esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### How do Meteorological Factors Contribute to the Relationships?
Meanwhile SIF and PAR showed a significant increase of 96.95% in the slope (PAR \(<\) 1250 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), K = 3.24; PAR \(>\) 1250 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), K = 6.39; Figure 5). With an increase in PAR, GPP had an obvious photo-inhibition phenomenon accompanied by a significant increase in SIF production. In consideration of the influence of VPD on GPP and SM on SIF on the daily scale (Table 1), we fitted the two sets of data on the 30 min timescale (Figure 5). With the increase of VDP, GPP showed a downward trend and there was a significant correlation between VPD and GPP which was not found in SIF (Figure 5, Table 1). With the increase of SM, SIF showed an upward trend and was significantly correlated with SM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{4}{c}{**Pearson’s Correlation Coefficient**} \\ \cline{2-5}  & \multicolumn{2}{c}{**SIF**} & \multicolumn{2}{c}{**GPP**} \\ \cline{2-5}  & **30 min** & **Day** & **30 min** & **Day** \\ \hline PAR & 0.872 ** & 0.544 ** & 0.627 ** & \(-\)0.495 ** \\ VPD & 0.203 ** & \(-\)0.237 & \(-\)0.043 & \(-\)0.498 ** \\ SM & 0.202 ** & 0.577 ** & 0.009 & \(-\)0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlations between canopy SIF and GPP with meteorological factors like PAR, VPD, and SM based on 30 min and daily data observed in oak canopy. The whole analyses were based on data on sunny days. ** for \(p\)\(<\) 0.01. Figure 5: The response of SIF-GPP to different meteorological factors in the 30 min timescale, (**a**), (**b**) and (**c**) represent PAR, VPD and SM, respectively. The solid line and dashed line represent the linear fit between SIF and GPP with meteorological factors, respectively. K and R\({}^{2}\) represent the fitting slope and regression coefficients, respectively. All data are for sunny days.
## 4 Results
### Evolution of Water Chemistry in the Upper Reservoir  
The following results are related to the hydrochemical context described at 2.3. Figure 8 shows the main results simulated with the hydrochemical groundwater model concerning the hydrochemical evolution of the water in the upper reservoir. Figure 7 displays the evolution of pH and the precipitated quantities of calcite, pyrolusite, and goethite.  
In the upper reservoir, the chemical equilibrium of the water with the atmosphere induced an increase in the concentration of dissolved O\({}_{2}\) and a decrease in the concentration of dissolved CO\({}_{2}\). In general, the increase in the dissolved O\({}_{2}\) caused the precipitation of pyrolusite and goethite (Equations (6) and (7)), whereas the decrease in dissolved CO\({}_{2}\) caused the precipitation of calcite (Equations (1) to (5)). The precipitation of these elements occurred from the first pumping-discharge cycle, during which the amount of precipitated minerals reached a maximum (1.46 \(\times\) 10\({}^{-6}\), 4.55 \(\times\) 10\({}^{-6}\), and 1.4\(\times\)10\({}^{-3}\) moles of precipitate/L of pyrolusite, goethite, and calcite, respectively) as shown in [9] in the scenario with only calcite. Precipitation continued during the following cycles, but the amount of precipitated minerals decreased gradually as the water pumped into the upper reservoir became depleted in calcium, manganese, and iron. The precipitated quantities were significant, especially at the beginning of the PSH activities. The quantity of precipitated calcite was three orders of magnitude higher than those of goethite and pyrolusite. Precipitated amounts of goethite and pyrolusite were similar, although slightly higher for goethite. Indeed, the iron concentration (4.7 \(\times\) 10\({}^{-6}\) mol/L) in groundwater was initially higher than that of manganese (1.5 \(\times\) 10\({}^{-6}\) mol/L). In addition, iron oxidation is preferential and has faster kinetics. Theoretically, calcite, pyrolusite, and goethite should form deposits in the upper reservoir, which would require periodic cleaning tasks. In practice, however, given the flows involved, turbulence in the upper reservoir could reduce these deposits. Overall, the volume of precipitated minerals can be relatively important  
Figure 8: Evolution of hydrogeochemical variables during pumping–injection cycles in the quarry. (**a**) Evolution of pH. (**b**) Evolution of the quantity of Ca\({}^{2+}\) in moles per liter. (**c**) Evolution of the quantity of Mn\({}^{2+}\) in moles per liter. (**d**) Evolution of the quantity of Fe\({}^{2+}\) in moles per liter.  
