esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 3 QR\(-\)LoRa Design
### Joint Frequency-Amplitude Demodulation  
#### 3.3.1 Frequency Dimension Demodulation  
The QR\(-\)LoRa frequency demodulation is the same as the standard LoRa. First, we multiply \(s_{(F_{0},T_{0})}(t)\) by \(downchipr(t)=e^{2\pi\left(\frac{tW}{2T}t-\frac{tW}{2T}t^{2}\right)},0\leq t\leq T\), obtaining  
\[s_{rx}(t)=s_{(F_{0},T_{0})}(t)downchipr(t) \tag{9}\] \[=\left\{\begin{array}{l}\frac{1}{T}(t+T-T_{0})e^{2\pi\left( \frac{tW}{2T}\right)t},0\leq t<T_{0}\\ \frac{1}{T}(t-T_{0})e^{2\pi\left(\frac{tW}{2T}\right)t},T_{0}\leq t<T-\frac{t_ {0}}{2W}\\ \frac{1}{T}(t-T_{0})e^{2\pi\left(-BW+\frac{t}{2T}\right)t},T-\frac{t_{0}}{2W} \leq t<T\end{array}\right.\]  
The duration of one chirp is \(\frac{2^{5F}}{BW}\) seconds. Sampling \(s_{rx}(t)\) with \(f_{s}=BW\), we obtain \(T_{0}=\frac{n_{0}}{BW}\), where \(n_{0}\) is the corresponding sampling point after \(T_{0}\) is digitized. The sampled received signal can be expressed as  
\[s^{\prime}_{rx}(n)=s_{rx}\left(\frac{n}{BW}\right) \tag{10}\] \[=\left\{\begin{array}{l}\left(\frac{n-n_{0}}{2T}+1\right)e^{2 \pi\left(\frac{t_{0}}{2T}\right)n},0\leq n\leq n_{0}-1\\ \left(\frac{n-n_{0}}{2T}\right)e^{2\pi\left(\frac{t_{0}}{2T}\right)n},n_{0} \leq n\leq 2^{5F}-1\end{array}\right.\]  
Next, a Discrete Fourier Transform (DFT) is performed on the signal \(s^{\prime}_{rx}(n)\), yielding  
\[S^{\prime}_{rx}(m)=DFT[s^{\prime}_{rx}(n)]=\sum\limits_{n=0}^{2^ {5F}-1}s^{\prime}_{rx}(n)W_{2^{5F}}^{mn} \tag{11}\] \[=\sum\limits_{n=0}^{m-1}\left(\frac{2^{5F}+n-n}{2^{5F}}\right)e^{ 2\pi n\left(\frac{n-n}{2^{5F}}\right)}\] \[+\sum\limits_{n=0}^{2^{5F}-1}\left(\frac{n-n}{2^{5F}}\right)e^{2 \pi n\left(\frac{n-n}{2^{5F}}\right)}\] \[=\left\{\begin{array}{l}\frac{2^{5F}-1}{2},m=F_{0}\\ \frac{1}{e^{2\pi\left(\frac{n-n}{2^{5F}}\right)}-1},m=else\end{array}\right.\]  
Note that \(W_{2^{5F}}^{mn}=e^{-2\pi\frac{tW}{2^{5F}}}\). When \(m=else\), we obtain  
\[\left\|S^{\prime}_{rx}(m)\right\|=\frac{1}{\left|2\sin\left(\pi\frac{F_{0}-m}{ 2^{5F}}\right)\right|} \tag{12}\]  
Equation (12) demonstrates that when demodulating the QR\(-\)LoRa symbol, almost all the energy will converge into a single peak with a height of \(\frac{2^{5F}-1}{2}\) at \(m=F_{0}\) while the energy at other frequencies will rapidly decay to close to 0. Accordingly, the receiver demodulates the frequency encoded value according to the position of the spectrum peak, as shown in Figure 5.
