esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Gaussian Processes Regression (GPR)  
A Gaussian process is a stochastic process that is formed by a collection of random variables and has a Gaussian probability distribution [70]. The major factor in the Gaussian process is the covariance function known as the kernel function. The learning problem in the Gaussian process amounts to adjusting the covariance hyperparameters. The mean function \(m(\mathbf{x})\) and covariance function \(k(\mathbf{x},\ \mathbf{x}^{\prime})\) characterize the Gaussian process \(f(\mathbf{x})\), which can be described as follows:  
\[m(\mathbf{x})=\ \mathbb{E}[f(\mathbf{x})] \tag{5}\]  
\[k\big{(}\mathbf{x},\ \mathbf{x}^{\prime}\big{)}=\ \mathbb{E}\big{[}(f(\mathbf{x})-m(\mathbf{x})) \big{(}f\big{(}\mathbf{x}^{\prime}\big{)}-m\big{(}\mathbf{x}^{\prime}\big{)})\big{]} \tag{6}\]  
\[f(\mathbf{x})\ \sim\ GP\big{(}m(\mathbf{x}),k\big{(}\mathbf{x},\ \mathbf{x}^{\prime}\big{)}\big{)} \tag{7}\]  
For simplicity, we usually consider the mean function to be zero.  
Similar to SVR, GPR has been advanced to solve complex nonlinear problems through projection of inputs into high dimensional space by applying highly flexible kernels, where such a technique is referred to as kernel trick. Additionally, GPR models have a great ability to provide the most informative feature (spectral band) from the input dataset. In this study, we used the sum of an RBF and a noise component function kernel:  
\[k\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}={\theta_{0}}^{2}exp\left(-\frac{\big{(} \mathbf{x}_{i}-\mathbf{x}_{j}\big{)}^{2}}{2\sigma^{2}}\right)+{\theta_{1}}^{2}{\delta _{ij}} \tag{8}\]  
where \(\theta_{0}\) is the scaling factor, \(\sigma\) is the length-scale, and \(\theta_{1}\) corresponds to the independent noise component.  
Beyond the kernel functions, alpha and n_restarts_optimizer, are significant for the GPR model. The results of hyperparameter tuning using GridSearch and 5-fold cross-validation are shown in Table 6 as follows. The remainder of hyperparameters for the GPR model are set as defaults.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 5 Numerical Results
### Performance Evaluation
As shown in Figure 5b, the proposed scheme provides more BS attempts than the other two schemes while maintaining the minimum number of total mobility attempts. In Figure 5c,d, the proposed scheme shows the best 3-dB loss probability and the lowest RLF rate. Thus, with the proposed scheme, the system can provide better channel quality to users while the uplink and downlink overhead are kept low. Figures 6 and 7 show the SINR distribution of users in the single-gNB and UDN environment, respectively. In the single-gNB case as shown in Figure 6, the IBH and the proposed scheme show the same performances while the conventional BM scheme shows slightly better performance due to better TRP selection based on highly frequent CSI-RS report which causes significant overhead usage. However, in the UDN case as shown in Figure 7, owing to better accuracy of beam-pair selection (i.e.,  
Figure 4: Simulation results in single-gNB environment. (**a**) overhead performance, (**b**) mobility attempts, (**c**) mobility failure rates, (**d**) 3 dB-loss probability. Figure 5: Results under the UDN scenario. (**a**) overhead performance, (**b**) mobility attempts, (**c**) mobility failure rates, (**d**) 3-dB loss probability. 3-dB loss probability) of the proposed scheme, the SINR also shows superior performance than the other two schemes. In particular, in terms of the SINR of the 5-% tile (i.e., the meaning of the outage performance), the proposed scheme is superior to the other schemes. If we use the multi-user-MIMO for increased throughput, the spectral efficiency performance of the proposed scheme can be worse than that of the BM scheme. This is because frequent periodic CSI-RS reports of the BM scheme makes it easier to find multiple-independent channel links. However, if we apply the periodic report in the proposed scheme as in the conventional BM scheme, we can achieve increased spectral efficiency by sacrificing the overhead performance as a trade-off.
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 2 Test Design
### Scheme of the Experiment
#### 2.5.2 Model Tunnel and Its Instrumentation  
The model tunnel is composed of 8 rings of segmental lining. For the purpose of transverse response, straight joints were adopted between lining rings, as shown in Figure 8a. As with the circumferential bolts, the longitudinal bolts between rings were replaced by 10 steel wires with a diameter of 0.8 mm. It was assumed that each ring would react in the same way under transverse excitation. To eliminate the effect of boundary, only the middle 4 rings out of 8 rings were used as instrumented ones.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Prototype** & **Model** \\ \hline Shear stiffness of grounds (N/mm) & 914,083 & 2926 \\ \hline Radial stiffness of linings (N/mm) & 7022 & 23.10 \\ \hline Transverse stiffness of linings (N/mm) & 994 & 3.15 \\ \hline Stiffness ratio & 920 & 929 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of soil and structure of prototype and model.  