and periodical cleaning tasks could be needed, which would affect the global efficiency of the PSH plant [9; 32]  
pH values in the upper reservoir increased drastically during the first pumping-discharge cycle as a result of CO2 degassing and calcite precipitation. After the first cycle, pH remained relatively constant throughout the following cycles, oscillating between 8.16 and 8.18. Note that incrustations were promoted under these values of pH, in accordance with calcite precipitation, and potentially needed cleaning operations.
###### Abstract
While these machine learning algorithms can achieve high accuracy and speed after training on massive amounts of data, they consume significant computational resources during pre-training. On the other hand, meta-heuristic algorithms like BA [33], Whale optimization algorithm (WOA) [34], ACO algorithm [35], and Red deer algorithm (RDA) [36, 37] simulate natural population behavior and are based on mathematical theories inspired by natural laws [38]. These algorithms require fewer mathematical conditions than methods like Lyapunov Vector Field (LVF) [39, 40], Unscented Information Filter (UIF) [41, 42], and traditional heuristics [43]. Improved meta-heuristic optimization algorithms offer fast and accurate solutions without pre-training. The TABU algorithm [7] and GA algorithm [44] have been applied to the THEMIS flat plate receiver based on the HFLCAL convolution method to flatten the flux distribution while minimizing spillage. The ant colony optimization algorithm (ACO) [45, 46] has been proposed to maximize the intercepted power and comply with the limits, regardless of the thermal receiver's shape. According to [PERSON] et al. [47], this method performs 2% better than the single-factor aiming strategy of the cylindrical receiver. It is also coupled with the local search algorithm (TLBO) of [PERSON] et al. [16] to increase convergence to the global optimum. In this paper, we propose a novel meta-heuristic method that does not require offline pre-training but is also computationally efficient without sacrificing optimality. This paper aims to reduce Particle Swarm Optimization (PSO) [48] complexity and shorten optimization time by modeling heliostats as intelligent agents (IA). PSO is an efficient metaheuristic inspired by social animal behaviors, known for quick convergence and adaptability in complex problem-solving, where particles share information and collaboratively move toward optimal solutions. Formulated in a physically meaningful way, the IA is connected to the physical heliostat, thus enabling the utilization of operational continuity to enhance the solution process. The proposed innovative mechanism incorporates the heliostat field aiming strategy of the previous period. Therefore, reinforcements are introduced into the classical PSO algorithm, resulting in the development of a new algorithm called Individual Update PSO (IUPSO). The paper primarily focuses on creating a dynamic aiming strategy for central receiver systems that can effectively handle the real-time impact of a cloud shadow on the heliostat field. The aim is to offer a more precise and efficient method for calculating the real-time aiming strategy of the heliostats under cloud shadow.
## 6 Atmospheric Gaseous Losses Profile for Aircraft-Stratospheric Basestation (SB) Link
### Complete Flight Profile Scenario
\(L\) can be expressed in dB as follows:  
\[L_{dB}=20\log(d_{cKm})+20\log(f_{GHz})+92.558\ \mathrm{dB} \tag{29}\]  
where \(d_{cKm}\) is the communication range in km and \(f_{GHz}\) is the carrier frequency in GHz. The path loss is displayed in Figure 11b and the total signal loss (atmospheric plus free space path losses) is shown in Figure 11c where the impact of path loss on the transmitted signal is dominant. Therefore, for FR'1 frequencies, the broadband aircraft-SB aeronautical network is mainly affected by the free-space path loss rather than atmospheric absorption losses. The same demonstrations for the FR'2 higher set of mmWave frequencies are shown in Figures 12a-c and 13 including atmospheric gaseous attenuation only, FSL, and the combined attenuation of both, respectively. As shown in Figure 12a, the atmospheric attenuation at 26 and 28 GHz are almost of the same profile with aircraft altitude and ground distance. However, higher atmospheric losses affect greatly the other higher frequencies (i.e., above 40 GHz), especially during the climbing/descending stage, while the losses drop to very low levels at the cruise heights with converging performance indicating low dependency of atmospheric losses at this height. In Figure 12b, the FSL variations are shown for FR'2 frequencies while the combined atmospheric attenuation and FSL is shown in Figure 12c where the higher impact of atmospheric attenuation appears clearly during climbing/descending stages. On the other hand, at cruise levels, the FSL is the dominant attenuation factor. \[d_{gSB}=\frac{h_{s}}{\tan\!\left(\alpha_{g}\right)} \tag{24}\]0 Flight signal attenuation performance for 5G frequencies up to 10 GHz in dB: (**a**) atmospheric gaseous attenuation, (**b**) free-space loss (FSL), (**c**) combined atmospheric gaseous attenuation and FSL performance. \[d_{gSB}=\frac{h_{s}}{\tan\!\left(\alpha_{g}\right)} \tag{24}\]1 _Cont._  
As shown in Figure 13, at the 66 GHz mmWave frequency, the atmospheric attenuation is very high and it is not feasible at all to use during the climbing/descending stage, while it does not exceed 20 dB at the cruise height.
