esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 2 FSO Communication Channel Effects and Their Mitigation Techniques
### Terrestrial Expansion of FSOC  
One of the benefits of FSO is that it can be configured with the optical relaying networks (ORN) to expand the distance of the data link between source and destination, as illustrated in Figure 15, wherever the LoS path is unavailable. In the ORN configuration set-up, FSO can reach longer link distances, in the order of 10s/100s of kilometers terrestrially, as it is supported by many nodes to receive, process, and forward the optical signal. The ORN can also support dual and multi-hop to provide communication in computer networks [77]. In the ORN configuration, FSO has no issues with the non-LoS path between Tx and Rx points. The only requirement for an ORN-based installation of FSO communication is the need for an optical transceiver within each node. Thus, the ORN can be adapted to the geography of Earth's contours through spacing of the nodes, as required, for several kilometers of data link connectivity.  
Figure 14: The low-pass filter in the output of the PD to minimize the output of noise caused by shot and thermal noise.  
Figure 13: The results of turbulence effects into signal reception by PD, producing noise instead of signal.  
The ORN implementation in FSO can be accomplished with either amplify-forward (AF) or amplify-receive-relaying (AR) configurations, as in Figure 16[78; 79]. However, the implementation of an optical amplifier in the ORN also increases the noise figure (NF), amplified spontaneous emission (ASE), and background noise. These issues can be solved by applying the appropriate fixed or adaptive filters for the reception of the signal in the AR and AF configurations. An optical band-pass filter (OBPF) can be implemented before the amplification process, as in Figure 17[79; 80]. Thus, a received signal by an optical amplifier in the configuration of AF and AR can accept a signal with a minimum of NF, ASE interactions, and background noise. Another issue with an optical amplifier is the fade-out condition whenever the received signal power falls below a threshold level.  
In addition to avoiding this issue, the implementation of OBPF and OSF in the node of the ORN means that an optical amplifier produces a signal with high gain and with minimum of noise modulation.
## 4 Results and Discussion
### Counter Pressure and Exhaust Time Analysis of the Chamber  
Counter pressure and exhaust time analysis of the chamber was analyzed using AMESim (Advanced Modeling Environment for Simulation of Engineering System) [6].  
The pressurization time of the filling vessel was determined based on the following assumptions, taking into account the boundary values of the design variables that have the greatest effect.  
* Chamber volume: 523 cc
* Temperature: 293 K
* Supply pressure: 5 bar
* Orifice area: 19.625 mm\({}^{2}\)
* Chamber initial pressure: 1.013 bar  
The pressurization and exhaust time according to the supply pressure condition of the chamber was analyzed as shown in Table 3 through the analysis of the AMESim model. The model to calculate the filling/exhaust time of the chamber was configured as shown in Figure 10. Figures 11-13 show the result of the analysis of the pressurization and exhaust time within the CO\({}_{2}\) carbonate gas at ambient temperature. The pressurization and exhaust time was analyzed to be approximately 0.2 s and 0.4 s, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Average** & **Standard** & **Dispe** & **Minimum** & **Middle** & **Maximum** \\  & **Deviation** & **R**sion & **Value** & **Value** & **Value** \\ \hline
501.9 & 0.38 & 0.14 & 501.3 & 502.0 & 502.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Repeatability measurement value of filling valve.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Average** & **Standard** & **Dispe** & **Minimum** & **Middle** & **Maximum** \\  & **Deviation** & **R**sion & **Value** & **Value** & **Value** \\ \hline
501.9 & 0.38 & 0.14 & 501.3 & 502.0 & 502.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy measurement value of filling valve.  
Figure 9: Repeatability measurement value of filling valve.  
Figure 11: Counter pressure and exhaust time of the chamber (5 bar).  
Figure 12: Counter pressure and exhaust time of the chamber (4 bar).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Supply Pressure Condition**} & \multicolumn{2}{c}{**AMESim Analysis**} \\ \cline{2-3}  & **Counter Pressuring Time** & **Exhaust Time** \\
**[bar]** & **[s]** & **[s]** \\ \hline
5 & 0.25 & 0.5 \\ \hline
4 & 0.23 & 0.45 \\ \hline
3 & 0.21 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pressure variation and time analysis of the chamber.  
