esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Numerical Simulation of Stress-Relief Groove Utility Analysis  
#### 3.1.1 Grooving of the Floor-Middle for Stress-Relief  
(i)  Concentration of stress in the surrounding Rock  
The analysis of a 1 m wide stress-relief groove, as shown in Figure 10, reveals that increasing the depth reduces stress concentration in the floor middle, though it persists on the floor side and shifts towards the far roadway. Stress concentration at the top floor also decreases. Vertical stress results show that increasing the groove depth causes the stress concentration to shift downward, with stress spreading further from the bottom floor.  
The peak points of horizontal and vertical stresses and their distances from the bottom floor and roadway centerline were analyzed (Figure 11). As groove depth increases, the peak horizontal stress rises, with a nearly constant vertical distance from the base floor and an increasing horizontal distance from the roadway centerline. For horizontal stress, the peak value increased from 43.00 MPa (no groove) to 45.11 MPa (2 m groove depth), shifting 1.75 m towards the far roadway. Similarly, the vertical stress peak increased from 29.28 MPa (no groove) to 29.97 MPa (2 m groove depth), with the peak point shifting 1.01 m towards the far roadway. These observations confirm the stress-relief effect of base floor grooving.  
Figure 10: Stress-relief effect of central grooving.  
Figure 9: SEGA algorithm flow.  
2. Trends in perimeter surrounding rock displacement Monitoring the maximum displacement (Tables 5 and 6) of the roadway floor and sidewalls revealed that increasing groove depth and width decreases the floor displacement, with depth having a more significant impact. Meanwhile, as groove dimensions grow, the sidewall displacement gradually increases, though the increase is relatively small. Thus, while grooving effectively relieves stress and reduces floor displacement, it can also damage surrounding rock and compromise sidewall stability.  
The volume of the plastic zone, expressed in m\({}^{3}\)/m, is summarized in Table 7. Before grooving, it was 62.26 m\({}^{3}\). After grooving, the plastic zone volume consistently decreased across all parameters. A 2 m groove depth notably reduces the plastic zone volume by 11 to 41 m\({}^{3}\), while a 1.5 m groove depth shows the largest volume compared to other depths, though still smaller than the pre-grooving volume. The 2 m \(\times\) 2 m groove achieves the lowest plastic zone volume of 11.824 m\({}^{3}\), indicating significant improvement over the initial state.
## 7 Discussion
In Figure 18b, the influence of time step size is investigated for 3 mm element size (1 s versus 5 s), with the conclusion that it is negligible too. The behaviour of the tested materials is significantly affected by temperatures. The Perzyna material model was designed for one temperature and was therefore also used in this paper. Modification of Perzyna model is possible in several ways. For example, in [42], a modification was tested that additionally adds a parameter as a function of temperature for the Anand model. It is not possible to directly compare the obtained material model parameters with other sources as they are not yet widely available in the literature (SACX0807). In addition, the parameters depend on the temperature and the way the mechanical tests are performed. The literature [27, 43], which is more similar to the problem presented here, can be recommended. The inclusion of additional experiments for parameter calibration or validation of their resulting design was also considered. For the selected material model, for example, this involved the problem of ratcheting. For three material models and two types of experiments, the identification procedure consisted of 10 optimisation steps (see Section 5.3). That is why we decided to test the basic procedure and not test temperatures or more complex states (ratcheting).
