esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Processing Methods
### Multi-Look Processing of the SAR Images  
#### 3.3.1 The Principle of Multi-Look Processing  
Multi-look processing refers to the incoherent superimposition of multiple independent images of the same scene, with the primary objective of suppressing speckle noise [64]. Reference [65] investigated the statistical characteristics of the phase in InSAR images under both single-look and multi-look conditions, and presented an analytical expression for the phase probability density function:  
\[p_{\varphi_{0}}(\varphi_{0})=\frac{\Gamma\Big{(}L+\frac{1}{2}\Big{)}\Big{(}1- \left|\rho\right|^{2}\Big{)}^{L}\beta}{2\sqrt{\pi}\Gamma(L)(1-\beta^{2})^{L+1 /2}}+\frac{\Big{(}1-\left|\rho\right|^{2}\Big{)}^{L}}{2\pi}F\Big{(}L,1;\frac{1 }{2};\beta^{2}\Big{)} \tag{10}\]  
where \(\beta=\rho\cos(\varphi_{0}-\overline{\varphi}_{0})\), \(\overline{\varphi}_{0}\) is the peak value of the phase distribution, and \(F\) is the Gaussian hypergeometric function, expressed as follows:  
\[F(a,b;c;x)=\sum_{m=0}^{\infty}\frac{(a,m)(b,m)}{(c,m)}\frac{x^{m}}{m!} \tag{11}\]  
Figure 4: Synthetic Aperture Radar imaging principle of the Back Projection Algorithm.  
where \((a,0)=1\), \((a,m)=a(a+1)\cdots(a+m-1)\), \(m\) is an integer, \(\rho\) is the coherence coefficient, and \(L\) is the numbers of looks. The coherence coefficient is defined as the degree of similarity in phase and intensity information conveyed by two complex echo signals acquired from the same area. The coherence coefficient, which ranges from 0 to 1, indicates the level of noise in the SAR images, with higher values signifying reduced noise. This coefficient is calculated using the following method:  
\[\rho=\frac{\left|\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}M(i,j)S^{*}(i,j) \right|}{\sqrt{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{n}|M(i,j)|^{2}\cdot\sum \limits_{i=1}^{m}\sum\limits_{j=1}^{n}|S(i,j)|^{2}}} \tag{12}\]  
where M and S are the two complex SAR images, \(i\) and \(j\) are the index values of the current pixel, and \(m\) and \(n\) represent the number of rows and columns in the local window.  
In this distribution, the relationship between the Standard Deviation (STD) of the phase distribution and the coherence coefficient as well as the number of looks can be observed. As illustrated in Figure 5, with the increase in both the coherence coefficient and the number of looks, the STD of the phase gradually decreases. Furthermore, when the coherence coefficient remains constant, an increase in the number of looks effectively reduces the STD of the phase. This indicates that multi-look processing can significantly mitigate the noise present in the phase, with the denoising effect becoming more pronounced as the number of looks increases.
## 4 Aeroelastic Stability Analysis
### Limit-Cycle Analysis  
Steady non-trivial solutions of the bifurcation Equation (27)-a describe periodic oscillations occurring on limit-cycles. By letting \(\dot{a}=0\) and solving the biquadratic algebraic equation, two explicit solutions are found (to within an unessential sign):  
\[a_{\pm}=u\sqrt{-\frac{2}{d_{5}}\left(d_{3}\pm\sqrt{\bigtriangleup(u)}\right)} \tag{30}\]  
where:  
\[\bigtriangleup(u):=d_{3}^{2}-\frac{4d_{5}\left(d_{1}u+d_{0}\right)}{u} \tag{31}\]  
They describe two velocity-dependent branches (denoted by the \(\pm\) index) of the bifurcation diagram \(a_{\pm}=a_{\pm}\left(u\right)\). Depending on \(u\), two, one o zero nontrivial real solutions exist. In order to discuss the number of solutions, here the interesting case in which \(A_{3}<0\) and \(A_{5}>0\) is considered, which entails \(d_{3}>0\) and \(d_{5}<0\). This case is typical, for example, of rectangular cross-sections with a 2:1 aspect ratio (see [29; 31]), according which the qualitative galloping response is plotted in Figure 2.  
