esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Folded Expanded Metal Mesh with Sound Absorption Performance
## 3 Measurement Results  
### The Single-Panel Structure  
Table 1 shows the EMM of four single panels that are divided into two different board thicknesses and the distance of the hole, with three different cavities (Type A to Type D). We tested and analyzed the sound-absorption performance of EMM of 12 specimens.  
The arrangement of the holes had a significant effect on sound-absorption performance when compared to the thickness of the board. Figures 6-8 show that increasing the thickness of the cavity mainly improves low-frequency sound-absorption performance. Furthermore, comparing the results of different EMM with the same cavity thickness showed the same trend in the sound-absorption coefficient curve. However, different cavity thicknesses had no significant effect on the weighted sound-absorption coefficient (\(\alpha_{w}\)), as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Arrangement of the Holes**} & \multirow{2}{*}{**Panel Thickness**} & \multirow{2}{*}{**Cavity**} \\  & \multicolumn{1}{c}{**Horizontal Distance**} & \multicolumn{1}{c}{**Vertical Distance**} & \multicolumn{1}{c}{**(mm)**} & \multicolumn{1}{c}{**(mm)**} \\  & \multicolumn{1}{c}{**(mm)**} & & & & \\ \hline A1 & & & & 210 & 0.30 \\ A2 & 1 & 2 & 0.5 & 260 & 0.30 \\ A3 & & & & 460 & 0.30 \\ \hline B1 & & & & & 210 & 0.05 \\ B2 & 2  
Therefore, we selected Type C, which had smaller hole distance and a thicker panel, for further development in the second part.  
Figure 8: The sound-absorption coefficient of EMM with a 460 mm cavity.  
Figure 6: The sound-absorption coefficient of EMM with a 210 mm cavity.  
Figure 7: The sound-absorption coefficient of EMM with a 260 mm cavity.  
Figure 11: The sound-absorption coefficient of 30 mm FEMM with a 450 mm cavity.  
Figure 12: The sound-absorption coefficient of 30 mm FEMM with a 200 mm cavity.  
Figure 13: The sound-absorption coefficient of 55 mm FEMM with a 450 mm cavity.  
As shown in Table 2, among FEMM, E6, E15, E16, and E27 had the greatest sound-absorption performance, and most of those had a rectangular structure. Regarding the trapezoidal structures, those with six convex forms (E5, E10, E14, E18, E24, and E30) had poor sound-absorption performance. Meanwhile, the trapezoidal structures with three convex forms (E4, E9, E17, E23, and E29) all achieved \(\alpha_{w}\geq 0.8\), except for E13. Using the trapezoidal concept to fold specimens, low material costs can be selected to achieve better sound-absorption performance.  
As described above, folding EMM has less impact on sound-absorption performance than changing the size of the cavity, but a folding structure improved the strength of the specimen.
# Inspiring Designers' Innovative Thinking: An Evolutionary Design Method for Product Forms
## 3 Empirical Study
### Extraction of Automobile Product Form Features
#### 3.2.2 Prediction Results for the Evolution of Automobile Product Form Features  
Based on Markov chain analysis and the actual situation, the predicted states can be divided into five state intervals according to the comparison of the results of the predicted and actual values [46].  
State 1: Severely overestimated, i.e., \(\delta(k)<-10\%\).  
State 2: Overestimated, i.e., \(-3\%>\delta(k)\geq-10\%\).  
State 3: Accurately assessed, i.e., \(-3\%\leq\delta(k)<3\%\).  
State 4: Underestimated, i.e., \(3\%\leq\delta(k)\leq 10\%\).  
State 5: Severely underestimated, i.e., \(\delta(k)>10\%\).  
