esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 4 Discussion  
### Microstructure Variation in Different Types of Remolded Fault Gouge  
To further investigate the mechanisms underlying the differences in shear performance of fault gouge under varying water contents and degrees of cementation, scanning electron microscopy (SEM) analyses were performed on fault gouge samples subjected to a normal pressure of 200 kPa.  
Figure 8a,b,d present scanning electron microscopy (SEM) images of fault gouge with 20% water content under three types of cementation degrees (0, 1%, 3%). At a cementation degree of 0%, large voids between particles were observed, indicating a loose structure with insufficient cementing material. This condition resulted in weak interparticle interactions and low shear strength. As the cementation degree increased from 0% to 3%, the amount of cementing substances formed by Portland cement gradually increased. These substances, together with montmorillonite, coated the surfaces of quartz and kaolinite particles, filling the interparticle voids and enhancing the bonding strength among them [43]. This change significantly improved the integrity of the fault gouge, reduced microcracking, and led to tighter interparticle connections. Macroscopically, both shear strength and cohesion increased with the cementation degree, as illustrated in Figures 5b and 6b. This indicates that the presence of cementing substances not only markedly enhanced the shear resistance of the fault gouge but also altered its failure mode from particle sliding to the fracturing of the cementing material.  
Figure 8c-e show SEM images of fault gouge with a cementation degree of 3% at varying water contents. At a low water content (10%), obvious microcracks were observed in the samples, attributed to the low plasticity and high rigidity resulting from insufficient water in kaolinite and montmorillonite. This further contributed to increased brittleness and reduced shear strength of the fault gouge (as shown in Figures 4 and 5). With increasing water content, more network-like cementing substances formed due to the swelling of montmorillonite particles as they absorbed water [44]. Consequently, microcracks decreased, the pore structure became denser, and the overall integrity of the samples was enhanced, leading to higher shear strength values at this stage [45]. However, when the water content continued to increase to 30%, a significant sliding zone was observed on the shear surface of the fault gouge sample [46]. This indicates that excessive water filled  
the voids between particles and cementing substances, weakening the cementing effect of the cementing materials, reducing the friction between particles, and changing the failure mode of the fault gouge from fracturing of the cementing material to wet sliding between particles. This led to a significant decrease in shear strength. This phenomenon correlates with the trend observed in Figures 7a and 8a, indicating that high water content diminishes the strength of the fault gouge to some extent.
# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 2 System Framework
### Definition of Aircraft Attitude Angle  
We define the aircraft local coordinate system \(O_{p}-x_{p}y_{p}z_{p}\), as shown in Figure 1. The center point (center of mass) of the aircraft is taken as the origin of its local coordinate system \(O_{p}\), the direction of the wing (pointing to the right wing) is defined as the \(O_{p}x_{p}\) axis,the direction of the nose is the \(O_{p}y_{p}\) axis, and the direction perpendicular to the aircraft is the \(O_{p}z_{p}\) axis. This coordinate system changes as the aircraft attitude changes. Solving the aircraft attitude is equivalent to finding the rotation angle between the current coordinate system and the initial coordinate system. In this system, the three Euler angles, namely, pitch, yaw, and roll, are used to describe the attitude of the aircraft. Pitch refers to rotation around the \(X\) axis and is also called the pitch angle \(\theta\). Yaw refers to rotation around the \(Z\) axis and is also called the yaw angle \(\varphi\). Roll refers to rotation around the \(Y\) axis and is also called the roll angle \(\gamma\).
## 4 Conclusions  
This study introduces an advanced design for a MIMO antenna engineered for operation in the 28 GHz frequency band. The MIMO antenna setup consists of four ports, featuring \(1\times 8\) series-fed arrays for each port, showing peak gain of 15.5 dBi and bandwidths spanning 2 GHz. The enhanced performance of this antenna design is a direct outcome of meticulously optimized antenna spacing and a decoupling strategy employing well-designed metamaterial cells, effectively reducing interference among antenna elements. The system demonstrates remarkably low mutual coupling, registering below \(-40\) dB, with envelope correlation coefficients of 0.00010, diversity gains approaching 10 dB, and a channel loss capacity of 0.11 bit/s/Hz across the examined frequency range. Experimental assessments have validated these enhancements, establishing the proposed design as a promising candidate suitable for a millimeter-wave communication networks.  