Preliminary, it is observed that \(d_{1}u+d_{0}\) is negative in the subcritical \(u<u_{c}\) range and positive in the supercritical \(u>u_{c}\) range. Then, the sign of the discriminant is discussed. Let:  
\[u^{*}:=\frac{4d_{5}d_{0}}{d_{3}^{2}-4d_{5}d_{1}}\equiv u_{c}\frac{1}{1-\frac{ d_{3}^{2}}{4d_{1}d_{5}}} \tag{32}\]  
be the value of velocity at which the discriminant vanishes, i.e. \(\bigtriangleup(u^{*})=0\). Since \(u^{*}<u_{c}\), it follows that:  
* when \(u<u^{*}\), it is \(\bigtriangleup(u)<0\), so that no real solutions \(a_{\pm}\) exist;
* when \(u^{*}<u<u_{c}\), it is \(\bigtriangleup(u)>0\); however, due \(d_{1}u+d_{0}<0\), it is \(\sqrt{\bigtriangleup(u)}<d_{3}\), so that both the \(a_{+}\) and \(a_{-}\) roots are real;
* when \(u>u_{c}\), in addition to \(\bigtriangleup(u)>0\), it is also \(d_{1}u+d_{0}>0\); consequently, \(\sqrt{\bigtriangleup(u)}>d_{3}\), which entails that only \(a_{+}\) is real.  
The two branches coalesce at \(u=u^{*}\), at which \(a_{+}=a_{-}=u^{*}\sqrt{-\frac{2}{d_{5}}d_{3}}\) and a turning point manifests itself.
# Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports
## 3 Case Studies
### Taekwondo  
A taekwondo demonstration was performed; in this study case, the dynamical force at the ankle of an athlete during a taekwondo kick was explored. For this scenario, a personally recorded video was used.  
In this video, a young female athlete performs a series of punches, stances, and kicks. However, we focused on a particular roundhouse kick with her left leg. A total of 342 frames were manually digitized, and 18 points were used per frame digitization from both left and right (R) sides: (LR toes, LR ankles, LR knees, LR hips, LR shoulder, LR elbows, LR wrists, LR fingers, chin, and forehead). The height and weight of the athlete were of 1.45 m and 35 kg respectively. Figure 3 shows the position, acceleration and estimated force at the left ankle of the athlete, during the roundhouse kick, as well as two manually digitized frames of the video. Here, force was obtained by multiplying the mass times the acceleration estimated by SkillSpector (\(\vec{F}=m\vec{a}\)).  
Figure 3: Temporal tracking of position, acceleration and estimated force of the left ankle of the taekwondoist during a high roundhouse kick. The insets at the top of the figure depict the sequence of movements that the taekwondoist was performing; preparing stance, and high kick.  
In Figure 3, it can be observed that, prior to the execution of the kick, the position (\(y\)-axis) remains at ground level, close to 0 m; then, the position constantly increases, representing the upward movement of the kick, reaching 1.6 m in the \(y\)-axis. Regarding the acceleration, similarly, it starts approximately at 0 m/s\({}^{2}\), representing no movement; the acceleration begins to increase after 0.4 s, reaches a peak acceleration of 65 m/s\({}^{2}\) at 0.85 s, and then decreases by the final stages of the kick. By analyzing the Force experimented in the athlete's left ankle, it can be observed that low forces are presented in the initial stage, intermediate forces (500-1000 N) are observed in different moments (0.6 s, 1.18 s), and a maximum force of approximately 2200 N at the 0.85 s mark, where the kick reaches its highest acceleration.  
The obtained maximal force of 2.2 kN for a 35 kg participant during a roundhouse kick lies within a consistent range, when compared to reports of average forces in the foot during roundhouse kicks up to 5.5 kN for 60 kg practitioners [100].
