esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 HydroSAR Product Portfolio and Product Algorithms
### Intermediary Data Products and Visual Aids
#### 4.2.2 RTC30 Product  
Previous research has demonstrated the benefits of radiometric terrain correction (RTC) processing for many mapping and hazard monitoring applications [10; 11; 40; 41]. The removal of topography-induced radiometric biases (topographic shading) from SAR images aids in the visual interpretation of SAR amplitude data by analysts. Furthermore, RTC processing reduces the requirement for exact repeat acquisitions in change detection algorithms by mitigating the differences between images which are introduced by changes in the observation geometries [10].  
Based on the end user input, HydroSAR's RTC30 product (Figure 5a) was designed as a 30 m RTC-processed SAR image product, and it serves as a visual aid and as an essential base dataset in the HydroSAR service. RTC30 forms the basis for most higher-level data products in the HydroSAR product portfolio (Figure 3).  
We utilized the pixel area integration approach proposed by [PERSON] [42] to perform RTC processing. We chose this approach as it was reported in several studies to provide the best RTC processing accuracy [43; 44; 45]. It was also adopted by the Analysis Ready Data (ARD) working group of the Committee on Earth Observation Satellites (CEOS) as one of the recommended approaches for generating terrain-normalized radar backscatter products.  
In this approach, radiometric normalization is performed by determining the actual area on the ground for every SAR resolution cell. According to [10], the average normalized backscatter coefficient \(\sigma^{0}\) for a pixel is calculated as follows:  
\[\sigma^{0}=\beta^{0}\cdot\frac{A_{\sigma^{0}_{\text{obs}}}}{A_{\sigma^{0}_{ \text{pa}}}}\quad[dB] \tag{1}\]  
with the (calibrated) radar brightness \(\beta^{0}\), ellipsoid reference area \(A_{\sigma^{0}_{\text{pa}}}\), and illuminated topographic pixel area \(A_{\sigma^{0}_{\text{pa}}}\)[46]. In our algorithm, the \(\sigma^{0}\) data from Equation (1) are further projected into the \(\gamma^{0}\) space using the local incidence angle \(\theta\):  
\[\gamma^{0}=\frac{\sigma^{0}}{cos(\theta)}\quad[dB] \tag{2}\]  
For land applications, \(\gamma^{0}\) provides the best measure for image classification and time series analysis [46]. Single Look Complex (SLC) Sentinel-1 Interferometric Wide Swath (IW) data, along with the 30 m resolution Copernicus GLO-30 DEM, are used to generate the HydroSAR RTC30 products. For ease of integration into our cloud-based workflows, RTC-30 data are generated as cloud-optimized GeoTIFFs. For more information on the RTC algorithm's implementation, see [39]. For information on the performance of the underlying RTC algorithm, please see [42].
## 2 Related Work
### Traditional Methods
Moreover, some traditional methods employ technologies like lasers, radar, and manual calibration, though these are generally applied for localized or point-based inspections and are ill-suited for large-scale, real-time track monitoring. Overall, traditional detection methods, through a \"manual + equipment\" combination, have partially met the safety requirements of conventional operational tracks. However, relying on manual inspections or single detection techniques is no longer adequate to keep pace with the rapid development of high-speed and heavy-load railways. These methods still exhibit significant shortcomings in detection efficiency, accuracy, and the identification of complex defects, necessitating the integration of emerging technologies such as computer vision and intelligent algorithms to achieve higher automation and greater robustness for online detection.
## 3 Results  
The analysis of varying the air gap width ('w2') within the bulk dielectric produced the data plotted in Figure 5. They indicate a significant impact on the force production of the artificial muscle fibers. As 'w2' was adjusted, the force output exhibited a peak at lower gap widths. Specifically, the force production was found to be optimal at 'w2' values around 50 \(\upmu\)m. While smaller gap widths continued to increase force density, practical manufacturing constraints make these smaller dimensions less feasible. Therefore, 50 \(\upmu\)m has been selected as the ideal parameter for 'w2', balancing enhanced force production with manufacturability.  
Figure 4: \(2\times 2\times 1\) air gap simulation plot showing stress in the bulk dielectric and fluid velocity in the air gaps.  
Figure 5: Parameter sweep of gap width ‘w2’ vs. force density for a \(2\times 2\times 1\) gap model.  
To determine the force density saturation of long fiber arrays, a longitudinal array sweep was conducted. Arrays of varying lengths and configurations were modeled to assess how the force density scales with the size of the array. During the length sweep, the gap width was set to a constant 50 \(\upmu\)m, due to the considerations explained above. The simulation results are shown in Figure 6. They indicate a trend toward force density saturation as the number of microcapacitors in the array increase. This saturation point indicates the maximum effective force production achievable before additional length no longer contributes significantly to increased force output. Comparison revealed that the gap model outperformed the non-gap model in terms of force density, demonstrating the advantages of the air gap design in enhancing actuator efficiency.  
