esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
## 2 System Configuration
### WDPS Configuration for Distributed Control of Heavy Haul Freight  
#### 2.3.1 WDPS-Based MIMO-OFDM  
It was judged that the snake motion of the heavy haul freight in domestic freight line with many tunnels and curved sections would affect the signal attenuation. Therefore, in this test, MIMO-OFDM-based WDPS, which is strong in multipath fading environment and capable of high-speed data transmission, is applied and its performance is verified. OFDM processes data in parallel, as shown in Figure 2, divides a high-speed data stream at low speed, and transmits them simultaneously using a carrier frequency. Since the symbol interval is increased by using a low-speed parallel carrier, ISI is reduced, and by using multiple carriers, there is a strong advantage in frequency selective fading [20; 21].  
Signal strength is affected by path loss, shading, and fading. In general, the signal strength (\(L_{s}\)) in a free space RSSI is defined as the ratio of transmit power (\(P_{t}\)) and receive power (\(P_{r}\)) as shown in  
Figure 2: MIMO-OFDM block diagram.  
the following Equation (1), and is expressed in dBm as a value reflecting the strength of the received signal [22].  
\[L_{s}=\frac{P_{t}}{P_{r}}=[10\log\Bigl{(}\frac{4\pi x}{\lambda}\Bigr{)}]^{2} \tag{1}\]  
Here \(d\) is the distance between the transceiver and \(\lambda\) is the wavelength. In general, the higher the frequency, the greater the loss of free space. In the case of a heavy haul freight, \(d\) is constant. Therefore, NLOS according to curve, gradient and tunnels are the main factors to degrade communication performance rather than free space path loss according to distance. Installing an appropriate transceiver location is important. Therefore, we had secured the communication coverage of heavy haul freight by applying WiFi transceiver with 1 km coverage and additionally repeater as shown Figure 3. To improve communication reliability, the WDPS, which was first applied in Korea, was resistant to multipath fading and employed MIMO-OFDM technology capable of transmitting large amounts of information [23].
## 3 Revisiting Data Annotation
### Generic ML-Based Approaches  
ML-based approaches leverage algorithms to automate or assist in data annotation and address the inefficiencies of traditional methods. In particular, active learning is a prominent technique in which a model selects the most informative unlabeled data points for human annotation. This minimizes labeling efforts while maximizing model performance. For instance, uncertainty sampling identifies instances where the model is least confident as seen in text classification tasks where active learning has reduced annotation needs by up to 50% [40]. Weak supervision enables the rapid labeling of large datasets using noisy or indirect signals such as heuristics or pre-trained models. Programmatic labeling, a key weak supervision method, involves defining labeling functions that assign labels based on data patterns. One example is identifying entities in text using syntactic rules [41]. Furthermore, transfer learning uses pre-trained models, like BERT for NLP or ResNet for computer vision, to perform initial labeling on new tasks. By fine-tuning these models on a small set of labeled data, they can adapt to specific annotation requirements. This has been demonstrated in applications like named entity recognition [42]. These methods reduce reliance on manual effort and allow more efficient use of human expertise.  
However, generic ML-based approaches have notable limitations. To begin with, active learning requires an initial labeled dataset and can be computationally intensive due to iterative model training. Similarly, weak supervision often produces noisy labels and may require additional denoising techniques such as majority voting among multiple labeling functions, which adds complexity [37]. Likewise, transfer learning suffers from negative transfer if the source and target domains differ significantly, leading to suboptimal performance. For example, a model pre-trained for general text classification may struggle with medical texts without extensive fine-tuning [43]. Moreover, these methods still require human input for initial labeling or rule definition. This process can be time-consuming and may demand domain expertise. These challenges highlight the need for more advanced techniques, such as generative AI and large language models (LLMs), which offer greater autonomy and adaptability. They also handle complex annotation tasks with minimal human input, further advancing the efficiency and quality of data annotation.
## 2 Designs for the Artificial Magnetic Conductor and Antenna  
### A Ferrite-Loaded Artificial Magnetic Conductor Design  
The complex permeability of the MP2106-0M0 ferrite is presented in Figure 1. The complex permeability consists of the real part (\(\mu^{\prime}\)) and imaginary part (\(\mu^{\prime\prime}\)). The permittivity of the MP2106-0M0 ferrite is 12. The permeability value is relatively high compared to the values of general commercial substrates in the HF, VHF and UHF bands. Due to the high permeability characteristics of the MP2106-0M0 ferrite, the unit cell structure has a high inductance value [18]. As a result, the unit cell of the AMC can be designed with a small size while operating in the HF, VHF and UHF bands.  