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 3 Problem Description and Model Formulation
### Mathematical Model  
The MILP-based optimization model \(\mathbf{P_{0}}\) is used to obtain the minimal service costs, which is shown as follows:  
\[\mathbf{P_{0}}\hskip 56.905512 ptMinf=\sum_{k}c_{1}\times x_{k}+\sum_{j}c_{2} \times v_{j}+\sum_{j}\sum_{i}2\times c_{3}\times z_{i}^{j}\times d_{i}^{j} \times r_{i} \tag{1}\]  
The objective function (1) aims to obtain minimal service costs, which include total construction costs for spare part centres, total usage costs for service vehicles, and total service mileage costs for service stations:  
\(s.\)\(t.\)  
\[u_{j}^{k}\leq x_{k},\forall j,\forall k \tag{2}\]  
\[\sum_{k}u_{j}^{k}\geq y_{j},\forall j,\forall k \tag{3}\]  
\[u_{j}^{k}\times D_{j}^{k}\leq D2_{\max},\forall j,\forall k \tag{4}\]  
\[\sum_{j}v_{j}\leq m \tag{5}\]  
\[C\times v_{j}\geq\sum_{i}z_{i}^{j}\times r_{i},\forall j \tag{6}\]  
\[\sum_{j}y_{j}=l \tag{7}\]  
\[\sum_{j}z_{i}^{j}=1,\forall i \tag{8}\]  
\[\sum_{i}z_{i}^{j}=y_{j},\forall j \tag{9}\]  
\[z_{i}^{j}\leq y_{j},\forall i,\forall j \tag{10}\]  
\[\sum_{j}\sum_{i}z_{i}^{j}=n \tag{11}\]  
\[z_{i}^{j}\times d_{i}^{j}\leq D1_{\max},\forall i,\forall j \tag{12}\]  
\[\sum_{i^{\prime}|(i,i^{\prime})\in A}w_{ii^{\prime}}^{j}-\sum_{i^{\prime}|(i^ {\prime},j)\in A}w_{ii^{\prime}}^{j}=z_{i^{\prime}}^{j},\forall i,\forall j,i \
eq j \tag{13}\]  
\[\sum_{i^{\prime}|(i,i^{\prime})\in A}w_{ii^{\prime}}^{j}\leq(M-2)\times z_{i^ {\prime}}^{j},\forall i,\forall j,i\
eq j \tag{14}\]  
\[\sum_{i^{\prime}|(i^{\prime},j)\in A}w_{ij}^{j}\leq(M-1)\times y_{j},\forall j \tag{15}\]  
\[w_{ii^{\prime}}^{j}\geq 0,\forall i,\forall i^{\prime},\forall j \tag{16}\]\[v_{j}\geq 0,\forall j \tag{17}\]  
\[y_{j},x_{k},u_{j}^{k},z_{i}^{j}=\{0,1\},\forall i,\forall j;\forall k \tag{18}\]  
Constraints (2) and (3) ensure that all these chosen service stations can be served by the spare part centres. Constraint (4) implies the maximal service distance for spare part centres. Constraint (5) restricts the number of service vehicles allocated to service stations, and constraint (6) ensures the service capacity of service stations. Constraint (7) defines the number of chosen service stations. Constraints (8) and (9) specify that one service unit can only be served by one service station. Constraints (10) and (11) introduce a limitation that all service units must served by the chosen service stations. Constraint (12) enforces the maximal service distance for the service station. Constraints (13)-(15) refer to Han and Zhang [37] and are extended for contiguity constraints in service regions served by service stations. Constraint (13) is applied to calculate the imaginary flow from the service unit to the chosen service stations. Constraints (14) and (15) are introduced to prohibit the imaginary flow from other service stations. Constraints (16)-(18) specify the range of decision variables.
## 3 Methodology
### Model Transformation Process
#### 3.1.2 Expressing Model Transformation Rules  
In this part, we present the transformation rules for the abstraction and concretization phases.  
* **Abstraction rules** The abstraction activity allows the engineering models to be represented as directed graphs. In this step, the SysML, Modelica, and Altarica metamodels are the source metamodels and the digraph metamodel is the target metamodel. To achieve the abstraction activity, we use the following transformation rules.  
For each source model (i.e., SysML, Modelica, and Altarica models), entities are transformed into vertices. The flows exchanged between entities are transformed into edges. The interaction points between entities define the direction of edges.  
