esa-sceva/satcom-chunk-collection huggingface dataset:
# DEM Simulation of the Load Transfer Mechanism of a GRPS Embankment with a Fixed Geogrid Technique
###### Abstract  
As a new technique, a fixed geogrid in a geogrid-reinforced and pile-supported (GRPS) embankments has been used to reduce the total and differential settlement. To investigate the load transfer mechanism of the fixed geogrid technique of a GRPS embankment, three discrete element method (DEM) models of pile-supported embankments were established, including an unreinforced embankment, a geogrid reinforced embankment, and a fixed geogrid reinforced embankment. The efficacy of the pile, the evolution law of the contact force chain and the axial force of the reinforcement, and the microscopic load-bearing structure of the soil were investigated. Numerical simulation results showed that the embankment self-weight and surcharge load were transferred to the pile through the soil arching and tensile membrane effect. The settlement could be effectively reduced via the addition of the reinforcement, and the fixed geogrid technique was more conducive to improving the load-bearing ratio of the pile than the traditional reinforcement technique. Compared with the traditional technique of a GRPS embankment, the fixed geogrid technique had a better effect on reducing the total and differential settlement. With the increase in the surcharge load and the settlement of the soft subsoil, the reinforcement transferred a greater load to the pile. The results also showed that the stress of the embankment fill was concentrated at the pile top in all three models. The GRPS embankment with a fixed geogrid technique had a lower soil stress concentration than the other two cases. The contact force chain and stress in the embankment also showed that the reformation of the microscopic load-bearing system of the embankment fill was the internal mechanism that caused the development of the soil arching and the redistribution of stress. Furthermore, the evolution of the fabric parameters in the arching area could reflect the evolution of the soil arching structure. In the fixed geogrid case, the proportion of the load transferred to the pile from the soil arching effect was reduced, and the vertical load transferred to the pile top by the tensile membrane effect accounted for 22-28% in this study. Under the combined effect of the tensile membrane and the soil arching, the efficacy of the pile could increase by 10%.  
p 2021 11 8814. [[https://doi.org/10.3390/app11198814](https://doi.org/10.3390/app11198814)]([https://doi.org/10.3390/app11198814](https://doi.org/10.3390/app11198814))
# Structural Evaluation of Cable Bolts under Static Loading
## 1 Introduction  
The application of cable bolts was first introduced in the 1960s as part of a temporary support system [1]. Due to the large demands of societal growth, the need for deep mining excavation has exceeded the technological and testing capabilities to facilitate a safe mining procedure. Civil tunnelling methods have ultimately been incorporated into deep mining excavations; thus, cable bolts are considered as a critical permanent support element [2]. Cable bolts are an effective method to provide reinforcement for the rock mass combined with a face support such as shotcrete and steel meshing [3, 4]. A cable bolt is a flexible steel tendon made up of twisted steel wires that are inserted within a duct and anchored by a plate to the rock surface [5]. They are often used in conjunction with a pre-stressing and grouting system to provide additional tensile forces and have been used as the primary reinforcement method for deep mining excavation. Whilst there are many factors that contribute to the overall strength of the combined support system, the cable bolt is notably of greater influence when considering the reinforcement of the rock capacity [6]. [PERSON] et al. (2018) [5] stated that whilst cable bolts are an effective method for reinforcement, there is much that is unknown about the nature of cable bolts subject to dynamic loading. This is due to the inaccessibility and restrictions of deep mining in situ testing and drop testing methods failing to capture in situ conditions. Bolts under static loading are well understood; however, in deep mining excavations, the presence of rock bursts induces sudden dynamic loading conditions and presents an additional potential failure mechanism. Therefore, it is important to understand cable bolt behaviour under dynamic loading conditions. This study conducted a parametric test examining the shear force, displacement and energy absorption capacity of a cable bolt subjected to loadingthrough a double shear test using Finite Element Analysis (FEA) and the Finite Element Modelling (FEM) software ABAQUS/Explicit. As a relatively unexplored field of study, the insights obtained from this study will significantly improve industrial understanding of cable bolt behaviour under static loading. The goal of the study was to test and draw valuable conclusions about the influence of various parameters when subject to a static and dynamic load. The development of the model is detailed with explanations for each design criterion that was required in developing this model. Considerations of various design parameters are discussed, followed by a summary of the results of the testing according to each parameter. A discussion is provided to examine the fundamental implications of the results and its consistency with current literature. This compares experimental data against existing literature and experimental results. Finally, a discussion of the limitations of the testing is undertaken and final conclusions and recommendations for further investigation are presented.