Conceptualization, F.N.A.; Methodology, N.A., K.A., S.A.K., A.M.A. and M.S.; Software, M.S.; Validation, F.N.A. and M.S.; Formal analysis, M.S.; Investigation, N.A., F.A. and M.S.; Writing--original draft, N.A., F.N.A. and S.O.M.; Writing--review & editing, K.A., F.A., S.O.M. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data supporting the findings of this study are already included within the article.  
The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2025).  
The authors declare no conflicts of interest.
# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 2 Our Proposed Methods
### Data Extraction  
Once the stego image has been decrypted, the embedded messages can be extracted. The first step of message extraction is to determine the embedding base.  
When dealing with HDR images in the RGBE format, we employ the six-level adaptive base technique, shown in Equation (1), which uses the intact E values to determine the embedding bases for the R, G, and B channels of the pixel. In the case of LDR images, we utilize the values \((\mu_{R},\mu_{G},\mu_{B})\) provided in the secret key and apply Equation (2) to determine the embedding base \((b_{R},b_{G},b_{B})\) for each channel of the pixel.  
Once the embedding bases have been determined, the secret digits \((d_{R},d_{G},d_{B})\) embedded in the R, G, and B channels of a stego pixel can be extracted using Equation (8). These secret digits are later used to produce the original binary secret bits:  
\[(d_{R},d_{G},d_{B})=(P_{R^{\prime}}\bmod b_{R},P_{G^{\prime}}\bmod b_{G},P_{B^ {\prime}}\bmod b_{B}) \tag{8}\]  
Continuing from our previous example in data embedding, the decrypted stego pixel \(\mathbf{P^{\prime}}=(P_{R^{\prime}},P_{G^{\prime}},P_{B^{\prime}})=(62,115,84)\), and the corresponding mean values \((\mu_{R},\mu_{G},\mu_{B})\) produced by the secret key are \((106.91,\,100.87,\,92.31)\). First, we apply Equation (2) to determine the embedding base \((b_{R},b_{G},b_{B})=(7,7,11)\), and then we use Equation (8) to extract the secret digits: \((d_{R},d_{G},d_{B})=(62\,\,mod\,7,115\,\,mod\,7,84\,\,mod\,11)=(6,3,7)\). In this way, our scheme correctly extracts secret messages that have been concealed in the stego pixel, \(\mathbf{P^{\prime}}\).
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 5 Fibers with Anti-Resonant Guidance
### Exchange of Gases with Atmosphere and Entry of Water  
Water vapor absorption is prominent in the spectral loss of the lowest-loss HCF reported so far [13], as shown in Figure 14. The water peak was 90 nm wide at an added loss of \(\geq\)0.2 dB/km, and 110 nm at \(\geq\)0.05 dB/km--the latter value is typical for conventional single-mode fibers conforming to the ITU-T G.652.D standard [29]. The shape of loss peak corresponds exactly to the absorption by water vapor in air [76].  
Researchers from the University of Southampton have recently (2022) measured the dynamics of gas exchange between un-sealed lengths of HC-PBGF and NANF fibers and the surrounding atmosphere, absorption and Raman (Stokes) spectra of contaminants inside fibers, the internal pressure of gas in a freshly drawn NANF, and the infiltration of liquid water into a vertically suspended HC-PBGF [69; 70]. Some findings are surprising, and important for the handling, splicing and repairs of HCFs and cables with them, as follows:  
* The inner pressure in a freshly drawn fiber is 20-29 kPa, only 1/4 th of the atmospheric pressure, despite the application of 6 kPa overpressure during drawing. This happened because the gas inside the preform was initially hot, \(\approx\)1400 \({}^{\circ}\)C during necking, and the pressure fell during cooling to room temperature, roughly by a factor of 5.7;
* The time required for the equalization of pressure inside a 55 m length of freshly drawn HC-PBGF with a 31.6 \(\upmu\)m core diameter open at both ends with a surrounding atmosphere was 2 h. For a 34 m long 7-tube NANF with a smaller core diameter (18.5 \(\upmu\)m), this time was longer--3 h;
* When a freshly drawn HC-PBGF (kept sealed after drawing, and cleaved 3 min before experiment) was vertically suspended, with one end sealed and the other immersed in pure water, the water column inside the fiber reached a height of 8.8 m after 40 h. For a pressure-equalized fiber, the result was still 2.1 m, as the water was driven into fiber by surface tension. The fiber tested was 26.5 m long.  