* **Concretization rules** The concretization activity allows updating engineering models depending on the changes provided in the abstracted models. This activity is considered the opposite process of the abstraction activity. Thus, the source metamodel is the digraph metamodel and the target metamodels are SysML, Modelica, and Altarica metamodels.  
The transformation process is defined as follows. For each updated abstracted model, we transform firstly the vertices to represent the entities of the target models. Then, we transform oriented edges to define the connections and the direction of exchanged flows between different entities.
# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 4 Experiment
### Case Study  
As shown in Figure 8, a representative case was chosen to illustrate visually how our model can improve the accuracy of prediction results. In this case, the defendant is charged with robbery. Theft and robbery are related to property, and their fact descriptions are similar, so many models have difficulty distinguishing them. In-depth analysis of the relevant legal articles reveals that one important difference between them is robbery often uses violence to seize the victim's property, and the crime of theft is mainly related to \"taking advantage of a person's surprise\" and \"secret manner\". In the charge prediction, the model can only correctly predict the charge when the distinguishable features in the fact description are extracted. As shown in Table 9, the charge of the case is accurately predicted by our model. On the contrary, other models did not focus on both \"property\" and \"violence\" and incorrectly predicted the crime of theft. For example, in MPBFN, by analyzing the relationship between the IJP subtasks and using the legal article and prison term to assist in the prediction of the charge, the case characteristics are extracted in depth; however, the information of \"hit\" and \"injury\" in the case is not paid extra attention to. However, in this paper, by constructing a heterogeneous graph of the charge information and using a DGCN to extract the heterogeneous graph features, our model can effectively capture the semantics related to the specific charge, which are assigned the red color in Figure 8, and, finally, combine external knowledge and fact description to accurately predict the charge in this case, which further proves that the feature augmentation based on graph attention network can effectively distinguish confusing charges.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Datasets** & \multicolumn{4}{c}{**Criminal-S**} & \multicolumn{4}{c}{**Criminal-M**} & \multicolumn{4}{c}{**Criminal-L**} \\ \hline
**Metrics** & **Acc.** & **MP** & **MR** & **F1** & **Acc.** & **MP** & **MR** & **F1** & **Acc.** & **MP** & **MR** & **F1** \\ \hline No-data & 91.1 & 60.4 & 59.7 & 58.4 & 92.9 & 67.2 & 71.3 & 68.2 & 95.5 & 76.2 & 76.0 & 74.9 \\ \hline No-feature & 93.1 & 60.7 & 61.2 & 60.3 & 93.8 & 67.1 & 71.9 & 68.1 & 95.1 & 75.1 & 75.8 & 74.2 \\ \hline Cross-loss & 92.4 & 59.7 & 60.2 & 59.1 & 93.6 & 68.1 & 71.3 & 68.7 & 94.9 & 77.3 & 76.2 & 75.1 \\ \hline Ours & 93.5 & 66.2 & 63.8 & 62.1 & 94.9 & 70.3 & 73.5 & 70.0 & 95.9 & 78.6 & 77.1 & 76.2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Macro-F1 values of various charges on Criminal-S.
# A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach
## 7 Conclusions  
In this paper, we proposed a new bio-inspired probabilistic VDTN router based on the combination of two swarm-based approaches, namely the FA and GSO metaheuristics, to solve the problem of SCF vehicle selection in VDTN routing. The proposed model alternates with a geography-based recovery forwarding to cover the lack of nodes in critical stages of bundle routing. The proposed swarm-based router considers both forwarding historic and current forwarding abilities of vehicles as routing parameters for the probabilistic evaluation of candidate SCF relay nodes for both FA and GSO methods.  
Following the collected performances from the simulated urban mobility scenario, the proposed swarm-inspired VDTN router has been successful in optimizing the bundle flooding and the average delivery delays, while it alternates the best performances in the delivery probability of the compared GeoSpray model and the optimized route length of the probabilistic ProPHET model. The proposed router showed also an effective management of large buffer cache capacities which characterizes vehicular nodes by the swarm-based controlled forwarding timing of bundles.  