# Dictionary Encoding Based on Tagged Sentential Decision Diagrams
## 3 Tagged Sentential Decision Diagrams
### Canonicity  
TSDDs, as a variant of SDDs, apply trimming rules that integrate the trimming rules of SDDs and ZSDDs. Hence, the trimming rules of TSDDs include the trimming rules of SDDs and ZSDDs that are shown in Figure 1a-d, and we do not introduce them in the following definition. Hence, the trimming rules of TSDDs start from (e). In addition, these rules also include five new rules. We then show the compression and trimming rules for TSDDs as follows:* Tagged compression rule: if \(\langle s_{i}\rangle=\langle s_{j}\rangle\), then replace \((\mathbf{T}^{1},\mathbf{T}^{2},\{(p_{1},s_{1}),\ldots,(p_{t},s_{i}),\ldots,\)\((p_{j},s_{j}),\ldots,(p_{n},s_{n})\})\) with \((\mathbf{T}^{1},\mathbf{T}^{2},\{(p_{1},s_{1}),\ldots,(p^{\prime},s_{i}), \ldots,(p_{n},s_{n})\})\), where \(\langle p^{\prime}\rangle=\langle p_{i}\rangle\vee\langle p_{j}\rangle\).
* Tagged trimming rules:
* If \(p=(0,0,\mathbf{1})\) and \(s=(0,0,\mathbf{0})\), then replace \((\mathbf{T}^{1},\mathbf{T}^{2},\{(p,s)\})\) with \((0,0,\mathbf{0})\) (shown in Figure 1e).
* If \(p_{1}=(\mathbf{T}^{3},\mathbf{T}^{4},\alpha)\), \(s_{1}=(\mathbf{T}^{2}_{r},0,\mathbf{1})\), \(s_{2}=(0,0,\mathbf{0})\), and \(\mathbf{T}_{3}\prec\mathbf{T}^{2}_{l}\), then replace \((\mathbf{T}^{1},\mathbf{T}^{2},\{(p_{1},s_{1}),\)\((p_{2},s_{2})\})\) with \((\mathbf{T}^{1},\mathbf{T}^{2}_{l},\{(p_{1},0,\mathbf{1}),(p_{2},0,\mathbf{0}) \})\) (shown in Figure 1f).
* If \(p_{1}=(\mathbf{T}^{3},\mathbf{T}^{4},\alpha)\), \(s_{1}=(\mathbf{T}^{2}_{r},0,\mathbf{1})\), \(s_{2}=(0,0,\mathbf{0})\), and \(\mathbf{T}_{3}\prec\mathbf{T}^{2}_{r}\), then replace \((\mathbf{T}^{1},\mathbf{T}^{2},\{(p_{1},s_{1}),\)\((p_{2},s_{2})\})\) with \((\mathbf{T}^{1},\mathbf{T}^{2}_{r},\{((0,0,\mathbf{1}),p_{1})\})\) (shown in Figure 1g).
