esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Analysis of Deformation and Stress Characteristics of Rock Bolts
### Simulation Analysis of Rock Bolt Deformation  
The horizontal stress of the roadway surrounding rock is an important factor affecting the deformation of rock bolts. With the increase in horizontal stress, the deformation of rock bolts increases significantly. At this time, the subsidence of the roadway roof, floor heave, and displacements of the two sides increase significantly. The angle between the axial direction of the roadway and the maximum horizontal stress is different, and the degree of its influence on the deformation of rock bolts also varies. When the axial direction of the roadway is parallel to the direction of the maximum horizontal stress, the deformation of the rock bolts is the smallest. When the axial direction forms an acute angle with the direction of the maximum horizontal stress, the deformation of the rock bolts is biased towards one side of the roadway. When the axial direction is perpendicular to the maximum horizontal stress, the influence is the greatest, and obvious deformation occurs in the rock bolts. The deformation of the rock bolts obtained according to the simulation calculation method is shown in Figure 11. The 1\({}^{\#}\) rock bolt shows obvious shear deformation at the interface between the roof and the coal seam. The shear dislocation direction is towards the middle of the roadway, and the dislocation distance is 13.3 cm. The 2\({}^{\#}\) rock bolt shows obvious shear deformation at the interface between the roof and the coal seam. The deformation form is similar to that of the 1\({}^{\#}\) rock bolt, and its lateral dislocation distance is 12.3 cm. Continuous bending deformation occurs in the lower part of the gangue, and the bending angle is 4.5\({}^{\circ}\). The 3\({}^{\#}\) rock bolt shows shear deformation at the interface between the roof and the hard coal, and the lateral dislocation distance is 4.1 cm. Compared with the 1\({}^{\#}\) and 2\({}^{\#}\) rock bolts, its bending deformation degree is smaller.  
The deformation form of the rock bolts in the field and the simulation is consistent, both deforming and expanding towards the interior of the roadway along the interface of the roof coal seam with the center line of the roadway as the axis. In terms of the degree of deformation, the lateral dislocations of the 1\({}^{\#}\) and 3\({}^{\#}\) rock bolts in the field and the simulation are 16.2 cm, 13.3 cm, 3.7 cm, and 4.1 cm, respectively. The errors are 0.179 and 0.098. To sum up, compared with the actual deformation of the roof rock bolts in the 41072 roadway of Changing Mine (Figure 6), the simulation results of the rock bolt deformation are relatively consistent in terms of the deformation form and degree, indicating that the model parameter selection is reasonable, the calculation sequence is relatively consistent with the actual excavation process, and the model can be used to analyze the deformation of the bolt body.  
Figure 10: Calculation Sequence.
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 5 Results and Discussion
### IEEE-39 Bus System
#### 5.1.2 Analysis of Frequency Stability  
Based on the generators' operation schedule in cases 0-2, after two HVDCs blocked separately, frequency deviation during simulation is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Cost/\$** & \begin{tabular}{c} **HVDC 1’s Average** \\ **Power/MW** \\ \end{tabular} & \begin{tabular}{c} **HVDC 2’s Average** \\ **Power/MW** \\ \end{tabular} & \
\begin{tabular}{c} **Iteration** \\ **Times** \\ \end{tabular} &
\begin{tabular}{c} **Computing** \\ **Time/s** \\ \end{tabular} \\ \hline Case 0 & 1,157,677 & 1000.0 & 1000.0 & 1 & 15.0 \\ Case 1 & 1,229,612 & 989.2 & 989.2 & 7 & 31.6 \\ Case 2 & 1,279,549 & 967.0 & 974.9 & 12 & 48.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating cost and HVDC power.  
Figure 3: The diagram of reformed IEEE 39-bus system.  
