esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 3 Results and Discussion
### Training of Super-Resolution Model  
The Real-ESRGAN model was trained using the Kaggle platform and the Python programming language. The training process was conducted on an NVIDIA Tesla P100  
\begin{table}
\begin{tabular}{c c} \hline
**Port Name** & **Number of Tiles** \\ \hline Busan & 6641 \\ Durês & 224 \\ Hong Kong & 9931 \\ Iskenderun & 547 \\ Piraeus & 13,001 \\ Santos & 222 \\ Taranto & 7004 \\ \hline \end{tabular}
\end{table}
Table 2: Number of tiles for each port for training of Real-ESRGAN model with PlanetScope imagery.  
\begin{table}
\begin{tabular}{c c c} \hline
**Port Name** & **Number of Tiles** & **Port Name** & **Number of Tiles** \\ \hline Amsterdam & 478 & Ningbo-Zhoushan & 157 \\ Bremerhaven & 232 & Piraeus & 940 \\ Busan & 230 & Qingdao & 449 \\ Durês & 422 & Rizhao & 476 \\ Hong Kong & 543 & Rotterdam & 978 \\ Iskenderun & 147 & Santos & 134 \\ Izmir & 183 & Taranto & 607 \\ Long Beach & 927 & Tianjin & 664 \\ Nantong & 561 & Zeebrugge & 192 \\ \hline \end{tabular}
\end{table}
Table 3: Number of tiles for each port for enhancement of Sentinel-2A imagery.  
GPU-16 GB. The model's hyperparameters were carefully selected and adjusted to optimize performance, considering the images' hardware capabilities and dimensions. A detailed overview of these hyperparameters is provided in Table 4. The training consisted of 200,000 iterations, with each set of 100 iterations taking approximately 2 min, resulting in a total training time of about 67 h.  
Dataset 2 was processed utilizing the trained Real-ESRGAN model. The resulting data were analyzed both quantitatively and visually. Sample enhancements for the ports of Piraeus, Amsterdam, Hong Kong, and Nantong are illustrated in Figure 3. A careful examination of Figure 3 indicates a marked improvement in the visual interpretability of the images. Ships nearby are now more distinct; the distinction between land and sea has been enhanced with greater clarity. Performance metrics, including MSE, PSNR, and SSIM, were calculated for a comprehensive quantitative assessment of the results. The relevant metrics were calculated as 80.63, 29.67 dB, and 0.84, respectively.
## 5 Comparison of Different Topologies
### Topology Comparisons at a Glance  
Table 2 summarizes the comparative analysis of the four topologies based on the assumptions described in Section 2. The topologies were compared with ten performance metrics, namely total energy consumption, energy distribution, load distribution, redundant communication, data reliability, scalability, latency, network connectedness, lifetime, and topology management overhead. We mark each topology out of four for each evaluation metric according to their performance with regard to the corresponding metric. Finally, the points for each evaluation metrics of each topology are added. The totals, which indicate the overall performance, for each topology are shown at the bottom most row of the table.  
\begin{table}
\begin{tabular}{|l|c|c|c|c|} \hline Evaluation metric & Flat & Cluster-based & Tree-based & Chain-based \\ Total energy consumption & 1 & 3 & 2 & 4 \\ Energy Distribution & 1 & 3 & 2 & 4 \\ Load distribution & 3 & 3 & 3 & 4 \\ Redundant Communication & 1 & 4 & 4 & 4 \\ Data reliability & 4 & 3 & 3 & 2 to 3 \\ Scalability & 2 & 4 & 3 & 2 to 4 \\ Latency & 4 & 3 & 3 & 2 to 4 \\ Network Connectedness & 1 & 3 & 3 & 3 \\ Lifetime & 2 & 3 & 3 & 4 \\ Topology management overhead & 4 & 3 & 2 & 4 \\ \hline Overall Scores & 23 & 32 & 28 & 32 to 37 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of different topologies.  
In our point system, 4 means excellent, 3 means good, 2 means fair and 1 means poor. Depending on the design structure of a topology, some fields of the table have a range of numbers presented as x to y. For example, with regard to latency, single chain-based topology shows fair results, whereas multi-chain topology shows excellent results. Thus for latency, the chain-based topology received 2 to 4. These points used here are entirely relative. For example, with respect to energy consumption, cluster-based topology is better than flat topology but not as efficient as chain-based topology. Note that this point system is used for the purpose of easy understanding and does not provide a standardized measure for comparison.  
The table shows that the chain-oriented topology scores the highest among four topologies, whereas the flat topology scores the lowest. The cluster-based topology performs better than the tree-based topology but not as efficiently as the chain-based topology. Nevertheless, there are some areas in the chain-based topologies to which special attentions should be paid by the designer to make the topology more efficient.