Figure 1: Complex permeability of the MP2106-0M0 ferrite.  
Figure 2a depicts the geometry of the designed unit cell of the artificial magnetic conductor (AMC). The unit cell structure consists of two substrates sandwiching the ferrite material. The two substrates are Taconic RF-35 substrates with a dielectric constant of 3.5, thickness of 1.52 mm and loss tangent of 0.0018, with the ferrite material, MP2106-0M0 (Laird [21]), between them, as denoted in blue in Figure 2a. In addition to the ferrite being located between each substrate, the patch of the AMC is on the upper side of the upper substrate and the ground of the AMC is on the lower side of the lower substrate. The lengths of one side of the square unit cell structure and the patch are \(W=100\) mm and \(a=50\) mm, respectively. The total thickness is 13.04 mm, which is the sum of the thicknesses of the two substrates (\(S_{h}=1.52\) mm) and the thickness of the ferrite (\(F_{h}=10\) mm). The boundary condition of the unit cell structure is illustrated in the Figure 2b. To simulate the reflection phase with a periodic boundary condition, each of the facing walls are set such that they are identical to those of a PEC (perfect electric conductor) and a PMC (perfect magnetic conductor). The waveport exists on the \(+z\)-axis, and the reflection phase on the surface of the unit cell was simulated.  
Figure 3 shows the simulated reflection phase of the proposed unit cell of the AMC. The \(-90^{\circ}\)-\(+90^{\circ}\) operating frequency bandwidth ratio is 24:1 (19-460 MHz), covering the upper frequency ranges of the HF band, the VHF band and the lower frequency range of the UHF band. Accordingly, it can be confirmed that the unit cell, which is \(0.006\times 0.006\times 0.0008\,\lambda_{L}^{3}\) (\(\lambda_{L}\) is the wavelength of the lowest operating frequency) in size, operates in the HF, VHF and UHF bands given the use of the high-permeability material MP2106-0M0 ferrite.
## 5 Results
### Example 1  
Firstly, we examine the one-dimensional example which serves as a univariate nonstationary growth model commonly employed in nonlinear filtering. The system is depicted as follows:  
\[x_{1}(k)=a_{1}x_{1}(k-1)+a_{2}\frac{x_{1}(k-1)}{1+x_{1}(k-1)^{2}}+a_{3}\cos(1.2 (k-1))+w_{1}(k-1) \tag{61}\]  
\[z_{1}(k)=\frac{{x_{1}}^{2}(k)}{20}+v_{1}(k) \tag{62}\]  
The parameters are selected as \(a_{1}=0.5\), \(a_{2}=2.5\), \(a_{3}=8\), \(K=100\) and \(\Upsilon=100\). The convergence rate in the MKMC-CSVSF filter gain is selected as \(\lambda=0.1\). The kernel bandwidth of the MKMC criterion is selected as \(\sigma=5\).  
Firstly, the system and measurement noises are characterized by a Gaussian probability functionas \(w_{1}(k-1)\sim N(0,0.06)\), \(v_{1}(k)\sim N(0,0.01)\). Then the ARMSE of various algorithms including the CKF, CSVSF and MKMC-CSVSF are calculated and presented in Table 1.  
Then, considering the dynamic uncertainties, the parameter \(a_{3}=8\) is changed to \(a_{3}=4\) at \(K=50\) during each Monte Carlo simulation. Meanwhile, the measurement error is substituted with a heavy-tailed non-Gaussian distribution, under the assumption that the measurement errors adhere to a mixed-Gaussian distribution \(w_{1}(k-1)\sim N(0,0.06)\), \(v_{1}(k)\sim 0.8N(0,0.01)+0.2N(0,10)\). The statistical results are presented in Table 2 in this scenario.  
