esa-sceva/satcom-chunk-collection huggingface dataset:
The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R\({}^{2}\) of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R\({}^{2}\) of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R\({}^{2}\) of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM\({}_{10}\) concentration caused by drilling operations in open-pit mines. meta-heuristic algorithm; PM\({}_{10}\) concentration; drilling operation; artificial intelligence; open-pit coal mine
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 3 Method
### Converged CST Module  
A convolutional neural network (CNN) is a simple and effective network for processing image tasks and has excellent performance in processing local features, but CNNs have some limitations in processing global information. The transformer model is relatively complex and has a poor ability to process local information, but it has a good ability to process global information with good long-term dependency. Inspired by ViTDet [34] and BoTNet [35], a hybrid model was constructed using a CNN and Transformer, replacing specific modules in the network and combining the advantages of both to construct a CST module. We used a Swin Transformer [36] with lower computational effort to fuse with CNN to obtain local information through the CNN, and then, the Swin Transformer captured global information and rich contextual information to improve the detection performance of small targets. As shown in the figure, each Swin Transformer contains two consecutive Swin Transformer Blocks using two modules, W-MSA (Window-based Multi-Head Self-Attention) and SW-MSA (Shifted Windows Multi-Head Self-Attention), followed by a 2-layer MLP (Multilayer Perceptron) with a GELU (Gaussian Error Linear Unit) activation function, and an LN (Layer Norm) layer is used prior to each MSA (Multi-Head Self-Attention) module and the MLP, and residual connections are used. Swin Transformer not only reduces computational complexity but also increases the ability to capture information at different scales through spatial dimensional mixing and improves detection performance for spatially small targets.  
Figure 2: Multi-scale feature fusion pyramid structure. (**a**) is a structural diagram of PANet network. (**b**) is the structural diagram of our proposed multi-scale feature fusion pyramid.  
Considering that Transformer requires a large amount of computation and memory consumption, and the feature images at the end of the network have low resolution, we chose to introduce the CST module at the end of the feature pyramid. The local information in the image was extracted using the CNN, and then the linkage of contextual information was strengthened by Transformer to improve the detection ability of the model for spatially small targets, and the structure is shown in Figure 3.
###### Abstract
Leaving canals without filling was reported as the least common error [18], proceeded by broken instrument/s [23]. Other than one study [39], there was no assumed significant difference between different academic grades of undergraduate students during RCT [24; 28; 38]. However, most of the surveyed studies, including the excluded studies, were cross-sectional as it conducted in a limited period and were performed either on one academic grade or in general, without identifying the actual improvement of hand skills of students between different academic grades. This was the aim of the current study: to measure the prevalence and technical quality of RCF by senior and undergraduate students, the type and location of teeth which showed higher RCF errors, and the most occurred error/s, done by the different academic grades. