esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 3 Materials and Methods
### Preprocessing  
For the noise in the ECG signal such as baseline drift and power frequency interference, we have used FIR bandpass filter with passband of 3-50 Hz to denoise the original ECG signal [2]. Then, the entire ECG signal was segmented into 1-min segments by referring to the annotations in the database. Based on the per-minute ECG segment, we used the Hamilton algorithm to locate the R peaks, and corrected the position of the R peaks to the maximum value, so as to ensure the accuracy of the R peaks detection. The RR interval signal was obtained by the interval between successive R peaks, and the RR interval outliers were removed with reference to the method of [24]. The RAMP signal was obtained by the amplitude of R wave. In particular, one of the simplest approaches to obtain an EDR (ECG-derived respiration) signal is by interpolating the RAMP signal [14], so the RAMP signal is also called the EDR signal.  
SpO2 and ECG recordings were collected simultaneously. Similarly, the entire SpO2 signal was split into 1-min segments, and segments that violated its physiological signifi  
Figure 2: An example of 1-min apnea and normal segments [23]. (**a,b**) are normal ECG and SpO2 segments; (**c,d**) are apneic ECG and SpO2 segments.  
cance (SpO2 values less than 50%) were removed [25]. Then, the RR interval signal, RAMP signal and SpO2 signal were used for subsequent feature extraction.
## Appendix A  
Consider the following Lyapunov function  
\[V=\frac{1}{2}z_{1}^{2}+\frac{1}{2}z_{2}^{2}+\frac{1}{2}z_{3}^{2}+\frac{1}{2} \varepsilon^{T}P_{1}\varepsilon+\frac{1}{2}\eta^{T}P_{2}\eta \tag{10}\]  
From (22), (30), and (59), the derivative of \(V\) is determined by  
\[\dot{V}\leq -k_{1}v_{1}^{2}-k_{2}v_{2}^{2}-k_{3}v_{3}^{2}+v_{2}\tilde{f}_{2}+v _{2}\tilde{s}_{e1}+k_{2}v_{2}\tilde{s}_{2}+v_{3}\tilde{f}_{3}+v_{3}\tilde{s}_{ e2}-v_{3}\tilde{s}_{2} \tag{11}\] \[-\bigg{(}\omega_{1}-\frac{1}{2}\lambda_{\max}(X_{1})k_{f_{2}}^{2} -1\bigg{)}\varepsilon^{T}\varepsilon+\frac{1}{2\omega_{1}^{2}}\lambda_{\max}(Y _{1})\delta_{1}^{2}\] \[-(\omega_{2}-1)\eta^{T}\eta+\frac{1}{2}\lambda_{\max}(X_{2})k_{f _{3}}^{2}\delta_{2}^{2}+\frac{1}{2}\lambda_{\max}(Y_{2})\frac{\delta_{2}^{2}} {\omega_{2}^{2}}\]  
Applying [PERSON]'s inequality to (11), we have  
\[\dot{V}\leq -k_{1}v_{1}^{2}-\bigg{(}\frac{k_{2}}{2}-1\bigg{)}v_{2}^{2}-\bigg{(} k_{3}-\frac{3}{2}\bigg{)}v_{3}^{2}+\frac{1}{2}\Big{(}k_{f_{2}}^{2}+k_{2}+k_{f_{3}} ^{2}+\lambda_{\max}(X_{2})k_{f_{3}}^{2}+1\Big{)}\varepsilon^{T}\varepsilon \tag{12}\] \[-\bigg{(}\omega_{1}-\frac{1}{2}\lambda_{\max}(X_{1})k_{f_{2}}^{2} -1\bigg{)}\varepsilon^{T}\varepsilon+\frac{1}{2\omega_{1}^{2}}\lambda_{\max} (Y_{1})\delta_{1}^{2}\] \[-\bigg{(}\omega_{2}-\frac{3}{2}\bigg{)}\eta^{T}\eta+\frac{1}{2} \lambda_{\max}(Y_{2})\frac{\delta_{2}^{2}}{\omega_{2}^{2}}\]  
Equation (12) can be rewritten as  
