esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Biomethane Potential  
The BMP batches were incubated for 105 days, showing the generally slow kinetics of the anaerobic degradation. The biogas yield is shown in Figure 2, compared to the inoculum baseline. Due to different technical issues, three samples were not quantified. The baseline of the graph is the methane yield of the inoculum at 731 \(\pm\) 60 mL after 105 days. The variation in the inoculum BMP results in a margin of error of approximately 16%, which affects all results in addition to the variations in the BMP samples. Due to differences in the obtained yields, both batches containing co-substrate are depicted separately. The highest yields were achieved by mixing the OMW with a slurry of organic market waste and by depolymerohized OMW mixed with retenate, both reaching 350 NL methane per kg of organic dry matter. The volatile solids content was reduced by 6.4 g and 7.8 g VS, respectively. Initial inhibition was observed for depolymerohized OMW and raw OMW with co-substrate, with lower biomethane production rates than the inoculum for 20 and 65 days, respectively. This is unexpected and likely due to the higher-than-expected and fluctuating biogas production of the inoculum, combined with the error margin of the measurement method. Raw OMW, raw OMW with co-substrate, and depolymerohyzed OMW with retenate showed retarded biogas production, with a second steep increase in methane production between days 30 and 45. This is likely linked to an adapted microbiome in the samples after extensive incubation [15]. Using digestate from an anaerobic digester that is fed with olive mill wastewater is an option to reduce this lag phase. [PERSON] et al. indicated that adding biochar is a viable option to stabilize the incubation phase [16]. By reducing the effect of the degradation of the inoculum on the overall result, increasing the VS ratio between the inoculum and substrate can increase the accuracy of the experiment.  
On day 6, the incubation temperature for all samples was 8 degrees higher than during the rest of the period due to a technical problem. It is noticeable that on this day, there is a decrease in methane production for most substrates, which is likely connected to the higher pressure in the vessel due to the higher temperature. The gas yield was adapted to normal conditions (273 K and 1.013 bar).  
Figure 1: Biological oxygen demand over 30 days of OMW-derived substrates.
## 3 The Proposed Solution
### Fuzzy Logic Controller
Finally, as shown in Table 3, the output value is determined through nine fuzzy rules based on the IF-THEN statement of classic programming languages. For instance, considering the rule \(7\), if the scheduled time is High and the consumer feedback is Low, then the new scheduled time will be Medium. The outputs of the inference mechanism are fuzzy output variables. The FLC must convert its internal fuzzy output variables into crisp values, through the defuzzification process, so that the actual system can use these variables. Defuzzification can be performed in several ways. In this paper, the centroid of area (COA) method [32] has been chosen. In this method, the centroid of each membership function for each rule is first evaluated. The final output is then calculated as the average of the individual centroid weighted by the membership values as follows:  
\[new\_scheduled\_time=\frac{\sum_{i=1}^{n}Out_{i}*C_{i}}{\sum_{i=1}^{n}C_{i}} \tag{2}\]  
where \(Out_{i}\) is the output of rule base \(i\) and \(C_{i}\) is the center of the output membership function. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rule** & **Consumer decision** & **Scheduled time** & **Feedback value** \\ \hline
1 & Accept & Low & Low \\
2 & Accept & Medium & Medium \\
3 & Accept & High & High \\
4 & Deny & Low & Medium \\
5 & Deny & Medium & High \\
6 & Deny & High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Inference rules for consumer feedback.
## 2 Methods
### Soil Moisture Collections and Analyses  
Sentinel-1 transmits C-band signals, which do not penetrate deeply into the soil column, and instead interact primarily with the soil surface. Because C-band signals respond strongly to moisture content, we calculated the correlation coefficients of four Sentinel-1 backscatter indices (VV, VH, VV/VH, and RVI) relative to measured values of soil volumetric water content (VWC) collected using a handheld sensor. Handheld collections were taken with an Acclima SDI-12 sensor in conjunction with Sentinel-1 overpasses at each of the fields on five dates in 2021 (March: 2 nd, 15 th, 26 th, and April: 19 th and 29 th). Seven surface level measurements at 2 cm depth were taken near each plot, with six being taken within a 0.5 m radius of the center of the plot and the seventh in the center.
