esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Safety Analysis and Regeneration Study of La-EPRC Particles  
#### 3.3.1 Desorption Experiments  
++  
The predominant factor contributing to heightened material desorption is likely the reaction of sulfate ions with phosphate precipitates. This reaction leads to the conversion of the precipitates into sulfate precipitates, resulting in the dissolution of phosphates. In practical engineering applications, a low-concentration sulfuric acid solution can be employed to facilitate the regeneration of La-EPRC particles, allowing for the recovery of phosphorus elements and the sustainable reuse of resources.
# Advances in High-Speed, High-Power Photodiodes: From Fundamentals to Applications
###### Abstract  
High-speed, high-power photodiodes play a key role in wireless communication systems for the generation of millimeter wave (MMW) and terahertz (THz) waves based on photonics-based techniques. Uni-traveling-photodiode (UTC-PD) is an excellent candidate, not only meeting the above-mentioned requirements of broadband (3 GHz\(\sim\)1 THz) and high-frequency operation, but also exhibiting the high output power over mW-level at the 300 GHz band. This paper reviews the fundamentals of high-speed, high-power photodiodes, mirror-reflected photodiodes, microstructure photodiodes, photodiode-integrated devices, the related equivalent circuits, and design considerations. Those characteristics of photodiodes and the related photonic-based devices are analyzed and reviewed with comparisons in detail, which provides a new path for these devices with applications in short-range wireless communications in 6G and beyond.  
high-speed photodiodes; high-power photodiodes; millimeter-wave; terahertz wave; photodiode-integrated devices; wireless communication +
Footnote †: journal: Applied  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 4 Numerical Results
### The Impact of Decision Trees on Performance in Random Forests Algorithm  
The final prediction result of the random forest is obtained by combining the results of all decision trees. Each decision tree outputs a classification label, and the model determines the final classification result by majority voting on the classification labels output by all  
Figure 5: Classification of modulations for FSO link in rainy weather. (**a**) Results of training set. (**b**) Results of test set.  
Figure 6: Prediction of modulations for FSO link in foggy weather. (**a**) Results of training set. (**b**) Results of test set.  
Figure 7: Classification of modulations for RF link in rainy weather. (**a**) Results of training set. (**b**) Results of test set.  
decision trees. In Figure 8, the relationship between the number of decision trees and accuracy is displayed. The experiment simulated the results from 50 trees to 1000 trees with a step of 50, each tree being run 100 times. In this experiment, a total of 500 decision trees were used, corresponding to an accuracy of 0.997 for FSO links in foggy conditions. In rainy conditions, the accuracy of the RF link is 0.989, and the FSO link accuracy is 0.975.  
In each decision tree, the feature selection at each node is random, and not all features are used for training. This random feature selection makes the rules of the decision tree more randomized, helping to prevent overfitting and improving the accuracy and stability of the data.
# Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis
###### Abstract
Because of the high number of scientific publications and their rapid expansion, the amount of research is now immense, and its content is so complicated that traditional ways of interpreting the smart city, such as literature reviews, content analysis, and case studies [13], may not be adequate to conduct quantitative analyses, analyze patterns, or make judgments without the assistance of computers. With so many scientific papers available, computer-assisted data summarization is essential [14]. [PERSON] and [PERSON] [15] suggest thematic analysis as a foundational method for qualitative analysis. They describe thematic analysis as a systematic approach for identifying, analyzing, and reporting recurring patterns or themes inherent in data, facilitating a comprehensive textual exposition. They argue that thematic analysis can extend beyond this, identifying diverse aspects within the domain under investigation. [PERSON] et al. [11] propose topic modeling as a suitable automated technique for the initial stages of thematic analysis, given that the \"themes\" of thematic analysis align conceptually with the \"topics\" in topic modeling. Topic modeling stands as currently the most widely used text-mining technique [16] to explore the interrelation between the gathered data and documents, validating models, condensing the collection of texts, and steering the investigation into its contents [17]. Topic modeling involves the task of uncovering the inherent thematic structure within a text corpus, often resulting in a presentation of the most frequent terms associated with each identified topic [18]. It eliminates the need for researchers to formulate coding sheets prior to analysis, offering a time-efficient method for conducting exploratory assessments on extensive paper collections [19]. Although topic modeling is particularly well-suited for big data, it lacks the subtlety inherent to human interpretation. However, [PERSON] et al. [11] argue that both approaches share a common objective: identify underlying themes hidden within the data. This study aims to investigate the underlying structure of the smart city paradigm by employing topic modeling and thematic analysis. It contributes to our understanding of the potential knowledge landscape, particularly within the field of smart city development, by identifying significant themes as well as subthemes. It also facilitates the establishment of a semi-automated novel methodology for understanding the domain. Additionally, smart city leaders can utilize the derived discoveries of the analysis to effectively oversee the smart city structure and tackle challenges within smart city environments. Moreover,we describe a semi-automated methodology for the thematic analysis of large corpora by utilizing a topic modeling technique and further qualitative analysis.
