esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Development and Optimization  
The experimental data described in the previous section were utilized to construct and optimize a mathematical model, biding environmental conditions, such as average temperature (\(T_{avg}\)) and rainfall (\(R_{sum}\)) in the selected season, with expected berry yield and the impact of the rootstock [44]. The calculation was performed in the MATLAB environment, more specifically with The Curve Fitting Toolbox and The Optimization Toolbox. The methodology was split into two parts. First, a raw fitting of the polynomial 3d surface of the selected parameter was performed versus experimental data. The polynomial has the following form:  
\[f\big{(}T_{avg},R_{sum}\big{)}=p00+p10*T_{avg}+p01*R_{sum}+p11*T_{avg}*R_{sum} +p02*R_{sum}^{2} \tag{1}\]  
The next step was intended to increase the accuracy by including interactions between modeled parameters. In addition, the impact of the monthly deviation between temperature and rain measured in the selected year was included. This was obtained by including the standard deviation (\(T_{SD}\), and \(R_{SD}\)) of these two parameters in the model equation. The calculations were performed by the fmincon algorithm [45], started within the GlobalSearch procedure, to limit the possibility of a local minimum trap [46]. The exact number and type of interaction was dependent on statistical analysis (see Results and Discussion Section). The final forms for the mentioned parameters are presented below:  
\[NC_{inter}\big{(}T_{avg},R_{sum}\big{)}=NC\big{(}T_{avg},R_{sum}\big{)}+BW \big{(}T_{avg},R_{sum}\big{)}*pBW\_nc \tag{2}\] \[+NB\big{(}T_{avg},R_{sum}\big{)}*pEB\_nc+T_{SD}*pT\_nc+R_{SD}*pR \_nc\] \[NB_{inter}\big{(}T_{avg},R_{sum}\big{)}=NB\big{(}T_{avg},R_{sum} \big{)}+NC\big{(}T_{avg},R_{sum}\big{)}*pNC\_nb\] (3) \[+EB\big{(}T_{avg},R_{sum}\big{)}*pEB\_nb+T_{SD}*pT\_nb+R_{SD}*pR \_nb\] \[BW_{inter}\big{(}T_{avg},R_{sum}\big{)}=BW\big{(}T_{avg},R_{sum} \big{)}+NC\big{(}T_{avg},R_{sum}\big{)}*pNC\_bw\] (4) \[+T_{SD}*pT\_bw+R_{SD}*pR\_bw\] \[EB_{inter}\big{(}T_{avg},R_{sum}\big{)}=EB\big{(}T_{avg},R_{sum} \big{)}+NC\big{(}T_{avg},R_{sum}\big{)}*pNC\_eb\] \[+NB\big{(}T_{avg},R_{sum}\big{)}*pBB\_eb+T_{SD}*pT\_eb+R_{SD}*pR \_eb\]  
where pX_x denominates the coefficients to be optimized within _fmincon_ algorithm [47].
# Effects of Biochar Production Methods and Biomass Types on Lead Removal from Aqueous Solution
## 2 Materials and Methods  
### Biochar Production  
Avocado seed, avocado peel, and grapefruit peel were obtained from local grocery stores and collected after consumption, while brown seaweed (_sargassum_) was collected from the Laguna Madre, a hypersaline lagoon near the shoreline in South Padre Island, Texas, USA. The biomass materials were washed thoroughly with tap water and then oven-dried at 105 \({}^{\circ}\)C for 24 h. The oven-dried biomasses were then ground using a coffee grinder (Figure 1).  
One group of biochars was produced through a tube furnace (Lindberg/Blue MTM Tube Furnace, ThermoFisher Scientific, Waltham, MA, USA) at 300 \({}^{\circ}\)C (Figure 2a). The pyrolysis temperature at 300 \({}^{\circ}\)C was chosen because low temperature biochars were found to contain active surface functional groups such as hydroxyl, phenolic, and carboxylic functional groups, and were favorable for removing heavy metals from aqueous solutions in previous studies [17,18]. The individual biomass was weighed and placed in a crucible and then placed inside the tube furnace. The tube furnace was purged with nitrogen (N\({}_{2}\)) gas at a rate of 5 L min\({}^{-1}\) for 1 h to remove oxygen from the system, and then the biomass was pyrolyzed for 1 h at 300 \({}^{\circ}\)C. The tube furnace was allowed to cool down to room temperature under a N\({}_{2}\) flowing condition to prevent any oxidation before collecting and weighing the biochar samples. Biochars produced through the tube furnace are henceforth abbreviated AST for avocado seed (the letter T indicating tube furnace), APT for avocado peel, GPT for grapfreruit peel, and BST for brown seaweed.  
