esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Effectiveness of Natural Carbon Sinks Through a Temperature-Dependent Model
## Appendix A Computation Details of Deseasonalization  
Deseasonalization has the goal of removing all seasonal information from a signal with a nonlinear trend. The seasonal component is assumed to be identical and cyclical every year.  
Following [38], the measured information samples \(y_{i}\) at month \(i\) are split into 3 components, the nonlinear trend model component \(\hat{y}_{i}^{trend}\), the yearly periodic model component \(\hat{y}_{i}^{periodic}\), and an unknown residual \(y_{i}^{residual}\):  
\[y_{i}=\hat{y}_{i}^{trend}+\hat{y}_{i}^{periodic}+y_{i}^{residual}\] (A1)  
The trend model is a polynomial model, here of order 3:  
\[\hat{y}_{i}^{trend}=a_{0}+a_{1}\cdot i+a_{2}\cdot i^{2}+a_{3}\cdot i^{3}\] (A2)and the yearly periodic model for monthly data is  
\[\hat{y}_{i}^{periodic}=a_{4}\cdot\sin(\frac{i\cdot\pi}{6})+a_{5}\cdot\cos(\frac {i\cdot\pi}{6})+a_{6}\cdot\sin(2\cdot\frac{i\cdot\pi}{6})+a_{7}\cdot\cos(2\cdot \frac{i\cdot\pi}{6}) \tag{10}\]  
First, the parameters of the trend model are estimated by ordinary least squares minimization over all n data points:  
\[\min_{a_{0},a_{1},a_{2},a_{3}}\sum_{i=0}^{n-1}\left(y_{i}-(a_{0}+a_{1}i+a_{2}i ^{2}+a_{3}i^{3})\right)^{2}\]  
The difference between the original data and the polynomialy approximated data are the detrended data  
\[y_{i}^{detrend}=y_{i}-\hat{y}_{i}^{trend} \tag{11}\]  
They are displayed as the blue graph in Figure 11. The detrended data are modeled with the periodic model by an ordinary least squares minimization:  
\[\min_{a_{1},a_{5},a_{6},a_{7}}\sum_{i=0}^{n-1}(y_{i}^{detrend}-y_{i}^{periodic })^{2}\]  
These optimization problems are solved with the class OLS from Python Statsmodels [19].  
The orange graph of Figure 11 shows the approximation of the detrended and zero mean data by the periodic model of sine- and cosine-functions up to degree 2.  
The goal of the process, the deseasonalized data, are the difference of the original data and the periodic model. Figure 12 shows the orginal CO\({}_{2}\) concentration data, the trend model, the sum of trend model and periodic model, and the deseasonalized CO\({}_{2}\) concentration data.
## 3 Control Design
### Adaptive Sliding Mode Trajectory Tracking Control Design  
#### 3.2.1 Virtual Control Law  
Define the position errors  
\[\left\{\begin{array}{l}x_{e}=x-x_{d}\\ y_{e}=y-y_{d}\end{array}\right. \tag{6}\]  
Differentiating the position errors (6), we have  
\[\left\{\begin{array}{l}\dot{x}_{e}=u\cos\psi-v\sin\psi-\dot{x}_{d}\\ \dot{y}_{e}=u\sin\psi+v\cos\psi-\dot{y}_{d}\end{array}\right. \tag{7}\]  
Define the velocity errors as  
\[\left\{\begin{array}{l}u_{e}=u-\alpha_{u}\\ v_{e}=v-\alpha_{v}\end{array}\right. \tag{8}\]  
where \(\alpha_{u}\) and \(\alpha_{v}\) represent virtual control laws.  
Figure 2: The structure of the proposed adaptive sliding mode control scheme for the underactuated USV.  
