esa-sceva/satcom-chunk-collection huggingface dataset:
# Gut Microbiome in Patients with Obstructive Sleep Apnoea
## 3 Results
### Correlation between Gut Microbiome and Demographics, Clinical Data and Markers of Disease Severity  
There was a significant indirect correlation between the TST90% and relative abundance of _Actinobacteria_ (\(\rho=-0.34\), \(p=0.04\)). The abundance of _Proteobacteria_ was significantly related to age (\(\rho=0.38\), \(p=0.02\)), BMI (\(\rho=0.39\), \(p=0.01\)), HDL-C (\(\rho=-0.53\), \(p<0.01\)), triglycerides (\(\rho=0.46\), \(p<0.01\)), AHI (\(\rho=0.33\), \(p=0.04\)), ODI (\(\rho=0.43\), \(p<0.01\)), and TST90% (\(\rho=0.43\), \(p<0.01\)). The abundance of _Gammaproteobacteriaabacteria_ was related to BMI (\(p=0.46\), \(p<0.01\)), CRP (\(\rho=0.40\), \(p=0.03\)), HDL-C (\(\rho=-0.54\), \(p<0.01\)), triglycerides (\(\rho=0.47\), \(p<0.01\)), AHI (\(\rho=0.36\), \(p=0.03\)), ODI (\(\rho=0.47\), \(p<0.01\)), and TST90% (\(\rho=0.41\), \(p=0.01\)). There was a significant relationship between the abundance of _Lactobacillus_ and age (\(\rho=0.36\), \(p=0.03\)), BMI (\(\rho=0.39\), \(p=0.01\)), HDL-C (\(\rho=-0.38\), \(p=0.02\)), triglycerides (\(\rho=0.38\), \(p=0.02\)), AHI (\(\rho=0.42\), \(p<0.01\)), ODI (\(\rho=0.31\), \(p<0.01\)), and Sleep% (\(\rho=0.42\), \(p=0.03\)). The abundance of the _Lactobacillus_ genus was related to age (\(\rho=0.37\), \(p=0.02\)), BMI (\(\rho=0.39\), \(p=0.01\)), HDL-C (\(\rho=-0.37\), \(p=0.02\)), triglycerides (\(\rho=0.34\), \(p=0.04\)), AHI (\(p=0.41\), \(p<0.01\)), and ODI (\(\rho=0.52\), \(p<0.01\)). Finally, _Roseburia_ was related to HDL-C (\(\rho=-0.36\), \(p=0.03\)) and triglycerides (\(\rho=0.34\), \(p=0.04\)).  
The abundance of the _Lactobacillus_ family and _Lactobacillus_ genus was higher in males (both \(p=0.04\)). The abundance of _Proteobacteria_ and _Gammaproteobacteriaa_ was higher in hypertension (both \(p<0.01\)). Similarly, the abundances of _Proteobacteria_ (\(p=0.02\)) and _Gammaproteobacteriaa_ (\(p=0.03\)) were higher in cardiovascular disease. The abundances of _Proteobacteria_ (\(p<0.01\)), _Gammaproteobacteriaa_ (\(p=0.03\)), and _Roseburia_ (\(p=0.04\)) were higher in dyslipidemiaaemia.  
Participants who took any medication had higher abundances of _Proteobacteria_ (\(p=0.01\)) and _Gammaproteobacteriaa_ (\(p=0.03\)). In particular, patients who took benzodiazep (\(p=0.04\)), proton-pump inhibitors (\(p=0.02\)) and laxatives (\(p=0.02\)) had higher diversity at genus level. In addition, subjects who took proton-pump inhibitors had higher abundance of _Proteobacteria_ (\(p=0.03\)) and _Gammaproteobacteriaa_ (\(p=0.02\)), those who took antibistamines had higher abundance of _Proteobacteria_ (\(p=0.04\)) and _Gammaproteobacteriaa_ (\(p=0.04\)), subjects on clopidogrel had lower abundance of _Proteobacteria_ (\(p=0.02\)) and _Gammaproteobacteriaa_ (\(p=0.04\)), and participants taking laxatives had higher abundance of the _Lactobacillus_ family (\(p=0.03\)) and _Lactobacillus_ genus (\(p=0.03\)).  