\[\dot{V} \leq-k_{1}v_{1}^{2}-\bigg{(}\frac{k_{2}}{2}-1\bigg{)}v_{2}^{2}- \bigg{(}k_{3}-\frac{3}{2}\bigg{)}v_{3}^{2} \tag{13}\] \[-\bigg{(}\omega_{1}-\frac{1}{2}(\lambda_{\max}(X_{1})+1)k_{f_{2} }^{2}-\frac{1}{2}(\lambda_{\max}(X_{2})+1)k_{f_{3}}^{2}-\frac{1}{2}k_{2}- \frac{3}{2}\bigg{)}\varepsilon^{T}\varepsilon\] \[-\bigg{(}\omega_{2}-\frac{3}{2}\bigg{)}\eta^{T}\eta+\frac{1}{2 \omega_{1}^{2}}\lambda_{\max}(Y_{1})\delta_{1}^{2}+\frac{1}{2}\lambda_{\max} (Y_{2})\frac{\delta_{2}^{2}}{\omega_{2}^{2}}\] \[\leq-\Gamma V+\Psi\]where  
\[\Gamma=\min\begin{bmatrix}2k_{1};k_{2}-2;2k_{3}-3;\\ \dfrac{2}{\lambda_{\max}(P_{1})}\left(\omega_{1}-\dfrac{1}{2}( \lambda_{\max}(X_{1})+1)k_{f_{2}}^{2}-\dfrac{1}{2}(\lambda_{\max}(X_{2})+1)k_{f _{3}}^{2}-\dfrac{1}{2}k_{2}-\dfrac{3}{2}\right);\\ \dfrac{2}{\lambda_{\max}(P_{2})}\left(\omega_{2}-\dfrac{3}{2}\right) \end{bmatrix}\]  
\[\Phi=\dfrac{1}{2\omega_{1}^{2}}\lambda_{\max}(Y_{1})\delta_{1}^{2}+\dfrac{1}{2 }\lambda_{\max}(Y_{2})\dfrac{\delta_{2}^{2}}{\omega_{2}^{2}}\]  
It can be seen that \(V\) reaches a arbitrarily small region whose bound is specified by the controller gains \(k_{1}\), \(k_{2}\), and \(k_{3}\) and the observer bandwidths \(\omega_{1}\) and \(\omega_{2}\).  
Hence, Theorem 3 is completely proven.
# Simulation of Hybrid Photovoltaic Solar Assisted Loop Heat Pipe/Heat Pump System
## 4 Simulation Results and Discussion
### Comparison of HP Mode and LHP/HP Mode under Typical Weather Conditions
#### 4.1.1 On Sunny Days in Spring or Autumn
To make the most utilization of energy-saving LHP mode, the optimal water temperature at the moment of switching LHP mode to HP mode was 32.5 \({}^{\circ}\)C by simulation. As a result, LHP mode was utilized for the first 260 min, and then HP mode was started with an average COP at 3.51 for the following 100 min. Because the water temperature was low at the beginning of the heating process, the compression ratio and electric consumption were both therefore lower, which resulted in a higher COP. With the water temperature rise, the working pressure difference of the compressor increased and the operation efficiency of the compressor decreased continuously, so the power consumption increased and COP dropped correspondingly. As seen from Figure 5, for independent HP mode, the instantaneous compressor power consumption increased from 310 W to 598 W, and the total power consumption was about 1.436 kW-h during the heating process. While for hybrid LHP-HP mode, the total power consumption was only about 0.853 kW-h, saving 40.6% compressor power in comparison to the independent HP mode. Figure 4: Variations of water temperature and COP with heating time for HP mode and LHP/HP mode. Figure 5: Variations of compressor work and overall efficiency with heating time for HP mode and LHP/HP mode. In addition, for LHP/HP mode, the overall photovoltaic/thermal efficiency of LHP mode was 0.41, which was lower than the independent HP mode. This was because the overall photovoltaic/thermal efficiency was mostly influenced by the variation of thermal efficiency, and the thermal efficiency of LHP mode was much lower than that of HP mode due to their different heat transfer performances. While after switching to HP mode, the overall photovoltaic/thermal efficiency obviously enhanced up to 0.743. The overall photovoltaic/thermal efficiency of the whole LHP/HP mode was about 33.7% lower than that of the independent HP mode.