State 3 does not appear in the error rate sequence \(\delta(k)\), and will no longer be counted when constructing the state transfer probability matrix. The relative error-corrected state transfer matrix \(P(0)\) can be obtained from Equations (14)-(16).  
\[P(0)=\begin{bmatrix}0&1/2&1/2&0\\ 1/2&0&1/2&0\\ 0&0&0&1\\ 0&1&0&0\end{bmatrix}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Generation** & \(\mathbf{X_{1}}\) & **Step Ratio** & \(\mathbf{X^{(0)}_{1}}\) & **Step Ratio** \\ \hline
1 & 0.255575 & - & 1.255575 & - \\
2 & 0.145162 & 1.761 & 1.145162 & 1.096 \\
3 & 0.17081 & 0.85 & 1.17081 & 0.978 \\
4 & 0.215254 & 0.794 & 1.215254 & 0.963 \\
5 & 0.154378 & 1.394 & 1.154378 & 1.053 \\
6 & 0.12882 & 1.198 & 1.12882 & 1.023 \\
7 & 0.148096 & 0.87 & 1.148096 & 0.983 \\
8 & 0.174433 & 0.849 & 1.174433 & 0.978 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The initial data step ratio test in GM(1, 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Generation** & **Original Value** & **Predicted Value** & **Residual** & \(\mathbf{\delta(k)\) (\%)} \\ \hline
1 & 0.255575 & 0.255575 & 0 & 0 \\
2 & 0.145162 & 0.167139 & \(-0.021977\) & \(-15.139\) \\
3 & 0.170810 & 0.165563 & 0.005247 & 3.072 \\
4 & 0.215254 & 0.163989 & 0.051165 & 23.816 \\
5 & 0.154378 & 0.162417 & \(-0.008039\) & \(-5.208\) \\
6 & 0.12882 & 0.160848 & \(-0.032028\) & \(-24.863\) \\
7 & 0.148096 & 0.159281 & \(-0.011185\) & \(-7.552\) \\
8 & 0.174433 & 0.157715 & 0.016718 & 9.584 \\
9 & - & 0.156152 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Predicted values and error rates for the product form feature data.  
Therefore, the ninth-generation state transfer probability matrix \(P(1)\) is obtained based on Equation (17).  
\[P(1)=\begin{bmatrix}1/4&0&1/4&1/2\\ 0&1/4&1/4&1/2\\ 0&1&0&0\\ 1/2&0&1/2&0\end{bmatrix}\]  
According to Table 7, the eighth generation is in state 4. According to the state transfer probability matrix \(P(1)\), the ninth generation has the highest probability of being in state 2, and the predicted value of the ninth generation is overestimated. Therefore, the median value of the state interval is taken to correct the prediction results, and the correction value is \(X^{(0)}{}_{1}(9)=0.156\times(1+0.5((-3\%)+(-10\%)))=0.14586\).
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 4 Linear Optimization
### Energy Minimization  
In FLMPC-One and FLMPC-Two, cross nodes multicast and relay nodes unicast data packet. In order to increase the lifetime of network, efficient energy consumption is necessary. Energy is mainly consumed by transmission or reception of data packets. Energy is also consumed in idle state, however, it is very minor, thus, we are not considering it. The objective function of energy can be expressed as:  
\[Min\sum_{t=1}^{t_{max}}E(t)\;\;\forall\;t\;\in\;R \tag{20}\]  
such that;  
\[L_{1} :E_{t}\leq E_{o} \tag{21a}\] \[L_{2} :DR\leq DR_{pr}\] (21b) \[L_{3} :E_{s}\geq E_{tx}\] (21c) \[L_{4} :LQ_{t}\geq LQ_{req}\] (21d) \[L_{5} :Min\sum_{dp=1}^{N}E_{tx}\;\forall\;i\;\in\;R\] (21e) \[L_{6} :Min\sum_{dp=1}^{N}E_{rx}\;\forall\;i\;\in\;R \tag{21f}\]  
The main objective of Equation (20) is to minimize the energy consumption for the total time (\(t_{max}\)) of transmission to prolong network lifetime. Equation (21a) shows that energy of sensor node at any time \(t\) must be less than or equal to the initial energy (\(E_{o}\)) supplied to the sensor node. It reveals sensor nodes have limited energy. In Equation (21b), \(DR\) must be less than or equal to \(DR\) which makes the data packet erroneous. If Equation (21b) evaluates to false then \(PER\) increases which results in packet drop. As a result, the data packet needs to be retransmitted and it increases energy consumption. Equation (21c) shows sensor node must have energy greater or equal to transmission energy when it has a data packet to send. Constraint in Equation (21d) ensures that the link quality at current time must be good. If it is not, data packet becomes erroneous and gets dropped at surface gateway. Equations (21e) and (21f) indicate the energy consumption for the total number of data packets (\(dp_{s}\)) transmitted and received in the network. We can calculate \(E_{tx}\) from Equation (8) and \(E_{rx}\) from Equation (9).  
