esa-sceva/satcom-chunk-collection huggingface dataset:
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## Appendix A Preservation of Antisymmetry and Jacobi Identity  
_Appendix A.1. Classical Case: Poisson Brackets_  
**Modified Bracket Structure** The generalized Liouville equation introduces:  
\[\frac{d\rho}{dt}=-[H,\rho]-T[\overline{S},\rho], \tag{1}\]  
where \([A,B]\) denotes the Poisson bracket. The total dynamical bracket becomes:  
\[[A,B]_{\text{total}}=[A,B]_{\text{PB}}+T[A,\overline{S}]_{\text{PB}}. \tag{2}\]  
**Antisymmetry** The standard Poisson bracket is antisymmetric (\([A,B]_{\text{PB}}=-[B,A]_{\text{PB}}\)). The entropy term inherits antisymmetry:  
\[T[A,\overline{S}]_{\text{PB}}=-T[\overline{S},A]_{\text{PB}}. \tag{3}\]  
Thus, \([A,B]_{\text{total}}=-[B,A]_{\text{total}}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equation** & **Mathematical Form** & **Physical Meaning** \\ \hline Modified Dynamics & \(\dot{p}=-\partial_{q}H+T\partial_{q}\overline{S}\) & Hamiltonian mechanics \\  & \(\dot{q}=\partial_{p}H-T\partial_{p}\overline{S}\) & with entropy forces \\ \hline Extended Entropy & \(\overline{S}=\sum_{\alpha}\big{[}S^{(\alpha)}(\cdots)+\mathbf{a}\cdot\big{.} \big{.}\big{.}\big{  
**Jacobi Identity** For the modified bracket:  
\[[A,[B,C]_{\text{total}}]_{\text{total}}+\text{cyclic}=[A,[B,C]_{\text{PB}}+T[B, \overline{S}]_{\text{PB}}]_{\text{PB}}+T[A,\overline{S}]_{\text{PB}}+\text{ cyclic}. \tag{10}\]  
Expanding and simplifying, the Jacobi identity holds due to the scalar nature of \(\overline{S}\)[6].
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 3 Operation Principles
### State 5 (\(t_{4}<t<t_{5}\), Figure 6e)  
At the time \(t=t_{4}\), since the forward diode current \(i_{D1}\) is zero, the magnetizing inductor \(L_{m1}\) is in series with the leakage inductor \(L_{r1}\), and resonates with the clamping capacitor \(C_{c1}\). The primary voltage \(v_{p1}\) is \(-d_{m1}V_{in}/(1-d_{m1})\), and the equivalent circuit is as shown in Figure 6e. The leakage inductor current \(i_{Lr1}\) is equal to the magnetizing inductor current \(i_{Lm1}\) and continues decreasing. Due to the resonance, the direction of the current \(i_{Lm1}\) is reversed during this state, forcing the stored energy of the magnetizing inductor \(L_{m1}\) to be recycled to the input power source. In this state, the clamp-capacitor voltage \(v_{cc1}\) and the leakage inductor current \(i_{Lr1}\) can be expressed as  
\[v_{cc1}(t)\ =\ V_{in}-[V_{in}-v_{cc1}(t_{4})]\cos\omega_{4}(t-t_{4})+i_{Lr1}(t_{4} )Z_{4}\sin\ \omega_{4}(t-t_{4}), \tag{18}\]  
\[i_{Lr1}(t)\ =\ i_{Lr1}(t_{4})\cos\omega_{4}(t-t_{4})+\frac{V_{in}-v_{cc1}(t_{4} )}{Z_{4}}\sin\ \omega_{4}(t-t_{4}), \tag{19}\]  
where \(\omega_{4}\ =\ \frac{1}{\sqrt{(L_{r1}+L_{m1})C_{s1}}}\), and \(Z_{4}\ =\ \sqrt{\frac{(L_{r1}+L_{m1})}{C_{s1}}}\). This state ends when the driving signal of the auxiliary switch \(S_{a1}\) disappears.
