esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 2 Algorithm
### Signal Denoising  
#### 2.2.1 MA Strength Judgment  
When the MA intensity is not large, the PPG signal is not much affected by the MA, and then the use of adaptive filtering may lead to the weakening of the real PPG signal. Therefore, firstly, the scalar sum is obtained for the original three-axis acceleration signals:  
\[Acc=\sum_{i=1}^{N}\sqrt{Acc\_x(i)^{2}+Acc\_y(i)^{2}+Acc\_z(i)^{2}} \tag{4}\]  
where \(N\) denotes the total number of sampling points within each window, and \(Acc\_x\)\((i)\), \(Acc\_y\)\((i)\), and \(Acc\_z\)\((i)\) denote the acceleration signals in the x, y, and z directions, respectively.  
If it is larger than the threshold value THsum(THsum = 900), it is determined as a strong MA; otherwise, it is a weak MA. As shown in Figure 2, the two plots represent the weak and strong motion artifacts, respectively. It is obvious from the figure that when the motion artifact is weak, the peak of the true heart rate is more prominent and is the dominant peak. On the contrary, under the strong motion artifacts, the amplitude of the peak of the true heart rate is significantly smaller than the amplitude of the MA peak, which is affected by the MA.
# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 3 Materials and Methods
### Multi-Source Data Fusion Based on a Spatio-Temporal Grid  
#### Data Preprocessing  
Evaluating the quality of incoming taxi trajectory data is a key issue to evaluate the accuracy and reliability of the developed model. Typical error data are as follows:  
* Location error: GPS coordinate data are missing or gives an incoherent location;
* Speed error: a taxi speed exceeds a driving speed threshold;
* Data source error: data are not generated by a taxi GPS device;
* Status error: a taxi is in an ineffective state such as fortification and outage in taxi running.  
The data preprocessing process first filters most common errors by observation and programming. After that, the application of Douglas-Peucker optimizes the trajectory point paths in order to resolve GPS drifts problems as suggested [32]. As illustrated in Figure 1, the track point \(P\) and the adjacent track points before and after it \(P_{p},P_{n}\) form a track triangle. The trajectory point \(P\) is projected onto the edge \(P_{n}P_{p}\), and its projection length is H. If H is greater than the specified drift error, this point \(P\) is withdrawn. The drift error is set to the size of the map grid.  
Then, the trajectory point \(P\) is projected onto the edge \(P_{n}P_{p}\), and its projection length is H. If H is greater than the specified drift error, delete it. In this paper, the drift error is set to the size of the map grid.
# Research on Performance Prediction Model of Impeller-Type Breather
## 1 Introduction
Based on the dimensional analysis, a dimensionless group equation in general form is established for comprehensive separation efficiency, breather separation efficiency, and ventilation resistance. Also, through the least squares method, the performance prediction model of the breather is obtained, which provides a reference for the design, evaluation, and optimization of the impeller breather.
## 5 Simulation and Analysis
### Case 1  
The control target of the converter was input current and output voltage, so it was necessary to verify the power conversion ability of the parallel converter. In this case, the filter inductance of the AC side was equal, and there was no ZSCC suppression measure. Here, \(L_{1}=L_{2}=3\)mH, and other parameters were the same as those listed in Table 1. The experimental results of 0.3-0.6 s are presented in Figure 9.  
Figure 9: Simulation waveforms in parallel system with equal inductance and without ZSCC suppression strategy: (**a**) waveform of rectification (input, three-phase AC-source voltage; output, DC voltage); (**b**) A-phase voltage and current waveform of converter 1.  
As shown in Figure 9a, the peak value of the three-phase AC voltage source was 200 V. Through the converter, the output voltage could reach the rated voltage of 450 V and remain stable. In addition, as can be seen in Figure 9b, the phase of the input voltage and input current of the converter were basically the same, and the power factor angle was close to zero, indicating that the reactive power could be effectively suppressed.