Hence, according to the aforementioned results, it is apparent that if the system and measurement noises are characterized by Gaussian statistics, the presentation of the CKF, CSVSF and the proposed MKMC-CSVSF demonstrates similar levels of accuracy in state estimation. However, in scenarios where non-Gaussian heavy-tailed noise coexists with system uncertainties, the proposed MKMC-CSVSF algorithm outperforms the other two methods. The ARMSE of the MKMC-CSVSF exhibits notably lower values and provides more accurate state estimation. Moreover, it is worth noting that the fuzzy rule employed for selecting the kernel bandwidth proves to be effective and optimal. This optimal se  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Filter Algorithm** & **ARMSE of \(x\)** \\ \hline CKF & 0.1788 \\ CSVSF & 0.2110 \\ MKMC-CSVSF (Fuzzy rule) & 0.1953 \\ MKMC-CSVSF (\(\sigma=0.5\)) & 0.2410 \\ MKMC-CSVSF (\(\sigma=2\)) & 0.2347 \\ MKMC-CSVSF (\(\sigma=5\)) & 0.2605 \\ MKMC-CSVSF (\(\sigma=10\)) & 0.2209 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ARMSEs of \(x_{1}\) with Gaussian noise.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Filter Algorithm** & **ARMSE of \(x\)** \\ \hline CKF & 0.5889 \\ CSVSF & 0.5409 \\ MKMC-CSVSF (Fuzzy rule) & 0.2876 \\ MKMC-CSVSF (\(\sigma=0.5\)) & 0.3054 \\ MKMC-CSVSF (\(\sigma=2\)) & 0.3257 \\ MKMC-CSVSF (\(\sigma=5\)) & 0.3559 \\ MKMC-CSVSF (\(\sigma=10\)) & 0.3932 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ARMSEs of \(x_{1}\) with non-Gaussian noise.  
lection of the kernel bandwidth mitigates filtering divergence issues while improving filtering accuracy.
## 3 Experimental Activities
### Investigation of the Fragmentation Process  
Investigating the physics behind orbital collisions, and in particular the production of new fragments, is important for understanding the evolution of the space debris environment. In this context, the University of Padova is studying the fragmentation process of both simple and complex targets, with particular focus on the characteristics and the distributions of the generated debris. This work, supported by the European Space Agency in the framework of a collaboration with SpaceDys [44], included a campaign of impact tests on aluminum and CFRP simple plates and sandwich panels and a catastrophic fragmentation experiment on a Picosatellite mock-up.  
Figure 5: Frames from impact test video showing the panel’s self-healing capability.  
Figure 6: Pictures of 3D-printed samples subjected to impact tests and the copper witness plates placed behind them [43]. On the left, impact tests on simple plates (2.9 mm projectile at 1 km/s, top, and 5 km/s, bottom). On the right, test on a multi-layer corrugated panel (2.9 mm at 4.8 km/s).
# Seismic Hazard Evaluation and Strain Dynamics in the
## 3 Methodology and Implementation
### Geodetic Strain Rate Analysis of the SFZ  
Seismic hazard analyses conducted using earthquake catalogs provide predictions about future hazards based on the past activities of faults. Nevertheless, these data have limited capacity to fully reflect the current seismic hazard potential of the faults. Therefore, the use of high-precision GNSS velocity data is crucial for understanding the current deformation state of faults. The reason for this is that strain accumulation on faults during the pre-seismic or inter-seismic period can be calculated using GNSS velocities. These strain rates directly reflect the energy accumulation processes on faults and their dynamic characteristics. Therefore, for a more comprehensive seismic hazard analysis that includes the present accumulation state of faults, it is essential to consider the faults' current strain build-up. This geodetic approach offers the opportunity to make valuable and more accurate assessments of future seismic hazards, in addition to analyses that are mostly limited to historical catalog data.  
In the study area, instrumental earthquake records highlight the high seismic activity, suggesting the possibility of stress accumulation. Furthermore, considering past earthquakes in the region, it is evident that the fault has the potential to cause significant damage to nearby settlements. Therefore, if the faults in the region have the potential to generate a destructive earthquake in the coming years, it is crucial to assess this risk not only theoretically but also based on geodetic data obtained from field observations. Such  
Figure 9: Temporal energy density results of earthquakes in the study area from 1900 to 2030, shown in 10-year intervals (red lines represent active faults in the study area).  
an approach is of great importance in mitigating potential losses in all terms. For this purpose, the GNSS velocity field consisting of 836 sites on the Anatolian Plate, developed by [PERSON] et al. [119], was used to calculate the accumulated strain rates on the segments forming the SFZ (Figure 10). The average RMS values for the GNSS sites used in this study are 0.18 mm/yr and 0.20 mm/yr for V\({}_{\text{east}}\) and V\({}_{\text{north}}\), respectively.  