In case 0, after HVDC 1 or 2 blocked, the frequency deviation is greater than 0.2 Hz except for hour 11 and 12, which cannot meet the requirement of the power system. The reason is that the two HVDCs are the operation of rated capacity so that less generators are turned on and the power system does not have enough reserve capacity. When the HVDC block happens, the frequency deviation drops under 49.8 Hz. In cases 1 and 2, the minimum frequency limit constraint is supplied into the model. It enforces more when the generator is turned on while the HVDC output decreases at the same time. As a result, more reserve capacity could be provided by generators and frequency deviation in all hours when they are within 0.2 Hz after HVDC 1 or 2 block. That is because the minimum frequency limits could raise the first frequency regulation capacity and improve the power grid's reliability.
## 3 Results
### VOCs-Induced NO Accumulation Promotes Fe Absorption and Availability
_Arabidopsis_ plants were grown on an alkaline MS medium (pH = 7.6) for seven days, then transferred to 1/2 MS medium containing 50 uM Fe(III)-EDTA with or without exposure to BF06 VOCs for 10 days. Roots were incubated with the NO fluorescent probe DAF-FM DA. NO-associated fluorescence was detected in roots of non VOCs-exposed (control), VOCs-exposed, VOCs + L-NAME-treated, and VOCs + c-PTIO-treated plants. Left lines were obtained under bright-field microscopy, middle lines were imaged under green fluorescence, and the bright and fluorescence images were then merged. or to mitigate chloride symptoms under Fe-limited conditions when plants were treated with 5 mM L-NAME or 1 mM c-PTIO (Figure 9). Additionally, the total chlorophyll content of BF06 VOCs-exposed plants was markedly decreased after treatment with L-NAME or c-PTIO (Figure 10A), which was in accordance with the phenotype observed. Furthermore, we determined the total Fe content in both the non-treated and treated plants. Treatment with L-NAME or c-PTIO abolished the promoting effects of VOCs on Fe uptake in plants. Consistently, the treated plants had less total Fe content than the non-treated plants, but no significant difference was observed under the conditions of no Fe source (Figure 10B). Moreover, we examined soluble Fe levels in the VOCs-exposed plants with or without treatment with L-NAME or c-PTIO. As shown in Figure 10C,D, the shoots and roots of non-treated plants accumulated more soluble Fe content than L-NAME or c-PTIO-treated plants under different Fe conditions. Conversely, the level of root cell wall Fe was observed to be notably lower in the non-treated plants compared with the L-NAME- or c-PTIO-treated plants (Figure 10E). Collectively, these results indicated that VOCs-induced root NO accumulation was essential for increasing the soluble Fe content and Fe absorption in plants.
### Public ECG Databases Review  
The PhysioNet Computing in Cardiology Challenge 2017 (cinc17) [8; 9] and the Telehealth Network of Minas Gerais (TNMG) [10] are among the most popular databases for heart rhythm classification. Table 1 highlights both databases, which differ in terms of the number of records, data acquisition (i.e., sampling frequency), condition, and annotated pathologies.  
The PhysioNet/Computing in Cardiology Challenge 2017 (cinc17) database from [8; 9] is a very popular database in studies where Deep Learning algorithms are implemented. The PhysioNet challenge presented the database focused on Atrial Fibrillation (AF) detection by differentiating the AF from noise, normal, or other rhythms.  
The database was recorded with the AliveCor device with a sampling frequency of 300 Hz and filtered with a band-pass filter. It contains 8528 single-lead ECG recordings from individual patients with a time length from 9 s to just over 60 s. It was evaluated and annotated by several cardiologist experts.  
The Telehealth Network of Minas Gerais (TNMG) database was designed for use in a comprehensive study on the classification performance of a Deep Neural model. The complete database is composed of 2,322,513 ECG records from 1,676,384 different patients. Only the test dataset is available to the public, and it contains 827 12-lead ECG records with a sampling frequency of 400 Hz from different patients and is annotated by three different cardiologists. The database contains six different ECG annotations: 1 st degree AV block (1 dAVb), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and sinus bradycardia (SB), AF, and sinus tachycardia (ST).
