esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Experimental Multipath Delay Profile of Mobile Radio Channels Over the Sea at 2 GHz  
The path loss model and measurement results for over-the-sea wave propagation were already studied at short distances in [8], which we have described in Section 4.1. However, the work [10] showed two campaigns and well extended the work presented in [8]. These two campaigns have a short-distance over-the-sea wave propagation (ranging up to 15 km) and a long-distance over-the-sea wave propagation (ranging up to 45 km). Channel properties such as probability density function (PDF), cumulative distribution function (CDF), RMS delay spread, and mean excess delay were presented for both of the campaigns. The measured PDFs were characterized by using a three-term Gaussian model. For a short-range campaign, a fixed dual-antenna channel sounder receiver was positioned in the harbor area. A high-speed craft contained a mobile single-antenna transmitter. For the long-range campaign, a fixed dual-antenna channel sounder receiver was placed on the coastline area close to the shore in an open sea. A research vessel contained a mobile single-antenna transmitter in this case. A simple channel model scenario, where the receiving antenna is fixed on shore, and the mobile transmitter is in the vessel is presented in Figure 12.  
After analyzing all the experimental results, it was found that the PDF of the mean excess delay and RMS delay in short-range measurements, adopted from the harbor area via an omni-directional transmitting antenna, are Gaussian-like or contain a peak that allowed the authors of [10] to model these channel parameters using the three-term Gaussian model. It was found that the reflection from the environment is too weak and very close to the LoS in the delay domain, when considering the long-range measurements taken from the coastline with a directional transmitting antenna. The main drawback of this paper is the lack of specific measurements for the specular and diffuse components of multipath propagation. Moreover, no specific path loss model has been elaborated in the measurement techniques.  
Figure 12: A scenario where an antenna is fixed on the shore and a mobile antenna is in the vessel.
# Space Robot Sensor Noise Amelioration Using Trajectory Shaping
## 2 Materials and Methods
### PID Controller  
Facilitating the study of input trajectory shaping methods, some control must be benchmarked. While whiplash control from the prequel in reference [11] was selected as the single feedforward control benchmark (to be discussed shortly in Section 2.6), a PID controller with structural filters was chosen as the single benchmark feedback control and was designed to control the motion of the reaction wheel using the procedures in the prequel (Equations (29)-(38) in reference [10]). The PID controller was designed to meet the following specification requirements: 15% overshoot and control bandwidth of 4 rad/s. It is assumed that the natural frequency of the closed loop response is equal to the control bandwidth. The rise time, damping ratio, settling time, and period are calculated according to Equations (10), (11), (12), and (13), respectively.  
\[t_{r}=\frac{1.8}{\omega_{n}}=\frac{1.8}{4}=0.45\;\mathrm{s} \tag{10}\]  
\[\zeta=\frac{-ln(0.15)}{\sqrt{\pi^{2}+ln^{2}(0.15)}}=0.517 \tag{11}\]  
\[t_{s}=\frac{4.6}{\zeta\omega_{n}}=\frac{4.6}{0.517*4}=2.22\;\mathrm{s} \tag{12}\]  
\[T\cong\frac{10}{\zeta\omega_{n}}=\frac{10}{0.517*4}=4.84 \tag{13}\]  
The proportional, integral, and derivative gain values are calculated according to Equations (14)-(16).  
\[K_{P}=I_{w}\bigg{(}{\omega_{n}}^{2}+\frac{2\zeta\omega_{n}}{T}\bigg{)}=1.537 \tag{14}\]  
\[K_{I}=\frac{I_{w}\omega_{n}^{2}}{T}=0.301 \tag{15}\]  
\[K_{D}=I_{w}\bigg{(}\frac{1}{T}+2\zeta\omega_{n}\bigg{)}=0.396 \tag{16}\]  
The PID controller and the flexible spacecraft system were modeled in MATLAB(r), and the simulation results are presented in Section 3.
# Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
## 4 Results
### Mapping of DSL  
As the uppermost limit of the frozen percolation radar zone, the DSL's position can shift significantly due to heavy snowmelt in summer. With the low temperatures in winter, very sporadic snowmelt was detected by the SSM/I in the coastal regions, indicating a stable position of winter DSL in the AP. On SAR images, the boundary between the frozen percolation and dry snow radar zone is seen as a transition from bright to dark. In Figure 10, we compared winter \(\sigma^{0}\), melting days, and the elevation of a profile across the Larsen G Ice Shelf and the upstream Swan Glacier (the green star shown in Figure 6c). Backscatter decreased significantly with the increase in elevation. SSM/I-derived melting days changed from two days to zero in the same time, indicating a summer with weak snowmelt. Backscatter was strong on the Larsen F Ice Shelf, and plummeted (under \(-6.5\) dB) when the elevation increases above 1114 m. The \(\sigma^{0}\) decreased to \(-15\) dB rapidly in places where no summer snowmelt was experienced. The clear boundary of the transition is the winter DSL, and the DSL elevation of profile AB is 1114 m.  