A second group of biochars were produced through BioCharlie Log (BioCharlie, America Sequesters CO\({}_{2}\), Clarence, NY, USA) (Figure 2b). The BioCharlie pyrolyzed the biomass by minimizing oxygen inflow and has been advertised as a DIY biochar maker. The individual biomass was weighed and placed inside the BioCharlie Log, which was pyrolyzed on a grill over a fire. The BioCharlie Log was removed from the fire and allowed to cool at room temperature before opening it to weigh the produced biochar. Biochars produced through BioCharlie are henceforth abbreviated ASB for avocado seed (the letter B indicating BioCharlie), APB for avocado peel, GPB for grapfreruit peel, and BSB for the brown seaweed.  
Figure 1: Ground biomass feedstock used for biochar production: (**a**) avocado seed, (**b**) avocado peel, (**c**) grapfreruit peel, and (**d**) brown seaweed.  
Figure 2: Two different biochar production methods employed in this study: (**a**) tube furnace that is commonly used for material synthesis; (**b**) BioCharlie Log, a Do-It-Yourself biochar maker (photo adapted from Amazon product page).  
For all the produced biochars, the percent yield of the biochars (mass fraction of biochar over dried biomass) was determined by:  
\[\text{Yield (\%)}=100\ \times\ (W_{f}/W_{i}) \tag{1}\]  
where \(W_{f}\) represents the weight of the produced biochars and \(W_{i}\) represents the weight of the dried biomass.
# Switchable Broadband-to-Tunable Narrowband Magnetic Probe for Near-Field Measurements
## 2 Magnetic Probe Design
Increasing the gap width \(t\) will increase the frequency since both the gap width \(t\) and the frequency\(f\) are directly proportional to each other, according to Equation (4). Figure 6:1 The surface current distribution of the narrowband magnetic probe: (**a**) top view; (**b**) perspective view. Figure 6:2 S-parameters of the proposed magnetic probe with and without using a PIN diode. The frequency shift achieved with different values of the gap width \(t\) has inspired the authors and led them to the next step by changing the position of the high sensitivity narrowband across several frequency bands like WiMAX, PAN, WLAN, etc. The second step is to use a varactor diode (reverse bias voltage) between the via-1 and the far side of the slotted patch gap (across the opening gap). The varactor diode will change the parasitic capacitance value, hence changing the slot's dimensions. Figure 15 depicts the geometrical shape upon inserting the varactor diode into the structure. Figure 16a shows the spice model of the varactor diode type SMV2201--040 LF, \(Ls\) = 0.45 nH, \(Cp\) = 0.075 pF, and \(Rs\) = 5.41 \(\Omega\), while its equivalent circuit is shown in Figure 16b. Figure 6:3 S11 parameters of the narrowband magnetic probe for different values of slotted gap t in mm. Figure 6:4 S11 parameters of the narrowband magnetic probe for different values of a slotted circle radius \(r\). Figure 17 presents the simulated S-parameter results for both the broadband mode and tuned narrowband mode. The sensitivity enhancement of the tuned narrowband mode is about 10 dB compared to the broadband mode through all frequency bands. Figure 6:5 Varactor diode model: (**a**) spice model; (**b**) equivalent circuit. Figure 6:6 S-parameters with different PIN diode and varactor diode switching modes. Figure 6:7 Use of the PIN diode and varactor diode in the magnetic probe. Based on the SMV2201-040 LF datasheet from the Skyworks website, the parasitic capacitance value will decrease as the reverse biasing voltage increases. This probe has been fabricated by the Pragoboard company [36]. Figure 18 demonstrates the manufactured shape fabricated by the Pragoboard company.
# WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
## 3 Results
### Carbon Fluxes and Wile
Figure 9: Monthly GPP boxplot (gC m\({}^{-2}\) day\({}^{-1}\)) for eddy covariance observed data (Tower) and MODIS—derived data (Satellite) for the: (**A**) Amazonia; (**B**) Caatinga; (**C**) Cerrado; (**D**) Pantanal sites. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Forest Type** & **Slope** & \(\mathbf{R^{2}}\) & **Reference** \\ \hline Alpine grassland & 1.30 & 0.50 & [PERSON] et al. [55] \\ Alpine grassland & 0.58 & 0.17 & [PERSON] et al. \\ Dry tropical forest & 0.24 & 0.27 & **This study** \\ Floodplain forest & 5.39 & 0.01 & **This study** \\ Primary forest & 5.34 & 0.02 & **This study** \\ Semi-deciduous forest & 0.49 & 0.32 & [PERSON] et al. [56] \\ Temperate grassland & 1.59 & 0.70 & [PERSON] et al., 2018 \\ Temperate grassland & 0.50 & 0.40 & [PERSON] et al., 2018 \\ Tropical grassland & 0.89 & 0.53 & [PERSON] et al., 2018 \\ Tropical peatland & 0.23 & 0.16 & [PERSON] et al. [57] \\ Tropical grassland & 0.91 & 0.63 & [PERSON] et al., 2018 \\ Tropical grassland & 0.89 & 0.53 & [PERSON] et al., 2018 \\ Wetland & 2.40 & 0.31 & **This study** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical summary of studies comparing MODIS GPP and eddy covariance estimations for different land covers using linear regression models. Figure 11: Monthly WUE (gC kg H\({}_{2}\)O day\({}^{-1}\)) boxplot for each site and studied period: (Amazonia: 2009\(-\)2011; Caatinga: 01/2014\(-\)07/2015; Cerrado: 2004\(-\)2006; Pantanal: 2015\(-\)2016). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Forest Types** & **WUE (g C kg-1H\({}_{2}\)O)** & **References** \\ \hline Wetland & 0.95 & **This study** \\ Boreal treeless wetland & 1.2 & [PERSON] et al. [58] \\ Floodplain forest & 1.61 & **This study** \\ Maritime pine & 1.69 & [PERSON] et al. [59] \\ Primary forest & 1.82 & **This study** \\ Deciduous broadleaf forest & 1.87 & [PERSON] et al. \\ Old-growth forest & 1.83 & [PERSON] et al. [60] \\ Evergreen broadleaf forest & 2.35 & [PERSON] et al. [61] \\ Conifer plantation forest & 2.53 & [PERSON] et al. [62] \\ Deciduous broadleaf forest & 2.57 & [PERSON] et al. \\ Eucalyptus plantation & 2.87 & [PERSON] et al. [63] \\ Ponderosa pine & 2.97 & [PERSON] et al. [64] \\ Evergreen broadleaf forest & 3.13 & [PERSON] et al. \\ Boreal aspen & 3.70 & [PERSON] et al. [65] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between mean annual WUE retrieved in the present study with values for different types of forests found in the literature.
## 5 Experimental Results
Although the differences in evaluation metrics are not substantial, HDBSCAN slightly outperforms DBSCAN in trajectory clustering. The results of this study demonstrate that HDBSCAN performs exceptionally well in big data clustering analysis. By leveraging the computational power of clustering algorithms, we can rapidly classify ship trajectories for maritime management, which is crucial for overseeing hazardous or suspicious vessel movements. The contributions of this study not only validate the practical effectiveness of HDBSCAN but also scientifically verify the quality of clustering results through evaluation metrics. Furthermore, it provides a reliable technical foundation for intelligent maritime management, offering significant implications for the future development of maritime intelligent transportation and navigational safety systems. Figure 5: Each cluster distribution.
## 4 Simulation Analysis of CRPDC
### Coil Currents under Different Capacatances  
The output current characteristics of the CRPDC with different energy-storage capacitances are further simulated, as shown in Figure 9. The frequencies of the input PWM signals are all set to be 5 kHz. The duty cycles of input PWM signals are all set to be 50%. The energy-storage capacitances are set to be 2 uF, 4 uF, 6 uF, and 8 uF, respectively. The power voltage is configured to be 24 V. The current ripples of proportional valve coil at different energy-storage capacitances are calculated to be 87.80 mA, 53.81 mA, 38.15 mA, and 29.57 mA (peak-to-peak values, results from the Multisim model in Figure 4a). The average currents at different energy-storage capacitances are calculated to be 1.139 A, 1.446 A, 1.564 A, and 1.616 A (results from the Multisim model). As can be seen from Figure 10, with the increase in capacitance, the average current increases slightly but the output current ripple obviously decreases. Hence, the current characteristic of CRPDC can be modulated by choosing the energy-storage capacitance.  
Figure 8: The current characteristics of the CRPDC under the different PWM duty cycles (results from Multisim model): (**a**) Average current; (**b**) current ripple (peak-to-peak value).  
