esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 2 Study Area and Data  
### Study Area  
The study area consisted of two regions with different climatic and environmental characteristics (Figure 1). The first study site (site A) was Sutter County in California State, United States, which is known for its high rice productivity. About 90% of site A is composed of vegetated areas, and almost 40% of the vegetated areas are paddy rice. Temperature ranges from 4.06 \({}^{\circ}\)C in January to 36.11 \({}^{\circ}\)C in July on average, and the annual precipitation is around 558.8 mm. In site A, rice is planted mid-May by dropping rice seeds into flooded paddies from small planes (i.e., direct seeding) and harvested between September and October (Figure 2). The second study site (site B) was Dangjin in South Korea. Site B is one of the highest rice production regions in South Korea. Site B has relatively heterogeneous land covers, with many fragmented patches when compared to site A. The annual mean temperature is 11.4 \({}^{\circ}\)C and the annual precipitation is 1158.7 mm. Since South Korea has a rainy season between June and July caused by the Asian Monsoon, it is not easy to collect cloud-free satellite optical sensor images during this time. Rice planting and harvesting in site B occur at similar time intervals as site A (Figure 2). Unlike site A, manual or machine-based rice transplantation in May is common in site B.
## 4 Channel State Prediction via ML Algorithms
### Data Acquisition and Preprocessing  
Ray tracing for data acquisition was performed with a 28 GHz carrier frequency, and the propagation environment was modeled to obtain propagation paths between Tx and Rx with the number of reflections up to 3. The data were collected from 10 different maps, and in each map, 10 different transmitter's positions for 10 trajectories were modeled. Separate datasets were created for AoA and AoD and for various step size values, namely for \(\Delta s=1,30,60,100\) cm. Each dataset \(\mathcal{D}_{X,\Delta s}\), \(X\in\{\text{AoA},\text{AoD}\}\), was then populated with sequences of size \(S=5\) of consequent frames extracted from longer trajectories. Finally, the size of the datasets for each step size was set to 30,000 observations. Each dataset was split so that 70 % was used for training the model and 30 % for testing. We processed each dataset separately, so in what follows, indices \(X\) and \(\Delta s\) are omitted when no confusion arises.  
To improve prediction performance, we rescaled data in a dataset so that all entries lay in the range \([0,1]\) with 0 corresponding to empty clusters. More formally, let \(\left(\widetilde{V}^{(i)}(s)\right)_{s=1,\ldots,S}\) denote the original (before scaling) \(i\)-th data point of a dataset \(\mathcal{D}\), \(i~{}=~{}1,\ldots,|\mathcal{D}|\), and let \(\widetilde{v}^{(i)}_{m,n}(s)\) denote the corresponding matrix entries, as previously. Then, after preprocessing, the dataset's data points have the entries  
Figure 7: Architecture of the employed DNN.  
\[v_{m,n}^{(i)}(s)=\frac{200-\widehat{v}_{m,n}^{(i)}(s)}{200-v_{\text{min}}(\mathcal{ D})} \tag{10}\]  
for all \(i=1,\ldots,|\mathcal{D}|,s=1,\ldots,S\), \(m=1,\ldots,K_{\phi}\), \(n=1,\ldots,K_{\phi}\), where  
\[v_{\text{min}}(\mathcal{D})=\min_{i,s,m,n}\widehat{v}_{m,n}^{(i)}(s). \tag{11}\]  
We also note that in each data point \(\left(V^{(i)}(s)\right)_{s=1,\ldots,S}\), frame \(V^{(i)}(S)\) represents the label and frames \(\left(V^{(i)}(s)\right)_{s=1,\ldots,S-1}\) the features.
# Anticipatory Classifier System with Average Reward Criterion in Discretized Multi-Step Environments
## 2 Materials and Methods
### Reinforcement Learning and Reward Criterion  
Reinforcement Learning (RL) is a formal framework in which the agent can influence the environment by executing specific actions and receive corresponding feedback (reward) afterwards. Usually, it takes multiple steps to reach the goal, which makes the process much more complicated. In the general form, RL consists of:  
* A discrete set of environment states S,
* A discrete set of available actions \(A\),
* A mapping \(R\) between a particular state \(s\in S\) and action \(a\in A\). The environmental payoff \(r\in R\) describes the expected reward obtained after executing an action in a given state.  
In each trial, the agent perceives the environmental state \(s\). Next, it evaluates all possible actions from \(A\) and executes action \(a\) in the environment. The environment returns a signal \(r\) and next state \(s^{\prime}\) as intermediate feedback.  
