correction method for micromechanical parameter calibration is proposed. The errors between the three groups of simulation results and laboratory test results are all less than 10%, and the crack distribution patterns show a high degree of consistency. The findings of this study provide a theoretical foundation and technical means for exploring the mechanical behavior and damage mechanism of moderately consolidated sandstones.  
discrete element method; micromechanical parameters; parameter calibration method; linear parallel bond model; moderately consolidated sandstones; intelligent dynamic regression selection 2025157086
## 6 Remaining Problems  
### 3-D Radiative Transfer: The LiDAR Problem  
Although we have mostly considered _plane-parallel_ systems so far with an emphasis on the coupling between the atmosphere and the underlying surface consisting of a water body or a snow/ice surface, there are many applications that require a three-dimensional (3-D) RT treatment. For a clear (cloud- and aerosol-free) atmosphere, 3-D effects are related to the impact of the Earth's curvature on the radiation field as discussed briefly in Section 5.4.3. To include Earth curvature effects it may be sufficient to employ a _\"pseudo-spherical\"_ treatment, in which the direct solar beam illumination is treated in spherical geometry [110; 113], whereas _multiple scattering_ is treated in plane-parallel geometry. In fact, this pseudo-spherical approach has been implemented in many RT codes [114; 115]. Also, there is a large body of literature on 3-D RT modeling with applications to broken clouds, and readers interested in this topic may want to consult [116] or visit the web-site [http://i3 rc.gsfc.nasa.gov/](http://i3 rc.gsfc.nasa.gov/).  
Finally, 3-D RT modeling may also be important for analysis and interpretation of LiDAR data. The classical \"searchlight problem\" [117], which considers the propagation of a laser beam through a turbid medium, is relevant in this context. Long-range propagation of a LiDAR beam has been studied both theoretically and experimentally [118]. Monte Carlo simulations are well suited for such studies [119], and use of deterministic models such the _discrete-ordinate method_, briefly discussed in Section 4, have also been pursued to investigate this problem [120; 121].
## 3 Instrument and Experiments
### The Radiometer and Observation Mode
Since the sun moves through the sky within this time interval, in order to reduce errors, the antenna direction also follows the sun. We needed to recalculate the solar azimuth and elevation prior to changing the antenna pointing position in real-time, and the azimuth and elevation step angles are the relative angle between the antenna beam direction and the sun; this relative angle was fixed for each observation. Common scan parameters are listed in Table 2.  
When tuning the antenna beam to scan the sun in the azimuth, a rotation of the antenna in the azimuth with a constant elevation does not describe a great circle on the sky-sphere, there were some distortions [1]. There is an extreme case when the azimuth of the antenna is rotated at an elevation angle of 90\({}^{\circ}\), but the antenna beam pointing would not move on the sky. For the distortion and the calibration method, [PERSON] and [PERSON] (2016) described it in detail [1]. We calculated the distortion angle by using their method. If we had not calibrated the angle distortion, it would have caused a large bias in the beamwidth measurement.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
###### Abstract  
This paper proposes a potential enhancement of handover for the next-generation multi-tier cellular network, utilizing two fifth-generation (5G) enabling technologies: multi-access edge computing (MEC) and machine learning (ML). MEC and ML techniques are the primary enablers for enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). The subset of ML chosen for this research is deep learning (DL), as it is a adept at learning long-term dependencies. A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Subsequently, edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40-60% improvement in overall throughput. Although the proposed scheme may increase the number of handovers, it is effective in reducing the handover failure (HOF) and ping-pong rates by 30% and 86%, respectively, compared to the current 3 GPP scheme.  
multi-tier cellular handover; multi-access edge computing; deep learning; long short-term memory; heterogeneous network; 3 GPP +
Footnote †: journal:
## 3 Materials and Methods
### Feature Reduction
#### 3.1.1 Linear Regression  
The Linear Regression model takes the form \(y=\beta_{0}+\beta_{1}X_{1}+\beta_{2}x_{2}+\ldots+\beta_{n}X_{n}+\epsilon\) where \(y\) is the (predicted) dependent variable, each \(x_{i}\) is an independent, \(\beta_{0}\) is the y-intercept, \(\beta_{i}\) is the regression coefficient of the i-th independent variable, and \(\epsilon\) is the model error. Multiple Linear Regression calculates the regression coefficients that lead to the smallest model error and then calculates the \(t\)-statistic and \(p\)-value for each regression coefficient. The \(p\)-value for coefficients indicates whether these relationships are statistically significant. If the \(p\)-value is greater than the significance level (0.05) for an independent variable, it will be omitted and not considered in predicting the dependent variable. However, in order to draw a valid conclusion through hypothesis testing (e.g., \(t\)-test or ANOVA), errors after modelling should follow a normal distribution.
