esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Plane Monolithic Integration of Scaled III-V Photonic Devices
###### Abstract  
It is a long-standing goal to leverage silicon photonics through the combination of a low-cost advanced silicon platform with III-V-based active gain material. The monolithic integration of the III-V material is ultimately desirable for scalable integrated circuits but inherently challenging due to the large lattice and thermal mismatch with Si. Here, we briefly review different approaches to monolithic III-V integration while focusing on discussing the results achieved using an integration technique called template-assisted selective epitaxy (TASE), which provides some unique opportunities compared to existing state-of-the-art approaches. This method relies on the selective replacement of a prepatterned silicon structure with III-V material and thereby achieves the self-aligned in-plane monolithic integration of III-Vs on silicon. In our group, we have realized several embodiments of TASE for different applications; here, we will focus specifically on in-plane integrated photonic structures due to the ease with which these can be coupled to SOI waveguides and the inherent in-plane doping orientation, which is beneficial to waveguide-coupled architectures. In particular, we will discuss light emitters based on hybrid III-V/Si photonic crystal structures and high-speed InGaAs detectors, both covering the entire telecom wavelength spectral range. This opens a new path towards the realization of fully integrated, densely packed, and scalable photonic integrated circuits.  
integrated photonics; silicon-on-insulator; III-V on silicon; hybrid photonic crystals +
Footnote †: journal: selected topics in materials science
## 3 Results and Discussion
### Splitting the Data into Calibration and Validation Sets  
In order to ensure the comparability of the quantification models on the basis of various approaches, the data were split into calibration and test sets. This process was performed in three steps. First, the samples of steel/ore were sorted by the increase in concentration of the element of interest. Second, the two smallest and the two biggest concentrations of the element in steel/ore were taken to the calibration set (except for the Ti in steel samples, where only the smallest and the biggest concentrations were excluded, as there were only seven samples in total). Finally, every third sample from the sorted data was taken to the test set, while all the other samples were taken to the calibration set. The samples of steels and ores were merged together into a single calibration set and  
Figure 3: “Measured vs predicted” plots for IC-PLS models: (**a**) calibration set of steel samples, test set of ore samples for Al; (**b**) calibration set of ore samples, test set of steel samples for Al; (**c**) calibration set of steel samples, test set of ore samples for Mn; (**d**) calibration set of ore samples, test set of steel samples for Mn. The green lines represent an ideal function y = x.  
a single test set. This procedure allowed for comparatively uniform distribution of the concentration levels of the target elements in the calibration and test sets of both ores and steels. Thus, for each element different calibration and test sets containing both ore and steel samples were created (Table S3, Supplementary Materials).
## 3 Results and Discussions  
### Water Demand, Setting Time and Soundness  
Table 2 provides results for setting times, water demand and soundness. It is observed that each blended cement, depending on its material composition, impacted water demand and setting times differently. Blended cement containing 30% silica fume (30 SF) and 30% calcined clay (30 CC) recorded the least and highest water demand, respectively, which were also reflected in the setting time results. If cement retains its volume after setting it is mostly considered to be sound. The amount of free lime, MgO and SO\({}_{3}\) contained in cement is the main cause of its excessive expansion [33]. From Table 2, it can be seen that all results for the soundness test fell below the EN-196-3-specified maximum limit of 5.0 mm. This is due to the low MgO and SO\({}_{3}\) content in the blended cements [33; 34]. The blended cements showed improved soundness as compared to the control.
