esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Ferulic acid (FeraA, 99%), cinnamic acid (CinA, \(\geq\)99%), caffe acid (CaffA, \(\geq\)98%), chlorogenic acid (ChlorA, \(\geq\)95%), 2,3-dihydroxybenzoic acid (DHB, 99%), 1-vinylimidazole (VIM, \(\geq\)99%), divinylbenzene (DVB, 80%), 2,2\({}^{\prime}\)-azobis(2-amidinopropane) dihydro-ride (AAPH, 97%), trifluoroacetic acid (TFA, 99%), diethyl ether (Et\({}_{2}\)O, \(\geq\)99.9%), ethylene glycol dimethacrylate (EGDMA, 98%), and sodium phosphate monobasic monohydrate (NaH\({}_{2}\)PO\({}_{4}\)\({}_{2}\)O, \(\geq\)99.0%), as well as basic, activated aluminum oxide (Al\({}_{2}\)O\({}_{3}\), approx. 150 mesh, standard grade) were all purchased from Sigma Aldrich (St. Louis, MI, USA), an affiliate of Merck (Darmstadt, Germany). Sodium phosphate dibasic dehydrate (Na\({}_{2}\)HPO\({}_{4}\)\({}_{2}\)H\({}_{2}\)O, \(\geq\)99.5%) and hydrochloric acid (HCl, 37% in H\({}_{2}\)O) was obtained from Merck. The 1,6-dibromohexane (98%) was obtained from Acros Organics affiliate to Thermo Fisher Scientific (Waltham, MA, USA). The solvents methanol (MeOH, LC-MS grade), acetonitrile (ACN, LC-MS grade) and ethyl acetate (EtOAc, 99.5%) were purchased from Th. Geyer GmbH & Co. KG (Renningen, Germany) and 1-decanol (for synthesis) and 1-propanol (99.8%) from VWR(tm) (Radnor, PA, USA), and affiliate of Avantor(tm) (Radnor, PA, USA). Sodium hydroxide (NaOH, \(>\)99%), ammonium hydroxide solution (30% NH\({}_{3}\) in water) and formic acid (FA, \(\geq\)98%) were obtained from Carl Roth(r) (Karlsruhe, Germany). Empty SPE cartridges (1 mL) and matching polyethylene frits with 20 \(\upmu\)m porosity were obtained from Supelco(r) (Bellefonte, PA, USA). Pre-packed Oasis(r) MAX cartridges (30 mg) were purchased from WatersTM (Milford, MA, USA).  
Water was obtained from a Milli-Q water purification system from Merck Millipore (Burlington, MA, USA). The phenolic acid standard stock solution mixture contained 500 mg L\({}^{-1}\) of each analyte, including FerA, CinA, CaffA, ChlorA and DHB in 20 vol.% MeOH in water. Working standards were daily prepared by diluting the standard stock solution with 20 vol.% MeOH in water. All stock solutions were stored at 4 \({}^{\circ}\)C. Three phosphate buffers (0.1 M) with a pH 6.6, 7.0 and 7.7 were prepared in 5 vol.% MeOH in water by dissolving certain quantities of Na\({}_{2}\)HPO\({}_{4}\) 2H\({}_{2}\)O and NaH\({}_{2}\)PO\({}_{4}\) - H\({}_{2}\)O. The pH was adjusted by adding HCl or NaOH solution.  
The DVB was extracted three times with 10% (\(w/v\)) NaOH solution and distilled under vacuum. The EGDMA was extracted with activated basic Al\({}_{2}\)O\({}_{3}\) prior its use.