There was no relationship between the abundance of investigated bacteria and smoking status, diabetes, arrhythmia, blood glucose, total cholesterol, LDL-cholesterol levels, TST, SPT, or ESS (all \(p>0.05\)).
## 3 MIMO Configuration  
The proposed olympeak antenna is transformed into a four element MIMO antenna system. The MIMO elements are transformed into a linear element, having 12 mm distance among each element centre to centre. The distance among the MIMO elements is kept at 1.2 \(\lambda\) at 28 GHz resonance. The dimensions of the MIMO antenna system is noted to be 48 \(\times\) 12 \(\times\) 0.254 mm\({}^{3}\). Figure 7 shows the proposed olympeak linear MIMO Antenna.  
The MIMO simulated s-parameters are shown in Figure 8. Figure 8 shows the reflection co-efficient response while the ports isolation are shown in Figure 8. The reflection co-efficient of the MIMO antenna is the same as the single element resonance response while for isolation characteristics, the first band offers the peak coupling value of 20 dB for side-by-side elements closest to each other, and for the second resonance, the enhanced isolation characteristics of \(>\)33 dB is observed.  
Figure 5: Efficiency and gain of the proposed antenna.  
Figure 6: Radiation patterns at 28 GHz frequency on plane (**a**) \(zy\) and (**b**) \(zx\).
## 5 Conclusions  
In order to automatically identify the defects of GIS and help avoid serious accidents resulting from insulation defects, the classification system using MFCC features and SMO-SVM multi-classifiers based on ultrasonic PD signals was proposed. The acoustic feature extraction of the system contains 12 static MFCC features and 24 delta MFCC features. The classification of the system is performed by  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pattern Recognition** & **Defect Type 1** & **Classified** & **Correctly Classified** & **Accuracy** & **Sensitivity** \\ \hline \multirow{4}{*}{SMO-SVM} & P & 20 & 20 & \multirow{4}{*}{93.75\%} & \multirow{4}{*}{94.14\%} \\  & F & 22 & 18 & & \\ \cline{1-1}  & C & 19 & 19 & & \\ \cline{1-1}  & M & 19 & 18 & & \\ \hline \multirow{4}{*}{SVM} & P & 18 & 18 & \multirow{4}{*}{82.50\%} & \multirow{4}{*}{83.18\%} \\  & F & 20 & 15 & & \\ \cline{1-1}  & C & 20 & 17 & & \\ \cline{1-1}  & M & 22 & 16 & & \\ \hline \hline \end{tabular}  
* Defect types are shortened as P, free metal particles; F, floating electrode; C, creeping discharge; and M, metal protrusions.  
\end{table}
Table 1: The results of insulation defect identification based on ultrasonic signals of GIS.  
Figure 8: Delta MFCC features calculated from static MFCC features under different kind of insulation defects. (**a**) Free metal particles; (**b**) Floating electrode; (**c**) Creeping discharge; (**d**) Metal protrusions.  
SMO-SVM. Through the experiments conducted on a 110 kV GIS with four kinds of insulation defects, it is verified that the system has excellent performance in the defect identification of GIS. With the optimization of SMO, the multi-classifier improves the accuracy from 82.5% to 93.75% and sensitivity from 83.18% to 94.14%. Furthermore, SMO-SVM is more efficient in training and testing samples than SVM. It is confirmed that the classification system with MFCC and SMO-SVM can be used to accurately and efficiently identify the defects of GIS.  
Conceptualization, W.Y. and Y.Q.; methodology, W.Y.; supervision, G.S. and X.J.; writing--original draft preparation, W.Y.; writing--review and editing, Y.X. and Y.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by China Postdoctoral Science Foundation (2019M661505).  
The authors declare no conflict of interest.