This contribution introduced the novelty of alternate swarm computation for VDTN routing and the hybridization of probabilistic and geographic SCF routing which opens further perspectives on the application of enhanced bio-metaheuristic techniques in the VDTN routing optimization. Other VDTN routing modes such as the knowledge-based forwarding can be used to cover the shortages of probabilistic and geographic DTN forwarding modes in the framework of the swarm-inspired optimization methodologies. Further work on the basis of this solution will focus on improving the quality of swarm computation techniques in order to improve the profitability of the SCF selection process.  
Research study and the conception of proposed solution have been realized by Y.A. and A.B. VDTN solution's coding and simulations using ONE have been realized by Y.A. and V.N.G.J.S. Manuscript writing has been realized by Y.A. Manuscript verification and corrections have been realized by Y.A., A.B. and V.N.G.J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We present our grateful thanks to [PERSON] for ensuring the availability of the GeoSpray router's ONE source code for the proposed solution's coding and the simulation comparisons.  
Figure 10: Number of dropped bundle copies.
# Effect of Seasoning Addition on Volatile Composition and Sensory Properties of Steved Pork
## 3 Results and Discussion
### Odour-Active Compounds Analysis of the Fresh and Stewed Pork
Linear aldehydes like pentanal, hexanal, heptanal, octanal, nonanal and decanal have been reported to be generated from lipid oxidation [27]. Moreover, these aldehyde compounds could be detected in different processing methods and may contribute grassy, fatty and fruity notes to overall aroma of the pork samples. Unsaturated aldehydes such as (\(Z\))-hept-2-anal, (\(E\))-oct-2-anal, (\(E\))-non-2-anal, (\(E\))-dec-2-anal, (\(E\),\(E\))-2,\(A\)-nonadienal, undec-2-enal, and (\(E\),\(E\))-2,\(A\)-decadienal are degradation products of linoleate and linolenate hydroperoxides [41]. Among them, there was no significant difference in undecem-2-al in FP, SP1, and SP2 (\(p>0.05\)), indicating that heating and salt treatment had no effect on the formation of undecem-2-al. On the other hand, the rest of the olefin aldehydes have relatively higher OAVs in SP\({}_{1}\) and SP\({}_{2}\). This showed that SP\({}_{1}\) and SP\({}_{2}\) could promote the increase of some unsaturated aldehydes. Benzeneacetaldehyde, with honey and sweet notes, is a well-known aroma component formed from Maillard reaction of phenylalanine [42] and the OAV in SP\({}_{5}\) were significantly higher (\(p<0.001\)) than that in other samples. 1,8-Cineole, anethole and estragole, with mint and aniseed flavour, were the most abundant in SP\({}_{3}\). The OAVs of oct-1-en-3-ol, (\(E\))-oct-2-en-1-ol, butane-2,3-dione, octane-2,3-dione and 2-pentylfuran were the highest in SP\({}_{1}\) and SP\({}_{2}\), followed by SP\({}_{3}\), SP\({}_{4}\), SP\({}_{5}\) and SP\({}_{6}\), of which oct-1-en-3-ol and octane-2,3-dione was shown to be richer in SP\({}_{2}\) than that in SP\({}_{1}\). Linalool and eugenol were found immediately when the spices were added to the cooked pork, which may be due to the flavour of the star anise itself. Dimethyl trisulfide, with fish and cabbage notes, was considered as the main sulphur-compound in SP\({}_{1}\), SP\({}_{4}\), SP\({}_{5}\), and SP\({}_{6}\).
# Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM
## 2 Stochastic Feature Selection  
Let \(\{\mathbf{Z}_{n},y_{n}\}_{n=1}^{N}\) be a set holding \(N\) vibration signals within a windowing approach. \(\mathbf{Z}_{n}\in\mathbb{R}^{W\times T}\) stores \(W\) row segments \(z_{w,n}\in\mathbb{R}^{T}\), at \(T\) time instants (\(w\in\{1,2,\ldots,W\}\)), and \(y_{n}\in\{1,2,\ldots,C\}\) is the output label for the \(n\)-th signal concerning \(C\) fault diagnosis/severity classes of a bearing installed on a rotating machine. Here, we introduce a Stochastic Feature Selection (SFS) approach to support fault diagnosis/severity classification from vibration signals, which comprises the following main stages: (i) Multi-domain feature estimation and (ii) Supervised relevance analysis under stochastic modeling. The following describes in detail each stage.
## 2 Materials and Methods
### Logics and Operation of the System
In each cycle, the system performs a single voltage reading, followed by a rapid burst of 100 current readings separated by a 100-us delay. A final 100-ms delay concludes each of the 10 cycles. The average voltage readings and the peak current value (converted to RMS current) were then used to calculate the electrical power delivered by the photovoltaic source. This averaging and sampling strategy results in a total data acquisition time of approximately 1.1 s for each of the 23 data points on the I-V curve, smoothing out noise and rapid fluctuations. The total system response time to generate a complete I-V curve is 45 s. This duration is a sum of the operations performed for each of the 23 measurement points, which includes \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**j** & **Binary** & **Res\({}_{1}\)** & **Res\({}_{2}\)** & **Res\({}_{3}\)** & **Res\({}_{4}\)** & **Res\({}_{5}\)** & **Res\({}_{6}\)** & **Res\({}_{7}\)** & **Res\({}_{8}\)** & **\({}_{\text{eq}}\) [\(\Omega\)]** \\ \hline
0 & 0000000 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \(\infty\) \\
30 & 0001110 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 1.159 \\
44 & 00101100 & 0 & 0 & 1 & 0 & 1 & 1 & 0 & 0 & 1.938 \\
63 & 0011111 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 0.556 \\
112 & 0111000 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 4.620 \\
217 & 11011001 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 1 & 0.809 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Truth table of resistor activation and equivalent resistance. \(1.1\) s for each of the 23 measurement points, a 500 ms delay immediately after the relays are switched, allowing the electrical circuit to stabilize, and a final 300 ms of delay for system pacing and to ensure the relays are fully reset before the next combination is activated.
## 3 Data and Methods
### 3D Geomechanical Modelling  
The experimental results obtained from triaxial tests are called static geomechanical parameters, which are known for their high accuracy [34]. However, due to sample discontinuities, acquiring continuous data solely through experiments remains unattainable. Dynamic geomechanical parameters can be derived using logging data such as p-wave time difference, s-wave time difference, and density (Figure 6). The limitation of logging calculations is the inherent inaccuracy of dynamic geomechanical parameters. The calculation formulas are as follows:  
\[\mu_{d}=\frac{\Delta t_{s}^{2}-2\Delta t_{p}^{2}}{2\Big{(}\Delta t_{s}^{2}- \Delta t_{p}^{2}\Big{)}} \tag{3}\]  
\[E_{d}=\frac{\rho}{\Delta t_{s}^{2}}\times\frac{3\Delta t_{s}^{2}-4\Delta t_{p} ^{2}}{\Delta t_{s}^{2}-\Delta t_{p}^{2}} \tag{4}\]  
where \(\Delta t_{s}\) is the p-wave time difference, s/m; \(\Delta t_{p}\) is the s-wave time difference, s/m; and \(\rho\) is the rock density, g/cm\({}^{3}\).  
By integrating mechanical experimental data with logging calculations, a successful relationship between dynamic and static geomechanical parameters in the study area was established, mitigating the limitations of individual methods and ensuring continuous and credible results (Figure 6).  
