esa-sceva/satcom-chunk-collection huggingface dataset:
# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 3 Methodology
### Transfer Learning  
In this study, the pretrained CNN models were utilized as feature extractors, with all layers frozen to preserve the rich feature representations learned from large-scale datasets such as ImageNet. Rather than fine-tuning the pretrained layers, a custom classification head was added on top of the frozen feature extractor. Freezing all layers may limit model adaptability to unique reflectogram features. However, given the relatively small dataset size, full freezing was adopted initially to leverage general features learned by CNN architectures. Future studies could explore partial fine-tuning of deeper layers to potentially enhance the sensitivity of the model to subtle defect features. The added layers include a flatten layer, followed by a fully connected dense layer with 128 neurons and ReLU activation, a dropout layer with a rate of 0.5 to prevent overfitting, and a final dense output layer with a sigmoid activation function to perform binary classification. The detailed architecture of the proposed model is illustrated in Figure 2.  
Layer sizes and dropout rates were selected based on standard practices and preliminary empirical evaluations. While extensive hyperparameter tuning was beyond the scope of this study, these configurations provided stable and satisfactory performance given the dataset size constraints. All layers were initially frozen due to dataset constraints, leveraging robust pre-trained features to avoid overfitting. Hyperparameters, including learning rate (\(1\times 10^{-4}\)), optimizer (Adam), batch size (32), dropout rate (0.5), and neuron count in classification layers (128), were chosen based on preliminary tests and common practices. An extensive ablation study examining partial unfreezing of deeper layers is recommended for future research to optimize model adaptability.  
This approach leverages the generalization capability of the pretrained models while allowing the newly added layers to specialize in the detection of pile defects in low-strain pile integrity tests. By freezing the pretrained layers, computational efficiency is improved, and the risk of overfitting to the relatively small dataset is minimized.
## 3 Methodology
### Graph Attention Networks  
Previous studies have shown that for the transductive task, since both the training and testing phases are based on the same graph structure, message passing over the graph in the form of convolution and can effectively capture local structure information between nodes [37]. However, for the Inductive task, the method uses a fixed neighbor node sampling strategy where each node only considers information from its one-hop neighbor nodes. This message passing method which DAGN chose may lead to loss of information or redundancy of information.  
In contrast, graph attention networks (GAT) use an adaptive attention mechanism that can better capture the relationships between nodes by assigning different weights to each node's neighbor nodes during message passing [38]. This approach allows for a better understanding of local and global information in the graph structure.  
By analyzing logical reasoning tasks, we can conclude that, as each question's passage and answer are distinct, every prediction is inferred based on different graph structures. Thus, this is a very typical Inductive task. For such tasks, compared to Graph Convolutional Networks that uniformly aggregate neighboring node features, Graph Attention Networks, which can automatically capture and learn heterogeneous relationships between nodes and assign unique weights to each adjacency relationship using the attention mechanism, are a more effective choice. Furthermore, Graph Attention Networks can utilize multi-head attention, implying they can learn multiple sets of attention weights in parallel, further enhancing the model's expressive capability.  
Figure 3: Visualization results of Position Encoding.  
Figure 4 shows the process of calculating the attention coefficient of GAT. For discourse unit \(i\), separately calculate its neighbors \(j\in\mathcal{N}_{i}\) and its own raw correlation coefficient \(e_{ij}\), then based on \(e_{ij}\) calculate the attention coefficient \(a_{ij}\).
# The Application of Satellite Image Analysis in Oil Spill Detection
## 2 Materials and Methods
### Methodology Description
#### 2.2.1 Satellite Missions  
Satellite images from the Sentinel-1, Sentinel-2 and Landsat 5, Landsat 7, and Landsat 8 systems were used for the analysis. Landsat mission products were selected for their long-term data coverage, Sentinel-2 images for their good spatial resolution, and Sentinel-1 due to the characteristics of the data that will allow to determine the thickness of the oil spot.  