In order to stabilize the position errors, the virtual control laws are designed as  
\[\left\{\begin{array}{l}\alpha_{u}=\dot{x}_{d}\cos\psi+\dot{y}_{d}\sin\psi-\frac{ \lambda x_{e}}{\sqrt{x_{e}^{2}+y_{e}^{2}+M}}\cos\psi-\frac{\lambda y_{e}}{\sqrt {x_{e}^{2}+y_{e}^{2}+M}}\sin\psi\\ \alpha_{v}=-\dot{x}_{d}\sin\psi+\dot{y}_{d}\cos\psi+\frac{\lambda x_{e}}{\sqrt {x_{e}^{2}+y_{e}^{2}+M}}\sin\psi-\frac{\lambda y_{e}}{\sqrt{x_{e}^{2}+y_{e}^{2} +M}}\cos\psi\end{array}\right. \tag{9}\]  
where \(\lambda\) and \(M\) are positive constants.  
Substituting (9) into (7), we obtain  
\[\left\{\begin{array}{l}\dot{x}_{e}=u_{e}\cos\psi-v_{e}\sin\psi-\frac{\lambda x _{e}}{\sqrt{x_{e}^{2}+y_{e}^{2}+M}}\\ \dot{y}_{e}=u_{e}\sin\psi+v_{e}\cos\psi-\frac{\lambda y_{e}}{\sqrt{x_{e}^{2}+ y_{e}^{2}+M}}\end{array}\right. \tag{10}\]  
Therefore, when \(u_{e}\) and \(v_{e}\) converge to zero, define the Lyapunov function \(V_{1}=\frac{1}{2}x_{e}^{2}+\frac{1}{2}y_{e}^{2}\), the time derivative of \(V_{1}\) is written as  
\[\dot{V}_{1} =x_{e}\dot{x}_{e}+y_{e}y_{e}\] \[=-\frac{\lambda\left(x_{e}^{2}+y_{e}^{2}\right)}{\sqrt{x_{e}^{2}+ y_{e}^{2}+M}}\leq 0 \tag{11}\]  
According to the above analysis, as long as we can stabilize the velocity errors \(u_{e}\) and \(v_{e}\), the position errors \(x_{e}\) and \(y_{e}\) asymptotically converge to zero.  
**Remark 1**.: _In [11], when \(\left|x_{e}\right|\) and \(\left|y_{e}\right|\) are relatively large, the virtual control laws can exceed the velocity range of USV, and they can lead to system instability or controller crash. However, the designed virtual control laws of this paper can avoid the above problem._
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 5 Conclusions  
### Emerging Strategies for Sticking Prevention  
Future perspectives for MXenes related to emerging strategies for sticking prevention could encompass several areas of development. Some potential directions for advancing the use of MXenes while addressing sticking prevention might include the following:  
Advanced intercalation techniques: enhancing and refining methods for intercalating compounds between MXene layers to prevent self-stacking. This could involve exploring a wider range of intercalants, including organic molecules with carefully tailored properties to effectively space MXene layers and prevent sticking.  
Tailoring surface functionalities: investigating and developing new approaches to modify MXene surface functionalities to mitigate the tendency to self-stack. By engineering the MXene surface at a molecular level, it may be possible to reduce the interlayer interaction forces, thereby preventing stacking without significantly compromising other desirable properties.  
Nanocomposite design: exploring the integration of MXenes into nanocomposite materials with specific structures that inherently prevent sticking. This could involve creating hybrid materials with MXenes dispersed within a matrix in a way that inhibits natural stacking tendencies.  
Machine learning and computational design: leveraging computational techniques and machine learning algorithms to predict and design effective sticking prevention strategies for specific MXene compositions. This approach could help accelerate the discovery of novel intercalation agents or surface modifications that minimize self-stacking.  
Innovative electrochemical energy storage device architectures: exploring the application of MXenes in novel device architectures that inherently minimize sticking. By integrating MXenes into innovative structures for energy storage devices, it may be possible to design systems that inherently prevent stacking while maximizing performance.
# Performance Evaluation of a Long-Span Cable-Slayed Bridge Using Non-Destructive Field Loading Tests
## 2 Non-Destructive Field Loading Test for Pingnan Xiangszhou Bridge
### Non-Destructive Field Loading Test for Pingnan Xiangszhou Bridge  
#### 2.3.1 Non-Destructive Field Loading Test Instrumentations  
The instruments for measuring strain, deflection, frequency, and cable force are shown in Figure 2. A multifunctional static strain test system was used in the bridge strain test. Each data acquisition module can measure 16 measuring points, and the communication distance between modules can reach up to 500 m. Eight test systems were used in the test. The deflection of the main girder was monitored by an electronic total station. Reflective lenses were installed at the test points, and the changes in elevation--and thus, deflection--at these test points were collected during the loading tests. Frequency was extracted using the wireless bridge modal test and analysis system. A high-communication-rate mode was used. Cable force was measured via radar interferometric deformation measurement technology, which extracts the frequency information of the received time signal via filtering and Fourier transform.  