## 2 Materials and Methods
### Data Analysis Methodology
#### 2.5.2 Mask-Region-Based Convolutional Neural Network (Mask R-CNN)
Another critical hyperparameter was the learning rate, which was selected at 0.001, which would allow the model to make gradual adjustments to the pre-trained weights in order to adapt to the new classes used in the annotated dataset. Further techniques such as Dropout and L2 Regularization were used to prevent overfitting and improve generalization. Dropout is a technique through which a random number of neurons present in the fully connected layers are deactivated or \"dropped-out\" during training, ensuring that the model becomes less reliant on particular neurons and develops a better learning of the features, hence preventing overfitting. A dropout rate of 0.25 was selected, meaning that 25% of the neurons were randomly deactivated at each training step. Similarly, L2 Regularization, also known as Weight Decay, adds a penalty for larger weights, promoting a more even distribution of weights and preventing overfitting. Through experimentation, a Regularization Parameter of 0.001 was found to be the most effective for this dataset. The dataset used consists of a total of 1164 images. The dataset was split into the following groups: 174 images showcasing on-field conditions for citrus groves, 30 images showcasing the effect of chromatic illumination on rust/lesions present on isolated citrus leaves, and 960 images of OTC- and Kocide-treated leaves taken in laboratory conditions. In order to test the efficacy of SAM for segmenting the image data taken, 100 images from on-field and laboratory conditions were each chosen and their segmentation masks were generated accurately and manually. The accuracy of the SAM's generated masks was tested by comparing the pixel locations of the generated and human-annotated binary masks. For the main pesticide detection, the 960 images taken in laboratory conditions were split into 768 training images for fine-tuning and 192 validation images.
## 4 Prediction Horizon Length and Convergence  
In this section, we derive sufficient conditions on the prediction horizon length to guarantee a decrease of the costs function and practical stability of the overall system. As the property of initial and recursive feasibility is needed to ensure that the algorithm does not terminate unexpectedly and a feasible solution is found for any time instant \(n\geq 0\), we recap shortly the assumptions and theorems which were already presented in Reference [10]:  
**Assumption 2** (Feasible initial conditions).: _Given a set of robots \([1:P]\), we have that \(z_{p}^{0}\in Z\) and for all \(p,q\in[1:P]\) with \(p\
eq q\) condition \(g_{q,0}^{p}(z_{p}^{0},q(z_{q}^{0}))\geq 0\) holds._  
Assumption 2 states that the scenario is well defined regarding the state and coupling constraints. Based on this assumption, we can conclude the following:  
**Lemma 1** (Initial feasibility).: _Consider a set of robots \([1:P]\) with the model in Equation (1) and the constraints in Equations (2) and (3) satisfying Assumption 1. If Assumption 2 holds, then there exists a solution to the OCP in Equation (16)._  
Concluding that there exists a solution to the initial optimal control problem (initial feasibility), recursive feasibility ensures that there exists a solution for any following time instant \(n>0\):  
**Theorem 1** (Recursive feasibility).: _Suppose a set of robots \([1:P]\) with the underlying model in Equation (1) and the constraints in Equations (2) and (3) satisfying Assumptions 1 and 2. If Algorithm 2 is applied, then the problem is recursively feasible, i.e., for all \(n\in\mathbb{N}_{0}\) and all \(p\in[1:P]\), there exists a solution to OCP in Equation (16)._  
The detailed proofs are presented in Reference [10].
# Kinematic Analysis and Motion Planning of Cable-Driven Rehabilitation Robots
## 3 Kinematics Analysis of Rehabilitation Robot
 \tag{19}\]  
by substituting (4) into (19),  
\[\dot{q}_{i}=-u_{i}^{T}K\dot{\theta}-(\underset{p}{\overset{O}{R}}Rb_{i}\times u _{i})^{T}\omega+u_{i}^{T}\dot{a}_{i} \tag{20}\]  
Therefore, the relationship between the joint angle, moving velocity of the cable winder and velocity of the cable is  