# Towards Accurate Children's Arabic Handwriting Recognition via Deep Learning
## 4 Results and Discussion
### Computational Complexity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Model**} & \multicolumn{2}{c}{**CNN**} & \multicolumn{3}{c}{**VGG-16**} \\ \hline
**Character** & **Class** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline \(\downarrow\) & **0** & 1.00 & 1.00 & 1.00 & 1.00 & 0.96 & 0.98 \\ \(\searrow\) & **1** & 0.97 & 1.00 & 0.98 & 0.88 & 0.98 & 0.93 \\ \(\searrow\) & **2** & 0.99 & 0.99 & 0.99 & 0.95 & 0.85 & 0.90 \\ \(\searrow\) & **3** & 0.99 & 0.99 & 0.99 & 0.96 & 0.97 & 0.97 \\ \(\searrow\) & **4** & 1.00 & 0.84 & 0.91 & 0.97 & 0.97 & 0.97 \\ \(\searrow\) & **5** & 0.86 & 1.00 & 0.93 & 0.95 & 0.95 & 0.95 \\ \(\
earrow\) & **6** & 0.99 & 1.00 & 0.99 & 0.96 & 0.96 \\ \(\searrow\) & **7** & 0.99 & 1.00 & 0.99 & 0.97 & 0.96 & 0.96 \\ \(\searrow\) & **8** & 0.99 & 0.97 & 0.98 & 0.92 & 0.96 & 0.94 \\ \(\searrow\) & **9** & 0.97 & 0.99 & 0.98 & 0.96 & 0.90 & 0.93 \\ \(\searrow\) & **10** & 0.98 & 1.00 & 0.99 & 0.98 & 0.97 & 0.97 \\ \(\searrow\) & **11** & 1.00 & 0.99 & 1.00 & 0.99 & 1.00 & 1.00 \\ \(\searrow\) & **12** & 1.00 & 1.00 & 1.00 & 0.93 & 0.91 & 0.92 \\ \(\searrow\) & **13** & 0.98 & 1.00 & 0.99 & 0.87 & 0.93 & 0.90 \\ \(\searrow\) & **14** & 1.00 & 0.99 & 1.00 & 0.93 & 0.93 & 0.93 \\ \(\searrow\) & **15** & 0.98 & 0.99 & 0.98 & 0.98 & 0.93 & 0.96 \\ \(\searrow\) & **16** & 1.00 & 0.97 & 0.98 & 0.96 & 0.97 & 0.97 \\ \(\searrow\) & **17** & 1.00 & 0.99 & 0.99 & 0.96 & 0.76 & 0.85 \\ \(\searrow\) & **18** & 0.99 & 0.99 & 0.99 & 0.94 & 0.95 & 0.95 \\ \(\searrow\) & **19** & 0.98 & 0.99 & 0.99 & 0.96 & 0.92 & 0.94 \\ \(\searrow\) & **20** & 0.99 & 0.98 & 0.99 & 0.99 & 0.96 & 0.97 \\ \(\searrow\) & **21** & 1.00 & 1.00 & 1.00 & 0.81 & 1.00 & 0.89 \\ \(\searrow\) & **22** & 1.00 & 0.98 & 0.99 & 1.00 & 0.88 & 0.94 \\ \(\
earrow\) & **23** & 1.00 & 1.00 & 1.00 & 0.97 & 0.95 & 0.96 \\ \(\searrow\) & **24** & 1.00 & 0.99 & 1.00 & 0.93 & 0.98 & 0.96 \\ \(\searrow\) & **25** & 1.00 & 0.99 & 0.99 & 0.95 & 0.97 & 0.96 \\ \(\searrow\) & **26** & 0.99 & 1.00 & 1.00 & 0.85 & 0.98 & 0.91 \\ \(\searrow\) & **27** & 1.00 & 1.00 & 1.00 & 0.93 & 0.93 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results and comparison of the CNN and VGG16 models on the AHCD dataset.
## 3 Results
### Validation: Water Temperature  
The water temperature modeling results were validated with the data observed daily at thirteen stations in the model domain. Box plots in Figure 5 were drawn at each station with three kinds of data: the daily observations, daily model results extracted following the observation methods (Table A2), and raw results of the model. The reason for presenting the daily model results and raw results separately is that the interval of model outputs was 12 min. This short interval of outputs allowed us to observe diurnal or even hourly variation  
Figure 4: Time-series comparison of the surface elevations at the **(a)** Hiroshima and **(b)** Osaka tidal stations. The air pressure (blue line) and wind (vectors) data were extracted at the location of the Osaka tidal station from the MSM dataset of the JMA.  
in temperature. However, direct comparison with the daily observations was not possible. To address this, we extracted the model results at the same time as the observations.  