## 2 Materials and Methods
### Hampson-Linde System Thermodynamics Model  
#### 2.6.1 Congruence to Ohm's Thermal Law  
The heat transfer prototypes were obtained by translating several variables from the electric domain to the thermal domain, as shown in Table 1. In addition, all voltage sources model fixed temperature points while current sources describe dissipated power.  
The relation between the last two variables from Table 1 and the mass of a certain substance is shown in Equation (8).  
\[C_{th}=m\cdot c, \tag{8}\]  
giving the equivalent thermal capacitance, as translated from the electrical domain.  
As for the ideal current sources' translation, the international standardized measurement unit is the Watt, also expressed as Joule per second; such a component was used as a model for the Joule-Thomson valve. Thus, there was a correlation between Ohm's Law for thermal circuits and the amount of energy released during decompression, as shown below using Equation (9). This resulted in several limitations, which will be discussed further in Section 4.  
\[P_{d}=\frac{Q_{gen}}{t}=\dot{Q}_{gen}=\dot{V}\cdot\frac{\rho^{2} \mathit{compensation}}{\rho_{table}(p,T)}\cdot c_{N_{2}}\cdot p\cdot\mu_{J-T}(p,T), \tag{9}\]  
with the meaning of the corresponding terms:  
* \(P_{d}\)--Dissipated power corresponding to Ohm's Law for thermal circuits;
* \(Q_{gen}\)--Stored heat (amount of transferred thermal energy);  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Variable** & **Description** & **Measurement Unit** \\ \hline Q & Heat transfer rate & J (Joule) \\ T & Temperature & K (Kelvin degree) \\ R\({}_{\text{th}}\) & Thermal resistance & K/W (Kelvin per watt) \\ C\({}_{\text{th}}\) & Thermal capacitance & J/K (Joule per Kelvin) \\ c & Specific heat capacity & J/kgK (Joule per kilogram Kelvin) 1 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Corresponding to the international metric system.  
\end{table}
Table 1: Electrical to thermal domain translation.  
\(\dot{Q}_{gen}\)--Rate of heat transfer;
* \(\dot{V}\)--Volumetric flow of compressor;
* \(\rho^{2}\)_compensation_--Square of compensation density is used to predict the dynamics more accurately upon heuristic optimization;
* \(\rho_{table}(p,T)\)--Nitrogen density as a function of pressure and current temperature;
* \(c_{N_{2}}\)--Specific heat capacity of nitrogen;
* \(p\)--Input pressure;
* \(\mu_{J-T}(p,T)\)--Joule-Thomson coefficient as a function of pressure and temperature;  
From this Equation, except for the compressor's volumetric flow, which is measured in liters per hour, and pressure variables expressed in bars, all other variables use international system measurement units. The dissipated power is measured in watts only if one translates the volumetric flow in cubic meters per second.
## 5 Computational Experiments
### Computational Experiments for Static Problems
#### 5.3.2 Experimental Results  
Figures 10 and 11 show the time required for transfer and the computation time required for route planning, respectively, for a comparison of route planning by each method when the number of AGVs is increased. In the route planning by CBS, the computation time became too long when the number of AGVs took larger than 10, and the route could not be found. Figure 10 shows that ADRPM and CBS approaches have the shortest planned path  
Figure 9: Routing result for two AGVs derived by the proposed method at each iteration. \((1)\), \((2)\), \(\cdots\), \((t)\) indicates the route plan pf AGV1, and \(1\), \(2\), \(\cdots\), \(t\) indicates the route plan pf AGV2.  
lengths, and the proposed method has longer path lengths than them and also shorter than the method using only Q-learning. When the number of AGVs is small (about 1-3 ), there is no difference in the length of the paths regardless of which method is used. However, as the number of AGVs increases, the difference in the lengths of the paths planned by each method increases.  
Figure 11 shows that the time required for route planning increases with the number of AGVs. For methods other than CBS, the computation time increases linearly with the number of AGVs. In addition, route planning using only Q-learning tended to take longer computation time overall compared to ADRPM and the proposed method. Comparing ADRPM and the proposed method, the change in computation time with the number of AGVs was almost identical, and the proposed method was able to generate routes faster, especially when the number of AGVs was less than 5. The above numerical experiments show the effectiveness of the proposed method in a static problem setting when the number of AGVs is small.
## 4 The Quadrature Chaos Shift Keying Communication System's Performance Analysis
### Result Analysis  
Figure 12 shows the BER graphs for both synchronization signal scenarios within the communication system. The blue curve reflects the results achieved with \(V_{C1}\) as a synchronization signal, whereas the red curve represents the results obtained with \(V_{C2}\) as a synchronization signal.  
