esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 4 Forecasting of Chaotic Time Series in Various Applications
### Communication Signals and Systems  
The complex weighted neural network algorithm in [25] solves the principal component analysis problem and high-resolution adaptive bearing prediction. The proposed approach is especially effective in circumstances where the hermit matrix progressively changes over time due to adaptive tracking. The BPNN tuned SVM grey model in [55] is used for forecasting the signal deviation time series. The anchor selection method is based on polynomial chaos expansions [86] for angle-of-arrival prediction-based positioning systems. The chaos algorithm in [92] is proposed for forecasting the radio wave propagation in the ionosphere. The proposed algorithm can also forecast a set of radio transmission signals at a fading amplitude time series location. The phase space reconstruction-LS-SVM in [98] is developed to predict FM radio's band occupancy rate in German Rohde, Schwarz company, and fixed radio monitoring station of Xihua University, USA. The proposed approach can be extended further to improve multistep time series prediction. The minimum phase-space volume-EKF equalization method presented in [106] is for forecasting the chaos in power line communications. The LLE, [PERSON]'s fractal dimension, and sample entropy techniques are used for predicting the fractals, chaos, and parametric entropy features of surface electromyography signals during dynamic contraction of biceps muscles under a varying load [127]. The proposed process can also be helpful in physiotherapy and athletic biomechanics for testing muscular fitness. A deterministic chaotic sequences method is developed to forecast quadrature baseband signals and orthogonal frequency division multiplexing-based cognitive radio channel [137]. The proposed approach can also be applicable to bit error rate performance, which is projected to improve if an appropriate power management method is used.
## 2 Outline of the Lane Multiplexing-Based Method for IBA Setting
### Issue Description of the IBA Based on Lane Multiplexing  
The IBA based on lane multiplexing is actually the BLITDM in aforementioned intersection space slices. The details of the time slice division and lane control strategy of general space slices are described in the references [19; 20]. This paper focuses on the IBA lane setting of intersection space slices. We consider here a typical physical environment of intersection space slices for the IBA based on lane multiplexing, as illustrated in Figure 4, where the VMS is installed at the upstream position of the weaving section at the intersection. The lane section from the VMS to the weaving section is defined as the merging section. The drivers can choose whether to travel in the bus lane of this space slice by observing the lane status information displayed on the VMS.  
In the intersection space slice, the virtual lane-borrowing vehicles can enter the bus approach from the merging section and the weaving section according to the IBA lane state of the space slice and then pass through the downstream intersection, where the IBA lane state is related to the signal cycle, current signal phase and queue length of the intersection. It should be noted that since the signal of urban intersection involves the coordinated control of traffic in the trunk line and region, the lane control strategy of IBA in this paper does not involve the signal control of intersection, so as to minimize the interference to the original traffic system.  
In this paper, we hope to provide a clear time allocation scheme for various types of vehicles using bus lanes and avoid the conflict of the right-of-way between different types of vehicles when using the lanes, where buses cannot be delayed by the general vehicle queuing at the intersection, whereas the general vehicles can effectively use the IBA lane to pass through the intersection without interfering with the bus running. In view of this, when considering the time-division multiplexing control of the IBA lane in intersection space slices, the following three key issues need to be addressed:  
1. Does each type of vehicle have the right to use the bus lane in this space slice at the current time?  
Figure 4: Typical physical environment of the IBA based on lane multiplexing.  
For these vehicles that are entitled to use, how long are they allowed to be used?
3. What is the appropriate length for the setting of the intersection space slice? In this way, it can satisfy the orderly and smooth lane changing of the virtual lane-borrowing vehicles in the relatively congested near-intersection area.
# A Calibration Method for Time Dimension and Space Dimension of Streak Tube Imaging [PERSON]
## 2 Coupling Mechanism
We detected the laser spot by a CCD, and the detection result is shown in Figure 3.  
According to imaging principles, it is theoretically possible for only one pixel of the CCD can be lit, disregarding the diffusion of the optical imaging system and electronic imaging system. This occurrence is contingent upon factors such as the size of the laser spot, the distance between the CCD and the target, the size of a CCD pixel, and the focal length of the receiving lens. However, more than 100 pixels were actually lit up with different intensities, as shown in Figure 3.  