# Gain-Scheduled Model Predictive Control for
## 4 Simulations  
To demonstrate the validity and superiority of the proposed gain-scheduled model predictive control method, a series of simulations for the CIPS are conducted in MATLAB/Simulink. In the first set of simulations, pulse noise is used as the external disturbance, and four cases are considered for the simulations. In the second set of simulations, Gaussian white noise is employed as the external disturbance, with four cases also considered for these simulations.
# APT Detection via Hypergraph Attention Network with Community-Based Behavioral Mining
## 2 Related Works  
### Behavioral Community Mining Techniques  
In the domain of behavioral community mining for cybersecurity, several studies have leveraged graph theory and community detection algorithms to model complex attack patterns.  
HERCULE [8] formulates multi-stage intrusion analysis as a community detection problem, constructing dynamic community sequences from contaminated log entries (e.g., malware binary analysis or website blacklists) via global graph modeling. By building multi-dimensional weighted graphs to correlate lightweight log entries, this approach identifies \"attack communities\" through community detection, enabling structured representation of multi-phase attack behaviors. T-Trace [9] introduces a tensor decomposition-based method for log community detection, extracting key behavioral patterns via event saliency scoring. This technique identifies abnormal communities associated with APT attacks in provenance graphs, providing fine-grained support for attack tracing by quantifying temporal and causal relationships in log data. KAIROS [10] employs a novel GNN-based encoder-decoder architecture to model the temporal evolution of provenance graph structures, quantifying the anomaly degree of each system event. Community detection is then used to reconstruct attack footprints from fine-grained anomaly scores, generating compact summary graphs that accurately describe the temporal correlation of malicious activities. DEPCOMM [11] addresses the dependency explosion problem in large-scale logs by partitioning provenance graphs into process-centric communities, compressing redundant edges from insignificant or repetitive system activities. Analyzing inter-community information flows generates semantic summaries for each community, refining high-value behavioral patterns from dependency graphs to facilitate lightweight representation for subsequent detection.  
However, these methods rely on rule-based or learning-driven strict community assignments, neglecting the overlapping semantic interactions where nodes may participate in multiple behavioral communities simultaneously. This oversight leads to higher false positives and negatives in complex attack scenarios, particularly when handling fine-grained kernel-level log semantics.
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 2 Image DNA Techniques
### Deep Learning-Based Approaches  
Deep learning-based methods utilize neural networks to automatically learn features from large datasets. Popular image recognition networks such as CNN (Convolutional Neural Network) [18], VGG [19], MobileNet [20], and ResNet [21] leverage pre-trained models (e.g., ImageNet [22]) to extract feature vectors representing probabilities across 1000 categories.  
VGG employs a simple design with 3 \(\times\) 3 convolutional kernels and identical padding, utilizing deep architectures with 16-19 layers to enhance representational power. In contrast, MobileNet focuses on lightweight and efficient architectures, though its simplicity may limit its expressiveness compared to high-performance models. ResNet introduces Residual Blocks to address vanishing gradient issues in deep networks, achieving superior performance with fewer computations than VGG.  
Autoencoders [23] are unsupervised learning models that compress input data into a low-dimensional latent space and reconstruct them through an encoder-decoder structure. They can generate variable-sized latent vectors by balancing compression efficiency with feature expressiveness. Self-supervised learning approaches such as SimCLR [24] and BYOL [25] learn image similarity and can be adapted for constructing image DNA. However, these methods require a dedicated training process, and incremental learning becomes challenging when filtering target images are continually added [26]. This necessitates the fine-tuning of incremental learning mechanisms for practical deployment.
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 3 Methodology
### Sensitivity Analysis  
#### 3.3.1 Extend Fourier Amplitude Sensitivity Test (EFAST)  
A sensitivity analysis of the Biome-BGC model was necessary to quantify the effects of multiple ecophysiological parameters on the model outputs. The forest flux site was mainly dominated by broadleaf Korean pine, so ecophysiological parameters of broadleaf Korean pine were used in a weighted average of the needle leaf forest (40%-50%) and broadleaf forest (50%-60%) based on the component percentage according to the forestry investigation.  