# Modeling and Simulation of a Wave Energy Converter _Nwave_
## 3 Modeling of the Device's Dynamics
### JONSWAP Wave Spectrum Model and Capture Width Ratio  
For irregular wave input, typical wave spectra include JONSWAP, Pierson-Moskowitz, Bretschneider and the Gaussian distribution [13]. We chose the JONSWAP wave model since the area where the _INWAVE_ device has been installed has \(\gamma=2.72\) of the JONSWAP spectrum [14]. When the significant wave height is \(H_{s}\) and the peak period is \(T_{p}\ (=2\pi/\omega_{p})\), it is expressed in the frequency domain with wave angular frequency \(\omega\) as:  
\[S_{JONSWAP}\left(\omega\right)=\frac{5}{16}H_{s}^{2}\left(1-0.287\ln\gamma \right)\frac{\omega_{p}^{4}}{\omega^{5}}\exp\left(-\frac{5}{4}\left(\frac{ \omega}{\omega_{p}}\right)^{-4}\right)\gamma^{\exp\left(-0.5\left(\frac{ \omega-\omega_{p}}{\omega\omega_{p}}\right)^{2}\right)}, \tag{18}\]  
where:  
\[\sigma=\left\{\begin{array}{ccc}0.07&for&\omega\leq\omega_{p}\\ 0.09&for&\omega>\omega_{p}\end{array}\right..\]  
Then, the wave power per unit crest length is defined as:  
\[J_{JONSWAP}=\frac{\rho g}{2\pi}\int_{0}^{\infty}S_{JONSWAP}\left(\omega\right) v_{g}\left(\omega\right)d\omega, \tag{19}\]  
where \(v_{g}\) is the group velocity given by:  
\[v_{g}=\frac{1}{2}\left[1+\frac{2 kh}{\sinh\left(2 kh\right)}\right]v_{p},\]  
and \(v_{p}\) is the phase velocity:  
\[v_{p}=\sqrt{\frac{g}{k}\tanh\left(kh\right)}.\]  
Here, the wave number \(k=2\pi/\lambda\) (\(\lambda\): the wave length) is obtained from:  
\[\omega^{2}=gk\ \tanh\left(kh\right).\]  
We also define the normalized wave power per unit crest length and unit wave amplitude as:  
\[J_{JONSWAP}^{*}=\frac{J_{JONSWAP}}{\left(H_{s}/2\right)^{2}}. \tag{20}\]  
This is only a function of the wave period and can be seen in Figure 10 with \(h=5\) m, which is the water depth of the installation area.  
In order to quantify the power performance with the consideration of the device's dimension, the capture width ratio (CWR) \(\eta_{1}\) is widely used and is defined as follows [15]:  
\[\eta_{1}=\frac{\bar{P}_{g}^{*}}{\int_{JONSWAP}^{*}}, \tag{21}\]  
where \(B=\sqrt{4A_{w}/\pi}\) is the characteristic dimension and \(A_{w}\) is the maximum horizontal cross-sectional area of the device. In this paper, for a cylindrical buoy, \(A_{w}\) is simply equal to \(\pi R^{2}\). Hence, \(B=2R\) where \(R\) is the radius of the buoy.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 3 System Model
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notion** & **Definition** \\ \hline \(\lambda_{\text{v}}\) & A parameter of the Poisson distribution that represents the traffic flow \\ \(\mu_{v}\), \(\sigma_{\text{v}}\) & \(\mu_{v}\) for the mean value and \(\sigma_{v}\) for the standard deviation of the speed \\ \(\text{d}_{\text{uk}}\) & The distance d between vehicle k and UAV u \\ \(\text{Puk}^{\text{LoS}}\cdot\text{Puk}\) & The probabilities of LoS and NLoS links corresponding to the UAV \\ \(\text{a}_{\text{a}}\), \(\text{b}\) & Constants determined by the environment and carrier frequency \\ \(\text{PL}_{\text{uk}}^{\text{LoS}}\cdot\text{PL}_{\text{uk}}^{\text{NLoS}}\) & The path loss expressions for LoS and NLoS links \\ \(S_{i}\), \(\text{R}_{\text{i}}\) & The average service time and remaining service time for priority i \\ \(S_{i}^{2}\) & The second moment of the average service time for priority i \\ \(\text{Wf}_{\text{i}}\) & The average waiting time for priority i \\ \(T_{i}\) & The overall delay, which is the average dwell time \\ \(\text{t}_{\text{r}}\) & The expectation of the passing time \\ \(T_{BSMi}\) & The interval for BSM transmission for vehicles in priority level i \\ \(\text{n}_{\text{a}}\) & The path loss exponent \\ \(\text{f}_{\text{c}}\) & The carrier frequency \\ c & The speed of light \\ \(\text{\eta LLoS}\cdot\text{\eta LLoS}\) & The average additional loss for free space propagation in LoS and NLoS connections, respectively \\ \(\text{PL}_{\text{uk}}\) & The average path loss \\ \(G_{uk}\) & The average channel gain \\ \(\text{SINR}_{\text{uk}}\) & The SNR received by the UAV u from the vehicle k \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notions used in the proposed analytical model. Figure 1: Vehicle-to-Vehicle Communication Model.