## 4 Results
### Risk Analysis and Mitigation  
During the development of the NeuroPredict platform, risk-related tests were carried out to identify shortcomings in the gathering, processing, and transmission of data under controlled laboratory conditions. These assessments were conducted by simulating potential points of malfunction in the platform workflow and observing how it behaves in specific load and atypical scenarios.  
To reduce the risks associated with sensor data integrity, session-level metadata, including sensor type, timestamp intervals, and preprocessing version, were included in all data-gathering modules. This configuration allowed for the finding of missing data segments or discrepancies caused by transitory disconnections or deviations between gathering channels. Internal logging throughout preprocessing demonstrated that most signal anomalies may be detected using vector consistency checks and temporal gap evaluation.  
Two edge-layer mitigating approaches hired for guaranteeing data consistency in the context of intermittent connectivity problems were local buffering and backup transmission approaches. Abnormal input structures were identified via preprocessing pipelines, enabling exclusion or reprocessing prior to the inference stage.  
By applying the classifier to simulated movements that have been observed to be highly susceptible to false positives, for instance unexpected transitions or high-impact non-fall activities, the fall detection functionality's performance sensitivity was assessed. Inaccurate classifications were identified and analyzed to facilitate future threshold and activity segmentation algorithm improvements. The development dashboard, which allowed each classification outcome to be linked to the original session and model version, was used to track outputs.  
To mitigate any potential security risks, data transmitted from edge devices was encrypted via HTTPS and stored on the ICIPRO private cloud infrastructure. During all testing, system access was restricted to authorized users using role-based permissions, and audit logs were recorded for each data upload and processing event. These findings show that the implemented mitigation approaches operate as intended in the laboratory environment. Although more evaluation of performance in real-world operating environments is required, the current configuration provides a validated basis for proceeding with semi-clinical testing.
# Underwater Acoustic Target Tracking: A Review
## 4 Theory of Target Tracking
### Target Detection  
In visual tracking, this process means determining the existence of the moving target in the image sequences and describing the characteristics of the target (such as the position of the target, the size of the external rectangle of the target). Currently, in the field of visual tracking, the commonly used moving target detection algorithms include background subtraction, frame difference, optical flow and mixture of the Gaussian model. Among varieties of target detection algorithms, background subtraction is the most frequently used one. The basic idea of it is to subtract the moving target from the difference image obtained by comparing the background image and the current image [39]. Frame difference is used to separate the moving target by comparing two images in consecutive frames [40]. Optical flow can detect the moving target without prior information about the scene. However, it is not suitable for real-time tracking owing to its relatively high time complexity of computation. A mixture of Gaussian model is aimed at modeling each pixel with a collection of K Gaussian distributions. Then the possibility of each pixel as the background is expressed by the corresponding weight which is calculated through accumulations of the historical input images.  
Distinct from the detection based on images, target detection in non-visual tracking is much simpler. An acoustic sensor accuses the detection of the target when the received signal strength exceeds a predefined threshold. It is true that there are many other sophisticated algorithms for detecting targets accurately. More relevant information can be found in other articles.
## 6 Results and Discussion
### Full Horizon (40-Day) Performance
#### 6.2.1 Disturbance Handling Strategy Performance  
As detailed in Table 7, NMPC-C and NMPC-CW achieved the highest final biomass (approximately 217.4 g m\({}^{-2}\) and 217.5 g m\({}^{-2}\), respectively) and, consequently, the highest  
Figure 11: Histogram of solver times for each NMPC configuration over the 40-day simulation horizon. Warm-started controllers (CW, PW, IW) achieve lower and more concentrated solve times.  
Figure 9: Trade-off between final biomass yield and total CO\({}_{2}\) cost for all NMPC controllers over the 40-day cycle.  