The agent's task is to represent the knowledge, using the policy \(\pi\) mapping states to actions, therefore optimizing a long-run measure of reinforcement. There are two popular optimality criteria used in Markov Decision Problems (MDP)--a _discounted reward_ and an _average reward_[24; 25].
## 2 Theory of EMD and EEMD  
### EMD Method  
EMD can decompose the original signal into a finite number of Intrinsic Mode Function (IMF) components and a residual component. The key to this method is that IMFs contain local characteristics belonging to different time scales of the original signal [35].  
IMF need to satisfy the following conditions: the difference between the extreme points number and zero points number being less than one; and at any part of the signal, the mean of upper envelope and lower envelope being equal to zero.  
The steps of the EMD method are as follows:  
Find the upper and lower envelope functions and the mean envelope function of original signal;  
Take the mean envelope function from the original signal function, produce a new signal, and determine whether the new signal satisfies IMF condition;  
If the new signal fits the IMF conditions, it can be identified as an IMF component; if not, repeat the above steps until the IMF component fits the conditions;  
Repeat the above steps several times to calculate the IMF components of various orders and the residual component of the original signal;  
Combine the IMF components with the residual component to obtain the original signal:  
\[x(t)=\sum_{j=1}^{n}C_{j}(t)+r(t)\]  
The EMD method is shown in Figure 1.  
Figure 1: Vibration signal analysis process of EMD.
## 6 Experimental Validation
### Analysis of Experimental Results  
Based on the experimental platform of human lung impedance tomography, the boundary electrode impedance measurement signals were acquired and fed into a PC. Image reconstruction was then conducted using various programmed imaging algorithms in MATLAB software. The resulting reconstructed image is shown in Figure 11.  
Figure 11: Comparative images of various algorithms for image reconstruction based on experimental data.  
Figure 10: Schematic diagram of the experimental setup for human thoracic impedance tomography.  
Evaluation of the image reconstruction results for various imaging algorithms discussed in this paper was performed using the image evaluation metric calculation method mentioned earlier. The evaluation metrics for the image reconstruction results, which were obtained based on experimental measurements, are presented in Table 2 and Figure 12.  
By visually observing the EIT reconstructed images based on experimental measurement data, it can be observed that they exhibit certain similarities with the EIT reconstructed images based on simulated data in terms of overall effects. However, due to the influence of contact impedance and noise signals, the reconstructed images based on experimental data exhibit less detail compared to the simulated reconstructed images, and certain distortions can be observed in some areas. Nevertheless, when comparing different imaging algorithms under the condition of experimental measurement data, it is found that the proposed soft-threshold segmentation method (STS) can effectively identify the pulmonary region of interest in the thoracic cavity. Combining the soft-threshold segmentation method (STS) with the reduced-order Tikhonov algorithm (RO-TK) and utilizing the segmentation-reduced-order-segmentation Tikhonov algorithm (STS-RO-STS-TK), the reconstructed images demonstrate a closer resemblance to the test model in terms of the reconstructed target shape. Furthermore, by calculating the evaluation metrics of the reconstructed images, it is found that the reduced-order Tikhonov algorithm (RO-TK) with image segmentation processing yields superior evaluation metrics compared to the standalone reduced-order Tikhonov algorithm (RO-TK). This further validates the effectiveness of the proposed method.
## 3 Test and Results  
The proposed MILP model is tested in the General Algebraic Modeling System (GAMS) v.24.1.3 with the objective of minimizing total cost. The commercial solver CPLEX v.12 by IBM [41] is used as a solution technique to provide optimal sizing of PV and ESS units by taking into consideration the harmonic current and voltage contents of smart home appliances.
## 2 Materials and Methods
### Unmanned Aerial Vehicle  
A Mavic Air drone (DJI\({}^{\circledR}\), Shenzhen, China) was adopted (see Figure 1). This drone had four propellers. The weight of this drone was only 430 g. At the ready-to-fly condition, this drone had a height and a diagonal size (propeller included) of 6.4 cm and 34.7 cm, respectively. This drone might also fly with a frame surrounding the outer edge of the propeller, which is designed to protect the propellers from contacting solids such as tree leaves (see Figure 1b). The diagonal size of this drone was 38.7 cm when the protector was used. This Mavic Air was tested under with and without protector modes.  