\[\dot{q}=-\Xi_{k}\dot{\Psi}+\Xi_{a}\dot{A} \tag{21}\]  
where  
\[\Xi_{k}=\left[ \begin{array}{cccc}{(u_{1}}^{T}K)^{T}&{(u_{2}}^{T}K)^{T}&{(u_ {3}}^{T}K)^{T}&\cdots&{(u_{m}}^{T}K)^{T}\\ \underset{p}{\overset{O}{R}}Rb_{1}\times u_{1}&\underset{p}{\overset{O}{R}} Rb_{2}\times u_{2}&\underset{p}{\overset{O}{R}}Rb_{3}\times u_{3}&\cdots& \underset{p}{\overset{O}{R}}Rb_{m}\times u_{m}\end{array} \right]^{T}\in R^{m \times 5},\]  
which is the first-order influence coefficient matrix of cable motion on hip motion. \[\Xi_{a}=\left[ \begin{array}{cccc}{[u_{1}}^{T}]&{[u_{2}}^{T}]&{[u_{3}}^{T}]& \cdots&{[u_{m}}^{T}]\end{array} \right]^{T} \tag{22}\]  
\[\dot{A}=\left[ \begin{array}{c}{[\dot{a}_{1}]}\\ {[\dot{a}_{2}]}\\ {[\dot{a}_{3}]}\\ \cdots\\ {[\dot{a}_{m}]}\end{array} \right]\cdots\dot{\Psi}=\left[ \begin{array}{c}\dot {\theta}\\ \omega\end{array} \right] \tag{23}\]  
By deriving the time at both ends of Equation (21), the kinematic acceleration of the cable-driven rehabilitation robot can be obtained as  
\[\ddot{q}=-\Xi_{k}\ddot{\Psi}-\dot{\Xi}_{k}\dot{\Psi}+\Xi_{a}\ddot{A}+\dot{\Xi }_{a}\dot{A} \tag{24}\]  
where  
\[\dot{\Xi}_{k}=\left[ \begin{array}{cccc}{((\omega\times u_{1})^{T}K+u_{1}}^{ T}{(\phi_{t}}^{T}V))^{T}&\cdots&{((\omega\times u_{m})^{T}K+u_{m}}^{T}{(\phi_{t}}^{T}V) )^{T}\\ \omega\times(\underset{p}{\overset{O}{R}}Rb_{1})\times u_{1}+(\underset{p}{ \overset{O}{R}}Rb_{1})\times(\omega\times u_{1})&\cdots&\omega\times( \underset{p}{\overset{O}{R}}Rb_{m})\times u_{m}+(\underset{p}{\overset{O}{R}} Rb_{m})\times(\omega\times u_{m})\end{array} \right]^{T} \tag{25}\]  
\[\dot{\Xi}_{a}=\left[ \begin{array}{cccc}{[\omega\times u_{1}]}^{T}&{[\omega \times u_{2}]}^{T}&{[\omega\times u_{3}]}^{T}&\cdots&{[\omega\times u_{m}]}^{T} \end{array} \right]^{T}\cdots\ddot{\Psi}=\left[ \begin{array}{c}\ddot{\bar{ \varphi}}\\ \varepsilon\end{array} \right] \tag{26}\]  
In summary, the kinematics of the rehabilitation robot are obtained through Equations (15), (21) and (24). The change in cable length is related to the position and orientation of the end lower leg, that is, the angle of the human hip joint. In the range of motion of the hip joint, reasonably plan the joint angle and inversely solve the orientation of the end lower leg through the analysis of kinematics theory, then the corresponding relationship between the angle change of human hip joint and the change of each cable can be obtained. Therefore, according to the angle relationship of the hip joint movement, the length change and motion law of the cable in the system can be obtained.
# Groundwater Mixing Process Identification in Deep Mines Based on Hydrogeochemical Property Analysis
## 2 Study Area
### Hydrogeological Setting  
There are four kinds of aquifer systems in the study area, including Cenozoic unconsolidated porous aquifers with the average thickness of 11-22 m, hydraulic conductivity of 0.48-4.60 m/day, and specific capacity of 0.04-0.94 L/(s\(\cdot\)m); Permian sandstone fractured aquifers with the average thickness of 3-50 m, hydraulic conductivity of 0.038-22.1 m/day, and specific capacity of 0.002-0.125 L/(s\(\cdot\)m); Carboniferous limestone fractured aquifers in Taiyuan Formations with an average thickness of about 53.87 m, hydraulic conductivity of 0.15-36.4 m/day, and specific capacity of 0.0034-11.4 L/(s\(\cdot\)m); and Ordovician limestone karst fractured aquifers with an average thickness of about 120 m, hydraulic conductivity of 1.77 m/day, and specific capacity of 0.633 L/(s\(\cdot\)m). Between the aquifers, there are many aquifuge layers, with the lithology of mudstone or siltstone. Four collapse columns are found in the study area. This study focuses on the groundwater hydraulic connection between Carboniferous limestone fractured aquifers in Taiyuan Formations and lower Ordovician limestone karst fractured aquifers through the collapse columns. Due to the needs of coal mining, drainage, and decreasing pressure required in Carboniferous Taiyuan limestone karst-fissure aquifer, which leads to different flow directions in Carboniferous Taiyuan limestone karst-fissure aquifer systems in the study area, as shown in Figure 2.  
