esa-sceva/satcom-chunk-collection huggingface dataset:
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 1 Introduction  
The generalized Hilbert transform (GHT) [1] improves the noise robustness and resolution of the Hilbert transform (HT) [2], by introducing the order parameter (a real number) to the time-frequency spectra, which are obtained by time-frequency analysis methods such as the short-time Fourier transform (STFT) [3], wavelet transform [4; 5; 6], S transform [7; 8], generalized S transform [9], EMD [10; 11; 12], VMD [13], modified short-time Fourier transform [14; 15; 16], synchrosqueezing transform [17; 18; 19; 20], and so on.  
Due to the good performance of noise robustness and resolution, GHT has been widely used in signal processing and interpretation. However, GHT results depend on the time-frequency spectra and the order parameter; and for a chosen order parameter, the output resolution of GHT is unique, which limits its application. In addition, the order parameter in GHT can only be chosen in a certain range; and the GHT result is not stable with respect to the chosen order parameter, which usually leads to poor-stability and low-resolution output.  
In order to improve the stability of GHT and broaden its order parameter chosen range, thus obtain high-stability and high-resolution results; we first extend GHT by introducing the sign function, and then propose the high-stability and high-resolution GHT (SGHT). In SGHT, due to the sign function, the order parameter can be chosen as any positive numbers. Thus, we can obtain high-stability and high-resolution GHT results. In addition, the frequency integral interval in GHT and SGHT is fixed, which limits the corresponding computation efficiency and output resolution. In order to improve thecomputation efficiency and output resolution of GHT and SGHT, and finally obtain high-efficiency and high-resolution results, we extend SGHT by introducing the two frequency integration parameters, and then propose the LP-order time-frequency spectra (LTFS). In LTFS, we can flexibly choose the order parameter and the two frequency integration parameters, and finally optimize the computation efficiency and output resolution of LTFS for more general application.  
The remainder of this paper is organized as follows. First, we introduce the principle of GHT and LTFS. Then, we use synthetic two-dimensional (2D) seismic data to test the capability of LTFS for edge detection, with the comparison of HT and GHT. Finally, we apply LTFS to actual three-dimensional (3D) seismic data, with comparison of HT, GHT, curvature [21; 22], and coherence [22; 23].
## 4 Innovation through SDN-Based Network Applications  
SDN has been a vehicle for network innovation in recent years, because it allows for more flexibility and extensibility than existing network architectures. New features or services that are added to an SDN-based network often require only a manageable number of control elements to be upgraded. Upgrades on forwarding elements are often unnecessary depending on the southbound interface. Thus, the separation of the control and data plane allows both planes to evolve more independently of each other compared to other network architectures. Network features and services can be dynamically added in the form of network applications, which facilitates their deployment. Multiple network applications have already been proposed and evaluated by the SDN research community, and we survey them in the following. They can be grouped in the areas of network management and traffic engineering, application server load balancing and network access control, SDN security, network virtualization and inter-domain routing.
## 3 Comparison of Power Generation of Wind Farm between MPPT and MPG
### Optimal Pitch Angle and Tip Speed Ratio Selection at 270\({}^{\circ}\) Wind Direction  
Similar to the tip speed ratio, as the pitch angle of WT\({}_{1}\) may change its power production and its downstream wind speed, the optimized pitch angle can be obtained as well in the case of the wind direction at 270\({}^{\circ}\) and ambient wind speed at 9 m/s. The total active power of WT\({}_{1}\) and WT\({}_{2}\) is shown in Figure 8 in terms of the tip speed ratio and the pitch angle of WT\({}_{1}\). It can be observed that the total active power is 0.37 pu by using the MPPT method with the pitch angle of 0\({}^{\circ}\) and the tip speed ratio of 7.55. However, the total active power increases to 0.39 pu if the WT\({}_{1}\) operates at the pitch angle of 1.8\({}^{\circ}\) and the tip speed ratio of 6.9.  
Compared with the MPPT method, the optimized pitch angle, tip speed ratio, the rotor speed, and active power of WT\({}_{1}\) are shown in Figure 9a. Meanwhile, the active power of WT\({}_{1}\), the active power of WT\({}_{2}\), and the total active power of the wind farm by using the MPPT and the MPG control are compared in Figure 9b. If the MPG control strategy is applied, it is evident that the active power of WT\({}_{1}\) is reduced, while the active power of WT\({}_{2}\) is increased. In respect to the total active power of the wind farm, it slightly increases by using the MPG control.  