When \(V_{C1}\) was utilized as the synchronization signal, a BER of 10% was achieved for an \(E_{b}/N_{0}\) of 8 dB; the curve demonstrates an almost linear descent with 2.4 powers of ten per decade for higher signal-to-noise ratio values. The red curve, on the other hand, depicts the noise immunity for the communication system that uses \(V_{C2}\) synchronization. The graph demonstrates a linear drop of 0.5 powers of ten per decade within the \(E_{b}/N_{0}\) ratio range of 20 dB to 30 dB. The curve's drop steepens to 1.8 powers of ten each decade between 30 dB and 40 dB.  
The cross-correlation and spectrum properties of chaotic oscillator signals explain the variances in the obtained BER curves. The cross-correlation coefficient between the signals comprising the information-carrying signal is one of the components contributing to these results. The cross-correlation coefficient between \(V_{C2}\) and \(V_{RL}\) is 0.11, as indicated in Table 1, resulting in greater noise immunity compared to the second example. Increasing the cross-correlation coefficient's absolute value reduces system noise immunity. Understanding the  
Figure 11: QCSK communication system hardware measurement block-scheme, where \(b(t)\) is information bit stream, \(X^{\prime}\) and \(Y^{\prime}\) are state variables generated in response chaos oscillator.  
cross-correlation coefficient between signals that create information is critical, as it is the foundation of information detection.  
The proposed QCSK communication system shows superior noise immunity in the AWGN channel compared to the FM-CSK [59], which achieves BER of 10% for the \(E_{b}/N_{0}\) of 17 dB. Moreover, the QCSK communication system demonstrates better noise immunity than the Time reversal-differential chaos shift keying communication system (TR-DCSK) [63], achieving a BER of 10% at an Eb /N0 of 9 dB. In addition, the QCSK communication system has slightly better noise immunity than the QCPSK [64] communication system, which achieves BER of 10% for the \(E_{b}/N_{0}\) of 8.5 dB. Furthermore, the QCSK data transmission system can achieve higher data transmission rates (up to 166.7 Kbps) compared to FM-CSK (up to 9 kbps) and QCPSK (up to 3 kbps), which shows the proposed system's suitability for IoT applications.
# Accelerate Incremental TSP Algorithms on Time Evolving Graphs with Partitioning Methods
## 2 Background and Related Work
### Time Evolving Graph  
Processing time-evolving graphs is challenging due to the changing topology of the graphs. These challenges are discussed in a survey [21; 22] where computation techniques on TEG are studied in three domains i.e., graph analytics, graph computing frameworks, and graph algorithms. As the changes in each graph happen frequently, not only the time taken to compute the graph will be expensive but it will even impact the performance of the underlying computing framework. [PERSON] et al. [23] have addressed this  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **Changes, Allowed** & **Distributed** & **Incremental** & **Partitioning** & **Result** & **Boundedness** \\ \hline [PERSON] et al. [16] & Insertions, & X & ✓ & X & Optimal & Unbounded \\  & Deletions & & & & & \\ \hline [PERSON] et al. [17] & Insertions, & ✓ & ✓ & X & Optimal & Unbounded \\  & Deletions & & & & & \\ \hline [PERSON] et al. [18] & Insertions; & X & ✓ & ✓ & Optimal & Locally bounded \\  & Deletions & & & & & \\ \hline [PERSON] et al. [19] & Insertions; & X & X & X & Approx. & Locally bounded \\  & Deletions & & & & & \\ \hline Anagnostopoulos & Edge- & X & X & X & Approx. & Relatively bounded \\  & Swapping & & & & & \\ \hline Anagnostopoulos & Edge- & X & X & X & Approx. & Relatively bounded \\  & Swapping & & & & & \\ \hline [PERSON] et al. [14] & Edge- & ✓ & ✓ & X & Optimal & Bounded (I-TSP) \\ (Previous work 1) & weight & & & & & \\ \hline Current work & Edge- & ✓ & ✓ & ✓ & Approx. & Unbounded \\  & weight & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparing state-of-the-art algorithms on the basis of approach used in implementation for TEG with this research work. The approach used is marked by ✓ and approach not used is marked by X.  
problem by proposing a partitioning algorithm for dynamic graphs in cluster computing frameworks. They have considered communication cost, the number of intra-node edges, and load distribution while determining the placement of the partitioned graph. However, it gets more challenging to determine a placement for newly arriving vertices in graph partitions. FENNEL [24] is a k-partitioning algorithm that takes decisions regarding the placement of newly arriving vertices in the graph. It uses a heuristic algorithm to place the new vertex in a partition that has either the largest number of neighbors or non-neighbors.