## 3 Materials and Methods
### Grid Attention Block (GAB)
#### 3.4.2 Complexity Analysis and Efficiency Optimization  
For the quadratic complexity problem existing in GAB, we have carried out some optimization processing operations. And we conducted a computational complexity analy  
Figure 4: The structure of Grid Attention Block (GAB).  
sis on Swin Transformer and GAB. The Swin Transformer attention mechanism operates within local windows of size \(M\times M\), leading to a complexity of the following:  
\[\mathcal{O}_{swin}=HWC+HWM^{2}. \tag{22}\]  
By contrast, the proposed \(GAB\) computes attention over \(K^{2}\) grid groups, each containing \(\frac{H}{K}\times\frac{W}{K}\) tokens. Its computational complexity is as follows:  
\[\mathcal{O}_{GAB}=2 HWC+\frac{H^{2}\mathcal{W}^{2}}{K^{2}}. \tag{23}\]  
While \(GAB\) enables richer long-range interactions beyond the local window constraints of the Swin Transformer, the quadratic term may raise concerns for high-resolution inputs. To mitigate this, we introduce a Top-k token selection strategy within each group: before applying attention, we select the most salient \(k\ll\frac{H}{K}\times\frac{W}{K}\) tokens based on activation strength. This effectively reduces the complexity to approximately the following:  
\[\mathcal{O}_{Top-k\;GAB}=HWC+HWk \tag{24}\]  
with \(k\) being a small constant, yielding linear complexity with respect to image size, and making \(GAB\) applicable to high-resolution super-resolution tasks.  
Although the underlying self-attention mechanism in GAB remains conceptually unchanged, we refine its structure by replacing the conventional MLP with SGFN, and further enhance its efficiency through the Top-k token selection scheme. These two enhancements work synergistically: SGFN improves local spatial reasoning, while Top-k attention reduces redundancy and accelerates computation. As confirmed by the ablation experiments in Table 1, GAB alone may slightly degrade performance due to overgeneralized attention, but its effectiveness is fully restored when paired with SGFN and the proposed Top-k optimization, demonstrating the importance of both selective interaction and structural coupling.
## 4 Results and Analysis  
The relationships established in the conceptual model were tested using the partial-least-squares approach to structural equation modeling (PLS-SEM). The application of the mentioned approach is very effective when working with a large number of constructs and relations that together create a complex model. In addition, the PLS-SEM approach demonstrated good analytical performance in working with non-normally distributed data and when testing the statistical significance of indirect relationships between latent variables [71]. Using the results of the identified indirect relationships and additional statistical analysis, the mediator effect can be discovered. An additional argument for the application of the mentioned approach was identified in previous studies of an interdisciplinary type. Similar to our study, previous studies on strategic management [72] and information systems [73] successfully applied the partial-least-squares approach to structural equation modeling. The SEM approach was deployed using a two-step procedure, as described by [PERSON] and [PERSON] [74], to validate the measurement model and to assess the quality of the structural model. Preliminary data preprocessing was conducted using version 24 of the statistical program SPSS. The study utilized SmartPLS 4.0 software to compute the essential indicators of the reflective model as defined.
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 4 Results
### Overall Accuracy of Inversion Result  
The data from the Aquarius LiDAR waveform were solved as survey data. A bathymetric inversion of the QuickBird image was carried out. Control and validation points were selected uniformly and randomly, and their numbers were 83 and 91, respectively. The distribution of the points is shown in Figure 10(a).  
The regression relationship between the survey data and the inversion data of the control points was established with Equation (8). The Newton iteration method was applied to accomplish the  
Figure 11: (**a**) Distribution of control and validation points; (**b**) depth inversion based on active and passive remote sensing combined.  
Figure 10: Scatter plot between the depth inversion result and the officially published value.  
regression of model parameters. The optimal regression parameters were \(a_{0},\ a_{1},m,\ \text{and}\ n\), and the \(R^{2}\) of the fitting was 0.96. Control and validation points were then used with ILCRM to retrieve the depth value. \(R^{2}\), MRE, and MAE were used as the evaluation indicators of accuracy. The overall accuracy of the inversion result is shown in Figure 12.  
Figure 11a,b show the scatter plots of the control points and the validation points, respectively. The derived depths of both points were well fitted with the survey data. However, the result of the validation points was more discrete than that of the control points, and the fitting coefficients of \(R^{2}\) were 0.95 and 0.96, respectively. The water depth of the inversion was in the range of 2-22 m. The overall MAEs were 0.80 m and 0.95 m, and the overall MREs were 7.0% and 8.9%, respectively.