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 5 Simulation  
In this section, performance of the proposed schemes, CRT Map and CBR, is evaluated through simulation studies. We first compare the proposed CBR with two existing routing protocols, GyTAR [18] and RIVER [16]. End-to-end delay, packet delivery ratio, routing overhead, and average hop count are evaluated. In end-to-end delay and average hop count, only successfully delivered packets are calculated. In addition, if a packet is carried by a vehicle, the hop count is not increased until the packet is forwarded. In packet delivery ratio, a Time To Live (TTL) is set for each packet. If a packet cannot be delivered to its destination in time, it is dropped. In routing overhead, all necessary control packets, including beacon, traffic collection messages, and information exchange messages, are taken into account. Then, we study the performance of the proposed CRT Map under different parameter settings. We measure the average deviation of the number of vehicles per road segment between the real world and the CRT Map.  
A real road map of 3000 m \(\times\) 3000 m urban scenario, extracted from Taichung City, Taiwan, is adopted in the simulation, as shown in Figure 6. There are 100 to 300 vehicles randomly deployed in the network. SUMO [30] is used to generate mobility model of vehicles. The average moving speed is between 30 and 80 km/h. The transmission range of each vehicle is 250 m with a channel capacity of 2 Mbps. The data packet size is 512 bytes with randomly chosen sources and destinations. Each simulation is run for 150 s and each result is an average of 100 simulations. Furthermore, the confidence intervals with a 95% confidence level are included. The counter CV used to control the exchange frequency of the CRT Map is first set as 1 to evaluate the routing performance, and then varied between 1 and 5 to study its effects.
# Robust Additive Value-Based Efficiency Analysis
## Appendix C Computation of Stochastic Acceptability Indices  
For illustrative purposes, we present the steps of computing the stochastic acceptability indices for the exemplary DMUs from Appendix B considering the root category (\(C^{(2)}_{1}\)). Firstly, we use the hit-and-run algorithm to generate the weight samples for all children of \(C^{(2)}_{1}\). Table 1 presents the five example samples. Let us emphasize that to obtain reliable estimates of stochastic acceptabilities, in practice, one uses a few thousand such samples. Secondly, we compute a value-based efficiency score for each considered DMU (see Table 2). Based on the efficiency scores, we determine the distance of each \(DMU_{0}\) to the best one as the difference between the maximal efficiency score obtained by any \(DMU_{k}\) for a given sample and the efficiency score of \(DMU_{o}\). For example, for sample 1 and \(DMU_{3}\), such distance equals \(0.773-0.542=0.231\). The efficiency rank of \(DMU_{o}\) is equal to the number of other DMUs, for which the efficiency score is better than that of \(DMU_{o}\) (for a given sample), increased by one. For example, for sample 1, \(DMU_{2}\) and \(DMU_{3}\) attained an efficiency score greater than \(DMU_{4}\), ranking it 3 rd. The distances to the best DMU and efficiency ranks for all exemplary DMUs and samples are presented in Table 2.  
Having determined the distances to the best DMU and efficiency ranks, we compute the stochastic indices. \(DAII\) is the share of the weight vectors for which the distance to the best DMU is within a given bucket. For illustrative purposes, we use only four buckets. For example, there are four out of five samples for which the distance of \(DMU_{4}\) to the best unit is in the interval \(b_{1}=[0,0.25]\), so \(DAII(DMU_{4},[0,0.25])=0.8\). Similarly, we calculate \(ERA\)Is as the share of samples for which a given DMU is ranked \(r\)-th. For example, \(DMU_{3}\) is ranked 2 nd only for sample 1, so \(ERAI(DMU_{3},2)=0.2\). Finally, \(PEOI\) for an ordered pair of units is computed as the share of samples for which the first DMU is at least as efficient as the other. For example, \(DMU_{4}\) is not worse than \(DMU_{3}\) for all samples except the first one, so \(PEOI(DMU_{4},DMU_{3})=0.8\). The results obtained by individual samples are averaged, providing the estimates of the expected distances \(Ed\) to the best DMU and expected efficiency ranks \(
# Off-Line Optimization Based Active Control of Torsional Oscillation for Electric Vehicle Drivetrain
## 3 Active Control System
### Off-Line Optimization with Real-Coded Genetic Algorithm  
The critical parameter, \(\eta\), in designing the LQR controller acts as the weighting matrix, and it effects the performance of controller directly. Typically, the choice of this parameter mainly depends on the designer's experience; such a subjective choice cannot guarantee an optimal result, and it may even introduce unknown side effects. In this article, the most appropriate value of \(\eta\) is calculated off line by genetic algorithm in order to seek possible optimal solutions to the problem. Genetic algorithm is an effective research technique based on the rules of natural selection and genetics. By genetic operations such as reproduction, crossover and mutation [33], it simulates the mechanism of survival competitions, the superior results survive while the inferior results are eliminated. GA has been applied to many kinds of optimization issues in engineering, due to its simple and effective implementation procedure [34; 35]. In order to unload torque in transmission with less oscillation in drivetrain, root mean square (RMS) of motor speed, torsional angle and rotary speed of drive shaft are introduced to constitute the fitness function, as their unit and magnitude are different, so that the above three indexes are normalized to be the fitness function \(f\), the optimization objectives can be expressed as Equation (23) where \(x_{\text{i}\text{pass}}(i~{}=~{}1,2,3)\) is open-loop drivetrain system state variable, \(N_{i}(i~{}=~{}1,2,3)\) is amount of sample points.  
