esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mathematical Modeling
By combining all the energy variations, substituting them into Equation (19), and finally gathering the virtual displacement \(\delta u\), \(\delta v\), \(\delta w\), \(\delta\beta_{1}\), and \(\delta\beta_{2}\), the five governing equations are expressed as in the following:  
\[\delta u:\ \frac{\partial N_{11}^{\sigma}}{\partial\alpha_{1}}+\frac{ \partial N_{21}^{\sigma}}{\partial\alpha_{2}}+\frac{Q_{13}^{\sigma}}{R_{1}}+ q_{1}=I_{0}\ddot{u}+I_{1}\ddot{\beta}_{1} \tag{33}\]  
\[\delta v:\ \frac{\partial N_{12}^{\sigma}}{\partial\alpha_{1}}+\frac{ \partial N_{22}^{\sigma}}{\partial\alpha_{2}}+\frac{Q_{23}^{\sigma}}{R_{2}}+ q_{2}=I_{0}\ddot{v}+I_{1}\ddot{\beta}_{2} \tag{34}\]  
\[\delta w:\ \frac{\partial Q_{13}^{\sigma}}{\partial\alpha_{1}}+\frac{ \partial Q_{23}^{\sigma}}{\partial\alpha_{2}}-\frac{N_{11}^{\sigma}}{R_{1}}- \frac{N_{22}^{\sigma}}{R_{2}}+q_{3}=I_{0}\ddot{w} \tag{35}\]  
\[\delta\beta_{1}:\frac{\partial M_{11}^{\sigma}}{\partial\alpha_{1}}+\frac{ \partial M_{12}^{\sigma}}{\partial\alpha_{2}}-Q_{13}^{\sigma}=I_{1}\ddot{u}+ I_{2}\ddot{\beta}_{1} \tag{36}\]  
\[\delta\beta_{2}:\frac{\partial M_{21}^{\sigma}}{\partial\alpha_{1}}+\frac{ \partial M_{22}^{\sigma}}{\partial\alpha_{2}}-Q_{23}^{\sigma}=I_{1}\ddot{v}+ I_{2}\ddot{\beta}_{2}, \tag{37}\]  
where \(I_{0}\), \(I_{1}\) and \(I_{2}\) being the normal, coupled normal-rotary and rotary inertia coefficients, respectively, which are defined below:  
\[I_{i}=\int_{-\frac{i}{2}}^{\frac{i}{2}}\rho(\zeta)\dot{\zeta}^{i}\bigg{(}1+ \frac{\zeta}{R_{1}}\bigg{)}\bigg{(}1+\frac{\zeta}{R_{2}}\bigg{)}d\zeta\ i=0,1,2. \tag{38}\]It should be noted that the stress results must be introduced, too. More specifically, \(N_{11}^{\sigma},\ N_{12}^{\sigma},\ N_{22}^{\sigma}\), and \(N_{21}^{\sigma}\) are the in-plane stress resultants, \(Q_{13}^{\sigma}\) and \(Q_{23}^{\sigma}\) are the transverse shear stress resultants, and \(M_{11}^{\sigma},\ M_{12}^{\sigma},\ M_{22}^{\sigma}\) and \(M_{21}^{\sigma}\) are the bending and torsional moments defined below:  
\[\{N_{11}^{\sigma},\ N_{12}^{\sigma},\ Q_{13}^{\sigma}\}=\int\limits_{\zeta}\{ \sigma_{11},\ \sigma_{12},\ k_{s}\sigma_{13}\}\bigg{(}1+\frac{\zeta}{R_{2}}\bigg{)}d\zeta \tag{39}\]  
\[K=\frac{1}{2}\int_{V}\rho(\zeta)\dot{U}^{T}\dot{U}dV, \tag{31}\]0  
\[K=\frac{1}{2}\int_{V}\rho(\zeta)\dot{U}^{T}\dot{U}dV, \tag{31}\]1  
\[K=\frac{1}{2}\int_{V}\rho(\zeta)\dot{U}^{T}\dot{U}dV, \tag{31}\]2  
It should be recalled that the definitions of the shear stress results require the introduction of the shear correction factor, which is usually taken as \(k_{s}=5/6\). The same assumption is herein introduced for our analyses.
