esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 3 Proposed Scheme
### Hierarchical Pyramid-like coefficient arrangement:  
Figure 2 shows an arrangement of the DCT coefficients in a hierarchical pyramid structure. The coefficients in each 8 \(\times\) 8 block are arranged in a parent-child relationship. The parent of a coefficient \(i\) is \(\left\lfloor\frac{i}{4}\right\rfloor\) for \(4\leq i\leq 63\), while the set of four children associated with \(j^{th}\) coefficient is \(\left\{4j,\,4j+1,\,4j+2,\,4j+3\right\}\) for \(0\leq j\leq 15\). The DC coefficient indexed as 0 is the root of the tree, which has only three children, e.g., 1, 2 and 3. The offspring corresponds to direct descendent in the same spatial location in the next finer band of pyramid. A tree corresponds to a node having 4 children which always form a group of 2 \(\times\) 2 adjacent pixels. In the bottom part of the Figure 2, arrows indicate the same indexed coefficients of other blocks those are grouped together so as to form an overall 3-level pyramid structure.
## 3 Experiment
### Quantitative Comparison between Real and Generated Digital Cores  
#### 3.6.1 Grayscale Value Distribution Analysis  
In assessing the efficacy of our generated digital cores, it is imperative to delve into a quantitative analysis. Here, we provide a comparative study of the distribution patterns observed in both real and generated digital rock images. The metrics used for this comparison, including their computational methods, can be referenced in Section 2.4.  
Figure 11a illustrates the distribution density of values from both real and generated images. Notably, the profiles of these distributions are strikingly similar, suggesting that the generated digital cores bear a close resemblance to the real counterparts in terms of their value distribution. As expressed in Equation (1), the bandwidth used in Figure 11a is determined using [PERSON]'s rule, which is calculated as \(n^{-1/5}\times\) sd, where n represents the sample size of 1000 and sd represents the standard deviation of the data. The kernel function used is the Gaussian kernel function:  
\[\text{K}(\text{u})=\frac{1}{\sqrt{2\pi}}\text{e}^{-\frac{\text{u}^{2}}{2}} \tag{8}\]  
Figure 11b presents box plots that capture the dispersion and central tendency of values from real and generated images. The median and interquartile range (IQR) from both categories demonstrate close proximity, further underscoring the alignment of generated cores with their real counterparts.  
The summary statistics for both categories are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Real Digital Rock** & **Generated Digital Rock** \\ \hline Mean & 0.5271451 & 0.5288587 \\ Standard Deviation & 0.13320786 & 0.15230845 \\ Minimum & 0.0 & 1.7905235 \(\times\) 10\({}^{-5}\) \\ Maximum & 1.0 & 0.9993653 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of statistical metrics between real and generated digital rock samples.  
Figure 11: Quantitative comparison of real vs. generated digital rock cores: (**a**) density distribution curve; (**b**) box plot.  
Table 2 underscores the proximate resemblance in terms of mean, standard deviation, and extremities between the real and generated images. Though there are minute deviations, especially in the minimum values for the generated images, these discrepancies are minuscule. Such consistency reaffirms the robustness of the image generation model in replicating the nuances of real digital cores.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
###### Abstract  
This paper presents an analytical exploration of sixth-generation (6G) satellite-terrestrial integrated networks, focusing specifically on their applications within air mobility operations, such as those involving unmanned aerial vehicles (UAVs). As the integration of satellite and terrestrial networks promises to revolutionize mobile communication by extending coverage and enhancing connectivity, this study delves into two critical aspects: link budget analysis and handover and mobility analysis for UAVs. The link budget analysis assesses the communication requirements necessary to ensure robust and consistent connectivity between satellites and UAVs, accounting for factors such as path loss, antenna gains, and power transmission. Meanwhile, the handover and mobility analysis investigates the challenges and solutions associated with UAVs transitioning between different network nodes and layers in a dynamic aerial environment. This paper utilizes theoretical models and simulations to provide insights into the design and optimization of these networks, aiming to enhance the reliability and efficiency of UAV operations in the context of the emerging 6G landscape. The findings propose not only technological advancements in network architecture but also practical guidelines for the deployment of UAVs in complex environments, marking a significant step toward the realization of a fully integrated, satellite-terrestrial ecosystem.  