\[\text{minimize}\qquad f\ =\ \frac{\sqrt{\frac{\sum_{j}^{j}-N_{1}}{N_{1}}x_{ \text{ij}}^{2}(\eta~{})}{N_{1}}}{\sqrt{\frac{\sum_{j}^{j}-N_{1}}{N_{1}}x_{ \text{ij}\text{pass}}^{2}}}+\frac{\sqrt{\frac{\sum_{j}^{j}-N_{2}}{N_{2}}x_{ \text{ij}\text{pass}}^{2}}}{\sqrt{\frac{\sum_{j}^{j}-N_{1}}{N_{2}}x_{\text{ j}\text{pass}}^{2}}}+\frac{\sqrt{\frac{\sum_{j}^{j}-N_{3}}{N_{3}}x_{\text{ j}\text{pass}}^{2}}}{\sqrt{\frac{\sum_{j}^{j}-N_{3}}{N_{3}}x_{\text{ j}\text{pass}}^{2}}}} \tag{23}\]  
with \(0.001\leq\eta~{}\leq 100\), s.t. \(x_{1}(\eta~{})<x_{1\text{pass}}\), \(x_{2}(\eta~{})<x_{2\text{pass}}\), \(x_{3}(\eta~{})<x_{3\text{pass}}\).  
Figure 6 illustrates structure and process of control system. In order to save time for control system processing, the weighting matrix \(\eta\) is optimized off line. The main parameters of GA are shown in Table 2.  
Figure 6: Structure of the active control system with off-line unit.
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 5 Experimental Setup and Results
### Range Extension  
In this case, our proposed range extension method is applied using digital delay offset in the received signal to realize larger range values such as 10 m. In Figure 9, ranging performance using the conventional (Figure 9a-c) and the proposed method (Figure 9d-f) is shown, when the target location is within the CPI, within the OFDM symbol duration, and beyond the OFDM symbol duration.  
Figure 9a is the range plot for the target placed at 0.6 m in the measurement, whereas Figure 9b shows the same target shifted to 10 m, which reduces processing gain and, in turn, performance degradation. Similarly, the target in Figure 9c is beyond the OFDM symbol duration and does not appear in the conventional OFDM-based processing. As a benefit, the results obtained through our proposed method are shown in Figure 9d-f, and all targets are identifiable for all the same cases. Multiple reflections from the target and reflection from the wall appear as mentioned in Figure 8a.  
Figure 8: Measurement results for different arrangements of static targets. (**a**) Range plot of a target when placed at 0.6 m. (**b**) Range plot for two targets placed at 0.65 m and at 0.6 m. (**c**) Results of two targets placed at 1.313 m and at 1.548 m, respectively.  
Figure 9: Range extension of the measurement results, where the range is extended through offset–delay. (**a–c**) Results of conventional approach for targets at 0.6 m, 10 m, and 17.65 m, respectively. (**d–f**) Results obtained through the proposed range extension method for the arrangements as for results in (**a–c**).
# MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel
## 2 Related Works
The focus of this paper is to reduce the impacts of geometrical loss in FSO, which depends on the spreading of the laser beam. The proposed implementation of the multiple beam increases the key generation rate and distance coverage of the multiphoton protocol.