# Task-Oriented Evaluation of the Feasible Kinematic Directional Capabilities for Robot Machining
## 2 Materials and Methods
### Manipulability Polytope  
The main disadvantage of the manipulability ellipsoid is the fact that it does not transform the exact joint velocity constraints into the task space [43]; however, it rather serves as a conservative approximation of the end-effector velocity capabilities since it relies on the Euclidean norm. In practical cases, the overall kinematic performance of the end-effector in a task space is bounded by each joint velocity limit [26]:  
\[\max\Bigl{|}\dot{\theta}_{i}\Bigr{|}\leq 1, \tag{8}\]  
where \(\dot{\theta}_{i}\) denotes the joint velocity of the \(i\)-th joint if we assume that the maximal joint velocity is limited by the unit value of 1. The bounds on the individual joint velocity form a convex joint velocity polytope \(Q\) with \(2^{n}\) vertices in the \(n\)-dimensional joint space, which can be written in the following compact form:  
\[Q=\Bigl{\{}\dot{\theta}\in\mathbb{R}^{n}\ |\ \left\|\dot{\theta}\right\|_{ \infty}\leq 1\Bigr{\}}, \tag{9}\]  
where \(\left\|.\right\|_{\infty}\) denotes the infinity norm or \(L_{\infty}\) norm, which is equal to the maximum absolute value of the vector's components. The linear mapping of \(Q\) from the joint to the task space (1) results in another convex polytope \(P^{trust}\) with the same number of vertices, which is called a twist manipulability polytope [15]:  
\[p^{trust}=\Bigl{\{}t\in\mathbb{R}^{m}\Bigr{|}\ t=J(\theta)\dot{\theta},\ \left\|\dot{\theta}\right\|_{ \infty}\leq 1\Bigr{\}}. \tag{10}\]  
Since we are interested in quantifying the ability to change the position and orientation of the end-effector along a specified task direction, the maximum feasible magnitude of the end-effector velocity along \(\hat{u}\) under the velocity constraints (8) can be defined as follows [28]:  
\[\mu_{\infty}=(\max\Bigl{|}\dot{\theta}_{i}^{*}\Bigr{|})^{-1}, \tag{11}\]  
where \(\dot{\theta}^{*}=\left[\dot{\theta}_{1}^{*},\dot{\theta}_{2}^{*},\dots,\dot{ \theta}_{n}^{*}\right]^{T}\) is the minimum infinity norm solution of \(\hat{u}=\hat{J}\dot{\theta}\). The end-effector velocity (11) can be derived from the quotient of the weighted task velocity vector Euclidian norm and the joint velocity vector infinity norm [44]:  
\[\mu_{\infty}=\|t\|/\left\|\dot{\theta}\right\|_{\infty}, \tag{12}\]  
where the joint velocity vector \(\dot{\theta}\) belongs to the input polytope Q (9), which is mapped into the output task space velocity polytope calculated by (1). Geometrically, \(\mu_{\infty}\) is the distance from the center of the output polytope to the point where the line along the desired direction of \(\hat{u}\) intersects the polytope.  
Similar to the manipulability ellipsoid concept, the manipulability polytope concept also suffers from physical inconsistency due to the unwanted mixing of the linear and angular velocity in a single scalar value. Furthermore, it may be subjected to an algorithm seeking a computationally complex and less tractable minimum infinity norm solution [35; 45; 46].