**Graphical Analysis:** Let us consider a scenario in which initial energy be 6 J, \(Tp=0.66\) mW (milliWatt), \(Pl=500\) bytes, \(DR=10\) KB/s, \(d=100\) m to 300 m and \(Rp=0.395\) mW. From \(d=100\) m to 300 m, the calculated value of \(E_{tx}\) is 3.3 mJ to 9.9 mJ and \(E_{rx}\) is 1.98 mJ to 5.93 mJ. So  
\[3.3\,\mathrm{mJ}\leq E_{tx}\leq 9.9\,\mathrm{mJ} \tag{22}\]  
\[1.98\,\mathrm{mJ}\leq E_{rx}\leq 5.93\,\mathrm{mJ} \tag{23}\]  
Figure 7 shows the intersection of lines which results in the bounded region which is called feasible region. Every point in this region results in feasible solution. Extreme values can be found at the corner points. Now examining vertices of the bounded region of Figure 7 as follows:  
* \(P_{1}(3.3,1.98)=3.3+1.98=5.28\) mJ
* \(P_{2}(3.3,5.93)=3.3+5.93=9.23\) mJ
* \(P_{3}(9.9,5.93)=9.9+5.93=15.83\) mJ
* \(P_{4}(9.9,1.98)=9.9+1.98=11.88\) mJ  
The above results show that values of the corner points lies within the range of initial energy. So all solutions are valid. When a sensor node transmits or receives a data packet, the value of energy is selected from this feasible region.
# TEDL: A Text Encryption Method Based on Deep Learning
## 12 Conclusions and Future Work  
In this paper, we propose a new text encryption method based on deep learning named TEDL. It is the first time to directly apply deep learning model to encryption, mainly utilizing the uninterpretability and time-consuming training features. The time-varying and self-updating characteristics of TEDL deal with the problem of key redistribution and the two-stage structure makes it hard to carry out brute-force attack and makes it efficient for communication. Moreover, TEDL bears other superior properties such as anti-interference, diffusion and confusion, high sensitivity, generality and so on, all of which have been confirmed through experiments.  
It is worth mentioning that both encrypted objects and models are expandable. Objects in various forms, such as binary numbers, texts, images, videos, or even multimodal information, can be encrypted with TEDL. For example, assuming TEDL adopts word embedding model and aims to encrypt binary numbers, a binary library can be constructed serving as public corpus while additional text in either original or binary form acts as an incremental corpus, then training performs on the synthetic corpus. Inspired by the fact that more and more objects can be embedded (e.g., the network [55]), it is natural that those  
Figure 19: Generality about models to be applied.  
objects can be encrypted by TEDL with embedding model, for which we might as well name as embedding encryption.  
As for the extensibility of models, all the models that satisfy the Model Requirements in Section 6.2 can be employed by TEDL, and it is easy to meet those requirements. For example, nearly all deep learning models own public training set and it is easy to get whether texts, images or videos on the Internet as long as their corresponding addresses exist, meeting Model Requirements 1 and 2. Besides, considerable models, such as CNN and LSTM, own a large number of parameters, satisfying Model Requirement 3. Moreover, as for supervisory models, they are still available, as long as labels are preconcerted without the necessity of secret, thus satisfying Model Requirement 4. Finally but not least, TEDL cannot only use for encryption. From the perspective of generating a key stream, such a large number of parameters in the deep learning model may be utilized, which is also worth exploring.  
Conceptualization, P.W. and X.L.; methodology, P.W. and X.L.; validation, X.L.; writing-original draft preparation, X.L.; writing-review and editing, P.W. All authors have read and agreed to the published version of the manuscript.  