Although kurtosis shows high values for extracting the impulsive feature [22], the background noises and rotating frequency-related interference harmonic components in the collected vibration signal would submerge the periodic impulse fault feature. As a result, the kurtosis value may not be able to reveal the useful information. The root-mean-square (RMS) value of the measured signal is applied in industry to measure the vibration intensity and indicate the incipient defect. However, the RMS value has limited applications because it is not sensitive to defect at its early stage, when the defect contributes little energy [23]. Moreover, these two indicators were originally developed in the field of statistics or information theory, thus they mainly focus on the general statistical distribution of a signal but may ignore the specific characteristics of mechanical signals [15]. Motivated by this, an autocorrelation function impulse harmonic to noise ratio index (ACFHNR) is proposed to measure the richness of the periodic impulse fault feature information in this paper. According to the proposed index, the ACFHNR based SVD-Teager energy operator (TEO) method for extracting the incipient fault feature from the vibration signal of the rolling bearing is presented. This paper is organized as follows: Section 2 introduces the theoretical background, and an ACFHNR index is proposed. Based on the proposed index, a novel method for extracting the incipient impulse fault feature of a rolling bearing using the ACFHNR based SVD-TEO is proposed in Section 3. Section 4 makes a simulation analysis of the proposed method. In Section 5, the proposed method is verified by experimental data, and Section 6 makes several conclusions.
# ITOC: An Improved Trie-Based Algorithm for Online Packet Classification
## 2 Related Work  
### Online Packet Classification Algorithm  
As we mentioned in the introduction, the work scene of online packet classification algorithms is quite different from the offline ones. So the online algorithms, which are developed from offline algorithms, changed a lot compared with the offline ones.  
In fact, most work on online packet classification algorithm is the algorithmic. For instance, some online decomposition methods, such as _a packet classification algorithm with incremental updates_ (PCIU) [8], design an incremental update process to get new bit-vectors when the ruleset changes. Owing to the update process, PCIU has a good update performance. However, it has the same shortcoming of poor scalability as other BV methods, even though it has good lookup performance.  
For decision-tree methods, a new method called CutSplit considered update latency. It separates rules into subsets and builds trees in each subset. When the ruleset changes, it only needs to rebuild a few of the trees. PartitionSort [18] is a method that first partition the rules into some sortable subsets and then build a structure called multidimensional interval tree to support search, insertion, and deletion. These methods have similar ideas. They divide the search space and build their structures in each subset.  
As TSS already meets the update requirement of online packet classification problems, the only shortcoming to be solved is the lookup speed. In TSS, the lookup speed is related to the number of tuples. TupleMerge [19] reduced the number of tuples by merging similar tuples.  
Although the online methods have good update performance, most of them have not been implemented in any actual environment, and some methods only provide test codes that only support 5-tuple test benchmarks, which are quite different from our SDN environment. In the SDN implementation environment, we use a southbound interface called protocol-oblivious forwarding (POF) [20] and the match fields could be longer and more complex. So we study the optimization ideas of the online methods and design our method based on trie structure.
## 3 Results and Discussion
### Zeta Potential and Average Particle Size Analysis
Through the above comparative analysis, it is evident that PEG is not suitable as a dispersant for an alumina polishing solution. Na(PO3)6 exhibits good dispersibility at an abrasive concentration of 20 g/dm\({}^{3}\) and a dispersant concentration of 0.5 wt%. On the other hand, phosphoric ester compounds, when used as dispersants, demonstrate excellent dispersibility within the concentration range of 0.5 wt% to 2.0 wt%. Particularly, at an abrasive concentration of 20 g/dm\({}^{3}\) and a dispersant concentration ratio of 1.0 wt%, phosphoric ester compounds show optimal dispersibility. By comparing the zeta potential, phosphoric ester compounds exhibit a superior dispersion stability compared to Na(PO3)6. Therefore, considering the effect of both dispersion stability on polishing solution and dispersibility on abrasive particles, the impact can be ranked as follows: phosphoric ester compounds > Na(PO3)6 > PEG.