Figure 8: Total active power of the wind farm in terms of the pitch angle and tip speed ratio of WT\({}_{1}\) at 270\({}^{\circ}\) wind direction and 9 m/s ambient wind speed.  
Figure 9: Comparison between the MPPT control and MPG control at the wind direction of 270\({}^{\circ}\): (**a**) pitch angle, tip speed ratio, rotor speed, and active power of WT\({}_{1}\); (**b**) active power of WT\({}_{1}\), active power of WT\({}_{2}\), and the total active power of the wind farm.
## 4 Results and Analysis
### The Effect of Harmonics  
Here, we want to show how the harmonics of natural frequencies mentioned in Section 4.3 impact the PCB in physical terms of displacement and corresponding strain. We primarily took 24.66 Hz as the fundamental natural frequency and observed the impact at its third-order harmonic of 73.98 Hz (which is the usual third harmonic observed within  
Figure 9: Effects of a natural frequency on the PCB (**a**) 21.97 Hz. (**b**) 23.67 Hz. (**c**) 47.12 Hz. (**d**) 74.63 Hz. (**e**) 212.75 Hz. (**f**) 222.69 Hz.  
the aircraft system environment). Maximum displacement and strain occurred at 73.98 HZ and was used to calculate the lifetime of the device. From Figure 10a,b, displacement and strain occur across the center of the board, either in the case of displacement where the heatsink is situated, or in the case of strain just in front of the heatsink. This transferred additional strain and stress into components situated in the vicinity of the heatsink, such as the capacitors, toroidal inductor, and transistors. However, from Figure 11, it can be seen that the impact of harmonics is not severe enough to cause a significant detrimental effect on the life of the board, with all components predicted to last well in excess of the 30-year lifetime with a probability of failure of 20% set during the analysis.  
Figure 10: (a) Displacement due to harmonic loading at 73.98 Hz. (b) Strain due to harmonic loading at 73.98 Hz.
###### Abstract  
Cylinder-shaped fuel pellets that were 3 mm in diameter and 3 mm in height, with a mass of 20 mg, were produced by compressing dry coal processing waste under the pressure of 5 MPa. The first group of pellets from coal dust with a particle size less than 140 \(\upmu\)m did not contain any additives. The pellets of the second group of fuel compositions contained an oil-impregnated porous polymer material particle with a size of 0.5 mm in the central part of the experimental sample. The particle was surrounded by coal dust from all sides. The ratio of components was 90:10% for coal dust: polymer particle. The latter value almost completely corresponds to the fraction of oil in the fuel composition, since the mass of a porous polymer material particle is negligible. The third group of compositions was a 70:30% mixture of coal dust with wood sawdust with a particle size less than 45 \(\upmu\)m, or 45-100, 100-200 and 200-500 \(\upmu\)m. The ignition and combustion of single fuel pellets were studied under radiant heating in an air medium while varying the temperature from 800 to 1000 \({}^{\circ}\)C. The processes during the fuel combustion were recorded by a high-speed video camera, and the concentrations of the main anthropogenic emissions in flue gases were measured by a gas analyzer. The main characteristics were established--ignition delay times (2-8 s) and duration of burnout (40-90 s)--at different heating temperatures. A difference was established in the combustion mechanisms of the pellets, when adding various components to the fuel mixture composition. This has a direct influence on the induction period duration and combustion time, other conditions being equal, as well as on the concentration of nitrogen and sulfur oxides in the flue gases. Adding an oil-impregnated porous polymer particle to the fuel composition intensifies ignition and combustion, since the times of ignition delay and complete burnout of fuel pellets under threshold conditions decrease by 70%, whereas adding wood sawdust reduces the content of nitrogen and sulfur oxides in the flue gases by 30% and 25%, respectively.  
fuel pellets; coal waste; wood sawdust; oil waste; combustion; anthropogenic emissions
# Remote Sensing for Irrigation of Horticultural Crops
## 4 Approaches to Precision Irrigation
### Baselines  
The theoretical definition of the two baselines, the lower baseline (well-watered crop, or more precisely non-water-stressed crop), and the upper baseline (completely water stressed crop).  