## 3 Results and Analysis  
### Geothermal Characteristics of Lushan Geothermal Springs  
#### 3.1.1 Geothermal Type and Geothermal Reservoir Structure  
All the five hot springs have convective geothermal characteristics. The water temperature of Shangtang, Zhongtang, and Xiatang geothermal fields is more than 60 \({}^{\circ}\)C and less than 90 \({}^{\circ}\)C, which belongs to hot water in low-temperature geothermal resources. The water temperature of Wentang and Shentang geothermal fields is less than 60 \({}^{\circ}\)C and more than 40 \({}^{\circ}\)C, which belongs to the category of warm water in low-temperature geothermal resources.  
All the hot springs are all controlled by fault structure, which belong to the fracture vein heat reservoir. The hot reservoir of Shangtang and Wentang consists of gneissic granite intruded by the Middle Proterozoic, the hot reservoir of Zhongtang and Xiatang consists of granite intruded by the early Cretaceous of the Mesozoic, the hot reservoir of Shentang consists of andesitic porphyrite of Xionger group of the Middle Proterozoic, and the underground geothermal water belongs to the fracture vein artesian water.  
The thickness of the overburden in Shangtang, Zhongtang, Wentang, and Xiatang is about 1-4 m, and that in Shentang hot springs is 13.6-17 m. Therefore, all the hot reservoirs layer in Lushan are not well insulated and heat protected. Because of the lack of overburden, geothermal water migrates from the depth to the surface along the fracture channel, directly exposing the surface to form hot springs or forming geothermal anomalies only covered by thin layers above.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 5 Discussion  
### Comparison and Validation of Results  
While remote sensing is the most-promising methodology in terms of achieving affordable and global SOC monitoring, the studies focusing solely on satellite imagery have been limited in scope (i.e., the number of soil samples used to develop and validate the models and the area covered).  
Table 8 shows the comparison between our results and other relevant approaches published in the literature. It should be noted that the highest R\({}^{2}\) was obtained by [PERSON] et al., but it relies on hyperspectral data and is limited to just 81 ground truth sample taken over an area just 7 km\({}^{2}\), making it very hard to judge how general this measure of performance is.  
Similarly, the recent approach proposed by [PERSON] et al. limits the models to specific areas, ranging between 2.2 and 425 ha, so the question of how their approach could be scaled to global- or continent-level SOC monitoring remains unanswered.  
The proposed approach, however, nearly matches the performance of [PERSON] et al. on CHLSOC, which covers an area more than 100,000-times larger in size and contains 85-times more data than were used by [PERSON] et al., making the evaluation performed in the study presented here much more extensive. Of the previous studies, the closest in size in terms of the number of samples is that of [PERSON] et al., but their dataset is still several orders of magnitude smaller and the study was restricted to Northern Iran. However, when evaluated on our entire dataset comprised on CHLSOC, LUCAS 2015, and LUCAS 2018, we were very near the performance achieved by [PERSON] et al. in terms of the variance explained by our model (R\({}^{2}\)). A valid comparison in terms of the RMSE is virtually impossible to achieve, since the range of SOC in different studies varies drastically.  
Figure 8: Grayscale representation of SOC values with validity overlay.  
In Table 6, we show the MAPE as a measure of the performance of our approach as well. This allowed us to conclude that, on average, our model missed the SOC value by 48.6%. While this certainly is a large margin of error, it is also a significant improvement when one takes into account that the current global estimates of SOC vary between 504 and 3000 Pg C [16].