In another experiment, the added spectral loss in HC-PBGF and five-tube NANF fibers was monitored, when a 150 m and 155 m length of each fiber were kept with one end open in the lab, at a controlled 40% relative humidity and 22 \({}^{\circ}\)C temperature for 10 months [70]. Then loss of NANF was stable throughout the test, while HC-PBGF exhibited an increase in loss of up to 10 dB at 1364 nm (SiOH absorption peak), and rising \"water (vapor) peak\" similar to the one shown in Figure 14. Fibers of the same type tested with fusion spliced SMF pigtails at both ends, and therefore sealed, exhibited stable loss for 3 months.  
The difference is explained by a dramatic, roughly 250:1, difference between the overlap of optical power distribution with glass parts in each fiber: 0.5-1.0% in HC-PBGF, and only 0.002-0.005% (30-50 ppm) in low-loss NANF. The added attenuation was about 24 dB lower in NANF for an identical concentration of water vapor inside.
## 2 Model
### L1 Regularization  
L1 regularization is introduced into the model to prevent overfitting and improve generalizability. The L1 regularization method introduces a penalty term in the loss function; the size of this penalty term is proportional to the L1 paradigm of the model parameters [33; 34]. The L1 regularization makes certain weight coefficients of the model become 0 during training, which can effectively reduce the complexity of the model. After  
Figure 4: Multi-head attention mechanism.  
adding L1 regularization in the model, the loss function of the model can be expressed as shown in Equation (11).  
\[Loss=\sqrt{\frac{1}{m}{\sum\limits_{i=1}^{m}(\mathbf{y}_{i}-\mathbf{Y}_{i})^{2}}}+\alpha \sum\limits_{j=1}^{n}{|w_{j}|} \tag{12}\]  
where the first term is the \(RMSE\) loss, \(\alpha\) is the regularity term coefficient, and \(w\) is the parameter of the model.
## 5 CFD Modeling  
The commercial CFD software package, Cradle SC/Tetra, typically used for thermofluid analysis, was used for this study. A 3-D model of the modified shearer in the longwall test gallery was created using a computer-aided design (CAD) package, PTC Creo Parametric 3.0 (see Figure 5). The 3-D model includes the test gallery, modified shearer, shields, and spill plates. The geometry of this arrangement offered no symmetry or periodicity; therefore, a full 3-D simulation was required. This increased the size of the computational domain, and hence the computational time and cost. In order to save computational time, the length of the panel upwind of the modified shearer in the computational domain was shortened by 5.1 m (16.7 ft), assuming streamlined airflow upwind of the shearer's headgate drum.  
First, the geometry was converted into an acceptable STEP file format (.stp) and imported into the Cradle SC/Tetra preprocessing package. The geometry was cleaned for isolated edges and overlapping and intersecting faces, and the computational domain was identified. The boundary surfaces were then registered, and the analysis parameters, such as analysis type (steady or transient simulation), fluid properties, turbulence model, initial conditions, boundary conditions, convergence criteria, and under relaxation factor, were applied. Next, the computational domain was discretized into small volumes creating a network of small elements, called mesh. Both analysis parameters and mesh files were imported into the Cradle SC/Tetra solver package, and simulation was run until a steady-state simulation condition was achieved. Finally, the simulation file was imported into the Cradle SC/Tetra post-processing package, and simulation results were analyzed with different methods, such as contour plots, vector plots, and time-series curves for appropriate graphical representations and reports. The simulations were performed with double precision on a Linux operating system with PC having four, 12 core, 2.4 GHz, AMD processors and 128 GB of RAM. Since the study aimed to validate the CFD model of the longwall gallery using the results of air quantity surveys performed in the longwall test gallery, a single-phase CFD simulation was performed.