* If \(p_{1}=(\mathbf{T}^{2}_{r},0,\mathbf{1})\), \(s_{1}=(\mathbf{T}^{3},\mathbf{T}^{4},\alpha)\), \(s_{2}=(0,0,\mathbf{0})\), and \(\mathbf{T}_{3}\prec\mathbf{T}^{2}_{l}\), then replace \((\mathbf{T}^{1},\mathbf{T}^{2},\{(p_{1},s_{1}),\)\((p_{2},s_{2})\})\) with \((\mathbf{T}^{1},\mathbf{T}^{2}_{r},\{(p_{1},(0,0,\mathbf{1})),(p_{2},(0,0, \mathbf{0}))\})\) (shown in Figure 1h).
* If \(p_{1}=(\mathbf{T}^{2}_{l},0,\mathbf{1})\), \(s_{1}=(\mathbf{T}^{3},\mathbf{T}^{4},\alpha)\), \(s_{2}=(0,0,\mathbf{0})\), and \(\mathbf{T}_{3}\prec\mathbf{T}^{2}_{r}\), then replace \((\mathbf{T}^{1},\mathbf{T}^{2},\{(p_{1},s_{1}),\)\((p_{2},s_{2})\})\) with \((\mathbf{T}^{1},\mathbf{T}^{2}_{r},\{((0,0,\mathbf{1}),p_{1})\})\) (shown in Figure 1i).  
A TSDD is compressed (resp. trimmed) if no tagged compression (resp. trimming) rules can be applied to it. The canonicity of compressed and trimmed TSDDs was proved in [8].
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
###### Abstract  
Real-time data transmission and reliable operation are essential for a tsunami monitoring system to provide effective data. In this study, a novel real-time tsunami monitoring system is designed based on a submersible mooring system. This system is equipped with a data acquisition and tsunami wave identification algorithm, which can collect the measured data of the pressure sensor and detect a tsunami wave in real time. It adopts the combination design of underwater inductive coupling transmission and a redundant BeiDou communication device on the water surface to ensure the reliability of real-time data transmission. Compared with traditional tsunami monitoring buoys, it has the advantages of reliable communication, good concealment, high security, and convenient deployment, recovery, and maintenance. The results of laboratory and sea tests show that the system has high reliability of data transmission, stable overall operation of the system, and good application prospects in the field of real-time tsunami monitoring and early warning.  
Keywords: tsunami monitoring; submersible mooring system; real-time data transmission; inductive coupling +
Footnote †: journal: Physics Letters B
## 4 Results and Discussion
### Scenario 3: Impulsive Sources with Time-Varying Characteristics  
This scenario considers noise sources having time-varying characteristics from the view point of the impulsiveness. At the start, the noise source is assumed to be modeled as a standard S\(\alpha\)S process of strongly impulsive nature having \(\alpha=1.4\), exhibits severely impulsive characteristics with \(\alpha=1.1\) during the middle, and finally returns to strongly impulsive behavior with \(\alpha=1.4\) for the latter part of the simulation. A typical signal drawn from such a standard S\(\alpha\)S process is shown in Figure 15, where the vertical axis is restricted to clearly show that the impulses are indeed more frequent in the middle part. The corresponding simulation results averaged over 100 realizations are presented in Figure 16. It is observed from the presented results that the existing algorithms work fine provided that the step size is adjusted to a small value, which surely results in an overall slow convergence speed. A large step size may improve the initial convergence speed; however, may result in a poor performance when impulsiveness increases during the middle portion of the simulation. The previous CCSS-NMFxLMS, the proposed FSS-MFxRRLS and proposed CCSS-MFxRRLS algorithms do not require any further tuning in the parameter values and keep the good performance for the whole duration of simulation. Furthermore, the proposed proposed CCSS-MFxRRLS algorithm demonstrates the best performance as compared with the rest of the algorithms.