The simulated numerical results in Figure 4 were validated by comparing them with the results of the previous simulations and first principle back-of-the envelope calculations [25].
## 3 Results and Discussion
### The Magnetoelectric Effect
Application considerations require that the multiferroic material exhibits the strongest possible magnetoelectric effect in the widest possible frequency range. Therefore, further work will be carried out to improve the technological process and select the appropriate quantity and type of dopant ions to obtain the strongest possible magnetoelectric coupling between electric and magnetic properties.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Thermochemical Processes
#### 2.1.6 Thermochemical Water Splitting  
Therochemical water splitting is a thermochemical process that uses high-temperature heat, normally resulting from concentrated solar power or the waste heat of nuclear power reactions, to promote a series of chemical reactions with the ultimate goal of decomposing water into oxygen and hydrogen [8].  
Since the direct thermal dissociation of water (thermolysis) only happens at very high temperatures, greater than 2227 \({}^{\circ}\)C, supplying heat to gaseous water (steam) is not an economically feasible way of carrying out this decomposition [8]. Thus, in thermochemical water splitting, chemical species are added to water to promote a set of reactions in series (a thermochemical cycle) that split water at lower temperatures (from 500 \({}^{\circ}\)C to 2000 \({}^{\circ}\)C). These intermediate species are fully recycled into their initial state by working in a closed cycle, so only water is consumed throughout the process.  
Many thermochemical cycles have been investigated in the literature, with over 300 cycles examined [9], but only some have been tested and proven to be technically feasible to produce large-scale hydrogen from a thermodynamic, cost, and efficiency perspective. As the number of steps in the cycle increases, the temperature required to perform water splitting decreases. The following Equations (1)-(4) represent a four-step thermochemical cycle [37]:  
\[H_{2}O+AB\to AH_{2}+BO \tag{1}\]  
\[AH_{2}\to H_{2}+A \tag{2}\]  
\[BO\rightarrow\frac{1}{2}O_{2}+B \tag{3}\]  
\[A+B\to AB \tag{4}\]
## 6 Discussion, Conclusions and Future Work  
### Discussion  
The simulation and analysis above shows that:  
1. When the LEO constellation is applied to TCN technology, switching is inevitable, but a certain anti-interference ability can be obtained through switching, and at the same time, it can provide optimization space for bandwidth resources, which can save bandwidth overhead to a certain extent.
2. As the switching time increases, the error of the algorithm also increases, therefore, in the design of engineering application, we should set the switching time according to the actual needs. The shorter the switching time, the better the algorithm performance, but frequent switching will increase the processing pressure and power consumption of the integrated system, while with a longer switching time, the algorithm performance is relatively poor, though it can take into account the development of other services, such as communications services.
3. The switching algorithm based on adjacent orbits is better than the algorithm based on the same orbit, therefore, in actual of engineering applications, satellites in adjacent orbits should be selected as much as possible to increase the geometric characteristics of the satellites and help improve positioning accuracy.
4. Compared with the high-orbit constellations such as MEO and IGSO, the performance of the alternate switching algorithm based on the LEO constellation is higher, and as the LEO satellites are essentially communication satellites which can eliminate the clock difference between the satellite and the user terminal through the FD system, they are therefore more suitable for ICN design.
5. Since LEO satellites are closer to the earth, the relative path loss is less and the signal propagation time delay is shorter. Multipath signal interference is therefore relatively weaker, and the signal interference from noise will be relatively reduced. Compared with the traditional MEO constellation and IGSO constellation algorithms, the switching algorithm based on the LEO constellation therefore has better anti-jamming performance and stronger algorithm robustness, and is more suitable for location service solutions in harsh and challenging environments.
6. From the experimental results, we also found that the impact of multipath and NLOS on navigation and positioning performance is more serious than noise interference, therefore, in actual engineering, more energy should be expended on the suppression of multipath and NLOS interference.
## 6 Conclusions  
This research paper presents a resonator layer combined with a cross dipole antenna for wireless communication bands, especially for the 2.2-2.7 GHz band. First of all, a cross dipole antenna was designed in an FIT based microwave simulator. Due to the close distances between each antenna elements constituting the cross dipole antenna, there were \(-15.6\) dB transmissions between each antenna element at 2.4 GHz. Various type resonator layers were then designed and imported into the Matlab environment for neural network processes. Additionally, an artificial neural network was trained by various types of resonators to create a minimum mutual coupling effect between each antenna element of cross dipole antenna. The obtained minimum mutual coupling at 2.4 GHz was \(-17\) dB with a resonator layer. Finally, a resonator layer for a minimum mutual coupling effect and thus for better isolation between antenna elements was fabricated in a microwave laboratory and tested experimentally. The obtained accurate results showed that the designed structure improves the isolation and reduces the transmission values significantly, which is a challenging problem in the antenna industry. In conclusion, it can be easily said that an artificial neural network provides a solution for antenna parameter enhancement applications with different frequency bands.  