Figure 7: Cross-sectional layout of sensors for the FF model (unit: mm).  
The assembled model tunnel was placed in the center of the ground in both plane as Figure 8b and elevation as Figure 7. The central axis of model tunnel is perpendicular to the long side of laminar box (direction of vibration). The vertical distance from the central axis of the model tunnel to the bottom of the laminar box is 1400 mm. The cover thickness of soil above the tunnel is one time of tunnel diameter, that is 933 mm. Both ends of the model tunnel are at an equal distance to the internal surface of the laminar box.  
The arrangement of accelerometers in the soil of ST model is consistent with that of FF model, except the one labeled one (AZ3) was occupied by the tunnel.  
Instrumentation on the tunnel is as Figure 9 indicated. There were 5 types of sensors attached to it. The parameters of these instruments are shown in Table 4. The sensors are shown in Figure 10. Two accelerometers, labeled A-top and A-bottom, were arranged at the  
Figure 8: Soil–tunnel model (unit: mm). (**a**) Model tunnel. (**b**) Plane layout.  
top and bottom of the tunnel, respectively, detecting the acceleration response of the model tunnel. Pairs of displacement gauges to record the extension/closure of longitudinal joints were arranged on the internal and external side of each longitudinal joint respectively, labeled D1 till D10 and marked as '-IN' and '-OUT'. Inductive displacement transducers were arranged to observe the cross-sectional deformations of the model tunnel, labeled BD1 till BD4, along diametral direction of tunnel's cross-section at 0 degrees (horizontal), 45 degrees, 90 degrees (vertical), and 135 degrees, respectively. Earth pressure gauges were arranged on the external surface of the model tunnel, labeled P1 till P4, and P6 till P8. Strain gauges, labeled SW1 till SW4, were arranged on the external surface of the model tunnel to observe the dynamic deformation of segments.
###### Abstract  
Wind turbines are under continuous development for large-scale deployment and oceanization, leading to the requirement of longer blades. The economic losses caused by blade replacement and shutdown have increased. The downtime caused by blade issues in a wind turbine is 8-20% of the total downtime. Many of these blade issues originate from the cracking of the blade trailing edge. The edge is more susceptible to damage due to the complex geometry, manufacturing technique, and operation conditions. The traditional design method and the expensive experimental research are not suitable for the accurate damage analysis of the trailing-edge adhesive because of simplifying assumptions and costs. This study aimed to investigate the influence of trailing-edge structural configurations on the shear fatigue life of the trailing-edge adhesive joint using finite element and stress transformation matrix (STM) methods. The structural configurations of the blade trailing edge included the position of unidirectional fiber layer (UD), chamfer of bonding line, prefabricated components, and outer over-lamination of the trailing edge. In this study, the finite element method was used to simulate the blade structure. The shell element was used for laminates, and the solid element was used for the trailing-edge adhesive joint. The basic shear fatigue properties of the adhesive were obtained by standard component tests. The shear fatigue life of the blade trailing-edge adhesive joint under given load conditions was calculated using the fatigue properties of the adhesive and STM method. The results showed that the angle of chamfering, location of UD, rigidity of the preform, and outer over-lamination all had an obvious influence on the fatigue damage of trailing-edge adhesive. The findings of this study can be used to guide blade structure design and blade production and maintenance.  
wind turbine blade; trailing edge; structural configuration; adhesive joint; shear fatigue damage; finite element method +
Footnote †: journal: Applied Sciences
###### Abstract  
For high-speed train, balise transmission module (BTM) system is easily interfered with by other equipment of the train. This could cause the train to malfunction. Studying the electromagnetic susceptibility (EMS) of the BTM is very important for the performance and efficiency of the train. In this paper, a modular, system-level modeling method is proposed to predict the EMS of BTM systems. Based on object-oriented technology and a modular method, the BTM system is disassembled into several modules according to the electromagnetic characteristics of the whole system rather than the physical structure. All the modules are mutually independent, and the total EMS could be evaluated by the output of them. The modules of three key elements of electromagnetic compatibility (EMC), i.e., sources, coupling paths, and sensitive equipment, are established by the theoretical method, full-wave simulation method, and black-box test method, respectively, and put into different layers. According to the functions of the BTM system, the EMS of BTM is given by analyzing the interrelation of input and output of modules. Results of the proposed model were verified by measurement.  