## 3 Results  
### Intelligibility Comparisons between Original Speech and Mosaic Speech  
Intelligibility (i.e., word identification accuracy) was obtained by counting the number of correct answers of all participants for all stimuli. Figure 2 shows how many words the participants identified correctly when presented in their original form. The native-English group performed almost perfectly, while the Indonesian participants scored close to 90% correct and the Chinese participants scored about 80% correct.  
Figure 2: Word identification accuracy (intelligibility) for original speech for each language group (English, \(n=19\); Indonesian, \(n=19\); Chinese, \(n=20\)). Error bars indicate standard error of means.  
The intelligibility scores for the mosaic speech stimuli are shown in Figure 3. Since the results of the Wilcoxon signed-rank test [39] had shown that there was no significant effect of half-phase or whole-phase starting phases on word intelligibility (\(p>0.05\)), and since investigating the effects of phase was not the main purpose of the present study, for convenience the scores were collapsed for subsequent analyses. Figure 3 shows that mosaicizing the original speech really affected the intelligibility. The intelligibility decreased by about 21% for the native-English group, by about 40% for the Indonesian group, and by about 25% for the Chinese group. An obvious reason for this is that when original speech is mosaicized, its signal degrades both in the temporal and the frequency dimension.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 3 The Proposed Method of Trust-Aware Reactive Ad Hoc Routing
### Performance Metrics
#### 3.4.2 Network-Wide Average Path Length  
When we incorporate trust into the routing protocol, it does not find the shortest paths anymore. It may pick more trusted but longer alternative routes. The metric called _average path length_ (\(PL_{avg}\)) helps us understand how the average path length was influenced by incorporating trust into the route discovery mechanism. The length is the number of hops from the source node to the destination node. \(PL_{avg}\) is formally defined as:  
\[PL_{avg}=\frac{\sum_{i=1}^{m}PL(P_{i})}{m}, \tag{4}\]  
where \(m\) is the number of paths \(P_{i}\) in the ad hoc network.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 6 Open Issues
### Quality of Service (QoS)  
While early versions of SDN and OpenFlow may not have explicitly supported differentiated QoS, recent developments have been made to incorporate QoS mechanisms into SDN networks. For example, the OpenFlow 1.3 specification includes support for QoS and traffic classification, allowing for the creation of virtual networks with different QoS requirements. Additionally, SDN controllers can implement QoS policies and traffic-engineering strategies to ensure that different types of traffic are handled appropriately. In order to enable end-to-end QoS signaling across distributed SDN domains, session control protocols can be used as a northbound interface. Using this approach, multiple applications can be managed in real time with flexible and differentiated QoS provisioning. The configuration of many QoS mechanisms currently requires human intervention, impairing network flexibility. In order to provide QoS support for SDN-enabled applications without using OpenFlow, approaches such as the OpenQoS framework have been developed, assisting network administrators in defining the QoS strategy that flows should adhere to and how the controller can allocate resources and differentiate services.  
Ad hoc networks lack centralized management, which can lead to issues with congestion and prioritization of messages. This can be especially problematic in vehicular networks, where the large number of vehicles on the road and their constantly changing positions can result in a significant amount of network traffic [156]. However, there are solutions being developed to address these issues, such as using intelligent routing algorithms and prioritizing certain types of messages, such as emergency messages, to ensure timely and reliable delivery.