## 3 Materials and Methods
### Identification of Peptides in Viscera Extracts
#### 3.7.2 Mass Spectrometry Analysis  
Peptides were analyzed in a nanoESI qTOF mass spectrometer (6600 plus TripleTOF, ABSCIEX, Framingham, MA, USA). A total of 5 \(\mu\)L of sample was loaded onto a trap column (ChromXP C18, 3 \(\mu\)m 120, 350 \(\mu\)m, 0.5 mm; Eksigent) and desalted with 0.1% TFA at a flow rate of 5 \(\mu\)L/min for 5 min. The peptides were then loaded onto an analytical column (3\(\mu\)C18-CL 120, 0.075 \(\times\) 150 mm; Eksigent) equilibrated in 5% ACN and 0.1% TFA. Elution was carried out with a linear gradient from 7% to 40% B in A for 45 min. (A: 0.1% formic acid (FA); B: ACN, 0.1% FA) at a flow rate of 300 mL/min.  
Sample was ionized by applying 3.0 kV to the spray emitter at 175 \({}^{\circ}\)C. Analysis was performed in a data-dependent mode. Survey MS1 scans were acquired from 350-1400 \(m/z\) for 250 ms. The quadrupole resolution was set to 'LOW' for MS2 experiments, which were acquired 100-1500 \(m/z\) for 25 ms in 'high sensitivity' mode. The following switch criteria were used: charge: 2+ to 4+; minimum intensity; 250 counts per second (cps). Up to 100 ions were selected for fragmentation after each survey scan. Dynamic exclusion was set to 15 s. The system sensitivity was controlled by analyzing 0.5 \(\mu\)g of K562 trypsin digestion (Sciex). In these conditions, 2230 proteins were identified (FDR <1%) in a 45 min gradient.
# Stabilization of Road Embarkments on Peat Soils Using Oil Shale Ash and Pozzolanic Additives
## Materials
### Silica Fume  
Silica fume (fine silica SiO\({}_{2}\)) is one of the best pozzolanic agents2. Two different types of silica fume were used: microscale SiO\({}_{2}\) of Egyptian origin (manufactured by Efaco) and nanoscale SiO\({}_{2}\) (manufactured by Cabot) with a microscale particle diameter. Later, a ready-made suspension of nanosilica fume produced by TamCem was also introduced. The main silica fume data are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Component**} & & **CFB 1** & **Cyclone PF 2** & **DeSO\({}_{\mathrm{x}}\) 3** \\  & & **Ash, \%** & **Ash, \%** & **Ash, \%** \\ \hline Quartz & SiO\({}_{2}\) & 16.8 & 9 & 12.2 \\ Orthoclase & KAlSi\({}_{3}\)O\({}_{8}\) & 12.5 & 2.3 & 0.4 \\ Illite + Illite-Smectic & Na\({}_{4}\)K\({}_{\mathrm{x}}\)(AlMg)\({}_{2}\)Si\({}_{4}\)O\({}_{10}\)(OH)\({}_{2}\)\(\cdot\)xH\({}_{2}\)O & 13.8 & & 0.4 \\ Belite & Ca\({}_{2}\)SiO\({}_{4}\) & 5.3 & 15.3 & 12.5 \\ Merwinite & Ca\({}_{3}\)Mg(SiO)\({}_{2}\) & 5.7 & 11.9 & 13.1 \\ C3A & 3 CaG\({}_{4}\)O\({}_{12}\)O\({}_{3}\) & & & 1.6 \\ Periclase & MgO & 2.3 & 3.2 & 2.5 \\ Melilite & (Ca,Na)\({}_{2}\)(Mg,Al)(Si,Al)\({}_{3}\)O\({}_{7}\) & 1.2 & 6.7 & 2 \\ Anhydrite & CaSO\({}_{4}\) & 9.5 & 7.5 & 8.7 \\ Lime & CaO & 10.8 & 34.5 & 11 \\ Calcite & CaCO\({}_{3}\) & 13.5 & 4.7 & 13.4 \\ Portlandite & Ca(OH)\({}_{2}\) & & 2.9 & 4.8 \\ Hematite & Fe\({}_{2}\)O\({}_{3}\) & 4.3 & 0.7 & \\ Pseudowollastonite & CaSO\({}_{3}\) & 3.6 & 0.6 & 1.7 \\ Bassanite & CaSO\({}_{4}\)\(\cdot\)0.5(H\({}_{2}\)O) & & & 1.5 \\ Hannebachite & Ca\({}_{2}\)(SO\({}_{3}\))\({}_{2}\)\(\cdot\)H\({}_{2}\)O & & & 13.4 \\ \hline \hline \end{tabular}  
* 1—circulating fluidized bed. 2—pulverised firing. 3—desulphurization.  