Figure 1: The main structural features of Pingnan Xiangszhou Bridge.  
Figure 2: Non-destructive field loading test instrumentation: (**a**) strain; (**b**) deflection; (**c**) frequency; (**d**) cable force.
## 3 Principles of the Scheme
### Adaptive Secondary Voltage Control (ASVC) Function
In the proposed method, however, any reactive power disturbance variable vector \(\mathbf{q}\) that satisfies \(J_{p}\mathbf{q}=\Delta\left|\mathbf{V_{p}}\right|\) can be utilized to determine feasible control signals that minimize \(\left\|\,\Delta\left|\mathbf{V_{L}}\right|\right|_{\infty}\). The least-norm technique [27], which is the most commonly used for solving an underdetermined set of linear equations, is applied to approximate \(\mathbf{q}\). In this case, the problem is to find \(\mathbf{q}\) that satisfies \(J_{p}\mathbf{q}=\Delta\left|\mathbf{V_{p}}\right|\) and minimizes \(\left\|\mathbf{q}\right\|_{2}\). Thus, the estimate of \(\mathbf{q}\) is given by  
\[\mathbf{q}^{\star}=J_{p}^{T}\left(J_{p}J_{p}^{
# The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin
## 4 Discussions
### Influence of Topographic Factors on GSE Change  
#### 4.2.1 Influence of Altitude on GSE Change  
As shown in Figure 9, glaciers in the Tailan River basin are primarily distributed on mountain peaks above an altitude of 3000 m. Over the past 22 years, almost all GSE in the basin have experienced a decrease. The overall trend indicates that the rate of decreasing GSE decreases as altitude increases. In the altitude range of 3000-4800 m, the GSE decreases sharply. It is obvious that there is a trend of the decreasing rate slowing down with altitude, and the GSE change is gradually transitioning from negative to positive in this range. This changing trend is because that zones with lower altitudes are more susceptible to temperature influences, with temperatures rising more quickly and resulting in greater glacial melting; thus, most GSEs show negative values. Above an altitude of 4800 m, the elevation of glaciers shows an increasing trend, because, that close to the ridgeline, rising altitudes and the effects of precipitation or snow accumulation can lead to glacier accumulation, so most GSE show positive values.  
To validate the elevation change values obtained in this paper, a comparison was made between the glacier elevation and typical monitored glaciers elevations from other zones, as shown in Table 7. [PERSON] et al. [73] investigated the elevation change pattern of glaciers in the Mont Blanc area and found that the rate of glacier elevation change in the Mont Blanc range was approximately 0.2-0.3 m\(\cdot\)a\({}^{-1}\) from 2003 to 2012. [PERSON] et al. [74] calculated the GSE change in the Tianshan Mountains area from 2000 to 2020 using the TanDEM-X radar data and SRTM DEM, and found that the glaciers in the eastern Tianshan Mountains had been accelerating thinning with a thinning rate of 0.76 \(\pm\) 0.05 m\(\cdot\)a\({}^{-1}\). Based on a time series of digital elevation models derived from satellite stereoscopic images, [PERSON] et al. [9] found that the GSE change rate in the Tianshan Mountains was \(-\)0.28\(\pm\) 0.20 m\(\cdot\)a\({}^{-1}\) from 2000 to 2016. The glaciers in the Tailan River basin are located in the southern area of the West Tianshan Mountains and the conclusion drawn in this study is consistent with that of [PERSON] et al., which is related to the quality of the DEM, the study area, and the observation time. The reliability of the results is mainly affected by the quality of the interferometric.  
Figure 9: Relationship between GSE change and altitude.
