esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results & Discussions  
### Field Monitoring Results  
Long-time series data of precipitation and volumetric water content at the Yindongzi landslide site during a four-year period from 21 May 2014 to 22 August 2017 were observed. Figure 4 shows the daily rainfall and accumulated annual rainfall during the monitoring period.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(c^{\prime}\) & soil cohesion for effective stress; (kPa) & 3.0 \\ \(\varphi^{\prime}\) & angle of internal friction for effective stress; (\(\circ\)) & 33 \\ \(\gamma_{\textit{s}}\) & dry soil unit weight; (kN/m\({}^{3}\)) & 15 \\ \(\beta\) & Slope angle; (\(\circ\)) & 60 \\ \(\theta_{\textit{r}}\) & residual water content; (-) & 0.05 \\ \(\theta_{\textit{s}}\) & saturated water content; (-) & 0.45 \\ \(Z\) & Soil depth;(m) & 0.5 \\ \(a\) & SWCC parameter of the Brooks–Corey model & 11 \\ \(n\) & SWCC parameter of the Brooks–Corey model & 0.53 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil parameters for the slope stability analysis. SWCC denotes soil water characteristic curves.  
The real-time monitoring has shown that the soil moisture content is strongly related to rainfall. Precipitation was notably seasonal, most of the precipitation was observed during the monitoring period, with most precipitation from May to September. Particularly, an extreme daily rainfall of 113 mm was recorded on 4 August 2015, while the rainfall intensity is lower than that in 2008 to 2013, and the maximum value of daily rainfall is recorded to be 250 mm in this area on 7 July 2013. This explains why there are no debris flows during these years, considering the rainfall intensity and total amount of rainfall may not be sufficient to cause displacements and inclination during the monitoring period. The soil moisture fluctuated significantly during the wetting and drying periods caused by evaporation and infiltration processes. Soil wetness increased episodically with respect to rainfall. Theoretically, the soil moisture content may exceed 0.45, because the porosity of the topsoil is reported to reach 0.49 in this area [20]. In the analyzed period, the soil moisture content ranged between about 0.03 and 0.17 in the topsoil. During the rainy season, soil moisture storage increased rapidly by an additional 5-13% caused by rainfall (Figure 5).
# Neurofeedback Effects on EEG Connectivity among Children with Reading Disorders: I. Coherence
### EEG Characteristics in Children with RD
Previous studies have reported that children with learning disorders have a different maturational process for EEG coherence than children with typical development [39]. Children with poor reading performance have shown higher coherence values, specifically in the delta [40], theta [28; 40], and beta frequency bands [28; 40; 41; 42]. In the alpha band, the results are not consistent. [PERSON] et al. [40] observed lower alpha coherence values in children with learning disorders than in controls; in contrast, [PERSON] et al. [42] and [PERSON] et al. [41] found higher alpha coherence values. However, it must be taken into account that the study by [PERSON] et al. [42] was the only one performed in a resting condition with eyes open; furthermore, in the study by [PERSON] et al. [41], the sample was very small and heterogeneous, since two of the seven children with dyslexia suffered from epilepsy and three suffered from ADHD. Usually, higher intrahemispheric coherence of the delta, theta, and beta bands occurs between regions of the left hemisphere, while lower intrahemispheric alpha coherence, described by [PERSON] et al. [40], occurs between leads of the right hemisphere. Regarding interhemispheric and intrahemispheric coherence, children with dyslexia are characterized by lower interhemispheric coherence and higher intrahemispheric coherence in the alpha frequency band compared to classmates of the same age [29].
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 2 Security AODV Routing Protocol
### The Process of Route Discovery
#### 2.4.4 Processing RREP Message  
After a node receives the RREP message, it first verifies its signature and hop number fields. Only when the verification is correct, the node stores the forward path corresponding to the message; otherwise, the message is discarded.  
Then, the node determines whether it is the destination node. If it is, the process of route discovery is finished. Otherwise, the RREP message is sent according to the reverse path in the routing table.