\end{table}
Table 2: Crystalline phases of oil shale ashes used in experiments, in percent by weight.
# AIS Data Vulnerability Indicated by a Spoofing Case-Study
## 3 Results
### An Overview of AIS Spoofing Incidents in the Last Years
A satellite that could only see an area about 5000 km wide, for example, the Gulf of Guinea in West Africa, received GPS-based position data from a ship AIS, showing the ship off Point Reyes in northern California. Their position reporting also confirmed the true positions of the ships before and after the \"displacement events\". However, it is still unclear whether these errors were caused by the ships' AIS or by an error or interference with the GPS navigation receivers. In the previously observed cases, it seemed obvious that a jamming device was nearby and affecting many ships. In these incidents, each ship was the only one in its vicinity, and the ships were thousands of miles apart [44]. Another example of spoofing was observed in July 2020, when one of the world's top fishing fleets was accused of misreporting its location to conceal illegal fishing activities in the EEZ around the Galapagos Islands. The ships reported a location on New Zealand via AIS that was about 10,000 km away from their observed location, when in fact, they were near the Galapagos Islands, where illegal fishing has occurred before. The HawkEye\({}^{360}\) satellite system, which provides a unique method for obtaining maritime awareness through RF geospatial intelligence, revealed many instances of suspicious vessel behavior, including evidence that many vessels in one of the world's top fishing fleets stopped transmitting AIS for eight or more hours and may have penetrated deep into the Galapagos EEZ, as shown in Figure 4[38,45]. This type of AIS \"cloaking\" is just one of many ways criminals use location spoofing of a GNSS-dependent system to support their refa  
Figure 4: Vessels in red color were disappearing from AIS tracking, the Galápagos Islands [45]. However, the question of the manipulation of AtoNs in Florida and the spoofing of the AIS system observed in late 2019 near Elba Island remains open. An investigation into the latter is presented in the next section.
## 4 Results and Discussion  
### Experimental Environment and Setup  
Our experimental platform consisted of a remote server with the Linux operating system, and the software and hardware environment included Python 3.6.9, CUDA 10.0, Pytorch-GPU 1.4.0, Pytorch-Geometric (PyG) 1.6.1 and a GeForce RTX 2080 Ti GPU. PyG is a geometric deep learning extension library based on Pytorch, which is suitable for handling graphs and other irregularly structured data. The construction and training of GNNs were implemented on PyG.  
During the training process, the Adam optimization algorithm was used for parameter learning. The learning rate was initialized to \(1\times 10^{-4}\). The weight decay coefficient was set to \(1\times 10^{-4}\), and the batch size was set to 1. In order to avoid the loss curve oscillation during the later stage of training, a learning rate decay strategy was adopted with the loss on the training set as index. When this indicator did not decrease for two successive epochs, the learning rate decreased to half of the original. The maximum epoch number was set to 100.  
