esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we propose a dynamic graph convolution-based spatio-temporal feature network (DG-STFN) to solve the problem of water demand forecasting. Given that existing methods generally lack the ability to jointly extract spatio-temporal correlations, our model introduces graph convolution to achieve joint extraction of spatio-temporal features in time series. To ensure effective aggregation of spatial features through graph convolution, we design a dynamic graph generation module. This module captures the spatial correlation of global relative stability and local dynamic changes in time series through an attention mechanism. Finally, extensive experiments on two datasets demonstrate that DG-STFN outperformed the baselines. Of course, our work currently has some shortcomings. In time series forecasting problems, the sparsity of the graph has a significant impact on the forecasting accuracy. However, in our study, we simply use threshold filtering and sampling methods to ensure the sparsity of the generated dynamic graph. In future work, we aim to quantify the impact of graph sparsity, and then design the corresponding graph sparsity loss function to further improve the prediction accuracy of the model.  
Conceptualization, [PERSON] and H.L.; methodology Z.J. and J.Y.; validation, [PERSON], [PERSON] and J.Y.; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Guangdong Basic and Applied Basic Research Foundation, grant number 2021A1515011913.  
**Institutional Review Board Statement:** Not applicable.  
Figure 2: Visualization of the prediction results for the same week using DG-STFN and the GCN-based baseline methods, on the test set of the WSD dataset. (**a**) Comparison of actual and predicted values of DG-STFN. (**b**) Comparison of actual and predicted values of ASTGCN. (**c**) Comparison of actual and predicted values of STGCN. (**d**) Comparison of actual and predicted values of DCRNN.
## 4 Results
### Pareto Fronts  
Figure 3 presents the Pareto fronts obtained by multi-criteria optimization, both with and without constraints on the damping ratio (\(\zeta\)). The results illustrate the trade-offs between two objectives: maximizing controllability, i.e., minimizing (\(J_{1}\)) for five Gramian-based metrics and minimizing RoCoF (\(J_{2}\)). Controllability, a maximization goal, was reformulated by minimizing the inverse of the controllability metric, thus simplifying the use of standard optimization algorithms, ensuring numerical stability, and making the Pareto front easier to interpret since lower values in both objectives mean better performance. Each colored series represents the Pareto front for one of the five controllability metrics. The spread of the Pareto fronts indicates the range of achievable compromises, each metric favoring a different balance between objectives. The optimal points (red circles) represent the points with the minimum Euclidean distance to the origin. Knee points (black diamonds) represent a compromise solution on the Pareto front, where competing objectives are balanced effectively [25].  
Unconstrained fronts reach lower \(J_{1}\) and \(J_{2}\) values, meaning they achieve better controllability and RoCoF performance numerically. However, this improvement in almost all cases comes at the cost of reduced damping ratios (\(\zeta\)), which can compromise stability. Without constraints on the damping ratio, the determinant of the controllability Gramian gives the best results in terms of controllability, while the maximum eigenvalue metric performs best for minimizing RoCoF. On the other hand, the constrained Pareto fronts offer more robust and balanced solutions, ensuring adequate damping ratios (\(\zeta\)) but at the expense of higher objective values. In the constrained case, the best RoCoF performance is again achieved using the maximum eigenvalue metric, whereas the best controllability is obtained using the trace of the controllability Gramian.
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 4 Results and Discussion
### Validation of Communication Parameters  
After adding the anti-interference scheme, the main performance indicators of the mine 5G system need to be guaranteed. Therefore, a comparative test of the upload rate and communication delay with and without the fusion scheme was carried out.  
In order to ensure the enhanced Mobile Broadband (eMBB) characteristics of the 5G communication systems and allow access to underground large-bandwidth data services, the upload rate of the base station should not be lower than 180 Mbps. Five upload-rate tests were carried out, and the average value was taken, as shown in Figure 9. It can be seen that the uplink rate between the terminal and the base station was stable within the range of 180 Mbps to 200 Mbps, and the uplink rate was not affected by the fusion anti-interference scheme.  
Figure 7: Comparing the interference suppression capability of different methods to 5 MHz crosstalk by output spectra: (**a**) Original signal without any suppression, (**b**) Signal after equalization suppression, (**c**) Signal after conventional blind source separation, and (**d**) Signal after the fusion anti-interference scheme.  
The communication delay tests were carried out in the main transport roadway, the fully mechanized mining face, and the underground substation. In both cases without fusion anti-interference and with fusion anti-interference, five tests were performed, as shown in Figure 10. We can see that the communication delay was largely unaffected by the fusion anti-interference scheme.  