The work is supported by National Key R&D Program of China (2018 YFD1100302), National Natural Science Foundation of China (No.61972082, No.72001213), and All-Army Common Information System Equipment Pre-Research Project (No.31511110310, No.31514020501, No.31514020503).  
Data variability statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 3 Experimental Validation of the Hydroelasticity Theory
### Numerical Setup  
A corresponding full-scale numerical model is established and the hydroelastic responses are calculated by the developed hydrodynamic code. The established whole ship hydrodynamic grid and the wetted grid under waterline are shown in Figure 4. The whole ship hydrodynamic grid which includes 2055 panels is used for the Froude-Krylov force and hydrostatic restoring force calculation; the underwater wetted grid, which includes 1308 panels, is used for diffraction force and radiation force calculation. In addition, the hull structure is discrete by 20 Timoshenko beam elements and the vertical vibration mode of the hull girder in vacuum is solved by the transfer matrix method (TMM).  
Two experimental conditions are selected for comparison with the numerical results: the extreme sea state Condition 1 (\(H_{1/3}\) = 15 m, \(T_{z}\) = 12.25 s, \(U\) = 5 knots) and the high speed Condition 2 (\(H_{1/3}\) = 9.2 m, \(T_{z}\) = 11.4 s, \(U\) = 24 knots). The long-crested irregular waves are generated using ISSC target spectra. The ship short-term responses within 1 h are simulated by the numerical hydroelastic code. Enough experimental time and samples are achieved by combining the time series at steady run region measured in several different runs.  
Figure 4: Hydrodynamic calculation grid.  
Figure 3: Model and tank facilities. (**a**) Model overview; (**b**) wave tank; (**c**) wave-maker.
# An Approach to Improve the Resolution of DLP 3D Printing by Parallel Mechanism
## 5 Conclusions  
In this work, to solve the problem of fabrication size and resolution of 3D printing based on DLP technology, an adjustment resolution approach based on parallel mechanism was proposed. And the prototype was built for experimental verification. The conclusions are as follows:  
1. Based on the projection stereolithography method, a DLP 3D printing prototype with parallel mechanism was established, which is different from the previous molding schemes with fixed molding resolution. It can adjust the resolution during the printing process to reduce the printing size of features.
2. Compared with previous single-axis or X-Y motion platform structures, the use of tripteron 3-DoF parallel mechanism improves the motion flexibility. After analyzing its kinematics, a control program was developed according to the forming process. With the low-cost optical projection equipment moving in space using parallel structure, the projection resolution was changed in order to adapt to smaller size printing.
3. The optimal molding processing parameters were obtained based on the orthogonal method. The forming contrast experiment was carried out on the prototype, and the results show that the fabrication resolution of features can be improved by about 1.3 times. The effectiveness of the approach was verified by experiments.  
Conceptualization, J.H. and J.X.; methodology, J.H. and B.Z.; software, B.Z.; validation, J.H. and B.Z.; formal analysis, J.H. and B.Z.; investigation, J.H. and B.Z.; resources, J.H.; data curation, B.Z. and Q.Z.; writing--original draft preparation, B.Z.; writing--review and editing, J.H., B.Z. and J.X.; visualization, J.H., B.Z. and Q.Z.; supervision, J.H.; project administration, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Henan Province Scientific and Technological Research Plan (Grant Nos. 222102220008, 212102210045), Fundamental Research Funds for the Universities of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Forming Method**} & \multicolumn{5}{c}{**Small Features Size (mm)**} \\ \cline{2-6}  & **1** & **2** & **3** & **4** & **5** \\ \hline Fixed resolution & 0.8689 & 0.8477 & 0.8543 & 0.8792 & 0.8278 \\ Adjustable resolution & 0.6689 & 0.6490 & 0.6495 & 0.6814 & 0.6358 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Features size of model by two methods.  
