esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Discussion
### Validation of Modified Method  
To better evaluate the results of selecting effective singular values based on the four methods mentioned above, this paper conducted statistical analysis about SNR on the de-noised signal, depending on SVD. The number of effective singular values was determined by each method.  
Table 1 shows the results of noise reduction according to the four methods. The bold numbers signify the highest values of the de-noised SNR. The table shows that the effective numbers determined by MPODSOISE are minimal at all noise intensities. This is because MPODSOISE only considers the influence of the maximum peak of the difference spectrum of the singular value, which causes the loss of useful components. Then MPOCSOISE and MMBDSOICE followed. MMBDSOICE performs the task of noise reduction best among all of the methods, which could be seen from the most improved SNR in the first column. As a result, MMBDSOISE is better able to capture the demarcation point of the spectrum than other methods. The comparison results in the last column show that noise will be retained as the number of effective singular values is slightly larger. This may cause a decline in the de-noised SNR as well.  
Figure 5: Frequency spectrum of original signal and de-noised signal using five different methods with initial SNR of \(-11.21\) db. (**a**) Fast Fourier transform (FFT) of original signal; (**b**) FFT of noisy signal; (**c**) FFT of de-noised signal based on MPODSOISE, MPOCSOISE; (**d**) FFT of de-noised signal based on MMBDSOISE; (**e**) FFT of de-noised signal based on MMBDSOISE.
# Sport Performance Analysis with a Focus on Racket Sports: A Review
## 3 Results
### SR2: A Systematic Review of Publications on Racket Sports
#### 3.2.5 Keyword Analysis
\begin{table}
\begin{tabular}{l c l c} \hline \hline
**Author Keywords** & **Occurrences** & **Title Words** & **Occurrences** \\ \hline performance analysis & 44 & tennis & 35 \\ racket sports & 13 & analysis & 20 \\ tennis & 8 & players & 16 \\ racket sport & 7 & badminton & 15 \\ badminton & 5 & performance & 13 \\ match analysis & 5 & padel & 11 \\ racquet sports & 4 & professional & 9 \\ sport analytics & 4 & elite & 8 \\ deep learning & 3 & match & 8 \\ notational analysis & 3 & table & 8 \\ sport performance analysis & 3 & serve & 6 \\ sports & 3 & success & 6 \\ table tennis & 3 & data & 5 \\ accuracy & 2 & game & 5 \\ coaching & 2 & network & 5 \\ disability & 2 & outcome & 5 \\ elite success & 2 & vision & 5 \\ equipment scaling & 2 & differences & 4 \\ fatigue & 2 & characteristics & 4 \\ hawk-eye data & 2 & player & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Most frequent words—author keywords and title words. Figure 7: Keyword analysis based on the frequency for a specific racket sport. **Summary:** Results from a review of the keywords and title were quite interesting. As of this writing, only 44 articles use the term \"performance analysis\". More than half of tennis publications define the sport, and all 15 badminton publications have the word \"badminton\" in the title. It is highly suggested that any future research should include the term \"performance analysis\" as a keyword and mention the sport's name in the title. These enhancements make it possible to search for information more precisely and accurately.
###### Abstract  
Energy security on islands is a challenging issue due to their isolation from energy markets and fossil fuel dependence. In addition, islands' average energy intensity has increased in recent years due to economic development. This research explores the environmental performance of two alternative non-variable bioelectricity feedstocks to increase energy resilience on islands. The study was developed for the Galapagos islands to address the environmental impacts from the direct use of waste cooking oil (WCO) and refined palm oil (RPO) to produce 1 MWh using the life cycle assessment methodological framework. A combination of primary and secondary data sources was used. The results show better performance for the electricity derived from WCO in all the impact categories considered when compared to RPO.  
biofuels; bioelectricity; islands; life cycle assessment; bioenergy; biomass; sustainable islands +
Footnote †: journal: Applied Sciences
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 3 Results
### Developed AWS-VPC  
#### 3.5.1 Live-Streaming System  
A live-streaming system was developed using a web real-time communication (WebRTC) API platform, which enables secure data communication between client applications [27]. The amount of ECG data received from a single mobile app for an hour is approximately 6.9 MB. With the help of AWS autoscaling function, the live-streaming system can receive ECG data from dozens of participants. To evaluate data integrity, a session ID (SessionID) and sequence number (SeqNum) of ECG packet data were generated. Once data communication started, a new SessionID was created and a SeqNum increased one by one. If data communication was interrupted and restarted, the SessionID and SeqNum were initialized. The live-streaming system performs the two major tasks. The first task is to display real-time ECG data from the web client. If web pages are opened by the web client, specific ECG data with participant numbers appear in the web client. A selective-forwarding unit (SFU) was used as a data-routing unit [28]. The SFU can receive multiple ECG streams and determine which ECG data would be displayed. The second task is to transmit ECG data with the SessionID and SeqNum to the API server.