The water temperature range varied depending on the region and depth. The temperatures observed at W3 showed a relatively low and narrow range from 23.04 to 26.13 \({}^{\circ}\)C. The daily model results obtained at the same stations also depicted a similar temperature pattern ranging from 22.51 to 25.64 \({}^{\circ}\)C. At W10, both the observations and model outputs showed slightly low temperatures, despite the observations being collected at the surface layer as the other observations were. Overall, the calculated water temperatures matched the observations well, except at locations W2, W4, W5, W6, and W13. The model underestimated the surface temperatures at W4, W5, W6 and W13, while at W2, the water temperature was overestimated. There was uncertainty regarding the observational depth, as described in Section 2.4. If the observations were conducted at a layer deeper than the surface, the observed temperature might be lower than the calculated value.  
The time series of observed and simulated temperatures in Figure 13 reasonably depict short-period variations and seasonal trends. The observations (black lines in Figure 13) collected over a year were obtained to show that the model could reproduce the seasonal temperature trend at each station. The observed data illustrate that the water temperature increased until the end of August and then started to decrease from the beginning of September. The model period was made relatively short to allow all seasons to be validated. However, the model results could sufficiently reproduce part of the seasonal trends. The model could also clearly generate the fortnight-scale temperature variations at W2 and W13. Even though the model overestimated (underestimated) the temperatures at W2 (W13), as mentioned above, the temperature variation was effectively reproduced. The raw model results (blue lines in Figure 13) including daily temperature ranges depict more detailed seawater temperature oscillations than the observations.
## 2 Material & Methods
### Mathematical Analysis of the Data
#### 2.6.2 Curve Fitting with a Sigmoid Function  
The recognition percentages versus the SNR can be classically represented by a sigmoid curve regression (Figure 2).  
Three parameters were considered on this curve:  
* the SNR corresponding to 50% of the maximum recognition denoted here by \(x_{50\%}\);
* the \"slope\" (SNR interval, given in dB, between 25 and 75% of the maximum recognition) which is denoted here by \(\Delta_{25-75\%}\);
* the top asymptote \(y_{\max}\) showing the maximum recognition score.  
These analytical values are represented on the sigmoid curve. The minimum recognition is 0% (measured for SNR = \(-3\) dB). Thus, the sigmoid equation is  
\[y=\frac{a}{1+e^{-b(x-c)}}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Channel**} & \multicolumn{2}{c}{**Centre**} & \multirow{2}{*}{**Analysis**} & \multicolumn{2}{c}{**Synthesis**} & \multicolumn{2}{c}{**Carrier**} \\  & & **Frequency (Hz)** & & & & \\ \hline  & & **Lower** & **Higher** & **Lower** & **Higher** & \\  & & **Cut-off** & **Cut-off** & **Cut-off** & **Cut-off** & \\ \hline
1 & 250 & 190 & 310 & 225 & 275 & sine \\
2 & 375 & 315 & 435 & 350 & 400 & sine \\
3 & 500 & 440 & 560 & 475 & 525 & noise band \\
4 & 625 & 565 & 685 & 60  
where  
* \(y\) is the recognition percentage,
* \(x\) is the SNR,
* \(a\) is \(y_{\max}\),
* \(c\) is \(x_{50\%}\), and
* \(b\) is linked to the slope: \(b=2.2/\Delta_{25\text{-}75\%}=>\Delta_{25\text{-}75\%}=2.2/b\).
# A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping
## 3 Proposed Method
### Exploration Reward  
The exploration reward is introduced to encourage the vehicle to explore unsurveyed areas that is far away from previous transects. By doing so, the collected data can be properly interpolated to obtain a reasonable map when the preset coverage rate is low or when the mission is terminated unexepctedly. Meanwhile, this strategy can avoid overlap and gather information efficiently at the early stage. The exploration reward \(E^{j}\) is defined as the minimum distance to the completed parallel transects:  
\[E^{j}=\left\{\begin{array}{ll}\min(y^{j}-\mathbf{W}_{y}^{2}),&\text{zonal transects}\\ \min(x^{j}-\mathbf{W}_{x}^{m}),&\text{meridional transects}\end{array}\right. \tag{3}\]  
where \(\mathbf{W}_{y}^{2}\) is all the past zonal transects and \(\mathbf{W}_{x}^{m}\) is all the past meridional transects.  