## 5 Experimental Validation and Benchmarks
### Quality of Service (QoS) Management
#### 5.4.2 Service Prioritization  
AI models play a crucial role in managing and prioritizing network traffic based on the specific requirements of different services, especially during periods of congestion. With increasing demand for diverse services such as Voice over IP (VoIP), video streaming, online gaming, and critical enterprise applications, it is vital to ensure that high-priority services receive the necessary resources to maintain their quality of service (QoS). During times of network congestion, AI-driven systems can dynamically adjust network resource allocations, ensuring that essential services are not impacted by less time-sensitive traffic [131].  
AI models leverage techniques such as machine learning and deep learning to analyze network conditions in real-time and determine which traffic requires higher priority. For  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **AI Model** & **Performance Improvement (\%)** \\ \hline Congestion Management & Deep Learning & 30 \\ Service Prioritization & Reinforcement Learning & 20 \\ Traffic Shaping & Neural Networks & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 9: AI in QoS management.  
example, VoIP and video streaming services are highly sensitive to latency and packet loss, making them prime candidates for prioritization. By using historical data and real-time traffic analysis, AI systems can predict periods of congestion and allocate bandwidth in a way that minimizes the impact on these critical services. This ensures that users experience minimal disruption, with high-quality calls and seamless video playback, even during peak usage times [131].  
Furthermore, AI models can be integrated with existing QoS frameworks to enforce dynamic policies that adapt to network conditions. For instance, AI can continuously evaluate the performance of different services and adjust priorities as needed [129]. In a network experiencing congestion, AI can dynamically adjust the prioritization of traffic, shifting bandwidth from less sensitive services (such as bulk data transfers or email) to services with stricter performance requirements (such as real-time communication). This flexibility allows for a more efficient use of available resources, ensuring that high-priority services are always given precedence.
## 5 Performance Evaluation
### Experiment
#### 5.4.3 Performance Comparison  
To verify that G-TIAG can classify attacks better than existing methods, we investigate the ability of the four classifiers mentioned in Section 5.1 and the recent study proposed by [PERSON] et al. [22] on the public dataset. We evaluate the classification ability using the average precision, recall, and F1-Score mentioned in Section 5.1, and the results are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **RF** & **SVM** & **CNN** & **GCN** & **RShield** & **G-TIAG** \\ \hline Average Precision & 0.86 & 0.83 & 0.81 & 0.84 & 0.82 & 0.89 \\ Average Recall & 0.88 & 0.75 & 0.77 & 0.82 & 0.84 & 0.88 \\ Average F1-score & 0.87 & 0.77 & 0.85 & 0.81 & 0.83 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of five classifiers.  
Figure 9: Classification results in ablation study. (**a**) Precision in ablation study. (**b**) F1-Score in ablation study.  
Compared with the other four classifiers, G-TIAG improves on all three indicators. The RF method reaches above 0.86 on all three indicators, but the stability of the classification results is found to be lower in the experiments. The recall and F1-Score of the SVM are around 0.75. The recall of the CNN is around 0.77, the GCN reaches about 0.81 in the three indicators, and the G-TIAG model has a stable classification result of about 0.89. Our method outperforms RShield by about 6% in the classification work of multiple types of attacks. We consider this because RShield is better at detecting complex multi-step attacks rather than multi-type attack identification. G-TIAG is better at classifying multiple attacks with unbalanced data because it can learn the difference in interaction patterns of different attacks in a specific period. Moreover, it can also learn the difference in the changes in interaction patterns of different attacks in a time period.  