This drone encompasses both visual and satellite positioning systems. It may hover and stabilize itself in the air because of these positioning systems. The horizontal and vertical hovering accuracies were \(\pm\)1.5 m and \(\pm\)0.5 m, respectively, when the satellite positioning system was used. The Mavic Air may fly when the wind was level 5 (8-10.7 m/s) or less. When the wind speed was near this limit, strong wind alarm appeared on the top of the monitor warning the operator to fly carefully.  
To navigate the drone, a remote controller and a smart phone are required. The remote controller has two joysticks, which allow commands of movements in eight directions (up, down, forward, backward, move left, move right, turn clockwise, and turn counterclockwise). A Mi\({}^{\circledR}\) 9 smartphone was attached to the remote controller as the monitor to display the flight information. The DJI GO\({}^{\circledR}\) app on the smartphone was adopted as the flight control software. The location of the drone during the flight was marked as a dot on either an e-map or a satellite ground image on the monitor. In addition to this information, the primary flight information includes horizontal distance to the take-off spot, altitude, remaining power, and intensities of remote and satellite signals. The horizontal distance to  
Figure 1: Mavic Air: (**a**) without protector and (**b**) with protector.  
the takeoff spot and altitude are displayed using digits on the bottom of the monitor. The intensities of remote and satellite signals and remaining power are displayed using icons on the top of the monitor.
## 5 Simulation Results and Discussions
As discussed in Figure 1, the performance gap between the proposed scheme and the conventional scheme C can be observed within the range of \(\Delta_{s}<15\) ppm, because the proposed scheme adopts \(D\)-symbol delay estimation using only two OFDM symbols. Regarding the number of flops, the complexity of the proposed frequency-offset estimation scheme is significantly decreased by 65% and 75.9% in comparison to that used in the existing schemes A and B, respectively, whereas a 94.1% complexity saving over the conventional scheme C is obtained. The uncoded bit error rate (BER) of the conventional and proposed estimation schemes versus SFO values is presented in Figure 3. Since we here focus on SFO estimation, a simple one-tap FEQ is adopted. We consider the situation where the received OFDM symbols are directly compensated in frequency domain once the SFO estimate is obtained every \(N_{o}\) symbol periods. The relative performance of the conventional and proposed methods is shown to be similar to that discussed in Figure 2. It is clear that the conventional schemes can have approximately the same BER performance as the proposed scheme within the range of \(\Delta_{s}<20\) ppm. More importantly, the proposed frequency-offset estimation \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Index \(m\)**} & \multicolumn{2}{c}{**Standard ROBO**} & \multicolumn{2}{c}{**High-Speed ROBO**} \\ \cline{2-5}  & **Subcarrier Distance \(F_{m}\)** & **\# of Subcarrier \(N_{m}\)** & **Subcarrier Distance \(F_{m}\)** & **\# of Subcarrier \(N_{m}\)** \\ \hline
1 & 676 & 54 & 816 & 54 \\
2 & 648 & 41 & 788 & 47 \\
3 & 674 & 6 & 777 & 13 \\
4 & 663 & 13 & 789 & 44 \\
5 & 396 & 44 & 769 & 71 \\
6 & 376 & 58 & 261 & 36 \\
7 & 388 & 13 & 251 & 66 \\
8 & 654 & 36 & 263 & 84 \\
9 & 644 & 79 & 241 & 24 \\
10 & 403 & 71 & 267 & 19 \\
11 & 381 & 43 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Subset configuration for ROBO modes. Figure 1: Mean square error (MSE) comparison between the proposed and conventional estimation schemes versus signal to noise ratio (SNR): (**a**) High-speed robust orthogonal frequency division multiplexing (ROBO) mode (**b**) Standard ROBO mode. scheme shows a strong insensitivity to the amount of SFO, whereas all conventional schemes suffer significant performance degradation at 20 ppm or higher. As predicted, the repeat coding enhances the frequency-offset estimation performance thanks to inherent frequency-diversity gain.
# Performance Prediction and Optimization of Multi-Mirror Combined Solar Dish Collector
## 3 Model Validation and Analysis Optimization  
### Compared to SolTrace  
SolTrace optical simulation software was developed by the National Renewable Energy Laboratory (NREL) and can be used to model, simulate, and analyze solar optical systems [33]. It adopts the principle of the ray tracing method, and considers the distribution of solar energy and optical error distribution. The users can set different solar position parameters and optical property parameters, to establish various concentrating systems such as solar tower, dish, and trough, and analyze the flux density. Therefore, the results calculated by SolTrace are often used to validate the optical model.  