Researchers are developing biodegradable and non-toxic hydraulic fluids that offer comparable performance to traditional mineral oils. These eco-friendly fluids reduce environmental impacts and improve the sustainability of hydraulic systems. Additionally, optimizing the fluid's viscosity and thermal properties can enhance the efficiency and responsiveness of the hydraulic cylinder. During the development of application software for mathematical modeling, as well as the identification and optimization of parameters, special attention was paid to the real needs of the practice of engineers. For this purpose, the original graphic 2D and 3D application software was used in real-time with simultaneous display and processing in 24 high-resolution windows. The design optimization of hydraulic cylinders has seen significant advances driven by innovations in materials, manufacturing, computational analysis, and intelligent systems. These developments have resulted in more efficient, durable, and environmentally friendly hydraulic cylinders. As research continues, the integration of digital technologies and sustainable practices will further enhance the performance and sustainability of hydraulic systems, solidifying their role in modern industrial applications. The article is organized as follows:  
* It firstly presents the theoretical foundations and methodologies developed for the design of hydraulic cylinders;
* It then describes the simulation techniques employed to predict the performance of these cylinders under various operating conditions;
* It then focuses on the optimization processes used to enhance the efficiency and power of the cylinders;* We next introduce the software tool developed to integrate these methodologies, including its features and user interface;
* We then provide a case study to demonstrate the practical application and effectiveness of the proposed solutions;
* All this is followed by concluding remarks and future work directions, in Section 4. The study of the influence of pressure pulsations on large-sized and high-power cylinders in hydraulic systems has benefited from extensive research contributions. These contributions have provided a deeper understanding of the underlying mechanisms, practical solutions for mitigating adverse effects, and improved design and control strategies for hydraulic systems. Ongoing research continues to refine these insights, driven by the need for more efficient, reliable, and durable hydraulic systems in various industrial applications.
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 3 Simulation
### Construction of Array Signals and Generation of Datasets  
In this paper, linear frequency modulation (LFM) was used to simulate the transmission signals of the active sonar system. An LFM signal refers to a signal whose frequency changes linearly with time and retains the characteristics of continuous signal and pulse. Using this signal in underwater target tracking can improve the anti-interference ability of active sonar equipment, realize the measurement function of long-distance and residence time resolution, and improve the detection performance of active sonar equipment.  
Figure 3: The simulation flowchart of the LSTM–Kalman filtering method.  
Figure 2: The internal structure of the LSTM-NN.  
In this experiment, the amplitude of the LFM signal was 1, the starting frequency was 200 Hz, the center frequency was 300 Hz, the sampling frequency was 65 kHz/s, the chirp rate was 200 Hz/s, the sound velocity in water was 1500 m/s, and the transmission duration of the transmitted signal was 1 s. Figure 4 shows the time domain waveform and frequency domain waveform of the transmitted signal. Figure 4 is the time domain waveform of the transmitted signal and Figure 4 is the frequency domain waveform of the transmitted signal.
## 3 Normalization Algorithms
### Hyperspectral Library\(-\)Based Normalization  
The key step to normalizing spectral radiation from different sensors is developing a hyperspectral library. We built a hyperspectral library by selecting images of relevant land cover ground types from the USGS and ASTER libraries, and simulated spectra from the PROSAIL model, spectra of GF\(-\)5, and Hyperion imagery. Each band's spectral response function from Sentinel\(-\)2A and Landsat\(-\)8 OLI images (Figure 2) was used in Equation (3) by using corresponding data types from all bands within the same range to obtain the normalized reflectance.  
\[\rho_{\lambda}=\int_{\lambda_{1}}^{\lambda_{2}}f_{i}\rho_{i}\mathrm{d}\lambda_ {i} \tag{3}\]  
Where \(\rho_{\lambda}\) is the normalized reflectance on the band (Table 1), \(\lambda_{1}\) and \(\lambda_{2}\) are the start-stop wavelengths of the band \(\lambda\), and \(f_{i}\) and \(\rho_{i}\) are the spectral response function and hyperspectral reflectance of band I, respectively. After applying Equation (3) to process each spectral dataset, differences among sensor bandwidths were removed and the corrected data regressed to calculate the transfer coefficient for each band between the two datasets.  