## 2 Materials and Methods
### Sample Collection and Analysis  
There are twelve boreholes and one potable well at the site for groundwater monitoring, all being located at the down gradient area of the petroleum spill (Figure 2). Perpendicular to the groundwater flow, three CPs were assigned for the plume assessment in this study, each of them containing two or three monitoring wells (Figure 2). All of the CPs were ensured by Formula (6) below, which ignored the influence of depth. The hydro-geochemical parameters were monitored from September 2009 to March 2010. These included six campaigns of TPH concentration sampled monthly, except for February 2010 from 13 monitoring wells. Other major ions including calcium, magnesium, sodium,potassium, chloride, and bicarbonate/carbonate were sampled in September 2009, January 2010, and March 2010 so that a good trend of hydro-geochemistry could be obtained. Electron acceptors and environment parameters including nitrate, nitrite, ammonium, total iron, iron(III), manganese, sulphate, sulfide, pH, oxidation reduction potential (ORP), conductivity, temperature, and salinity were also analyzed in September 2009, October 2009, November 2009, January 2010, and March 2010. The environment parameters were tested in situ during field sampling using Hanna, and then the sample for TPH, major ions, and electron acceptors were taken for further laboratory analysis. The TPH concentration before the pumping test was higher than after, and it showed that petroleum hydrocarbon concentration decreased with depth. To ensure the effect of MNA, the surface concentration of TPH was used in the estimation.  
Figure 1: Geological cross-section A-A’ of the field site.  
Figure 2: Site overview with the location of the employed wells and control planes.  
The sample for TPH analysis was taken with amber glass bottles. For data quality control, samples for Fe/Mn were added with hydrochloric acid, and samples for sulfide were added with 1 mol/L sodium hydroxide and 1 mol/L zinc acetate as chemical stabilizers. The sampling bottles were attempted with no head space and sealed with aluminum foil paper, shipped avoiding sunlight, and kept at 4 \({}^{\circ}\)C for further lab analysis.  
Total petroleum hydrocarbons were detected by infrared spectrophotometry (JDS-108U+). K\({}^{+}\) and Na\({}^{+}\) were determined by flame atomic absorption spectrophotometry; Ca\({}^{2+}\), Mg\({}^{2+}\), NO\({}_{3}\)\({}^{-}\), Cl\({}^{-}\), and Fe(III) were determined by the titration method; and SO\({}_{4}\)\({}^{2-}\) was determined by the turbidity method. Analytical methods adopted the Quality Standards for Groundwater (GB/T 14848-2017) [49] of China. The other main instruments used to determine the groundwater properties included a multiparameter water quality analyzer (Hach-HQ40).
## 2 Methodology
### Decomposition Methods
#### 2.1.1 Empirical Mode Decomposition  
Serving as an adaptive and highly efficient decomposition approach, the empirical mode decomposition (EMD) is proposed by [PERSON] et al. [49] to analyze nonlinear and nonstationary time series, which solves the problem that the full description of the frequency content cannot be obtained by single Hilbert transform. Certainly, this decomposition method is based on some assumptions: (1) the original signal at least contains one maximum and one minimum; (2) the characteristic time scale is defined by the time lapse between the extrema; and (3) only when the data totally lack extrema but contain inflection points can it be differentiated once or more times to reveal the extrema. Through the EMD method, the time series data can be converted into a finite and often small number of intrinsic mode functions (IMFs). As for the original data \(X_{t}\), the sifting process of EMD is illustrated as follows [49]:  
1. Apply a cubic spline line to connecting all the local maxima and minima to produce the upper envelope and lower envelope, respectively.