DSL in the AP was mapped by using the winter Sentinel-1 mosaic. Dry snow radar zones were mapped based on the decision tree. After clustering based on the classification aggregation in the ENVI software, the boundary of the dry snow patches larger than 25 km\({}^{2}\) was extracted. The dry snow radar zone and DSL are shown in Figure 11a. Very good agreement was found between the dry snow regions detected by SAR and radiometer (Figure 11b). Compared with the melting days derived by SSM/I, DSL is mainly distributed in the areas melted for one to three days in summer, suggesting that the SSM/I recognized a slightly larger melt area. DSL distributed along the mountainside varied with latitudes and elevations, especially for the Palmer Land (Figure 11c).  
DSL agreed well with contour lines locally, but DSL elevations varied with the latitudes. The mean elevations of the DSL of the Palmer Land were calculated at a 0.5\({}^{\circ}\) interval of latitude from 74\({}^{\circ}\)S to 70\({}^{\circ}\)S. As shown in Figure 12, DSL elevations increased from south to north.  
Figure 9: Comparison of 2-m air temperature (red solid line) and melt areas detected by Sentinel-1 (cyan stems) and the SSM/I (green areas). Melting signals were detected when mean air temperature rose above 266 K (red dotted line).  
Figure 11: (**a**) shows the dry snow radar zone (yellow area) and DSL (red line) derived by Sentinel-1. The DSL was overlapped with melting days and elevation in (**b**,**c**), respectively.  
Figure 10: Winter \(\sigma^{0}\) (blue line), melting days (green line), and elevation (black line) along a profile across the Larsen F Ice Shelf and the upstream Swan Glacier (corresponding to the star in Figure 6c). The red line marks the location of the dry snow line (DSL). The backscatter map at the bottom left corner shows the route of profile AB (yellow line).
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
###### Abstract  
Wildfire's are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem.  
wildfire severity prediction; deep neural networks; multi-channel attention-based analysis 1111060. [[https://doi.org/10.3390/app112211060](https://doi.org/10.3390/app112211060)]([https://doi.org/10.3390/app112211060](https://doi.org/10.3390/app112211060))
# A Distributed File-Based Storage System for Improving High Availability of Space Weather Data
## 5 Experiment
### Mean Time of Files Replication  
Overall, the results showed that replication using the IPFS + IPFS-Cluster was faster than Rsync. This was because the IPFS replicated all files using chunks formed by the hash function, which has a small and fixed size.  
Figure 11 presents the replication time of files as a function of latency when all files were only replicated from Computer 3 to Computer 1 and Computer 2. The main observation was that the latency had a more significant impact when replicating large files. File size determined the duration of replication due to the bandwidth limit. The effect of latency also appeared to be very significant in the replication time. It could be seen that high latency made a considerable difference compared to low latency. It took 332.35 s to replicate files of 1 GB (\(100\times 10\) MB) when the default latency was considered, and 427.45 s when the latency of 20 ms was used for the IPFS + IPFS-Cluster. Similarly, in Rsync, it was 335.57 s at the default latency and 414.02 s when the latency was 20 ms.  
Another interesting observation was that file replication by IPFS + IPFS-Cluster experienced a slight drop within small and large latencies where it took 47.91 s and 43.2 s for file sizes of 100 MB (\(100\times 1\) MB). On the other hand, file replication by the Rsync had a rising trend when latency increased. Replication took 7.49 s to 12.83 s for the file size of 25.6 MB (\(100\times 256\) kB) and 29.55 s to 62.92 s with the file size of 100 MB (\(100\times 1\) MB). This might be due to the more stable bandwidth used or minimal usage sharing Wi-Fi with other users.
## 4 Experimental Design and Results  
This section evaluates the performance of the proposed HEMOGWO through two sets of experiments. The first set aims to assess the algorithm's effectiveness in solving multi-objective optimization problems. HEMOGWO is compared against several representative multi-objective optimization algorithms, including SMOGWO, MOGWO, MOEAD_DE, MOPSO, and NSGA-III. All algorithms are tested on 17 widely recognized benchmark functions to provide a comprehensive evaluation of the optimization performance and improvement capability of HEMOGWO. The second set of experiments focuses on evaluating the adaptability and robustness of HEMOGWO in solving combinatorial optimization problems of varying scales. Tests are conducted on a series of real-world service composition models to further verify the algorithm's generality and practical applicability. All experiments are conducted under the following hardware and software environment: Windows 10 operating system, Intel Core i5-12400F processor (2.5 GHz) (Intel, Santa Clara, CA, USA), 16 GB RAM, and MATLAB R2020b as the development and execution platform.  
