esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### SampEn Parameters  
The two most commonly used entropy analysis methods for biological data are ApEn [28] and SampEn [30]. ApEn was proposed by [PERSON] in 1991 but has shortcomings, which were addressed in the SampEn algorithm proposed by [PERSON] in 2000. These shortcomings include self-matching of vectors resulting in bias, inconsistent results due to differing input data lengths, and a requirement for fixed parameters. SampEn does not calculate self-vectors, is independent of data length, and has been demonstrated to produce consistent results [36]. SampEn is more reliable for short-step data sets, is less sensitive to changes in data length, and has fewer relative consistency problems [37].  
In the literature, the parameter \(m\) is typically set to 2; studies have shown that \(m=2\) produces reasonable results for theoretical data and results in few self-matches for experimental data. Because m does not have an upper bound in SampEn, comparing results between studies is challenging if \(m\) is increased to 3.  
In 2002, [PERSON] proposed permutation entropy (PE) [38], which is an appropriate complexity measure for chaotic time series in the presence of dynamic and observational noise. In 2016, [PERSON] proposed dispersion entropy (DE) [39], which is superior to PE for discriminating between groups in a data set of real-valued signals. Its computation time is less than those of SampEn and PE for long signals--the DE analysis of signals with size 12,000 is 8 s faster than the SampEn analysis [39]. However, the sample size \(N\) of the data segments in this study were typically less than 400; hence, processing speed was not a concern. Although ApEn and SampEn are still the most widely used entropy methods in biomedical research, DE may be superior for future research involving large data sets.  
The experiment in this study was an incremental exercise experiment in which cyclists began riding at 60 W and the power was increased by 30 W every 3 min for five stages [31]. However, previous studies did not measure SV to judge the ACP stage. Moreover, the different setting of r in SampEn produced different results. If \(r=0.1\) or \(0.15\), the SampEn RR value decreased with exercise time; by contrast, if \(r=0.2\) or \(0.25\), SampEn RR first increased and then decreased.  
Figure 8: The results of SampEn RR and SampEn SV in the three groups.  
The objective of this study was to identify indicators for determining the sub-max stage to predict the ACP stage. Based on previous studies, the SampEn trends of \(r=0.2\) and \(r=0.25\) were unique and this may be an observable indicator. Hence, SampEn RR with \(r=0.2\) may be an indicator of sub-max training. Further, \(r=0.2\) is also the parameter set in many SampEn papers. Therefore, the SampEn parameters of \(r=0.2\), \(m=2\), and \(N=\) input data were used in this study.
## 2 System Description
### Water Pump Model  
Because the flow rate of each circulation step in the system varies with time, the variable frequency pump was applied and the power consumption was calculated using the following equation:  
\[P_{wp}=\frac{\rho gqH}{3.6\times 10^{6}\eta} \tag{11}\]  
where \(q\) is the flow rate of the circulating water pump, m\({}^{3}\cdot\)h\({}^{-1}\). \(H\) is the head of the circulating water pump, m. \(\eta\) is the efficiency of the variable frequency pump.  
The head is calculated according to the law of similarity:  
\[\frac{q}{q_{0}}=\frac{n}{n_{0}}=k \tag{12}\]  
\[\frac{H}{H_{0}}=\bigg{(}\frac{n}{n_{0}}\bigg{)}^{2}=k^{2} \tag{13}\]  
where \(q_{0}\) is the rated flow rate of the variable frequency pump, m\({}^{3}\cdot\)h\({}^{-1}\). \(H_{0}\) is the rated head of the variable frequency pump, m. \(n_{0}\) and \(n\) are the rated and actual speed of the variable frequency pump, respectively, r\(\cdot\)min\({}^{-1}\). \(k\) is the speed ratio.  