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} **The** \\ **Continent** \\ \end{tabular} } & \multicolumn{1}{c}{**The Study**} & \multicolumn{1}{c}{**Location**} & \begin{tabular}{c} **Type of Study** \\ **(Type of Course)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Treated Tenth** \\ **(or Canals)** \\ \end{tabular} & \begin{tabular}{c} **Adequate** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Not Adequate** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{**Africa**} & [PERSON] et al., 2016 [14] & Sudan & In-vivo (5 th year) & 173 T (324 RC) & 55.5\% & 44.5\% \\ \cline{2-6}  & [PERSON] et al., 2015 [15] & Libya & In-vivo (4 th and 5 th year) & 128 T (284 RC) & 53.9\% & 46.1\% \\ \cline{2-6}  & [PERSON] et al., 2011 [16] & Sudan & \begin{tabular}{c} In-vivo (4 th and 5 th year) \\ \end{tabular} & 166 T (265 RC) & 52.7\% & 47.3\% \\ \hline \multirow{4}{*}{**Europe**} & [PERSON] et al., 2018 [17] & UK & \begin{tabular}{c} In-vivo (4 th and 5 th year) \\ \end{tabular} & 222 T (381 RC) & 66.4\% & 33.6\% \\ \cline{2-6}  & [PERSON] et al., 2014 [18] & Serbia & In-vivo (5 th year) & 322 T (565 RC) & 74.2\% & 25.8\% \\ \cline{2-6}  & [PERSON] et al., 2017 [19] & Finland & \begin{tabular}{c} In-vivo \\ (Undergrad) \\ \end{tabular} & 105 T (167 RC) & 65.7\% & 34.3\% \\ \cline{2-6}  & [PERSON] et al., 2018 [20] & Greece & \begin{tabular}{c} In-vivo \\ (Undergrad) \\ \end{tabular} & 244 T (349 RC) & 40.4\% & 59.6\% \\ \hline \multirow{4}{*}{**America**} & [PERSON] et al., 2021 [21] & Brazil & \begin{tabular}{c} In-vivo (4 th and 5 th year) \\ \end{tabular} & 368 T (442 RC) & 93.8\% & 6.2\% \\ \cline{2-6}  & [PERSON] et al., 2019 [22] & Brazil & \
\begin{tabular}{c} In-vivo (4 th and 5 th year) \\ \end{tabular} & 274 T (381 RC) & 71.9\% & 28.1\% \\ \cline{1-1} \cline{2-6}  & [PERSON] et al., 2018 [23] & Brazil &
\begin{tabular}{c} In-vivo (4 th and 5 th year) \\ \end{tabular} & 397 T (480 RC) & 92.7\% & 7.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Adequacy of root canal treatment conducted by dental students in the last 10 years.
## Application of Perturbation Method
### Nondimensionalization  
The following dimensionless quantities are introduced  
\[P=\frac{qa^{4}}{E_{0}t^{4}},\ \ \eta=1-\frac{r^{2}}{a^{2}},\ W=\frac{w}{t},\ S= \frac{N_{r}a^{2}}{E_{0}t^{3}}. \tag{38}\]  
Equations (31) and (32) are transformed into their dimensionless forms, i.e.,  
\[\frac{d^{2}}{d\eta^{2}}\bigg{[}(1-\eta)\frac{dW}{d\eta}\bigg{]}=\frac{S}{4K} \frac{dW}{d\eta}-\frac{P}{16K} \tag{39}\]  
and  
\[\frac{d^{2}}{d\eta^{2}}[(1-\eta)S]+\frac{V}{2}\bigg{(}\frac{dW}{d\eta}\bigg{)} ^{2}=0 \tag{40}\]  
where  
\[K=K^{+}+K^{-}=\frac{D^{*}}{E_{0}t^{3}}=\frac{D^{+}}{E_{0}t^{3}}+\frac{D^{-}}{ E_{0}t^{3}},\ V=\frac{e^{\alpha_{1}}-1}{\alpha_{1}e^{\alpha_{1}T_{2}}},\ T_{1}=\frac{t_{1}}{t},\ T_{2}=\frac{t_{2}}{t}. \tag{41}\]  
The boundary condition (33c) becomes  
\[W=0,\ \lambda_{1}\frac{d^{2}W}{d\eta^{2}}-\frac{dW}{d\eta}=0\ \text{and}\ \lambda_{2}\frac{dS}{d\eta}-S=0,\ \text{at}\ \eta=0 \tag{42}\]  
where  
\[\lambda_{1}=\frac{2K}{K^{+}(1+\mu^{+})+K^{-}(1+\mu^{-})},\ \lambda_{2}=\frac{2A_{11}}{A_{11}-A_{12}}= \frac{2}{1-\mu^{+}}. \tag{43}\]  
Especially, using the introduced parameters \(\lambda_{1}\) and \(\lambda_{2}\), the other three types of boundary conditions may be described as, for rigidly clamped, \(\lambda_{1}=0\); for movably clamped, \(\lambda_{1}=\lambda_{2}=0\), and for simply supported, \(\lambda_{2}=0\). The introduction of \(\lambda_{1}\) and \(\lambda_{2}\) also indicates that the solution obtained under boundary condition (33c) may serve as a general solution to describe other three edge constraints. In addition, the axisymmetric conditions at the center, i.e., Equation (30), becomes  
\[\frac{dW}{d\eta}=0(\
eq\infty)\ \text{and}\ S\
eq\infty,\ \text{at}\ \eta=1 \tag{44}\]
## 7 Conclusions  
We have defined the Hostile-Direction Aware Routing Protocol (HDARP) that avoids detection by adversary aircraft in airborne tactical networks. HDARP uses directionalantennas and avoids adversary detection by using so-called situation awareness (SA) data to determine a route such that the packets are not detected by hostile aircraft. SA data are needed in order for the pilots to perform their mission.  