To comprehensively evaluate the performance of HEMOGWO, two widely adopted multi-objective performance metrics are employed: Generational Distance (GD) and Inverted Generational Distance (IGD) [28].  
The GD metric quantifies the average distance between the obtained approximation set (PF\({}_{\text{know}}\)) and the true Pareto front (PF\({}_{\text{true}}\)), and is calculated using Equation (25).  
\[GD=\frac{\sqrt{\sum_{i=1}^{n}\min(d_{vi}^{2})}}{v} \tag{25}\]  
where \(v\) represents the total number of Pareto solutions obtained, and \(d_{vi}\) denotes the Euclidean distance between the \(i\)-th obtained PF\({}_{\text{know}}\) and the closest PF\({}_{\text{true}}\). A smaller GD value indicates better convergence performance of the algorithm.  
The IGD is calculated by measuring the average distance from the true Pareto front to the approximate solution set, serving as a metric for both the convergence and diversity of the solution set. The formula for calculation is given in Equation (26).  
\[IGD=\frac{\sqrt{\sum_{i=1}^{n}\min(d_{ui}^{2})}}{u} \tag{26}\]  
where \(u\) represents the total number of Pareto solutions obtained, and \(d_{ui}\) denotes the Euclidean distance between the \(i\)-th PF\({}_{\text{true}}\) and the closest PF\({}_{\text{know}}\). A smaller IGD value indicates superior performance of the algorithm in approximating the true Pareto front.
## 3 Results and Discussion
As can be observed in Table 2, when the same IOP was considered, the maximum von Mises stress level rose with keratoconus evolution, and both values had higher values than those obtained with [PERSON] eye's geometry. These results are alienated by those presented by other authors [13; 28; 29], however, to our knowledge, this is the first study that compare at the same time the methods of displacements and prestress on real geometric models of patients, analyzing the influence of the stress field of both methods on healthy patients and in an early stage of the disease. Finally, and for validation purpose, the intraocular pressure (IOP) was directly applied to the free stress geometry of the theoretical [PERSON] model and patient-specific models, the results obtained showed that this IOP distorts the deformation field in the tissue of the corneal structure as shown in Figures 7-9. On the models, a tangent elastic module was derived in the central zone from the stress and strain values. An average value of 0.34 MPa was obtained for [PERSON] eye's geometry (15 mmHg), a value of 0.37 MPa for the G0 geometry (18 mmHg), and 0.37 MPa for the G1 geometry (16 mmHg). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Geometry**} & **Patient-Specific** & \multicolumn{2}{c}{**Average Maximum**} & \multicolumn{2}{c}{**Average Maximum**} \\  & **IOP (Pa)** & **von Mises Stress in** & **IOP (Pa)** & **von Mises Stress in** \\  & & **the Central Zone (Pa)** & & **the Central Zone (Pa)** \\ \hline Navarro & 15 & 15,885 & 15 & 15,885 \\ G0 & 18 & 19,503 & 15 & 16,253 \\ G1 & 16 & 19,641 & 15 & 18,413 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Maximum von Mises stress level in central zone of the cornea (Pa). Figure 7: Von Mises strain distribution in measurement phase for [PERSON]’s geometry. The tangent elastic modulus obtained in the central zone of the modelled corneas is in the range of values reported by other authors in inflation tests [39]; therefore, our models are consistent with the research developed. The values obtained are limited to the material model chosen and the parameters implemented, that depends on patient-specific tests. This factor does not affect to the results of this research: The two implemented methods, displacement method and prestress method, were equivalent, and the same stress distribution in the measurement phase was obtained. The displacement method allowed the stress-free geometry to be directly recovered and it proved to be a less complex method from the computational point of view. Regarding the clinical application of simulation models, the main challenges are defined by a realistic simulation in real time [40; 41]. In this sense, our research team developed specific real-time patient 3D geometric models [30; 31; 32; 33]. The accuracy of these geometric models allows for a good quality meshing, which leads to the efficient generation of specific models used in preoperative analysis or for surgical planning.
## 5 Conclusions and Future Work Recommendation  
We have proposed a novel wave-shaping technique, GSC, for the reduction of sidelobes of OFDM signal. The proposed technique allows the desired portion of the signal to pass and blocks the undesired portion, _i.e._, the sidelobes. The performance comparison of the proposed technique in different spectrum-sharing scenarios with already existing sidelobe suppression techniques is done through simulations, which show that the proposed technique achieves more than 90 dB reduction in sidelobes as compared to the existing techniques.  
In the future, one can use the proposed approach for direction of arrival estimation of plane waves, as well as spherical waves. Moreover, the proposed scheme can also be tested for independent null steering.
