esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Imaging Techniques for the Study, Conservation and Management of Rock Art
## 4 Discussion  
### Working Area 1  
This panel has been studied several times throughout history because it is one of the best known of the world's rock art. If we compare the new results (Figure 18b) with the first publication in 1911 (Figure 18a), we can appreciate the following.  
[PERSON] [18] affirms that all the animals were made in sienna, except for one black and one orange boson, and several of them are superimposed on negative hands. [PERSON] [19] reported, based on hyperspectral remote sensing results, that there are two phases of hands and two phases of animals (first in ochre and second in black) between them.  
The analysis derived from hyperspectral analysis data for the same area shows not only that it was capable of separating pigments, but also that new figures were emerging.  
What has been traditionally represented is a red bison with the back well drawn, but the front part imperfect and confused. Thanks to the techniques employed, it was possible to clearly document the boundaries of the contours of the figure and carry out a formal reconstruction. Spectral differentiation was also performed according to the different mineralogical compositions and the identification of areas affected by leaching and calcite coating due to lower signal intensity.  
Below the bison, the silhouette of another one facing to the left can be seen. Crossing the rear extremities of the bison (in yellow) and superimposed on the hand, a bovine silhouette can be seen, arranged to the left and somewhat wild. In addition to the above, new figures such as a horse's head appear in the central part of the bison.
# Evaluating Optical Clock Performance for GNSS Positioning
## 4 Clock Stability Analysis
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Power-Law Noise** & **Coefficient** & **Allan Variance Relationship** \\ \hline White Phase Noise (WPN) & \(h_{2}\) & \(\frac{4\pi^{2}\tau^{2}\sigma_{y}^{2}(\tau)}{3\tau_{3}}\) \\ Flicker Phase Noise (FPN) & \(h_{1}\) & \(\frac{4\pi^{2}\tau^{2}\sigma_{y}^{2}(\tau)}{1.038+3\pi(2\tau_{3}\tau)}\) \\ White Frequency Noise (WFN) & \(h_{0}\) & \(2\pi^{2}\sigma_{y}^{2}(\tau)\) \\ Flicker Frequency Noise (FFN) & \(h_{-1}\) & \(\frac{\sigma_{y}^{2}(\tau)}{2\pi(\tau)}\) \\ Random Walk Frequency Noise (RWFN) & \(h_{-2}\) & \(\frac{3\sigma_{y}^{2}(\tau)}{2\pi^{2}\tau}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The relationship between AVAR and clock power-law noises [51]. In terms of computational activities, ADEV values of atomic clocks assembled on 122 satellites are estimated separately for each clock product first, which are then further calculated to determine the average of all 12 clock products over 2022. Then, the averaged ADEV values of each satellite are categorised into different groups or types (see SV types in Table 2). The mean values are calculated afterwards to compare the generations of GNSS satellite clocks with the optical clocks.
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 2 Materials and Methods
### Propagation Models
#### 2.4.2 Two-Ray Model  
The two-ray model is a fundamental concept in radio wave propagation, closely related to line-of-sight (LOS) transmission. This model simplifies the understanding of signal behavior by considering two main paths: a direct path from the transmitter to the receiver and a ground-reflected path. The model analyzes how these two waves interact--constructively or destructively--depending on the phase in which they reach the receiving antenna, thus influencing the received signal strength. It is an important parameter for predicting signal variations in environments where ground reflections are likely to occur, offering a clearer understanding of the complexity of signal propagation in outdoor scenarios.  
In this stage, the two antennas were positioned such that gaps were introduced between absorbers, and their positions were changed with each measurement. This setup allowed the formation of both a direct wave and a ground-reflected wave, as illustrated in Figure 10.  
For these measurements, gaps were added and repositioned between the absorbers to create multiple signal propagation paths. The varying positions of these gaps influence the phase relationships between the received signals, highlighting the impact of multipath propagation on the received signal strength. The antenna shown at the top of Figure 10 is the transmitting antenna. In the Figure 10b, a gap is illustrated at position 4.  
As part of the experiment, the influence of both the number of gaps and their positions was studied, as shown in Figure 11.  
Figure 9: Setup of 90\({}^{\circ}\) change in polarization, where A—Antenna A; B—Antenna B; F—Oscilloscope and G—Signal generator.