One of the advantages of HDARP, besides avoiding adversary detection, is that it uses already existing SA data for network topology control and routing decisions as well. Based on evaluations using realistic directional antenna models and flight scenarios from the defense industry, we have shown that the major tactical advantage of avoiding adversary detection comes at a relatively small, and clearly acceptable, cost in terms of somewhat longer packet lifetime and marginally lower packet delivery rate. Our evaluations also show that SA data provide a highly efficient way of representing network topology control, and the number of sent packets using HDARP is on a par with the best routing algorithms that do not avoid adversary detection. This means that the cost of avoiding adversary detection in terms of a slight increase of the number of sent packets is negligible.  
In general, in this paper, we focus on the algorithmic properties of the protocol and not on the radio transmission protocol. In future work, we will consider looking at the effects of the radio protocol as well: examples include path loss, fading, and shadowing aspects. Further, we might also consider additional flight scenarios, different relations between flight speed and message propagation latencies. Finally, we have not tracked the beam width in our simulations. Theoretically, a narrow beam width decreases the probability of detection but potentially increases the average path length. Conversely, a wide beam width enables direct communication with more nodes, i.e., multicast, but potentially increases the probability of detection. We think that the trade-off between these two is a very interesting aspect that we consider for future work
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 3 Synthesis Methods and Characterization
### Wet Chemical Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dopant** & **Dopant** & & & & \\
**Element** & **Precursors** & & & & \\ \hline La & LaCl\({}_{3}\cdot\)7H\({}_{2}\)O & 5\% mol & H\({}_{2}\)O \({}^{a}\) & 123 nm \({}^{1}\) & [116] \\  & CeCl\({}_{3}\cdot\)7H\({}_{2}\)O & 1\% mol & H\({}_{2}\)O \({}^{a}\) & 20–30 nm \({}^{1}\) & [171] \\  & Ce(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O & 0.1–5\% mol & H\({}_{2}\)O \({}^{b}\) & 70–85 nm \({}^{1}\) & [172] \\  & Nd(NO\({}_{3}\))\({}_{3}\cdot\)6H\({}_{2}\)O & 5\% mol & H\({}_{2}\)O \({}^{a}\) & 101 nm \({}^{1}\) & [172] \\  & Sm & Sm(NO\({}_{3}\))\({}_{3}\cdot\)6H\({}_{2}\)O & 1–4\% mol & H\({}_{2}\)O \({}^{a}\) & 35 nm \({}^{1}\) & [193] \\  & EuCl\({}_{3}\cdot\)6H\({}_{2}\)O & 5\% mol & H\({}_{2}\)O \({}^{a}\) & 79 nm \({}^{1}\) & [150] \\  & Eu(NO\({}_{3}\))\({}_{3}\cdot\)5H\({}_{2}\)O & 5\% mol & CH\({}_{3}\)OH \({}^{a}\) & 9 nm \({}^{2}\) & [164] \\  & Gd(NO\({}_{3}\))\({}_{3}\cdot\)6H\({}_{2}\)O & 5\% mol & CH\({}_{3}\)OH \({}^{a}\) & 9 nm \({}^{2}\) & [164] \\  & Gd(CH\({}_{3}\)CO\({}_{2}\))\({}_{3}\) & 2–30\% mol & CH\({}_{3}\)CH\({}_{2}\)OH & 4 nm \({}^{2,3}\) & [163] \\  & \(\mathrm{NH_{4}VO_{3}}\) & 1\% mol & H\({}_{2}\)O \({}^{a}\) & 47 nm \({}^{2}\) & [194] \\  & Mn(NO\({}_{3}\))\({}_{2}\) & 0.5–3\% mol & CH\({}_{3}\)CH\({}_{2}\)OH & 50–120 nm \({}^{2}\) & [183] \\  & MnCl\({}_{2}\)4H\({}_{2}\)O & 1–5\% mol & CH\({}_{3}\)OH & 100 nm \({}^{2}\) & [126] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dopant ions and their respective precursors for the synthesis of ZnO nanoparticles by wet chemical methods.