## 3 Discrete-Time 2 DOF Robot Manipulator Model
### Discrete-Time Sliding Mode Controller
\begin{split} s_{D}^{l}(k+1)=&\omega_{1}^{l}(k)S \Big{(}\chi_{1}^{l}(k)\Big{)}\\ &-\chi^{ld}(k+1)+\tilde{\omega}_{2}^{l}u^{l}(k)+\tilde{\Delta}_{ 2}^{l}(k),\end{split}  \tag{16}\]  
Following the procedure explained in [14], the control law can be defined as  
\[u^{l}(k)=\left\{ \begin{array}{cl}\tilde{u}_{eq}^{l}(k)&\text{for }\left\|\tilde{u}_{eq}^{l}(k)\right\|\leq u _{0}^{l}(k)\\ u_{0}^{l}(k)\frac{u_{s}^{l}(k)}{\left\|u_{s}^{l}(k)\right\|}&\text{for } \left\|\tilde{u}_{eq}^{l}(k)\right\|>u_{0}^{l}(k)\end{array} \right. \tag{17}\]  
where \(u_{0}^{l}(k)\) represents the input signal (torques \(\tau_{1}(k)\) and \(\tau_{2}(k)\) of the system in Equation (3)), with  
\[ \begin{split}\tilde{u}_{eq}^{l}(k)=&\underbrace{-\left[ \tilde{u}_{2}^{l}\right]^{-1}\left[-s^{l}(k)+\tilde{f}_{2}^{l}(\chi^{l}(k),k) \right]}_{\tilde{u}_{eq}(k)}\\ &+\underbrace{\left[\tilde{u}_{2}^{l}\right]^{-1}\left[\tilde{f}_{2}^ {l}\Big{(}\chi^{l}(k-1),k-1\Big{)}\right]+\tilde{u}_{eq}^{l}(k-1)}_{\tilde{u} _{eq}(k)},\end{split}  \tag{18}\]  
and  
\[u_{s}^{l}(k)=-\left[\tilde{u}_{2}^{l}\right]^{-1}\tilde{f}_{2}^{l}\Big{(}\chi ^{l}(k),k\Big{)}. \tag{19}\]  
In Equation (18), \(\tilde{u}_{eq}(k)\) belongs to the part that rejects the nonlinear dynamics of the RHONN in Equation (4), and \(\tilde{u}_{eq}(k)\) reduces the effect of the unknown identification error \(\tilde{\Delta}_{j}^{l}(k)\) of Equation (14). The results of the closed-loop system are shown in the next section. Now, following the procedure explained in [25]:  
\[ \begin{split}\bar{w}_{2}^{l}u_{0}^{l}(k)&>\delta^{l},\\ \delta^{l}=&\Big{(}\big{\|}f_{2s}^{l}(k)\big{\|}+ \big{\|}\tilde{\Delta}_{2}^{l}(k)\big{\|}\Big{)},\\ f_{s}^{l}\Big{(}\chi^{l}(k),k\Big{)}&=-\chi_{r}^{ l}(k)+\chi_{r}^{ld}(k)+f_{r}^{l}\Big{(}\chi^{l}(k),k\Big{)}\,\end{split}  \tag{20}\]  
the control law in Equation (17) is able to drive the system in Equation (15) within a small neighborhood of the sliding manifold \(z_{2}^{l}(k)=0\). Thus, the control error \(z_{2}^{l}(k)\) satisfies  
\[\Big{|}z_{2}^{l}(k)\Big{|}=\Big{|}\chi_{2}^{l}(k)-\chi_{2}^{ld}(k)\Big{|}\leq O (T_{s}). \tag{21}\]  
The sliding mode motion in an \(O(T_{s})\) boundary layer of \(z_{2}^{l}(k)=0\) is described by the first-order system:  
\[z_{1}^{l}(k+1)=\mathbf{k}_{1}^{l}z_{1}^{l}(k)+\bar{w}_{1}^{l}z_{2}^{l}(k)+ \tilde{\Delta}_{1}^{l}(k). \tag{22}\]  
By direct inspection of Equation (22), there exists \(k_{1}^{l}\) such that \(\forall k^{l}>k_{1}^{l}\), the position control error \(z_{1}^{l}(k)\) satisfies  
\[ \begin{split}\big{\|}z_{1}^{l}(k)\big{\|}\leq&\delta _{1}^{l},\\ \delta_{1}^{l}=&\frac{1}{\gamma}\Big{|}\tilde{\Delta }_{1}^{l}(k)\Big{|}+O(T_{s}).\end{split}  \tag{23}\]  
It is worth mentioning that the identification error \(\tilde{\Delta}_{1}^{l}(k)\) can be made arbitrarily small by adding more higher order terms into the neural identifier in Equation (4), increasing the number of adjustable weights [12]. The \(\frac{1}{\gamma}\) is a positive constant and must be modified to get a convergence of the sliding mode \(\delta_{1}^{l}\) variable.
# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
## 2 Materials and Methods
### Floor Classification Results  
Floor classification is the second step in HF-ML localization and positioning. For the classification, the generated data and the predictions produced by the selected RF algorithm according to the results presented in the previous section are used. The results obtained with the ML methods are presented in Table 7. The accuracy value in this table is provided for both training and testing. The value given for the training is the average of the results obtained in the 10-fold cross-validation stage. The precision, recall, and F1 values obtained for the testing are calculated according to the distribution weights since there are five different output values and these outputs are in different numbers in the dataset. The HF tag is added when the generated data are combined with the predictions of RF building classification. In general, when the performance metrics are considered, the positive effect of HF is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ML Method**} & \multicolumn{2}{c}{**Accuracy**} & **Precision** & **Recall** & **F1** \\ \cline{2-6}  & **Training** & **Testing** & **Testing** & **Testing** & **Testing** \\ \hline NB & 0.673 & 0.670 & 0.722 & 0.670 & 0.674 \\ KNN & 0.963 & 0.964 & 0.964 & 0.964 & 0.964 \\ DT & 0.987 & 0.993 & 0.993 & 0.993 & 0.993 \\ RF & 0.993 & 0.995 & 0.995 & 0.995 & 0.995 \\ AB & 0.916 & 0.897 & 0.896 & 0.897 & 0.896 \\ SVM & 0.605 & 0.620 & 0.790 & 0.620 & 0.569 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Building classification results.  
Figure 5: Confusion matrix of random forest (RF) building classification.  
The performance metrics obtained as a result of latitude regression are presented in Tables 10 and 11. Considering the general performance metrics, application of HF has a negative effect on latitude estimation. When the algorithms are examined for MSE metrics, DT appears to be the most successful algorithm. While the MSE value of the regression with DT is 190.452, the MSE value of HF-DT is 1284.751. The results obtained with DT for training data have a very low MSE value of 2.171.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 3 Research Method
### The Coordinate Registration of the Point Cloud and BIM
#### 3.2.2 Fine Registration between Point Cloud and BIM  
After coarse registration, the point cloud model and BIM roughly overlap in the main direction. However, the registration accuracy still does not meet the requirements of geometric accuracy evaluation. Next, a trimmed iterative ICP algorithm based on the point-to-line distance is proposed for the fine registration between the point cloud sliced inner wall lines and the BIM inner wall lines. The \"inner wall lines\" refer to the projection of inner wall surfaces and inner surfaces of exterior walls in the XOY plane. In this study, the inner surfaces of exterior walls are used in most cases.  
Figure 3: Workflow of coarse registration.  
Figure 2: (a) Before coarse registration; (b) after coarse registration.  