# Multiple-Valued Logic Modelling for Agents Controlled via Optical Networks
## 1 Introduction
### Data Protection of Stationary Agents in Classical and Quantum Network Nodes
Although modern QKD systems did not obtain enough parameters for mass stream secret coding by the most secured one-time cipher pad (OTP) coding method and have some quantum and classical vulnerabilities of their optical and computer components, they are hoped to provide large additional technical, competence, and cost barriers for eavesdroppers [22; 23; 60]. The drawbacks of modern fiber optics QKD lines refer to the real limited length of quantum key transfer (less than 80-100 km), low fan-out coefficients, the possibility of physical damage of a QKD line, and the threat of being suppressed by intense optical noise. Current designs of quantum memory [61] are too far from practical needs, and modern QKD schemes cannot provide reliable data storage and exclude illegal modifications. Moreover, quantum networks together with ordinary ones can suffer from data leakages carried out by disloyal personnel [62]. Nevertheless, the research of QKD schemes continues, and in order to raise the security level of energy power systems [63], the trusted server with \"star\" architecture was combined with the large number of QKD lines. Other attempts [64; 65] also aimed at the realization of trusted nodes by various quantum schemes. The possibility of using atmospheric quantum laser lines for agents was principally demonstrated by key distribution between ground stations and satellites [66; 67], as well as by experiments with an airborne platform [68]. However, satellite QKD links can only be a partial solution for mobile agents, as data exchange sessions are scheduled by timetables of orbital flights, but their positioning can be somewhat unpredictable. Thus, the schemes and methods discussed above do not provide ready solutions for data protection in mass hardware robotic agents and easy ways to avoid expensive trusted servers. That is why one of the opportunities for agents and MASs is to use the principles of the linked list and the distributed ledger taken from BC methods [37; 38; 39]. Cryptocurrencies mining and stock trading seem not to be actual tasks for robotic agents, but new verification schemes are necessary for the autonomous confirmation of their rights and powers, licenses, as well as for the verification of technology data, and breakdown restoration. In contrast to cryptocurrencies mining schemes [53; 54], the verification of data in the collective robotic protocol can be based on the simple majority voting of participants, which are to be involved based on mutual interests or service payments, but not on mining. Respectively, the term \"entry\" but not \"transaction\" is used further.
## 3 Proposed Method  
SegNet network retains the location information of the object through the max-pooling indices. The U-Net network realizes the combination of the low-level image features and the high-level image features through the multi-level jump structure, which can extract fuller features and reduce the dependence on the number of samples. Thus, we improve U-Net and SegNet networks to propose a new multi-scale deep features fusion and cost-sensitive loss function based multi-object classification network for remote sensing images, named MFCSNet.  
As shown in Figure 3, the improved network in this paper consists of the encoding part (on the left side) and the decoding part (on the right side). The multi-scale feature encoding structure is used to extract each scale features of the input image continuously. This process includes convolution layers and pooling layers. Then the up-sampling is continuously performed by the decoding structure. To accurately retain the location information of the object features, the up-sampling is achieved by the max-pooling indices structure.  
Figure 3: The proposed network structure.  
In order to make full use of the shallow features, such as texture and edge of the image, the corresponding feature map in the feature coding structure is continuously combined during up-sampling. Thereby, the overall classification performance of the network can be improved. To improve the classification accuracy for the objects with a small number of samples, we propose a cost-sensitive loss function, which improves the classification performance of the network. The network details of each part will be discussed in the following.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 2 Related Work  
### Medical Image Segmentation  
Deep learning methods based on convolutional neural networks (CNNs) have demonstrated excellent performance in medical image segmentation. [PERSON] et al. [7] proposed U-Net, which uses a fully convolutional network for feature extraction and fusion. Subsequent variants [8, 14, 15] have further demonstrated the effectiveness of the U-shaped architecture. [PERSON] et al. [16] developed a brain tumor segmentation network that utilizes a residual architecture and multi-scale feature extraction to improve performance. [PERSON] et al. [17] proposed RU-Net, which integrates batch normalization with a residual network and employs a pooling index transmission mechanism to enhance spatial correlation for efficient segmentation. Transformer has also been widely applied in medical image segmentation. [PERSON] et al. [18] proposed TransUNet, which combines the hybrid structure of CNN and Transformer, utilizing the local feature extraction of convolutions and the global modeling capabilities of Transformers, respectively. [PERSON] et al. [19] proposed a scheme for segmenting 3D images into 3D patches to obtain a global representation. SwinUNETR [20] further improved segmentation accuracy by incorporating the Swin Transformer [21] module, which replaces the traditional downsampling and upsampling processes.  