All experiments adopted five folds and five runs of cross validation. The mean values of recall, precision and \(F1\_score\) were utilized as evaluation indicators and their corresponding variance values were given. Recall rate reflects the probability that a positive sample is not missed. Precision rate reflects the probability that a positive sample is not wrongly classified. \(F1\_score\) is the harmonic average of these as a comprehensive index. The detailed definition can be seen in the following formulas:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{11}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{12}\]\[F1\_score=\frac{2}{\left(\frac{1}{\text{recall}}\right)+\left(\frac{1}{\text{ precision}}\right)} \tag{13}\]  
where _TP_ represents the number of positive samples that were correctly classified. _FN_ represents the number of negative samples that were wrongly classified, and _FP_ represents the number of negative samples that were correctly classified.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 4 Proportional-Integral-Derivative Parameter Design
### Controller Parameter Decision
#### 4.3.1 Tuning Rule  
On the pair of plant parameters \(\tau_{0}\) and \(\tau_{a}\) with the established robust stability \(M_{s}^{d}\), the controller parameters are expressed in terms of the normalization parameters for the plant model as:  
\[\kappa_{p} =\alpha_{0}+\alpha_{1}\tau_{0}^{\alpha_{2}} \tag{23}\] \[\tau_{i} =\beta_{0}+\beta_{1}\tau_{0}+\beta_{2}\tau_{0}^{2}+\beta_{3}\tau_ {0}^{3}\] (24) \[\tau_{d} =\gamma_{0}+\gamma_{1}\tau_{0}+\gamma_{2}\tau_{0}^{2} \tag{25}\]  
where \(\alpha_{i}(i=0,1,2)\), \(\beta_{j}(j=0,1,2,3)\), and \(\gamma_{k}(k=0,1,2)\) are defined as follows:  
\[\alpha_{0} =a_{00}+a_{01}\tau_{a} \tag{26}\] \[\alpha_{1} =a_{10}+a_{11}\tau_{a}\] (27) \[\alpha_{2} =a_{20}+a_{21}\tau_{a}\] (28) \[\beta_{0} =b_{00}+b_{01}\tau_{a}\] (29) \[\beta_{1} =b_{10}+b_{11}\tau_{a}\] (30) \[\beta_{2} =b_{20}+b_{21}\tau_{a}\] (31) \[\beta_{3} =b_{30}+b_{31}\tau_{a}\] (32) \[\gamma_{0} =c_{00}+c_{01}\tau_{a}\] (33) \[\gamma_{1} =c_{10}+c_{11}\tau_{a}\] (34) \[\gamma_{2} =c_{20}+c_{21}\tau_{a} \tag{35}\]  
where the coefficient parameters in Equation (26)-Equation (35) in the servo optimization are shown in Table 1, and those in the regulation optimization are also shown in Table 2.  
\begin{table}
\begin{tabular}{l c c c c} \hline \(M_{s}^{d}\) & **1.4** & **1.6** & **1.8** & **2.0** \\ \hline \(a_{00}\) & \(0.2130\) & \(0.2778\) & \(0.3281\) & \(0.3098\) \\ \(a_{01}\) & \(-0.4643\) & \(-0.6376\) & \(-0.8185\) & \(-0.7722\) \\ \(a_{10}\) & \(0.4361\) & \(0.5803\) & \(0.6932\) & \(0.8100\) \\ \(a_{11}\) & \(-0.3767\) & \(-0.4236\) & \(-0.3308\) & \(-0.4577\) \\ \(a_{20}\) & \(-1.0067\) & \(-1.0169\) & \(-1.0150\) & \(-0.9861\) \\ \(a_{21}\) & \(1.7509\) & \(1.7951\) & \(1.9003\) & \(1.8503\) \\ \hline \(b_{00}\) & \(1.1368\) & \(1.1451\) & \(1.2097\) & \(1.3995\) \\ \(b_{01}\) & \(-1.6140\) & \(-1.1310\) & \(-0.7911\) & \(-1.9403\) \\ \(b_{10}\) & \(-0.0394\) & \(0.3152\) & \(0.4516\) & \(0.1364\) \\ \(b_{11}\) & \(1.4393\) & \(0.0802\) & \(-1.2593\) & \(2.0622\) \\ \(b_{20}\) & \(0.1724\) & \(-0.0447\) & \(-0.1094\) & \(0.1498\) \\ \(b_{21}\) & \(-0.9219\) & \(0.3521\) & \(1.6861\) & \(-1.2358\) \\ \(b_{30}\) & \(-0.0326\) & \(0.0265\) & \(0.0354\) & \(-0.0201\) \\ \(b_{51}\) & \(0.2070\) & \(-0.1725\) & \(-0.5677\) & \(0.2429\) \\ \hline \(c_{00}\) & \(-0.0190\) & \(0.000066\) & \(0.0047\) & \(0.0091\) \\ \(c_{01}\) & \(-0.1314\) & \(-0.0898\) & \(-0.0615\) & \(-0.0129\) \\ \(c_{10}\) & \(0.3193\) & \(0.2819\) & \(0.3377\) & \(0.3596\) \\ \(c_The controller parameters are optimized in the limited range. Although the range can be expanded, the tuning rule is redesigned when the range is changed.