The PL-ICP algorithm uses the point-to-line distance as an error measure [38], and its objective function seeks to minimize the sum of squared distances between the target point and the line segment that connects two adjacent points, as shown in Figure 4. PL-ICP is a robust algorithm that can effectively eliminate the influence of outlier points on the matching results. In this study, the incorrectly located inner wall lines are considered outliers, and two strategies are used to locate the correctly constructed \"inner wall lines\": (1) a distance threshold is set (\(r\) = 0.01 m) to eliminate the corresponding points with larger distances; and (2) a trimmed strategy [53]. In each iteration, we sort the distances of corresponding point pairs and select the top \(\alpha\) (\(\alpha\) = 80%) points for the next iteration. This robust strategy implicitly indicates that only the correct position construction is involved in the registration. The distance measurement used by the PL-ICP is a point-to-line distance and the distance \(r\) is calculated using Equation (3) as follows:  
\[r_{i}=r\big{(}T_{k}p_{i},\ p_{i}^{\prime}\big{)}=\big{\|}\big{(}T_{k}p_{i}-p_{ i}^{\prime}\big{)}\cdot n_{i}\big{\|}_{2} \tag{3}\]  
where \(T_{k}=\begin{bmatrix}R_{k}&t_{k}\\ 0&0\end{bmatrix}\), \(p_{i}\) is the 3D coordinate of the target point of the design model, \(p_{i}^{\prime}\) is the midpoint of the line connecting the two nearest target points in the point cloud, and \(n_{i}\) is the normal vector of \(p_{i}^{\prime}\).  
After the transformation matrix \(T_{k}\) is obtained using the PL-ICP algorithm, a coordinate transformation is performed on the coarse matching point cloud using transformation matrix \(T_{k}\). Note that if the coarse registration step is omitted, it may lead to an upside-down situation in the point cloud registration [54]. Through the above steps, the registration of the point cloud with BIM is finally achieved.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 4 Performance Evaluation  
### Platform Setup  
The simulator designed in this paper was implemented in Python language. The simulator was built on a computer with an Intel Xeon E5-1620 3.6 GHz processor, 32 GB of memory, and the Ubuntu 18.04 operating system. All the simulations in this paper were implemented under this platform.
## 3 Results and Discussion
### Data Analysis
#### 3.3.4 Responses Calculation
The array trace energy after the removal of only position errors is calculated to be \(E_{\mathit{x}}=2237\), which is almost equal to that of the ideal case, leading to a normalized array trace energy \(E_{\mathit{Nx}}^{\prime}=0\) dB. Similar results were also reported by previous studies [30; 31], as discussed in the next section.
## 2 Notations and Methodology
### EO Performances of the Active Region of the MZM
We find that it is possible to fit the dash blue curve with polynomial functions, leading to simple relationships between \(\alpha_{\text{MZI}}\), \(\Delta\varphi\), \(\delta_{\text{PN}}\) and the doping concentrations \(N_{A}\) and \(N_{D}\) (Equations (2)-(5)) for the structures showing the best compromises between efficiency and losses for an efficiency up to \(20^{\circ}\)/mm. \[\alpha_{\text{MZI}}\left[\text{dB/mm}\right]=6.68\times 10^{-4}\Delta\varphi^{2}-2.51\times 10^{-1}\Delta\varphi+2.84 \tag{2}\]  
\[10\text{log}(N_{D}\left[\text{cm}^{-3}\right])=-2.89\times 10^{-4}\Delta \varphi^{2}+5.37\times 10^{-2}\Delta\varphi+16.43 \tag{3}\]  
\[10\text{log}(N_{A}\left[\text{cm}^{-3}\right])=-4.32\times 10^{-4}\Delta\varphi^{2}+7.14\times 10^{-2}\Delta\varphi+15.79 \tag{4}\]  
\[\delta_{\text{PN}}\ \left(\text{nm}\right)=\ -15.61\times 10^{-5}\Delta\varphi^{4}- 29.18\times 10^{-3}\Delta\varphi^{3}+\ 20.45\times 10^{-1}\Delta\varphi^{2}-64.52\times \Delta\varphi+800.88 \tag{5}\]  
Figure 4 provides a graphic interpretation of the fitted relations compared to the simulated points. It allows a quick access of all parameters of the active region. The electro-refraction effect is more important for holes than for electrons in this range of doping levels, and since the depletion variation is always more important in the less doped region, a larger variation of concentration of holes is obtained when \(N_{A}<N_{D}\). In order to place this variation close to the center of the waveguide (where the maximum of the optical power is located), the position of the p-n junction is shift towards the n-doped side, so we have \(\delta_{\text{PN}}>0\). Finally, the magnitude of the electro-optical effect is directly correlated with the amplitude of the carrier variations, high modulation efficiency implies high doping levels. However the depletion width is then reduced, so the position of the p-n junction will be closer to the center of the waveguide (\(\delta_{\text{PN}}\) decrease). Figure 3: Diagram of optical losses as a function of the phase shift for an absolute bias voltage of 2.4 V at 1310 nm. The purple area includes all the simulated couples of doping level. The dash blue curve is the best case described by the Equation (3).