## 3 Control Strategy Development and Optimization  
### PID Controller  
PID controller is a simple process controller that consists of three control terms: proportional, integral, and derivative. PID control strategy has been adopted in various industrial applications due to its simplicity, easy troubleshooting, and straightforwardness in implementation [50]. The PID control has been applied for many control applications, such as Quadrotor [51; 52], power system [53; 54], brushless direct current (BLDC) motor [55; 56], and many other applications. Figure 15 shows the detailed block diagram of the PID control strategy for controlling the vehicle speed [km/h], as described in Figure 6 and the reference speed or drive cycle [km/h] is the input to the controller. The PID controller provides the optimal torque [Nm] and speed to the motor [rpm], which regulates the vehicle's speed for optimal consumption. The VW Crafter powertrain represents the vehicle subsystem containing the powertrain, including the engine/motor, battery, and transmission (gearbox), as described in the block diagram of Figure 6. The engine is controlled by its separate PID controller in the engine management responsible for the optimal torque and speed from the engine.  
The PID can be expressed mathematically in s-domain (\(G(s)\)), which can be expressed as in [57] as follows:  
\[G(s)=K_{p}+\frac{K_{i}}{s}+K_{d}s \tag{23}\]  
where \(K_{p}\) is the proportional gain, \(K_{i}\) is the integral gain, and \(K_{d}\) is the derivative gain. These parameters determine the performance of the controller. The trial and error method for determining the values of this gains is time consuming and may not yield satisfactory performance for nonlinear system. Therefore, the nonlinear dynamics of the HEVs system, disturbances, and other external effects, necessitate the optimal design of the PID controller in this research. As a result, an enhanced GA-PID is proposed in order to achieve an optimal balance between the \(K_{p}\), \(K_{i}\), and \(K_{d}\) for the desired performance.
## 3 Methods
### Evaluation Method  
By constructing the hierarchical structure model and its importance judgment matrix, the weight value of each evaluation indicator was obtained with the modified scaling method, and then the comprehensive index was calculated.  
(1)  Establish an importance matrix, \(A\).  
\[A=\begin{bmatrix}C_{11}&\cdots&C_{1n}\\ \vdots&\ddots&\vdots\\ C_{n1}&\cdots&C_{nn}\end{bmatrix}\]  
where \(n\) is the number of indicators, and the relative importance is checked from Table 1.  
Figure 2: Comprehensive evaluation frame of geo-environment suitability.  
(2) Identify the weights  
Based on the importance judgment matrix, the maximum eigenvalue and eigenvector were obtained [39], and then the eigenvector was normalized to calculate the weight value of different indicators [40,41]. Moreover, a consistency test of the judgment matrix should be carried out.  
The calculation equation of the product of each row element (\(M_{i}\)) is:  
\[M_{i}=\Pi_{j=1}^{n}\text{C}_{ij}\]  
The calculation equation of normalized eigenvector (\(W_{i}\)) is:  
\[W_{i}=\frac{\sqrt[n]{M_{i}}}{\sum_{i=1}^{n}\sqrt[n]{M_{i}}}\]  
The calculation equation of eigenvalue (\(\lambda_{i}\)) is:  
\[\lambda_{i}=\sum_{j=1}^{n}C_{ij}W_{j}\]  
The calculation equation of maximum eigenvalue (\(\lambda_{max}\)) is:  
\[\lambda_{max}=\sum_{i=1}^{n}\frac{\lambda_{i}}{nW_{i}}\]  
The calculation equation of consistency ratio (\(CR\)) is:  
\[CR=\frac{(\lambda_{max}-n)/(n-1)}{RI} \tag{1}\]  
where \(RI\) is the mean random consistency index, which can be checked from Table 2. Meanwhile, \(CR\) needs to be less than 0.1.  