We found that radiation from two images has a strong linear relationship with a fitted correlation coefficient approaching 1 (Figure 3). Adjacent bands have little influence on satellite image band radiation (Table 2). Except for band 4, all other coefficients of corresponding bands are close to 1. It is clear from these relationships that the radiometric difference caused by bandwidth effects can be linearly calibrated. If linear regression is used for the radiometric normalization of two images, the bandwidth variation detected is the linear regression coefficient, which is not necessary to conduct separated processing.  
Figure 3: Normalization coefficient and errors by SpecLib\(-\)Single.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sentinel\(-\)2A** & **Landsat8** & **Band\(-\)1** & **Band\(-\)2** & **Band\(-\)3** & **Band\(-\)4** & **Band\(-\)5** & **Band\(-\)6** \\ \hline Band\(-\)1 & \(1.1105\) & \(0.0329\) & \(-0.0052\) & \(0.1199\) & \(-0.0023\) & \(0.0091\) \\ Band\(-\)2 & \(-0.0871\) & \(0.9900\) & \(0.0523\) & \(-0.1169\) & \(0.0089\) & \(-0.0262\) \\ Band\(-\)3 & \(0.0626\) & \(0.0802\) & \(1.1079\) & \(-0.1476\) & \(-0.0111\) & \(0.0234\) \\ Band\(-\)4 & \(-0.0614\) & \(-0.0279\) & \(-0.0660\) & \(1.4037\) & \(0.0049\) & \(-0.0104\) \\ Band\(-\)5 & \(-0.0093\) & \(-0.0020\) & \(0.0000\) & \(0.0038\) & \(0.9994\) & \(0.0020\) \\ Band\(-\)6 & \(0.0095\) & \(0.0025\) & \(0.0020\) & \(0.0031\) & \(-0.0021\) & \(1.1088\) \\ Bias & \(0.0002\) & \(0.0001\) & \(0.0002\) & \(-0.0001\) & \(0.0000\) & \(0.0002\) \\ R2 & \(1.0000\) & \(1.0000\) & \(1.0000\) & \(0.9999\) & \(1.0000\) & \(0.9999\) \\ RMSE & \(0.0015\) & \(0.0006\) & \(0.0006\) & \(0.0024\) & \(0.0005\) & \(0.0019\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Normalization coefficient and errors by SpecLib\(-\)Multi.  
Figure 2: The spectral response of Sentinel\(-\)2A and Landsat\(-\)8 OLI.
## 3 Proposed Method
### Network Structure
#### 3.2.3 Multi-Scale Feature Fusion Branch  
To improve the recognition accuracy of the SAR image classification model, it is necessary to fuse local and global features from different scales. Meanwhile, to preserve both local and global features as much as possible, a multi-scale feature fusion branch was implemented. The multi-scale feature fusion branch can adaptively fuse local features, global representations from different scales, and semantic information from the previous level fusion based on the input features. The workflow of the multi-scale feature fusion branch is shown in Figure 1. We capture the shallow local and global features of stage 1 and the deep local and global semantic information of stage 3 through a multi-scale feature fusion branch. Finally, the feature information of each fusion path is fused to gradually fill in the semantic gaps, and the final output is obtained by connecting the global average pooling layer and the fully connected layer.  
As shown in Figure 3, the multi-scale feature fusion branch consists of the FFU and down-sampling operation. Among them, the FFU consists of a LN layer, a convolution layer with a convolution kernel size of \(1\times 1\), and a hard swish activation function. The specific processes of stage 3 are shown in Figure 2c. First, the FFU output features \(F_{i-2}\) of the previous stage are down-sampled through the convolution layer with the convolution kernel size of \(1\times 1\) and an average pooling layer (Avgpool); then, the channel dimensions are aligned to facilitate subsequent fusion operations. The fusion feature \(\hat{F}_{i-2}\) obtained after down-sampling is fused with the local feature \(C_{i}\) and the global feature \(S_{i}\) from the ConvNeXt-SimAM and the Swin Transformer branches, respectively, and normalized by the LN layer. Subsequently, a convolutional layer with a convolution kernel size of \(1\times 1\) and a hard swish activation function are adopted. Finally, the feature \(F_{i}\) fused in this stage effectively captures both the local features and global semantic information of the SAR image. The resulting feature \(F_{i}\) is subsequently classified through the global average pooling and fully connected layers. The process of this stage is described in the following equations.  