## 4 Experimental Results and Discussion
### LP Characters Segmentation Results Analysis  
Regardless of the variety of license plates, the proposed framework effectively segments all the characters of almost 97% of license plates with 98% precision, as shown in Table 3, which is quite good in the case of multinational VLPs. The major reason for high accuracy for multinational VLPs is that our proposed approach first eliminates the redundant part of license plate by extracting the region of interest (ROI). It consists of the largest area based on unique color that is the salient feature of majority  
Figure 14: Samples images of LPs from the test dataset.  
of the country's LPs. Then, a character height estimation filter is used, which adaptively detects the length of characters in any type of license plate. The accuracy and precision are defined as  
\[\begin{array}{l}Accuracy=\frac{NMC_{\text{LPs}}}{T_{\text{LPs}}}\\ Precision=\frac{CS_{\text{C}}^{2}}{CS_{\text{C}}+FS_{\text{C}}}\end{array}\]  
and  
\[\begin{array}{l}T_{\text{LPs}}=NMC_{\text{LPs}}+MC_{\text{LPs}}\end{array}\]  
where \(NMC_{\text{LPs}}\) (no-missing character license plates) are license plates of all detected characters and \(MC_{\text{LPs}}\) (missing-characters license plates) are LPs having partially detected characters. \(CS_{\text{C}}\) represents the correctly segmented characters and \(FS_{\text{C}}\) denotes the falsely segmented characters of no-missing character license plates (\(NMC_{\text{LPs}}\)) and only these LP characters are further used in recognition stage, as the LPs with missing characters (\(MC_{\text{LPs}}\)) are useless for further processes.
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 3 PID Controller Parameter Optimization
### Basic Principles of Genetic Algorithm  
The genetic algorithm is based on the principles of natural selection and genetic mechanisms from biological evolution theory. It solves optimization problems by simulating  
Figure 3: Longitudinal PID control model.  
the processes of reproduction, mutation, and survival of the fittest. The basic steps are as follows:  
1. Initialize Population: Randomly generate a set of possible solutions, with each individual representing one solution.
2. Fitness Evaluation: Use a fitness function to evaluate the quality of each individual.
3. Selection: Select the better individuals as parents based on their fitness, with higher chances for superior individuals to be chosen.
4. Crossover: Perform crossover operations between parent individuals to generate new individuals, combining genes from both parents.
5. Mutation: Randomly mutate some individuals to increase population diversity.
6. Iteration: Repeat the above steps to create a new population until the optimization conditions are met.
7. Output: The algorithm ultimately outputs the individual with the highest fitness as the optimal solution.  
This process enables the genetic algorithm to progressively approach the global optimal solution.
## 4 The Proposed Method and Principle
### The Adaptive Interference Cancellation Method  
Reference [18] introduced an adaptive method to eliminate the magnetic interference with a pair of magnetometers, and its theoretical model is shown in Figure 6, where \(x(n)\) represents the ambient magnetic field, \(e(n)\) is the magnetic interference, and \(w_{1}(n)\) and \(w_{2}(n)\) represent the intrinsic noises of OPM-I and OPM-II, respectively. The output readings of the two magnetometers are expressed as \(y_{1}(n)\) and \(y_{2}(n)\).  
Figure 4: Compensation results of OPM-II: (**a**) calibration flight data and (**b**) the power spectral density of the calibration flight data.  
Figure 5: The correlation coefficient of the interference field in OPM-I and OPM-II.  
The model can be expressed as a set of equations, as shown in Equation (8). The interference magnetic field elimination is the solution of an adaptive coefficient \(k\).  