For comparison, in a lawnmower path, the vehicle will navigate in a nearby transect once the current transect is finished, while in a spiral path, the vehicle will choose a far transect [16]. As a result, lawnmower type paths focus on sequential individual sections and spiral type paths precedently explore the outline of a region [20]. Take curve fitting as an analogy, lawnmower methods get the data points from one end to another, spiral methods get the data points from two ends to the center, in comparison, \(E^{j}\) tends to get the points from the ends and the middle. The latter operation could provide sufficient data to bound the uncertainty when interpolating the bathymetric over the workspace.
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 7 Positioning Simulation Results
### Example 2
(**a**) \(N=1\), \(\overline{N}_{\text{BS}}=5\); (**b**) \(N=8\), \(\overline{N}_{\text{BS}}=5\); (**c**) \(N=8\), \(\overline{N}_{\text{BS}}=10\). It can be noted that in this example, the tail end of the error CDF of OLS-MLE is lower than in any other result in this paper. For example, with \(\overline{N}_{\text{BS}}=5\) and \(N=1\) the 90 th percentile error is 352 m. This shows that occasionally, with a small number of contacted BSs (and large \(\sigma/\alpha\)), MLE positioning can cause larger errors when the PL model does not fit the true distribution. In this example, the OLS fitted model overestimates the large link distances more than in Examples 1 and 3. In this example, because the link distance overestimation is so large, it could be beneficial to include more censored BSs in the case of OLS-MLE. For example, in case of \(\overline{N}_{\text{BS}}=5\) and \(N=1\), the OLS-MLE 90 th percentile error is 246 m with a 2300 m radius (\(\overline{PL}(d)-2\cdot\sigma=PL^{*}\)) and an average of 760 censored BSs around the OLS-OTP result. Apparently, the MLE positioning may help only if the overestimation in the link distances is not excessively large.
# Research of Adaptive Extended Kalman Filter-Based SOC Estimator for Frequency Regulation ESS
###### Abstract
There are various methods for estimating the SOC, including the Coulomb counting method, open-circuit voltage (OCV)-based SOC estimation, model-based SOC estimation, and SOC estimation using machine learning. For example, Various methods of SOC estimation were introduced in detail by [PERSON] et al. [21; 22]. An equivalent circuit model (ECM) is a good way to analyze the internal resistance characteristics and time constants by electrically modeling the battery. However, the model parameters vary depending on the cathode material of the battery, and the parameters change with temperature and battery aging, which requires a great number of experimentation and analysis. Analysis of ECM models with different chemical materials and temperature was performed by [PERSON] et al. [23]. ECM is a vital element for SOC estimation in various techniques such as Luenberger observer, extended Kalman filter, adaptive extended Kalman filter (AEKF), unscented Kalman filter (UKF), particle filter (PF), sliding mode observer (SMO), etc. Model parameters are a very important factor in these techniques, and model parameters may be change with battery aging [21]. At this time, the accuracy of SOC estimation methods can be greatly degraded, so research for estimating capacity and model parameters as well as deterioration is being carried out. The dual extended Kalman filter is typical, and the SOC estimating filter and the capacity estimating filter work together to improve SOC estimation performance, even when battery aging proceeds. Recently, a lot of researches have been conducted to estimate the battery state through various techniques of machine learning. These data driven techniques are methods of analyzing the correlation coefficient from the voltage, current, temperature, deterioration information, etc., acquired from the battery and using the data for computer learning to predict the state of the battery. For example, a study was conducted to estimate the SOC of a battery in Urban Dynamometer Driving Schedule (UDDS) operating patterns using neural network (NN) by [PERSON] et al. [24], and a study was conducted to estimate the SOC by designing a deep neural network (DNN) and learning the data by [PERSON] et al. [25]. A support vector machine (SVM), which is one of the machine learning regression techniques, was used to estimate the SOC of an EV battery in Reference [26]. In this paper, the extended Kalman filter (EKF) algorithm, which is one of the model-based SOC estimation methods, is used to estimate the SOC. When significant noise is generated in the operation data of the BESS outputted through the FR ESS algorithm, it is verified that the SOC can be accurately estimated using the EKF algorithm. The SOC estimation performance of the adaptive extended Kalman filter (AEKF), which automatically updates the noise covariance in the algorithm according to the input data, is compared and verified.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Cobalt-Doped Nickel Oxide Films  
[PERSON] and co-workers [165] reported on the transparent Co-doped nickel oxide films [2015] deposited on glass substrates. The XRD studies confirmed polycrystalline, the cubic phase, and the most intense peaks corresponding to the (111) and (222) planes. No significant move in the Raman spectrum could be seen and the structure was not changed during the cobalt (Co)-doping process.