The lower baseline (non-water-stressed crop) depends on the plant species, and even among varieties of the same species, which have different canopy and leaf structures. The energy balance of crops may differ consistently, since each canopy structure has its own radiometric properties, which can facilitate in some cases eradiation capture, and, in other cases, that which can escape excessive radiation loads.
## 2 Background and Related Work
### XAI in Forensic Computing  
Digital forensic investigations demand rigorous standards of transparency, traceability, and interpretability to ensure the admissibility and reliability of evidence generated by AI-based tools [6]. It has been highlighted that the adoption of explainable artificial intelligence (XAI) significantly enhances the legal defensibility of forensic evidence by enabling human-understandable justifications for machine learning decisions. XAI not only improves trust in automated outputs, but also empowers analysts to reconstruct digital events, trace decision paths, and identify anomalies relevant for expert testimony. Complementarily, it has been argued that XAI contributes to ethical compliance, bias mitigation, and stakeholder confidence in forensic and cybersecurity contexts [11]. Aligned with this perspective, recent studies have proposed structured methodologies for integrating XAI into forensic workflows, emphasizing five key phases: (i) identification of relevant traffic featuresto enhance interpretability and detection accuracy; (ii) acquisition and preprocessing of network datasets to ensure data quality; (iii) preservation of feature integrity to maintain the legal validity of SHAP/LIME outputs; (iv) analysis via interpretable ML/DL models for post hoc transparency; and (v) presentation of results in reproducible and legally admissible formats [6; 12; 13; 14]. Collectively, these contributions support the growing consensus that XAI is not merely a technical enhancement, but a fundamental requirement for the operational, ethical, and judicial integration of AI into digital forensics.
## 4 Experiments and Results  
### Datasets and Preprocessing  
The BraTS dataset, synonymous with the Brain Tumor Segmentation Challenge, is a publicly available medical imaging resource that serves as a foundation for the research and development of algorithms aimed at segmenting brain tumors. It is a collection of MRI images from a multitude of patients with brain tumors, sourced from various medical centers. For the BraTS 2021 challenge, a significant number of cases were employed, 1251 for training and 219 for online validation [7; 46; 47], which has gained considerable traction. The dataset encompasses 1251 cases with ground truth annotations provided by certified neuroradiologists, while the ground truth for the 219 validation cases remains undisclosed to the public, with evaluation results accessible solely through the online validation process. Our approach involved the utilization of 80% of the 1251 training cases and 20% of the 1251 cases for offline validation. Additionally, we have submitted our model for evaluation on the Synapse platform, which can be accessed at [[https://www.synapse.org/#platform](https://www.synapse.org/#platform)]([https://www.synapse.org/#platform](https://www.synapse.org/#platform)) (accessed on 29 November 2024).  
To facilitate the accurate segmentation of brain tumor images by our network, we commenced by integrating the BraTS 2021 dataset into our program during the preprocessing phase. We employed simpleITK [48] and MONAI for image processing, employing the Z-score normalization method to standardize each image. Subsequently, we minimized the background while ensuring the inclusion of the entire brain and randomly downsampled the image from an initial size of 240 \(\times\) 240 \(\times\) 155 to a reduced size of 128 \(\times\) 128 \(\times\) 128. We clipped all intensity values to the 1 st and 99 th percentiles of the non-zero voxel distribution within the volume. In our research, we implemented several data augmentation techniques, including channel rescaling within the range of 0.9 to 1.1, channel intensity shifting between \(-\)0.1 and 0.1, the addition of Gaussian noise with a mean of 0 and a standard deviation of 0.1, channel dropping, and a random flipping probability of 80% along each spatial axis. We employed a strategic training regimen for our model during the training phase, and following model optimization, we resized the images back to their original dimensions. Ultimately, we submitted our results to the official platform for assessment.