The efficiency \(\eta\) consists of three parts: the pump efficiency \(\eta_{\text{p}}\), the motor efficiency \(\eta_{\text{e}}\), and the inverter efficiency \(\eta_{\text{t}}\):  
\[\eta=\eta_{\text{p}}\eta_{\text{e}}\eta_{\text{t}} \tag{14}\]The three are calculated according to the empirical equations [35]:  
\[\eta_{\rm P}=\eta_{\rm p0}\sin\Bigl{(}k\cdot\frac{\pi}{2}\Bigr{)} \tag{15}\]  
\[\eta_{\rm e}=0.94187[1-\exp(-9.04k)] \tag{16}\]  
\[\eta_{\rm t}=0.5067+1.283k-1.42k^{2}+0.5842k^{3} \tag{17}\]
## 3 Test for GNSS Monitoring System  
This test aims to estimate the accuracy of satellite positioning processed with GNSS sensors and noise reduction algorithm, and to distinguish the GNSS signal component. As shown in Figure 1, three control points were arranged in an open space. Monitoring Point B was coaxially mounted with a prism and a GNSS satellite antenna, the latter of which was able to receive GPS, BDS, and GPS + BDS satellite positioning signals. The support platform of Measuring B allows three-direction adjustment, as shown in Figure 2. Point A was arranged for a precision leveling measurement station and a total station was installed at the point with a centering device. Point P was for the rearview prism of the total station. The Point D was the reference station for GNSS satellite monitoring, equipped with a GNSS receiver and an antenna. The initial observation time lasted for 1 h in the precision experiment; after the displacement of the adjustable platform at Measurement Point B was adjusted, the duration of each observation was 30 min.  
Figure 1: Layout of measuring points.  
Figure 2: The control point and measured point of GPS.  
Figure 3 shows the results of the accuracy test, the results of precision leveling, and GNSS signal express the same trends with the actual displacement variations at the measurement point. The optimal accuracy is found in precision leveling, with both planar and vertical accuracy higher than 1 mm; GNSS data obtained by BDS + GPS and noise reduction algorithms achieve the planar accuracy higher than 1 mm, and the vertical accuracy higher than 3 mm.  
Figure 4 shows the continuous observation of Point B, throughout which process the position of Point B remained unchanged. The stability experiment employed the stationary wavelet transform and the empirical noise reduction mode for experimental data; the calculations were carried out for 30 min, 1 h, 2 h, and 4 h respectively. As shown in the figure, with the GNSS positioning method, as the calculation duration extends, variation range of the monitoring data narrowed gradually, leading to smaller variance among the observed data. From the accuracy and stability experiment of GNSS, it can thus be concluded that the GPS + BDS data and the 4 h calculation scheme result in higher data positioning accuracy.
## 1 Introduction  
Entities responsible for the regulation of public transportation on roads need data that allow them to verify that the operations of transportation companies are executed according to plan. Thus, these agencies frequently request the data from the companies in charge of providing this service that includes trip starting time, time history of stops, frequency and trips performed. In many cases, these data are difficult to provide because the companies do not have the necessary technology to acquire them. Thus, the data must be recorded manually, which is susceptible to errors. Authorities not only request these data for supervisory reasons but also use them to improve the planning of operations, for example, optimizing frequency, adjusting stop schedules to actual needs, improving infrastructure design of the transportation network, modifying the locations of stops, and improving stop infrastructure as a function of their use by travelers. The present article describes a system that allows regulatory agencies to perform these functions. The amount of data obtained by the system is massive, and the techniques used to manage the data are inspired by systems of ubiquitous data management.  
The following section states the objectives and requirements of the system. The third section describes various studies related to the objective of defining the challenges and the novel nature of the proposal. The description of the system is addressed in the fourth section. The fifth section presents a practical application of the system. The main conclusions are stated in the last section.
# EUV-Induced Plasma: A Peculiar Phenomenon of a Modern Lithographic Technology
## 3 Bulk Materials Facing EUV Photon Induced Plasma  
Current DUV lithography tools use refractive optics (i.e., lenses) for imaging. However, imaging in EUV lithography (EUVL) tools is achieved by using multilayer Bragg reflective mirrors (MLMs) since EUV radiation would be fully absorbed by these lenses. These mirrors have forty or more bilayers of molybdenum (Mo) and silicon (Si) [3]. As extensively discussed in the previous section, the presence of a low pressure (1-10 Pa) background gas results in the creation of EUV-induced plasma that is directly adjacent to these mirrors [10; 34; 64]. The interaction with this plasma can have both beneficial and negative effects on the (long term) mirrors' surface conditions. In this section, we first review the observations of plasma-induced surface processes. Subsequently, the relevant particle fluxes towards plasma facing surfaces are discussed with a focus on plasmas in H\({}_{2}\). Finally, the studies that attempted to identify the processes that dominantly impact the surface conditions are reviewed.