## 3 Distributed Target Tracking Systems
### Estimation Refinement with ANN
#### 3.3.3 Covariance Intersection (CI)  
For obtaining global estimates, we adopt covariance intersection to perform data fusion. The CI method of [4] provides the best estimate given the information available, which takes a convex combination of mean and covariance estimates that are represented in information space. Since these typical runs are independent, the general form is  
\[P_{cc}^{-1} = \omega_{1}P_{a_{1}a_{1}}^{-1}+\cdots+\omega_{n}P_{a_{n}a_{n}}^{-1} \tag{3}\] \[P_{cc}^{-1}c = \omega_{1}P_{a_{1}a_{1}}^{-1}a_{1}+\cdots+\omega_{n}P_{a_{n}a_{n} }^{-1}a_{n} \tag{4}\]  
where \(\sum_{i=1}^{n}\omega_{i}=1\), \(n>1\), \(a_{i}\) is the estimate of the mean from available information, \(P_{a_{i}a_{i}}\) is the estimate of the variance from available information, \(c\) is the new estimate of the mean, and \(P_{cc}\) is the new estimate of the variance.
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 3 Results and Discussion
### Evaluation Metrics
#### 3.2.3 Ablation Experiment  
To further verify the effectiveness of AMMFNet, the ablation experiments were designed to verify the effectiveness of each part of the model. The experimental results are shown in Table 9. When the RGB image is used as the input reference model, the vegetation indices can reflect more sufficient physiological information of apple plants than the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Input** & **Subsetace/\%** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **FI/\%** \\ \hline \multirow{3}{*}{ResNet18} & RGB & 83.99 & 78.26 & 78.53 & 79.92 & 79.22 \\  & MS & 82.02 & 76.34 & 77.69 & 77.59 & 77.64 \\  & VIs & 87.58 & 80.71 & 80.46 & 81.77 & 81.11 \\  & **AMMFNet** & **92.92** & **85.43** & **85.89** & **86.54** & **86.21** \\ \hline \multirow{3}{*}{ResNet34} & RGB & 82.38 & 77.64 & 78.06 & 79.83 & 78.93 \\  & MS & 83.10 & 77.37 & 78.04 & 79.38 & 78.70 \\  & VIs & 86.20 & 80.15 & 80.95 & 81.93 & 81.44 \\  & **AMMFNet** & **87.02** & **81.88** & **82.71** & **83.99** & **83.34** \\ \hline \multirow{3}{*}{ResNet50} & RGB & 83.83 & 78.56 & 79.56 & 81.04 & 80.29 \\  & MS & 85.80 & 79.51 & 80.10 & 81.20 & 80.72 \\ \cline{1-1}  & VIs & 86.40 & 80.28 & 80.97 & 82.09 & 81.53 \\ \cline{1-1}  & **AMMFNet** & **87.38** & **81.80** & **82.61** & **83.65** & **83.13** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of classification results between AMMFNet and single-source input model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation Metrics** & **Aphid** & **Alternaria** & **Mosaic** & **Brown Spot** & **Gray Spot** \\ \hline Accuracy/\% & 97.54 & 99.01 & **100.0** & 97.41 & 98.47 \\ Recall/\% & 79.63 & 78.75 & 66.77 & **95.64** & 86.09 \\ Precision/\% & 94.02 & 95.91 & **100.0** & 97.49 & 85.64 \\ \hline \hline \end{tabular} The bold content is the column optimal value.  
\end{table}
Table 8: Single-class apple disease and pest classification results of AMMFNet.  