(3) Calculate the suitability comprehensive index, \(SI\).  
\[SI=\sum_{i=1}^{n}u_{i}\cdot w_{i};i=1,2,\ldots,n \tag{2}\]  
where \(u_{i}\) is the score value of each indicator, \(w_{i}\) is the weight of each indicator, \(n\) is total number of indicators.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Order-Number & 11 & 12 & 13 & 14 & 15 \\ \hline \(RI\) value & 1.51 & 1.48 & 1.56 & 1.57 & 1.59 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean random consistency index (RI) values of 11–15 order judgment matrix.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale** & **Meaning of the Scale** \\ \hline Scale = 1 & Equal importance, two indicators contribute equally to the object \\ Scale = 1/9 & Extreme unimportance, the evidence favoring one indicator over \\  & another is of the lowest possible order of affirmation \\
1/9 \textless{} Scale \textless{} 1, & More and more importance, judgment more and more strongly \\
1 \textless{} Scale \textless{} 9 & favors one indicator over another \\  & \multicolumn{2}{c}{Extreme importance, the evidence favoring one indicator over} \\ Scale = 9 & another is of the highest possible order of affirmation \\ \hline \hline \end{tabular} Annotation: the relative importance of the indicators being compared is closer together when the scale is equal to 1 [33].  
\end{table}
Table 1: Scale of relative importance between different indicators.
# Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting
## 4 Numerical Results
### Result of the Energy Deviation of the PV Site  
The frequency of occurrence of energy deviation was analyzed for each scenario and case on a date with a high level of PV generation. Figure 11 shows the frequency of the occurrence of energy deviations. In order to maximize the revenue from incentives, most cases occurred with an energy deviation of 5-6% or less compared to case 1. Among the cases, case 5 had the highest frequency of occurrence in the 5-6% range and the lowest frequency in the 7-8% range. These results show the outcome of efforts to reduce the energy deviation by less than 6% to maximize the revenue from the incentive. In case 2, the frequency of occurrence in the range of 7-8% was higher than that of other cases. As case 2 only allows for adjustment of the generation schedule and does not include ESS operation or PV curtailment, it aims to increase the probability of benefit from the incentive, even if only slightly. The frequency of occurrence of case 5 in the range of energy deviation of 3-4% was the lowest among all cases. It aims at improving the revenue from MP within the energy deviation rate of 6%, which can qualify for the incentive, even if the frequency of occurrence of energy deviation changes by 5-6%. It demonstrates that if the unit price of the incentive program was designed in a step function, the optimal scheduling result was often determined at the point of the unit price changes. In other words, if the energy deviation exceeds 8%, whether the value is large or small, it will be reduced to 8% or less to receive the incentive. However, it should be noted that even if the energy deviation had been determined to be less than 5%, it may have increased within 5-6%. Therefore, when designing an incentive or penalty program, it seems that it is significant to consider the effect of the unit price.