Conceptualization, E.O. and O.A.; methodology, E.O. and F.O.A.; software, E.O. and F.O.A.; validation, M.K., O.A. and F.O.A.; formal analysis, O.A.; investigation, E.O.; resources, F.O.A.; data curation, M.K.; writing\(-\)original draft preparation, E.O.; writing\(-\)review and editing, Y.I.A. and M.K.; visualization, E.O.; supervision, O.A. and M.K.; project administration, M.K.; funding acquisition, L.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Grant no. 2017 YFA0204600), the National Natural Science Foundation of China (Grant no. 51802352) and the Fundamental Research Funds for the Central Universities of Central South University (Grant no. 2018 zzts355).  
The author would like to thanks for Iskenderun Technical University and Central South University for the technical supports.  
The authors declare no conflict of interest.
###### Abstract  
Usage of Unmanned Aerial Vehicles (UAVs) for different tasks is widespread, as UAVs are affordable, easy to manoeuvre and versatile enough to execute missions in a reliable manner. However, there are still fields where UAVs play a minimal role regardless of their possibilities. One of these application domains is mobile network testing and measurement. Currently, the procedures used to measure the main parameters of mobile networks in an area (such as power output or its distribution in a three-dimensional space) rely on a team of specialized people performing measurements with an array of tools. This procedure is significantly expensive, time consuming and the resulting outputs leave a higher degree of precision to be desired. An open-source UAV-based Cyber-Physical System is put forward that, by means of the Galileo satellite network, a Mobile Data Acquisition System and a Graphical User Interface, can quickly retrieve reliable data from mobile network signals in a three-dimensional space with high accuracy for its visualization and analysis. The UAV tested flew at 40.43 latitude and \(-\)3.65 longitude degrees as coordinates, with an altitude over sea level of around 600-800 m through more than 40 mobile network cells and signal power displayed between \(-\)75 and \(-\)113 decibels.  
Unmanned Aerial Vehicle; mobile network; Cyber-Physical System +
Footnote †: journal: Sensors
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 2 Methodology
### Method Overview  
In this paper, 4841 images with fixed viewing angle were used as the experimental database. Due to the interference from the moving objects (e.g., hull and clouds in the sky) in the image, the images need to be preprocessed to obtain the effective subregions of the image for subsequent feature extraction. Feature extraction was performed on the effective subregions, and the feature parameters were then imported into the Support Vector Regression models for training and evaluation. After finding the Support Vector Regression Models of the subregions, error weight analysis is used for the final evaluation. The proposed method for the visibility estimation is shown in Figure 1. Compared with the approach in [25], the proposed method in this paper can give more robust and accurate visibility estimation by fusing the estimated results of each subregion through error weight analysis.
# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 5 Discussion
### Discussion on the Arrival Time Prediction for Geoeffective CMEs  
Here, we first discuss the relationship between the predicted transit time of our method and the actual transit time on the testing set. As shown in Figure 9, the different blue dots represent the different CME events. The black dashed line representing the predicted transit time is equal to the actual transit time. The two green dashed lines show the deviation from the black line and the deviation is 5.8 h, which represents the MAE of our method. From the distribution of the dots, we can find that most dots are between the two green dashed lines and the dots are scattered close to the black dashed line. This proves that our method is effective for most CME events, and the prediction error of most CME events is less than 5.8 h. Specifically, we use the probability of detection (POD) to  
Figure 8: The performance of our method in geoeffectiveness prediction with different thresholds on the testing set.  
Figure 7: The confusion matrix of the geoeffectiveness prediction results of our method on the testing set.  
quantify the probability that the CME prediction error is less than the MAE of our method, and the formula is defined as follows:  
\[POD=\frac{Hits}{Hits+Misses}, \tag{10}\]  
where the absolute prediction errors less than 5.8 h are called Hits and others called Misses. The POD of our method is 60.7%.  