## 4 Discussion and Conclusions  
### Analysis of the Impact of Probability Maps on Segmentation Quality  
The results confirm that the proposed approach using crack probability maps as the fourth input channel for segmentation models led to improved quality metrics in most cases. The most significant positive impact was observed for the FastFCN and ENet models, where the F1 score increased by 10.95-15.52%, and the IoU improved by 15.74-25.91%. This highlights the effectiveness of the approach, especially for models which initially demonstrated lower segmentation accuracy.  
The key findings for the impact of the proposed approach on various metrics include the following:  
* Precision improved in 7 out of 12 cases, with the maximum increase of 13.64% being found for FastFCN (IoU). However, deterioration of 7.03% was observed in the case of U-Net, indicating a reduced ability to filter false-positive segmentations.  
A possible explanation for the reduced ability of certain models to filter false positives lies in the accumulation of errors from both the segmentation model and the classification ensemble. To verify this hypothesis, future work will focus on improving the classification model ensemble and exploring the possibility of integrating the probability map into deeper layers of the segmentation networks rather than only in the initial stages.  
Figure 3: Examples of segmentation using three-channel images and four-channel images, with a probability map as an additional channel.  
In the case of U-Net, the observed degradation might be attributed to its architectural characteristics, specifically the use of skip connections between the encoder and decoder layers. This design allows the probability map, which has a low resolution, to propagate directly to the output, potentially influencing the final segmentation results in an unintended manner:  
* Recall improved in 9 out of 12 cases, demonstrating the models' enhanced ability to detect cracks. The highest increase of 26.32% was observed for ENet (loss), while the maximum deterioration (0.52%) was recorded for HRNet.
* The F1 score, which balances precision and recall, improved for most models. Improvements were observed in all cases except for U-Net (loss).
* The IoU increased for most models, confirming that probability maps helped improve the accuracy of the predicted masks.  
Thus, the proposed approach achieved significant improvements for certain segmentation models, particularly FastFCN, ENet, and DeepLabV3+, while some metrics for U-Net showed deterioration.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### Experimental Settings and Parameters  
We run the pre-training SSL pipeline for 100 epochs with a learning rate of 0.001 and a cosine annealing schedule. For the augmentation (A) task, we used a batch size of 500 for the EuroSAT and BigEarthNet pretraining and a batch size of 900 for SEN12 MS. We trained the pretext task on one GPU with 12 Go of memory for the EuroSAT and BigEarthNet datasets, and on a multi-gpu machine for SEN12 MS dataset to accelerate the learning process. The region based pretext (RA) task was applied to SEN12 MS using the same hyperparameters as for (A), with a batch size of 500.  
Next, we copied the parameters of the ResNet-50 backbone trained with SSL into the corresponding layers of the U-Net. We train the network in two ways: fine-tuning **(FT)** and transfer learning **(TL)**. The first one, FT, corresponds to training all the layers of the U-NET on the ship detection task, while for the latter, TL, we froze the layers of the backbone.  
For the ship detection task, both in the TL and FT modes, we train the network with 100 epochs, with a batch size of 20 and a learning rate of 0.001.  
We evaluate our method as a one class object detection algorithm, using the pycocotools package. We focused on object-wise precision, recall and F1-score (harmonic mean of precision and recall) metrics. We also compute the recall for each ship size (a ship is considered as small if its area is under 2500 m\({}^{2}\), otherwise it is considered as being large), and for each ship location (moored ships or sailing ships).
## 3 Results and Discussion
### Effects of SO\({}_{2}\) and H\({}_{2}\)O on NO Removal Activity
#### 3.3.3 Tg-Dta  
To distinguish the different sulfate species formed during the sulfation process, the fresh and used catalysts were examined using TG-DTA.  