# In-Field Quantum-Protected Control-Based Key Distribution with a Lossy Urban Fiber Link
## 7 Methods  
### OTDR and Transmitrometry  
The following is the method that we use to identify points of local losses and their magnitude from a reflectogram, namely in Figure 2. First, the raw trace is smoothed using a moving average filter. Then, we calculate the gradient of this smoothed signal at every point. In regions exhibiting only natural losses, the gradient remains within predefined threshold values; any gradient excursion beyond these thresholds is taken as evidence of a local loss event. The magnitude of the local loss is obtained from the power difference between the start and end points of the identified local feature.  
Applying this method to our 4 km fiber link, we measure a total natural loss of approximately 0.635 dB (13.6%), in agreement with the expected Rayleigh scattering for this distance. At the beginning of the link, the first peak in the trace corresponds to the light reflection on the input connector, followed by three localized losses measured at 0.543 dB (11.8%), 0.010 dB (0.2%), and 0.439 dB (9.6%), respectively. At the opposite end, the final peak corresponds to the reflection on the isolator, while two additional connectors within [PERSON]'s trusted zone give loss values of 0.708 dB (15.0%) and 0.398 dB (8.8%). Consequently, the total event loss--determined as the sum of all event losses outside [PERSON]'s and [PERSON]'s laboratories--amounts to 0.993 dB (20.4%).  
During transmittometry, [PERSON] sends a signal with power \(P_{\text{A}}=100\) nW, modulated at a frequency 25 MHz. [PERSON] then receives this signal, performs a Fourier transform, and obtains its output spectral power \(P_{\text{B}}\). He communicates \(P_{\text{B}}\) back to [PERSON] through a classical channel. Because  
\[P_{\text{B}}=P_{\text{A}}G(1-r_{\text{E}})(1-r_{\text{L}})(1-r_{0}), \tag{2}\]  
where \(G\) is the amplification factor of [PERSON]'s EDFA and \(r_{\text{L}}\) is the loss on the connectors within [PERSON]'s and [PERSON]'s laboratories (corresponding to the blue and red regions in Figure 2), the users calculate \(r_{\text{E}}\) using  
\[r_{\text{E}}=1-\frac{P_{\text{B}}}{P_{\text{A}}G(1-r_{0})(1-r_{\text{L}})}. \tag{3}\]  
Our OTDR measurements indicate that, over a 12-hour period, \(r_{0}\) and \(r_{\text{L}}\) remain constant within 0.2%, while \(P_{\text{A}}\) is preserved through the control of [PERSON]'s laser. Detector's dark noise and the amplified spontaneous emission (ASE) from the EDFA may contribute to the uncertainty in the transmittometry measurements. To eliminate potential systematic errors arising from possible drifts in \(G\), \(r_{0}\), or \(r_{\text{L}}\), we perform OTDR every two hours and recalibrate the parameters in Equation (3). In principle, OTDR can be carried out more frequently if needed.
## 3 Modeling and Formulation for the Optimization Problem
### Constraints  
**Robust voltage range for PV installation:**  
Generally, all nodes' voltage constraint is expressed as Equation (5):  
\[V_{min}\leq V_{m}(t)\leq V_{max}. \tag{5}\]  
A stipulated voltage range of a low voltage distribution area under a pole transformer is set to be within 101 \(\pm\) 6 V in the Electricity Act of Japan. According to [20], the distribution voltage will fluctuate up to 6.5 V in a low voltage area under the high penetration of RES. In order to preserve robustness, thus a restriction has been put on the general voltage limit (95-107 V) as 101.5 V-105 V in this work. By configuring all pole transformers at a transformation ratio of 6600 V:105 V, the \(V_{min}\) and \(V_{max}\) are assigned 6380/101.5 V (0.967 pu) and 6600/105 V (1.0 pu), respectively.  
**Power flow constraints regarding reverse power flow and the power factor:**  
The active and reactive power flow constraints at an interconnected point are written as follows:  
\[P_{f}^{min}(t)\leq P_{f}(t)\leq P_{f}^{max}(t) \tag{6}\]  
\[Q_{f}^{min}\leq Q_{f}(t)\leq Q_{f}^{max} \tag{7}\]  
To compensate for reverse power flow in the static simulation, a flexible boundary is set as the active power flow. In order to avoid unnecessary use of control devices, the active power flow limit is shaped in such a way that a lower amount of control is necessary. Distribution losses also depend on  
\begin{table}
\begin{tabular}{l l} \hline Line impedance at each section & 0.04 + j0.04 pu \\ Rated capacity of PV node & 0.08 pu (400 kW) \\ Rated capacity of PV-interfaced inverter \(S_{PV}\) & 0.08 pu (400 kW) \\ Large BESS capacity \(C_{LB}\) & 20.0 pu (100 MWh) \\ Rated capacity of BESS-interfaced inverter \(S_{LB}\) & 1.0 pu (5 MW) \\ Rated Capacity of SVC \(S_{SVC}\) & 0.05 pu (250 kVar) \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the distribution system.  
power flow fluctuation; hence, the bandwidth of active power flow is set to \(\pm\)0.25 pu. It is essential to note that the bandwidth and shape should be set by considering the installation rate of PV amount and the load curve profile. On the other hand, the acceptable range of reactive power flow considers the power factor (PF) value, which depends on active power flow [42].  