## 1 Introduction
The stability of the system is relatively high [11], whereas fuzzy PID is typically suitable for single-variable or simple nonlinear systems and cannot directly handle constraints [12]. Compared with sliding-mode control, ESO-MPC optimizes the control input to smooth the control signal and avoid the common chattering problem in sliding-mode control. ESO-MPC can also handle constraints explicitly, whereas sliding-mode control is often difficult to use to directly handle constraints [13]. Compared with LQR, ESO-MPC can handle nonlinear systems and constraints, whereas LQR is only applicable to linear systems. ESO-MPC has stronger robustness when it estimates disturbances and unmodeled dynamics through ESO, whereas LQR is more sensitive to model accuracy and disturbances [14]. In summary, ESO-MPC can explicitly handle system constraints (input and state constraints) and estimate disturbances and unmodeled dynamics through ESO, has strong robustness, and is suitable for nonlinear and multivariable systems [15]. However, the computational complexity is high, and the hardware requirements are high, so the real-time performance may not be as good as control methods such as LQR.
## 2 Methodology and Literature Search Strategy  
This review was conducted through a comprehensive literature search utilizing established academic databases, including PubMed, Scopus, and Web of Science, to identify relevant studies. The search employed combinations of keywords such as \"vertebral augmentation devices\", \"vertebroplasty\", \"kyphopplasty\", \"vertebral compression fracture\", \"cement leakage\", and \"biomechanical stability\". The timeframe included all articles published in English up to November 2024. The search strategy was specifically designed to capture both preclinical and clinical studies, ensuring a thorough evaluation of the biomechanical impact and clinical outcomes associated with third-generation vertebral augmentation systems such as the SpineJack(r), Vertebral Body Stenting System(r) (VBS), and OsseoFix(r). Relevant data on clinical and radiological outcomes were qualitatively synthesized to provide a comprehensive analysis of the advancements and limitations of these technologies. Studies were excluded if they were not published in English or lacked sufficient methodological detail.
# Hierarchical Gradient Similarity Based Video Quality Assessment Metric
## 1 Introduction  
Recently, video quality assessment (VQA) metrics which can evaluate the video quality consistent with the human perception have received increased attention. VQA metrics are generally classified into three categories, full-reference (FR), reduced-reference (RR), and no-reference (NR) metrics. A full-reference (FR) metric aims to evaluate the qualities of distorted videos with the full available reference videos. Peak signal-to-noise ratio (PSNR) and Mean square error (MSE) [1, 2] are the most widely used FR metrics. These indices are simple to calculate and can be conveniently adopted in video and image applications, such as image processing and video coding [3, 4, 5]. However, they show poor consistency with subjective evaluations [6].  
Many efforts have been made to investigate the FR VQA algorithms. Structural similarity index (SSIM) [7] is the most popular metric. The comparison functions of luminance, contrast, and structure are designed and combined to obtain the overall quality. SSIM-based VQA metrics have been proposed by introducing motion information, temporal weighting schemes, and multi-scales-based schemes [8, 9, 10]. These metrics are developed based on the assumption that the degradation of perceptual qualities is highly related to the change of the structural information. Moreover, gradient-based metrics have been proposed to describe the loss of the structural information [11, 12]. In [12], edge-strength similarities were calculated for all pixels to acquire the overall quality score for each frame. In [13], the gradient-based 3-D structure tensors were decomposed to evaluate the video perceptual quality. Spatio-temporal gradient features were extracted to derive the 3-D structure tensor, and the corresponding eigenvalues and eigenvectors were used to evaluate the video perceptual quality.  
In this letter, a VQA metric is designed based on a hierarchical gradient similarity model. This model is inspired by functional principles of the processing hierarchies in the primate visual system [14], which is characterized by a sequence of visual areas. These areas are modeled by hierarchical gradient measures to evaluate the score of each frame. The evaluation of visual attention similarity by an efficient measure is also involved in the proposed metric. Then, an averaging operation is performed to obtain the final score of the video sequence. Experimental results show that the proposed VQA metric outperforms the state-of-the-art VQA metrics.
