esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Accuracy Evaluation
### Calibration Results Analysis  
Ten camera calibration tests were performed, manually and automatically, using the suggested algorithm. Each test was characterized by a reprojection error, and the results of comparing the manual and automatic calibration modes are presented in Figure 4a.  
To calibrate the wheels, two sets of experiments were performed. The first one-used the calibration matrix that was initially the best (with the least reprojection error) and then the automatic calibration at each stage. As a reference calibration, the same value of the calibration coefficient was used, which made it possible to achieve a minimum deviation from a straight line. The deviation value is measured in meters, and the positive direction of the measurement is directed to the right side along the movement of the robot. The deviation of the first test is presented in Figure 4b, and the results of the second test can be found in Figure 4c.  
The tests found that the suggested solution, on average, shows that the results are not much worse, than the classical manual solution when calibrating the camera, as well as when calibrating the wheels with a well known calibrated camera. However, when calibrating both the wheels and the camera, the wheel calibration can be significantly affected by the camera calibration effect. As a result of testing, a clear relationship was found between the reprojection error and the straight line deviation.  
Figure 3: Measurement of wheel calibration error after driving along a two-meter line.
# Hybrid Flow Shop Scheduling Problems Using Improved Fireworks Algorithm for Permutation
###### Abstract  
Prior studies are lacking which address permutation flow shop scheduling problems and hybrid flow shop scheduling problems together to help firms find the optimized scheduling strategy. The permutation flow shop scheduling problem and hybrid flow shop scheduling problems are important production scheduling types, which widely exist in industrial production fields. This study aimed to acquire the best scheduling strategy for making production plans. An improved fireworks algorithm is proposed to minimize the makespan in the proposed strategies. The proposed improved fireworks algorithm is compared with the fireworks algorithm, and the improvement strategies include the following: (1) A nonlinear radius is introduced and the minimum explosion amplitude is checked to avoid the waste of optimal fireworks; (2) The original Gaussian mutation operator is replaced by a hybrid operator that combines Cauchy and Gaussian mutation to improve the search ability; and (3) An elite group selection strategy is adopted to reduce the computing costs. Two instances from the permutation flow shop scheduling problem and hybrid flow shop scheduling problems were used to evaluate the improved fireworks algorithm's performance, and the computational results demonstrate the improved fireworks algorithm's superiority.  
Keywords:permutation flow shop scheduling problem; hybrid flow shop scheduling problem; improved fireworks algorithm; scheduling; makespan +
Footnote †: journal: Applied Sciences
## 5 Performance Evaluation  
### IDBN Performance Evaluation  
For the heavy-duty robot training dataset, there is a large number of redundant data in the NSL-KDD dataset, and there are also existing problems, such as data imbalance, which can't fully reflect the ability of identifying unknown samples [42]. Therefore, it is necessary to preprocess the dataset. Table 11 shows the details of dataset preprocessing, and the training set and test set are obtained.  
In this experiment, 20%, 40%, 60%, and 100% of the optimized NSL-KDD dataset were extracted and trained according to the SVM, BP, IDBN, and DBN algorithms. Finally, we obtained the accuracies of each algorithm with different data volumes and the corresponding training time.  
As shown in Figure 8 and Table 2, it is found that the proposed IDBN has significantly improved the identification ability and detection efficiency of cyber-domain intrusion compared with the traditional DBN. As for the BP algorithm and SVM algorithm, IDBN has a prominent advantage in accuracy on the basis of similar time. In addition, we find that a high detection accuracy can be obtained by using a 40% of the NSL-KDD dataset, and there is also a great advantage in terms of efficiency. Therefore, IDBN security checks for the heavy-duty robot use the 40% of NSL-KDD dataset as the training set, not only improving the detection capability of the cyber-domain intrusion, but also having a good efficiency.