Figure 10: Modeling of the chamber counter pressure and exhaust time.  
According to the result of the experiment, the filling time of the chamber was induced as shown below:  
\[t=(0.92-1/P_{cs})\times 7.04\times V/S\times 10^{-5} \tag{1}\]  
Equation (1) can be deduced from the experiment formula and is a combination of linear and exponential modelling. The pressurization time in the chamber in the actual design was approximately 0.26 s and 0.5 s was spent for the exhaust time, confirming that the pressurization time and the exhaust time were similar to the design time and the result of the AMESim model [6].
## 2 Materials and Methods
### Energy Management Strategy
#### 2.4.2 Mode Stability Control  
The proposed real-time EMS aimed to minimize energy loss. By calculating the instantaneous power consumption of the battery in each working mode, the mode with the lowest energy consumption is selected as the current working mode. However, working conditions in the field are complex and changeable, which will cause the working mode to change frequently with the driving conditions. Excessive mode switching will cause the motor speed toosellate greatly in a short period of time, which will affect the ride comfort of the tractor. At the same time, the operation of the electromagnetic brake will increase energy consumption, so excessive mode switching should be avoided.  
The energy consumption between the current mode and the next mode is sometimes very close, and the EMS still chooses the working mode with less power consumption according to the minimum energy consumption principle, which means that the energy saved by mode switching is negligible. To solve this problem, a penalty factor was introduced into the optimization objective function to reduce unnecessary mode switching, which was only allowed in the case of obvious energy saving. To increase the mode switching threshold, the penalty factor was only applied to the non-working mode, which means that the power of the current working mode was used as the benchmark without any penalty. At the same time, the power of other modes will add extra costs, so the system may keep the current mode working when comparing the energy consumption of all the modes.  
The penalty factor was defined as a step function of the power consumption variation between different operating modes, expressed as follows.  
\[\lambda=\left\{\begin{array}{ll}0.8,&\Delta P\leq 50\\ 1,&\Delta P>50\end{array}\right.,\qquad\qquad\Delta P=\left|P_{e\_current}-P_{e\_ other}\right| \tag{25}\]  
where \(\lambda\) is the penalty factor; \(P_{e\_current}\) and \(P_{e\_other}\) denote the instantaneous energy consumption of the battery in the current working mode and non-working modes, respectively; and \(\Delta P\) is the power consumption difference between the current working mode and the other two non-working modes.  
The new objective function after introducing the penalty factor can be expressed as  
\[P_{e\_other}=\lambda^{-sign(P_{e})}P_{e} \tag{26}\]  
The simulation model for the DMET equipped with a DMCPS was implemented in MATLAB/Simulink (R2018a), as shown in Figure 6. This simulation model was mainly composed of the working cycle model, driver model, EMS model, battery model, transmission model, and the tractor dynamic model.  
Figure 6: Simulation model for a DMET equipped with the DMCPS.
## 4 Use Case
### Aeroelastic Analysis with Flutter Verification  
The mid-fidelity tool ASWING was used to model aircraft aeroelasticity [24; 25] ASWING couples interconnected nonlinear (specifically, Bernoulli-Euler) beam models with a general extended lifting line approach. The ASWING software enables the calculation of aircraft deflections, axial strains and shear stresses, aircraft aerodynamic properties, and aircraft stability derivatives and eigenvalues based on input data, including geometric, structural, and aerodynamic parameters airfoil sections along the aircraft wing and other surfaces. During analysis, aircraft stability derivatives and eigenvalues were used because the different outputs were identified using other methods. The model used as the input to ASWING was calculated to form the parametric CAD model (Figure 20); material data assumed for analysis and load distribution were identified using XFLR5 [26]. The data were integrated into excel file and subsequently transferred to a proper format which was used as the input to ASWING (Figure 21).  
The flutter speed and actual flutter frequency are indicated by one of the eigenvalues crossing over from left to right of the imaginary axis. Flight-dynamic instabilities (e.g., spiral mode) will also have eigenvalues on the right side of the imaginary axis, but these will typically be at a near-zero frequency and will have completely different eigenmodes.  