## 3 Results and Discussion
### GC-IMS Analysis of Volatile Profile of Dried Citrus Peel as Effected by Drying Methods
#### 3.3.1 Aldehydes and Alcohols  
Aldehydes were the most widely detected compounds in dried citrus peel, with 16 aldehydes detected in Figure 5b. Among these aldehydes, most aldehydes contained no more than 10 carbons. The aldehydes were reported from degradation of fatty acid [43]. It was shown that aldehydes were important odorants, imparting the flavor of grass, fruit, almond, and sweet [44]. Hexanal and (Z)-3-hexenal were reported to be important odorants in the headspace above the Pontianak peels [35]. Dried Ponkan peel showed higher concentration of furfurral and benzaldehyde than dried Chachi peel, in agreement with GC-MS analysis; while dried Chachi peel showed higher concentration of 3-methylbutanal (malt) and (E)-2-heptenal (soap, fat, almond [44]) than dried Ponkan peel. AD was unfavorable to preserve (E)-2-pentenal (strawberry, fruit, tomato [44]). The detected alcohols were only smaller than aldehydes with 14 alcohols detected as shown in Figure 5a. Alcohols were also important odorants in citrus fruit: linalool and 1,8-cineole were characterized as the most potent odorant in citrus fruit oil [32; 33; 35]. Linalool, with the brightest points (implying high concentration) in GC-IMS analysis, was also detected in GC-MS analysis, and higher concentration of linalool was observed in dried Ponkan peel through linalool-polymer point, in agreement with GC-MS analysis. It can be also found from Figure 5a that n-hexanol (resin, flower, green [44]) was characteristic for SD peel; and FD was favorable to preserve 2-methyl-1-butanol (wine, onion [44]).
# Deployment Strategy of Shore-Based Cooperative Units for the Internet of Inland Vessels
## 5 Conclusions  
The work explained the architecture and business model of the inland riverboat networking system, with the functions and scenarios of the ship-shore communication service as well as the communication requirements analyzed. Modeling was performed from the system-service arrival process, the data-packet generation rules of each service, and the transmission process to calculate the amount of ship-to-shore communication data of the system. Then, a coverage model of two-layer heterogeneous network communications was established from the comprehensiveness of the base-station signal coverage.  
Considering the types and configurations of deployed base stations, a multi-objective optimization model was established with the goals of power consumption, cost, and data transmission rate. The multi-objective optimization model was solved by the genetic algorithm. Finally, the proposed deployment strategy was verified through simulation cases. The simulation results showed that the proposed deployment strategy could deploy regional shore-based collaborative units. The deployment costs of shore-based collaborative units were reduced when the communication needs were satisfied.  
Writing--original draft, P.L.; Supervision, C.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflict of interest.
## 4 Tuning Parameters and Results Assessment
### Parameter Optimization and Accuracy Assessment  
#### 4.2.1 The kNN Algorithm  
The kNN algorithm is a non-parametric method based on the idea that k nearest samples to the current sample will be determined, then the Euclidean distance is computed. The chosen k value has a profound impact on the performance of the kNN algorithm [21]. Depending on the specific function (classification or regression), the analysis for selecting the k value has certain differences. However, the simplest way to find the k value is to check the average precision score using different k values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \multirow{2}{*}{**Classification**} & \multicolumn{3}{c}{**Regression**} \\ \cline{3-5}
**(Optimized Parameter)** & & **Whole Floor** & **Center Area** & **Edge Area** \\ \hline kNN (k) & 5 & 3 & 3 & 3 \\ SVM (Gamma and C) & 16 and 8 & 128 and 16 & 128 and 16 & 128 and 16 \\ DT (Max-depth) & 8 & 8 & 6 & 8 \\ RF (Tree) & 16 & 12 & 16 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal parameters.  
Figure 10: K-Fold Cross-Validation (CV).  
The best overall value of k is chosen, based on the highest precision scores and the highest values of the corresponding CV score. In our case, k was in the range of 2 to 20.  
* Classification Function  
To optimize the k value for the classification process using the kNN algorithm, the mean of the precision score and CV score was compared for each k value. As depicted in Figure 11, when k increased from 8 to 20, the mean score gradually decreased for both CV and precision. The highest precision score, 0.92, appeared at k = 5. At k = 5, the CV score was 0.96, which ranks second of all the cases. Therefore, we adopted k = 5 as an optimal value for the classification function.  
* Regression Function  
To maximize the positioning accuracy in the regression process, we conducted a detailed survey of the relationship between the average positioning accuracy, standard deviation, and different k values. In Figure 12a, the best mean of positioning error was obtained when k = 2. However, the standard deviation for k = 2 was worse than that of k = 3, in which the error increased, but not significantly (3.6%). From these considerations, k = 3 was used to estimate the position for the whole floor before area division. Similarly, the same values of k were also selected for the center area and the edge area (see Figure 12b,c)  
Figure 11: Mean score vs. number of nearest neighbors.  