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
## 2 Preliminaries
### Quadrotor under Inner Loop Controller  
In agreement with [16; 18; 19; 21; 24; 25; 26], the inner loop controller in (3)-(5) applied to dynamic models (1) and (2) leads to the second order system  
\[\ddot{\mathbf{q}}=T(\psi)K_{u}\mathbf{u}-K_{v}\mathbf{q}+\delta, \tag{6}\]  
where  
\[\mathbf{q}=[x\;y\;z\;\psi]^{\top}\in\mathbb{R}^{4} \tag{7}\]  
contains the position in three-dimensional space \((x,\;y,\;z)\) and the yaw angle \(\psi\), \(K_{u}\in\mathbb{R}^{4\times 4}\) and \(K_{v}\in\mathbb{R}^{4\times 4}\) are positive definite diagonal matrices containing the parameters of the vehicle and the inner controller, explicitly given as  
\[K_{u}=\text{diag}\Big{\{}g\theta_{\max}\quad g\phi_{\max}\quad\dot{z}_{\max}/ \tau_{z}\quad\dot{\psi}_{\max}/\tau_{\dot{\psi}}\Big{\}} \tag{8}\]  
and  
\[K_{v}=\text{diag}\Big{\{}d_{x}\quad d_{y}\quad 1/\tau_{z}+d_{z}\quad 1/\tau_{ \dot{\psi}}+d_{\psi}\Big{\}}, \tag{9}\]  
the matrix \(T(\psi)\;\in\mathbb{R}^{4}\) is a transformation matrix given by\[T(\psi)=\begin{bmatrix}\cos(\psi)&\sin(\psi)&0&0\\ \sin(\psi)&-\cos(\psi)&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix},\]  
and \(\delta\in\mathbb{R}^{4}\) represents constant external disturbances that are bounded by \(\|\delta\|\leq\delta_{B}\in\mathbb{R}\) with \(\delta_{B}>0\). Finally, the vector [24; 25]  
\[\mathbf{u}=[u_{\theta}\;u_{\phi}\;u_{z}\;u_{\dot{\phi}}]^{\top}\in\mathbb{R}^{4}\]  
is a nondimensional input vector for the inner control loop with the following parameters:  
\(u_{\theta}\quad\) is an angular position command related to the displacement along the \(x\)-axis.  
\(u_{\phi}\quad\) is an angular position command related to the displacement along the \(y\)-axis.  
\(u_{z}\quad\) is a velocity command related to the displacement along the \(z\)-axis.  
\(u_{\dot{\phi}}\quad\) is a velocity command related to the rotation around the \(z\)-axis.  
A graphical representation of the quadrotor dynamics (1)-(2) under the inner loop controller (3)-(5), which result in (6), is provided in Figure 1.  
It is worth noticing that the inner loop controller given in Equations (3)-(5) is used only for demonstrative purposes, and any other inner loop controller can be used. In this sense, as long as the second-order system in Equation (6) is recovered, the proposed outer loop controller (presented in the next section) can be applied.
# A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
## 2 Raman Spectral Imaging System  
### System Design  
Figure 1 shows a schematic of the 1064 nm dispersive Raman spectral imaging system. The Raman spectrograph uses a high throughput volume phase grating (VPG) (BaySpec, Inc., San Jose, CA, USA) optimized for 1064 nm laser excitation. A concave lens (Lens 1) in the spectrograph guides the incoming light to the VPG, where the light is diffracted into different angular output paths. The concave lens (Lens 2) reflects the dispersed light to the 512 pixels Indium-Gallium-Arsenide (InGaAs) detector (Nunavut, BaySpec, Inc., San Jose, CA, USA). The detector is thermoelectrically deep cooled to \(-55\)\({}^{\circ}\)C during spectral acquisition to minimize the dark current. The detector is connected to the computer using a USB cable for detector control and data transfer.  
A 1064 nm laser module (MiniLite, BaySpec, Inc., San Jose, CA, USA) with a power of 600 mW is used for sample excitation. A fiber-optic Raman probe (RPB, InPHotonics, Inc., Norwood, MA, USA) is used to focus the laser on the sample surface and acquire the scattering signal. A long-pass filter assembled in the probe eliminates the light at and below 1064 nm. The probe is coupled to the Raman spectrograph via a bifurcated optical fiber that consists of excitation fiber and collection fiber. The excitation fiber (diameter 105 \(\upmu\)m) carries the laser from the source to the Raman probe, is connected to the output of laser source by angled physical contact (APC) connector. The laser is focused on the sample surface by an extension tip of the probe. The end face polished at an 8-degree angle in the APC connector allows any light that is reflected back to the source to reflect out into the fiber cladding, achieving a better return loss. The collection fiber (diameter 200 \(\upmu\)m) transfers the acquired scattering Raman signal to the spectrometer, is connected to the spectroscopy by fiber-optic connector (FC). The Raman probe uses bandpass, diachronic and edge filters for separating the excitation light and the scattered signal. The probe has a focal length of 7.5 mm and spot size of 158 \(\upmu\)m. A two-axis motorized positioning table (MAXY4009W1-S4, Velmex, Bloomfield, NY, USA) is used to hold and move the sample in two perpendicular directions. The positioning table can move through an area of 127 mm \(\times\) 127 mm with a displacement resolution of 6.35 \(\upmu\)m. Stepper motor controller (Velmex, Bloomfield, NY, USA) is programmed to control its movement. Sample movement at an adjustable step size below the fixed position Raman probe allows accumulation of Raman spectral image of the entire surface area of the sample, which can be analyzed both spectrally and spatially.  