2. Calculate the mean of upper and lower envelope, \(m_{1}\), which then is used to obtain the first component, \(h_{1}\), as shown in the following equation: \[X_{t}-m_{1}=h_{1}\] (1)
3. Check whether the \(h_{1}\) is an IMF, which satisfies two conditions (see [49] for detail), or not. If not, treat it as the original data and repeat step 1 and 2 \(k\) times until \(h_{1k}\) is an IMF, designated as \(c_{1}\), that is \[h_{1(k-1)}-m_{1k}=h_{1k}=c_{1}\] (2) and the residue, \(r_{1}\), is denoted as: \[X_{t}-c_{1}=r_{1}\] (3)
4. Regard \(r_{1}\) as the new original data and tackled with the same sifting process presented above in order to further extract the information of longer period components contained in it. Repeat this process until the last residue becomes a monotonic function from which no more IMF can be extracted. The results are then shown as below: \[r_{1}-c_{2}=r_{2},\;r_{2}-c_{3}=r_{3},\ldots,\;r_{n-1}-c_{n}=r_{n}\] (4)
5. Sum up Equations (3) and (4), and the ultimate decomposition results of EMD are obtained as follows: \[X_{t}=\sum_{i=1}^{n}c_{i}+r_{n}\] (5) where \(c_{i}\) is the \(i\)th component, and \(r_{n}\) is the final residue.
# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 3 Experimental Validation and Results Analysis
### Robustness Evaluation of the Improved Algorithm
#### 3.6.1 Sesame Weed Dataset  
This paper utilized a dataset uploaded to Kaggle ([[https://www.kaggle.com/raviraisi-nh45/crop-and-weed-detection-data-with-bounding-boxes](https://www.kaggle.com/raviraisi-nh45/crop-and-weed-detection-data-with-bounding-boxes)]([https://www.kaggle.com/raviraisi-nh45/crop-and-weed-detection-data-with-bounding-boxes](https://www.kaggle.com/raviraisi-nh45/crop-and-weed-detection-data-with-bounding-boxes)) (accessed on 20 June 2023)) for sesame weed detection. This dataset comprises 1300 images featuring sesame crops and various types of weeds, each labeled with bounding box annotations. The images have a size of 512 \(\times\) 512 pixels, and the image labels follow the YOLO format.  
Given the relatively limited number of images, various data augmentation techniques, such as rotation, translation, brightness variations, etc., were employed. After augmentation, the total dataset consists of 2546 images, with 1872 images for training, 468 for validation, and 206 for testing.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Params** & **GFLOPs** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **P** & **R** \\  & **(M)** & & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline YOLOv5s & 7.1 & 15.8 & 76.3 & 53.5 & 76.9 & 71.5 \\ YOLOv5s + C3F & 6.8 & 14.9 & 77.7 & 53.4 & 77.9 & 72.8 \\ YOLOv5s + CA & 7.7 & 16.7 & 77.9 & 53.9 & 77.8 & 71.8 \\ YOLOv5s + CAR & 7.16 & 16.0 & 77.5 & 53.7 & 79.8 & 72.7 \\ YOLOv5s + SNE & 7.1 & 15.8 & 77.6 & 57.9 & 76.8 & 73.0 \\ YOLOv5s + C3F + CA & 7.5 & 15.8 & 78.1 & 53.8 & 78.4 & 73.6 \\ YOLOv5s + C3F + CA + SNE & 7.5 & 15.8 & 78.9 & 58.2 & 78.7 & 73.6 \\ YOLOv5s + C3F + CA + SNE + CAR & 7.65 & 16.1 & 79.5 & 58.6 & 81.3 & 74.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments.