Figure 12: The mean and standard deviation of positioning errors vs. number of nearest neighbors by: (**a**) whole floor, (**b**) center area, and (**c**) edge area.
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 6 Conclusions  
Various MIMO antennas function within the sub-6 GHz frequency range, using an RTS880 substrate. One design features a single circular antenna that operates between 2.96 GHz and 7.1 GHz. A MIMO of two 1 \(\times\) 2 arrays was created to support dual-band operation, covering both 2.27 GHz to 4.94 GHz and 5.53 GHz to 7.84 GHz, as a measured result, resulting in a maximum gain of 8.3 dBi, a maximum radiation efficiency of up to 95% and demonstrating strong MIMO performance. Additionally, a MIMO of two 1 \(\times\) 4 arrays was designed for triple-band coverage at 2.33 GHz to 4.02 GHz, 4.25 GHz to 4.4 GHz, and 4.6 GHz to 6.62 GHz as a measured result, yielding a maximum gain of 10.9 dBi, maximum radiation efficiency of up to 97.46% and effective MIMO performance metrics: ECC values were below 0.006 and 0.003 for the respective bands, while CCL values were under 0.4 and 0.3, respectively. Finally, the three fabricated antennas were experimentally tested and implemented using the MIMO Application Framework for 5G systems, demonstrating operational effectiveness in 5G applications.  
Conceptualization, H.A. and A.M.A.; Methodology, H.A. and A.M.A.; Software, H.A.; Validation, H.A. and A.M.A.; Formal analysis, A.M.A.; Investigation, A.M.A.; Resources, A.M. and A.N.; Writing--original draft, H.A.; Writing--review & editing, A.M.; A.N. and M.A.-Z.; Supervision, A.M.A.; A.M.; A.N. and M.A.-Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Single Element** & **MIMO of Two 1 \(\times\) 2 Arrays** & **MIMO of Two 1 \(\times\) 4 Arrays** \\ \hline DL transmission rate (Mbit/s) & 4.6 & 13.7 & 13.7 \\ UL throughput (Mbit/s) & 9.1 & 23.2 & 27.4 \\ \hline \hline \end{tabular}
\end{table}
Table 10: MIMO application framework performance parameters.  
**Acknowledgments:** The authors thank the Egypt-Japan University of Science and Technology, Egypt, and the Egyptian Science, Technology and Innovation Funding Authority (STDF) for facilitating the testing of the designed antennas at the Next Generation Wireless Communication Technologies Laboratory for Smart Cities through STDF Project No. 38285.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 4 Feature Layer Fusion of Multi-Source Heterogeneous Sensors Based on 1D-CNN
### Interpretation of Result
#### 4.5.3 Computational Time and Complexity of Algorithm Training  
As one of the two performance evaluation indexes of the algorithm, time complexity qualitatively describes the algorithm's running time, which is of great significance for the selection, construction, and optimization of the algorithm. For the convolutional neural network algorithm adopted in this paper, its time complexity is mainly reflected in the speed of the training process of the data set. If the complexity is excessive, the training time of the model will be long, resulting in a waste of time and economy. So it is of great significance to evaluate the algorithm's time complexity.  
According to the concept of time complexity of the convolutional neural network, its time complexity mainly includes the calculation amount of the convolutional layer and fully connected layer in the model. The time complexity calculation formula of a single convolutional layer is shown in Equation (15), and the time complexity calculation formula of a single fully connected layer is shown in Equation (16) [25]. Table 4 shows the specific structure of the one-dimensional convolutional neural network adopted in this paper. Therefore, this algorithm's computational time and complexity during model training are shown in Table 5.  
\[T\text{ime} \sim\text{O}(M^{2}\cdot K^{2}\cdot C_{in}\cdot C_{out}) \tag{15}\]  
\[T\text{ime} \sim\text{O}((2\times C_{in}-1)\cdot C_{out}) \tag{16}\]  
where \(M^{2}\) is the area of the output feature map, \(K^{2}\) is the area of the convolution kernel, \(C_{in}\) is the number of input channels, and \(C_{out}\) is the number of output channels.