## 2 Materials and Methods
### Data
#### 2.2.3 Model Forcing Data  
EVI and the land surface water index (LSWI) were used as input data for the satellite based VPM. Both were acquired from Moderate Resolution Imaging Spectroradiometer reflectance (MOD09A1 V05) product ([[http://ladsweb.nascom.nasa.gov/](http://ladsweb.nascom.nasa.gov/)]([http://ladsweb.nascom.nasa.gov/](http://ladsweb.nascom.nasa.gov/)); the spatial resolution is 500 m, and the time resolution is 8 days). EVI was calculated from the red, blue, and near-infrared bands [43], while LSWI was calculated as the normalized difference between the near-infrared and short-infrared spectral bands [34; 44].  
The interpolated meteorological dataset (temperature and precipitation) was downloaded from the data resources service website of the Chinese Ecosystem Research Network ([[http://www.cnern.org.cn](http://www.cnern.org.cn)]([http://www.cnern.org.cn](http://www.cnern.org.cn))). The gridded meteorological data were generated from 1098 ground meteorological stations using ANUSPLIN Interpolation software [45].
# Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition
## 3 Saliency Map-Based Local Feature Descriptors for Egocentric Action Recognition
### Local Spatial and Temporal Information Calculation  
**SM-HOG and SM-HMG:** SM-HOG calculates spatial gradient information for each input video frame \(\mathcal{F}r_{i}\). By extending SM-HOG, SM-HMG performs an efficient temporal gradient calculation between each pair of neighboring frames (\(\mathcal{T}D\)) prior to the entire SM-HOG process using Equation (1).  
\[\mathcal{T}\mathcal{D}_{(j,j+1)}=\frac{\partial(\mathcal{F}r_{j},\mathcal{F}r_ {j+1})}{\partial t}. \tag{1}\]  
**SM-HOF and SM-MBH:** The gradients in SM-HOF are implemented using the Horn-Schunck (HS) [40] optical flow method; the calculated flow vector \(\overrightarrow{\mathcal{O}\mathcal{F}_{j,j+1}}\) is also used in SM-MBHx and SM-MBHy. Because \(\overrightarrow{\mathcal{O}\mathcal{F}_{j,j+1}}\) is a complex typed vector, SM-MBHx and SM-MBHy use its imaginary (\(\mathcal{IF}_{j,j+1}\)) and real (\(\mathcal{RF}_{j,j+1}\)) parts, respectively.  
The gradients in the \(x\) and \(y\) directions for SM-HOG, SM-HMG, SM-HBHx, and SM-MBHy are summarized below:  
Figure 1: The framework of the proposed approach as shown in the upper row, and the outline of the main steps of the saliency map-enhanced LFD extraction approach (SMLFD) as illustrated in the lower row using the histogram of motion gradients (HMG) approach with 8 bins and 18 blocks.  