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 2 Experiments and Data  
### Mechanism of Detection  
Since the shapes of falling raindrops are always nonspherical, the depolarization of GNSS signals is inevitable when it passes through rain area [25]. Polarimetric phase shift, which is used to describe depolarization characteristic, is defined as  
\[\Delta\phi=\phi_{h}-\phi_{v} \tag{1}\]  
where \(\phi_{h}\) and \(\phi_{v}\) is the horizontally and vertically polarized phases, respectively. In theory, the polarimetric phase shift, can be obtained from  
\[\Delta\phi=\int_{L}K_{DP}dl \tag{2}\]  
where \(L\) is the path length through rain area; \(K_{DP}\) is the specific differential phase, which is related to rain rate and more specifically to the characteristics of rain particles. However, the \(K_{DP}\) here comes from forward-scattering of particles rather than back-scattering of particles. For a polarimetric weather radar, whose signals are always in S-, C-, or X-band, its \(K_{DP}\) can be observed [8; 9]. Here, the detectability of \(K_{DP}\) from L-band signals is uncertain. Thus, an experiment is necessary and essential.
## 2 Materials and Methods
### Statistical Analysis  
Fluxes, meteorological and soil half-hourly and daily averaged values were synchronized with the SIF data between 6:00 A.M. and 6:30 P.M. The relationships between observed factors were analyzed by Pearson correlations, and linear and hyperbolic regression functions at the same temporal scale. The coefficient of determination (R\({}^{2}\)), RMSE and the slope of the regression were calculated.  
To understand the main factors controlling the relationship between SIF and T, a least absolute shrinkage and selection operator (LASSO) regression analysis [25] was conducted with T as target variable, and observed meteorological and soil data (PAR, VPD, air temperature, SWC and soil temperature) and SIF as input variables. The LASSO regression model was fitted on the pooled data of hourly and daily scales separately. Each dataset was split into training and test samples. The training sample consisted of the first 85% dataset of each site, while the test sample consisted of the remaining 15%. All variables were normalized by the max-min method with the original data reflected to [0,1] and the data structure retained. Extreme outliers (values beyond \(\pm\)3 standard deviations) in inputs and outputs were removed. The regularization parameter lambda in the LASSO regression was determined using a 10-fold cross-validation within the training set. The selected lambda was the largest value and the mean squared error (MSE) lay within one standard error of the minimum MSE [13].
# Adaptive Antenna Array Control Algorithm in Radiocommunication Systems
## 4 Model and Experimental Tests of an Adaptive Microstrip Antenna Array
The reference signal is described by the following relationship:  
\[r=A_{r}e^{i\psi_{r}} \tag{15}\]  
where:  
\(A_{r}\) is the amplitude of the reference signal;  
\(\psi_{r}\) is the amplitude of the reference signal with a random value. Reference correlation vector:  
\[\mathbf{S}=\mathbf{X}_{x}r \tag{16}\]The vector of weight coefficients is determined from the following relationship:  
\[\mathbf{W}=\mathbf{\Phi}^{-1}\mathbf{S} \tag{17}\]  
In order to obtain a graph of the antenna's radiation power characteristics as a function of the scanning angle, we use the following relationship:  
\[P(x)=\left|\mathbf{W}^{T}\mathbf{X}(x)\right| \tag{18}\]  
where:  
\(x\) is the scanning angle \(-\pi\leq x\leq+\pi\);  
\(P(x)\) power radiated in the \(x\) direction;  
\(\mathbf{X}(x)\) an auxiliary vector specifying the signal parameters in the \(x\) direction. \[\mathbf{X}(x)= \begin{bmatrix}1\\ e^{-j\varphi(x)}\\ e^{-j\varphi(x)}\\ e^{-jn\varphi(x)}\\ \end{bmatrix}  \tag{19}\]  
where  
\[\varphi(x)=\pi sin(x) \tag{20}\]  
is the phase shift between subsequent elements due to the value of angle \(x\). Using the Mathcad mathematical program, the possibility of directing the main antenna beam in the direction of the desired signal was examined [25]. Selected radiation characteristics of the eight-element antenna array, Figure 8, for signals coming from different directions are presented below (Figures 9-13). Figure 8: Eight-element antenna array on a dielectric substrate: physical implementation. Figure 9: Radiation characteristics of an 8-element array. Figure 11: Radiation characteristics of an 8-element array. Figure 10: Normalized directivity characteristics of an 8-element array before and after adaptation in polar coordinates. (**a**) The signal falls at an angle of 10. (**b**) The signal falls at an angle of 30\({}^{\circ}\). Figure 12: Radiation characteristics of an 8-element array, black line. Figure 13: Radiation characteristics of an 8-element array.