mobility analysis; handover; 6G; satellite-terrestrial networks; link budget Article
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 2 Related Works
### Dynamic Satellite Network Topology
Despite extensive research and analysis by numerous scholars on the design and optimization of satellite network topologies, most simulations encounter various issues. These challenges include insufficient satellites incorporated in testing, high algorithm complexity, overlooking the periodicity and regularity of prominent constellations, and proposed optimization algorithms that do not apply to mega-satellite networks. Previous work cannot balance the shortcomings mentioned above while improving the overall performance of the constellation network. Therefore, it is necessary to combine the characteristics of numerous temporary dynamic links in the constellation and propose a new link allocation strategy. This strategy should calculate and filter satellites that meet the conditions for link establishment, as well as select the optimal topological configuration, to minimize the total time delay of space communication and improve the transmission efficiency of the links.
# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
## 4 Experiments  
### Experimental Settings  
#### 4.1.1 Dataset Description  
We conducted extensive evaluations on two widely used public EEG datasets: the Bonn dataset and the CHB-MIT dataset.  
The Bonn dataset contains EEG recordings collected at the University of Bonn, consisting of five sets (labeled Z, O, N, F, and S) with 100 single-channel EEG segments each. Each segment has a duration of 23.6 s, sampled at 173.61 Hz, resulting in 4097 data points per segment. Sets Z and O contain surface EEG recordings from five healthy volunteers in awake states with eyes open and closed, respectively. Sets N and F include intracranial recordings from five patients during seizure-free intervals, with Set N recorded from the hippocampal formation and Set F from the epileptogenic zone. Set S consists of seizure activity recordings from all recording sites exhibiting ictal activity. The EEG signals were recorded using a 128-channel amplifier system with a common average reference.  
The CHB-MIT dataset comprises long-term EEG recordings from 22 pediatric subjects with intractable seizures at Boston Children's Hospital. The recordings include 23 cases with 664 continuous EEG recordings, each approximately one hour in duration, sampled at 256 Hz using the international 10-20 electrode placement system. The dataset contains 198 seizures with varied durations, marked by experienced epileptologists. We preprocessed the data using a sliding window approach with a window size of 40 samples and 50% overlap between consecutive windows.  
For both datasets, we implemented a rigorous data split strategy:  
* Training set: 80% of the data, used for model training and parameter optimization;
* Validation set: 10% of the data, used for hyperparameter tuning and early stopping;
* Test set: 10% of the data, used exclusively for final performance evaluation.  
To ensure patient-independent evaluation, we performed stratified splitting at the patient level rather than the segment level, ensuring that segments from the same patient do not appear in different sets. This approach provides a more realistic assessment of the model's generalization capabilities.
## 5 Conclusions and Future Work  
We introduced a position-rate hybrid control that can increase the efficiency with which an operator completes a specific dual-arm teleoperation task called MADMOFEM, which is time-consuming, complex, and highly difficult. Furthermore, the operator is required to complete the task with incomplete information. To decrease the cognitive load and a heavy workload, we first decomposed the task into a sequence of subtasks. The process recognizer, based on the relative distance between the slave and the target, recognizes which subtask index each arm currently corresponds to.  
Depending on the subtask index, the degrees of freedom for human manipulation are minimized by constraining the input space using the input-space controller, and either the position or rate control is selected by the control-mode selector. Experiments were conducted to compare general position control and hybrid controllers, including freedom constraints of master devices, to verify to what extent the proposed system increased the operator's efficiency. By allocating a control mode method suitable for the situation due to the hybrid controller, the workload was reduced by reducing the working time and the distance the master moved. Furthermore, we confirmed that the cognitive load of the operator was reduced through constraining the degrees of freedom of the master device suitable for the subtask.  