# Design of Novel Fiber Optical Flexible Routing System
## 1 Introduction
DWDM, ROADM, OXC, and optical interleavers can be used to automatically configure all signal wavelengths on ROADM nodes to increase backbone bandwidth or provide flexible bandwidth allocation techniques. DWDM technology can effectively expand the transmission capacity of a single optical fiber. ROADM and OXC can dynamically adjust or allocate network resources to meet the needs of dynamic networks. Because of their ability to dynamically adjust, we can avoid changing the network architecture in the event of future bandwidth shortages. In DWDM optical networks, in order to upload/download the required signal wavelengths or to exchange the required signal wavelengths, this must be achieved by some common passive optical components, such as fiber Bragg grating (FBG), arrayed waveguide grating (AWG) [17; 18; 19], optical switches, optical circulators, and so on [20; 21]. AWG is an important element commonly used to realize ROADM. In 2018, [PERSON] proposed a bi-directional ROADM based on a pair of N \(\times\) N cyclic AWGs and tunable FBGs over N wavelength channel configurations [22]. [PERSON] et al., used several optical switches and a Mach-Zehnder interferometer to achieve the aim of adding or dropping multiple optical signal wavelengths at the same time [23]. Common OXC systems are mostly implemented using passive optical fiber components [24]. As far as we know, some published literature uses optical circulators and tunable FBGs to build OXC systems [25; 26]. This architecture successfully completed the purpose of multi-signal wavelength switching. However, if the OXC function is performed using FBG, when the signal wavelength of the network increases, the number of FBGs must be increased, so the flexibility is insufficient. In recent years, the collocation of OXC and ROADM systems has been widely discussed [27; 28; 29]. It can be seen that these two systems are very important under the all-fiber network architecture. In DWDM network, the combination of multi-functional systems are very important. Especially in next generation high-speed optical networks, the distribution or integration of optical signal wavelengths is an important task. In this study, we implemented a routing device with three different functionalities in the same router configuration, namely ROADM, OXC and a bidirectional interleaver. The proposed router can be used as a DWDM metropolitan area network, inter-domain switching medium (OXC function), and can help connect new and old systems (interleaver function) to increase network flexibility. Furthermore, we used optical switches to select the desired channel in this study, which is different from previous studies using the channels required for FBG modulation [3]. In addition to greatly improving the response speed of optical signal exchange, the system's homodyne crosstalk will also increase by about 30 dB when the ROADM is executed, which improves the performance of the system.
# Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System
## 2 Design Variables and Objectives of Planar Circular Coil
Then the coil structure can be represented by the inner diameter (\(D_{in}\)), the turning pitch (\(p\)), and the number of turns (\(N\)). Constrained by the mechanical structure in Figure 2, the coils' maximum outer diameter (\(D_{out}\)) is 300 mm. If the outside diameter of the coils is greater than 300 mm, remove it from the coils group in the simulation calculation to guarantee that the optimal parameters may be reached within a restricted size. To summarize, the design objectives for the WPT system are \(K\) and \(\eta\), and corresponding design variables are the inverter frequency \(f\), the inner diameter of coils \(D_{in}\), the turn pitch \(p\), and the number of turns \(N\). By combining the actual conditions, some of the variables' ranges are removed. The final design variables and ranges are presented in Table 1 below.
# Research on Space Maglev Vibration Isolation Control System Modeling and Simulation
## 2. Maglev Vibration Isolation System Design  
### Overall Scheme Design  
The attitude and orbit control system of a spacecraft consists of three parts: measurement component, control component, and execution component. The control component is responsible for communicating with each individual machine and calculating the attitude and orbit information of the spacecraft, as well as control instructions. The maglev system belongs to the executing components, including maglev actuators, controllers, displacement sensors, and a set of two module connection release devices. The core of the maglev system is mainly reflected in non-contact, including non-contact position sensors and non-contact actuators. The maglev system uses non-contact sensors to measure the relative position and attitude between the payload module and the support module. The scheme adopts a master-slave collaborative control strategy with active control of the payload module and passive control of the support module following the payload module. The control strategy is shown in Figure 1.  
The maglev system adopts a configuration of '4 bar + bidirectional 4-channel actuators + primary and backup redundancy of control system'. This system adopts a four-bar actuator configuration scheme, and the actuator coil adopts a bidirectional four-channel structure, which has the function of simultaneously outputting bidirectional force values and high precision. The controller adopts a one-to-four architecture (1 control board  
Figure 1. The master-slave collaborative control strategy.  
+ 4 power boards), each power board has four independent drive outputs, which can meet the bi-directional force output of the actuator through four independent controls. At the same time, the high-force current and high-precision current in each direction can be synchronously output. The scheme configuration is shown in Figure 2. There are four boxes in the figure, the green box represents the controller, the purple box represents control board, the blue box represents power board, and the red box represents actuator.