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 5 Experiments
### Experimental Setup  
The proposed system contains adjustable parameters, including filter size, the number of filters, the number of layers, and the number of hidden units (for LSTM), which may be tuned optimally to reduce the prediction error. In contrast, these differences in parameters achieved complexity in the proposed CRNN architecture. We performed 12 experiments with different parameters to study what combination of variables would achieve the highest accuracy for the system. Table 4 provides a snapshot of the configurations and results of a selection of the experiments undertaken. GTCC and MFCC were generated with MATLAB for each audio file in the database using Hamming windows with a window length of 30 msec and a window step of 20 msec. This resulted in [26 50 1] matrices, which were fed into the architecture as inputs. An NVIDIA GeForce RTX 2080 Ti graphics processing unit with 11 GB RAM was employed for training, with a batch size of 128 samples for 15 epochs and a learning rate of 0.0009, using [PERSON]'s adaptive gradient descent method as the optimizer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**\# Experiment**} & \multirow{2}{*}{\begin{tabular}{c} **\# Convolution** \\ **Layers** \\ \end{tabular} } & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{\begin{tabular}{c} **\# Hidden Units** \\ **in LSTM Layer** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Training** \\ **Accuracy (\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Validation** \\ **Accuracy (\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Testing** \\ **Accuracy (\%)** \\ \end{tabular} } & \multirow{2}{*}{**Time Taken**} \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{5} & Filter Size = 5 & & & & \\ \cline{2-5}  & & \# Filters \(\simeq\) 12 & & & & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{5} & Filter Size = 10 & & & & \\ \cline{2-5}  & & \# Filters \(\simeq\) 32 & & & & \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{3} & Filter Size = 10 & & & & \\ \cline{2-5}  & & \# Filters1 = 32 & & & & \\ \cline{2-5}  & & Filter Size = 5 & & & & \\ \cline{2-5}  & & \# Filters2 = 12 & & & & \\ \cline{2-5}  & & Filter Size = 15 & & & & \\ \cline{2-5}  & & \# Filters3 = 64 & & & & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{5} & Filter Size = 4 & & & & \\ \cline{2-5}  & & Filter Size = 6 & & & & \\ \cline{2-5}  & & \# Filters2 = 12 & & & & \\ \cline{2-5}  & & Filter Size = 8 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Snapshot of selected experiment configurations and results.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 2 Materials and Methods
### Control Model of the System
#### 2.3.3 Pure Pursuit Model  
The pure pursuit model is a method based on the geometric principle, in which the center of the rear axis of the spraying robot is the tangent point and the longitudinal direction of the body is the tangent line, and the spraying robot travels along the arc by controlling the steering angle. As shown in Figure 8, the spraying robot tracks the path AB, the current point C (\(x_{c}\), \(y_{c}\)) and the forward-looking distance \(l_{d}\) are known, and the point G (\(x_{g}\), \(y_{g}\)) with the distance \(l_{d}\) from the current position on the path is obtained. G is the next tracked point. R is the radius of the driving arc of the rear axle at a given steering angle, \(\alpha\) is the angle between the body and the next tracked point. \(v_{c}\) is the linear velocity at the center of the rear axle, \(\omega_{c}\) is the steering angular velocity around the OC.  
\[R=\frac{l_{d}}{2\sin\alpha} \tag{18}\]  
The steering angular velocity can be expressed as follows:  
\[\omega_{c}=\frac{V_{c}}{R}=2V_{c}\sin\alpha/l_{d} \tag{19}\]  
Figure 8: The pure pursuit model of the spraying robot.  
Figure 7: The kinematic model of the spraying robot.  
In each control cycle, the next point to be tracked is updated, the desired steering angle of the spraying robot in that control cycle is calculated and further the spraying robot path tracking is achieved by controlling the steering angular velocity \(\omega_{c}\).
# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML
### Use Cases of the TinyML
#### 2.7.3 Face Detection  
A crucial application case for the smart IoT environment is face recognition. Long-range face detection without batteries, which is readily accessible, may surely enhance human face tracking in a number of applications. In [48], a time-of-flight sensor and a low-power camera module (such as the HM01B0) are paired with an ARM Cortex-M4F MCU (e.g., VL53L1X). The LoRa transceiver module is used to interact with a distant system (e.g., SX1262). The BQ25504 chip controls the system power. Initially, the flight time unit is engaged automatically (e.g., 1 Hz).The MCU is programmed to wake up if it notices any motion. Images captured by the camera are 320 \(\times\) 320 pixels. There are VGA and QVGA options available with this configuration. At 30 frames per second, this uses just 1.1 mW of power. Two layers of convolution are utilized in this mode, along with two levels of convolution-MaxPool. Three dense layers help in facial detection in the end. The CelebA dataset is used in the article to train a face classifier on the MCU using the TensorFlow  
Figure 7: Wireless EMG + accelerometer acquisition device [30].  