The results of the initial analyses indicated the risk of flutter at a speed of 20 m/s, (Figure 22), which is a speed exceeding the operating speed. A sensitivity analysis was performed during the flutter analysis. The influence of the position of the wing mass was  
Figure 21: Data processing sequence for aeroelastic analysis using ASWING.  
Figure 20: Geometric model used for ASWING analysis.  
investigated. The first simplified analysis assumed that the solar panels are positioned proportionally to the wing area. The model was then refined by adding mass points symbolizing solar panels at a distance of 0.4 of the wing chord from the leading edge. In the last iteration of calculations, the influence of shifting the mass of solar panels by 80 (mm) towards the leading edge was investigated. Sensitivity analysis showed the possibility of turning the critical flutter velocity in these cases to 25 and 35 m/s, respectively.
# A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Turkiye
## 3 Ground-Motion Prediction Models (GMPMs)
### Comparisons  
Figure 12 shows the comparison between the observed data and the predictions proposed in this study for two magnitudes (\(M_{\rm{W}}\) 5.5 and 7.2, which are close to the lower and upper limits of the dataset) and strike-slip faulting; the comparison is made for PGA, PGV, and PSA (at \(T\) = 0.2 s and 1.0 s). This comparison is made for \(V_{530}\) = 760 m/s, which represents rock sites. A visual inspection of the plots shows that our prediction curves are generally in agreement with the observed data.  
The median 5%-damped response spectra of our proposed model are compared with some of the most commonly used local and global GMPMs, which are based on similar parameters and developed for shallow crustal earthquakes (Figure 13). In the comparisons, \(M_{\rm{W}}\) 6.0 and \(R_{\rm{JB}}\) 100 km, and \(M_{\rm{W}}\) 7.2 and \(R_{\rm{JB}}\) 15 km are used for magnitude and distance attenuation curve combinations, respectively. In both cases, a strike-slip fault scenario is considered for \(V_{530}\) = 760 m/s.  
Figure 11: Magnitude-dependent heteroscedasticity between events for (**a**) \(T\) = 0.2 s, (**b**) \(T\) = 1.0 s, (**c**) \(T\) = 0.5 s, and (**d**) \(T\) = 3.0 s.  
The selected GMPMs for this comparison were partially developed using the NGA-West2 database (ASK14; BBSA14; CY14; CB14; I14), while the others were developed using the Pan-European datasets (ASB14; Beal14; KAAH15; Leal19; Keal20; B21; IS23). While California and Taiwan earthquakes dominate in the NGA-West2 models, the records from Turkiye, Greece, Italy, and Iran are predominant in the Pan-European predictive models [4]. Figure 13 shows the comparisons with five NGA-West2 and seven European models for moderate-magnitude (\(M_{W}\) = 6.0) and \(R_{JB}\) 100 km and large-magnitude (\(M_{W}\) = 7.2) and \(R_{JB}\) 15 km scenarios. Overall, the predictions of the model we recommended are close to those of the other selected GMPMs. However, for the large-magnitude (\(M_{W}\) = 7.2) scenario, the proposed model gives somewhat lower estimates in the 0.2 to 1.0 second period range compared to the selected models. Similar prediction values are also observed in the B21 local model developed by [PERSON] et al. [9] using earthquake records from Greece. Here, the variation in the compared prediction models may be influenced by the quantity and quality of the databases used, as well as by local seismotectonic characteristics.  
Figure 12: Comparisons of observations for PGA, PGV (top row), and PSA at \(T\) = 0.2 s and \(T\) = 1.0 s (bottom row) for \(M_{W}\) 5.5 and 7.2 events from strike-slip faulting at rock sites (\(V_{S30}\) = 760 m/s), as estimated by the predictive models used in this study.