## 6 Discussion
### Effect of Manufacturing Related Parameters and Structure-Dynamic Aspects  
There are different reasons for the discrepancy between the calculated and measured vibration behaviour. As mentioned before, manufacturing related parameter, as thickness variance, the realistic boundary conditions, the value and direction of pre-stress and relaxation effects influence the resulting resonance behaviour. The clamping of the film has to be considered as a compliant boundary; however the determination of the elasticity and damping of the adhesive bonding is difficult. Other important aspects are temperature and frequency dependent material properties of the films, as well as the influence of moisture absorption on the resulting vibration behaviour. The used material properties are gained by measurements of injection moulded samples using granulate which is pre-dried prior to processing [30]. Before testing, specimens are conditioned, thus the values of material testing can differ from the properties of finished parts. Further factors affect the physical properties, such as varying processing conditions, orientation of macromolecules and fillers, internal stresses, moisture, annealing and environmental conditions [30]. Consequently, the applied film should be tested inside the cavities during application. For the application as an acoustic liner in aero engines the resulting mass of the honeycomb core needs to be decreased. Compared to conventional HR-Liner with rigid walls the usage of flexible walls reduces the mechanical compression resistance and enhances the risk of a stability failure. The results of the compression test show deviations due tolerances during manufacturing, as well as levelling of the core during compression testing. A promising approach, a design with different wall thicknesses, as well as the usage of alternative manufacturing technologies could be chosen.
# Laser-Induced Breakdown Spectroscopy for Determination of Spectral Fundamental Parameters
## 5 Experimental Determination of Spectral Parameters by LIBS  
The main applications of LIBS to the determination of the fundamental spectral parameters of many emission lines of several elements, are summarized in two Tables (Table 1 for the Stark broadening coefficients and Table 2 for the transition probabilities), and briefly discussed in the following.
# Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement
## 6 Conclusions  
In this paper, we proposed a coverage algorithm based on relative localization to perform the coverage task for the group of UAVs in the case that only one UAV knows its position in advance. Because the performance of coverage placement and the position estimation of the UAVs influence each other, the proposed optimal multi-UAV placement strategy takes into account both RL localization geometry optimization and coverage placement optimization. The simulation results show that the coverage placement algorithm based on relative localization is able to achieve the coverage task and that the coverage performance is improved by the proposed optimal multi-UAV placement strategy. Our future work will include a discussion for limitations in terms of energy and computing resources and the algorithm test in a real experiment.  
Conceptualization, Z.Z. and X.X.; methodology, Z.Z. and X.X.; software, Z.Z.; validation, X.X., J.C.; formal analysis, J.C.; investigation, X.X.; resources, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z., X.X. and W.M.; visualization, X.X.; supervision, W.M.; project administration, X.X.; funding acquisition, W.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grants NSFC 61803105, U1911401, Guangdong Introducing Innovative and Entrepreneurial Teams (2019 ZT08X340) of \"The Pearl River Talent Recruitment Program\" of Guangdong Province.  
Figure 8: The minimization process of the location uncertainty. Blue lines represent the flight trajectory of the UAVs. The red ellipses represent the corresponding depict of the location uncertainty in each step.  
Figure 9: The process of coverage task performing, k = 200 s.
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
## 2 System Model
### Cooperative PD-NOMA
#### 2.3.1 The Direct Transmission Phase  
As already mentioned in the PD-NOMA transmission, the LED source sends \(M\) signals to the users based on the PD-NOMA concept as shown in Figure 2.  