## 3 The Bike-Share Case
### Evaluation--Causal Mapping
In this simplified example, we observe multiple feedback loops with varying strengths, delays and non-linear relationships (e.g., the effect of bike availability on the provider's risk perception and user perception), making a closed-form solution unfeasible. Based on the causal representation of the inventory imbalance problem, we might hypothesise about the potential root causes (see numbered flags in Figure 4). These are described below. The baukling behaviour reduces recorded demand, effectively censoring flows (1). When used for forecasting, this results in a pessimistic outlook on future demand (2). In other words, historical issues hinder the ability to predict future demand accurately, worsening the situation over time. Additionally, inefficiencies in incorporating real-time availability or overemphasising it (where demand outliers or temporary fluctuations heavily influence decisions) (3) affect the provider's perception of stock levels. Lastly, the rules for triggering incentives, based on perceived and desired bike availability (4), impact their effectiveness. The causal map's feedback-oriented view helps explain the dynamic behaviour behind inventory imbalance (the current state). Having identified the leverage points, we might further hypothesise a mental model that addresses the problem--the envisaged future state. Our proposed mental model likely involves a mix of human judgment and ML. For addressing bike-share inventory imbalance, several factors need to be considered: initiating incentives cannot be fully algorithmic due to budget constraints and other balancing methods; user response to station saturation is non-linear; it is better to base outage risk on patterns of actual and projected bike availability, rather than reacting to temporary demand fluctuations; and policy robustness is preferable over chasing \"optimal\" incentive schemes.
## 4 Proposed Control and Incentive Mechanism
### Control System  
At time \(t\), the charging station elicits the drivers' preferences and then solves an optimal control problem. The preferences reported by the drivers are denoted as \(\mathbf{b}_{t}=\{\mathbf{b}_{it}\}_{i\in I}\). In general, we can assume that \(\mathbf{b}_{it}\in\mathbf{\Theta}_{it}\), meaning that the reported preferences are within the set of drivers' preferences. However, these reported preferences do not need to be truthful, meaning \(\mathbf{b}_{it}\
eq\mathbf{\theta}_{it}\)[35].  
**Assumption 4**.: _The reported preferences \(\mathbf{b}_{t}\) belong to the set of the drivers' preferences \(\mathbf{\Theta}_{t},\mathbf{b}_{t}\in\mathbf{\Theta}_{t}\)._  
When the preferences are reported, the control system will find the optimal mode for all connected EVs (\(\mathbf{z}_{t}\)) and the amount of power supplied to and from EVs (\(\mathbf{u}_{t},\mathbf{v}_{t}\)) concerning their state of charge (\(\mathbf{x}_{t}\)) and battery capacity \(\mathbf{C}\) and their drivers declared preferences \(\mathbf{b}_{t}\).  
So, the control system solves the following optimization problem:  
minimize \[\sum_{i=1}^{l}c_{it}(\mathbf{b}_{it},\mathbf{a}_{it})+c_{0t}(\mathbf{a}_{t},\gamma)\] (14) s.t. \[\mathbf{u}^{+}\cdot\mathbf{z}_{t}-\mathbf{u}^{+}\leq\mathbf{v}_{t}\leq 0\] (15)\[0\leq\mathbf{u}_{t}\leq\mathbf{u}^{+}\cdot\mathbf{z}_{t} \tag{16}\]  
\[\mathbf{x}^{-}\leq\mathbf{x}_{t}+\frac{1}{\mathcal{C}}\cdot(\mathbf{u}_{t}+\mathbf{v}_{t})\leq \mathbf{x}^{+} \tag{17}\]  
\[0\leq\sum_{i=1}^{l}(u_{it}+v_{it})\leq\mathbb{E}[s_{t}] \tag{18}\]  
\[\mathbf{z}_{t}\in\{0,1\}^{I} \tag{19}\]  
This problem leads to mixed-integer linear programming. The results will represent a set of socially efficient control policies at the time \(t\) for each EV connected to the charging station. Since the costs of all participants, as well as the charging station operator, are minimized, it can be noted that the social welfare of the system is ensured. Social welfare, or the optimal \"cost\" function, is a function that represents minimal discomfort across all participants:  
\[W_{t}(\mathbf{b}_{t}):=\inf_{\mathbf{a}_{t}}\Big{(}\sum_{i=1}^{l}c_{it}(\mathbf{b}_{it}, \mathbf{a}_{it})+c_{0t}(\mathbf{a}_{t},\gamma)\Big{)}, \tag{20}\]  