# Recent Advances in the Heterologous Biosynthesis of Natural Products from _Streptomyces_
## 3 Selecting a Suitable Host for Heterologous _Streptomyces_ Products  
### Streptomyces  
Heterologous expression is a promising tool for exploring compounds from silent BGCs or increasing the titers of natural products. _Streptomyces_ produces numerous biologically active compounds with various effects, including agrobiological and pharmacologic agents. Many _Streptomyces_ strains grow slowly, are difficult to genetically manipulate, and can produce many types of compounds. Thus, to select strains for the activation or enhancement of target products, host strains should contain some attractive features, such as fast growth, a well-studied secondary metabolism, and being easily genetically manipulated. Based on this, some _Streptomyces_ hosts have been used for heterologous expression, such as _S. albus_, _Streptomyces amboticiens_, _S. avernitilis_, _Streptomyces coelicolor_, _Streptomyces fradiae_, _Streptomyces roseporus_, _Streptomyces toyocaneisis_, _S. venezuelae_, and _S. lividans_[56]. Therefore, heterologous expression in _Streptomyces_ enables the activation or enhancement of the production of secondary metabolic products. For example, albucidin was produced in _S. albus_ Del14 from _S. albus_ subsp. _chlorinus_ NRRL B-24108 [57], kinamycin from _Streptomyces galtieri_ Sgt26 was successfully biosynthesized in _S. albus_ J1074 [58], and thaxtomins were successfully produced at a 10-fold higher titer in _S. albus_ J1074 than in native _Streptomyces scabiei_[59].  
_Streptomyces_ host can also be used for the production of metabolites from rare _Actinomyces_. Some rare _Actinomyces_ exhibit slow growth and carry a smaller population than _Streptomyces_, which is also considered a bioactive compound source. Some compounds were discovered from rare _Actinomycetes_ via heterologous expression in a _Streptomyces_ host, such as chuangxinomycin (from _Actinoplanes_) [60], tunicamycin (from _Actinosynmema_) [61], huimycin (from _Kutzneria_) [62], thiocarbine (from _Micromonospora_) [63], rangenicin (from _Nocardi_) [64], kocurin (from _Nonomuraea_) [65], GE2270 (from _Planobispora_) [66], shinorine (from _Pseudonocardiia_) [67], taromycin B (from _Sacharomonospora_) [68], and A201A (from _Sacharothrix_) [69].
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 6 Conclusions  
The optimization of the MOD_17 model using refined input data improved the forest GPP behavior compared with applying the original MODIS model driven by default parameters. The optimized model was then incorporated in the Biome-BGC model exploiting the powers of both models for the simulation of forest carbon fluxes. The incorporation is also beneficial for the calibration of ecophysiological parameters and can make Biome-BGC more resistant to the impact of environment variability. The key ecophysiological parameters were determined with a sensitivity analysis on Biome-BGC using EFAST, a global analysis method that could provide the first order sensitivity index and the total order sensitivity index of the parameters to model predictions. The results demonstrated that FLNR, SLA, and FRC:LC were the parameters most sensitive for the model output.  
A data assimilation method using EnKF has been designed to improve the simulation of carbon fluxes and reduce the errors based on time series remote sensing observations (GLASS LAI). This method takes errors in observations, input data and model structure into account to ensure that the output behavior is consistent with the observations. Eight-day GLASS LAI products were assimilated into the calibrated Biome-BGC model from 2003 to 2007 at a Changbai Mountains forest flux site to track the model over time. It was found that GPP behavior was smoothed by the EnKF, especially in spring, with the performance generally improving significantly (R\({}^{2}\) = 0.92, RMSE = 1.261 gC\(\cdot\) m\({}^{-2}\cdot\) d\({}^{-1}\)).  