BTM; electromagnetic susceptibility; modular system-level modeling +
Footnote †: journal: Applied Sciences
## 2 Results and Discussion  
### Selection of Marker Peptide Candidates  
Target proteins are typically enzymatically digested into peptide fragments using trypsin, and mass spectrometry (MS) is highly effective in simultaneously detecting these peptides with high sensitivity, allowing for species identification based on interspecies differences in amino acid sequences. Gelatins from the antlers, hides, and bones of sika deer were hydrolyzed with trypsin, and the resulting peptide mixtures were analyzedusing ultra-high-performance liquid chromatography coupled with quadrupole Q-Exactive-Orbitrap mass spectrometry (UHPLC-Q-Exactive-Orbitrap MS). High-resolution mass spectrometry data of enzymatically digested peptides from all sika deer gelatin samples were obtained (Figure S1). Using PeptideShaker software (version 3.0.8) [39], 134 peptides from antler gelatin, 157 peptides from hide gelatin, and 172 peptides from bone gelatin were identified (Tables S1-S3).  
To identify marker peptides for distinguishing gelatin from sika deer, 28 marker peptide candidates were first selected based on the following criteria (Table 1). Peptides readily detectable in sika deer gelatin were chosen as marker candidates (P1-P12) if they met the following conditions: (1) Peptides with relative intensities > 50% in the high-resolution mass spectrometry data of antler, hide, and bone gelatin. (2) Peptides identified with 100% confidence using PeptideShaker software. Using these conditions, 12 candidate peptides (P1-P12) were identified, with 9 originating from the type I collagen \(\alpha\)1 chain (P1, P3-P5, P7, P8, and P10-P12) and 3 from the type I collagen \(\alpha\)2 chain (P2, P6, and P9). Additionally, peptides previously reported in the literature as deer-specific markers (R1-R16) were also chosen as marker candidates in this study [39; 30; 26; 32; 33; 34; 26; 30]. Among these, 4 peptides (R1, R7, R8, and R16) originated from the \(\alpha\)1(I) chain, while 13 peptides (R2-R6 and R9-R15) were derived from the \(\alpha\)2(I) chain.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **t\({}_{\text{R}}\)** & _m/z_ & **Charge** & **Sequence 1 ** & **Chain** \\  & **(min)** & & & \\ \hline P1 & 2.34 & 418.7223 & 2 & GPAGGPQGPR & COL1A1 \\ P2 & 2.48 & 4
## 2 Material and Methods
### Nodal Connectivity Optimization Design Model
Newly created points make a polyline-wired structure, creating a new relief pattern for each cell at the boundary region. Polylines replace old relief topology with a new active pattern that must be closed-loop. Given the new nodal formation, the Voronoi cell structure has no sharp corners, and all polygons look topologically correct, ensuring better nodal connectivity when strut thickness is applied. The algorithm is critical to eliminating small gaps and grooves in the structure's configuration, preventing residual stresses from occurring, which is the starting point for a lattice structure to be topologically validated and gives the possibility to make results more uniform and reliable. The stochastic open cellular structure consists of solid struts surrounding a void space. When analyzing bending-dominated stress states of stochastic open cellular structure, it is crucial to consider significant mechanical strength criteria in correlation with relative density. Having started from the fact that cellular solids are characterized by their relative density [26], for which the structure is defined as follows:  
\[\frac{\rho_{ls}}{\rho_{m}}=\left(\frac{t}{L}\right)^{2} \tag{3}\]  
Figure 4: Algorithm for tracking topological formation and defining tolerance value to cull unwanted points. Figure 3: Error in nodal formation due to piling up of numerous topology data during tessellation. where \(\rho_{ls}\), is the density of the lattice structure, \(\rho_{m}\) is the solid prime material's density, \(L\) is the cell size, and \(t\) is the thickness of the cell edges. It follows from this that relative density plays a critical role in mechanical property determination. However, controlling the number of struts for different seed orientations in stochastic tessellation, and thus relative density, which varies depending on the strut number, has been challenging. Having analyzed this, the Gibson-Ashby model was the starting point for most papers that have tackled this issue, where remote compressive stress exerts a force \(F\cong\sigma L^{2}\) on the cell edges, causing them to bend, leading to a bending deflection for a value \(\delta\):  
\[\delta=\frac{FL^{3}}{E_{m}I} \tag{4}\]  
This expression in Equation (4) is related to a horizontally oriented strut length \(L\), loaded at its mid-point by force \(F\), causing deflection, where \(E_{m}\) is the modulus of the solid of which the strut is made, and \(I\) is the second moment of area of the cell edge of the circular cross-section. The approximate compressive strain experienced by the entire cell is equal to \(\varepsilon=\frac{2\delta}{L}\). Assembling these values will give the following relationship:  
\[\frac{E_{ls}}{E_{m}}=\left(\frac{\rho_{ls}}{\rho_{m}}\right)^{2} \tag{5}\]
# A Fast Ray-tracing Method for Locating Mining-induced Seismicity by Considering Underground Voids
## 2 Methodology  
As the propagation medium of the wave in the voids is air, the wave velocity in the voids is 340 m/s. Besides, the energy attenuation is more significant than that in the non-voids. The propagation medium of the wave in the non-voids is the rock mass, and the propagation velocity of seismic waves in rocks is much higher than 340 m/s [31,32]. Obviously, for small-scale multi-voids of an underground mine, the wave propagation is mainly affected by the voids. Therefore, we divide the monitoring area into two media. The wave velocity in these two media is very different and the shortest path of the wave from the source to the receiver generally bypasses the void. Therefore, we can assume that the shortest path of the wave does not pass through voids.  