In order to ensure the reliability of the result comparison, the parameters set in SolTrace are consistent with the proposed model, where the system optical error is 2 mrad, the circumsolar ratio is 0.05, and the system focal length is 5 m. The reflected light intensity distribution was obtained by convolving the optical error distribution of 2 mrad with a standard solar intensity distribution function with a circumsolar ratio of 0.05, and was imported into the SolTrace code. By adjusting the radius r of the receiver, the change of the system intercept factor can be obtained. Figure 4 shows the validation results for a 3 m \(\times\) 3 m combined dish system with a circular receiver. On the whole, the presented method is in good agreement with the results of the SolTrace method. From Figure 4a, it can be seen that certain errors exist when calculating the interception factor of the combined dish system, in which the average relative error is 0.51%, and the maximum relative error is 1.14%, which occurs when the receiver radius is 0.02 m. In general, the comparison between the proposed model and SolTrace showed excellent consistency. It can be seen in Figure 4b that the intercept factor of the combined system with different system focal lengths was calculated using two methods. The average relative error was 0.06%, and the maximum one was 0.18% when the system focal length was 12 m.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Heat Loss Rate q** & **The Heat Loss Coefficient \(\xi\)** \\ \hline Receivers with glass window & \(1.67\times 10^{3}\) W/m\({}^{2}\) & 18.177 \\ Receivers without glass window & \(1.63\times 10^{4}\) W/m\({}^{2}\) & 168.25 \\ \hline \end{tabular}
\end{table}
Table 1: Value of the heat loss coefficient \(\xi\)= q/DNI used for two kinds of receiver.  
In addition, from the view of the calculation speed, taking the Intel i7-6700 CPU 3.40 GHz computer as an example, only one single processor was used to calculate the intercept factor of 3 m \(\times\) 3 m combined dish system when the receiver radius was 0.1 m. The result shows that the proposed method took 4.3 s, and the SolTrace code needed 142.1 s. Therefore, the calculation results are reliable and accurate, and the calculation speed is faster.  
Figure 4: Validation of the intercept factor of 3 m \(\times\) 3 m combined dish system with different receiver radius (**a**), and system focal length (**b**).
## 2 Materials and Methods
### Synthesis and Characterisation of MeJA Nanocomites  
MeJA-doped nanoparticles (Nano-MeJA) were synthesised using a simple and eco-friendly protocol, as previously described [27; 28]. Firstly, amorphous calcium phosphate nanoparticles (ACP) were precipitated just after mixing an aqueous solution (2 L) containing 0.2 M CaCl\({}_{2}\cdot\)2H\({}_{2}\)O and 0.2 M Na\({}_{3}\)(C\({}_{6}\)H\({}_{5}\)O\({}_{7}\))\(\cdot\)2H\({}_{2}\)O with a phosphate solution of equal volume containing 0.12 M K\({}_{2}\)HPO\({}_{4}\) and 0.1 M Na\({}_{2}\)CO\({}_{3}\). The mixture was agitated for 5 min and then ACP precipitates were collected and repeatedly washed with ultrapure water by centrifugation (3700 rpm, 15 min, 4 degC). Nano-MeJA was obtained by adding 5 mL of MeJA solution to 40 g of ACP nanoparticles dispersed in 2 L of water. The mixture was kept under magnetic stirring for 24 h and then Nano-MeJA was collected by centrifugation (3700 rpm, 15 min, 4 degC).  
Nanocrystalline apatite (Ap) nanoparticles were synthesised by slightly modifying the batch precipitation protocol described above. Two solutions, (A) CaCl\({}_{2}\cdot\)2H\({}_{2}\)O (0.1 M) and Na\({}_{3}\)(C\({}_{6}\)H\({}_{5}\)O\({}_{7}\))\(\cdot\)2H\({}_{2}\)O (0.4 M) and (B) K\({}_{2}\)HPO\({}_{4}\) (0.12M) and Na\({}_{2}\)CO\({}_{3}\) (0.1 M), were mixed (1:1 \(v/v\), 4 L total) and kept for 24 h at 60 degC. After that, the precipitates were collected and repeatedly washed with ultrapure water by centrifugation (3700 rpm, 15 min, 4 degC). The Ap nanoparticles were then functionalised with MeJA (Ap-MeJA) through the same protocol previously described for ACP nanoparticles.  