With the development of deep learning frameworks, the accuracy of tumor segmentation has significantly improved. However, this is accompanied by an increase in memory consumption and the number of parameters. In particular, in 3D medical image segmentation, 3D image data require the network to process a large amount of voxel information, leading to a significant increase in computational complexity. In addition, larger models typically require more parameters to learn and memorize complex feature patterns, further burdening the memory of the computing device. As a result, despite the significant advances in accuracy of these state-of-the-art (SOTA) models, the increased computational costs and hardware requirements limit their widespread use in clinical practice.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 4 Validation of Parabolic Mesh Reflector
### Near-Field Antenna Measurements  
Antenna pattern measurements were realized with a near-field plane polar system at A-METLAB facilities at Kyoto University, composed of a robot scanner, a dual-linearized probe, and a computer subsystem. The helical antenna was placed at position \(z=-2\) cm, considering the parabolic and helical antenna's phase center. Measurements were realized in the frequency range of 1.1-1.55 GHz. From the sampled radiating near-field, the data could be later converted into far-field. The measuring probe was located in the ceiling, and the antenna under test (AUT) on a lifting table over a turn plane on the ground. Figure 8b illustrates the planar near-field measurement system setup. The distance from probe to AUT was 1.1 m. The probe gathered 57 points within a radius of 2.7 m at 0.09 m spacing, and 91 points on a polar fashion at 2\({}^{\circ}\) step. Far-field points were derived, 201 points in the azimuth and elevation directions, ranging from \(-60^{\circ}\) to \(+60^{\circ}\), at step angle of \(0.6^{\circ}\). Results are depicted in Figure 10, showing simulated and measured far-field antenna directivity at center frequency.  
Directivity measured was 29.86 dB, with peak slightly deviated from the boresight by 0.38\({}^{\circ}\). Highest measured SLL was 15.8 dB, and axial ratio 2.94 dB at center frequency. A summary of simulated and measured results are in Table 7 for minimum, center and maximum frequencies. From the near-field measurements, holographic maps were also derived, displaying amplitude and phase data of co-polarization field in center frequency in Figure 11.  
Figure 9: Computed cloud-to-cloud (C2C) absolute distances in millimetres between reflector model and scanned areas of reflector ribs, rims and center plate.  
Figure 10: Simulation and measurement results at center frequency of RHCP directivity in the: (**a**) E-plane; (**b**) H-plane; and axial ratio in the: (**c**) E-plane; and (**d**) H-plane.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Co-pol (dBic)** & **AR (dB)** & **HPBW** & **SLL (dB)** & **Cross-pol (dBic)** \\ \hline Measured at \(f_{min}\) & 29.8 & 2.25 & 4.2\({}^{\circ}\) & \(-\)16.86 & 12.9 \\ Measured at \(f_{c}\) & 29.84 & 2.94 & 3.8\({}^{\circ}\) & \(-\)15.86 & 12.8 \\ Measured at \(f_{max}\) & 29.7 & 2.37 & 4.1\({}^{\circ}\) & \(-\)15.2 & 13.8 \\ Simulated at \(f_{c}\) & 30.6 & 2.2 & 4\({}^{\circ}\) & \(-\)15.2 & 24.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of results of near-field antenna measurements at minimum, center, and maximum frequencies (\(f_{min}\), \(f_{c}\), \(f_{max}\)) regarding: peak co-polarization at boresight (Co-pol), axial ratio (AR) at boresight, half-power beam width (HPBW), sidelobe levels (SLL) and cross-polarization at boresight (Cross-pol).
## 2 Materials and Methods
### Experimental Setup and Design
#### 2.1.2 Design of Experiments  
To investigate whether the factors listed in Table 2 influenced the positioning accuracy, time required for positioning tasks, user experience, presence, and workload, the different levels of the factors (e.g., 40 mm or 110 mm diameter) were varied and tested. Owing to the high number of factors, a classical experimental design where only one factor is varied (e.g., the diameter) and the other factors are kept constant would have led to an enormous experimental effort. Therefore, we used the design of experiments (DOE) method in this study. The DOE is a standardized methodology for the planning and statistical evaluation of experiments [57]. This methodology aims to learn as much as possible about the relationships between influencing factors and results with as little experimental effort as possible. The information obtained is statistically confirmed, and the effects of the input variables on the output are quantifiable [58]. In the DOE, several factors are varied simultaneously. The factors are varied according to a specific system (test design) such that the influence of a single factor can be statistically calculated [59].  
In this study, a fractional factorial I-optimal response surface test design with 35 trials (joystick) and 32 trials (rotary control) was created using Design Expert 13 software (Stat-Ease, Minneapolis, MN, USA). In I-optimal designs, the algorithm selects points that minimize the integral of the prediction variance in the entire design space [60,61]. Table 3 lists an excerpt from the test design for the rotary control as an example. The levels of the factors listed in Table 2 were varied systematically. The complete test designs can be found in the Supplementary Material (cf. Tables S1 and S2).