Figure 8a shows the TG and Differential Scanning Calorimetry (DSC) curves of F-Mn-Ce/Al\({}_{2}\)O\({}_{3}\), in which the TG curve presented one primary weight loss at approximately 80 \({}^{\circ}\)C, with the DSC curve revealing a corresponding valley. The weight loss was attributed to the desorption of adsorbed water on the catalyst [41]. In the TG-DTA curves of S-Mn-Ce/Al\({}_{2}\)O\({}_{3}\) (Figure 8b), there are three main weight losses at approximately 80 \({}^{\circ}\)C, 370 \({}^{\circ}\)C, and 790 \({}^{\circ}\)C. The weight loss at 370 \({}^{\circ}\)C was attributed to the decomposition of NH\({}_{4}\)HSO\({}_{4}\) into (NH\({}_{4}\))\({}_{2}\)S\({}_{2}\)O\({}_{7}\) and H\({}_{2}\)O, and the (NH\({}_{4}\))\({}_{2}\)S\({}_{2}\)O\({}_{7}\) decomposition generated NH\({}_{3}\), SO\({}_{2}\), H\({}_{2}\)O, and N\({}_{2}\), as reported in the literature [42; 43]. According to the report, the decomposition temperature of Ce\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) or Ce(SO\({}_{4}\))\({}_{2}\) was greater than 700 \({}^{\circ}\)C [34]. Therefore, sulfated CeO\({}_{2}\) decomposition or desorption can explain the weight loss peak at 790 \({}^{\circ}\)C. Upon completion of the TG-DTA analysis, it can be concluded that ammonium hydrogen sulfate and sulfated CeO\({}_{2}\) were formed during the reaction process in the presence of SO\({}_{2}\).  
Figure 8: Thermogravury and Differential Thermal Analysis (TG-DTA) curves of fresh and used catalysts: (**a**) F-Mn-Ce/Al\({}_{2}\)O\({}_{3}\); (**b**) S-Mn-Ce/Al\({}_{2}\)O\({}_{3}\).
## 3 Results
### Classification Results and Accuracy Assessment  
#### 3.3.1 Airborne and Sentinel-2A Image Classification Results  
Table 5 lists the field area and the cotton root rot-infested areas from classification maps generated from the airborne images and Sentinel-2A images for Fields 1-8. The cotton root rot classification results generated from the airborne images showed that the total infested area for the eight fields was 560,286 m\({}^{2}\) with an average infestation percentage of 12.86% for the whole study area. The percentage of root rot-infested areas for Fields 1-8 ranged from 0.35% for Field 3 to 48.16% for Field 7. Among them, Fields 1-3 and 5 had relatively low level of infestation (low infestation), while Fields 4 and 8 had medium level of infestation (moderate infestation), and Fields 6 and 7 had high level of infestation (severe infestation). The cotton root rot classification results generated from the Sentinel-2A images showed more root rot-infested areas. The classification result of the spectral model showed that the total infested area estimated was 752,623 m\({}^{2}\) and the average infestation percentage was 17.27%, and the percentage of root rot-infested areas for Fields 1-8 ranged from 0.26% to 61.66%. For the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Regression** \\ **Model** \\ \end{tabular} } & \multicolumn{4}{c}{**Training Set**} & \multicolumn{4}{c}{**Test Set**} \\ \cline{2-9}  & **Acc (\%)** & **Pre (\%)** & **Rec (\%)** & **F (\%)** & **Acc (\%)** & **Pre (\%)** & **Rec (\%)** & **F (\%)** \\ \hline Spectral & 95.96 & 96.53 & 95.21 & 95.86 & 93.10 & 100.00 & 89.29 & 94.34 \\ Texture & 95.62 & 95.24 & 95.89 & 95.56 & 90.80 & 98.00 & 87.50 & 92.45 \\ Spectral-texture & 98.65 & 99.30 & 97.94 & 98.62 & 96.55 & 100.00 & 94.64 & 97.25 \\ \hline \hline \end{tabular} Note: Pre: Precision; Acc: Accuracy; Rec: Recall; F: F-score.  
\end{table}
Table 4: Classification accuracy of binary logistic regression (BLR) models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Regression Model** & **Variable** & **Regression Coefficient** & **Significant Level** \\ \hline Spectral & NDVIPSRI & 1.421116.997 & 0.0280.000 \\ Texture & Mean 1 Entropy 2 & \(-0.2325.751\) & 0.0000.000 \\ Spectral-texture & NDWI1 RDVIMean 1 Entropy 2 & 76.280—221.9401.3136.747 & 0.0090.0030.0060.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected features and regression parameters.  
texture model, the total infested area was estimated to be 1,139,019 m\({}^{2}\) with an average infestation percentage of 26.13%, and the percentage of root rot-infested areas for Fields 1-8 ranged from 1.54% to 75.05%. For the spectral-texture model, the total infested area was 811428m\({}^{2}\) and the average infestation percentage was 18.62% with the infestation percentage among the eight fields ranging from 1.22% to 57.50%. Clearly, the classification results of the spectral model were most similar to those of the airborne images, followed by the spectral-texture model and the area of root rot identified by the texture model was the largest.