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 3 Materials and Methods
### Research Methods  
#### 3.2.1 Remote Sensing Water Balance Method  
Groundwater recharge is defined as the difference between rainfall and evapotranspiration and runoff when employing the remote sensing water balance method. The following Formula (1) [23; 24] was used to calculate groundwater recharge:  
\[R=P-S-ET-\Delta S \tag{1}\]  
where \(R\) is the groundwater recharge amount (mm); Rainfall (mm); \(S\) is the annual average runoff (mm); \(ET\) is the evapotranspiration (mm); \(\Delta S\) is the store changes (mm) for soil water. Details of the method can be found in reference [25].
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 3 Microbial Safety of Meat and Meat Products  
Food safety is a high need for wellbeing specialists and purchasers around the world. Meat, being the highest consumable food product that is an ideal vehicle for microbial development, has been brought to the front line. Health hazards correlated within meat items are of chemical, physical and biological types. Microbial agents may leads to serious health implications including p severe, mild, and chronic gastro-intestinal diseases. Therefore, various food safety and quality systems are being adopted in industries guaranteeing the wellbeing and safety of meat-based items [20].  
A detailed technique for managing microbiological issues in meat items involves the execution of procedures or interventions that: (i) minimize or prevent transfer and access of microbes to the products; (ii) overcome starting contamination by inactivating or removing micro-organisms that have already gained access; (iii) kill or inactivate micro-organisms over products; and (iv) prevent or delay microbial growth that has received access or has not been disabled. Proper management and implementation of these approaches leads to the safety of meat products and minimally decreases the frequency of microbial meat-borne diseases. It is clear that updating food-related services or customer behavior should also ensure improved safety of meat and meat products [20; 21].  
Research has been conducted in recent years that there is nonstop variation and improvement in the opposition of pathogenic microorganisms to anti-microbials and possibly to conventional food safeguarding boundaries such as low pH, heat, dryness, cold temperatures or minimum water activity, and other chemical preservatives. Moreover, there is proof of the presence of strains of pathogenic bacteria with upgraded capacity for endurance in their hosts, low infective dosages, and expanded destructiveness, now and then after exposure to basic environmental pressure [22].
## 2 The \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) Filter
### Problem Definition
As indicated in the description of the filtering process above, the input of the filter is \(x_{\rm o,k}\). Although eight outputs can be considered (predicted target position \(x_{\rm p}\), velocity \(v_{\rm p}\), acceleration \(a_{\rm p}\), and jerk \(j_{\rm p}\), and smoothed target position \(x_{\rm s}\), velocity \(v_{\rm s}\), acceleration \(a_{\rm s}\), and jerk \(j_{\rm s}\)), this paper focuses on the accuracy of \(x_{\rm p}\) because many applications of the tracking filters are focused on the prediction of the position of the target. This study assumes that the error in \(x_{\rm o}\) is due to white Gaussian noise, and the variance of the position measurement error \(B_{\rm x}\) is known.
###### Abstract  
Gene electrotransfer (GET) has gained significant momentum as a non-viral gene delivery method for various clinical applications, primarily in the cancer immunotherapy and vaccine development space. Preclinical studies have demonstrated exogenous gene delivery and expression in various tissues, including the liver, skin, cardiac muscle, and skeletal muscle. However, protein replacement applications of this technology have yet to be fully actuated. Plasmid DNA skeletal muscle delivery has been shown to maintain expression for up to 18 months. In the current study, we evaluated localized skeletal muscle delivery for protein replacement applications. We developed localized in vivo electro gene therapy (_liveGT_) protocols utilizing mono- and biphasic pulse sequences for localized pulse delivery directly to skeletal muscle with a custom monopolar platinum electrode. Plasmid DNA encoding human insulin and human glucokinase were chosen for this study to evaluate the _liveGT_ platform for protein replacement potential. Initial in vitro GET was performed in mouse myoblasts to evaluate human insulin and glucokinase co-delivery. This was followed by _liveGT_-mediated reporter gene delivery in the skeletal muscle of Sprague-Dawley rats for pulse sequence selection. Protein replacement potential was evaluated in healthy (non-diabetic) rats with _liveGT_-mediated human insulin and glucokinase co-delivery to skeletal muscle. Human and rat insulin levels were measured via ELISA over the course of 3 months. Fed-state blood glucose measurements were monitored in correlation with serum human insulin levels. _LiveGT_-mediated skeletal muscle reprogramming successfully produced physiological levels of human insulin in serum over the course of 3 months. Hypo- and hyperglycune events were not observed. Therefore, _liveGT_ is a safe and viable platform for potential protein replacement therapies.  