In our current teleoperation system, the process recognizer outputs the result of the subtask index based on the position of the slave and the target. If the mobile robot at the bottom of the system moves or the position of the target changes, the relative position of the target changes; as a result, the position of the target needs to be changed. Therefore, in future work, a robust process recognizer that uses a deep-learning model whose inputs are camera images taken at a remote site will be developed so that the recognizer can be used even when changes occur in an unstructured environment.  
**Author Contributions:** Supervision and Funding Acquisition, G.-H.Y.; Writing-original draft Preparation, J.H.; Writing-Review & Editing, G.-H.Y.; Conceptualization, J.H. and G.-H.Y.; Data acquisition and analysis, J.H. and G.-H.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Korea Institute of Industrial Technology (KITECH) through the In-House Research Program (Development of Core Technologies for a Working Partner Robot in the Manufacturing Field, Grant Number: EO220009).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Simulation Results
### Result of Pattern 1  
The ITSO-INC performance in solving MPPT problems was evaluated for pattern 1. The results of ITSO-INC were also compared with CS, TSO, and INC. The PV power curves obtained using the TSO, ITSO-INC, CS, and INC are shown in Figure 9. Pattern 1 had a peak power of 6166.24 w. Obviously, the steady-state fluctuation of MPPT based on ITSO-INC was very low, reaching a maximum power of 6166.72 w. Compared with INC, TSO, and CS, ITSO-INC reached the maximum power point at an extremely fast speed, with a tracking efficiency of 99.95%. The peak power of TSO, CS, and INC MPPT methods in pattern 1 were 6165.51 w, 6153.07 w, and 6145.29 w, and the tracking efficiency was 99.95%, 99.75%, and 99.58%, respectively. In terms of tracking efficiency, these three basic methods required 0.34 s, 0.275 s, and 0.41 s, respectively. ITSO-INC only needed 0.048 s to reach GMPP, which was the fastest speed of the four methods. As shown in Table 8, ITSO-INC had a higher accuracy and tracking efficiency in MPPT than CS, INC, and TSO.  
The PV duty cycle curves obtained using TSO, ITSO-INC, CS, and INC are shown in Figure 10. It can be seen in the figure that the four methods can maintain zero stable state occupation after a certain period of time. Compared with TSO and CS, the new method can quickly maintain stability after small frequency shocks and converge to the maximum power point. It is proved that the stability of the ITSO-INC algorithm was better than CS and TSO algorithms in the case of pattern 1.
# Analysis of the Effect of Pulse Length and Magnetic Field Strength on Nonlinear Thomson Scattering
## 3 Results
### Space Radiation Properties of Electrons  
The parameter group settings of Figure 6 are the same as above, where its radiated power magnitude is normalized by the maximum value of every parameter group itself. The maximum radiated power and the corresponding maximum radiated polar angle for each specific parameter group will be given in the next section.  
The above figure is drawn according to the spherical coordinate system of the electron spatial radiation, where the horizontal and vertical axes are \(\sin\)\(\sin\)\(\varphi\) and \(\sin\)\(\cos\)\(\varphi\), respectively, where \(\varphi\) is the azimuthal angle and \(\theta\) is the polar angle, as shown in Figure 1. The corresponding \(z\)-axis coordinate value represents the radiation power magnitude \(dP(t)/d\Omega\). From the figure, it is evident that as the strength of the magnetic field increases, the radiated power of the electrons of different solid angles tends to be the same, and phenomena involving significant magnitude changes in Figure 6 (\(L=2\lambda_{0}\); \(B_{0}=0.1 KT\)) become much less pronounced. The corresponding collimation improves, indicating that the radiation is more concentrated.  
Figure 6: Space radiation properties of electrons.  