\[Gx_{k}=\begin{cases}\frac{\partial(\mathcal{F}r_{j})}{\partial x}&\text{if SM-HOG},\\ \frac{\partial(\mathcal{T}\mathcal{D}_{j,j+1})}{\partial x}&\text{if SM-HMG},\\ \frac{\partial(\mathcal{I}\mathcal{F}_{j,j+1})}{\partial x}&\text{if SM-MBHx}, \\ \frac{\partial(\mathcal{R}\mathcal{F}_{j,j+1})}{\partial x}&\text{if SM-MBHy}. \end{cases}Gy_{k}=\begin{cases}\frac{\partial(\mathcal{F}r_{j})}{\partial y}& \text{if SM-HOG},\\ \frac{\partial(\mathcal{T}\mathcal{D}_{j,j+1})}{\partial y}&\text{if SM-HMG}, \\ \frac{\partial(\mathcal{I}\mathcal{F}_{j,j+1})}{\partial y}&\text{if SM-MBHx}, \\ \frac{\partial(\mathcal{R}\mathcal{F}_{j,j+1})}{\partial y}&\text{if SM-MBHy}. \end{cases} \tag{2}\]  
where \(k=i\) and \(i\in[1,f]\) for SM-HOG, and \(k=j\) and \(j\in[1,f-1]\) for the others. Please note that the above derivative operations are usually practically implemented using a convolution with a Haar kernel [41]. Then, the magnitude \(\mathcal{M}_{k}\) and orientation \(\mathcal{O}_{k}\) of the temporal and spatial information about the pixels in each frame are calculated as:  
\[\mathcal{M}_{k}=\begin{cases}|\mathcal{O}\mathcal{\bar{T}}_{k}|&\text{if SM-HOF},\\ \sqrt{Gx_{k}{}^{2}+Gy_{k}{}^{2}}&\text{otherwise}.\end{cases}\qquad\qquad \mathcal{O}_{k}=\begin{cases}\frac{f(\mathcal{O}\mathcal{\bar{T}}_{k})}{ \arctan(\frac{Gy_{k}}{Gx_{k}})}&\text{if SM-HOF},\\ \arctan(\frac{Gy_{k}}{Gx_{k}})&\text{otherwise}.\end{cases} \tag{3}\]  
where \(\mid\cdot\mid\) and \(\frac{f(\cdot)}{\arctan(\cdot)}\) denote the magnitude and orientation of the complex-typed vector, respectively.
# Low Complexity List Decoding for Polar Codes with Multiple CRC Codes
## 1 Introduction
In Section 2, the SCL decoding and its operational complexity are introduced, and in Section 3, the encoding and decoding methods for the multi-CRC scheme, and the optimization algorithm for CRC positions are explained. Section 4 provides the performance and complexity of the proposed and conventional schemes, and Section 5 concludes this paper.
## 2 Data Availability
### Remote Sensing Data  
The moderate-resolution imaging spectroradiometer (MODIS) surface reflectance data were obtained from MCD43A4.061. The reflectance values across all bands served as the input for the BESS model and for the calculation of LAI. It is worth noting that MODIS LAI tends to significantly underestimate crop LAI [73]. Measured LAI values were provided  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Site** & **Latitude** & **Longitude** & **Crop Type** & **Year** & **LAI** \\ \hline US-Ne1 & 41.165\({}^{\circ}\)N & 96.477\({}^{\circ}\)W & Maize & 2001–2012 & — \\ US-Ne2 & 41.165\({}^{\circ}\)N & 96.470\({}^{\circ}\)W & Maize–Soybean & 2001–2012 & Literature * \\ US-Ne3 & 41.180\({}^{\circ}\)N & 96.440\({}^{\circ}\)W & Maize–Soybean & 2001–2012 & Literature * \\ Daman & 38.853\({}^{\circ}\)N & 100.376\({}^{\circ}\)E & Maize & 2018, 2019, 2021 & measured \\ Fenzidi & 41.153\({}^{\circ}\)N & 107.653\({}^{\circ}\)E & Maize & 2017, 2018, 2020 & measured \\ \hline \end{tabular}  
* the LAI data for the site were partially obtained from the literature [72].  