The Sentinel-1 mission was designed as a constellation of two satellites: Sentinel-1A launched on 3 April 2014 and Sentinel-1B, launched on 25 April 2016. The satellites are in synchronous orbit with the Sun at an altitude of 693 km, with an inclination of 98.18\({}^{\circ}\). Each satellite is equipped with an advanced C-SAR radar, regardless of weather conditions. The Sentinel-1 mission was developed to ensure the continuity of Earth's radar mapping. It is characterized by a short revisit time, which is 12 days. This makes the mission suitable for monitoring land and ocean surface changes, as well as for security and emergency responses [36]. Sentinel-2 is a European mission for multi- and multi-spectral imaging with wide-range data acquisition and high resolution. It consists of two satellites, rotating in the same orbit synchronized with the movement of the Sun, 180 \({}^{\circ}\) apart from each other. The position of each satellite is measured by a dual-frequency Global Navigation Satellite System (GNSS) receiver. The full specification is designed to ensure a high reimaging frequency of 5 days at the equator. This guarantees the continuity of data collection and delivery.  
Landsat 5 is the longest-running Earth observation satellite in history. It was launched on 1 March 1984, with a Delta 3920 rocket launched from the Western Test Range at Vandenberg Air Force Base, California, and spent 29 years collecting and sending information about our planet. It was designed and built at the same time as Landsat 4 and had the same hardware specifications: a multispectral scanner system (MSS) and thematic mapping (TM) instruments. Landsat 7 was launched on 15 April 1999, from the same base as Landsat 5, on a Delta II launch vehicle. It is in a polar orbit, i.e., at a tilt of 98\({}^{\circ}\), synchronized with the azimuthal position of the Sun, which means that it scans 100% of the Earth's surface, which takes 16 days. The main instrument on board the Landsat 7 satellite is the Enhanced Thematic Mapper Plus (ETM +), a \"whisk broom scanner\" image sensor. Landsat 8 is another American Earth observation satellite that began its mission on 11 February 2013. It was launched into space with an Atlas V rocket from the same base as its predecessors, Vandenberg, California. The mission ensures continuous acquisition and sharing of Earth data thanks to the use of two sensors: Operational Land Imager (OLI) and Thermal InfraRed Sensor (TIRS) [37].  
Figure 2: Data processing workflow.
## 3 Results and Discussion
### Extraction Process Optimization
It has been underlined that increasing the R\({}_{\text{mol}}^{\text{D/A}}\) up to a certain point could favor polyphenol extraction due to the decrease in viscosity and  
Figure 1: Prediction profilers displaying the overall desirability of GL–Lys, GL–Pro (untransformed data), and GL–Arg DESs (transformed data) along with the optimum \(R_{\text{L/S}}\), \(C_{\text{DES}}\), and \(T\) values. higher diffusivity [48]. Thus, using a higher \(\rm R_{mol}^{D/A}\) of (7:1) in the case of GL-Arg, compared with a \(\rm R_{mol}^{D/A}\) of (3:1) in the case of GL-Lys and GL-Pro appears as evidence that the \(\rm R_{mol}^{D/A}\) is of a significant influence on polyphenol extraction optimization processes. Operating in similar optimal extraction conditions, the extraction with GL-Lys yielded 54.83% and 40.32% higher \(Y_{TP}\), compared with that attained with GL-Pro and GL-Arg, respectively, whereas, extraction with GL-Arg afforded 24.32% higher \(Y_{TP}\), compared with that attained with GL-Pro. This finding suggests the suitability of the GL-Lys for efficient polyphenol extraction, which would possess stronger hydrogen bonding with polyphenols of the plant material regardless of its \(\rm R_{mol}^{D/A}\) and the alkalinity condition, compared with the other solvent essayed [49; 50]. As a result, it could be concluded that the observed difference in polyphenol yield is mainly attributed to the hydrogen bond acceptors used in this study. Consequently, tailoring the physicochemical properties of the HBAs would be promising in order to enhance polyphenol extraction. With regard to the temperature effect, an observed favorable effect on polyphenol extraction is reported. Indeed, increasing temperature has shown to lead to an increase in polyphenol extraction yield by improving solubility and hydrolytic reactions [51; 52]. This is in accordance with the outcome of the present work. A high significance was observed in all three DESs, and the same optimum extraction temperature (80 \({}^{\circ}\)C) was determined. These results showed a similarity with the investigations of [PERSON] et al. [53], who found the highest polyphenol extraction at 80 \({}^{\circ}\)C. In addition to this finding, other investigations on kinetics portrayed that diffusion is directly proportional to temperature and follows the Arrhenius law [54; 55]. However, at temperatures higher than a certain point, with longer exposure times, it would reduce the polyphenol extraction yield and diversity. That temperature level varies in different scientific works, which probably depends on the extraction solvent and plant material, along with some other related factors as well [56]. Figure 2 illustrates the effect of the experimental variables on the extraction yield through a 3D plot.
# Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils
## 4 Conclusions  
This research utilized an extensive database of low-organic-carbon soil samples from across the Czech Republic to develop reliable sorption models for predicting \(K_{\text{d}}\) of tetrachlororetiene. Based on the data and analyses conducted, this work classified low-organic-carbon soils into three distinct texture-based classes: loam to sandy, clay, and silty loam, highlighting the significant role of soil texture and composition in modeling contaminant behavior. OC content and SSA were identified as the dominant predictors of \(K_{\text{d}}\), with clay and sand content also contributing significantly to its variability. These findings reinforce the importance of texture-specific modeling for accurately predicting the behavior and transport of PCE in low-OC soils. Multivariate linear regression models were developed and demonstrated improved predictive accuracy over univariate approaches, explaining substantial variance in \(K_{\text{d}}\) values. Statistical tests validated the need for separate models for each soil class, ensuring precise sorption predictions. These models are specifically designed for soils with low OC content (up to 1%) and may not be applicable to soils with higher OC content. Soils with higher OC content were not included in this study, and for such soils, older, simplified models based on \(f_{\text{OC}}\) are generally more appropriate. Our research highlights that multivariate regression models are not only effective but also cost-efficient, using affordable inputs and eliminating the need for expensive soil characterization methods. They provide a practical tool for predicting the distribution and transport of contaminants in low-OC soils, supporting both environmental and health risk assessments, while also guiding effective remediation strategies and monitoring at contaminated sites. While linear models facilitate comparisons across different soil types, they may not fully capture nonlinear sorption behavior, especially under extreme conditions. Nonlinear models could improve accuracy but introduce added complexity, making direct  
Figure 5: Comparison of the measured and predicted \(K_{\text{d}}\) values of used samples using the multivariate linear regression models for Classes 1, 2 and 3.  
comparisons and evaluations more challenging. Our linear multivariate regression models strikes a balance by providing reliable predictions without this added complexity, making it suitable for a wide range of applications. Future research could explore the application of these models to other contaminants and soil types, as well as their integration into large-scale environmental monitoring and risk management systems. By refining and expanding these models, we can improve our ability to predict contaminant behavior in diverse soil environments, contributing to enhanced environmental protection and public health outcomes.
## 3 Design and Electromagnetic Field Analysis of IPM Synchronous Motor
### Electromagnetic Field Analysis and Results  
The electromagnetic field analysis was performed by connecting a current source and equally inputting a constant current according to the speed. The analysis range was the main rotation section, which was between 3500 and 4500 rpm. A comparative analysis was performed at the current phase angle of 40\({}^{\circ}\) to analyze the maximum torque characteristics because the maximum torque has been reported which is generally shown at a current phase angle of 35-45\({}^{\circ}\) if the phase angle is controlled [19].  
Figure 1 shows the analysis model for electromagnetic analysis and flux flow during simulation. IPMSM generates maximum torque because it adds magnetic torque from permanent magnets and reluctance torque from magnetic resistance differences depending on rotor position. Therefore, it is designed to use both magnetic torque and reluctance torque using SKD11 between magnets, using a magnesium alloy that allows flux to flow through the stator.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Winding Diagram** & **Parameters** & **Unit** & **Value** \\ \hline \multirow{6}{*}{
\begin{tabular}{c} **Winding Diagram** \\ \end{tabular} } & Number of Slot & Slot & 36 \\  & Number of Pole & Pole & 42 \\  & Outside Diameter & mm & 81 \\  & Inside Diameter & mm & 60 \\  & Stack Length & mm & 20 \\  & Air Gap & mm & 0.4 \\  & Number of Turns & Turns & 10 \\  & Number of Strands & Turns & 6 \\  & Phase Resistance & m\(\Omega\) & 0.202 \\  & Slot Fill Factor & \% & 43.69 \\  & Rated Mechanical Power & W & 2000 \\  & Rated Rotational Frequency & Hz & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the stator.  