The velocity field used contains the most comprehensive velocity data currently available for the Anatolian Plate. Strain rates on the segments were calculated at approximately \(\sim\)10 km distances using GNSS velocities from 20 sites covering the study area. The results indicate that the strain rates reach their maximum at the western boundary where the zone intersects with the GFZ and are non-negligible at the eastern boundary of the zone.
## 3 Results
### CNN-SVM Prediction Model Improved by the Black-Winged Kite Algorithm
#### 3.3.2 Model Performance Comparison  
To validate the effectiveness of the model optimization results, we uniformly used the training and test samples obtained through SG-1 DR preprocessing as divided in Table 2. This study selected five modeling methods--CNN, SVM, CNN-SVM, BKA-CNN, and BKA-CNN-SVM--to compare the prediction accuracy of SOM content. The modeling results are shown in Table 7. To visually understand the differences between the predicted and actual values for each model, regression scatter plots of the predicted and actual values for each model's test set were drawn, as shown in Figure 10. The closer the fitted regression line is to the 1:1 line, the more accurate the model's predictions. The BKA-CNN-SVM model was the best in all four evaluation metrics, with its scatter points more evenly distributed around the 1:1 line. The RMSE of the validation set was 3.042, which decreased by 2.022, 2.123, 1.599, and 1.702 compared to other models. The R\({}^{2}\) was 0.930, which increased by 12.3%, 13.3%, 9.2%, and 9.9% compared to other models. The MAE was 2.254, which decreased by 1.716, 2.137, 1.487, and 1.221 compared to other models. The MARE was 0.1, which decreased by 0.077, 0.083, 0.078, and 0.063 compared to other models. The RPIQ was 1.436, which decreased by 3.560, 2.819, 2.316, and 0.876 compared to other models. It can be seen that the BKA-CNN-SVM model has a high degree of matching between predicted and actual values, indicating that the addition of SVM improves the generalization ability of regression predictions. The Black-winged Kite Algorithm significantly enhances the accuracy of the CNN-SVM network, showing a more significant and positive impact on the effective management of forest soil in the study area and enhancing the scientific and practical effectiveness of soil improvement strategies.  
Figure 9: SVM parameter optimization process. (**a**) shows the optimization situation of parameter c. (**b**) shows the optimization situation of parameter g.
## 5 Discussion
### Research Challenges and Opportunities  
Optimizing data governance is a key challenge for improving data-driven CIM systems. Data standardization is crucial in order to address multi-source data fusion issues, and technologies such as machine learning and big data analytics can significantly enhance the data quality and consistency. This can improve the accuracy and timeliness of decision support, especially in dynamic environments like smart traffic management, where data integration from various sensors is essential. Enhancing the real-time processing capabilities is another critical challenge. Distributed computing and edge computing are promising solutions to optimize the computational efficiency and reduce the response time, enabling CIM to handle large-scale urban data efficiently. These technologies are essential for scenarios requiring rapid decision making, such as emergency response and real-time urban management. Cross-departmental data sharing remains a significant hurdle. Addressing this requires both technical and policy solutions. Federated learning allows departments to collaborate on data analysis without sharing sensitive information, ensuring privacy while improving the decision quality. Meanwhile, stricter data-sharing standards and privacy regulations must be developed to ensure secure and compliant cross-departmental data exchanges. Lastly, social responsibility and ethical concerns are crucial for the future of CIM. Balancing data sharing with privacy protection and ensuring the fair and equitable use of public data will be central to advancing CIM systems. Technologies like blockchain and federated learning can enhance transparency, compliance, and data security, ensuring that CIM systems adhere to ethical standards while improving urban management.
# Numerical Simulation of Fluid-Structure Coupling for a Multi-Blade Vertical-Axis Wind Turbine
## 4 Results and Analysis
### Pressure Field Analysis
The pressure distribution curve of the six blades has a sharp trend change, dropping rapidly as the measurement point value increases before rising quickly after reaching the bottom, with a maximum value of 2.63 \(\times\) 10\({}^{6}\) Pa and a minimum value of 1.05 \(\times\) 10\({}^{6}\) Pa.