\end{table}
Table 1: Summary of flux sites information.  
for the Fenzidi and Daman sites, while LAI values for certain years at US-Ne2 and US-Ne3 were extracted from the literature [72]. To estimate LAI values for other years, a machine learning model (ExtraTreesRegressor) was employed, demonstrating strong simulation performance (R\({}^{2}\) = 0.94) (Figure 1).  
The clumping index (\(\Omega\)) is another vegetation canopy structure index, quantifying the degree of deviation in leaf spatial distribution from a random pattern in BESS [74; 75]. The \(\Omega\) values were estimated using the MODIS BRDF product at a resolution of 500 m [76; 77]. \(\Omega\) values range from 0 to 1, where 1 indicates a randomly distributed canopy. Smaller \(\Omega\) values indicate a more clustered canopy structure.
# Terpenes as Potential Anti-Alzheimer's Disease Agents
## 2 Activities of Terpenes on the Mechanisms Associated with AD
### Inhibition of NFTs Formation
#### 2.3.3. Diterpenes  
Carnosic acid (**59**) is a diterpene extracted from rosemary (_Rosmarrius officinalis_ L.) [89]. It also readily penetrates the BBB into the brain [95]. It reduces the production of inflammatory mediators like TNF-\(\alpha\), IL-1\(\beta\), IL-6, and NO [96]. Other diterpenes like the ginkgolides A (**60**), B (**61**), and C (**62**) isolated from the leaves of _Ginkgoa biloba_ L. inhibit the production of IL-1\(\beta\) and TNF-\(\alpha\)[97; 98; 99]. However, these compounds have difficulty entering the brain through the BBB [100].  
Considering the nor-diterpenes (**3-6**), besides BACE1 and \(\tau\) phosphorylation inhibitors, all of them reduce the production of ROS [101].
# Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework
## 5 Conclusions  
In this work, we presented an emulation framework that leverages on a combination of SDN and container management with Kubernetes for the emulation of both control- and data-planes of disaggregated optical networks. Our legacy proposal was reviewed including the architectural details and the classical optical network agent structure that leveraged on the Netcope2-Sysrepo framework. Then, we presented the Containerized Framework for emulating SDN-DONs paying special attention to the coexistence of the server-client architecture based on Kubernetes master nodes and the client-server structure that used NETCONF in the SDN controller-optical agent interaction. We further detailed the software agent structure which includes NETCONF server, YANG validator, XML/JSON config database and YANG models, whose combination offers versatility for accommodating the wide variety of optical components and systems. The implementation of the agents in this emulating system was deployed in Docker containers allowing advanced and efficient management by Kubernetes. We described all the configuration and settings for performing the proof-of-concept that consisted in an 18-node European topology with Cassini transponders. We reported the successful status request and configuration settings of optical-layer parameters such as transmitted and received optical power. Given the successful interaction between the control and data plane positions, our proposal demonstrates an adequate approach for managing an operationally complex carrier-grade transport infrastructure with SDN-based disaggregated optical systems.  
Figure 10: Snapshot of NETCONF-based queries to change the output power of the laser in an emulated optical agent commanded from ONOS.  
Figure 9: Snapshot of NETCONF-based queries about the status of the configuration in an optical agent commanded from ONOS.  
**Author Contributions:** Conceptualization, F-J.M.-M., M.G. and I.I.-C.; methodology, F.-J.M.-M. and M.G.; software, I.I.-C. and S.Z.; validation, F.-J.M.-M.; formal analysis, F.-J.M.-M. and P.P.-M.; investigation, F.-J.M.-M. and S.Z.; resources, P.P.-M.; writing--original draft preparation, F.-J.M.-M.; writing--review and editing, M.G., I.I.-C., S.Z., and P.P.-M.; supervision, P.P.-M.; funding acquisition, P.P.-M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded Spanish Government: ONOFRE-2 project under Grant TEC2017-84423-C3-2-P (MINECO/AEI/FEDER, UE) and the Go2 Edge project under Grant RED2018-102585-T; and by the European Commission: METRO-HAUL project (G.A. 761727).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.