# Status of the SXFEL Facility
## 2 The SXFEL Test Facility
### Performance of the SXFEL-TF  
The SXFEL-TF has been designed and constructed to be a flexible facility for testing various advanced FEL seeding concepts. A number of different operating modes have been considered for the SXFEL-TF. Here we only present some typical results for the two-stage HGHG cascade, the EEHG-HGHG cascade and single stage EEHG. More details of the simulations can be found in references [26; 28].  
Using the parameters listed in Table 1, Figure 6 shows the simulation results for the cascaded HGHG. The simulations were carried out by the time-dependent mode of GENESIS [29]. The seed power used for the first stage HGHG is about 200 MW. The whole electron beam was tracked though the two stages to obtain realistic simulation results. One can find in Figure 6 that a coherent soft X-ray radiation pulse at 8.8 nm with peak power exceeding 200 MW can be produced through the two-stage HGHG scheme.  
Besides the conventional cascaded HGHG, we also want to perform some proof-of-principle experiments for novel schemes. As shown in Figure 1, the first stage of the SXFEL-TF is a typical EEHG, which employs two modulator-chicane sections to introduce the echo effect into the electron beam. Therefore, the SXFEL provides a perfect platform for testing the cascaded EEHG-HGHG scheme. The simulation results for this scheme are shown in Figure 7.  
Figure 5: Layout of the undulator line.  
Figure 6: Simulated FEL performances of cascaded high-gain harmonic generation (HGHG) at the SXFEL-TF: final output pulse at 8.8 nm in time (**left**) and spectral (**right**) domain.  
Moreover, we also have the plan of operating a single-stage EEHG at ultra-high harmonics for directly generating the 8.8 nm radiation pulse [29]. The main simulation results for this case are shown in Figure 8. In the single stage EEHG, long seed laser pulses can be adopted to fully cover the electron bunch, which results in a much higher output pulse energy and much narrower output bandwidth.
# A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication
## 2 Related Work
### Application of Large Language Models  
In recent years, LLMs have developed rapidly and become a research hotspot in China. They provide brand-new solutions for semantic communication, natural language processing, and other fields.  
LLMs possess robust semantic understanding capabilities and find extensive applications in semantic communication. Specifically, they are employed in two primary ways. First, LLMs calculate semantic loss and assess semantic importance to optimize the semantic compilation code process [14; 17]. Second, they serve as semantic knowledge bases, facilitating precise extraction and recovery in semantic communication [18; 19]. However, LLMs require extensive computational and storage resources to function. Integrating LLMs into semantic communication systems poses significant challenges to meeting resource requirements. Additionally, LLMs suffer from the problem of \"hallucination\", which reduces accuracy on professional issues [20]. Currently, leveraging LLMs to enhance semantic communication remains an open research question. Harnessing LLMs' extensive knowledge reserves and learning abilities to improve semantic communication represents a crucial development direction in this field.  
In the realm of knowledge graphs, the application of LLMs has also become a prominent research area. LLMs have a powerful semantic comprehension ability, which enables them to perform tasks such as corpus collection and knowledge extraction, offering novel approaches to knowledge graph construction. Performance comparable to other state-of-the-art methods can be achieved if LLMs are fine-tuned via prompt engineering [21]. For instance, [22] introduced a scheme for clinical tasks that integrates few-sample learning techniques, achieving high performance in named entity recognition. Similarly, [9] proposes a method for constructing a Chinese medicine knowledge graph based on LLMs, significantly reducing manual effort and enhancing construction efficiency.  