\[\hat{F}_{i-2}=\text{Avgpool}(f^{1\times 1}(F_{i-2})) \tag{6}\]  
\[F_{i}=\text{hardswish}(f^{1\times 1}(\text{LN}(\text{Concat}(\hat{F}_{i-2},S_{i},C_ {i})))) \tag{7}\]  
\[O=\text{FC}(\text{glo\_avg\_pool}(F_{i})) \tag{8}\]  
where \(O\) represents the final output of the model, FC represents the fully connected layer, and glo_avg_pool represents global tie pooling.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### Summary of Works in Handover Methods

Table 1: Comparative summary of handover decision-making algorithms. highlights methodological diversity while helping to identify critical limitations that restrict the practical deployment of many existing models. AI-based approaches utilize machine learning, deep learning, and reinforcement learning to enable adaptive and predictive HO decisions. These methods are effective in modeling complex mobility behavior and enhancing QoS. However, their applicability in real-time systems is constrained by limited interpretability, the need for large training datasets, and significant computational overhead, which is particularly problematic in energy-sensitive or latency-critical environments. Fuzzy logic-based approaches offer interpretable, rule-based reasoning that handles uncertainty well in HO scenarios. They are lightweight and transparent, making them suitable for constrained devices. Nonetheless, such models lack adaptability in highly dynamic settings and often require manual tuning, limiting their scalability. Hybrid approaches, which integrate AI techniques with fuzzy logic systems, aim to balance learning capability with interpretability. These models show promise in adapting to real-time variations while maintaining clear decision logic. However, they introduce increased complexity, integration overhead, and a lack of validation in real-world 6G deployments. Despite advances in handover efficiency across all categories, many models still lack energy-awareness, which is critical for ultra-dense smart city scenarios involving thousands of mobile and IoT devices. Without considering energy constraints, these algorithms may lead to unnecessary signaling and battery depletion. Additionally, secure and real-time deployment capabilities remain underexplored. Most models are tested only in simulation environments and rarely account for latency, dynamic interference, or authentication mechanisms during HO. To address these challenges, future work must prioritize the development of lightweight, secure, and context-aware HO algorithms. This includes integrating federated learning, energy-aware optimization, and blockchain-based mechanisms to enable privacy-preserving, tamper-proof, and interoperable handover solutions for 6G networks.
# Fault Classification and Localization Scheme for Power Distribution Network
## 7 Fault Classification and Location with Renewable Energy Distributed Generation
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class** & **TP** & **FP** & **Precision** & **Recall** & **F-Measure** & **ROC** \\ \hline AG & 0.919 & 0.000 & 0.906 & 0.902 & 0.981 & 0.930 \\ BG & 0.902 & 0.000 & 0.919 & 0.920 & 0.960 & 0.926 \\ CG & 0.911 & 0.000 & 0.909 & 0.908 & 0.955 & 0.955 \\ AB & 0.958 & 0.000 & 0.916 & 0.922 & 0.927 & 0.940 \\ AC & 0.911 & 0.003 & 0.905 & 0.903 & 0.948 & 0.910 \\ BC & 0.925 & 0.000 & 0.900 & 0.943 & 0.920 & 0.928 \\ ABG & 0.908 & 0.012 & 0.906 & 0.951 & 0.916 & 0.955 \\ ACG & 0.922 & 0.000 & 0.944 & 0.922 & 0.920 & 0.945 \\ BCG & 0.917 & 0.025 & 0.920 & 0.908 & 0.911 & 0.931 \\ ABC & 0.925 & 0.012 & 0.925 & 0.909 & 0.945 & 0.952 \\ Avg & 0.920 & 0.008 & 0.915 & 0.919 & 0.938 & 0.937 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: SVM classification with two cycles post-fault with PV and WE sources. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Fault Type** & **TP** & **FP** & **Precision** & **Recall** & **F-Measure** & **ROC** \\ \hline AG & 0.988 & 0.000 & 0.990 & 0.905 & 0.982 & 0.910 \\ BG & 0.900 & 0.000 & 0.981 & 0.955 & 0.922 & 0.955 \\ CG & 0.921 & 0.030 & 0.970 & 0.951 & 0.991 & 0.958 \\ AB & 0.980 & 0.045 & 0.968 & 0.958 & 0.955 & 0.909 \\ AC & 0.952 & 0.033 & 0.970 & 0.903 & 0.951 & 0.922 \\ BC & 0.911 & 0.022 & 1.000 & 0.957 & 0.966 & 0.975 \\ ABG & 0.899 & 0.055 & 0.960 & 0.975 & 0.952 & 0.966 \\ ACG & 0.855 & 0.099 & 1.000 & 0.971 & 0.922 & 0.952 \\ BCG & 0.950 & 0.011 & 0.955 & 0.908 & 0.981 & 0.916 \\ ABC & 0.991 & 0.044 & 0.980 & 0.909 & 0.931 & 0.929 \\ Avg & 0.935 & 0.034 & 0.977 & 0.939 & 0.955 & 0.939 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: SVM classification from half a cycle post-fault with PV and WE sources.