# Wind Turbines Optimal Operation at Time Variable Wind Speeds
## 6 Case Study
### PI-Based Control of the PMSG's Optimal Power
Thus, based on this tuning method applied in successive simulations, so that the power oscillations are reduced and the PMSG's angular speed value, \(\omega\), is comparable to the PMSG's optimal angular speed value (meaning that the wind turbine operates at the MPP), the coefficients of the PI/PID controller were determined. For example, at \(t=570\) (s), the PMSG's angular speed value is \(\omega(570)=141.76\) (rad/s) and \(\omega_{optim}(570)=23.091S_{w}(570)=23.091=\left(-1.5724\times 10^{-6}570^{2}+7.2086 \times 10^{-4}\times 570+6.24\right)=141.78\) (rad/s). From the simulations, it results that the PMSG's power oscillations are lowest for \(k_{p}=10\) and \(k_{i}=2700\). Thus, the differential equations system in Equation (59), together with the initial conditions in Equation (60), can be written as  
\[\left\{ \begin{array}{l}1.15\times 10^{5}\frac{d\omega(t)}{dt}\omega(t)=2.2566 \times 10^{6}\Big{(}\frac{\left(-1.5724\times 10^{-6}t^{2}+7.2086\times 10^{-4}t+6.24 \right)}{\omega(t)}-2.6247\times 10^{-2}\Big{)}\\ -58.617\frac{\left(-1.5724\times 10^{-6}t^{2}+7.2086\times 10^{-4}t+6.24 \right)}{\omega(t)}e^{\left(-1.5724\times 10^{-6}t^{2}+7.2086\times 10^{-4}t+6.24 \right)^{3}}\\ -P_{PMSC\_PI(t)}\\ \frac{dP_{PMSC\_PI(t)}}{dt}=10\frac{d\omega(t)}{dt}-23.091\times 10\Big{(}-3.14 48\times 10^{-6}t+7.2086\times 10^{-4}\Big{)}\\ +2700\Big{(}\omega(t)-23.091\Big{(}-1.5724\times 10^{-6}t^{2}+7.2086\times 10^{-4 }t+6.24\Big{)}\Big{)}\end{array} \right. \tag{62}\]  
\(\omega(0)=144.09\)  
\(P_{PMSG\_PI}(0)=4.6297\times 10^{5}\)  
the solutions of which are the \(P_{PMSC\_PI}(t)\) and \(\omega_{optim}(t)\) curves. In conclusion, by using a PI-type regulator, the resulting PMSG optimal power, \(P_{PMSG,PI}(t)\), oscillates around the optimal values of the \(P_{PMSGqtim}(t)\), with amplitudes up to \(\pm 20\%\), as can be seen in Figure 3.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 2 Related Work
### Section Summary  
This section reviews traditional methods, deep learning approaches, and semantic-guided techniques for Low-Light Image Enhancement (LLIE). Traditional methods like histogram equalization and Retinex models improve brightness and contrast but suffer from computational complexity and noise sensitivity. Recent deep learning advances include supervised (LLNet, MBLLEN, SID), unsupervised (EnlightenGAN), semi-supervised (DRBN), and zero-shot methods (ExCNet, RRDNet), which enhance performance yet face challenges like high computational costs and data dependency. Semantic-guided methods optimize enhancement through semantic constraints, categorized into loss-level (through adaptive loss functions) and feature-level (via semantic priors) strategies. Future research should focus on refining existing approaches by deeper integration of semantic information to improve enhancement quality and broaden applicability.
## 2 System Model
### Performance Metrics
#### 2.4.3 Short-Time Objective Intelligibility (STOI)  
STOI [24] evaluates speech intelligibility by measuring the correlation between the temporal envelopes of the original and processed speech signals. This metric is especially sensitive to time-domain degradations that affect human speech comprehension, making it complementary to PESQ. STOI scores range from \(-1\) to \(1\), where higher scores indicate greater intelligibility.  
To ensure robustness, the selected performance metrics are computed across a dataset of speech signals transmitted under varying SNR levels and different fading channel conditions. This evaluation framework uses multiple metrics to thoroughly assess the system's capability to maintain both speech quality and intelligibility while operating in practical communication environments.
## 5 Conclusions  
By exploring the unique characteristics of the PolSAR data, we have presented a new method that achieves excellent accuracy in PolSAR classification. The main contributions of this work lie in the following three aspects. First, we proposed a method of pre-processing the PolSAR data to facilitate subsequent work. Second, a novel CNN framework which consists of two CNNs was presented to extract and fuse the polarization feature and spatial feature of the pre-processed data. Last but not least, visualization of the CNN was applied to help us tune the parameters of the model. We carried out the experiments on 14 types of land cover classes, and the results show that our model is superior to the classical classification methods such as SVM, and Maximum Likelihood. Compared with a single CNN, our method still has higher accuracy due to its P-S features.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (61071139; 61471019; 61671035), the Aeronautical Science Foundation of China (20142051022), the Pilot Project (9140A07040515 HK01009), the Scientific Research Foundation of Guangxi Education Department (KY 2015 LX443), and the Scientific Research and Technology Development Project of Wuzhou City, GuangXi, China (201402205).  
**Author Contributions:** [PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] performed the experiments. [PERSON], [PERSON], [PERSON] analyzed the data and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.