The PF of the interconnection point is maintained from 0.85-1.0. Reactive power flow limits are determined from the active power flow value. Thus, the upper and lower reactive power limit are rewritten as follows:  
\[Q_{f}^{min}(t)=-P_{f}(t)*\tan(\cos^{-1}(0.85))\]  
\[Q_{f}^{max}(t)=P_{f}(t)*\tan(\cos^{-1}(0.85)).\]
# Examining the Optimal Use of WBG Devices in Induction Cookers
## 2 Power Semiconductor Selection for Series Resonant Half-Bridge Converter
### Si-Based Power Semiconductor Selection  
Modern induction cookers prefer Si-based IGBTs thanks to their low saturation voltages (\(V_{CE_{sat}}\)) and moderate switching capacities, which are limited due to tail current phenomena occurring during turn-off. As can be seen from the operating conditions explained in the previous sections, conduction loss is one of the most important factors affecting total losses. That is the reason IGBTs are preferred in this application. There are plenty of manufacturers that produce Si-based IGBTs for half-bridge converters. 600-650 V and 40-50 A IGBTs are mostly used in series resonant half-bridge converters. This section tries to find out the most suitable IGBT by comparing their performances with selected WBG devices.  
The datasheet parameters are used preliminarily to compare IGBTs. The first parameter is the saturation voltage. Figure 4a,b show saturation voltages of IGBTs for different junction temperatures. The second parameter is the diode forward voltage. Figure 5a,b show forward voltage drops for different junction temperatures. As mentioned above, conduction loss is the major contributor to total switch loss. Therefore, \(V_{CE_{sat}}\) is the most crucial parameter to determine IGBT.  
Figure 4: IGBT saturation voltages at different junction temperatures: (**a**) IGBT \(V_{CE_{sat}}\) voltages at 25 \({}^{\circ}\)C junction temperature; (**b**) IGBT \(V_{CE_{sat}}\) voltages at 175 \({}^{\circ}\)C junction temperature.  
When \(V_{CE_{sat}}-I_{C}\) characteristics are compared at different junction temperatures, Infineon's IHW50N65R6 and Onsemi's FGHL75T65 MQDT IGBTs are suitable for high-power applications. However, IHW50N65R6 is selected for comparison with the WBG devices since this IGBT is specifically produced for induction heating applications and is preferable both in terms of cost and technical specifications. When comparing \(V_{F}-I_{F}\) characteristics that are at different junction temperatures, IHW50N65R6 is not the best option to choose; however, diode conduction loss is a small portion of the total conduction losses, hence making IHW5065R6 the best selection for this application. IHW50N65R6 has Reverse Conduction IGBT (RC IGBT) technology, which integrates the body diode into the IGBT die so it only uses one die. This is advantageous in terms of manufacturing, cost, packaging, and EMI.  
Figure 5: Diode \(V_{F}\) voltages at different junction temperatures: (**a**) Diode \(V_{F}\) voltages at 25 \({}^{\circ}\)C junction temperature; (**b**) Diode \(V_{F}\) voltages at 175 \({}^{\circ}\)C junction temperature.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 2 Damage Detection Using the Quasi-static Displacement Influence Lines of Bridges
### Damage Detection Using the Null Space Method Based on Quasi-static Displacement Influence Lines
#### 2.2.4 Procedure of the Proposed Method  
Based on the above description of the proposed method, the algorithm framework considers three main states: the reference state of the bridge, the healthy state of the bridge and the unknown state (damaged or not) of the bridge. Usually, the first quasi-static load test is the reference state of the bridge and the null space of the Hankel matrix \(\mathbf{N}_{r}\), described by Equations (8) and (9), is determined by using the displacement ILs measured in this state. The healthy state is defined as the state in which we believe the bridge to be in good condition. For the healthy state, the metric of the damage feature \(\gamma_{c}\) and the damage detection threshold \(\lambda\) are obtained by using the measured displacement ILs and the generated \(\mathbf{N}_{r}\) under the reference state. For the unknown state, the metric of damage feature \(\gamma_{d}\) is calculated using the measured displacement ILs, after which the determination regarding the presence of damage is made by evaluating whether \(\gamma_{d}\) is larger than the threshold \(\lambda\). The detailed procedure is illustrated schematically in Figure 2.