The morphology and size of Nano-MeJA and Ap-MeJA were evaluated by transmission electron microscopy (TEM) with a LIBRA 120 PLUS instrument (Carl Zeiss SMT, Centre for Scientific Instrumentation of the University of Granada, CIC-UGR) operating at 120 kV. To this aim, Nano-MeJA and Ap-MeJA nanoparticles were ultrasonically dispersed in ethanol, and some drops of the slurry were deposited on 200 mesh copper grids covered with thin amorphous carbon films. ImageJ software (version 1.48v; NIH, Bethesda, MD, USA) was used to analyse the size of the nanoparticles (\(n=40\)).
## 5 Discussion  
The combination of supervised learning, big data technologies, and high-performance computing has opened up new possibilities for deciphering, measuring, and comprehending data-heavy operations in agricultural environments.  
This study analyzed different supervised learning techniques, specifically LR and SVR, to predict the internal temperature of a greenhouse. The main objective was to identify the best model based on an acceptable temperature range with a 2 \({}^{\circ}\)C hysteresis. The MAE metric was used to measure the accuracy of the models by calculating the distance between the predicted values and the actual temperature points.  
The results show that the MAE metric for both LR models exceeded 2 \({}^{\circ}\)C, with the multiple regression algorithm using PLS performing the worst in winter with a hysteresis of 3.49 \({}^{\circ}\)C. However, the two SVR models remained below 2 \({}^{\circ}\)C, except for the SVR BRF in winter, which had a hysteresis of 2.64 \({}^{\circ}\)C. It was noted that both LR and SVR BRF models struggled to adapt to temperature peaks, resulting in higher MAE values in winter. On the other hand, the SVR Polynomial model proved to be the best in all seasons, with the lowest MAE values. It showed great adaptability over time, and the ability to model data in N  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Season** & **R2** & **RMSE** & **MAE** & **MAPE** \\ \hline Fall & 0.9808 & 1.7431 & 0.2856 & 0.0124 \\ Winter & 0.9984 & 0.6760 & 0.2929 & 0.0123 \\ Spring & 0.9924 & 1.0362 & 0.7444 & 0.0270 \\ Summer & 0.9999 & 0.0549 & 0.04222 & 0.0015 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation results for SVR using polynomial kernel model.  
Figure 10: Comparison of the metrics results for each season and models proposed. \(\mathbb{R}^{2}\) (**a**), RMSE (**b**), MAE (**c**) and MAPE (**d**).  
dimensions and complexity, making it an excellent model for temperature forecasting in greenhouses, especially during summer, with an MAE of 0.04.  
Based on our analysis, the SVR with the polynomial kernel model had the highest forecast accuracy during summer among all the algorithms tested. This model's performance is comparable to existing ones, as shown in Table 7. Some studies presented inferior results, such as [PERSON] et al. [32], using SVM and [PERSON] et al. [34], using SVR with fuzzy logic. [PERSON] et al. [35], compared various models; specifically, the SVR model had lower precision. [PERSON] et al. [33], had similar results using SVR BRF but only evaluated the error using the MSE metric; hence, more information is needed to confirm the model's accuracy.
## 2 Materials and Methods
### Multi-Software Approach for Network Modeling
#### 2.3.3 Model Calibration and Simulation  
Hydraulic model calibration is essential for ensuring simulation accuracy and reliability in real-world conditions [35]. By leveraging this multi-source approach, the study successfully reconstructed a reliable representation of the water distribution system, forming the foundation for hydraulic simulations and network optimization in WaterGEMS. Calibration was conducted to align the simulated results with the observations. The hydraulic model was validated using SCADA telemetry, manual field measurements, and historical water consumption records. The key parameter adjustments included:  
* Modifications to the Hazen-Williams roughness coefficients to match the observed head losses [15; 28; 29].
* Optimization of pump curves to reflect the actual operational performance based on SCADA telemetry [6; 21; 29; 36].
* Demand allocation and demand patterns tuning through iterative simulations and establishing pressure zone boundaries to optimize supply efficiency and reduce leakage risks based on historical usage data [7; 8; 11; 12; 20].
* Incorporation of reservoir storage capacities and elevations to analyze system-wide pressure variations [3; 4; 6; 18].  
The model was run iteratively until the simulated results closely matched the observed data, with calibration accuracy assessed using statistical indicators such as Nash-Sutcliffe efficiency (NSE) and root mean square error (RMSE) [37; 38; 39; 40; 41; 42; 43].