Figure 9: \(Cont.\)
## 4 Rician K-Factor Characteristics
### Rician K-Factor in Simple Propagation Environment
This means that the path power of other scattered waves is low at \(D\) = 10 m. Therefore, the Rician K-factor becomes higher as the distance \(D\) increases, so stable A2G communication can be expected. The Rician K-factors of the measured and analytical results decrease between \(H\) = 10 m and 20 m regardless of the distance from the building. Then, the contribution rate of building reflection also increases with increasing the UAV altitude. On the other hand, the Rician K-factor increases above \(H\) = 21 m because building reflections shown as the blue line in Figure 12c do not reach the receiving point. Moreover, the Rician K-factor increases above \(H\) = 25 m because the reflection waves through the building shown as the blue and purple lines in Figure 12c do not reach the receiving point, and then the contribution rate of the diffraction wave also decreases. This indicates that, as the UAV altitude increases, the contribution rate of ground reflection becomes very high, and the Rician K-factor increases and approaches a constant value. On the other hand, the received power decreases with increasing distance between the transmitting and receiving antennas. Therefore, it is essential to consider the trade-off between UAV altitude and received power when designing the communication system. However, the Rician K-factor of the measured results at an \(H\) = 30 m is significantly higher than that of the analytical value. Moreover, it can be confirmed that the variance of the Rician K-factor of the measured values is larger than that of the analytical values, as shown in the box-and-whisker plots in Figure 12a,b. It has been suggested that various factors such as the surface properties of buildings, the reflective properties of the ground, and the vibration of the UAV can affect the propagation of the direct and scattered wavesin real environments. Further verification is required to determine whether this is due to local variations in the scattered wave or measurement error.
## 4 Workload and Data Center Modeling  
A complete model for data center behavior and workload demand is presented in this section using two methods. The first method uses analytical and statistical probability to describe data center provisioning and workload demands. The second method uses deep machine learning by correlating the input and output behavior of the system. Figure 7 shows the general architecture of workload demands and the data center with its internal modules. Workload demands are described by bunches of jobs that include tasks submitted to the data center queue (the Submit Tasks ellipse in Figure 7). Data center resources (Resources Machine List) are configured on the fly to match the requested demands in an elastic way by increasing or decreasing the provisioned resources. The system receives submitted jobs/tasks as a script file descriptor, which specifies the needed resources, similar to HPC job submission. The submitted tasks module acts as a proxy to communicate with the resources scheduler that masters the resource reconfiguration and assigns tasks to computational nodes with proper reconfiguration. The machine list is updated by the data center capacity planning, which is the total resource capability. The data center is scaled all the time by adding new servers or removing failed machines. The operation configuration works with the scaling action to determine the direction and reflect it in the machine classes. The machine list is also updated by the setup configuration, which works to accommodate new tasks and by the terminate configuration, which works to release the resources from terminated or failed tasks. The failed tasks will be resubmitted by en-queuing them in the Submit Tasks. The two models must represent the whole architecture accurately that will be used to validate and test our proposed optimized machine learning elastic model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \multicolumn{3}{c}{Google and Alibaba Traces Section Symbols} \\ \hline \(RD\) & workload resources demand \\ \(PR\) & data center resource provision \\ \(OP\) & over-provisioning case, (\(OP=PR>(RD+B)\)) \\ \(FP\) & fit-provisioning, (\(FP=(RD+B)\geq PR>RD\)) \\ \(UP\) & under-provisioning (\(UP=PR\leq RD\)) \\ \(B\) & base value \\ \(LB\) & Lower Bound, (\(LB=RD\)) \\ \(UB\) & Upper Bound, (\(UB>RD+B\)) \\ \(T_{w}\) & time window interval \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key Symbols Used in the Analysis and their Definitions.  
Figure 7: Architecture of the data center and workload models.
# Multi-Class Phased Prediction of Academic Performance and Dropout in Higher Education
## 4 Materials and Methods
### Model Evaluation and Feature Importance  
To assess model performance, the balanced accuracy and the global \(F1\) score were used, which are adequate metrics for models built with imbalanced datasets [23]. \(F1\) score is the harmonic mean between precision and recall and is computed according to the following equation:  
\[F1=\frac{TP}{TP+\frac{1}{2}(FP+FN)} \tag{1}\]  
where \(TP\) is the number of true positives, \(FP\) is the number of false positives, and \(FN\) is the number of true negatives.  