Then, we discuss the detailed comparison of our method for CMEs in the testing set with four well-known CNN models (VGG, Resnet, MobileNetv2, Resnext). As shown in Figure 10, the red dash-dotted line indicates the average absolute errors of CNN methods and its value is 8.1 h. The blue dots (53.5%) represent the events that our method has a better performance and the error is less than 8.1 h. The green dots (14.2%) represent the events that our method has a better performance but the error is greater than 8.1 h. The red dots (14.2%) represent the events that our method performs worse and the error is less than 8.1 h. The purple dots (17.8%) represent the events that our method performs worse and the error is greater than 8.1 h. In general, our method performs better for 67.7% CME events.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 7 Endoscopy
### Detection  
Researchers explore 3D CNNs to extract spatial-temporal features and employ a 3D FCN for detecting polyps in colonoscopy videos. Deep CNN architectures are used to classify gastrointestinal endoscopy video frames as abnormal or normal, and deep saliency detection algorithms are applied to detect salient points in abnormal images. Various models based on GoogleNet, AlexNet, VGG 16, and reinforcement learning are proposed for detecting lesions from wireless capsule endoscopy frames. A framework for ureteral orifice detection from ureteroscopy videos achieved high accuracy, although the number of false positive and false negative classifications is notable. Polyp detection from colonoscopy videos is addressed using a bootstrapping method, combining a base detector and temporal consistency verification.  
Endoscopy segmentation and detection tasks present several challenges. Separate training data are required for every kind of ulcer or pathology, and further confirmation is often necessary for accurate diagnoses, especially when lesions are histologically indistinguishable from other conditions.
###### Abstract  
Salt crystallization represents one of the primary forms of weathering encountered in rock cultural heritage sites, with sulfate weathering having particularly notable destructive effects. This study focuses on sandstone and limestone, using them as test materials to conduct simulation experiments on sulfate weathering under specific environmental conditions. The experimental process involved documenting the surface morphology of the rock samples and analyzing changes in indicators such as wave velocity, hardness, composition, and pore size distribution. The degree of damage to the two types of rock was evaluated using the entropy weight-TOPSIS method, and the sensitivity of different weathering indicators in assessing the weathering of the two rocks was also discussed. The results revealed that sandstone exhibited obvious surface damage under sulfate erosion, with dissolution holes and pits surrounding the rock samples, while limestone primarily suffered damage at its edges. There were notable differences in the rate of attenuation observed in the macro and micro indicators between the two rock types. The wave velocity of both types of rocks exhibits linear attenuation while the intensity undergoes exponential change. It is worth noting that sandstone hardness demonstrates a pattern of \"fast-rapid-slow-stable\" decline characteristics, whereas limestone follows an exponential trend with an initial fast decline followed by a slower decline. Additionally, sandstone exhibited significantly greater damage and weathering thickness compared to limestone, owing to the involvement of complex and diverse physical and chemical reactions. The pore damage factor and macro-level indicators of the rock samples could be fitted using exponential and linear functions, respectively, although the fitting curves differed distinctly. The sensitivity indicators reflecting the weathering state of sandstone and limestone under sulfate erosion varied, with mass loss applicable to sandstone and porosity to limestone. Overall, with our research findings, we aim to provide a theoretical foundation for the anti-salian and precision protection of rock cultural heritage sites.  
rock cultural heritage sites; sandstone; limestone; sulfate weathering; damage characteristics; differentiation +
Footnote †: journal: Applied Sciences
## 3 Results
### Evaluation of TSNP Application in Biosensing and Diagnostics
#### 3.4.2 Sensitivity Test  
The sensitivity test changes the refractive index of the medium in which the AuTSNPs are suspended, and can provide a measure of the nanoplates sensitivity without interfering with their stability. AuTSNP solution was added to increasing concentrations of sucrose solutions to evaluate the potential of the nanoplates for their use in sensing applications when there is a variation in the nanoplates' surrounding medium. Results (Figure 10) are in agreement with the reported outcomes by [PERSON] et al., where the LSPR\({}_{\lambda\text{max}}\) of the spectra increases linearly as the sucrose concentration increases [54; 56].
## 3 Results and Discussion
### OAM Detection Under Different Data Volumes  
The recognition ability of the network for distorted images before and after transfer learning under different datasets is shown in Figure 5. Figure 5 indicates that the accuracy of OAM modal detection increases with the increase in dataset sample size. When each OAM modality contains 100 image data and the total dataset reaches 1500, the classification performance of the modality classifier for distorted LG beams is close to saturation. In the  
Figure 4: Partial light intensity distribution images of LG beams with different topological charges transmitted through different ATs, with a transmission distance of z = 1000 m.  
Figure 3: The overall process of OAM modal detection.  
case of \(C_{n}^{2}=1\times 10^{-16}\) m\({}^{-2/3}\) and \(C_{n}^{2}=1\times 10^{-15}\) m\({}^{-2/3}\), the detection accuracy can reach 100%. When the turbulence strength reaches \(C_{n}^{2}=1\times 10^{-14}\) m\({}^{-2/3}\), the detection accuracy before and after transfer learning is 78.11% and 90.73%, respectively, which is 12.62% higher than the network detection accuracy before transfer. In addition, the performance of the Transfer OAM mode classifier does not significantly improve with the increase in training data size, indicating that the classifier requires 100 OAM mode images for each class. Therefore, in the following discussion, 100 intensity images with different OAM modes are selected for each class as training data.