Since the two factors work together, the deconstruction of two components on the time axis and space axis is more complex. If this problem cannot be solved, the range and spatial accuracy of STIL will be reduced.
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 4 Results
### Simulated S11 Parameters  
In this subsection, the impact of the antenna feeding as well as the impact of the antenna-skin distance (a-s) for S11 parameter is studied with the layer model. Discrete port as well as coaxial cables with thicknesses 6.5 mm and 1 mm are used in the simulations. With discrete port, the antenna can be attached directly on the skin layer in the simulations. Thickness of the coaxial cable determines the antenna-skin distance in the simulation model, as seen in Figure 5a. With the coaxial cable thickness of 6.5 mm, the antenna-skin distance is 3.2 mm, whereas with the coaxial cable thickness 1 mm, the antenna-skin distance is 0.94 mm. Furthermore, we evaluate a case whereby we set the antenna with 1 mm coaxial cable directly located on the skin layer so that the skin layer is partially cut below the coaxial cable to enable direct contact between the skin and the antenna, as shown in Figure 5b.  
Figure 4: Flexible antenna on the hand phantom in Satimo.  
Figure 6 presents the S11 results obtained with these different cases. As observed, the feeding and the antenna-skin distance has clear impact on the S11 results. The closer the antenna is to the skin, the better the antenna matching obtained for the whole frequency range 2-11 GHz. This result is positive since the wearable flexible antenna is targeted to work directly on the skin contact. The best matching is obtained with the discrete port.
## 3 Sound Propagation and Interference Simulation  
### Sound Propagation and Interference Model  
We first build a one-dimensional sound propagation model to confirm agreement between SPH simulation results and theoretical solutions. In this model, the sound propagates from \(x<0\) to \(x\geq 0\) (\(x\) denotes the direction of propagation), and the SPH computational region is from \(-10\) m to \(90\) m. The sound source is a plane wave, and the region of sound source is from \(-10\) m to \(0\) m, while the region of sound propagation is from \(0\) m to \(90\) m. The sound wave is at [\(-10\), \(0\)] when \(t=0\) s. When \(t=0.25\) s, the distance of sound propagation is \(85\) m, that is the sound wave is located at [\(-10\), \(85\)].  
The sound pressure of the acoustic wave in \(x<0\), namely the source of the acoustic wave, can be written as:  
\[p(t,x<0)=P_{\mathrm{A}}\sin(\mathit{ot}\ \text{-}\ \mathit{kx}) \tag{17}\]  
where \(t\) denotes time, \(x\) is the geometric position in the propagation direction, \(P_{\mathrm{A}}\) denotes the amplitude of the sound wave, \(\omega\) is the angular frequency of the sound wave and \(k=\omega/c_{0}\) is the wave number. The initial values of the amplitude and angular frequency are set to be \(P_{\mathrm{A}}=50\) Pa and \(\omega=50\) rad/s, respectively. In addition, the sound speed \(c_{0}\) is set to be \(340\) m/s, and the density of the propagation medium is \(1.0\) kg/m\({}^{3}\).  
Similarly, a sound interference model is built, as well. In the interference model, the region of sound interference goes from \(-10\) m to \(80\) m. In the computational region, a sound wave with sound pressure of \(30\) Pa and angular frequency of \(100\) rad/s transmits along the \(x\) axis; at the same time, another sound wave with sound pressure of \(10\) Pa and angular frequency of \(100\) rad/s comes from the opposite direction. After \(0.15\) s, the sound interference is generated by two sound waves.  
The SPH algorithms with and without added artificial viscosity are used to simulate the sound propagation and interference. These simulation results are then compared to theoretical solutions.
## 2 Results and Discussion
### Statistical Analysis
#### 2.3.3 Differences between Solvents in Extraction Using Solvents  
Table 8 presents the results of the analysis of differences in the scope of the SLE method. The analysis had a two-stage character using the Friedman ANOVA test due to the small size of the groups in the measurements.  
As it turned out, the analysis of the differences between the groups of solvents in the method with solvents allowed us to find no differences in all the time intervals. This means that regardless of the solvent used, the intensity of the volatile compounds obtained was similar. In the case of differences within the solvent groups, it turned out that there was a border difference effect in the case of the use of methanol (\(p\) = 0.050), indicating a higher intensity of volatile compounds in the case of the measurement of 24 h than in the case of 48 h and 72 h.
# Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications
## 3 Results
### Bandpass Filtering Results  
The same example provided in Figure 4 but referring to the BPF step is shown in Figure 5. Clearly, the HPF does eliminate the baseline drifts not removed by the MA corrections due to their local and non-linear action. Results relevant to the LPF smoothing are similar for all eight pipelines. However, the amount of removed HF noise presents interesting differences. Namely, only the two Hybrid-MARA approaches appear to have had significant smoothing in the previous MA reduction step when applying SG smoothing as a last step of the algorithm scheme.  
It is noteworthy that the wavelet method, which reduced MAs, did not perform effective filtering in both the stop bands: LF, for baseline drift reduction, and HF, for signal smoothing. This is a direct consequence of the data-driven setting of the normality range of the method, with no a-priori definition of signal vs. noise scales. Therefore, the noisy scales of slow baseline drifts and fast HF noise were preserved. Removal of unwanted bands was left to the next BPF phase, which confirms the need of this further step.  
The modification of the spectral content performed by the BPF is analyzed in Table 1, where the I/O power ratio (output over input %) is shown both in the passband (within band, \(PR_{wb}\)) and stopband (outside the band, \(PR_{ob}\)), within and outside the \(f=[0.01-0.2]\) Hz range. Due to the BPF linearity, I/O differences should be attributed to the method-specific impact of LPF and HPF transition bands in the specific algorithm. Apparently, all methods, with the only exception of TDRR, displayed a large power content at the edges of the passed bandwidth, where power loss occurred, compared to the ideal BPF. This led to 20% or more power loss in the ideal passed bandwidth. Such non-ideal loss was reduced to about 10% in TDRR. As for \(PR_{ob}\), the ideal stopband behavior should be a 0% power ratio, which should be best approximated away from the cutting edges. Therefore, the larger values above 1% displayed by the Hybrid-MARA approaches should indicate the  
Figure 5: Single-channel graphical representation of the effect of BPF over \(\Delta OD\) signals across pipelines. \(\Delta OD\) signals are represented with respect to the MA-corrected signals (blue line-input signal) and after BPF step (red line-output signal).  
presence of significantly greater power in the outside band transition regions. These results suggest that the BPF order and design could be profitably adapted to the previous MA reduction step.
# Simulation Study of Thermal-Mechanical Coupling Fretting Wear of Ti-6 Al-4V Alloy
## 4 Results and Discussion
### Effect of Temperature on Wear  
The results with and without temperature are compared to analyze the effect of temperature on wear. Figure 8a shows the contact pressure distribution for the two cases with plasticity considered. The position of the contact pressure change is the same for both cases. The distribution of the contact pressure is similar, which means that their wear profiles are similar. A closer look reveals that the contact pressure value without considering the temperature is slightly larger and its distribution range is slightly smaller. The reason for this phenomenon lies in the fact that temperature affects frictional and wear characteristics. Due to the difference in the degree of wear, temperature is more affected by wear.  
Figure 8b shows the wear depth distribution of the contact surface in both cases. The two wear profiles are similar, which is consistent with the analysis of the contact pressure. Wear depth and wear width are greater when temperature is considered. The friction caused by the wear process increases the temperature of the contact surfaces. The coefficient of friction decreases with increasing temperature, but the coefficient of wear increases with increasing temperature, which leads to more intense wear. It can be concluded that an increase in temperature has a catalytic effect on wear for such thermal-induced softening materials. Therefore, the wear resistance of such materials in practical engineering can be improved by reducing the working or ambient temperature.
## 1 Introduction  
Mobility is a basic necessity for people; as a result, modern societies are concerned with developing transit systems that satisfy this need. Because a critical aspect of transit systems is their accessibility, authorities have developed guidelines, recommendations, and initiatives with the objective of permitting access to transportation for all people, including people with special needs [1,2]. According to the United Nations, the world population in 2011 consisted of seven billion people [3]. The percentage of the population that functions with a disability is approximately 15%, which comprises the largest minority population in the world. Due to the increased life expectancy of the population in economically developed countries, the percentage of the population that live with a disability has increased.  