Figure 8: Comparing the interference suppression capability of different methods to 20 MHz crosstalk by output spectra: (**a**) Original signal without any suppression, (**b**) Signal after equalization suppression, (**c**) Signal after conventional blind source separation, and (**d**) Signal after the fusion anti-interference scheme.  
Figure 9: Upload rate test.  
Through the verification of the upload rate and communication delay of the 5G communication systems, it can be seen that, after the fusion anti-interference scheme was added, the main communication indicators of 5G were not affected. Taking the 5G remote control of the fully mechanized excavator on the road surface as an example, considering the control signals such, as the cutting speed of the fully mechanized excavator and the start and stop commands, the communication uplink rate needed to reach 160 Mbps, and the overall control signal delay was less than 22 ms. This method can meet the requirements of fully mechanized excavators on a road surface and the application requirements of 5G remote control of mobile phones.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 8 Conclusions  
This article presents and justifies different tools for data augmentation and autoencoder (or GAN) neural networks training. Additionally, the training source codes and the trained neural networks are attached to a proper repositories.  
The novel method of image filtering for the autoencoders provides better neural network pretraining. Thanks to simple blurring algorithms (Gaussian blur and median filtering), the operations are fast and easy to perform, while the gain in the metrics varies from 20 to 50%! The algorithm is also very easy to implement in a real deployment environment, as it does not use complex data or correlated imagery. The only cost of the algorithm is a higher CPU load during the image pre-processing.  
Neural networks created in this way are perfect for transfer learning or further statistical or analytical processing. The networks presented here are convolutional, which allows for very quick calculations using GPU, but times under 250 ms are achieved with PC class CPU. Additionally, proper pruning of the given neural networks can be performed depending on the requirements. It was not performed to allow for the fine-tuning of the neural networks for the final task or the use of more advanced methods of pruning [37].  
In the future, other architectures and neural network types (such as GANs) could be tested by implementing the proposed image pre-processing.  
This research received no external funding.  
Not applicable.  
The authors would like to thank European Space Agency for access to the TerraSAR-X database and Capella Space Program for Open Data Imagery used as examples in the article.  
The author declares no conflict of interest.  
Source Codes: Source codes available on Github service under link: [[https://github.com/Krisequ/-sar_filtered_labels_experiment](https://github.com/Krisequ/-sar_filtered_labels_experiment)]([https://github.com/Krisequ/-sar_filtered_labels_experiment](https://github.com/Krisequ/-sar_filtered_labels_experiment)).  
The model weights are available for download under the link attached in the README.md of the GitHub repository above.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ CNN & Convolutional Neural Networks \\ GAN & Generative Adversarial Network \\ DNN & Deep Neural Networks \\ MAE & Mean Absolute Error \\ MSE & Mean Square Error \\ SAR & Synthetic Aperture Radar \\ SNR & Signal to Noise Ratio \\ SSIM & Structural Similarity \\ YOLO & You Only Look Once--Neural Network Family \\ \end{tabular}
## 3 Materials and Method
### Data Augmentation
#### 3.2.2 Medical Image Data Augmentation  
The quality of medical imaging data directly impacts the performance of models in diagnostic tasks. However, due to issues such as insufficient samples or class imbalance in real-world data collection, raw data often cannot meet the deep learning models' need for large-scale, high-quality training datasets. Data augmentation techniques generate more diverse samples by applying a series of transformations to the raw data, as shown in Figure 1, while retaining key image features, thus significantly improving the model's generalization ability.  
Figure 1: Visualization of different image data enhancement methods: (**a**) rotation; (**b**) salt-and-pepper noise; (**c**) contrast adjustment; (**d**) shearing; (**e**) cropping; (**f**) flipping.  
Common medical image augmentation methods include random rotation and flipping, which modify the spatial arrangement of the image to increase data diversity. For example, for a 2D image \(I(x,y)\), the random rotation operation can be expressed as  
\[I^{\prime}(x^{\prime},y^{\prime})=I(x\cos\theta-y\sin\theta,x\sin\theta+y\cos \theta), \tag{9}\]  
where \(\theta\) is the rotation angle and \((x^{\prime},y^{\prime})\) are the new coordinates after rotation. Random flipping reverses the image along either the horizontal or vertical axis to generate augmented images. Gaussian blur and sharpening techniques adjust the spatial frequency distribution of the image to simulate different imaging conditions. Gaussian blur can be applied by convolving the image \(I(x,y)\) with a Gaussian kernel \(K\) as follows:  
\[I^{\prime}(x,y)=\sum_{u=-k}^{k}\sum_{v=-k}^{k}K(u,v)I(x+u,y+v), \tag{10}\]  
where \(K(u,v)=\frac{1}{2\pi\sigma^{2}}\exp\left(-\frac{u^{2}+v^{2}}{2\sigma^{2}}\right)\) and \(\sigma\) controls the degree of blurring. Conversely, sharpening enhances edge details in the image, increasing its spatial frequency. Synthetic noise augmentation adds random noise to the image to improve the model's robustness to varying image qualities. If the original image is denoted by \(I(x,y)\) and the noise image by \(\eta(x,y)\), the augmented image is  
\[I^{\prime}(x,y)=I(x,y)+\eta(x,y), \tag{11}\]  
where \(\eta(x,y)\) can be Gaussian noise (with mean 0 and standard deviation \(\sigma\)) or salt-and-pepper noise, depending on the task requirements. The addition of noise simulates fluctuations in image quality due to different imaging devices or environments, thereby enhancing the model's adaptability to diverse data.  