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 4 Experiments and Discussion
### Distributed Targets Simulation Experiments  
In this subsection, distributed target simulation experiments are conducted. The image used for simulation is acquired by Sentinel 1A in March 24 th, 2019. This scene is located at Coastal Waters of Great Barrier Reefs in Australia with 20 m resolution. The parameters are the same as for multi-channel TOPSAR in Table 1 except that the resolution is now 20 m. The scene is 10 km in azimuth and 50 km in range. The original scene and imaging results with errors and after error compensation are shown in Figure 15. Adding the errors in Table 2, it can be seen that the image with errors (Figure 15b) is smeared and ghost targets occur. Ghost targets are mainly resulted from the phase inconsistency errors because this is one of specific symbols of phase inconsistency errors. Besides the overlay of ghost  
Figure 14: The performance of CDC error estimation method versus _SNR_ (**a**) and _Fu_ (**b**).  
Figure 13: The performance of CDC error estimation method versus _SNR_ (**a**) and _Fu_ (**b**).  
targets, the CDC error is another factor that leads the smeared effect. It is not easy to see the effects of DRR error in the image directly because it mainly has an effect on the azimuth resolution and intensity loss. After error correction, the image (Figure 15c) is well focused, as same as the image simulated without errors (Figure 15a).
## 4 Results  
Wildfires mapped using the proposed methodology reported a burned area of around 1400 km\({}^{2}\) (NBR: 1398.3 km\({}^{2}\); BAIS2: 1196.6 km\({}^{2}\); BAIS2\(\alpha\): 1384.4 km\({}^{2}\)) in the Italian territory caused by 2017 wildfires. The EFFIS \"MODIS burnt areas\" products reported an area of 1404.6 km\({}^{2}\). Distribution of the burned areas is shown in the maps in Figure 4.  
Omission and commission errors generated during the four procedure steps using the three spectral indices are reported in Table 3.  
Figure 4: **(a)** Spatial distribution of the EFFIS “MODIS burned areas” reference products and Copernicus EMS rapid mapping activations (areas of interest are overlaid); **(b)** spatial distribution of the identified burned areas; **(c)** focus map of the identified burned areas over the Calabria region; **(d)** focus map of the identified burned areas over the Sicily region.  
Based on the comparison with the Copernicus EMS product (Table 3), the procedure generated in Step A a high omission error of around 75% and a commission error of around 20-25%. Step B, the sieve filtering, slightly reduced the commission error and increased the omission error. Step C, the region-growing algorithm, helped to enlarge the core burned areas and reduce the omission error by around 30-35%, while increasing the commission error by about 5%. Step D contributed to a reduction of the commission error by only around 1% while negatively affecting the omission error, which slightly increased.  
Considering the overall procedure, the NBR and dNBR spectral indices reported the best classification performances, while BAIS2 and dBAIS2 spectral indices resulted in the lowest commission error (Table 3).  
Post-fire dates identified from the S2 data were detected on average five days after the post-fire dates reported in the EFFIS product (Figure S2a) and one day before the post-fire dates corresponding to the satellite observations used to produce the Copernicus EMS rapid mapping activations (Figure S2b).