Transit systems provide services with the objective of facilitating access to transit networks; however, these services are designed for people with full physical and cognitive abilities. In this paper, we present the case of an intelligent system for public transit by highway, in which the main objective is to facilitate access to public transit for people with special needs, e.g., the blind, the deaf, people with reduced mobility, or seniors. An important feature of this system is deployment using prevalent components in highway public-transit infrastructure, e.g., sensors, mobile communications systems, and positioning systems. The system is designed to operate in non-urban transport contexts, e.g., isolated rural areas, where the availability of basic infrastructure, such as electricity and communications infrastructures, is not always guaranteed. To construct the system, the principles and techniques of Ubiquitous Computing and Ambient Intelligence have been utilized. The Ubiquitous Computing paradigm has been used for context awareness: to recognize different environments in the transit network and implement smart services. The principles and objectives of Ambient Intelligence have inspired the design and development of services that improve the accessibility of public transit for people with special needs, using sensors and mobile communications.  
This paper is structured as follows: in the following section, we present a synthesis of related research. In the third section, we present the objectives that have been established and the technological challenges that are encountered. The system is described in the fourth section. The fifth section is devoted to a case study example. The sixth section assesses the impact of the proposed system. The main conclusions and future works are presented in the seventh section.
# Rosemary Leaf Extract Inhibits Glycation, Breast Cancer Proliferation, and Diabetes Risks
## 4 Conclusions  
In this study, RE demonstrated an inhibitory effect on AGEs, CML, and protein glycation formation. In addition, RE had the potential of inhibiting breast cancer cell proliferation. The risk of the diabetic rat was also proven to be lowered by the RE treatment via mediating blood glucose, serum malondialdehydedehyde (MDA), cholesterol (CHO), triglycerides (TG), low-density lipoproteins (LDLs), anti-oxidation capacity (T-AOC), and superoxide dismutase (SOD) activity. Additionally, the liver function of the diabetic rat was improved by RE, through maintaining alkaline phosphatase (ALP), glutamate pyruvate transaminase (GPT), and glutamate oxaloacetate transaminase (GOT) activities.  
Conceptualization, D.X.; formal analysis, Y.Y.; methodology, L.Z.; software, B.A.; resources, J.H.; data curation, X.Z.; writing--review and editing, Y.S.; supervision, Z.S. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Normal Control** & **Diabetes Control** & **DRE Treatment** \\ \hline \multirow{4}{*}{**Blood lipid**} & **HDL-C (mmol/L)** & \(1.45\pm 0.32\)a & \(0.92\pm 0.22\)a & \(1.37\pm 0.02\)a \\  & **LDL-C (mmol/L)** & \(0.37\pm 0.01\)a & \(0.71\pm 0.14\)ab & \(0.49\pm 0.14\)a \\  & **TG (mmol/L)** & \(0.80\pm 0.05\)a & \(1.35\pm 0.08\)c & \(1.21\pm 0.05\)b \\  & **T-CHO (mmol/L)** & \(2.48\pm 0.42\)a & \(2.97\pm 0.12\)a & \(2.63\pm 0.17\)a \\ \hline \multirow{4}{*}{**Liver Function**} & **ALP (U/100 mL)** & \(43.81\pm 3.64\)a & \(366.95\pm 10.99\)b & \(349.25\pm 7.21\)b \\  & **GOT (U/L)** & \(47.34\pm 2.42\)a & \(64.93\pm 2.35\)c & \(50.53\pm 2.28\)b \\ \cline{1-1}  & **GPT (U/L)** & \(37.37\pm 1.23\)a & \(64.07\pm 3.29\)c & \(50.61\pm 1.67\)b \\ \hline \hline \end{tabular} HDL-C: high-density lipoprotein-cholesterol; LDL-C: low-density lipoprotein-cholesterol; TG: triglycerides; T-CHO: total cholesterol; ALP: alkaline phosphatase; GPT: glutamate pyruvate transaminase; GOT: glutamate oxaloacetate transaminase; DRE: diabetic group with rossary extract treatment.  
\end{table}
Table 4: Lipids profiles and liver function parameters in the experimental rats.  
**Funding:** This research was supported by the National Natural Science Foundation of China (31772096), the Key Technologies Research and Development Program of Hainan (ZDXM2014104), Central Public-interest Scientific Institution Basal Research Fund for Innovative Research Team Program of CATAS (NO. 17 CXTD-05, NO.1630092019001).  