Our method achieves good classification performance based on unbalanced data, including multiple attack types. G-TIAG can learn the differences in interaction patterns from the graph structure and the differences in the changes of interaction patterns at different times. Then, our work discusses the effects of GRU and self-attention on classification performance. The introduction of GRU and self-attention can improve classification performance, and the contribution of GRU is more significant than that of self-attention. We consider that GRU focuses on the correlation between adjacent inputs because the changes between adjacent inputs for different attacks are also different. Finally, after comparing with classifiers and existing studies, we can conclude that G-TIAG has strengths and outperforms other methods in classification work for multiple types of attacks with imbalanced data.
## 1 Introduction
In this paper, we investigate two typical energy balance algorithms and analyze their advantages and disadvantages. [PERSON] et al. [16] proposed an Energy-Balancing unequal Clustering Approach for Gradient based routing (EBCAG). The nodes are dynamically organized into clusters, which achieves a balance between CHs and member nodes in a cluster. However, this method increases the energy cost of the whole network. In [17], the authors designed an Energy Balancing Cluster Head (EBCH) method to balance the energy consumption of CHs in different annuli. However, they didn't consider the unbalanced workload or the energy consumption between CHs and common nodes. To solve the above problems, a type of annulus-based energy balanced data collection method is proposed in this paper. Without loss of generality, the circular network is firstly divided into several annulus-shaped sectors of different sizes. Nodes in the same annulus-sector form a cluster. The main contributions of this paper are the following:  
* We analyse the energy consumption of nodes in each annulus and conclude that the CHs in different annuli should be selected from different sizes of regions, which can balance their energy consumption. According to this conclusion, the length of the radius of each Region for Candidate Cluster Headers (RCCH) is calculated. * A data forwarding strategy with the help of the Candidate Cluster Headers (CCHs) is proposed to reduce the possibility of buffer overflow during data uploading. With the collaboration of CCHs, the energy consumption of CHs is reduced and the energy consumption between CHs and CCHs is approximately balanced in a round of data gathering. * In order to achieve balanced energy consumption between CHs and common nodes, we adopt wireless charging technology. In each annulus, there is a WCV that is responsible for recharging the CHs and CCHs in the same annulus under the constraint of minimizing the recharging cost. Therefore, the energy efficiency of the network is improved and the energy hole problem is effectively alleviated. It is worth mentioning that the proposed method is not only adaptable for circular networks, but also can be applied to networks of other shapes. For example, in a large-scale farmland with regular or irregular shapes, the static sensor nodes are randomly deployed and form several clusters. They upload data to the base station (the monitoring center) in a multi-hop way. In order to maintain the long-term running of the network, a number of WCVs move in this farmland according to some predetermined trajectories (e.g., the field ridges) and recharge the relay nodes and cluster heads. Thus, the growth of crops can be monitored for a long time. The remainder of this paper is organized as follows: related works and the network model are described in Sections 2 and 3, respectively. In Section 4, we propose a type of data forwarding strategy with the help of the candidate cluster heads. Then, a multi-MCV based recharging scheme for maximal benefit is described in Section 5 and experimental results are shown in the next section.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 3 Methodology
### Phase 3--Alternative Mitigation Strategies Evaluation  
In this phase, alternative mitigation strategies are identified and evaluated to address the seismic risks outlined in previous phases. The assessment incorporates adjustments to the components' vulnerabilities and the power station's overall layout, reflecting the impact of the proposed mitigation measures.  
* Mitigation alternatives identification--possible mitigation strategies are identified to enhance the power station's seismic resilience. These strategies could involve structural upgrades, system redundancies, improved emergency response plans, or other relevant measures.
* Updating the components' vulnerability and system layout--based on the mitigation strategy and measures, the vulnerability of each component and the overall system layout are re-evaluated to reflect the expected impact on seismic resilience. This step includes updating fragility parameters, adjusting the system's mechanical and functional relationships, and implementing other relevant modifications to its layout.
* Benefit-to-cost analysis--this process assesses the economic effectiveness of the proposed mitigation strategies by examining the balance between expected risk reduction and the costs of implementation. The analysis aims to evaluate each strategy's worth and identify the most cost-effective option. This process compares the anticipated benefits of a mitigation measure, in terms of reduced economic losses from earthquakes, against the estimated costs of implementation. Decision trees are employed to systematically compare and quantify the benefits and costs of alternative strategies.