## 2 \(p\)-\(q\) Method Using Clarke Transform for Three-, Five-, and Two-Phase Systems
### Taking the Clarke Transform for a Non-Symmetrical System  
Since for non-symmetrical system is valid  
\[x_{a}(t)+x_{b}(t)+x_{c}(t)\
eq 0, \tag{23}\]  
because of including a zero-phase sequence component \(x_{0}(t)\) into Clarke transform [1; 25; 26; 27],  
\[\begin{bmatrix}x_{a}(t)\\ x_{\beta}(t)\\ x_{0}(t)\end{bmatrix}=\frac{2}{3}\begin{bmatrix}1&-\frac{1}{2}&-\frac{1}{2}\\ 0&\sqrt{3}&-\frac{\sqrt{3}}{2}\\ \frac{1}{2}&\frac{1}{2}&\frac{1}{2}\end{bmatrix}\begin{bmatrix}x_{a}(t)\\ x_{b}(t)\\ x_{c}(t)\end{bmatrix}. \tag{24}\]  
The inverse transformation into \(a,b,c\)-system can be obtained using the inverse transformation matrix. Consequently, the power in a non-symmetrical system also features an instantaneous zero-sequence component \(p_{0}(t)\).  
\[\begin{bmatrix}p_{a,\beta}(t)\\ q_{a,\beta}(t)\\ p_{0}(t)\end{bmatrix}=\frac{3}{2}\begin{bmatrix}u_{a}(t)&u_{\beta}(t)&0\\ -u_{\beta}(t)&u_{\alpha}(t)&0\\ 0&0&\frac{u_{0}(t)}{3}\end{bmatrix}\begin{bmatrix}i_{a}(t)\\ i_{\beta}(t)\\ i_{0}(t)\end{bmatrix}. \tag{25}\]  
The phase power components \(p_{a,b,c}(t)\) can be expressed by the inverse transform of this Equation (23) or by introducing a new transform [7]; the following equations are obtained  
\[\begin{bmatrix}p_{a}(t)\\ p_{b}(t)\\ p_{c}(t)\end{bmatrix}=\begin{bmatrix}p_{ap}(t)\\ p_{bp}(t)\\ p_{cp}(t)\end{bmatrix}+\begin{bmatrix}p_{aq}(t)\\ p_{bq}(t)\\ p_{cq}(t)\end{bmatrix}+\begin{bmatrix}p_{a0}(t)\\ p_{b0}(t)\\ p_{c0}(t)\end{bmatrix}. \tag{26}\]  
Instantaneous reactive powers in each phase \(p_{aq}(t)\), \(p_{bq}(t)\),\(p_{cq}(t)\) make no contribution to the instantaneous power flow in the three-phase non-symmetrical system. The instantaneous active power flow is represented by the sum of \(p_{a,\beta}(t)\), and \(p_{0}(t)\) because the sum of the instantaneous reactive powers \(p_{aq}(t),p_{bq}(t)\),\(p_{cq}(t)\) is always zero [7].
## 2 Materials and Methods
### Development and Cross Validation of a Gaussian Process Model to Predict Unit Load Segment Bending
In the more traditional approach towards developing response surfaces, the experimenter used the sequential experimentation philosophy, working towards the identification of a small region of interest in order to determine the optimum solution, typically approximated as a low-order polynomial. This study sought to replicate the actual response throughout the whole response region, not only the lowest or highest pallet deflection, since optimization was not the only objective. In order to satisfy this, a space filling design was used in a manner that could cover the complete response region. Additionally, no replication was possible since the finite element model developed was a deterministic model. A space filling design that satisfied the requirements by spreading the design points evenly throughout the region of experimentation was the Latin Hypercube Design (LHD). A Latin Hypercube, with 40 runs and 4 dimensions (40 \(\times\) 4 matrix), was generated using the computer software JMP Pro 15, which conducts a random permutation to determine the values of each variable for each run. Table 2 presents the factor level combinations for each model run. The same design was used independently for the model of each quantity of columns (3, 4, 5, or 6 columns). A total of 160 finite element model simulation responses were required. To fit the deterministic responses from the finite element simulations, a Gaussian process model was used. The Gaussian process model is \(y=\mu+z(\mathrm{X})\), where \(z(\mathrm{X})\) is Gaussian stochastic process with covariance matrix \(\sigma^{2}\mathrm{R}(\theta)\). \(\mathrm{R}(\theta)\) is a correlation matrix with the elements shown in (2). Values for \(r_{ij}\) are the correlations between responses at two design points. The parameters \(\mu\) and \(\theta_{s}\), s = 1, 2,..., \(k\) were estimated using the method of maximum likelihood. \[r_{ij}=e^{-\sum_{s=1}^{k}\theta_{s}(x_{i_{n}}-x_{j_{n}})^{2}} \tag{2}\]  
Predicted values for the responses of the Gaussian process model are generated from (3). \(\hat{\mu}\) and \(\hat{\theta}\) are the maximum likelihood estimates of the model parameters \(\mu\) and \(\theta\), respectively, and \(r^{\prime}(x)=[r(x_{1},\ x),r(x_{2},\ x),\ldots,\ r(x_{n},\ x)]\). This prediction equation contains one term for each of the design points in the original experiment.