With the model calibration and data assimilation, GPP behavior showed good agreement with EC measurements at the forest flux site, and it is possible to simulate other forest processes (e.g., evapotranspiration, respiration) based on this strategy as well, both on a local and a regional scale.  
This work was supported by the National Basic Research Program of China (973 Program) under grant 2013 CB733404. The eddy covariance measurements in this study were obtained from ChinaFlux. We would also like to thank Beijing Normal University for providing the GLASS products. The soil data set has been provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn))).  
[PERSON] is the principal author of this manuscript, having written the majority of the manuscript and contributed at all phases of the investigation. [PERSON] contributed to the interpretation of methods and provided suggestions about the structure. [PERSON] and [PERSON] contributed to the field logistic and design. [PERSON], [PERSON], and [PERSON] contributed to the data processing.  
The authors declare no conflict of interest.
###### Abstract  
Atmospheric depositions have been recently recognized as an important source of nutrients for off-shore marine systems, in line with the coastal input and physical exchange. The input of nutrients with atmospheric depositions can change their inventory and ratio in the euphotic zone, thus increase the rate of primary production and the type of predominant phytoplankton. The influence of atmospheric depositions, temporal variations of this influence and consequences of this deposition have been neglected. Monitoring of nutrients in atmospheric depositions of Crimea in 2015-2020 has allowed studying of multi-scale variations in their input to coastal areas and scaling the effects of this input. It has been found that the contribution of dry deposition in the total flux of nutrients is more significant for silicates and phosphates. Intra-annual variations in concentrations of nitrogen reveal a maximum in an urbanized area for the cold period of year, due to burning of extra fuel. On the contrary, increasing concentrations of nitrogen have been detected in a rural area in warm period. High values of concentrations of phosphorus and silica are typical for dry summer period and associated with dust transport from natural and anthropogenic sources. The N:P:Si ratio in the atmospheric depositions has been significantly shifted towards nitrogen as compared to the stoichiometric ratio. The results obtained in this work suggest that additional flux of nutrients with atmospheric depositions is minor at the annual scale, but it may change the local inventory and C:N:P ratio in the surface layer of the sea on a daily-time scale. The input of nutrients with atmospheric depositions can lead to additional (up to twofold) production of organic matter and result in additional oxygen consumption, when this surplus organic matter sinks and is oxidized, thus supporting suboxic conditions in near-shore areas.  
atmospheric depositions; nutrients; inorganic nitrogen; inorganic phosphorus; silicon; Black Sea; ratio of nutrients; oxygen consumption Article
# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
## 5 Conclusions  
In this paper, the CEEMD method is used to adaptively reduce the noise of complex vibration signals, but the pseudo components after CEEMD decomposition seriously reduce the diagnostic efficiency. The article proposes a new method of adaptively removing noise components, applying the proposed method to the composite fault simulation signal and the vibration signal of mining machinery, accurately extracting the composite fault characteristics, and verifying the feasibility of the method by comparing it with the traditional CEEMD. Finally, the following conclusions are obtained:  
1. Determine the best scale factor \(Q\) = 10 in the MSE by simulation signals with different SNR, and then employ it to calculate the MSE values of noise signals, simulation signals, and vibration signals, which provide support for optimizing the CEEMD method.
2. Calculate the MSE value of 10,000 sets of random noise, and use the minimum value as the threshold, remove the IMFs component signal greater than the threshold, achieve the purpose of CEEMD noise reduction, and verify the feasibility of the minimum value as the threshold.
3. Through CEEMD decomposition of the reconstructed signal, the MSE value of each component signal is calculated. The MSE value is compared with the dynamic threshold value, and if there is an IMFs component signal greater than the threshold, it is removed, the remaining signal is reconstructed, and CEEMD decomposition is performed again. After multiple iterations, when it is finally shown that there is no case where the MSE value of the component signal is greater than the threshold, the iteration is terminated. Finally, the obtained signal is analyzed by the frequency spectrum to determine the fault characteristics.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; and [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key Research and Development Program of Shanxi Province (International Cooperation) (201903D421051, 201803D421028); Youth Fund Project of Shanxi Province (201901D211210).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 20: MSE value of the component signal (reconstructed signal is decomposed by CEEMD).