EFAST was applied to analyze the sensitivity of the carbon flux to ecophysiological parameters in this study. This method was widely used to estimate the expected value and variance of the outputand the contribution of the input parameters to uncertainty and sensitivity of the outputs [45]. The output Y of a model simulator can be calculated as follows:  
\[\mathrm{Y}\,=\,f(\mathrm{X})\,=\,f(\mathrm{X}_{1},\,\mathrm{X}_{2},\ldots,\, \mathrm{X}_{\mathrm{n}}), \tag{2}\]  
where \(\mathrm{X}_{\mathrm{i}}\) (i = 1,2, \(\ldots\),n) represents each input parameter, which has a range of variation to show its uncertainty. The total variance of the model output (\(\mathrm{V}_{\mathrm{Y}}\)) is:  
\[V_{Y}=\sum_{i}V_{i}+\sum_{i}\sum_{j>i}V_{ij}+\sum_{i}\sum_{j>i}\sum_{k>j}V_{ ijk}+...+V_{1,2,...,n}, \tag{3}\]  
where \(\mathrm{V}_{\mathrm{i}}\) is the first order conditional variance of \(\mathrm{Y}\) given that the input \(\mathrm{X}_{\mathrm{i}}\) has a fixed value \(\mathrm{x}_{\mathrm{i}}\). \(\mathrm{V}_{\mathrm{ij}}\) is the second order conditional variance when \(\mathrm{X}_{\mathrm{i}}=\mathrm{x}_{\mathrm{i}}\) and \(\mathrm{X}_{\mathrm{ij}}=\mathrm{x}_{\mathrm{ij}}\). \(\mathrm{V}_{\mathrm{ijk}}\) and \(\mathrm{V}_{1,2,...,n}\) are the higher order variance of interaction among multiple variables.  
The first order sensitivity index, \(\mathrm{S}_{\mathrm{i}}\), is defined as, \(\mathrm{S}_{\mathrm{i}}=\mathrm{V}_{\mathrm{i}}/\mathrm{V}_{\mathrm{Y}}\). This quantifies the effect of varying \(\mathrm{X}_{\mathrm{i}}\) alone, but averaged over variation in other input parameters. The value of \(\mathrm{S}_{\mathrm{i}}\) is between 0 and 1. The second and higher order sensitivity indices are: \(\mathrm{S}_{\mathrm{ij}}=\mathrm{V}_{\mathrm{ij}}/\mathrm{V}_{\mathrm{Y}}\) and \(\mathrm{S}_{\mathrm{ijk}}=\mathrm{V}_{\mathrm{ijk}}/\mathrm{V}_{\mathrm{Y}}\). The total order sensitivity index (\(\mathrm{S}_{\mathrm{t}}\)) was introduced as the sum of all the above indices, including the contributions of individual parameters and the interactions between a specific parameter and other parameters.