## 2 Cold-Sky Calibration Simulator
### Cosmic Background Radiation during Calibration  
Sufficiently stable targets such as galactic poles are used in external calibration measurement, which can provide an image level of about 3.5 K with spatial homogeneity over large regions and good temporal stability [1; 3; 4; 8]. Using the deep-sky view, the absolute calibration of the radiometer is performed, whose procedure is called 'flat-target transformation' [14]. The bright temperature map of the L-band cold sky is established in both declination and right ascension in equatorial coordinates using the J2000.0 or B1950 system [15]. The brightness temperature of the cold-sky target is rather constant for both polarizations [3; 4]. The nominal SMOS sky map is derived from the Reich and Testori continuum map [15]. During the cold-sky view, the main lobes of the antenna look up toward the cold sky and the back lobes look toward Earth, and each antenna of the synthetic aperture radiometer moves along the spacecraft's orbit. The components that contribute to  
Figure 4: (**a**,**b**): The angle of the direct and reflected sun directions with respect to the antenna’s boresight, as a function of the day of the year, while the satellite operates on a typical sun-synchronous orbit and observes Earth. The eclipse is not considered here. (**c**): The angle of the direct moon directions with respect to the antenna’s boresight, for which the situation when the moon disappears below the satellite’s horizon during the orbit is not removed.  
Figure 5: An antenna brightness temperature map of the sun. The green and blue dots represent the brightness temperatures at the footprints’ center.  
the BT of the antenna FOV can be obtained by the position of the satellite, which is given by the ellipsoid equation in Cartesian coordinates in an Earth-centered inertial reference frame, and the Sky Tb can be obtained by integrating over the antenna pattern. The area observed by the radiometer antenna is converted into right ascension and declination under the corresponding coordinate system, and the cold-sky scene is interpolated to the image observed by the radiometer. The contribution of the celestial sky is obtained at each point of the satellite. The cross-correlations between the pairs of antenna elements are carried out to synthesize the visibility function.
## 4 Results
### Arrhythmia Classification Using DA-BSMs for Coarse-Grained Databases
#### 4.2.2 Evaluation with PTB-XL Database
[43] utilized an autoencoder-based anomaly detection method designed to distinguish AFIB from the SR by measuring reconstruction errors. Despite achieving a robust F1-score of 0.925 with a single lead, this method's dependence on significant resampling restricts its scalability. [PERSON] et al. [44] proposed a lightweight CNN utilizing entropy-based features extracted from normalized ECG signals, achieving computational efficiency but a comparatively lower F1-score of 0.8910, highlighting its limitations in classifying complex arrhythmias like AFIB. In contrast, the proposed DA-BSM framework demonstrated competitive performance (F1-score: 0.9267) while requiring only a single lead and eliminating the need for data resampling. By effectively bridging the gap between fine-grained and coarse-grained annotations, the DA-BSM ensures strong adaptability and consistent generalization performance across heterogeneous ECG databases and diverse clinical environments. To visually illustrate the benefits of the DA-BSM framework, Figure 7 presents t-SNE visualizations of the ECG data distributions from the PTB-XL database at different stages of the proposed method. In this figure, panel (A) depicts the raw ECG data distribution without any preprocessing or domain adaptation, showing considerable overlaps between rhythm classes (SR and AFIB). Panel (B) illustrates the limited class separation obtained by training exclusively with WSL using coarse-grained annotations without pretraining. Panel (C) demonstrates the improved separability achieved by pretraining alone on the fine-grained MITDB dataset, though the overlaps between classes remain noticeable. Finally, panel (D) shows distinct and clearly separated rhythm clusters resulting from the combined application of pretraining and weakly supervised adaptation. These visual results further underscore the effectiveness of the proposed approach in enhancing class distinction, robustness, and generalization performance across heterogeneous ECG databases. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Author** & **Features** & **Classifier** &
\begin{tabular}{c} **Data** \\ **Sampling** \\ \end{tabular} & **Leads** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline [PERSON] & FD-ECG & \multirow{2}{*}{CNN} & Resampling & 12 & 0.930 & 0.980 & 0.960 & - \\ \cline{1-1} [PERSON] et al. [43] & Raw ECG & Autoencoder & Resampling & 1 & 0.930 & 0.925 & 0.925 & 0.930 \\ \hline [PERSON] et al. & Entropy + & \multirow{2}{*}{CNN} & \multirow{2}{*}{None} & \multirow{2}{*}{1} & \multirow{2}{*}{0.889} & \multirow{2}{*}{0.893} & \multirow{2}{*}{0.891} & \multirow{2}{*}{0.892} \\
[44] & ECG & & & & & & \\ \hline Proposed work & DA-BSM & CNN & None & 1 & 0.922 & 0.932 & 0.927 & 0.978 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of the proposed DA-BSM and state-of-the-art methods for arrhythmia classification on the PTB-XL database (SR vs. AFIB; number of samples: SR = 16,782, AFIB = 1514. ).