\[\begin{array}{l}y_{1}(n)=s(n)+e(n)+w_{1}(n)\\ y_{2}(n)=s(n)+ke(n)+w_{2}(n)\end{array}. \tag{8}\]
## 2 Optimization Design Process and Modeling Method
### Pump Model
#### 2.2.2 Optimization Objectives
Secondly, calculate weight vector:  
\[\mathbf{\omega}_{i}^{*}=\left(b_{1},b_{2},\ldots,b_{N}\right)^{\mathrm{T}}, \tag{4}\]  
where \(i=1,2,3,\ldots,N\); \(b_{i}=\sqrt[3]{a_{1}\cdot a_{2}\cdot\ldots a_{iN}}\)  
Thirdly, calculate the normalized weight value for each weight vector obtained from Equation (4):  
\[\omega_{i}=b_{i}/\sum_{i=1}^{N}b_{i}. \tag{5}\]  
Fourthly, sum the elements in each column:  
\[S_{i}=\left(\sum_{j=1}^{N}a_{1j},\sum_{j=1}^{N}a_{2j},\ldots,\sum_{j=1}^{N}a_{ Nj}\right)^{\mathrm{T}}. \tag{6}\]  
Fifthly, calculate the maximum eigenvalue of the matrix. It would be used to calculate the following consistency index. \[\lambda_{\mathrm{max}}=\sum_{i=1}^{N}\mathbf{\Omega}_{i}\cdot S_{i}, \tag{7}\]  
where \(\mathbf{\Omega}_{i}=\left(\omega_{1},\omega_{2},\ldots,\omega_{N}\right)\)  
Sixthly, consistency test. A consistency ratio (\(CR\)) is obtained in addition to the corresponding principal eigenvector when using FAHP for criteria weighting. It represents the priority vector which is integrated by the intended weights. The consistency ratio is used to measure if this eigenvector estimates the weight vector well. It is obtained by division of the consistency index (\(CI\)) and the appropriate average random consistency index (\(RI\)):  
\[CR=\frac{CI}{RI}, \tag{8}\]  
\[CI=\frac{\lambda_{\mathrm{max}}-N}{N-1}, \tag{9}\]  
where \(\lambda_{\mathrm{max}}\) is the maximum eigenvalue of the comparison matrix, and \(N\) is the order of objective matrix. The value of the \(RI\) is related to \(N\), which can be obtained from Table 2. If \(CR\) is greater than 0.1, the comparison matrix is inconsistent and should be revised. The second step for the optimization is to use the intelligent method. The impeller parameters mentioned before, the impeller outlet diameter \(D_{2}\), blade outlet width \(b_{2}\), blade number \(Z\), blade outlet angle \(\beta_{2}\), blade inlet angle \(\beta_{1}\), leading edge tangential angle \(t_{3}\), trailing edge tangential angle \(\phi\), and blade thickness \(\delta\), are used for variable parameters to find the optimal combination at this step. Batch command is used to drive the following cycle of the numerical simulation process. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(N\) & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 \\ \hline \hline \(RI\) & 0 & 0 & 0.58 & 0.9 & 1.12 & 1.24 & 1.34 & 1.41 & 1.45 & 1.49 & 1.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Random consistency index (\(RI\)).
## 3 Results and Discussion
### MEG/EEG-Based Hyperscanning Research
#### 3.1.3 Motor Synchronization  
The motor synchronization application makes up 23% (14/60) of the research done with the MEG/EEG neuro-imaging methodology [41; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113]. The goal of these studies was to look at how participants are able to coordinate their actions with each other. This was most commonly done by asking participants to perform a motor task such as rhythmically tapping their fingers [41; 104; 105],following someone's lead (imitation) to tap the sensors at the same time [102; 106; 108; 109; 110], or other motor/selective attention tasks [68; 107; 111; 112; 113]. Motor synchronization studies were also done via a computer by asking participants to try to coordinate their response times to different matching/shapes games.  
_Setting_. [PERSON] et al. [68] was the only study which explored the shared and complementary mental models in a natural setting. It performed EEG mapping of two brains engaging in the real-world interactive motor task of juggling. The researchers increased difficulty by having participants juggle an increasing number of objects.  
_Orientation_. Less than half the studies, 43% (6/14), were done with participants facing each other, while 57% (8/14) of the studies employed the side-by-side orientation [102; 103; 104; 105; 106; 110; 111; 112; 113]. The studies that were done with participants facing each other used motor synchronization tasks like finger tapping. For example, [PERSON] et al. [113] investigated sub-band modulations in the mu domain in pairs performing different social coordination tasks--variations of finger tapping and motion imitation. [PERSON] et al. [108] explored neural signatures of leaders and followers in terms of hand kinematics using dual-MEG.  
_Group Size_. All 14 studies (100%) looked at the neural responses between pairs of participants, because they all focused on neuro-synchrony and mirror-neuron responses between pairs.