Therefore, we believe that a knowledge graph construction method tailored to semantic communication can be developed using LLMs. This approach can accommodate knowledge evolution's dynamic nature during the semantic communication processes.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 1 Introduction  
The expansion of 5G technologies has ushered in a new era of communications with promises for high-speed mobile broadband communications, ultra-low latency applications, massive device connectivity, Internet of Things (IoT) support, and more. As 5G usage and coverage continue to increase in the near future for industries and consumers alike, it also brings with it a new range of security challenges and considerations. In order to uphold the promises of this technology, the confidentiality, integrity, and availability of the network are of utmost importance during its ongoing integration with critical infrastructure, government operations, and medical operations. This survey paper compiles a comprehensive review of academic and industry literature on current and future 5G security mechanisms in a systematic approach with a particular focus on the necessitiesand proposed methods for integrating Zero Trust Architecture (ZTA). Our paper aims to facilitate knowledge and research that is applicable not only to academia but also to industry and government users.  
The challenges in 5G security are numerous and complicated. Researchers and standards organizations, like 3 GPP, have outlined security structures and provisions that encompass the entirety of 5G. Even with such extensive considerations, there will always be security concerns post-design, whether inherent in the operations, due to misconfigurations by a vendor, or because of other factors outside of the control of the architect or operator. In 4G LTE (Long-Term Evolution), the predecessor to this current generation of cellular networks, security vulnerabilities were identified where a user's identity could become exposed whenever network authentication was requested [1]. This fault in LTE leads to a decrease in user identity protection and a lack of forward secrecy. For 5G to continue being a backbone of our technological society, all avenues of security need to be challenged and reviewed, and systems need to be fully compliant with established standards. Implementing robust security measures in 5G is not just a significant technical challenge but also vital for building trust with service users.
# Research on Slope Stability Based on Bayesian Gaussian Mixture Model and Random Reduction Method
## 3 Comprehensive Safety Factor
### Establishment of Training Sets for Reduction Coefficients and Dissipated Energy  
To build a training set, FLAC3D was utilized for conducting numerical simulations of a slope model employing the Mohr-Coulomb constitutive model. A computational program was developed based on Equation (5) to calculate the dissipated energy under independent reductions in cohesion (\(c\)) and internal friction angle (\(\varphi\)). The computational program utilizes FLAC3D's built-in FISH programming environment. The range of reduction coefficients for \(c\) and \(\varphi\) was set to [1,1.5] with an initial step size of 0.1. To optimize computational efficiency, data requiring over 100,000 convergence steps in FLAC3D were eliminated. The local refinement of step sizes (0.01 and 0.001) was applied to specific intervals, resulting in a final dataset of 609 entries. Partial results are summarized in Table 3, while the complete dataset is illustrated in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{c}\)**-Reduction** & \(\mathbf{\varphi}\)**-Reduction** & **Dissipated Energy/J** \\ \hline
1 & 1.1 & 241.4 \\
1 & 1.2 & 241.5 \\ \(\dots\) & \(\dots\) & \(\dots\) \\
1.368 & 1.366 & 3387.7 \\
1.366 & 1.368 & 3572.2 \\
1.366 & 1.37 & 3783.9 \\ \(\dots\) & \(\dots\) & \(\dots\) \\
1.5 & 1 & 408 \\
1.5 & 1.1 & 674.4 \\
1.5 & 1.2 & 1272.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reduction coefficients and dissipated energy.  
Figure 5: Three-dimensional relationship between reductions and dissipated energy.  
In geotechnical engineering, to solve the system of differential equations governing the medium's response (equilibrium, geometric compatibility, and constitutive equations) along with boundary conditions, the Finite Difference Method first discretizes the problem domain and establishes a computational grid. Subsequently, at each grid node, it utilizes Taylor series expansions to approximate the spatial derivatives in the governing equations as finite differences using function values at adjacent nodes. These finite difference approximations are then substituted back into the original differential equations, thereby transforming them over the entire solution domain into a system of linear or nonlinear algebraic equations with respect to nodal unknowns (such as displacements or stresses). Finally, the approximate solution is obtained by numerically solving this algebraic equation system.  