Hena Province (Grant No. NSFRF210339) and Key Discipline of Mechanical Engineering in Henan Polytechnic University and the University Doctoral Fund (Grant No. B2016-24).  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 7 Conclusions and Future Insights  
The proposed SiM-RPO and CoSiM-RPO algorithms are presented for the reliable and persistence operation of the network. For the MSs' path determination, the network is split into four equal squares. To cover all the network and collect more information, the triangular paths are defined for the MSs. The MSs move in the network to pick information and data directly from the nodes. In CoSiM-RPO, for correct data delivery, the data are determined at the MSs. If the data satisfy the criteria of the correct data reception, then this is considered successful delivery of the data. Otherwise, the MSs demands retransmission of the data from the relay, which enhances the probability of correct data reception. The proposed algorithm enhances the performance of the UA-WSNs in terms of successful data reception with minimum energy cost and delay and has more stability than the existing counterpart algorithms. In the future, energy harvesting techniques will be utilized for further improvement of the network stability.  
[PERSON] proposed and implemented the main idea under the supervision of [PERSON] and co-supervision of [PERSON], respectively. [PERSON] and [PERSON] contributed in refining the flow of the manuscript. [PERSON] and [PERSON] responded to the reviewer's comments and streamlined the overall manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work in part through research group NO (RG-1438-034). The authors also extend their appreciation to the University of Malaya for funding in part under the Postgraduate Research Grant (PG035-2016A).  
The authors declare no conflict of interest.
## 2 Introduction of pH Value Regulation System and Analysis of Regulation Process
### Analysis of Liquid Fertilizer pH Regulation Process
#### 2.2.1 Objective Function and Design Variables  
The ionization process of m-membered acid is described below. First level ionization  
\[H_{m}A\rightleftharpoons H_{m-1}A^{-}+H^{+}\]  
\[K_{a1}=\frac{\big{[}H^{+}\big{]}\big{[}H_{m-1}A^{-}\big{]}}{\big{[}H_{m}A \big{]}} \tag{1}\]  
Secondary ionization  
\[H_{m-1}A^{-}\rightleftharpoons H_{m-2}A^{2-}+H^{+}\]  
\[K_{a2}=\frac{\big{[}H^{+}\big{]}\big{[}H_{m-2}A^{2-}\big{]}}{\big{[}H_{m-1}A^{ -}\big{]}} \tag{2}\]  
\(m\)-level ionization  
\[HA^{(m-1)-}\rightleftharpoons A^{m-}+H^{+}\]  
\[K_{am}=\frac{\big{[}H^{+}\big{]}\big{[}A^{m-}\big{]}}{\big{[}HA^{(m-1)-}\big{]}} \tag{3}\]  
The ionization process of \(n\)-membered bases in liquid fertilizer can be expressed as:  
First level ionization  
\[B(OH)_{n}\rightleftharpoons B(OH)_{n-1}{}^{+}+OH^{-}\]  
\[K_{b1}=\frac{\big{[}B(OH)_{n-1}{}^{+}\big{]}\big{[}OH^{-}\big{]}}{\big{[}B(OH) _{n}\big{]}} \tag{4}\]  
Secondary ionization  
\[B(OH)_{n-1}{}^{+}\rightleftharpoons B(OH)_{n-2}{}^{2+}+OH^{-}\]  
\[K_{b2}=\frac{\big{[}B(OH)_{n-2}{}^{2+}\big{]}\big{[}OH^{-}\big{]}}{\big{[}B(OH )_{n-1}{}^{+}\big{]}} \tag{5}\]  
\(n\)-level ionization  
\[K_{a1}=\frac{\big{[}H^{+}\big{]}\big{[}H_{m-1}A^{-}\big{]}}{\big{[}H_{m}A \big{]}} \tag{1}\]0  
\[K_{a1}=\frac{\big{[}H^{+}\big{]}\big{[}H_{m-1}A^{-}\big{]}}{\big{[}H_{m}A \big{]}} \tag{1}\]1  
where \(K_{b1}\), \(K_{b2}\), \(\ldots\), \(K_{bn}\) are the ionization equilibrium constants. The ionization equilibrium of water can be expressed as:  
\[K_{a1}=\frac{\big{[}H^{+}\big{]}\big{[}H_{m-1}A^{-}\big{]}}{\big{[}H_{m}A \big{]}} \tag{1}\]2  
\[K_{a1}=\frac{\big{[}H^{+}\big{]}\big{[}H_{m-1}A^{-}\big{]}}{\big{[}H_{m}A \big{]}} \tag{1}\]3where \(K_{w}\)= \(10^{-14}\). Let \(x_{i}\) be the total ionic concentration of the acid or the total ionic concentration of the base in the fertilizer mixture, then  