insulin; gene electrotransfer; _liveGT_; gene therapy; glucose; protein replacement therapy
# Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works
## 3 Materials and Methods
### Sigmoidal Models
#### 3.3.4 Gompertz Model  
It is a sigmoid curve that describes asymptotic growth as the slowest at the end of a given period or the maximum of a given variable [27; 28]. Concerning the construction field, it was applied to model the planning of activities for the control of rigid pavement works. It is particularly suitable for situations where the growth of a variable accelerates rapidly in the initial phases, but then gradually decelerates, approaching asymptotically an upper boundary [29]. In the context of rigid pavement construction, this behavior reflects the reality of many projects, where costs tend to increase rapidly at the beginning, due to the mobilization of resources and the execution of the first phases of the project, and then stabilize as the project progresses towards completion.  
In this model, time is considered the independent variable driving project progress, while cost is the dependent variable reflecting the accumulation of expenses over time. The Gompertz model has been demonstrated to effectively capture the asymmetric nature of cost growth, thus providing a valuable instrument for the forecasting and management of cost escalations throughout the project life cycle. The Gompertz model equation, as applied to the planning and control of rigid pavements, is expressed by Equation (20).  
\[f(x)=ce^{-e^{-(x-b)/a}}-ce^{-e^{(b/a)}} \tag{20}\]  
The initial phase is characterized by a rapid acceleration, followed by a significant deceleration. The limits of this function are identical (0 and c), yet its inflection point is earlier. This adaptation is particularly relevant for projects characterized by accelerated mobilization yet also by concurrent deceleration of final activities, thereby affecting overall  
Figure 6: [PERSON] model.  
project progress. The lower limit (0) establishes the initial point for the delineation of milestones, while the upper limit, c, stipulates the physical objectives. The growth rate (x) is a metric of efficiency, with low values indicating delays and high values suggesting quality risks.  
Demonstrations of the limits of extremes of intervals 0 and 1 are as follows.  
Verification of \(f(0)=0\)  
\[f(x)=c(e^{-e^{-(x-b)/a}}-ce^{-e^{(b/a)}})\Rightarrow f(0)=0 \tag{21}\]  
Limit when \(x\rightarrow\infty\):  
\[e^{-e^{-(x-b)/a}}\to 0\Rightarrow e^{-e^{-(x-b)/a}}\to e^{-1} \Rightarrow f(x)\to c(1-e^{-b^{\prime}/a}) \tag{22}\]  
Derivative:  
\[f^{\prime}(x)=\frac{c}{a}e^{-(x-b)/a}-e^{-e^{-(x-b)/a}} \tag{23}\]  
\(f(x)\) is the valued cost of the project ($), \(a\) is the parameter that reflects the rate at which costs increase over time, \(b\) represents the moment in time when costs increase at the highest rate, \(c\) indicates the expected total cost of the project in the long term, and \(x\) is the project execution time (Month) (See Figure 7).