No-load simulation was performed through an electromagnetic analysis program to analyze cogging torque and back-EMV, and Figure 2 represents the analysis results. The cogging torque was measured at 0.00363 \(Nm_{peak-peak}\) at 10 rpm, and back-EMV was measured at 5.58 \(V_{ph\_rms}\) at 1000 rpm.  
Figure 3 shows the motor performance with respect to the rotational speed when the motor is driven in the main driving section. It is challenging to exceed an output power of 2000 W in similar-sized surface permanent magnet commercial motors. However, this model has an output power of 2604 W, which is superior to conventional commercial motors because IPM motors with higher protrusion ratios (\(L_{d}>L_{q}\)) compared to SPM motors integrate the reluctance torque of the motor to increase efficiency and torque. It was also found that the efficiency was 90.7%, which is the maximum value at 4500 rpm. Therefore, the utility and applicability are high because the flight duration and distance of the UAV are extended; it is easy for the motor to rotate at high speed. Moreover, the output power and efficiency of the proposed motor improve compared to those of the conventional SPM motor.  
Figure 1: Analysis model and electromagnetic flux of the IPM motor.  
Figure 2: No-load analysis results of the IPM motor. (**a**) Cogging torque at 10 rpm; (**b**) Back-EMV at 1000 rpm.
## 3 Results and Discussion
### XRD Analysis  
Figure 9 shows the hydration product analysis of DSPB after 60 min of reaction. The main hydration products were C-S-H gel, ettringite, gypsum, Ca(OH)\({}_{2}\), and CaCO\({}_{3}\), as well as the incompletely reacted CaO and CaSO\({}_{3}\). It can be seen from the XRD pattern that the effects of different dosages of HCDA, PCE, and SS on the early hydration products were relatively small, which indicates that these factors have no obvious influence on the mechanism of hydration but only contribute to the hydration rate. The most diffraction peaks in the system were those of Ca(OH)\({}_{2}\) and CaCO\({}_{3}\) due to HCDA's high f-CaO content, which reacted with water to generate Ca(OH)\({}_{2}\). Only a portion reacted with GBFS, and most unreacted Ca(OH)\({}_{2}\) remained as flocculent colloids, which subsequently underwent carbonation with atmospheric CO\({}_{2}\) to form CaCO\({}_{3}\). The flocculating structure of Ca(OH)\({}_{2}\) colloids and CaCO\({}_{3}\) precipitation contributed to the decrease in slurry fluidity.  
Figure 9: XRD analysis of DSPB after 60 min of reaction.  
Ca(OH)\({}_{2}\) involved in the reaction can accelerate the dissolution of silica-oxygen tetrahedra and aluminum-oxygen tetrahedra in GBFS and react with them as well as with SO\({}_{4}\)\({}^{2-}\) in solution to generate C-S-H gel and ettrinigite, and the generation of hydration products promotes the establishment of the microstructure, which is the main factor leading to the change in the rheological properties [73].
## 2 Materials and Methods
### Elaborating the Models in MATLAB-Simulink DC-DC Buck Converter  
#### 2.2.1 Control Algorithms of the DC-DC Converters for Following Up the Maximum Power Point  
Photovoltaic systems can provide a maximum power to a load or into an electrical network at a unique running point, named the MPP--maximum power point. The geometric location of this point has a nonlinear variation with the solar radiation incident on the photovoltaic cell and its temperature. Thus, for the photovoltaic system to run at the MPP, a follow-up regulator will be needed for the MPPT. When the ratio between the power derivate and the derivate of the voltage of the photovoltaic system \((dP_{FV}/dV_{FV})\) is equal to zero, the maximum power point (MPP) will be reached. Practically, to reach the maximum power point while running, the voltage of the photovoltaic system \(V_{FV}\) has to be adjusted so that it increases when the slope \((dP_{FV}/dV_{FV})\) is positive, and decreases when the slope \((dP_{FV}/dV_{FV})\) is negative. The adjustment law of a regulator that follows up the continuous extraction of the maximum power is given by the relation [23]:  
\[V_{opt}=k_{G}\int\frac{dP_{FV}}{dV_{FV}}\ dt\ \approx k_{G}\int\frac{\Delta P_{FV}}{ \Delta V_{FV}}\ dt \tag{7}\]  
where \(V_{opt}\) is the optimal voltage at maximum power, \(k_{G}\) is a proportionality constant of amplification, and \(dP_{FV}\) and \(dV_{FV}\) are the power and voltage variations between two running points. The block diagram of the implementation of the control algorithm MPPT including the adjustment of the voltage \(V_{FV}\) is presented in Figure 2[23].  