Accuracy is the fraction of correct predictions out of the total number of predictions and is computed according to Equation (2),  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{2}\]  
where \(TN\) is the number of true negatives.  
Moreover, since it is important to understand the models' behavior for each class, the individual F1 scores were also analyzed. The confusion matrices, which provide more detailed information regarding miss-classification for each class, were also obtained and analyzed.  
Data splitting and model training were repeated 10 times with different random number generator seeds, both for the train-test split procedure and for the cross-validation and classifier. This allowed for checking the dependency to the randomness of each model. The overall estimates of the evaluation metrics were obtained by the average values and respective standard deviation values across the 10 runs of the procedure.  
To better understand how the learners perform the classification tasks, the feature importance for the best three models, built with each dataset, were computed. These computations are easily performed for the Random Forest based models since they are provided by one of the attributes of the model's object. In such cases, the feature importance is computed as the total reduction of the criterion (the Gini impurity index) brought by that feature on average over all the trees in the forest.
# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
## 4 Discussion
We conclude that feature extraction of fine-tuned CNNs yields better classification results than end-to-end training with the softmax layer for classification. It goes in line with information in Tables 3 and 6, that the best performing method for AID and NWPU-RESISC45 data set classification is based on feature extraction (multiple) and three-dimensional (3D) pooling of extracted features. * Performing experimental research into classification of remote sensing datasets with RBF SVM classifiers, shows that it is a superior classification technique compared to softmax classification. For the AID dataset RBF SVM classifier outperforms softmax classification in all cases, except in a few of the simulation scenarios with linear decay scheduler. The situation with NWPU-RESISC45 dataset follows the previous example: from 16 simulation scenarios (for both 20%/80% and 10%/90% train/test split ratios) softmax classifier is better than RBF SVM classifier in only two cases. From Tables 1, 2, 4, and 5 it is noticeable that it yields better classification accuracy for each examined dataset in most of the simulation cases. * Comparing the linear decay scheduler and cyclical learning rate for RBF SVM classification, leads to similar conclusions as for linear SVM classification. The linear decay scheduler shows better experimental results for bigger training sets (50% training set for AID dataset and 20% training set for NWPU-RESISC45 dataset). Cyclical learning rates appear more suitable for aerial scene classification under smaller training sets.
## 2 Materials and Methods  
### GF Setting in Lokomat  
The Lokomat (Hocoma AG, Volketswil, Switzerland) is the most well-known and widely used commercial robotic exoskeleton for RAGT [26; 27]. The Lokomat can be adapted to the characteristics of the occupant by adjusting the geometric length of the exoskeleton and the cuff size and position. Special pediatric exoskeleton parts are available for femur lengths ranging from 0.21 to 0.35 m, making RAGT for children possible [28].  
In the Lokomat system, the exoskeleton robot moves the hip and knee joints based on a predefined gait pattern in the sagittal plane and is combined with a treadmill and body weight support (BWS) system [26]. The Free-D module, which supports pelvic rotation, was not used in this study. The main training variables of the Lokomat are treadmill drive speed, BWS, and GF [14]. GF represents the assistive force in the range of 0-100%. The impedance controller of the Lokomat can guide the movements of the lower limbs when there is a difference between the actual robot joint angles and the required angles, that is a deviation from the predefined joint trajectory. The GF level determines the amount of assistive force used to push the hip and knee joint movements back toward a predefined trajectory. When the GF was set to 100, the exoskeleton robot operated in a well-known position control mode. This mode does not allow deviations from the reference pattern and does not require active patient participation [26; 29]. Conversely, when the GF was set to zero, the robot did not generate any force, allowing free leg movements. Hence, even with a low GF during RAGT, the participant must voluntarily contract their muscles and actively participate in the RAGT, granted the physical therapist is providing guidance to avoid range of motion (ROM) decrease.
## 2 Study Area and Data
### Data Source  
The interferometric data used in this study were provided by the Alaska Satellite Facility's HyP3 service [30]. The interferometric images were generated using a temporal baseline of 60 days and a spatial baseline of 100 m, resulting in a total of 592 images. The data covered the period from March 2018 to July 2022, with a revisit cycle of 12 days and a spatial resolution of approximately 5 m \(\times\) 20 m. The orbit direction was descending, and the polarization mode was VV, with a wavelength of 5.63 cm and an incidence angle of 42.35\({}^{\circ}\).  
Figure 1: Location of the study area, with an indication of the administrative boundaries of Kunming City.