## 5 The Proposed Wilkinson Divider Design Process  
The proposed WPD is obtained by incorporating the designed filters into the conventional WPD structure. The structure of the proposed divider is depicted in Figure 17. In the divider structure, one open-ended stub and two filters are used to suppress unwanted harmonics. The RT/Duroid 5880 substrate is used to simulate the proposed divider.  
The frequency response of the proposed Wilkinson power divider is depicted in Figure 18. As the results show, the designed divider has good performance at an operating frequency of 1.5 GHz. The proposed WPD only has a 0.1 dB insertion loss (S\({}_{12}=-3.1\) dB). Both of the input and output return losses are higher than 30 dB (S\({}_{11}=-35\) dB, S\({}_{22}=-30\) dB), and an output ports isolation (S\({}_{23}=-32\) dB) of more than 32 dB is obtained for the proposed design. Moreover, the proposed divider provides wide rejection band from 2 GHz to 20 GHz, with an attenuation level of more than 20 dB, which suppresses the 2 nd-13 th unwanted harmonics.  
Figure 17: The structure of the proposed Wilkinson power divider.  
A comparison between the designed divider and some reported works is listed in Table 4. The designed divider shows good performance compared to the other similar works. The proposed divider has good performance at both the operating frequency and at higher frequencies.
## 1 Introduction  
Through the last decade, research on a new field of activity, energy harvesting (EH), has gained recognition both in scientific and industrial environments. It denotes the effort to develop small distributed renewable sources of energy and the need to power up low-voltage autonomous electrical devices and batteries. Many EH devices have already found application in a few technical solutions i.e., automotive [1], wireless sensor networks (WSN) [2], health-monitoring devices [3], and civil engineering [4]. The main advantage of the aforementioned device is its substitutability for a battery as a source of energy. The energy is scavenged from the environment, examples of which can be solar energy, tides, gravitational force or mechanical vibrations [5]. Most of the currently designed systems are electromagnetic, piezoelectric and electrostatic [5; 6; 7; 8; 9; 10; 11].  
From the application point of view, research aimed at increasing the density of recovered energy is important for systems generating small portions of energy in a fairly wide frequency band [12; 13; 14]. That is why energy harvesting systems with non-linear behavior are researched [15; 16; 17; 18; 19; 20]. The main advantages of non-linearities introduced to the system are the possibilities to modify the resonance behavior [21; 22], to obtain good off-resonance performance [23; 24; 25] or to attain multi-resonance [26].  
Our proposed concept is based on an electromagnetic system with a rotational pendulum. Energy harvesters with a rotational pendulum as a resonator can evolve in time with a large amplitude. Earlier works on the system's development started from theoretical analysis of its dynamics and studies on its response by different excitation conditions [27; 28; 29; 30; 31; 32]. Next, several systems were built, optimized and tested for different applications, primarily energy harvesting from human motion [33; 34; 35; 36; 37] and automotives [38; 39]. In our system we focused on introducing double potential well by mounting adjustable additional magnets inside coils expecting non-linear behavior brought by magnetic interactions. The magnet's position and its repelling alignment will enable pendulum's rotation only by specific excitation frequency which was found experimentally. To obtain a pure system's response, the corresponding parts were made with a 3D printing technique. Some improvements connected with design had to be introduced after performing tests. The system was validated with mathematical model and variables used in the pendulum's equation of motion were based on simulation and experiment. Voltage and power outputs were measured with various excitation frequencies.
# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
## 2 Preliminaries  
### Discrete Wavelet Transform  
The wavelet transform has been widely employed in image processing, pattern recognition, signal denoising and other fields [25; 26]. It removes noise from the real signal. As one of the main techniques for denoising signals, wavelet transforms greatly improve the accuracy of the time series prediction model. It decomposes the input signal into several low-frequency and high-frequency components. Wavelet transform includes two types:continuous wavelet transform (CWT) and discrete wavelet transform (DWT). The definition of CWT is defined as follows:  
\[CWT_{f}(u,s)=<f(t),\psi_{u,s}(t)>=\int_{-\infty}^{+\infty}f(t)\frac{1}{\sqrt{s}} \psi^{*}(\frac{t-u}{s})dt \tag{1}\]  
where \(u\), \(s\), \(f(t)\), \(\psi(t)\) and \({}^{*}\) denote the translation factor, scale factor, real signal, mother wavelet and implies complex conjugate, respectively.  
DWT is a Mallat algorithm proposed in 1988. It is widely used in signal decomposition. It reduces the computational complexity and improves the data compression ability, and effectively avoids information redundancy caused by a continuous wavelet transform. The definition of DWT is as follows [27; 28]:  
\[DWT(j,k)=2^{-\frac{1}{2}}\sum_{i=0}^{L-1}W(t)\psi^{*}(\frac{t-k\cdot 2^{j}}{2^{j} }) \tag{2}\]  
where \(j\), \(k\), \(L\), \(W(t)\) and \(\psi(t)\) denote the scale factor, translation factor, length of \(W(t)\), input signal and mother wavelet, respectively.  
Discrete wavelet decomposition is a multiscale analysis tool that is used to reveal the hidden characteristics of the signal. Due to the nonstationary, fluctuating timing of wind speed, the output power of the wind turbine is unstable. A discrete wavelet transform is used to decompose the original wind speed data into low-frequency components and high-frequency components. The three-level decomposition process using the wavelet transform is shown in Figure 1 [29; 30].
## 3 Results
### Water Level Difference  
The discharge capacity of the cascade permeable dam can be reflected by the water level difference between the front and behind of the dam body. Where there is a larger water level difference, there is a lower discharge capacity. In order to compare the discharge capacity of the cascade permeable dam under different working conditions, curves of the relative water level difference of 1-3# dam were calculated, as shown in Figure 5. As the flow condition near the outlet is free flow, the water level behind the dam remains stable and the water level difference changes slightly, so 4# dam is not discussed. The overall trend of the water level difference of the cascade permeable dam with large and small particle sizes is increasing. At the end of the experiments, the water level differences of 1-3# dam are 1.80-2.45, 1.30-2.40, and 1.30-2.80 cm (D = 3-5 mm) and 2.50-4.25, 2.05-4.10, and 2.00-3.60 cm (D = 1-2 mm), respectively. Correspondingly, the change rates are 0.03-0.16, 0.02-0.05, and 0.01-0.06 cm/H and 0.31-3.00, 0.12-0.83, and 0.06-0.24 cm/H.  
It is clear that the water level difference of dams with a small particle size rises more obviously and at a faster rate, mainly due to the smaller pore volume of the dams being easily blocked by sediment particles. For permeable dams with a small particle size, when the incoming discharge is small and the flow velocity is low, the sediment particles are more likely to penetrate into the interior of the dam, which weakens the permeability of the dam. The water level difference of 1# dam is the largest and the rate of rise is relatively fast, indicating that the permeability of 1# dam is poor under the smaller discharge. The water level difference of 1# dam decreases monotonically with increasing discharge under the same sediment concentration. At this time, a large number of sediment particles enter the subsequent dams with high velocity, causing the water level difference of 3# dam to rise monotonically with increasing discharge, indicating that the permeability of 3# dam is gradually increasing.  
Figure 5: Curves of the water level difference of the dams along the flow direction.  
For the permeable dams with a large particle size, the water level difference of 2# and 3# dam increases with increasing discharge for the same sediment concentration, indicating that the permeability of 2# and 3# dam gradually decreases with increasing discharge, and the permeability of 3# dam is better than that of 2# dam, in general. When the discharge increases to 700 L/H, the change in the water level difference of 3# dam tends to decrease first and then increase with increasing sediment concentration, and this phenomenon is mainly caused by the increase in the hydrodynamic force.