**Acknowledgments:** We thanks to the National Natural Science Foundation of China (31772096), the Key Technologies Research and Development Program of Hainan (ZDKJ2017004), Central Public-interest Scientific Institution Basal Research Fund for Innovative Research Team Program of CATAS (NO. 17 CXTD-05, NO.1630092019001).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Influence of the Flexible Tower on Aeroelastic Loads of the Wind Turbine
## 2 Analytical Study
### Eigenvalue Analysis  
The eigenvalue problem of the structure is to analyze its free vibration. To calculate the natural frequency and modal vibration pattern of the tower system, the effect of damping is generally neglected. Removing the damping matrix and external excitation of Equation (1), the free vibration equation of the tower can be obtained as follows:  
\[M\ddot{x}(t)+Kx(t)=0 \tag{17}\]  
The characteristic equation of the tower can be solved as:  
\[\Big{(}K-\omega^{2}M\Big{)}\phi=0 \tag{18}\]  
where \(\omega\) denotes the frequency of free vibration, and \(\phi\) denotes the time-independent nth-order vector. \(\phi=\left[\begin{array}{cccc}\phi_{1}&\phi_{2}&\cdots&\phi_{n}\end{array}\right]\), \(\phi_{n}\) denote the nth-order vibration column vector of the structure.  
Wind turbines operating in different seas have different foundation characteristics. [PERSON] et al. [11] studied and obtained classical values of foundation stiffness for three actual operating wind turbines. Based on this study, 12 foundation cases are selected in this paper to study the effect of flexible foundations on the towers' inherent frequency. The wind turbine numerical model parameters used for simulation in this paper were selected from the NREL 5 MW offshore horizontal axis wind turbine [12]. The first three orders of tower inherent frequencies are calculated as shown in Table 1.  
From Table 1, it can be concluded that the rotational stiffness has a greater effect on the first two orders of the towers' natural frequency and a smaller effect on the third order of the towers' natural frequency. The horizontal stiffness of a tower on the natural frequency of the tower is opposite to the effect of rotational stiffness. For a tower with a rigid foundation, the first three orders of inherent frequency are also calculated, as shown in Table 2.  
From Tables 1 and 2, it can be concluded that the flexible foundation of a tower has an important effect on the free vibration of the tower. Therefore, the flexibility of the tower needs to be taken into account in the dynamic analysis of offshore wind turbines.
## 3 Performance Analysis
Figure 12: Normalised average residual battery level achieved by the CoAP, MQTT-SN and WPT-MQTT-SN protocols in a Two-Tier star based topology nodes deployment. Figure 8:0 Comparison of MQTT-SN protocols without and with WPT for different Publishing rates in Two-Tier star based topology. It is worth noticing that this represents a worst-case scenario, where performances degrades, but it is interesting to evaluate the effects of WPT. Since most of the CHs are far from the ES or are not in its coverage range, the network nodes cannot properly recharge their battery and, thus the overall network consumption of the proposed approach is similar to the MQTT-SN standard, as shown in Figure 8:1  
Moreover, increasing the publishing rate the network consumption gets worse for all the solution and the effect of WPT becomes irrelevant, as depicted in Figure 8:2 In conclusion, the proposed WPT-MQTT-SN approaches is able to achieve remarkable efficiency in the presence of controlled topology where the ES node deployment can be spatially optimised, which represent the most common case in IWSANs. To improve the accurateness of the results analysis, we have compared the performance achieved by the considered protocols (i.e., CoAP, MQTT-SN and WPT-MQTT-SN) over three different topologies (i.e., Random Mesh, 2-Tier and Unbalanced Random Mesh) in the same simulation time interval. The results, in terms of normalised lowest/highest residual energy and lowest/highest network lifetime are pointed out in Tables 3 and 4, respectively, all confirming the previous considerations within a more reliable min-max confidence interval. Figure 8:3 Comparison of WPT for different Publishing rates in Unbalanced Random Mesh topology. Figure 8:4 Normalised average residual battery level achieved by the CoAP, MQTT-SN and MQTT-SN-WPT protocols in a Unbalanced Random Mesh topology nodes deployment.