framework. In the wireless camera detection planning mode, it achieves 97% accuracy. The practical IoT-based face detection system is depicted in Figure 8. For the COVID-19 to be introduced, face mask identification has been crucial. The creation of face mask recognition systems that are TinyML aware has been tried by a number of research teams. In [130], for example, a convolution neural network that performs facial recognition utilizing 496 KB of buffer memory is built using an ARM Cortex-M7 MCU running at 480 MHz. The model, which was trained using TensorFlow, requires 138 KB of RAM at 30 frames per second during post-quantization. In [131], a visual face device that detects coughs is displayed. When a cough is detected, the face visor may be switched on and off.
## 3 Results
### The Fungal Data Set
\begin{table}
\begin{tabular}{c c c c} \hline
**Genus** & **Fungal Name** & **References** & **Environment** \\ \hline \multirow{2}{*}{_Emericella_} & _Emericella ruler_ & [31] & C-NHE \\ \cline{2-3}  & _Emericella_ sp. & [49; 75] & C-HE, C-NHE \\ \hline _Engyodontinu_ & _Engyodontinu_ sp. & [45; 69] & C-HE, NC-HE, C-NHE \\ \hline \multirow{2}{*}{_Epicoccum_} & _Epicoccum nigrum_ & [20; 28; 31; 32; 46; 49; 56; 58] & C-HE, NC-HE, C-NHE, O-SPE \\ \cline{2-3}  & _Epicoccum_ sp. & [27; 65; 87; 90] & C-HE, NC-HE, C-NHE, O-SPE \\ \hline \multirow{4}{*}{_Eurotium_} & _Eurotium halophilicum_ & [104] & C-NHE \\ \cline{2-3}  & _Eurotium herbariorum_ & [68] & C-HE \\ \cline{2-3}  & _Eurotium_ sp. & [47; 65; 89; 92] & C-HE, NC-HE, C-NHE \\ \hline \multirow{4}{*}{_Exophiliala_} & _Exophiliala angulospora_ & [42; 98] & C-HE \\ \cline{2-3}  & _Exophiliala moilliae_ & [85] & C-HE \\ \cline{2-3}  & _Exophiliala_ sp. & [23; 42] & C-HE \\ \hline \multirow{2}{*}{_Fomitopsis_} & _Fomitopsis vinosa_ & [63] & C-NHE \\ \hline \multirow{4}{*}{_Fusarium chlamydosporum_} & [51] & C-HE \\ \cline{2-3}  & _Fusarium culmorum_ & [31] & C-NHE \\ \cline{2-3}  & _Fusarium equiseti_ & [53] & SC-NHE \\ \cline{2-3}  & _Fusarium fujukuroi_ & [29; 30; 32; 74] & C-HE, NC-HE \\ \cline{2-3}  & _Fusarium oxysporum_ & [23; 28; 31; 32; 38; 46; 49; 58; 67; 73; 105] & C-HE, NC-HE, C-NHE \\ \cline{2-3}  & _Fusarium proliferation_ & [77] & C-NHE \\ \cline{2-3}  & _Fusarium sportrichioides_ & [38] & C-HE \\ \cline{2-3}  & _Fusarium_ sp. & [10; 23; 24; 27; 31; 39; 43; 47; 62; 64; 82; 91; 94; 106] & C-HE, NC-HE, C-NHE \\ \hline \multirow{2}{*}{_Fuscororia_} & _Fusoporia_ sp. & [66] & C-NHE \\ \hline \multirow{2}{*}{_Fusidium_} & _Fusidium viride_ & [48] & C-NHE \\ \hline \multirow{2}{*}{_Ganoderma_} & _Ganoderma_ sp. & [66] & C-NHE \\ \hline \multirow{4}{*}{_Gliomastix_} & _Gliomastix tumulicola_ & [23; 25] & C-HE \\  & (syn. _Acemonium tumulicola_) & [23; 97] & C-HE \\ \cline{2-3}  & _Gliomastix_ sp. & [58; 97] & C-HE \\ \hline \multirow{2}{*}{_Gloiothele_} & _Gloiothele_ sp. & [66] & C-NHE \\ \hline _Helminthosporium_ & _Helminthosporium_ sp. & [65; 75; 82] & C-NHE, O-SPE \\ \hline \multirow{4}{*}{_Humicola_} & _Humicola fuscoatra_ & [49] & C-NHE \\ \cline{2-3}  & _Humicola udagaguvae_ & [38] & C-HE \\ \cline{2-3}  & _Humicola sp._ & [31] & C-NHE \\ \hline \multirow{2}{*}{_Hyphodontia_} & _Hyplodontia alutaria_ & [66] & C-NHE \\ \cline{2-3}  & _Hyphodontia sp._ & [66] & C-NHE \\ \hline _Hyphodontiella_ & _Hyphodontiella_ sp. & [66] & C-NHE \\ \hline _Hypholoma_ & _Hypholoma_ sp. & [66] & C-NHE \\ \hline _Idriella_ & _Idriella_ sp. & [58] & C-HE \\ \hline \multirow{2}{*}{_Kendrickiella_} & _Kendrickiella physcomyces_ & [23; 24; 107] & C-HE \\ \hline \multirow{2}{*}{_Kernia_} & _Kernia geniculotricha_ & [67] & SC-NHE \\ \cline{2-3}  & _Kernia hipprocepida_ & [67] & SC-NHE \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## The Formulation for the Virtual Initial Condition
### The Virtual Initial Conditions for Arbitrary Force
Then the stability is dependent on the \(\beta\), \(\gamma\). ```
Step 1.0 Initial calculation
1.1 DFT for \(g_{1}(t)\) to calculate coefficients \(A_{\text{j}}\) and \(B_{j}\) by Equations (36) and (37). 1.2 \(u_{0}^{t}=u_{0}+u_{0}^{v21}+u_{0}^{v3}\), \(v_{0}^{t}=v_{0}+v_{0}^{v1}+v_{0}^{v3}\)
1.3 \(\hat{u}_{0}^{t}=\frac{\beta-(t-1\eta)k\hat{u}_{0}}{\beta\alpha}\)
1.4 select \(\Delta t,\ \beta,\ \gamma\)
1.5 \(\hat{k}=(1+i\eta)k+\frac{1}{\beta\Delta t}m\)
2 Step 2.0 Calculations for each time step
2.1 \(\hat{f}_{n+1}=f_{n+1}+[\frac{1}{\beta\Delta t}u_{n}^{t}+\frac{1}{\beta\Delta t }\hat{u}_{n}^{t}+(\frac{1}{2\beta}-1)\hat{u}_{n}^{t}]m\)
2.2 \(u_{n+1}=\hat{f}_{n+1}/k\)
2.3 \(\hat{u}_{n+1}=\frac{\gamma}{\beta\Delta t}(u_{n+1}-u_{n})+(1-\frac{\gamma}{ \beta})\hat{u}_{n}+(1-\frac{\gamma}{\beta})\hat{u}_{n}\Delta t\)
2.4 \(u_{n+1}^{v1}=-i\frac{\text{Re}(v_{n+1})+a\omega\text{Re}(u_{n+1})}{a\omega}v_{ n+1}^{v1}=i[\frac{\text{Re}(v_{n+1})+a\omega\text{Re}(u_{n+1}))+\omega b \text{Re}(u_{n+1})]}\)
2.5 \(\hat{u}_{n+1}^{t}=\text{Re}(u_{n+1}+u_{0}^{v1}+u_{0}^{v3}\), \(v_{n+1}^{t}=\text{Re}(v_{n+1})+v_{n+1}^{v1}+v_{0}^{v3}\)
2.6 \(\hat{u}_{n+1}^{t}=\frac{f_{n+1}-(1+i\eta)k\hat{u}_{n+1}}{m}\)
```  
**Algorithm 1** Newmark method with the virtual initial condition for arbitrary force. From Equation (14), the correct theoretical solution is  
\[g_{1}(t)=\frac{A_{0}}{2}+\sum_{j=1}^{N/2-1}\Big{(}A_{j}\cos\theta_{j}t+B_{j} \sin\theta_{j}t\Big{)}+\frac{A_{N/2}}{2}\cos\theta_{N/2}t, \tag{35}\]2  
However, the wrong solution by Equation (12) with only the real initial conditions is  
\[g_{1}(t)=\frac{A_{0}}{2}+\sum_{j=1}^{N/2-1}\Big{(}A_{j}\cos\theta_{j}t+B_{j} \sin\theta_{j}t\Big{)}+\frac{A_{N/2}}{2}\cos\theta_{N/2}t, \tag{35}\]3  