When \(i\) is an acid:  
\[K_{a1}=\frac{\big{[}H^{+}\big{]}\big{[}H_{m-1}A^{-}\big{]}}{\big{[}H_{m}A \big{]}} \tag{1}\]4  
When \(i\) is a base:  
\[K_{a1}=\frac{\big{[}H^{+}\big{]}\big{[}H_{m-1}A^{-}\big{]}}{\big{[}H_{m}A \big{]}} \tag{1}\]5  
Since the solution must always remain electrically neutral, the charge balance equation yields:  
\[ \begin{array}{l}\sum\limits_{i=acid}\left\{\left[H_{p_{i}-1}A^{-}\right]+2[H_ {p_{i}-2}A^{2-}]+\cdots+p_{i}[A^{p_{i}-}]\right\}+\left[OH^{-}\right]\\ =\sum\limits_{i=base}\left\{\left[B(OH)_{n_{i}-1}^{+}\right]+2\left[B(OH)_{n_{ i}-2}^{2+}\right]+\cdots+n_{i}[B^{n_{i}+}]\right\}+\left[H^{+}\right]\end{array}
# On the Control and Validation of the PARA-SILSROB Surgical Parallel Robot
## 5 Conclusions  
This study focused on the development of the control system of a robot designed for single-inclision laparoscopic surgery. The system utilizes the master-slave architecture, where the master console consists of two commercially available 3D Space Mouse devices and a GUI. The slave consists of a six-DOF parallel robot, which guides the laparoscope and includes two modules for the manipulation of active surgical instruments. A state machine was integrated into the PLC that controls the robot's operations and incorporates specific control functions. The effectiveness of the control system was validated by comparing the desired input signals for the end effector with the actuators' response signal.  
The robotic system was validated in laboratory conditions through a two-stage battery of tests. The first stage assessed the robot's functionality using a surgical kit to simulate a variety of operations and procedures used in robot-assisted surgery, including manipulation, resection, and suturing. The second stage simulated an esophagectomy and reconstruction performed during the Ivor-Lewis procedure. For this simulation, a torso was used in which the main organs pertinent to this surgical procedure were accurately positioned. The organs used in this testing stage were modeled using image segmentation based on helical CT scans, and their simplified model was printed using the PolyJet 3D printing technique with soft materials.  
Future research will focus on designing our custom active instruments, and additionally, efforts will concentrate on improving the current graphical interface, developing a surgical simulator to improve the learning curve, and finding ways to integrate tactile/haptic feedback into the active instruments by using two haptic devices and force sensors mounted on the flanges of the active instruments, along with enhanced visualization through a 3D HD laparoscope.-  
Conceptualization, D.P.; methodology, B.G., C.V. and D.C.; software, A.-D.C.; validation, A.C., C.P., E.M. and N.A.H.; formal analysis, B.G., D.C., A.P., A.C., C.R. and N.A.H.; investigation, A.P.; resources, D.P.; data curation, B.G. and C.V.; writing--original draft preparation, A.P., B.G. and A.-D.C.; writing--review and editing, D.P.; visualization, A.C.; supervision, D.P.; project administration, D.P. and D.C.; funding acquisition, D.P. and D.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project New smart and adaptive robotics solutions for personalized minimally invasive surgery in cancer treatment--ATHENA, funded by the European Union--NextGenerationEU and Romanian Government, under National Recovery and Resilience Plan for Romania, contract no. 760072/23.05.2023, code CF 116/15.11.2022, through the Romanian Ministry of Research, Innovation and Digitalization, within Component 9, investment I8.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.