The MPPT algorithm may be implemented on a DC-DC converter connected between a photovoltaic system and a consumer (load or electric network), as shown in Figure 3, to  
Figure 2: Control diagram of the MPPT and voltage adjustment \(V_{FV}\).  
adjust the voltage (or current intensity) on the output of the photovoltaic system, so that the maximum power available is extracted.  
Most frequently, the MPPT algorithms Perturb and Observe (P&O) and incremental conductance (IncCond) are used. In the case of these two algorithms, the voltage of the photovoltaic system is adjusted in order to follow up the maximum power point being established, which represents the voltage on the maximum power point (MPP) by moving the current running point of the photovoltaic system along the P-V diagram as follows: on the left side of the maximum power point MPP if \(dP/dV>0\), and on the right side of the maximum power point MPP if \(dP/dV<0\). The coordinates of the maximum power point for the photovoltaic system result from the P = f(U) diagram are as follows: \(P_{max}=448.7\) W, \(V_{max}=29.6\) V (Figure 4).  
Figure 4: The coordinates of the maximum power point for the photovoltaic.  
Figure 3: Block diagram of the connection of a photovoltaic system to the consumer.
# Lightweight Object Detector Based on Images Captured Using Unmanned Aerial Vehicle
## 2 Materials and Methods
### Materials
#### 2.1.2 Fasternet Block  
FasterNet discovers similarities between feature maps, improves feature extraction efficiency, and optimizes the overall model using redundancy [27]. The core features of FasterNet optimize the overall model, considering that partial convolution (PConv) only treats a portion of the input feature map channels as a representation of the entire feature map for feature extraction. By keeping the other channels unchanged, the retained channels can be utilized in subsequent processing carried out in the pointwise convolution (PWConv) layer. This allows feature information to flow through all the channels, ensuring the completeness of the feature information. In traditional convolution, the convolution kernel performs a sliding calculation on all channels of the entire input feature map, whereas PConv only convolves a portion of the input channel, and the remaining channels are passed directly to the output. PConv divides the channel of the input feature map into two parts: One part is used for convolution calculation, and the other part remains unchanged, which allows PConv to effectively extract spatial features while reducing the amount of computation and memory access. In the FasterNet Block, the PConv layer is usually followed by two point convolution (PWConv) layers, forming an inverted residual block structure. This structure enables FasterNet to reduce the amount of computation and memory access while maintaining high-precision feature extraction capabilities. The structure of the PConv convolution is illustrated in Figure 1.  
We assume that the number of channels of the input and output feature maps is equal and denoted as \(c\) ; meanwhile, \(w\) and \(h\) are the width and height of the input image, and \(K\) is the channel size. The FLOP of PConv is as follows:  
\[h\times w\times k^{2}\times c_{p}^{2} \tag{1}\]  
When the ratio r to \(c_{p}\) is \(1/4\) (i.e., r = \(\frac{c_{p}}{c}\) = \(1/4\)), the FLOPs for partial convolution are only \(1/16\) of those for normal convolution, while memory access for partial convolution is described as follows:  
\[h\times w\times 2c_{p}+k^{2}\times c_{p}^{2}\approx h\times w\times 2c_{p} \tag{2}\]  
Therefore, partial convolution requires only \(1/4\) of the memory access of normal convolution.  
Figure 2 illustrates the overall design of the FasterNet Block module, which is designed to incorporate the concepts of partial convolution (PConv) and the Inverse Residual Block. The PConv layer is immediately followed by two convolutional layers, which are used as part of the Inverse Residual Block for the further processing of features. The use of normalization and activation layers after the intermediate convolutional layers helps to preserve the diversity of the features and prevents the problem of vanishing or exploding gradients during the training process.