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 3 Basic Algorithms
### APF Method  
#### 3.2.1 Gravitational Potential Field  
Here, we set the initial destination point as the origin of the gravitational potential field. which generates a force that attracts the robot toward this specific location. In fact, the function of a field of gravitational potential is generally described as:  
\[U_{att}(q)=\frac{1}{2}k_{att}d^{2}(q,q_{goal}) \tag{6}\]  
In Equation (6), \(q\) represents the current position (CP) of the robot, \(q_{goal}\) is the target point (TP), \(k_{att}\) is the gravitational coefficient, and \(d(q,q_{goal})\) is the distance between the CPof the robot and the TP. The strength of the gravitational field (equivalent to gravitational force per unit mass) can be mathematically described as the spatial derivative that points in the direction opposite to the gradient of the scalar gravitational potential function.  
\[F_{att}(q)=-\
abla U_{att}(q)=k_{att}(q_{goal}-q) \tag{7}\]  
The attractive force acting on the robot diminishes nonlinearly as its proximity to the target position (TP) increases. At the point of spatial coincidence between the robot and the TP, this force field achieves an equilibrium state with vanishing magnitude.
# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 3 Methods
### Fine-Tuned Pruned Network  
Armed with the insights from our channel importance analysis, we proceed to the crucial step of network pruning. Our goal is to significantly reduce the model size and computational requirements while maintaining high dehazing performance for a static scene. We establish pruning criteria based on a combination of our channel importance scores and a threshold determined through a series of ablation studies. This threshold is carefully calibrated to strike an optimal balance between model compression and performance preservation. We develop an progressive thresholding mechanism that considers both the global distribution of channel importance scores and local patterns within each layer, ensuring a nuanced approach to pruning. The pruning process itself is executed with meticulous attention to maintaining network stability. We implement a gradual pruning schedule, removing channels in small batches and allowing the network to adjust after each pruning step. This iterative approach helps mitigate the potential for sudden performance drops and allows the remaining channels to compensate for the removed ones. Post-pruning, we conduct another round of fine-tuning to recover and optimize performance with the reduced architecture. This fine-tuning phase employs a specialized learning rate schedule and loss function weighting that we developed specifically for pruned networks. Our approach places increased emphasis on preserving the high-frequency details that are crucial for effective dehazing, counteracting the potential loss of model capacity due to pruning. To evaluate the efficacy of our pruned network, we conduct a comprehensive performance analysis. We compare the pruned model against both the original pretrained network and the fine-tuned unpruned version across a range of metrics. Our results demonstrate that we achieve a substantial reduction in model size (up to 70% fewer parameters) and computational cost (up to 60% reduction) in FLOPs(Floating Point Operations) while maintaining dehazing quality within 5% of the unpruned model on key perceptual metrics. We provide detailed visualizations comparing the output of the pruned network with its unpruned counterpart, highlighting the preservation of critical dehazing capabilities.
# Revamping Fluid Catalytic Cracking Unit, and Optimizing Catalyst to Process Heavier Feeds
## 3 Results
### Performance of the FCC Unit before and after the Revamp
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **10 September–11** & **11 September–13** & **2 October–3 October 2022** \\
**Date** & **September 2018** & **September 2022** & **after Revamp** \\  & **before Revamp** & **after Revamp** & \\ \hline \hline
**FCC Operating conditions** & & & \\ Straight-run VGO flow rate, t/h & 205 & 222 & 225 \\ FCCPT trough-put, t/h & 225 & 200 & 230 \\ H-Oil VGO in FCCPT feed, wt \% & 20 & 0 & 5 \\ FCCPT Feed density at 15 \({}^{\circ}\)C, g/cm\({}^{3}\) & 0.915 & 0.9227 & 0.9198 \\
5\%, \({}^{\circ}\)C & 344 & 354 & 350 \\
10\%, \({}^{\circ}\)C & 367 & 373 & 369 \\
50\%, \({}^{\circ}\)C & 442 & 445 & 444 \\
90\%, \({}^{\circ}\)C & 518 & 522 & 520 \\
95\%, \({}^{\circ}\)C & 532 & 539 & 537 \\