Figure 1: Generalized stratigraphy of the study area.  
Figure 2: Groundwater flow field of Carboniferous Taiyuan limestone karst-fissure aquifer systems in the study area. There are three main flow directions: northeast, southeast, and southwest, in this groundwater flow field.
## 5 Experimental Verification of FFvI Controller
The ability to change the stiffness during operation is shown in Figure 22. The module is first positioned to \(\alpha_{xref}=10^{\circ}\), \(\alpha_{yref}=5^{\circ}\) at a requested aggregate pressure of 3 bar. Then, the aggregate pressure is changed step-wise to 6 bar, 9 bar, 12 bar and 15 bar, respectively. A change in aggregate pressure corresponds to a change in the stiffness of the system. As seen from Figure 22, the pressure and therefore stiffness can be changed online. This change \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sinus Signal** & \(\mathbf{\pi_{xref}}\) & \(\mathbf{\pi_{yref}}\) \\ \hline amplitude [\({}^{\circ}\)] & 10 & 10 \\ period [s] & 2\(\pi\) & 2\(\pi\) \\ phase [rad] & 0 & \(\pi/2\) \\ bias [\({}^{\circ}\)] & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sinus reference signal parameters. Figure 21: Comparison PID controller vs. FFvI controller—input sinus wave. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha_{x}-8^{\circ}\to 8^{\circ}\) & FFvI & PID & \(\alpha_{y}10^{\circ}\rightarrow-10^{\circ}\) & FFvI & PID \\ \hline rise time [s] & 0.42 & 0.4 & rise time [s] & 2.74 & 4.13 \\ settling time 5\% [s] & 2.03 & 5.42 & settling time 5\% [s] & 3.43 & 4.5 \\ first maximum [s] & 0.54 & 2.15 & first maximum [s] & 0.65 & 7.45 \\ overshoot [\%] & 21.1 & 10.3 & overshoot [\%] & 3 & 3 \\ \hline \(\alpha_{x}8^{\circ}\rightarrow-8^{\circ}\) & FFvI & PID & \(\alpha_{y}-10^{\circ}\to 10^{\circ}\) & FFvI & PID \\ \hline rise time [s] & 0.5 & 1.23 & rise time [s] & 0.53 & 3 \\ settling time 5\% [s] & 4.46 & 5\(\circ\) & settling time 5\% [s] & 2.73 & 8.04 \\ first maximum [s] & 0.57 & 3.4 & first maximum [s] & 0.64 & 7.5 \\ overshoot [\%] & 8 & 21.3 & overshoot [\%] & 5 & 9.6 \\ \hline performance criteria & FFvI & PID & & & \\ \hline IAE & 40.9 & 79.3 & & & \\ ISE & 461.9 & 758.2 & & & \\ ITAE & 481.2 & 954.1 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison. produces a momentous destabilization of the system resulting in slight position loss. The maximum error for our test was at the transition between aggregate pressure 12 bar and 15 bar with maximum error for \(\alpha_{xref}=1.3^{\circ}\), \(\alpha_{yref}=1.65^{\circ}\). This can be again, attributed to nonsynchronous pressure change between the bellows and possible measurement errors in the original feed-forward controller input data. Therefore, instead of a sharp aggregate pressure step a smooth aggregate pressure transition should decrease this issue.
## 1 Introduction and Motivation
### Contributions  
The main contributions of this work are as follow:  
* A novel scheme for energy efficient hierarchical clustering to be used with cooperative MIMO communication for a wide area sensor coverage with higher network lifetimes in WSNs. The algorithm is distributed and scales to large networks.
* An energy efficient cooperative clusterhead selection algorithm which minimizes energy expenditure.
* Performance analysis of our scheme against prevalent schemes for cooperative MIMO. Performance is analyzed on the basis of extended network life time, energy conservation and minimization of number of hops from clusterhead (CH) to central coordinating station (CCS).