Figure 10: CO\({}_{2}\) budget decomposition for each NMPC configuration, showing total CO\({}_{2}\) supplied, CO\({}_{2}\) lost through ventilation, and CO\({}_{2}\) assimilated by the crop.  
net profit (approximately 2.658 HfI m\({}^{-2}\) and 2.663 HfI m\({}^{-2}\), respectively). This superior productivity can be attributed to the controllers' tendency to maintain high CO\({}_{2}\) concentrations during periods of sufficient light, thereby maximizing photosynthetic activity, as evidenced by the higher assimilation rates shown in Figure 10. However, this reactive and aggressive approach--due to the controllers' lack of future disturbance information--resulted in the highest rates of constraint violations among all strategies, particularly for temperature and humidity setpoints (Figure 8). While these controllers incurred the greatest operational costs related to CO\({}_{2}\) injection (Figures 7 and 9), their heating costs were lower than those of the predictive controllers (Figure 7). This suggests that C and CW responded only to current conditions and did not proactively increase heating in anticipation of future humidity or temperature violations. Despite higher CO\({}_{2}\) consumption, their biomass cost (C\({}_{bio}\)) remains competitive due to substantial yield gains.  
The integration of future disturbance information, whether from an ML-based preview (P, PW) or an ideal forecast (I, IW), enabled these controllers to proactively adjust control actions in anticipation of upcoming environmental fluctuations. As a result, they achieved lower overall constraint violations--particularly for temperature and relative humidity--compared to the feedback-only controllers (Figure 8). This improved constraint adherence was primarily accomplished through more conservative CO\({}_{2}\) dosing as well as strategic increases in heating to preempt periods of elevated humidity or temperature (Figure 6). While these controllers reduced total CO\({}_{2}\) costs (Figures 7 and 9), they exhibited slightly higher heating costs than their feedback-only counterparts (Figure 7), reflecting a deliberate trade-off between resource efficiency and stricter climate control.
# Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry
## 2 Methods
### Soil and Rover Properties  
Each terrain type is associated with a specific set of parameters that characterize the wheel-ground interaction (i.e., \(c,\ \phi,\ n,\ k_{c},k_{\phi},\ K_{x},\ K_{y}\), and \(\rho\), mentioned in Section 2.1). The numerical simulations presented in this work are carried out through a modeling of the Martian surface to mimic several operative conditions. Our mission scenarios account for cloddy, mixed drift-cloddy and drift soils, which are common terrain types on Mars. The physical properties of these soil are reported in Table 2[26; 27].  
An accurate definition of the dynamical equations requires a 3D modeling of the rover. We implemented rover's main body (Figure 2), wheels and joints by using geometrical properties of ESA ExoMars rover [25], as shown in Table 1. The rover has six wheels, each with its own steering mechanism. The control system of the actuators is supposed to realize the Ackermann condition [28; 30].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter [Unit]** & \(c\) [Pa] & \(\phi\) [deg] & \(n\) [-] & \(k_{c}\) [N/\(m^{n+1}\)] & \(k_{\phi}\) [N/\(m^{n+2}\)] & \(K_{x}=K_{y}\) [M] & \(\rho\) [kg/m\({}^{3}\)] \\ \hline Cloddy soil & 170 & 37\({}^{\circ}\) & 1 & 1400 & 820,000 & 0.016 & 1550 \\ Mixed drift-cloddy soil & 220 & 33.1\({}^{\circ}\) & 1 & 1400 & 820,000 & 0.016 & 1350 \\ Drift soil & 530 & 26.4\({}^{\circ}\) & 1 & 1400 & 820,000 & 0.016 & 1150 \\ \hline \end{tabular}
\end{table}
Table 2: Soil properties.