Figure 1: Schematic of 1064 nm dispersive Raman spectral imaging system.
## 3 Results and Discussion
### Reducing Power  
The reducing capacity of a compound indicates its potential antioxidant activity, as the presence of reductants (such as antioxidant substances) in the samples causes the reduction of the Fe\({}^{3+}\)/ferricyanide complex to the ferrous form. Therefore, Fe\({}^{2+}\) can be monitored by measuring the formation of [PERSON]'s Prussian blue at 700 nm [36]. Figure 3 depicts the reducing power of the different percentages of IDE loaded in SLN and NLC using the potassium ferricyanide reduction method, as reported in the literature [24]. The reducing power of SLN after loading 1% or 1.5% w/w IDE increased with the increase of the sample concentrations and showed a statistically significant greater value compared to IDE-loaded NLC (\(p<0.05\)).  
Figure 2: Calorimetric curves, in heating mode, of the following lipid nanocarriers: (**a**) unloaded solid lipid nanoparticles (SLN), SLN loaded with idebenone (IDE) 1% w/w (SLN IDE 1), SLN loaded with idebenone (IDE) 1.5% w/w (SLN IDE 1.5); (**b**) unloaded nanostructured lipid carriers (NLC), nanostructured lipid carriers loaded with IDE 1% w/w (NLC IDE 1), nanostructured lipid carriers loaded with IDE 1.5% w/w (NLC IDE 1.5).
# Calculation of Theoretical Travel Time and Automatic Picking of Actual Travel Time in Seismic Data
## 3 Automatic Picking of the Seismic Phase Actual Travel Time
Where the actual arrival time of the first arrival phase manually picking from the waveform recorded at the \(j\)th station is denoted as \(T_{ij}^{obs}\), and its theoretical arrival time is denoted as \(T_{ij}^{cal}\), then the travel time residual of this phase \(t_{ij}\) is \[t_{ij}=T_{ij}^{obs}-T_{ij}^{cal} \tag{20}\]  
The average walk time residual \(\overline{t_{i}}\) is:  
\[\overline{t_{i}}=\frac{1}{N}\sum\
olimits_{j=1}^{N}t_{ij} \tag{21}\]  
The relative travel time residual \(r_{ij}\) is:  
\[r_{ij}=t_{ij}-\overline{t_{i}} \tag{22}\]  
Equation (19) can be used to quickly calculate the arrival time difference data of all \(N\) waveforms with respect to the \(j\)th waveform, a total of \(N\) (where the arrival time difference of the \(j\)th waveform with respect to itself is 0 s), denoted as \(Y_{i,j1}^{obs}\)\(\cdots\)\(Y_{i,j(-1)}^{obs}\)\(\cdots\)\(\cdots\)\(Y_{i,j(+1)}^{obs}\)\(\cdots\)\(Y_{i,j(+1)}^{obs}\). The theoretical arrival time of each waveform can be calculated from the theoretical arrival time of each waveform to the first phase, and the theoretical arrival time of the \(j\)th waveform can be calculated as \(Y_{i,j1}^{cal}\)\(\cdots\)\(Y_{i,j(-1)}^{cal}\)\(\cdots\)\(0\)\(\cdots\)\(Y_{i,j(+1)}^{cal}\)\(\cdots\)\(Y_{i,jN}^{cal}\), which  
\[Y_{i,jk}^{obs}=T_{ij}^{obs}-T_{ik}^{obs} \tag{23}\]  
\[Y_{i,jk}^{cal}=T_{ij}^{cal}-T_{ik}^{cal} \tag{24}\]  
Make  
\[\overline{Y_{i,jk}^{obs}}=\frac{1}{N}\sum\
olimits_{k=1}^{N}Y_{i,jk}^{obs} \tag{25}\]  
\[\overline{Y_{i,jk}^{cal}}=\frac{1}{N}\sum\
olimits_{k=1}^{N}Y_{i,jk}^{obs} \tag{26}\]  
Then  