## 6 Strategies for Sensor Failure Detection and Identification  
Automatic sensor failure detection and automatic identification can be done using three strategies: network-level strategy, homogeneous strategy and heterogeneous strategy.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 4 Results  
The received signals were demodulated by MATLAB. The demodulation depends on the signal energy, and the threshold value for making decisions for each symbol was set by trial and error  
\begin{table}
\begin{tabular}{c c} \hline
**Frequency (kHz)** & **Amplitude (mV)** \\ \hline
460 & 285.8 \\
**470** & **280.5** \\
480 & 283.0 \\
**490** & **279.3** \\
500 & 264.3 \\
510 & 225.1 \\ \hline \end{tabular}
\end{table}
Table 1: Ultrasonic transducer amplitude test for the frequency selection.  
Figure 8: Frequency spectrum of the ultrasonic transducer.  
Figure 9: (**a**) Baseband digital signal at 500 bps; (**b**) Transmitted BFSK signal; (**c**) Received BFSK signal in water environment.  
The processing is shown in Figure 10. The BER was calculated in increments of 0.5 cm from 0.5 to 5 cm, as shown in Figure 11. Owing to the data storage limitations of the oscilloscope, the BFSK experiment was limited to a 100 bit stream, and the OOK and BPSK modulation experiments were limited to a 1000 bit stream. Over the three demodulation schemes, BFSK had the lowest BER. This indicates that an ultrasonic wave has better transmission in water than in air. Therefore, we could use the transducer inside the body. The BER for BFSK modulation remains the same as the distance increases. The stability of the BER for BFSK modulation results from the smaller binary stream storage used in the experiment. OOK and BPSK modulation schemes have a small BER for short distances, and then the trend becomes irregular. When ultrasonic waves propagate in air or water, the energy of the received signal is attenuated as the distance increases. Even though the results do not exhibit any increasing trend with distance, the BERs of the three modulation schemes are acceptable.
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Algorithm Performance Based on Geological Data Types and Optimal Parameters  
We present a detailed summary of the accuracy rates obtained for each type of geological data. These results are based on the best parameters identified and optimized to maximize the performance of the three classifiers tested. The analysis highlights variations in accuracy based on characteristics of geological data and the ability of algorithms to exploit this information, providing a clear comparison of their relative effectiveness.  
Table A1, presented in Appendix A, provides the performances of the Gradient Boosting Classifier (GBC) and Random Forest (RF) using three sets of parameters: PCAP, DGP, and DOLIP. Geographic parameters (PGs) produce the best scores overall, as evidenced by the 90% obtained for alluvium with RF and the 65% for pyroclastics with XGBoost. Random Forest stands out as the best-performing algorithm in the majority of cases.  
Figure 11 presents the data from Table A1 in histogram form.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & **Time (ms)** & **Accuracy (\%)** & **Precision (\%)** & **MSE** & **F-Score (\%)** & **Recall (\%)** & **MCC (\%)** \\ \hline Gradient Boosting & 11.12 & 30.03 & 27.32 & 6.65 & 27.32 & 30.03 & 12.16 \\ XGBoost & 4.37 & 26.28 & 25.99 & 6.84 & 25.99 & 26.28 & 09.42 \\ Random Forest & 2.45 & 26.28 & 25.80 & 6.85 & 25.80 & 26.28 & 09.31 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of algorithm performance on Data from OR Spectrometric Parameters.  
Figure 10: (**A**) Confusion matrix and (**B**) ROC curve for the Gradient Boosting model on Data from OR Spectrometric Parameters.  
We will then combine the best parameters, which include:  
* Data from OLI spectrometric parameters: These data allowed the Random Forest algorithm to achieve the best accuracy (52.59%) and higher MCC (30.32%) while having a low execution time (2.60 ms).
* Data from PCA parameters: They allowed Random Forest to achieve an accuracy of 41.98% and an MCC of 28.23%, with an execution time of 3.14 ms, thus showing good performance on data transformed by dimensional reduction.
* Data from geographic parameters: Although slightly inferior in performance compared to the OLI spectrometric parameters, they allowed Random Forest to achieve a competitive precision of 49.14% and an MCC of 37.39%, with a time execution time of 2.58 ms, confirming their relevance for classification tasks.