## 3 Problem Formulation
### Optimization Problem Formulation
#### 3.1.3 Multiobjective Problem Formulation  
As detailed in Sections 3.1.1 and 3.1.2, there are two distinctive objectives included in our problem; i.e., maximization of connected users--as given in (20)--and minimization of the energy consumption of the UAV BSs--as given in (22). In this work, we aim at optimizing the both objectives--(20) and (22), simultaneously. In this regard, we developed the following optimization model:  
\[\begin{array}{rl}\max_{\vec{C}_{\mathrm{M}}\in\mathbb{R}^{3}}&h(\vec{C})\\ \mathrm{s.~{}t.}&\mathrm{C_{1}}\mathbf{:}&h_{\min}\leq h_{\mathrm{d}}\leq h_{\max},\\ &\mathrm{C_{2}}\mathbf{:}&\angle A_{1}+\angle A_{2}+\angle A_{3}+\angle A_{4}<2\pi, \\ &\mathrm{C_{3}}\mathbf{:}&\theta<\pi,\\ &\mathrm{C_{4}}\mathbf{:}&P_{\mathrm{t}}\leq P_{\max},\end{array} \tag{23}\]where \(h:\mathbb{R}^{3}\rightarrow\mathbb{R}\) is the objective function, and \(h(\vec{C})=w_{1}f(\vec{C})-w_{2}g(\vec{C})=w_{1}n_{\text{c}}-w_{2}E_{\text{T}}\) in this case. Here, \(w_{1},w_{2}\in\mathbb{R}\) are coefficients used for two purposes:  
* To prioritize one objective over the other. For example, a mobile network operator may not be interested in the energy consumption much and focuses only on covering as much as users as possible for a short duration, and it would choose \(w_{1}\gg w_{2}\). On the other hand, if the operator ranks both objectives equally, then it would choose \(w_{1}=w_{2}\). Therefore, \(w_{1}\) and \(w_{2}\) allow the operators to rank the objectives according to their requirements.
* To make the units of both \(f(\vec{C})\) (unitless) and \(g(\vec{C})\) (in Joules) the same, since \(h(\vec{C})\) includes the summation of \(f(\vec{C})\) and \(g(\vec{C})\). To this end, while \(w_{1}\) is chosen to be unitless, \(w_{2}\) is in (1/Joules).
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 3 Design of Data Naming Mechanism
### Data Naming Mechanism
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Name** & **Block Number** & **Source Node Information** \\ \hline /Data/Prefix1/Video/20210322/ & 2 & B \\ \hline /Data/Prefix1/Data/20210420/ & 1 & C \\ \hline /Data/Prefix1/Video/20210420/ & 3 & C \\ \hline /Data/Prefix1/Video/20210421/ & 3 & A \\ \hline \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of published information. block sequence number information to automatically generate a common request packet as '/Source/Host-A/Data/Prefix/Version/Timestamp/NumberX/'. After automatically generating multiple interest request packets, different nodes are requested for different content blocks concurrently, and finally the demand data are obtained through multiple paths in parallel transmission mode to speed up the content transmission speed and reduce the load of the content provider.
###### Abstract  
Predicting battery states such as the voltage and state of charge (SOC) can help us monitor lithium batteries more efficiently during usage. This study proposed a predictive model for the lithium battery voltage and SOC by combining a second-order RC equivalent circuit model with a multi-head attention Bidirectional Long Short-Term Memory (MHA-BiLSTM) neural network. The equivalent circuit model simulates long-term charge-discharge cycles in Simulink, providing essential data for model training. The BiLSTM model, enhanced by the multi-head attention mechanism, is used for accurate short-term predictions of the battery voltage and SOC. The experimental results demonstrate that the proposed MHA-BiLSTM model outperforms other models in the prediction accuracy, achieving an R\({}^{2}\) of 0.91, with the lowest RMSE of 0.0567 and MAPE of 0.0095. This hybrid approach effectively captures the dynamic behavior of the battery and reduces predictive errors, making it a promising solution for battery health monitoring and management.  
Keywords: lithium battery prediction; equivalent circuit model; multi-head attention; bi-directional long short-term memory Article
## 3 Results
### Doping of HA Nanoparticles
#### 3.3.4 Silver-Doped Nano-Hydroxypaatite (Ag-Nano-HA)  
Silver (Ag) is a highly effective antibacterial agent widely recognized for its ability to prevent microbial colonization while maintaining biocompatibility and low toxicity at controlled concentrations [51]. Its antibacterial efficacy is attributed to its interaction with bacterial cellular components, particularly the plasma membrane. Silver disrupts electron transfer processes, inhibits ATP synthesis, and stimulates the production of reactive oxygen species (ROS), which collectively lead to bacterial cell damage and death [43; 52].  