In this example, the problem was solved by the Newmark method with \(\gamma=\frac{1}{2}\), \(\beta=\frac{1}{4}\). The numerical results obtained using \(\Delta t=0.01s\) are compared with the theoretical solution, as shown in Figure 1, in which NMV is numerical result obtained by algorithm 1, NMR is numerical result obtained by the Newmark method with real-valued initial conditions. It can be seen from Figure 1 that if directly using the real initial conditions \(u_{0}=1\) and \(v_{0}=0\) by the Newmark method, the displacement blows up to give meaningless results which converge to the wrong solution of Equation (48). However, the displacement with initial conditions \(u_{0}^{t}\) and \(v_{0}^{t}\) by Equation (16) converges to the correct theoretical solution of Equation (47). Therefore, the Newmark method is stable, but the solution is unstable for the real initial conditions. After applying the proposed virtual initial condition, the divergent term is eliminated to make the solution stable.
## 3 Results
### Fatty Acids Characterization  
Field and Tank cultivated samples were richer in total fatty acids than the cultivated samples under the controlled laboratory conditions. Table 2 shows the Tank T was richer in saturated fatty acids (SFA, 2186.69 \(\mathrm{\SIUnitSymbolMicro g^{-1}}\) dw), while aquarium C was the poorest (1257.64 \(\mathrm{\SIUnitSymbolMicro g^{-1}}\) dw). Furthermore, tank T was also the highest in monounsaturated fatty acids (MUFA, 305.74 \(\mathrm{\SIUnitSymbolMicro g^{-1}}\) dw) and polyunsaturated fatty  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Days of Culture** & **Weight Average (g)** \\ \hline
00 & 1.75 \(\pm\) 0.59 \\
07 & 2.28 \(\pm\) 0.16 \\
14 & 2.38 \(\pm\) 0.11 \\
21 & 2.78 \(\pm\) 0.24 \\
28 & 3.32 \(\pm\) 0.41 \\
35 & 4.00 \(\pm\) 0.92 \\
42 & 4.25 \(\pm\) 1.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average weight of _C. jubata_ macroalgae during cultivation under controlled conditions. The values indicate the average weight of the macro algae \(\pm\) standard deviation.  
acids (PUFA, 137.96 \(\upmu\)g\(\cdot\)g\({}^{-1}\) dw) concentration respectively, whereas aquarium B presented the lowest concentration on MUFA (266.03 \(\upmu\)g\({}^{-1}\) dw) and PUFA (20.79 \(\upmu\)g\({}^{-1}\) dw). However, the samples of the field had the highest concentration of highly unsaturated fatty acids (HUFA, 2602.41 \(\upmu\)g\(\cdot\)g\({}^{-1}\) dw) and the lowest was aquarium B (506.73 \(\upmu\)g\({}^{-1}\) dw). Referring to the total fatty acid composition, field samples presented the highest value (5289.05 \(\upmu\)g\(\cdot\)g\({}^{-1}\) dw) while aquarium B showed the lowest (2105.43 \(\upmu\)g\({}^{-1}\) dw) content (Figure 3).  