**Kw** & **11.89** & **11.81** & **11.84** \\ Sulphur, \% & 1.98 & N.D. & N.D. \\ Concarbon, \% m/m & 0.48 & 0.26 & 0.18 \\ HTVGO flow rate, t/h & 203 & 172 & 203 \\ HTVGO density at 15 \({}^{\circ}\)C, g/cm\({}^{3}\) & 0.906 & 0.903 & 0.896 \\ \(\Delta\) Density, kg/m\({}^{3}\) & 9 & **21** & **23.8** \\
**Kw** & **12.05** & **12.06** & **12.15** \\ Sulphur, \% & 0.192 & 0.102 & 0.14 \\ H-Oil VGO flow rate, t/h & 52 & 78 & 47 \\ H-Oil VGO in the blend HTVGO-H-Oil & 27.5 & 31.2 & 18.8 \\ VGO, wt \% & 27.5 & 31.2 & 18.8 \\ H-Oil VGO density at 15 \({}^{\circ}\)C, g/cm\({}^{3}\) & 0.9273 & 0.9526 & 0.9403 \\
5\%, \({}^{\circ}\)C & 330 & 300 & 296 \\
10\%, \({}^{\circ}\)C & 353 & 324 & 322 \\
50\%, \({}^{\circ}\)C & 422 & 397 & 395 \\
90\%, \({}^{\circ}\)C & 494 & 497 & 474 \\
95\%, \({}^{\circ}\)C & 517 & 523 & 504 \\
**Kw** & **11.62** & **11.17** & **11.31** \\ Sulphur, \% & 0.624 & 0.622 & 0.366 \\ Concarbon, \% m/m & 0.15 & 0.21 & 0.11 \\ FCC feed flow rate, t/h & 255 & 250 & 250 \\ FCC slurry in FCC feed, \% & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LNB FCCU performance before and after the revamp.
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 2 Model Development
### First Order Radiative Transfer Model  
The vegetation model applied to a GNSS-R bistatic radar can be divided into two types, one of which is an approximate field method for weak scattering media. For vegetation, a strong scatterer is discrete, and the dielectric constants of such scatterers are much larger than those of air. Therefore, it is more suitable to use radiation transfer theory to solve the scattering problem. At the same time, the vegetation model can be divided into a continuous model and a discrete model. The continuous vegetation model treats the vegetation layer as a continuous fluctuating medium. The discrete vegetation model considers that the vegetation layer can be treated as a single scatter with different size, shape, and spatial distribution probability. In this study, when analyzing vegetation, a double layer noncoherent scattering model for high crown layers was employed, which divides the vegetation layers above the surface into the crown and the trunk layer [24]. The model is based on the Stokes matrix of a single scatterer. It obtains the corresponding phase matrix and finally derives the phase matrix of the respective scatters.
# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 2 Materials and Methods
### Improvements in VIEM-YOLOv8n Model  
To address the challenges of complex, diverse target features and target overlap in the chaotic stacked sugarcane detection, we made several significant improvements to the YOLOv8n model to enhance its detection performance and robustness.  
First, we replaced the backbone network of YOLOv8n with VanillaNet. VanillaNet simplifies unnecessary depth, skip connections, and complex operations (such as self-attention mechanisms), adopting a more lightweight design. This improvement effectively enhances the model's feature extraction capability and significantly improves computational efficiency. Especially in resource-constrained application scenarios, it ensures good inference speed and accuracy.  
Second, we introduced the InceptionNeXt deep convolutional architecture, which employs a multi-branch convolution design. This design decomposes large convolution kernels into multiple smaller ones, both improving computational efficiency and expanding the receptive field. As a result, the model can more accurately detect sugarcane targets of different shapes and sizes, especially in complex stacked situations, effectively capturing the key information of the targets.  
Third, in the feature fusion module C2F, we integrated an efficient channel attention mechanism (ECA). ECA captures channel interactions in a way that does not involve dimensionality reduction, effectively enhancing the model's attention to key features in stacked sugarcane. This improvement boosts the model's recognition ability in complex backgrounds, reducing the loss of critical features and thus improving detection accuracy.  
Finally, to improve the model's detection accuracy in overlapping target scenarios, we optimized the boundary box regression loss function by adopting the Modified Probabilistic Distance Intersection over Union (MPDIOU) loss function. This loss function significantly improves accuracy in overlapping target detection by minimizing the Euclidean distance between the predicted and ground truth keypoints, reducing false positives and false negatives.  
Through these improvements, the VIEM-YOLOv8n model not only significantly enhances the accuracy of sugarcane target detection but also ensures high efficiency in complex scenarios, making it more suitable for real-world agricultural tasks.