## 3 Implementation Methodology
### Feature Engineering  
In regard to selecting features in the predictive models, the hourly AQI readings with the highest index out of 6 pollutants: O\({}_{3}\), PM\({}_{2.5}\), PM\({}_{10}\), CO, SO\({}_{2}\), and NO\({}_{2}\) are selected. To convert the time-window-specific concentration of 6 pollutants, the AQI Taiwan Guidelines [18] are adopted and the AQI is manually calculated using the following Equations (1) and (2), where index values of O\({}_{3}\), PM\({}_{2.5}\), and PM\({}_{10}\) are needed to define AQI in Taiwan, and the lack of one or more of these values will significantly reduce the accurate assessment of current air quality.  
\[AQI=\left\{\begin{array}{l}max[I_{O3},I_{PM2.5},I_{PM10},I_{CO},I_{SO2},I_{NO 2}]I_{O3},I_{PM2.5},I_{PM10}\
eq\varnothing\\ \varnothing\textit{otherwise}\end{array}\right. \tag{1}\]  
Pollutant concentration (\(value_{i}\)) is converted to pollutant index (\(I_{i}\)) by the following formula:  
\[I_{i}=\begin{array}{l}LB_{j}+\frac{value_{i}-lb_{i}}{ub_{i}-lb_{i}}\times \left(UB_{j}-LB_{j}\right)\end{array} \tag{2}\]  
where \(i=\) O\({}_{3}\), PM\({}_{2.5}\), PM\({}_{10}\), CO, SO\({}_{2}\), NO\({}_{2}\); \(j\) denotes which level in AQI system occupied by the concentration of the specific pollutant using categories of good, moderate, unhealthy which includes specific groups, unhealthy, very unhealthy, and hazardous. The data transformation defines the time-window-specific concentration to calculate \(I_{i}\) values. For example, based on the AQI from Taiwan's EPA website [18], the concentration \(value_{O3}=0.06\) ppm will fall in the interval with \(lb_{O3}=0.055\) ppm and \(ub_{O3}=0.070\) ppm corresponding to the \"moderate\" pollutant level with \(LB_{moderate}=51\) and \(UB_{moderate}=100\). The \(value_{O3}\) is defined by matching either of two conditions: if the 8-h average concentration is more precautionary for a specific site and is also below 0.2 ppm, then this value is used; otherwise, the 1-h average concentration will be considered. Both \(value_{PM2.5}\) and \(value_{PM10}\) are the moving average values which consider two time-windows, i.e., the last 12 h and 4 h (see Table 1). Other variables, such as \(value_{CO}\) and \(value_{NO2}\) only account for a single time window, i.e., last 8 h and 1 h, respectively. Meanwhile, \(value_{SO2}\) emphasizes the 24-h average concentration if the 1-h average concentration exceeds 185 ppb; otherwise, the 1-h average value will be used.  
The AQI mechanism introduces several new variables to train the prediction model (Table 1). For several pollutants, time windows other than hourly are more sensitive in determining AQI; therefore, the prediction interval related to the accuracy of long-term predictions is under investigation to clarify the time dependency between consecutive data points. As the AQI calculation is already established, the future value of the AQI readings in three different time intervals will be regarded as target variables and are summarized in Table 2.