## 3 Results
### Conserved Motif Analyses of M. longifolia TPSs  
TPS harbors conserved structural features such as the RR(X)8W motif in the N-terminal domain and DDXD and NSE/DTE motifs in the C-terminal domain, which play important roles in the catalytic function of TPS [12; 43]. In our study, conserved motifs were analyzed in _M. longifolia_ TPSs, and significant differentiation was found between different subfamilies (Figure 4). The RR(X)8W motif is conserved in the TPS-b subfamily and plays a role in initiation of the isomerization cyclization reaction [44]. Both the TPS-b and TPS-g subfamilies are angiosperm monoterpene synthases, but the TPS-g proteins do not contain this motif. The TPS-g proteins are required for the biosynthesis of acyclic monoterpenes, which form floral volatile organic compounds (VOCs) [45]. It has been reported that the TPS-a subfamily encodes only sesquiterpene synthase, and the second arginine of the RR(X)8W motif is not conserved [46]. The NSE/DTE motif is conserved in most subfamilies except for the TPS-c subfamily. The RXR motif is conserved in the TPS-a and TPS-b subfamilies. The DDXD motif is the most conserved motif among these TPSs and is conserved in the TPS-a, TPS-b, TPS-e, TPS-f, TPS-g subfamilies but not the TPS-c subfamily (Figure 4). The DDXD motif is involved in the coordination of divalent ions and water molecules and the stabilization of the active site [47; 48]. The TPS-c proteins are not expected to have this domain as they do not cleave the prenyl diphosphate unit; however, they contain a DXDD motif that is critical for the protonation initiate reaction [49].  
Figure 3: Scaffold localization of _TPS_ genes in _M. longifolia_ genome sequence assembly. The _M. longifolia_ genome sequence assembly contains 12 large scaffolds encompassing 462.6 Mb, and the 63 _TPS_ genes are mapped to nine scaffolds based on their localization information. The Y-axis represents the length of the scaffolds. _TPS_ genes of _TPS-a, TPS-b, TPS-c, TPS-e, TPS-f,_ and _TPS-g_ subfamilies are indicated in blue, orange, purple, green, red, and gray fonts, respectively. The tandem duplication and segment duplication _TPS_ genes are indicated in red lines.
## 4 Simulation and Experimental Analysis
### Dataset for Load Forecasting  
The databases used in the paper were obtained from the station in the southwest of China with an AC power voltage of 35 kV. The dataset contains a 3-year power load profile with the sampling time of 15 min. The dataset is a mixed dataset that contains different types of loads such as resident load, commercial load, and industrial load. The dataset was pre-processed in order to separate the relevant data and select the predictive features in the models. Here, we separated the dataset into different types for the load forecasting based on days and season characteristics. The pre-processing of the dataset is shown in Section 3.2, and the forecasting models were trained and tested using a 1-year sample dataset where the first 80% is used for model training and the remaining 20% is used to test the performance of the proposed model.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
###### Abstract
Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Tras-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (\(R^{2}\)) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (\(R^{2}=0.68\)). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics. P 2024 6 2024 2028 1 Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Tras-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal, [EMAIL_ADDRESS]