These data augmentation techniques play a crucial role in addressing issues such as insufficient samples and class imbalance in medical image datasets. They help generate a more diverse set of training samples, improving the model's robustness and performance in real-world applications.
# Experimental Study on Engineering Properties of Cemented Soil with High Water Content
## 3 Test Results
### Cemented Soil Failure Mode Analysis  
The photographs of typical failure modes of the cemented soil specimens are presented in Figure 10. The specimens had a cement content of 18% and were cured for 28 days. The confining pressures were 0 kPa, 100 kPa, 200 kPa, and 400 kPa, respectively. As shown in Figure 10, four typical failure modes occurred in this study. In the UCS tests (the confining pressure was 0), the vertical crack occurred in the cemented soil specimen, as shown in Figure 10a. It can be considered that the tension failure occurred in the UCS tests. The photograph of the cemented soil specimen under the confining pressure of 100 kPa after the test is shown in Figure 10b. The failure mode was between the tension failure and shear  
Figure 8: Correlation between the UCS and cement content.  
Figure 9: Correlation between the deformation modulus and UCS of the cemented soil.  
failure, which was similar to the test results of [PERSON] [21]. It can be considered as a transition mode between tensile failure and shear failure. When the confining pressure reached 200-400 kPa, the single shear failure or conjugate shear failure occurred in the cemented soil shear tests, as shown in Figure 10c,d. In this research, the failure angle in the cemented soil specimen after single shear failure was basically between 63\({}^{\circ}\) and 72\({}^{\circ}\), which indicates that in the unconsolidated-undrained triaxial test, the failure mode will change from tensile failure to shear failure as the confining pressure increases.
# Functional and Healthy Features of Conventional and Non-Conventional Soundoughs
## 3 Nutritional and Functional Features of Sourdough
### Enzymatic Activities with Beneficial Effects on Human Health  
It is known that cereal grains are significant sources of minerals such as magnesium, potassium, iron and zinc. However, they also contain phytic acid or myo-inositol hex-akiphosphate (IP6) (1-4% of dry weight) [57; 58]. This compound is an anti-nutritional factor for humans and animals, in fact, the central hexaphosphate ring is highly charged due to six anionic groups and acts as a chelator of dietary minerals reducing their bioavailability [59; 60]. Sourdough fermentation, owing to the pH reduction, provides suitable conditions for cereal endogenous phytase activities [36; 61]. The phytases that take part in the process are also exogenous and they are produced by a large number of microorganisms among which are sourdough yeasts and LAB. Their activity reduces to less than a half the phytate content of whole wheat bread and allows to increase bioavailability of minerals, free amino acids, and proteins. A recent screening on 152 LAB, isolated from cereal-based substrates, revealed a widespread capacity of the isolates (95%) for degrading phytic acid. Among the isolates, strains _L. brevis_ LD65 and _L. plantarum_ PB241, showed the highest phytase activity; on the contrary, _Weissella confusa_ strains showed low or no phytase activity [62].  
Allergies, intolerances, and sensitive individuals are also positively influenced by the LAB present in the sourdough, as they can show specific enzymatic activities toward gluten proteins. The proteolytic activity carried out by endogenous and exogenous proteases during the fermentation process with sourdough seems to lead to a complete hydrolysis of gluten, meeting the needs of individuals affected by celiac disease [32].
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 3 Materials and Methods
### Methods  
Three algorithms are utilized for the detection of swarming: SVM, k-NN, and the newly proposed U-Net CNN algorithm. In our detection methodology, we have incorporated two arbitrary preprocessing methods (sound-based and image-based) and trained the three different classifiers. In addition, U-Net CNN classifier training was performed on an image-based preprocessing method, while for k-NN and SVM a raw sound-based preprocessing method was used. This preprocessing method includes low pass filtering, Fast Fourier Transformation, and discretization of the frequency responses in the swarming interest range of 350-600 Hz using ten 25 Hz bins and aggregating the amplitude responses (in mV Root Mean Square--RMS).  