# The Stability of New Single-Layer Combined Lattice Shell Based on Aluminum Alloy Honeycomb Panels
## 1 Introduction
Using piecewise first-order beam theory, they analyzed the overall buckling of the honeycomb structure. [PERSON], [PERSON] et al. [11; 12] studied the instability of a honeycomb core with a uniform wall thickness under uni- and bidirectional in-plane compression and out-of-plane compression. Using experimental studies and numerical simulations, [PERSON], [PERSON] et al. [25; 26] studied the macroscopic deformation and plastic instability of a honeycomb structure with uniform wall thicknesses under unidirectional in-plane compression. It is obvious that the existing studies focus on the aluminum alloy lattice shell structure or the honeycomb panel itself. Almost no studies of a combined aluminum alloy lattice shell structure, in which an aluminum alloy honeycomb panel reinforces the lattice shell have been conducted. There are some topics that should be addressed, such as the stability of these relatively lightweight and soft combined lattice shells and the difference between its unstable failure mode and that of ordinary single layer lattice shells that lack aluminum alloy. In this paper, the authors conducted experimental  
Figure 1: Schematic diagram of a large-span single-layer aluminum alloy honeycomb panel combined lattice shell: **(a)** The overall structure; **(b)** A local area, magnified. studies on the stability of this new structure to defects to investigate the structure's destabilization failure mechanism and the effect of geometric imperfections on stability. The authors also verified the reliability of the finite element method. The results of this paper could provide a theoretical basis for making and revising the design codes for this new combined lattice shell structure.
## 5 Experiment and Result Analysis
### The Experimental Results
The experimental results are shown in Table 2. The accuracy rate and recall rate of the original Relu activation function for fault phase selection reach the lowest 95%. The accuracy, precision and recall of the PRelu activation function and F1 value reached 97.75%, 97.76%, 97.75% and 97.74%, respectively. The Mish activation function in GTCN shows that the accuracy, precision, and recall of fault arc phase selection are 98.63%, while the F1 value drops slightly to 98.26%. The experimental results show that GTCN will appear negative during the propagation process, so the failure of Relu function reduces the accuracy of phase selection classification. In the process of GTCN iteration, the Mish activation function can better capture category features than PRelu activation function in the feature classification task. Therefore, the Mish activation function is used in GTCN for low-voltage three-phase fault arc phase selection. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activation Function** & **Accuracy** & **Precise** & **Recall Rate** & **F1 Value** \\ \hline Relu & 95.00\% & 95.22\% & 95.00\% & 95.02\% \\ PRelu & 97.75\% & 97.76\% & 97.75\% & 97.74\% \\ Mish & 98.63\% & 98.63\% & 98.63\% & 98.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fault phase selection accuracy of GTCN under each activation function. Figure 11: Confusion matrix of GTCN three-phase fault arc detection.
## 11 Current Studies and Future Directions
### Research Directions
Developing novel NFV approaches for future smart cities able to meet the required network scalability and cost efficiencies. This includes implementing continuous integration and deployment pipelines for automated testing, integration, and deployment of virtualized functions. The proposed NFV approaches should ensure compatibility with upcoming technologies and establish interoperability between various virtualized functions by referring to standardized interfaces and protocols. Also, this direction includes developing automated testing frameworks to ensure the reliability and robustness of virtualized functions. * Innovating novel frameworks for integrating all smart city applications over a single platform. Innovating novel frameworks for integrating all smart city applications over a single platform. This includes designing the integration framework with a plugin architecture, allowing easy integration of new applications and services. * Ways to assist dense deployment and ultra-reliable latency communications required by most smart city applications  
This direction includes implementing small cell networks, including picocells and femtocells, to enhance capacity in densely populated areas. Thus, research in improving the performance of heterogeneous network (HetNet) architecture will assist the evolution of future smart cities. Furthermore, implementing advanced beamforming techniques to increase coverage and reduce interference in densely populated regions is recommended.
# A Low Phase Noise Crystal Oscillator with a Fast Start-Up Bandgap Reference for WLAN Applications
## 2 Circuit Composition of Low Phase Noise Crystal Oscillator
### Design of Low Phase Noise Crystal Oscillator
#### 2.2.3 Design of Broadband LDO
Figure 14 depicts a comparison of the phase noise of a crystal oscillator with and without the proposed RC low-pass filter. The plot reveals that the inclusion of the filter leads to a significant reduction of approximately 11.21 dBc/Hz at 100 kHz offset in the phase noise of the crystal oscillator. The results demonstrate the effectiveness of the proposed approach in minimizing the phase noise of the oscillator. Figure 13: (**a**) RC low-pass filter, (**b**) logarithmic plot of RC low-pass filter.