# Photonic Crystal-Based Sensors for Detecting Alcohol Concentration
## 3 Results and Discussion
### Ethanol Concentration Examined by PS Opal and TiO\({}_{2}\) Inverse Opal Structures
The sensitivity was enhanced about 73.4%, as we calculated and compared them from typical method (5242 nm/RIU, \(\Delta n\) = 0.0248) and the present method (9090 nm/RIU, \(\Delta n_{\rm a}\) = 0.0143) in Table 2. Our experimental results show that the sensitivity is not consistent with the predicted value by the Equation (5). However, similar results were also observed in the ref. [3] for a PS inverse opal structure. Their sample shows that a peak wavelength shift of \(\sim\)120 nm as the ethanol concentration varies from 10% to 80% and the sensitivity is approximately 4800 nm/RIU. Finally, these results also revealed that 600 \({}^{\circ}\)C sintered TiO\({}_{2}\) inverse opal structure possesses the highest sensitivity for ethanol concentration because the refractive index of the titania structure is altered with the sintering temperature. Different sintering temperatures form different crystalline phases. As the sintering temperature increased from 500 to 600 \({}^{\circ}\)C, the anatase phase of TiO\({}_{2}\) in the inverse opal structure is gradually reduced, while the rutile phase is increased [20], increasing the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ethanol Concentration** & **Refractive Index** & **Average Refractive Index (\(n_{\rm a}\))** & **The Wavelength (\(\lambda\))** \\
**(v/v\({}_{0}\))** & **from Table 1** & **from Equation (4)** & **from Table 1** \\ \hline
80\% & 1.3623 & 1.7391 & 670 (nm) \\
10\% & 1.3375 & 1.7248 & 540 (nm) \\ - & \(\Delta n\) = 0.0248 & \(\Delta n_{\rm a}\) = 0.0143 & \(\Delta\lambda\) = 130 (nm) \\ Sensitivity (\(S\)) & 5242 nm/RIU & 9090 nm/RIU & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculating the sensitivity of the 600 \({}^{\circ}\)C sintered TiO\({}_{2}\) inverse opal sensors by Equations (4) and (5). refractive index [36]. The peak position of reflectance spectra can be used for detecting the organic solvents with different refractive indices. This TiO\({}_{2}\) inverse opal could be used for quantitative detection of common organic solutions in the corresponding range [3]. As shown in Equations (3) and (4), the sensitivity of the ethanol concentration could be enhanced as \(n_{\text{a}}\) was increases because the red shift (\(\Delta\Delta\)) increases with \(\lambda_{\text{max}}\).
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 6 Conclusions  
This research was conducted to solve unmanned surface vehicle (USV) dynamic environmental problems as well as convergence problems in evolutionary algorithms. This research proposes a neuronal genetic algorithm that utilizes neural network inputs for processing with a genetic operator. The modifications in this research were carried out by applying a partially exponential-based fitness function to the neuronal genetic algorithm. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Result Comparison of Baseline and Proposed Method** & \(t\)**Critical** & \(t\)**Stat** & \(p\)**-Value** \\ \hline Total genome (convergence speed) & 1.69 & 1.71 & 0.049 \\ Travel distance & 1.69 & \(-\)1.95 & 0.029 \\ Travel time & 1.69 & 2.14 & 0.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(t\) Critical, \(t\) Statistics, and \(p\)-Value for experimental results in a dynamic environment. We also implemented an inverse time variable to the fitness function. Both modifications aim to produce faster convergence. Based on the experimental results and compared to basic neural-network-based genetic algorithms, the proposed method can produce faster convergent solutions and requires a smaller total genome. This improvement was declared significant using statistical test methods. Then, for the travel distance parameter, the proposed method provides better performance in static environments. For travel time, the proposed method provides significantly better performance in dynamic environments. This proves that, in exponential space, slightly different output fitness values will be more visible because there is the influence of power values. In addition, an inverse of the time parameter allows the USV to move in a shorter time to obtain a greater fitness value. Therefore, this form of modification can be applied in other genetic algorithm research to produce better convergence speeds. There are many kinds of improvements that can be implemented in the future. The challenge associated with travel distance may be resolved by considering the radius value between the center of the map and the position of the USV when moving, although this is not necessarily relevant to real-world cases. Other research on unaddressed USV problems, such as autonomous navigation and cognitive planning in a complex environment, can be carried out using other GA modifications. Conceptualization, N.H., W.D. and H.N. ; methodology, N.H.; software, N.H.; validation, N.H., W.D. and H.N. ; formal analysis, N.H.; investigation, N.H.; resources, N.H.; data curation, N.H.; writing--original draft preparation, N.H.; writing--review and editing, N.H. and W.D. ; visualization, N.H.; supervision, H.N. ; project administration, H.N. All authors have read and agreed to the published version of the manuscript.