RGB image. Therefore, the subset accuracy rate predicted by the 22 vegetation indices is 3.59% higher than the RGB single-source image input result, and the improvement is the largest. And the use of the RF-ML algorithm can further improve the accuracy of model recognition. And RGB images can compensate for the local texture details that vegetation indices lack. Therefore, fusing these two types of images improves the subset accuracy by 6.21% compared to the baseline model. In addition, for two kinds of multi-source images, using data-level feature fusion can improve the quality of feature representation, while using feature-level fusion will cause the lack of detailed features of the diseases and insect pests, which will reduce the accuracy of the model, as shown in Table 10. After feature fusion, channel attention is used to re-weight the features, which further improves the classification accuracy by 2.72%. Ultimately, AMMFNet achieves a total improvement of 8.93% in subset accuracy compared to the baseline model.
## 5 Passive Dual-Band Phase-Gradient Metasurfaces
### All-Dielectric Unit Cells  
All-dielectric unit cells are explored in [85; 86; 87; 88; 89] for various beam steering and phase correction applications. Dual-frequency or multi-wavelength meta-molecules are presented in [83]. A hexagonal unit cell operating at two different frequencies and providing independent phase control is designed using four nano-posts placed at the center of the hexagonal cell. Three nanoposts are used to control the cell response at the higher frequency, while one nanopost is used to control the response at the lower frequency. The diameter of the nano-posts is varied to achieve phase variation, keeping their length constant. Using a holey substrate, a dual-band all-dielectric unit cell is also reported in [90] for dual-band phase correction in TAs. However, the structure may not be suitable for designing PGMs. A crucial obstacle exists in creating all-dielectric dual-frequency metasurfaces. While several designs necessitate more significant physical footprints, others involve complex machining operations, presenting substantial challenges in the manufacturing process. In addition, the design of all-dielectric unit cells to achieve dual-frequency band capabilities may raise problems regarding feasibility.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 1 Introduction
### EEG Signals into Frequency Sub-Bands: Comprehensive Categorization  
The EEG signals are represented as the linear combination of many frequencies' sub-bands in which each of them bears a unique set of brain activity. Normally, delta waves refer to unconsciousness and deep sleep. Theta waves are associated with meditation, creativity, and insomnia, while alpha waves relate to tranquility, relaxation, and reduced concentration. Beta waves are affixed to anxiety, concentration, and active, busy, or deep-thinking mental activities. Gamma waves are attributed to higher-level mental activities that involve more consciousness or perception [12; 13]. This classification induces structure and reproducibility when employed in clinical and research settings, and it enables healthcare providers to analyze brains and diagnose disorders more efficiently. A visualization of each wave type can be seen in Figure 1[12; 13; 14].
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 7 Issues and Challenges of Node Clustering in CR-WSNs
### Busy Receiver Problem  
The busy receiver problem may occur in CR-WSNs because of the multichannel network. Transmitters cannot find their receivers on a channel because the receivers are busy on other channels (either transmitting or receiving). Thus, the busy receiver problem increases the dropped packet rate and wastes channel bandwidth.
## 2 Materials and Methods
### Satellite Data and Processing
#### 2.3.2 Vegetation Indices  
We assessed the mangrove vegetation cover by way of four optical-related vegetation indices that are widely used in land cover characterisation [14; 78; 81; 82], i.e., NDVI (Normalised Difference Vegetation Index) [83], NDMI (Normalised Difference MoistureIndex) [15], EVI (Enhanced Vegetation Index) [84], and SAVI (Soil-Adjusted Vegetation Index) [85]. The S1 images were first pre-processed with a speckle filter (Lee refined) at a window size of 7 \(\times\) 7 pixels [81; 86]. A ratio channel (VV/VH) from the backscattering was generated. Lastly, the mean value of all S1 images was employed. This mean function makes the S1 composite less susceptible to variation in image acquisition [76; 77]. With this additional dataset, each S1 mosaic had two bands and one index, while S2, L7, and L8 had 10, 6, and 7 spectral bands, respectively, and each was composed of four vegetation indices (Table 1). Table 2 specifies the vegetation indices formulas used in the analysis. Figure 5 describes the flowchart of the image processing procedure.