## 4 Further Discussion and Future Work
In the future, the framework will be improved by implementing weather datasets to provide a more comprehensive overview of the impact of weather on traffic resilience. Lastly, the proposed framework will be implemented to access traffic resilience assessments of past hurricane scenarios before being implemented for future hurricane scenarios. Additionally, the integrated data-driven predictive resilience framework will be adopted for different disaster scenarios, such as wildfires and major flooding.
## 2 Ge Growth Techniques
### Crystalline Ge Growth with Graded SiGe Buffer Layers  
In the early stage of crystalline Ge film epitaxy on Si wafers, a compositionally graded SiGe region was commonly adopted as buffer layer. This approach was first adopted in the SiGe/Si system by [PERSON] _et al_. [5] and later improved by [PERSON] _et al_. in 1990 [10]. Multiple buffer layers with increasing Ge content were adopted to relax the high strain between Ge and Si, which minimizes dislocation nucleation and reduces the threading dislocations. The final strain-relaxed Si\({}_{1\textrm{-}\textrm{x}}\)Ge\({}_{\textrm{x}}\) layers grown on these graded layers showed low density of threading-dislocations, 4 \(\times\) 10\({}^{5}\) cm\({}^{-2}\) for x = 0.23 and 3 \(\times\) 10\({}^{6}\) cm\({}^{-2}\) for x = 0.50.  
However, the graded SiGe buffer method usually requires a thick 10 um buffer for pure Ge epitaxy on Si, while in modern Si photonics technology, Ge photodetectors are favorably fabricated in close adjacency with Si optical waveguide facilitating evanescent or butt-coupling of the optical power. As a result, a new technique with thin buffer layers is still needed.
## 5 Fine-Grained Network Congestion Detection Algorithm Based on Flow Watermarking
### Analysis of SRW in Micro-Burst Traffic Model  
Figure 6 shows an example of the decoding result of watermark '00' when micro-packet delay and micro-packet loss occurs. If some packets in the first interval are delayed until the second interval, the decoder obtains that \(Y_{k2}\) is less than the average value and \(Y_{k3}\) is greater than the average value. The decoded watermark is '00'. Similarly, if some packets in the third interval are lost, \(Y_{k2}\) is less than the average value and \(Y_{k3}\) is greater than the average value. The decoded watermark is still '00'. Therefore, when slight packet delay and packet loss occurs, the watermark decoding result may be deceptive due to the small change in the number of packets, which will affect the judgment of the changes in network status.
## 1 Introduction  
Wavefront coding technology [1] has been proposed to extend the depth of field (DoF) without sacrificing resolution or luminous flux. This technology has been widely used in various optical systems, such as miniature optical endoscopes [2], microscopes [3], and conformal domes [4].  
The phase mask plays a key role in the image quality of wavefront coding systems. Many optimization algorithms were applied to obtain an optimal phase mask, such as simulated annealing (SA) [5] and genetic algorithm (GA) [6]. These algorithms have achieved good results in DoF extension. However, the optimal solution and calculation time of SA are related to the cooling rate, number of temperature iterations, and temperature range. These parameter settings are very complicated, and it takes many attempts to obtain the right value. Otherwise, it is easy to fall into the local optimal solution. GA requires sufficient mutations. Otherwise, GA will fall into a local optimal solution. However, sufficient mutations will prolong the optimization time.  
People constantly pursue a fast, simple optimization algorithm that does not easily fall into the local optimal solution. Therefore, an improved particle swarm optimization (PSO) is proposed here as an optimization algorithm for optimizing phase masks in the design of wavefront coding systems. Based on PSO [7, 8], we cancel the velocity range in PSO and introduce the restriction conditions, which not only restrict the optimization range of the phase mask parameter but also guarantee the quality of image recovery in the later stage. The improved PSO is simple to use, and only the number of iterations and the population of particles need to be set. The improved PSO can effectively jump out of the local optimal solution while guaranteeing the convergence speed and improving the optimization efficiency of the optical design process.  