such that constraints (15)-(19) apply. Solving the presented optimal control problem results in determining a socially efficient control policy \(\mathbf{a}_{t}^{*}\) with:  
\[W_{t}(\mathbf{b}_{t}):=\sum_{i=1}^{l}c_{it}(\mathbf{b}_{it},\mathbf{a}_{it}^{*})+c_{0t}(\bm {a}_{t}^{*},\gamma,P_{PV,t}). \tag{21}\]
## 4 Multi-Hop Data Forwarding Strategy Based on Annulus-Sectors
### The Length of Radius of the RCCH
As shown in Figure 5, if and only if the boundaries of the RCCH and the annulus-sector are tangent, the value of \(r_{1}\) is the maximum, so it needs to meet the following expression:  
\[r_{1}\leq(R/2k)\sin(\pi/n) \tag{19}\]  
According to Equations (15), (16), (18) and (19), the following equation can be obtained:  
\[r_{k-1}\geq\sqrt{\frac{(N_{k-1}+N_{k}-1)e_{r}+(N_{k-1}+N_{k})e_{t}}{\pi\rho((N _{k}-1)e_{r}+N_{k}e_{t})}} \tag{20}\]  
Similarly, according to Equations (15) and (19), the following expression can be obtained:  
\[r_{k-1}\leq\sqrt{\frac{(N_{k-1}+N_{k}-1)e_{r}+(N_{k-1}+N_{k})e_{t}}{\left( \left(N_{1}-1+\sum\limits_{m=2}^{k}N_{m}\right)e_{r}+\left(N_{1}+\sum\limits _{m=2}^{k}N_{m}\right)e_{t}\right)}}\times\left(\frac{R}{2k}\right)\sin\!\left( \frac{\pi}{n}\right)} \tag{21}\]
# Reliability Study on FRP Composites Exposed to Wet-Dry Cycles
## 5 Conclusions  
Forty-eight FRP samples, including 24 CFRP samples and 24 GFRP samples, were tested to evaluate the deterioration of the FRP properties under long-term wet-dry cycles exposure. The results illustrate that the failure modes of FRP samples at different corrosion times are not significantly different. The wet-dry cycles have a significant adverse influence on the tensile strength, have a certain adverse effect on the elongation, and a very limited influence on the elastic modulus of FRP. Moreover, GFRP is more susceptible to the wet-dry cycle exposure than CFRP. In the probability analysis, the tensile strength of the FRP materials with/without the wet-dry cycles both fit the Normal distribution and the Weibull distribution. However, the probability distributions of FRP tensile strength under each condition are taken as Normal distribution because of its simple computational modeling.  
Figure 10: The effects of standard deviations of the load on the GFRP specimens: (**a**) Change of \(\beta\) with wet-dry cycles; and (**b**) change of \(P\) with wet-dry cycles.  
Figure 9: The effects of standard deviations of the load on the CFRP specimens: (**a**) Change of \(\beta\) with wet-dry cycles; and (**b**) change of \(P\) with wet-dry cycles.  
The decreased tendency of \(\beta\) with the increasing of wet-dry cycle is similar to an exponential function. In the beginning, \(\beta\) rapidly reduced with the increase of wet-dry cycles, and then \(\beta\) decreased very slowly with the increase of wet-dry cycles when \(\beta\) reduced to a small value. The boundary value of the two stages for both CFRP and GFRP is about 1000 times. Subsequently, through the analysis of the design tensile strength of ACI-440, TR-55, GB 50608-2010 and GB 50367-2013, European Fib Bulletin 14 and Italian CNR guidelines on the \(\beta\), it is found that only the \(\beta\) of CFRP composite under the design value of the TR-55 code can always stay above 3.2, which means long-term safety can be guaranteed. However, for the GFRP composite, all the four codes cannot guarantee the long-term safety of the GFRP composite and the partial safety factors in these codes are still not conservative. The reliability of the FRP composite will visually decrease as the standard deviation of the load increases. When the standard deviation is 2\(\sigma_{\text{eg}}\) or more, the \(\beta\) is less than 3.0 under 4000 times wet-dry cycle exposure, and the failure probability increases very obviously. Therefore, the design value of tensile strength needs to be further conservative when the standard deviation of the load is large.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; all the authors analyzed the data and wrote the paper.  
This research was funded by National Natural Science Foundation of China (No. 51578428) and Special Project on Technical Innovation of Hubei (No. 2016 AAA025).  
The authors