## 3 Dataset Collection  
### Simulation Training Data Collection  
To achieve end-to-end intelligent parking model pre-training and obtain a large amount of parking data, this paper constructs a simulated parking environment using the Carsim/PreScan/Simulink framework, Figure 3 shows the flow chart of simulation data acquisition. This approach simulates an intelligent parking process closely resembling actual parking environments. The primary advantages of using a simpler simulation environment include the following:  
1. Reduced computational complexity: This allows for faster model training, which is particularly beneficial during the initial model development and testing stage.
2. Faster convergence: A simple environment with fewer variables can help the model converge more quickly during pre-training, enabling more effective fine-tuning of model parameters and performance evaluation.
3. Ease of implementation: A simpler simulation environment is easier to build and enables more extensive data collection, saving time and resources during development.  
In this study, the driver's parking control within the simulated parking environment is achieved using the Logitech G29 driving simulator. Parking loop views are recorded with PreScan's integrated video recording function, while the car dynamics model is built using Carsim, replacing the in-built car model in PreScan. The steering wheel angle, vehicle speed, and ultrasonic radar distance data for the simulated car are recorded through Simulink. Each frame of the surround-view data and ultrasonic sensor data collected by the simulation platform automatically match the corresponding driving decision, eliminating the need for additional space-time synchronization.  
Skilled drivers collected the experimental data by manually driving the main vehicle's simulator (Figure 4). The resolution of the captured screenshots was set to 540 \(\times\) 960, with a frame rate of 20 fps. To enhance the diversity of the dataset, multiple skilled drivers performed various parking environments from different starting points in multiple parking experiments (as depicted in Figure 5), the black area is the starting position of parking, and the arrow points to three different angles of parking starting. This data collection process yielded a total of 648,350 frames of parking data.  
Figure 3: Simulation data acquisition flowchart.  
The dataset was divided into training, validation, and test sub-sets in an 8:1:1 ratio to evaluate the model's performance during training and assess its ability to generalize to new, unseen data.  
Although the simplified simulated parking environment offers several advantages, it may only partially capture the intricacies of actual parking environments. To address this limitation, this paper employs a two-step approach: first, the intelligent parking model is pre-trained in the simulation and then retrained using transfer learning methods with a dataset from actual parking environments and more complex environments. This strategy ensures that the end-to-end intelligent parking model achieves high accuracy and better generalization when applied in real-world settings.
## 3 Results
### Engineering geology
#### 3.3.1 Geological Disasters and Disaster-Inducing Factors
This reflects the combined regulatory effect of natural environmental conditions and anthropogenic disturbance on hazard occurrence and distribution. In terms of disaster type distribution, collapses are primarily concentrated in Zones I and II, with 910 and 963 occurrences, respectively; conditions favoring collapse development include densely developed fault zones, fractured lithologies, and steep slopes. Zones III and V recorded 168 and 255 collapses, respectively, reflecting moderate elevation gradients and rock weathering. In contrast, the flat terrain of Zone VII corresponds to just 29 recorded collapse events. Landslides are most concentrated in Zone II, with 1739 occurrences, driven by steep topography and rock softening under concentrated rainfall. Zone I follows with 1012 landslides, largely attributed to frequent tectonic activity. Zones III and IV recorded 334 and 89 landslides, respectively, concentrated in mountainous areas with pronounced relief. Zone VIII recorded only seven landslides, consistent with the limited conditions for landslide initiation in flat terrain. Debris flows are densest in Zone I, with 3960 recorded events, followed by Zone II with 2105 occurrences. Both are influenced by a combination of intense precipitation, abundant loose surface material, and steep slope convergence. Debris flows in Zone IV (632) and Zone III (380) are associated with snowmelt, short-duration rainfall, and abundant weathered debris. Zones VIII (119) and VII (109) recorded significantly fewer debris flows, primarily due to low precipitation and limited source material. In summary, Zones I and II--characterized by active tectonic processes and complex terrain--serve as core development areas for collapses, landslides, and debris flows (CLD). Other zones, constrained by gentler slopes, limited material sources, and lower precipitation, exhibit reduced hazard development. Collectively, this distribution pattern underscores the joint control of geological background, topographic relief, climatic conditions, and engineering geological (lithological) factors over the spatial pattern of CLD hazards across the Qinghai-Tibet Plateau.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 2 Related Work
### Neural Network Models  
Some ionospheric models use neural networks and machine learning to model the ionosphere. These models are based on data-driven techniques that learn from the historical and observational data of the ionosphere. In [44; 45], processing algorithms and measurements from reference stations were used to compute the ionospheric delay at a regional level with high accuracy but with a certain latency. Other research, such as [31], used reference stations along with Machine Learning (ML) algorithms and neural networks to establish accurate values of the ionospheric delay at a regional level. Since there are many neural network-based models in the literature, it has been hard to find a relevant benchmark among them, and therefore, they are not included in our comparisons.