This paper is organized as follows. Section 2 provides the theoretical analysis of the wavefront coding technology, PSO, and phase mask parameter optimization based on improved PSO. Section 3 shows the simulation results and analyzes the performance of the improved PSO based on the simulation results, as well as a comparison with SA and traditional PSO. Section 4 shows the experimental results, which verify that the cubic phase mask optimized by the improved PSO can achieve DoF extension in the wavefront coding system. The conclusions are given in Section 5.
## 4 Conclusions  
We conducted 22 shear tests with different cover plates, prevailing torques, and grit-blasting surface treatments, and we investigated the slip load and slip factor of the GFRP-GFRP slip-critical connections. We conducted 16 pre-tension force relaxation tests. The long-term performance of the GFRP-GFRP slip-critical connections was investigated. An explanation of the force transformation mechanism of the GFRP-GFRP slip-critical connections, based on the adhesion theory of friction for this type of connection, was discussed. Based on the experimental and theoretical analysis of test results, the following conclusions can be drawn:  
1. The slip factor of the G-S24# connection with grit-blasting treatment stainless-steel cover plates can steadily reach 0.45; this slip factor value could satisfy the requirements of engineering applications. The main component of the friction force with the GFRP-GFRP slip-critical connections was likely the deformation force. A large deformation force could steadily transfer the force between the stainless-steel cover plates and GFRP plates. The grit-blasting surface treatment increased the number of asperities and roughness on the faying surface of stainless-steel cover plates and increased the deformation force. The grit-blasting surface treatment of the GFRP plate could damage the mechanical performance of GFRP plate and the slip factor increase can be small; therefore, the grit-blasting surface treatment of the GFRP plate should be avoided.
2. The slip factor changed with the variation in the prevailing torque effects of the G-S24# connection. When the prevailing torque equaled 6 N-m, the maximum slip factor of 0.45 occurred. The prevailing torque was not large. A large prevailing torque can flatten the asperities and reduce the deformation force. The reduction in the deformation force caused the slip load reduction.
3. Compared to the G-G connection, the G-S24# connection demonstrated good long-term slip resistance. The residual rate of the slip load of the G-S24# connection was 91% after 30 days of preloading time. The mechanical performance of the G-S24# connection can be relied upon over the service life by considering the relaxation effect.  
Figure 11: Comparison of the slip load residual rate after the bolt pre-tension relaxation tests.  
**Author Contributions:** Formal analysis, Y.P.; software, J.Z.; supervision, J.D.; validation, Y.P.; writing--original draft, Y.P., Z.W.; writing--review and editing, Y.P., W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Project of National Natural Science Foundation of China: 51408307 and Project of Nanjing Gongda Construction Technology Co., Ltd.: 2019 RD06.  
**Acknowledgments:** This research was funded by Project of National Natural Science Foundation of China grant number [51408307] and Project of Nanjing Gongda Construction Technology Co., Ltd. grant number [2019 RD06].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### PAH Components  
Table 1 presents the results data for each PAH concentration component at different combustion periods with the highest POSS bio-char moisture content (60% d.b.) and 0.1 m/s clean air speed. In this experiment, the PAH compounds from the POSS bio-char combustion could be classified into two groups, 2-3-rings and 4-6 rings. Phenanthrene, Acetnaphthene, Fluorene, Naphthalene, Acetnaphththene, and Anthracene were the 2-3 rings of PAH compounds [48,49], while the 4-6-ring PAHs were Indeno[1,2,3-cd]pyrene, Fluoranthene, Benza[a]anthracene, Chrysene, Pyrene, Benzo[b]fluoranthene, Benzo[k]fluoranthene, Benzo[a]pyrene, Dibenz[a,h]anthracene, and Benzo[g,h,i]perylene [48,49]. The influence of fuel moisture content and the combustion period on the concentration of 2-3-ring and 4-6-ring PAHs was like the influence on the total smoke particle concentration and PAH concentration. This result showed that low concentrations of PAHs after a long period of burning may result, in part, from the decomposition of the PAHs. This agreed with the results reported by [15,28,38,39]. Figure 8 shows the mass fraction profile of the 16 PAHs in the smoke particles generated from the POSS bio-char change during the combustion process at various clean air speeds (data not shown for 0.2 m/s). The total concentration of  
Figure 5: The temperature of 15% d.b. of POSS bio-char moisture content for different clean air speeds and distance locations above the base of the fire. Figure 6: MMAD emission from the POSS bio-char combustion for different moisture contents. the 2-3-ring PAHs value was lower than the 4-6-ring PAHs value at the initial combustion period, with the highest moisture content of the POSS bio-char (60% d.b.). However, the total concentration of the 2-3-ring PAHs value continually increased until it was higher than the 4-6-ring PAHs value during the middle and final combustion periods. The results showed that an increase in clean air speed and time period had a strong influence in decreasing the 4-6-ring PAHs values. This was probably the result of the decomposition and evaporation of the 4-6-ring PAHs. Figure 9 shows that the decrease in the POSS bio-char moisture content had a high influence on decreasing the 4-6-ring PAHs values. This was because the degree of incomplete combustion decreases after the evaporation of the water in every biomass fuel [50; 51], including the POSS bio-char in this experiment.