# Special Issue: Quality of Service in Wireless Sensor/Actuator Networks and Systems
## 2 Summary of Contributions
It is fundamental to check if/how WSAN application requirements can be met, for instance concerning timeliness (e.g., maximum end-to-end delay of 3 s), reliability (a packet error rate below 1%), and scalability (a network must support up to 1000 nodes and cover an area with a 2 km radius), so that network parameters such as bit rate, nodes'/clusters' duty-cycle, TDMA slots reservation, and routers' buffer size can bed tune/optimized. The authors of [7] wrap up relevant work on _sensor network calculus_, a mathematical methodology that has been tailored (actually by the first author, over a decade ago) for worst-case analysis and dimensioning in sensor networks. The paper describes tools and applications of sensor network calculus and points out new research directions, such as encompassing stochastic models, downstream control traffic, and mobility support. Last (but definitely not least), the authors of [8] address decision-centric resource management in QoS-aware WSAN systems, enabling the optimization of communication protocols, data storage, and scheduling policies for critical data collection, specifically for meeting decision needs. Toward this end, resource management heuristics/algorithms are proposed that meet at least the three following conditions: (i) collected data must have enough quality to support a user's decision; (ii) it should be recent (fresh) enough; (iii) the user's decision (based on the collected data) must meet the decision deadline. What sensors need to be activated to take the best decision at the lowest possible (resources) cost? How often do nodes need to sense the physical world and to communicate data? In which order (scheduling) should they be sampled? The proposed methodology and architecture (dubbed _Athena_) answers these questions, and has been evaluated (via simulation) considering a post-disaster route-finding scenario, proving to outperform traditional alternatives.
## 1 Introduction  
Satellite communication (SatCom) is increasingly seen as an indispensable part of the next-generation wireless communications networks, which can provide a higher rate, broader coverage, lower energy consumption, more robust performance, and higher spectrum utilization [1, 2]. Moreover, to make up for its shortcomings and further enhance the quality of service (QoS), SatCom is commonly combined with terrestrial communication. However, security issues arise due to the broad coverage of the satellite beams and the openness of wireless communication. In this paper, we adopt covert communication technology to solve them.
# The Application of Satellite Image Analysis in Oil Spill Detection
## 2 Materials and Methods
### Methodology Description  
In our study, we strongly emphasize the applicability of the solution obtained and its potential for replication in similar research. Therefore, Figure 2 shows the flow of activities to achieve the objectives set in practice.  
As shown in Figure 2, Landsat data were used for long-term analyses using the Google Earth Engine. Sentinel missions were used to analyze oil spill to better spatially define the results and divide them into passive and active techniques. In addition to the processes presented to detect changes in water and oil leaks, spatial indices have been used to correctly understand the situation in the field. We used, respectively, NDVI (Normalized Difference Vegetation Index) to study vegetation, NDWI2 (Second Normalized Difference Water Index), and SWM (Sentinel Water Mask) to monitor water in water reservoirs and NDSII (Normalized Difference Snow and Ice Index) to assess the impact of snow cover on the results. The abbreviations are listed in alphabetical order in Abbreviations.