## 3 Results
### RMSE and RRMSE of V1 Relative to VM
#### 3.2.2 Distribution Characteristics of RMSE and RRMSE at Different Visibility Levels  
As shown in Figure 3a, the RMSE of V\({}_{\rm I}\) relative to V\({}_{\rm M}\) increased with increasing V\({}_{\rm M}\) for all three visibility meters, exhibiting a linear relationship (R\({}^{2}>\) 0.97). This means that the larger the V\({}_{\rm M}\), the greater the \(\Delta\)V. The \(\Delta\)V of LT\({}_{05}\) was the smallest at each V\({}_{\rm M}\) level; the \(\Delta\)V of FD was the largest when 368 m \(<\) V\({}_{\rm M}\)\(\leq\) 847 m and 6403 m \(<\) V\({}_{\rm M}\)\(\leq\) 9595 m; the \(\Delta\)V of LT\({}_{23}\) was the largest when V\({}_{\rm M}\)\(\leq\) 368 m and 847 m \(<\) V\({}_{\rm M}\)\(\leq\) 6403 m. Xiamen Airport is located near the sea, where the visibility level has significant spatial-temporal variations. Runway 23 was constructed on land reclaimed from the sea and frequently affected by sea fog; therefore, the visibility at the end of runway 23 is generally lower than that at other localities. Thus, the \(\Delta\)V of LT\({}_{23}\) was especially large at a low V\({}_{\rm M}\). In contrast, the \(\Delta\)V of FD was higher than those of the two sets of LT at a high V\({}_{\rm M}\) (\(>\)6403 m) due to the limitations of the forward-scatter meter's detection principle. Consistent with relevant results, the scattering error increased at high visibility [32; 33]. In addition, at V\({}_{\rm M800}\) (V\({}_{\rm M}\)\(<\) 800 m, the same below), which has a crucial impact on aviation operations, the \(\Delta\)V of FD was also greater than those of the two sets of LT when the visibility was at approximately the minimum operating standards for Xiamen Airport (RVR \(>\) 400 m for take-off and RVR \(>\) 550 m for landing; 368 m \(<\) V\({}_{\rm M}\)\(\leq\) 847 m).  
The RRMSE of V\({}_{\rm I}\) relative to V\({}_{\rm M}\) exhibited a power relationship with V\({}_{\rm M}\) (R\({}^{2}>\) 0.96) for all of the instruments (see Figure 3b). As V\({}_{\rm M}\) increased, RRMSE decreased rapidly; when V\({}_{\rm M}\)\(>\) 1692 m, the decreasing curves started to level off, with a difference in RRMSE between adjacent V\({}_{\rm M}\) levels of less than 6%; when V\({}_{\rm M}\)\(>\) 3016 m, the RRMSE of each instrument was consistent. The RRMSE of LT\({}_{05}\) was the smallest at each V\({}_{\rm M}\) level; the RRMSE of LT\({}_{23}\) was the largest when V\({}_{\rm M}\)\(\leq\) 368 m and 847 m \(<\) V\({}_{\rm M}\)\(\leq\) 3016 m; and the RRMSE of FD was the largest when 368 m \(<\) V\({}_{\rm M}\)\(\leq\) 847 m.  
From the above analysis, we can infer that, among the three sets of visibility meters, the V\({}_{\rm I}\) of LT\({}_{05}\) was the closest to V\({}_{\rm M}\) at each V\({}_{\rm M}\) level. At V\({}_{\rm M800}\), when V\({}_{\rm M}\)\(\leq\) 368 m, the deviation of V\({}_{\rm I}\) from V\({}_{\rm M}\) was the most significant for LT\({}_{23}\); when V\({}_{\rm M}\)\(>\) 368 m, the deviation was the greatest for FD.