Upon training and testing the models, the authors evaluated the SVM and k-NN classifiers in terms of accuracy, precision, recall, and F1 score. The mathematical formulas used for the algorithms' evaluation process are presented in Appendix B. Average accuracy metric was also used for the cross-comparison of SVM, k-NN, and U-Net CNN algorithms (see Appendix B). The following subsections present the implementation of both methods on the sound-recorded data before the training process.
# Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core
## 4 Analysis Method
### Methodology
#### 4.2.2 Acquisition  
Since the Raspberry Pi Model 3 B has a removable storage in the form of a microSD card, and it is physically accessible for the investigators, an offline acquisition is the best approach to follow. This process is carried out by executing the following actions:  
* If the system is on, it is shut down. To do so, first, the system is turned off using the menu of the operating system, and then the Raspberry Pi is disconnected from the power supply. This guarantees that the storage does not suffer any damage or data loss.
* The microSD card is extracted from the board and inserted into a microSD to SD card adapter with write blockage capabilities.
* The adapter is then inserted into the forensic computer, making sure that the write blocker is on.
* The FTK Imager tool is launched and an image file of the storage is created.
* Once the image file is created, the hash value of the image is compared with that of the microSD card in order to guarantee that the data has not been altered.
* Finally, the image file is copied into a different storage location to ensure that at least one other copy is available in case the first one gets damaged.  
The authors opted to create an image file of the storage instead of cloning it since it allows the analysis of the different scenarios simultaneously while consuming less physical resources. In addition, it also eases the preservation and storage of the data as the image files are saved on the forensic computer.  
Regarding the preservation of the acquired image, it can be seen that no special measures are taken, just the essential ones necessary to certify that the data do not vary during the analysis of each scenario, thereby preserving the integrity of the evidence. In addition, every time that the image is mounted in the system, the hash value is calculated beforehand to assure that it has not been tampered with by an external element, and the read-only method is used.
## 3 Numerical Modeling Calculation
### Numerical Simulation Steps  
The three-dimensional simulation considers a three-step excavation method under various levels of surrounding rock conditions, and the excavation diagram is shown in Figure 2.  
To conduct the calculation, the model followed the following steps [19; 20]:  
1. Advanced support: The mechanical strength parameters of the rock and soil within the advanced support range were increased.
2. Excavation of upper steps: The upper steps were excavated.
3. First support of the upper steps: Initial support was provided for the excavated upper steps.
4. Excavation of 10 steps (24.0 m) on the upper bench: The upper bench was excavated, followed by excavation of the middle bench.
5. Primary support for the middle step: The middle step received primary support.
6. Construction of two excavation steps (7.2 m) for the middle step: Two excavation steps were performed for the middle step, followed by excavation of the lower step.
7. Initial support for the lower steps: Initial support was provided for the lower steps.
8. Excavation of the inverted arch: The inverted arch was excavated after three excavation steps (10.8 m) of the lower step without initial support.  
The elastic-plastic body had an excavation cycle of 1000 steps for each step. After completing the cycle, initial support was applied, followed by another cycle of 1000 steps. For the creep model calculation, each step had an excavation cycle time of 36,000 s (10 h). After completing the cycle, initial support was applied, and 1000 steps were cycled. The excavation durations were as follows: 17 days for a 30 m closure distance, 22 days for a 40 m closure distance, 25 days for a 45 m closure distance, 29 days for a 50 m closure distance, 35 days for a 55 m closure distance, and 38 days for a 60 m closure distance.  
Figure 2: Diagram of three-step excavation.
# Extremely Low-Profile Monopolar Microstrip Antenna with Wide Bandwidth
## 2 Antenna Design and Analysis
### Operation Principle and Field Distributions
On a similar principle, in the proposed antenna, the degenerated mode generates two frequencies with the same electric field distribution as a result of the correlation between the horizontal resonator of the top gap-coupled patch and the vertical substrate thickness of the cavity between the top patch and bottom ground plane. Thus, the optimum dimensions of the proposed antenna can be expanded to a wide operating bandwidth by using an extremely thin substrate thickness without adding a multiple resonator, multi-layered substrate, etc. The detailed analysis results of the above two frequency regulations and couplings are further explained in following section. Figure 3: Design concept with simulated electric-field distributions at TM\({}_{02}\) modes and their electric-field vector magnitudes in cut plane: (**a**) even TM\({}_{02}\) mode of lower resonant frequency of 5.264 GHz and (**b**) odd TM\({}_{02}\) mode of higher resonant frequency of 5.897 GHz.