\[ \begin{array}{l}y_{ij}=\overline{Y_{i,jk}^{obs}}-\overline{Y_{i,jk}^{cal}}= \frac{1}{N}\sum\limits_{k=1}^{N}\left(Y_{i,jk}^{obs}-Y_{i,jk}^{cal}\right)\\ =\frac{1}{N}\sum\limits_{k=1}^{N}\left[\left(Y_{i,j}^{obs}-Y_{i,j}^{cal}\right) -\left(Y_{i,k}^{obs}-Y_{i,k}^{cal}\right)\right]\\ =\frac{1}{N}\sum\limits_{k=1}^{N}\left[t_{ij}-t_{ik}\right]\\ =t_{ij}-\overline{t_{i}}=r_{ij}\end{array}  \tag{27}\]  
That is, the relative travel time residuals of Equation (22). The whole process is completely automated by the computer program, which largely improves the efficiency  
Figure 3: The cross-correlation functions of two time series. and accuracy of data processing. \(T_{ij}^{obs}\) in Equation (20) represents the actual arrival time picking manually, and \(T_{ij}^{obs}\) in Equation (23) represents the actual arrival time variable, which is not involved in the actual operation.
###### Abstract  
Time series remote sensing products with both fine spatial and dense temporal resolutions are urgently needed for many earth system studies. The development of small satellite constellations with identical sensors affords novel opportunities to provide such kind of earth observations. In this paper, a new dense time series 30-m image product was proposed respectively based on an 8-day, 16-day and monthly composition. The products were composited by the Charge Coupled Device (CCD) images from the 2-day revisit small satellite constellation for environmental monitoring and disaster mitigation of China (HJ-1A/B). Taking the Zoige plateau in China as a case area where it is covered by highly heterogeneous vegetation landscapes, a detailed methodology was introduced on how to use 183 scenes of CCD images in 2010 to create composite products. The quality of the HJ CCD composites was evaluated by inter-comparison with the monthly 30-m global Web-Enabled Landsat Data (WELD), 16-day 500-m MODIS NDVI, and 8-day 500-m MODIS surface reflectance products. Results showed that the radiometric consistency between HJ and WELD composited Top Of Atmosphere (TOA) reflectance was in good agreement except for May, June, July and August when more clouds and invalid data gaps appeared in WELD. Visual assessment and temporal profile analysis also revealed that HJ possessed better visual effects and temporal coherence than that of WELD. The comparison between HJ and MODIS products indicated that HJ composites were radiometrically consistent with MODIS products over areas consisting of large patches of homogeneous surface types, but can better reflect the detailed spatial differences in regions with heterogeneous landscapes. This paper highlights the potential of compositing HJ-1A/B CCD images, allowing for providing a cloud free, time-space consistent, 30-m spatial resolution, and dense in time series image product. Meanwhile, the proposed products could also be treated as a reference to generate regional or even global composited products for the on-going satellite constellations and even for the forthcoming satellite missions such as Sentinel-2A/B.  
Keywords:time series; dense; image composition; HJ; Zoige plateau; heterogeneous regions; WELD +
Footnote †: journal: Remote Sensing