## 1 Introduction
[16] proposed a thermal error prediction method based on the digital twin's long and short-term memory (DT-LSTM) and developed a system for the thermal error prediction problem of thermal characterisation of the spindle of CNC machine tools. The Particle Swarm Optimisation (PSO) algorithm was used with the LSTM for thermal error prediction and fusion, which improves the accuracy and robustness of thermal error prediction. [PERSON] [17] proposed a high-fidelity digital twin model of aircraft structure, integrating the calculation of structural deflection and temperature response to flight conditions to predict the integrity and lifetime of aircraft structure. [PERSON] et al. [18] developed a digital twin-driven intelligent health management method for monitoring and evaluating the gear surface degradation process, assessing the surface wear of gearboxes, revealing the gear wear propagation characteristics, and achieving an accurate prediction of the RUL of gearboxes. [PERSON], [PERSON] et al. [19] address the problem of wind speed prediction and, based on the digital twin technology, propose a hybrid time series prediction model based on the integration of the empirical modal decomposition (EEMD), the long-short-term memory (LSTM) neural network, and the Bayesian optimisation (BO). The forecasting model is comprehensively compared with methods including persistence models, ARIMA, LSTM neural networks, BO-LSTM neural networks and EEMD-LSTM neural networks through case studies. At present, experts and scholars have begun to explore the application of digital twin technology in digital processing. Still, the research content is mainly focused on the level of model definition, system analysis, etc., which is difficult to land and promote. In this regard, this paper establishes the digital twin system architecture for the processing of high-precision optical components, constructs a high-fidelity digital twin model of a centring lathe, carries out a technical exploration of the development process of the twin model and twin data based on Unity3d, and proposes the method of VMD-GRU feature fusion to develop the digital twin intelligent service to monitor the critical components of the centring lathe intelligently.
# Influences of Temperature and Substrate Particle Content on Granular Sludge Bed Anaerobic Digestion
## 1 Introduction  
Up-flow anaerobic sludge bed reactors (UASBs) are normally used for the treatment of substrates with a low level of suspended solids such as industrial wastewater. Their attributes, such as low cost, high efficiency, and low footprint, also make them attractive for the treatment of particle-rich substrates such as sludge and manure slurries that are available in large quantities worldwide [1]. Solid accumulation and granular sludge floatation leading to losses of biomass are known challenges in the treatment of wastewater with a high level of suspended solids [2], but high anaerobic digestion (AD) efficiency was also reported for particle-rich substrates [3]. Traditionally, continuous stirred tank reactors (CSTR) are used to treat particle-rich substrates such as manure slurries but, due to drawbacks such as long hydraulic retention time (HRT) and large reactor volume requirement, high-rate AD reactors are becoming more popular. High-rate reactors are characterized by long sludge retention time (SRT), short HRT, and efficient degradation of organic substances. Long SRT is achieved because of microbial aggregation phenomena forming a granular sludge bed that stays in reactors for a relatively long time. The use of high-rate reactors, however, has its own drawbacks with regard to particle-rich substrates. Accumulation of solid particles in the sludge bed is considered a problem [4]. Moreover, the interaction of solid particles with microorganisms in the sludge bed, as well as the extent of the contribution of solid particulates to biogas production, is not clear. Due to these reasons, high-rate reactors are mostly used for the treatment of substrates with low solid content, mostly industrial wastes.  
Nevertheless, [PERSON] et al. [3] showed that particle-rich substrates (swine manure slurry) could also be treated using high-rate lab-scale UASB reactors. Solid particulates are thought to undergo disintegration and hydrolysis before the rest of the anaerobic digestion process takes place. Some researchers treat disintegration and hydrolysis as a single step, while others do not. In this paper, we treat them as distinct steps. The rate-limiting step in AD of particle-rich substrates is usually disintegration/hydrolysis. Solid particulates disintegrate relatively slowly and tend to accumulate, making it challenging to adopt high-rate reactors for particle-rich substrates. The aim of this study is to contribute to the development of high-rate reactors for particle-rich substrates. For this, it is important to understand how the suspended solid content influences granular sludge bed AD by identifying disintegration and hydrolysis patterns of particulates and their dependence on temperature.