The \(k\) receiver applies SIC to the \(k\)-th user, this removes the signal of the other users with higher power allocation. The received electrical power is expressed as:  
\[P_{n_{k}}=P_{elec}h_{k}a_{k}^{2}. \tag{13}\]  
The received signal to interference plus noise ratio (SINR) for the \(M\)-th user to decode the \(k\)-th user's signal for \(k<M\) is represented as in [16] by:  
\[SINR_{M,k}=\frac{\left(h_{M}a_{k}\right)^{2}}{\sum_{l=k+1}^{M}\left(h_{M}a_{l} \right)^{2}+\frac{1}{\rho_{k}}} \tag{14}\]  
where \(\rho_{k}=\frac{P_{elec}}{\sigma_{n_{k}}^{2}}\) represents the transmitted SNR. After the \(M\)-th user decodes and subtracts other users' signals, its own message is decoded with \(SINR_{M,M}=\rho(h_{M}a_{M})^{2}\). The achievable data rate for the \(k\)-th user is given as in [11] by:  
\[R_{k}=\left\{\begin{array}{ll}\frac{1}{2}\ \text{log}_{2}\bigg{(}1+\frac{ \left(h_{k}a_{k}\right)^{2}}{\sum_{l=k+1}^{M}\left(h_{k}a_{l}\right)^{2}+\frac {1}{\rho_{k}}}\bigg{)},&k=1,\cdots,M-1,\\ \frac{1}{2}\ \text{log}_{2}(1+\rho_{k}\Big{(}h_{k}a_{k})^{2}\Big{)},&k=M\\ \end{array}\right.. \tag{15}\]  
The above equation is conditioned that the \(k\)-th user has to successfully detect the signal for all lower order users by satisfying the following condition:  
\[R_{k\to j}=\left\{\begin{array}{ll}\frac{1}{2}\ \text{log}_{2}\left(1+ \frac{\left(h_{k}a_{j}\right)^{2}}{\sum_{l=j+1}^{M}\left(h_{k}a_{l}\right)^{2 }+\frac{1}{\rho_{k}}}\right)\geq\overline{R}_{j},&j\leq k,j\
eq M\\ \frac{1}{2}\ \text{log}_{2}(1+\rho_{k}\Big{(}h_{k}a_{j})^{2}\Big{)}\geq\overline{R}_ {j},&j=k=M\\ \end{array}\right. \tag{16}\]  
where \(R_{k\to j}\) is the rate that the \(k\)-th user needs to detect the message signal for the \(j\)-th user and \(\overline{R}_{j}\) is the targeted data rate for the \(j\)-th user to successfully detect its own message signal.  
Figure 2: Power domain non-orthogonal multiple access (PD-NOMA)-VLC concept.
## 4 Research Implications and Limitations
Analyzing the quality of studies has also contributed to increased accuracy and precision of data extraction results, as it gives more credibility to the fact that the data extracted comes from reliable studies. This study has two major limitations. First, the limited number of sources searched (only four sources were selected) and second, the fact that only automatic search was performed, in which the use of keywords does not encompass all studies that use information technology aimed at combating money laundering for the following reasons:  
* The keywords searched may not all be explicit in the search places such as in the title, keywords or abstract. * Relevant works may not be found because the String search may not contain the full set of keywords required because of their variety. Finally, it should be added that, although the guidelines suggested by Reference [11] were used for this systematic review, there was a deviation from the suggested procedures. Instead of a group of researchers, only 3 researchers extracted the data in this research. Although the practice of analysis by only one researcher is adopted in some studies, it is possible that some collected data may have some description or classification questioned even with the triple validation in this study.
###### Abstract  
The corona-stabilized switch has the potential to be a high repetition rate pulsed-power switching device, but there has been limited investigation into its repetitive breakdown stability and insulation recovery characteristics. Repetitive breakdowns of gas are characterized by a memory effect, where the subsequent breakdown process is inevitably influenced by the preceding one. However, there are still some issues that require further exploration in the current research on the mechanism of memory effect on repetitive breakdown characteristics. To clarify the factors and mechanisms that affect the repetitive breakdowns of corona-stabilized switches, this paper introduced optical observation methods into the experimental investigation. Through optical-electrical coupled diagnosis, the repetitive breakdown stability and insulation recovery performance of corona-stabilized switches under different working conditions and repetition frequencies were analyzed. The monotonic promoting effect of corona stabilization on switch insulation strength recovery is proposed as well as the non-monotonic and complex regulatory mechanism of corona stabilization on repetitive breakdown stability. The research results provide a theoretical and practical basis for clarifying the mechanism of repetitive corona-stabilized breakdowns and optimizing the design of corona-stabilized switches.  
pulsed power switch; pulse repetition frequency; repetitive breakdown stability; corona stabilization effect; memory effect +
Footnote †: journal: Applied Sciences