## 3 Proposed Method  
DL, decision tree, random forest, and mind map models can be expressed as directed graphs and can be represented as matrices. Therefore, these learning processes can be performed with GNN and GCN. The nodes in the hidden layers can be seen so that it becomes visible and transparent. The procedure for white boxing is as follows:  
\begin{tabular}{l l} (1) & Make directed graphs of the DL, decision tree, random forest, or mind map; \\ (2) & Convert the directed graphs to matrices; \\ (3) & Use these matrices as input of the training samples of GNN and GCN; \\ (4) & Visualize the node in the hidden layers of the GNN and GCN, which results in white boxing of DL and the other models. \\ \end{tabular}  
The image is processed as local data separated by grids, and several layers of CNN are superimposed to grasp the overall image of the object. In the graph, Euclidean distance is not relevant, and the data points related to the object are combined and treated as a data set. The adjacency matrix \(A\) expresses whether or not there is a connection relationship between nodes, and the degree matrix \(D\) expresses how many edges are connected to each node. In addition, the Laplacian matrix is a representation of these together: a normalized Laplacian matrix obtained by normalizing the \(L=D\)-Laplacian matrix is actually used.  
A graph is a data structure in which nodes (vertices) are connected by edges (branches) that represent adjacency relationships. Graphs can be represented mathematically by matrices, and this GCN uses the following two matrices: the aforementioned adjacency matrix and a feature matrix:  
\begin{tabular}{l l l l l} (1) & Adjacency matrix: a matrix that represents the connection relationship between nodes. \\ (2) & Feature Matrix: a matrix representing the feature vector of each node. \\ \end{tabular}  
The example in the following figure (Figure 2) can be represented by an adjacency matrix and a feature matrix.  
Orange letters represent each node. For example, in the adjacency matrix, the third column of the first row contains 1 because node \"a\" and node \"c\" are connected. In the feature matrix, \(x_{1}\) and \(x_{2}\) indicate features, as shown in Figure 2.  
GCNs are techniques for applying deep learning to graph data. GCN is a convolutional neural network that quantifies each node with structure. Taking into account the structure means that the node's focusing point is digitized and takes into account what kind of node it is attached to.  
Let \(G=(V,\,E,\,X)\) denote an undirected and unweighted graph comprising a set of nodes \(V\), a set of edges \(E\), and a set of node feature vectors \(X=\{x_{1},\,\ldots,\,x_{n}\}\) corresponding to nodes in \(V\), where \(x_{u}{\in}R^{M}\). Let \(n=|\,V\,|\,\) denote the number of nodes in the graph and \(A\in R^{n\times n}\) be the adjacency matrix (Equation (1)), where element \(A_{ij}=1\) if nodes i and j  
Figure 2: Matrix representation of graphs. (**a**) adjacency matrix, (**b**) feature matrix.  