Silver-doped hydroxyapatite (Ag-HA) nanoparticles, synthesized through methods such as precipitation at 100 \({}^{\circ}\)C, have shown potent antibacterial effects against both Gram-positive and Gram-negative bacteria [46]. The antibacterial properties of Ag-HA are primarily driven by the release of Ag\({}^{+}\) ions, which interact with bacterial membranes to inhibit growth and promote lysis. Despite the cytotoxic potential of Ag\({}^{+}\) ions, studies indicate that their concentrations in most Ag-nano-HA samples remain below the toxic threshold for human blood, minimizing cytotoxic effects on human cells across a wide range of applications [45].  
The safety and efficacy of Ag-nano-HA can be optimized by precisely regulating the amount of silver ions during synthesis. This careful control ensures that Ag\({}^{+}\) ion release provides sufficient antibacterial activity without compromising biocompatibility. These properties make Ag-nano-HA an attractive material for use in bone tissue engineering, particularly in preventing infections around implants and scaffolds.
## 4 Nonlinear Mathematical Model of the Bearing Stationary State
### Bearing Model with Throttle SECT
For \(i=k\) for the left and right sweep formulas, we have a system of matrix equations similar to (71):  
\[\left\{ \begin{array}{c}\overline{\mathbf{v}}_{k-1}=X_{k}\overline{\mathbf{ v}}_{k}+\overline{y}_{k},\\ \overline{\mathbf{v}}_{k+1}=V_{k}\overline{\mathbf{v}}_{k}+\overline{z}_{k}. \end{array} \right. \tag{73}\]  
Substituting (73) into (72), we transform System (70) into the form  
\[\left\{ \begin{array}{c}\overline{\mathbf{v}}_{k}=A_{8}\overline{d}_{k}- \overline{C}_{6},\\ \overline{d}_{k}=\overline{d}_{p},\end{array} \right. \tag{74}\]  
where  
\[A_{8}=A_{7}^{-1},\overline{C}_{6}=A_{8}\overline{C}_{5},\]  
\[A_{7}=A_{5}V_{k}+A_{6}X_{k}+6E,\,\overline{C}_{5}=\overline{C}_{4}+A_{5} \overline{z}_{k+1}+A_{6}\overline{y}_{k-1}.\]  
The nonlinear system (74) was solved by the Seidel iterative method [34]. The initial values of the pressure vectors \(\overline{P}_{k},\overline{P}_{p}\) were taken to be the pressure values calculated by Formulas (17) in the absence of a bearing load. These values were substituted into the right-hand side of the first equation of (74), then \(\overline{\mathbf{v}}_{k}\). were calculated. Since \(\overline{\mathbf{v}}_{k}^{j}=\big{(}P_{k}^{2}\big{)}^{j}\), the new value of the pressure \(P_{k}\) at the grid nodes was found by the formula \(P_{k}^{j}=\sqrt{\overline{\mathbf{v}}_{k}^{j}}\). New values of the pressure \(P_{p}\) were found by solving the second equation of System (74), which is also nonlinear. Taking into account the fact that \(P_{p}^{j}\in\left[P_{k^{\prime}}^{j},P_{s}\right]\), the solution of this equation for each value \(j=0\), \(1\),..., \(m\) was found by the bisection method. Further, the new pressure values were again substituted into the right side of the first equation of (74), and the process was continued until its convergence. The process was stopped after the conditions were met:  
\[\left|\left(P_{k}^{j}\right)_{\pm i+1}-\left(P_{k}^{j}\right)_{a}\right|<\delta,j=0,1,\ldots,m,\]  
where \(\alpha\) is the iteration number, and \(\delta\) is the calculation accuracy (in the calculations, \(\delta=10^{-4}\) was taken). After finding \(\overline{\Psi}_{k}\) by Formulas (55) and (66), the reverse left and right sweeps were performed, as a result of which, the field of pressure squares \(P^{2}\) in the bearing gap were obtained, and then, the force reactions \(W\) and \(W_{p}\) were found.