Figure 3: Sum of fatty acid group and total fatty acid expressed in \(\upmu\)g\(\cdot\)g\({}^{-1}\) dw from samples of _C. jubata_ obtained from cultivated in indoor balloons (A, B and C), cultivated in outdoor tank (T) and collected from the field (F). The same upper letters (a, b, c) mean there are no differences between the treatments (cultivation type). Data as mean value, distribution and variance; \(n=3\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**A**} & \multicolumn{3}{c}{**B**} & \multicolumn{3}{c}{**C**} & \multicolumn{3}{c}{**F**} & \multicolumn{3}{c}{**T**} \\ \hline C14.0 & 244.83 & \(\pm\) & 34.54 & 209.08 & \(\pm\) & 51.07 & 233.02 & \(\pm\) & 11.43 & 343.22 & \(\pm\) & 9.74 & 353.59 & \(\pm\) & 20.52 \\ C16.0 & 1013.99 & \(\pm\) & 144.06 & 1052.6 & \(\pm\) & 217.31 & 964.31 & \(\pm\) & 38.88 & 1707.55 & \(\pm\) & 44.05 & 1723.46 & \(\pm\) & 100.96 \\ C18.0 & 116.05 & \(\pm\) & 24.74 & 47.53 & \(\pm\) & 15.68 & 60.31 & \(\pm\) & 6.42 & 53.99 & \(\pm\) & 4.71 & 109.64 & \(\pm\) & 9.11 \\ \(\sum\) SFA & 1374.88 & \(\pm\) & 200.39 & 1311.87 & \(\pm\) & 283.84 & 1257.64 & \(\pm\) & 40.01 & 2104.76 & \(\pm\) & 54.10 & 2186.69 & \(\pm\) & 113.37 \\ C16.1 & 122.91 & \(\pm\) & 22.26 & 39.32 & \(\pm\) & 2.68 & 96.65 & \(\pm\) & 12.43 & 102.91 & \(\pm\) & 4.26 & 1169.69 & \(\pm\) & 11.89 \\ C18.1 &
## 4 Conclusions  
The soils from the Almaden and Guadalmez synclines demonstrated very high local background concentrations of some elements of metallogenic interest: Hg (2.9 mg kg\({}^{-1}\)), Pb (56.5 mg kg\({}^{-1}\)), and Sb (4.1 mg kg\({}^{-1}\)) on average.  
Figure 6: Spatial distribution of factors. A geological scheme is provided as a reference with the main mines indicated, separated by elements or genetic type.  
Cluster analysis differentiated two groups of elements, the most distinct of which was composed of ore-deposit-forming elements (Hg, Sb, Pb, and As), which appeared together with EC, SOM, and S in the Almaden syncline and together with P and Ag in the Guadalmez syncline.  
Factor Analysis differentiated three factors of geogenic elements: F1 (Al, Be, Co, Cr, Cu, Fe, Ni, P, Sc, V, and Zn); F2 (Al, K, Mg, Hg, La, Ni, and P); and F3 (Ca, Ba, Co, Mn, and Sc); and two factors related to ore deposits, F4 (Pb and Zn) and F5 (As and Sb).  
The non-existence of a factor for Hg ore deposits can be explained by the predominantly atmospheric origin of the element fixed in the surface soil and the deleting of outliers of natural and anthropogenic origins.  
The mineralization factors (F4 and F5) showed areas favorable to the discovery of new mineral deposits, partially confirmed in F5 maps, to the SE of the main Sb mines of the Guadalmez syncline.  
Some of these areas coincided with discovered mineralized zones, specifically in the area SE of a derelict Sb mine, confirming the usefulness of these datasets and statistical tools in areas with recent mining activity.  
A combination of cluster analysis and FA was useful in detecting prospective areas in datasets from mining districts, although some limitations were detected for the prospection of Hg, due to its ability to transform to a gaseous state and then be transferred to the soil.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122211351/s1](https://www.mdpi.com/article/10.3390/app122211351/s1)]([https://www.mdpi.com/article/10.3390/app122211351/s1](https://www.mdpi.com/article/10.3390/app122211351/s1)), Table S1: Pedological parameters and elemental composition of samples, major (in %) and elements (in mg kg\({}^{-1}\)).  
Data curation, J.I.B., S.L., S.R., A.C.G.-V. and E.G.-O.; Formal analysis, J.M.E.; Funding acquisition, J.I.B., Investigation, J.I.B., S.R. and A.C.G.-V.; Methodology, S.L. and J.M.E.; Project administration, P.H.; Resources, A.C.G.-V.; Writing--original draft, J.I.B., S.L., J.M.E., E.G.-O. and P.H. All authors have read and agreed to the published version of the manuscript.  
This study has been financed by project PCI2019-103779 (Spanish Ministry of Science and Education), and it is also a contribution to the AUREOLE Project, funded by the ERA-MIN2 European Project.  
This study does not requite ethical approval.  
Raw analytical data are available for other researchers after justified requirement to the corresponding authors.  
The authors declare no conflict of interest.