It is worth noting that when the pulse length is too large, i.e., \(L=20\lambda_{0}\), the collimation of the electron's radiation deteriorates slightly with the growth of the magnetic field strength \(B_{0}\). This is because the spatial radiation of electrons is more closely related to the trajectory of motion. With the increase in the magnetic field strength \(B_{0}\), the electron motion trajectory changes from a spiral with a constant radial radius to a spiral whose radial radius first decreases and then stays constant. As shown in the previous section, a decreasing radial radius corresponds to a larger radiation emission. Moreover, since the direction of the radiation is the same as that of the motion of electrons, the collimation of radiation corresponds to poorer collimation.
## 2 Methodology
### Wavefield Modeling Based on Acoustic Wave Equation  
[PERSON] et al. [37] and [PERSON] et al. [38] derived wavefield modeling and waveform inversion-based location methods, by taking advantage of numerically solving the acoustic wave equations in time domain and frequency domain, respectively, and this study will carry out wavefield modeling under the same framework. The scalar acoustic wave equation in frequency domain can be expressed as follows:  
\[-\frac{\omega^{2}}{c^{2}(\mathbf{x})}u(\mathbf{x},\omega)=\
abla^{2}u( \mathbf{x},\omega)+S(\omega)\delta(\mathbf{x}-\mathbf{x}_{s}) \tag{10}\]  
where, \(c(\mathbf{x})\) indicates the compressional P wave velocity at point \(\mathbf{x}\) in the medium; \(S(\omega)\) represents the frequency spectrum of the source-time function \(S(t)\), usually showing complex spectrum characteristics.  
Through a series of derivations (see [37] for more details), sensitivity kernels with respect to velocity model parameters for wavefield disturbance \(\delta u\) at a sensor caused by the source spatial location disturbance are obtained by using the popular adjoint method. On this basis, in accordance with the objective function defined by waveform cross-correlation travel time difference and sensitivity kernels, \(\Delta T_{i}\widetilde{\left(X_{r}\right)}\) and disturbance of model parameters in the wavefield propagation space (especially in first Fresnel zone surrounding the main energy ray path) can be obtained.  
\[\Delta T_{i}(\mathbf{x}_{r})=\int_{\Omega}K(\mathbf{x}_{i},\mathbf{x}_{r}, \mathbf{x}_{s})\frac{\delta c(\mathbf{x})}{c(\mathbf{x})}\mathrm{d}\mathbf{x} \tag{11}\]  
where, \(K\) is the cross-correlation travel time sensitivity kernel and \(\delta c(\mathbf{x})\) represents the P wave velocity perturbation at point \(\mathbf{x}\).  
\[K(\mathbf{x}_{i},\mathbf{x}_{r},\mathbf{x}_{s})=-\int_{0}^{T}\big{[}2c^{2}( \mathbf{x})\
abla u^{+}\Big{(}\mathbf{x},t^{2};\mathbf{x}_{r}\Big{)}\cdot \
abla u\Big{(}\mathbf{x},t^{2};\mathbf{x}_{r}\Big{)}\big{]}\mathrm{d}t \tag{12}\]  
where, \(u^{+}\Big{(}\mathbf{x},t^{2};\mathbf{x}_{r}\Big{)}\) is the adjoint wavefield in the media excited by a virtual adjoint source defined at the sensor \(\mathbf{x}_{r}\) (the detailed derivation refers to [37]). The sensitivity kernel of travel time has very complex intensity and spatial distribution, which reflects the frequency-dependent weighted influence of velocity perturbation in the Fresnel zone on the final travel time anomaly \(\Delta T_{i}(\mathbf{x}_{r})\) recorded by sensor. It reveals the nature of finite frequency effects in actual wavefield propagation.
# Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM
## 4 MPRM Module
### Path Search Based on the Dijkstra Algorithm  
The Dijkstra algorithm is one of the most famous algorithms for searching the shortest path. It uses the breadth-first search to solve the single-source shortest path problem of a weighted directed graph or an undirected graph. Given the roadmap \(G=\{E,V\}\), the starting vertex \(s\in V\), and the target vertex \(g\in V\), the shortest path from the starting point to the target point can be searched. Since the similarity with the task path is taken into account when constructing the roadmap, the algorithm follows the two principles of the shortest path and the highest similarity.
## 2 Background  
### Post-Quantum Cryptography  
As quantum computing advances, existing encryption methods used in classical computers, such as RSA, AES, and ECC [22; 43; 44], are becoming increasingly vulnerable. While these methods are effective against conventional mathematical attacks, they can be rendered ineffective by quantum algorithms that exploit entanglement and parallelism [45; 46; 47; 48]. To address this, many industries and researchers most widely used post-quantum cryptography (PQC) algorithms to ensure secure encryption in the quantum era. PQC algorithms are based on mathematical problems that are difficult to solve even with quantum computing, such as lattice and multivariate polynomial problems. In addition, PQC can be integrated into existing computing systems, offering practical compatibility and flexibility during the transition to quantum-safe security technologies [26; 49; 50].  
Among various PQC approaches, lattice-based cryptography is based on computationally hard problems such as the Shortest Vector Problem (SVP) and Learning With Errors (LWE), which remain intractable even for quantum computers [51; 52]. These problems are defined in high-dimensional Euclidean space and are resistant to known quantum algorithms. A lattice-based encryption algorithm is used for key encapsulation to ensure secure key exchange. Kyber [53], based on the LWE problem and selected for NIST standardization, offers strong security and efficient performance. Kyber uses compact key sizes, performs structured matrix operations over polynomial rings, and supports parallel computation through simple and regular arithmetic patterns. These properties allow compatibility with classical systems and internet protocols, and enable efficient execution on manycore architectures such as modern CPUs and GPUs. Kyber is suitable for secure integration into distributed and cloud systems.
## 5 Analysis of Cracking and Damage in Wharf Structures under Impact Loading
### Damage Identification Based on Modal Flexibility Using Finite Element Simulation
#### 6.2.3 Setting Damage Conditions for the Wharf Structural Pile  
During normal operations of high-piled whares, the foundation piles typically endure vertical loads as well as lateral loads caused by various factors such as ship impacts, shoreline deformations, and ground settlement. Compared to other large civil engineering structures, the foundation piles of high-piled whares are subject to complex stress conditions over extended periods, making them more susceptible to damage. In practical engineering, local damage to foundation piles generally manifests as concrete cracking, spalling, and steel reinforcement corrosion, which essentially result in a reduction in local stiffness at the damaged areas. In finite element models, this damage is usually simulated by reducing the elastic modulus, as follows:  
\[E^{d}=(1-\alpha)\times E \tag{14}\]  
where \(\alpha\) is the degree of damage (percentage of stiffness loss); \(E^{d}\) is the elastic modulus after damage; \(E\) is the elastic modulus in the undamaged state.  
The upper sections and intertidal areas of the offshore side front piles are prone to damage; therefore, this study sets damage segments in these locations. Five levels of local stiffness loss are considered: 0% (undamaged), 5%, 10%, 20%, and 30%, representing minor to moderate damage, to investigate the cumulative impact on the mechanical performance of the foundation piles. The fifth offshore side front pile in the positive x-direction is selected as the damaged pile. This pile is divided into 24 segments with a total of 25 nodes, with each segment being 0.6 m long. The segments are numbered from top to bottom as Units 1 to 24, and the nodes are numbered from top to bottom as nodes 1 to 25. units 6 and 12 are designated as the damaged units. Figure 34 illustrates the segmentation of the pile foundation and the setup of the damage elements.  
Figure 34: Schematic diagram of segmentation and damage units for the damaged pile.  
Figure 33: Application of fixed constraints at pile bottoms and finite element model meshing. (**a**) Application of constraints at the pile Bottoms; (**b**) Finite element model mesh division.