A visualization of dissipated energy contours for selected slope cases is shown in Figure 6.  
When cohesion (\(c\)) and internal friction angle (\(\varphi\)) are reduced proportionally, this scenario can be considered a special case within random reduction paths. The corresponding variations in dissipated energy are illustrated in Figure 7.
## 5 Challenges and Future Directions
### Dynamicity  
In real-world scenarios, network topologies exhibit dynamic characteristics with nodes and edges appearing or disappearing, or the input data change over time. Within an NDT where the two-way communication enables the real-time collection of data, the ability to exploit and understand these dynamic network behaviors becomes crucial. Static graphs are stable so they can be modeled feasibly, while dynamic graphs introduce changing structures. Thus, new GNN models are needed to adapt to the dynamicity of the network topology because re-training the model can be computationally expensive. Finding more efficient ways to update node representations in response to graph changes in network topology is an ongoing research problem. In this context, _GraphSAIL_ has been proposed for the first time [123]. It addresses the incremental learning on GNN for recommender systems, which deals with the edges between nodes. Incremental learning [132; 133] is a method that allows a model learning from new data without having to retrain from scratch, which could be particularly beneficial in dynamic environments where data continually evolve. Also, incorporating other machine learning techniques can enhance the performance of GNNs with dynamic network topology. Integrating FL [130; 131; 134; 135; 136] with dynamic GNNs can be a promising direction for building adaptable models for Next-generation networks. For example, FL allows dynamic GNNs adaptation to changes in data distribution across devices. In particular, using an asynchronous update allows process of fast nodes without waiting for slower or disconnected ones [125]. Another strategy is to train GNNs with FL while accounting for clients that may drop out during the process [124]. All these can be beneficial for dynamic GNNs with Next-generation networks, where the relationships between nodes may be changing over time, for straggler nodes, added, disconnected, or updated nodes.
# Contribution of Atmospheric Fallout to the Soil-Root-Leaf Transfer of PAHs in Higher Plants
## 5 Conclusions  
The No. 227 OECD Guideline was modified in our study to suggest treatment mechanisms simulating wet deposition. Treatment implied spraying the aerosol extract on these surfaces; therefore, air-aerial parts-root and soil-root-aerial parts exposure pathways could be separately investigated. As the soil was directly sprayed with the sample under laboratory conditions, other pollution sources could be excluded. Measuring PAH accumulation in different parts of Eruca sativa, the results clearly show that wet deposition can be an important source of soil PAH concentrations. PAHs deposited on the soil will accumulate in the roots and gradually move to the above-ground parts of the plant. This will also result in the transfer of PAHs with higher molecular weights, although in much lower concentrations. Considering accumulation in the leaves--which are the edible parts of leafy vegetables--direct atmospheric deposition via precipitation is the main pathway. Rocket can accumulate PAHs with even higher molecular weights in considerable quantities. The main benefit of the proposed treatment protocol is that wet deposition-associated PAH accumulation can be investigated in other crop species, which might have different accumulation capacities.  
Conceptualization, K.H. and N.K.; methodology and investigation, K.H., B.E.-V., S.T. and G.T.; data curation, K.H.; visualization, K.H. and S.T.; writing--original draft preparation, N.K.; supervision, project administration, funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript.  
Financial support was provided by the NKFIH-872 project 'Establishment of a National Multidisciplinary Laboratory for Climate Change'.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The authors thank the ELGOSCAR-2000 Environmental Technology and Water Management Ltd. (Head Office: 164 Soroksari u. H-1095 Budapest, Laboratory: H-8184 Balatonfuzfo) for conducting analytical measurements and the HUN-REN-PE Air Chemistry Research Group (University of Pannonia) for providing the samples.  
**Conflicts of Interest:** Author [PERSON] was employed by the company ELGOSCAR-2000 Environmental Technology and Water Management Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.