## 4 Description of the Physical System
### Governing Equations for the Dispersed Phase  
Simulation of the flow laden with solid particles is developed through the Lagrangian+ Eulerian approach. When the interaction between the continuous phase and the dispersed phase takes place, the dynamic interaction between both phases interact can be unidirectional or bidirectional. Because of the low volumetric fraction of the dispersed phase in this study case (3-5%), it is assumed that coupling between the phases is unidirectional (one-way coupling), the particles do not have a considerable effect on the physical properties of the fluid. According to this and keeping in mind that in Fluent [37], the Lagrangian approach is used to track the solid particles dispersed in the continuous phase, this study will use the Lagrangian approach. For just one disperse particle, the equilibrium equation is given as:  
\[\frac{\partial u_{pi}}{dt}=F_{D}\big{(}u_{i}-u_{pi}\big{)}+\frac{\big{(} \rho_{\rho}-\rho\big{)}g_{i}}{\rho_{\rho}}+F_{i} \tag{8}\]  
where \(u_{pi}\) is the velocity of the particles, \(F_{D}\big{(}u_{i}-u_{pi}\big{)}\) is the drag force that acts over the particles, \(\rho_{\rho}\) is the density of the particles, \(g_{i}\) is the gravitational acceleration, and \(F_{i}\) corresponds to the additional forces that can act upon the particle, such as Brownian force, the Saffman and Magnus force, the added mass force, electromagnetic forces, and Basset force, among others; all these forces are expressed per mass unit. For spherical particles, drag force is given as:  
\[F_{D}=\frac{18\mu}{\rho_{\rho}d_{\rho}}^{2}\frac{C_{D}Re}{24} \tag{9}\]  
The Reynolds number is expressed as:  
\[R_{e}=\frac{\rho d_{\rho}|u_{p}-u\big{|}}{\mu} \tag{10}\]  
where \(d_{\rho}\) is the diameter of the particle, \(u\) is the velocity of the fluid, \(u_{p}\) is the velocity of the particle, \(\rho\) is the density of the fluid, and \(\mu\) is the dynamic viscosity of the fluid.
# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
###### Abstract  
The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.  
Sentinel-2; remote sensing; precision agriculture; crop monitoring 2020, 10, 641; doi:10.3390/agronomy10050641
# Automatic Age and Gender Recognition Using Ensemble Learning
## 3 Dataset and Methods
### Ensemble Learning
#### 3.3.4 Hyperparameter Optimization  
ML algorithms have a set of hyperparameters that control the learning process. The process of determining the optimum values of these parameters is known as hyperparameter optimization and is one of the most important steps affecting model performance. There are various methods for hyperparameter optimization [39]. In this research, grid search cross-validation (GridSearchCV) was used in parameter optimization of base-level classifiers. GridSearchCV is one of the most commonly used methods for optimizing the hyperparameters of a machine learning model. In this method, grid search and cross-validation (CV) techniques were used together to find the best parameter for the model. The grid search method uses a brute-force search technique and systematically explores all possible combinations of hyperparameter values in a given range. CV is a data-splitting method used to evaluate the generalization performance of the model. In CV, the available dataset is divided into multiple folds, and one of these folds is used for validation, while the other folds are used for training. The process is repeated each time using a different fold for the validation set. Finally, the performance of the model is calculated by averaging the results from each validation step. In the GridSearchCV method, the CV step is repeated for all parameter combinations examined by the grid search, and the optimum parameters are determined according to the classification accuracies. In this study, a ten-fold CV was used for hyperparameter optimization. The investigated parameter ranges and determined optimum values for each classifier are given in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Classifier** & **Range of Hyperparameters** & **Selected Values** \\ \hline \multirow{4}{*}{SVM} & C: [1, 10, 100, 1000] & 1000 \\  & gamma: [0.1, 0.01, 0.001, 0.0001] & 0.0001 \\  & kernel: [’linear’, 'rbf’, 'poly’] & rbf \\ \hline \multirow{4}{*}{KNN} & n\_neighbors: [1, 2,..., 26] & 1 \\  & metric: [’Euclidean’, ’Manhattan’, ’Minkowski’] & Manhattan \\ \hline \multirow{2}{*}{RF} & max\_features: [’sqrt’, ’log2’] & Log2 \\  & n\_estimators: [100, 300, 500, 700] & 700 \\ \hline \multirow{2}{*}{E-TREE} & max\_features: [3, 5, 7, 9, 11] & 9 \\  & n\_estimators: [100, 200,..., 1000] & 900 \\ \hline \multirow{4}{*}{LR} & C: [0.001, 0.01, 0.1, 1, 10, 100, 1000] & 0.01 \\  & Penalty: [’11’, ’l2’] & l2 \\ \cline{1-1}  & solver: [’lbfgs’, ’liblinear’, ’newton-cg’] & newton-cg \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters for the base-level classifiers.