are connected by some edge in \(E\), and \(A_{ij}=0\) otherwise. As the input data of GCN, the following two matrices are used.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 5 Controversies in Thermal Conductivity Measurements
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**NPs** & **BF** & **Size [nm]** & **Concentration** & **Keff Increase [\%]** & **Method** & **Ref.** \\ \hline \multirow{9}{*}{**TiO2\({}_{2}\)**} & & 21 & 0.1 vol\% & About 11.1 & THW & [119] \\  & & 100 & 0.10, 0.15, 0.21 & & 16.7 for TiO2-0.25\% & THW & [120] \\  & & & and 0.25 vol.\% & & \\  & & 10, 30, and 50 & 0.005, 0.01, 0.1, & & & \\  & & 0.5, and 1 vol.\% & 0.4 & Theory & [121] \\  & Water & 5 & 0, 0.1, 0.5 and & & & \\  & & 1.0 vol.\% & 6.55 & TPS & [122] \\  & & 21 & 0.1–0.5 vol.\% & 7.28 & & [123] \\  & & 10 & 3 vol.\% & 11.4 & THW & [114] \\  & & 34 & 3 vol.\% & 8.7 & THW & [114] \\  & & 21 & 2 vol.\% & 7 & THW & [56] \\  & & 40 & 2.6 vol.\% & 6.5 & THW & [110] \\  & & 70 & 3 vol.\% & 6.4 & THW & [114] \\  & & 20 & 2 vol.\% & 4.2 & THW & [124] \\  & Water:EG & 40 & 0.2 to 0.8 vol.\% & 24 at VF 0.8\% and & THW & [125] \\  & & 5 & 7 vol.\% & 19.52 & THW & [126] \\  & & 15 & 5 vol.\% & 18 & THW & [127] \\  & EG & 10 & 3 vol.\% & 14.4 & THW & [114] \\  & & 34 & 3 vol.\% & 12.3 & THW & [114] \\  & & 70 & 3 vol.\% & 7.5 & THW & [114] \\  & & \(\mathcal{O}\)10\(\times\)40 & 5 vol.\% & 33 & THW & [34] \\  & DI & 15 & 5 vol.\% & 30 & THW & [34] \\  & DI & 20.5 & 1 vol.\% & 14.4 & THW & [28] \\  & & 21 & 3 vol.\% & 7.2 & 3\(\omega\) & [128] \\  & & 45–65 & 1, 1.5, 2, 3, and & & & \\
**SiC** & Water & 45–65 & 0.5, 1.0, 2, 3 and & & & \\  & & 45–65 & 4 vol.\% & 4.8 & THW & [105] \\  & & 35–45 & 0.5, 1.0, 2, 3 and & & & \\  & & 35–45 & 4 vol.\% & 3.42 & THW & [105] \\  & & 29 & 6 vol.\% & 52 & SSCB & [108] \\  & Water & 25 & 7.5 vol.\% & 32.3 & THW & [130] \\  & Water & 25 & 7.5 vol.\% & 32.3 & THW & [130] \\  & & 55–66 & 2 vol.\% & 24 & THW & [129] \\
**CuO** & & 33 & 4.68 vol.\% & 16.5 & TSHW & [131] \\  & Water + 0.5 & & & & \\  & wt\% CMC & 40 & 0.2–1.0 wt\% & 29\% & THW & [132] \\  & EG & 55–66 & 2 vol.\% & 21 & THW & [130] \\  & MEG & 33 & 1 vol.\% & 9 & THW & [40] \\  & MEG & 25 & 7.5 vol.\% & 21.3 & THW & [129] \\  & EO & 55–66 & 2 vol.\% & 14 & THW & [130] \\
**CeO2\({}_{2}\)** & EG & 10–30 & 2.5 vol.\% & 22 & THW & [132] \\  & Water & 10 & 3 vol.\% & 14.2 & THW & [133] \\  & Water & 60 & 3 vol.\% & 7.3 & THW & [134] \\  & EG & 30 & 3 vol.\% & 21 & THW & [133] \\  & EG & 50 & 2.4 vol.\% & 13 & THW & [135] \\
**WO3** & EG & 38 & 0.3 vol.\% & 13.8 & THW & [28] \\  & Kerosene & 15 & 1–vol.\% & 34.6 & THW & [136] \\  & & 15–23 & 3 vol.\% & 11.5 & THW & [137] \\
**Fe3O4** & Water & 15–20 & 4.8 vol.\% & 2.9 & 3\(\omega\) & [138] \\  & & 15–20 & 1 vol.\% & 1.1 & 3\(\omega\) & [138] \\
**NiFe2O4** & DI & 8 & 2 vol.\% & 17.2 & THW & [139] \\
**MgO** & EG/Water & 40 & 3 vol.\% & 34.43 & THW & [140] \\
**Cu** & Water & 75–100 & 0.1 vol.\% & 23.8 & THW & [141] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._