# A New Shear Strength Model with Structural Damage for Red Clay in the Qinghai-Tibetan Plateau
## 4 Discussion  
### Analysis of Shear Strength  
To further analyze the deterioration of the shear strength of red clay after FTCs, the deviatoric stress at an axial strain of 15% is taken as the peak deviator stress. Figure 7 shows the peak deviator stress of specimens subjected to FTCs under different confining pressures. The strength is significantly deteriorated by the FTC process. The peak deviator stress gradually declines with the increase in FTCs, yet the rate of deterioration slows down. As the radial stress increases, the range of peak deviator stress reduction increases. Furthermore, the reduction process of the shear strength can be classified into three phases: rapid reduction, slow reduction, and stabilization. Among them, the attenuation degree of the shear strength is the most significant in the rapid reduction stage where the shear strength decreases by approximately 30% under four radial stresses. Additionally, the peak deviator stress after seven FTCs is approximately 65% of the initial strength.  
According to the value of failure strength in Figure 7, the stress Mohr circle and strength envelope under different confining pressures are drawn with the shear stress as the ordinate and the normal stress as the abscissa. The intercept between the strength envelope and the ordinate is cohesion, and the angle between the strength envelope and the abscissa is the internal friction angle. According to this method, the cohesion and internal  
Figure 6: \(T_{2}\) distribution curve of red clay.  
Figure 7: Variation curves of peak deviator stress of specimens under four confining pressures.  
friction angle of red clay are calculated. The shear strength parameters of the red clay are illustrated in Figure 8. As the number of FTCs increases, the cohesion gradually declines to stable, showing a characteristic of deceleration degradation. The cohesion decreases the most after the first FTC, accounting for 46.26% of the total degradation. After three FTCs, the cohesion deteriorates to about 83% of the total degradation. After five FTCs, the cohesion changes weakly and the change trend gradually tends to be stable. It can be seen that the decreasing trend of the shear strength and cohesion is similar, indicating that cohesion is the most important factor for the shear strength of red clay. In addition, the internal friction angle also displays a decreasing tendency, but the change range is small compared with the cohesion. The curves of the measured values of the cohesion and internal friction angle with the number of FTCs were both in line with the logistic model. The specific fitting functions relationship can be expressed as Equations (2) and (3):  
\[c=31.5+16.7/\left(1+0.9N^{1.6}\right) \tag{2}\]  
\[\varphi=6+2.2/\left(1+0.1N^{3.2}\right) \tag{3}\]  
where \(c\) and \(\varphi\) are the cohesion and internal friction angle, respectively. \(N\) is the number of FTCs.
## 4 Experimental Result and Discussion
### Rice Mapping
It can be seen that the accuracy of the rice map strongly depends on the training and test datasets. The GSO statistical data should be a common dataset to evaluate different algorithm performances. Besides, the ML approach has proved its effectiveness compared to the traditional threshold-based method. For different machine learning strategies, such as ensemble learning (i.e., RF in Reference [30]) and SVM in our study, 10 m resolution of VVVH gives the best results for rice classification in the RRD. However, to conclude that the ML method is more optimal than thresholding technique, it is necessary to assess the same data set. In our study, the SVM classifier was selected based on the experimental results of some previous rice classifications, the effectiveness of the SVM classifier in binary classification problems and its convenient deployment with the Python programming language and Scikit-learn library. As a result, our rice map was created for the winter-spring season in the RRD region including 11 provinces from the time-series S1A data. The OA is 90.50% with a difference of 12,544 hectares to the GSO statistics. The detailed comparison is listed in Table 7.
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 5 Conclusions  
We proposed an Energy-aware Grid-based 3D-Obstacle Avoidance during path planning missions. The algorithm is named EAOA and has two phases. It first generates an original path offline using the area top-view grid, then scans the frontal view of the area to detect an obstacle along the drone's path. The algorithm re-plans the path online to find a detour and bypass the obstacle. The results show a gain in completion time and energy saving. We aim in the future works to test the algorithm in an actual flight and study the behavior of the algorithm in a noisy environment and in the presence of dynamic obstacles or pop-up threats. We also intend to boost up the performance of the algorithm by applying more optimization using dynamic programming.  
Conceptualization, A.M.; Investigation, A.G. and A.M.; Supervision, A.G.; Validation, A.M.; Writing--original draft, A.G. and A.M.; Writing--review and editing, A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank the editors and the anonymous reviewers, for their valuable comments which improved the clarity and quality of the paper.  
The authors declare no conflict of interest.