The heterogeneity and anisotropy of the formation, rapid lithological changes, and sudden fractures can lead to variations in velocity, density, and geomechanical parameters. According to the definition of acoustic impedance, this variable's magnitude correlates closely with velocity and density, offering insights into geomechanical parameter trends. In this study, static geomechanical parameters serve as constraints, guided by acoustic impedance trends. Geostatistical analyses such as Cokriging and sequential Gaussian simulation were employed to develop complex and accurate 3D heterogeneous rock mechanics models of the study area [35; 36; 37; 38].  
Figure 5: **(A)** Different stages of a cumulative acoustic emission curve. Region A\(-\)B represents the closing of cracks, and B\(-\)C the linear elastic deformation. C\(-\)D is the stable, and D\(-\)E the unstable fracture propagation. Reprinted with permission from ref. [30]. Copyright 2012 Elsevier. **(B)** Sketch diagram of core sampling. Reprinted with permission from ref. [33]. Copyright 2009 Springer Nature.
# A Novel OFDM-Based Time Domain Quadrature GSM for Visible Light Communication System
## 2 Channel Model  
Figure 1 shows the geometrical setup of a general indoor VLC-MIMO system, which consists of \(N_{t}\) LEDs and \(N_{r}\) photodiodes (PDs). The LEDs are mounted on the ceiling for stable illumination and the user is equipped with multiple PDs, which are all oriented vertically downwards and upwards, respectively. In addition, we assume that the PDs are located at 0.85 m above the receiving plane. Since the total received optical power of the line-of-sight (LOS) link exceeds at least 95% of the total amount, only the LOS link transmissions are considered in this work.  
Assume \(L\) is the inverse fast Fourier transform (IFFT) length always employed in the OFDM module. Let \(\mathbf{H}\in\mathbb{R}^{N_{r}\times N_{t}}\) denote the MIMO channel matrix, \(\mathbf{s}\in\mathbb{R}^{N_{t}\times L}\) denote the transmitted signal matrix, and \(\mathbf{n}\in\mathbb{R}^{N_{r}\times L}\) denote the additive white Gaussian noise (AWGN) with zero mean. Thus, the received signal \(\mathbf{y}\in\mathbb{R}^{N_{r}\times L}\) can be expressed by  
\[\mathbf{y}=\mathbf{H}\mathbf{s}+\mathbf{n}. \tag{1}\]  
In addition, \(\mathbf{H}\) can be determined by  
\[\mathbf{H}=\begin{bmatrix}h_{11}&\cdots&h_{1N_{t}}\\ \vdots&\ddots&\vdots\\ h_{N_{t}1}&\cdots&h_{N_{t}N_{t}}\end{bmatrix}, \tag{2}\]  
where \(h_{\lambda\mu}(\lambda=1,2,\ldots,N_{r};\mu=1,2,\ldots,N_{t})\) is the direct-current channel gain between the \(\mu\)-th LED and the \(\lambda\)-th PD. As the LEDs follow the Lambertian radiation pattern [21], \(h_{\lambda\mu}\) can be calculated by  
Figure 1: Geometric setup of a general indoor VLC-MIMO system.  
\[h_{\lambda\mu}=\frac{(w+1)\rho A}{2\pi d_{\sigma}^{2}}\text{cos}^{w}(\varphi_{o}) T_{s}(\theta_{o})g(\theta_{o})\cos(\theta_{o}), \tag{3}\]  
where \(w=-\ln 2/\ln(\cos(\Psi))\) represents the Lambertian emission order, \(\Psi\) is the semi-angle at half power of the LED, \(\rho\) is the responsivity, and \(A\) is the PD's active area. In addition, \(d_{\sigma}\), \(\varphi_{o}\), and \(\theta_{o}\) are used to denote the distance, the angle of emission, and the angle of incidence between the \(\mu\)-th LED and the \(\lambda\)-th PD, respectively. \(T_{s}(\theta_{o})\) is the gain of optical filter, \(g(\theta_{o})=\frac{n^{2}}{\sin^{2}\Phi}\) is the gain of optical lens, where \(n\) is the refractive index and \(\Phi\) is the half-angle field of view (FOV) of the optical lens.