The steps of the proposed method are as follows: (1) To establish the surface model of the underground voids, this paper uses the 3D modeling software based on the triangular patch modeling. (2) Interpolate nodes on the edge of the surface model according to a certain step length, and the step length controls the accuracy of ray tracing. (3) The key step of the proposed method is to calculate the adjacent nodes set of each node, and the definition of adjacent points is in the method section of the paper. (4) Based on [PERSON]'s principle, the Dijkstra algorithm is used to calculate the minimum value of the arrival time of each node, and then the shortest path from the source to the sensor is obtained. In summary, we propose a new ray-tracing method without meshing. This new method avoids dividing a large number of grids and provides a fast and accurate method for microseismic source location in underground mining.
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
## 2 Wind Energy Conversion System Description
### PMSG Model  
The voltage equations of the PMSG in decoupled \(dq\)-axes rotating reference frame are expressed as [21]:  
\[\left\{\begin{array}{l}u_{ds}=R_{s}i_{ds}+p\psi_{ds}-\omega_{e}\psi_{qs}\\ u_{qs}=R_{s}i_{qs}+p\psi_{qs}+\omega_{e}\psi_{ds}\end{array}\right., \tag{7}\]  
where \(u_{ds}\), \(u_{qs}\) are the stator winding voltages in \(dq\)-axes. \(i_{ds}\), \(i_{qs}\) are the stator winding currents in \(dq\)-axes. \(\psi_{ds}\), \(\psi_{qs}\) are the stator winding magnet fluxes in \(dq\)-axes. \(\omega_{e}\) is the rotor speed, \(R_{s}\) is the resistance of the stator windings.  
The magnet flux equations of PMSG in \(dq\)-axes rotating reference frame are expressed as \[\left\{\begin{array}{l}\psi_{ds}=L_{d}i_{ds}+\psi_{f}\\ \psi_{qs}=L_{q}i_{qs}\end{array}\right., \tag{8}\]  
where \(\psi_{f}\) is the permanent magnet flux. \(L_{d}\), \(L_{q}\) are the stator winding inductances in \(dq\)-axes.  
The electromagnetic torque of PMSG is given as:  
\[T_{e}=n_{p}[\psi_{f}i_{qs}+(L_{d}-L_{q})i_{ds}i_{qs}], \tag{9}\]  
where \(n_{p}\) is the pole pairs of the PMSG.  
The motion equation of the PMSG is expressed as:  
\[T_{L}-F\omega-T_{e}=J\frac{d\omega}{dt}, \tag{10}\]  
where \(T_{L}\) is the input load torque, \(F\) is friction coefficient, \(J\) is the moment of inertia, \(\omega\) is the mechanical rotor speed.
## 4 Experiments
### Backtesting Experiment  
To validate the performance of IGWO-MALSTM in real-world trading scenarios, this study conducts a backtesting comparison using three strategy models. Correlated futures data of rebar and hot-rolled coil were selected, with the 5 min K-line data of rebar used for model training. The backtesting was performed on the 5 min K-line data of hot-rolled coil from 2019 to 2024. Backtesting uses entirely independent data (January 2019-December 2024, 5 min K-line) not overlapping with the training period (December 2020-March 2023). This ensures the trading strategy is evaluated on unseen market regimes. The three strategies include the original Rbreaker algorithm, the Rbreaker-LSTM-Attention strategy (Rbreaker-LA), and the proposed Rbreaker-IGWO-MALSTM strategy.  
In the Rbreaker-LA strategy, an LSTM-Attention model was introduced to predict market trends (i.e., price increase or decrease), providing auxiliary signals to enhance the decision-making process of the Rbreaker strategy. Building upon this, the Rbreaker-IGWO-MALSTM strategy incorporates the IGWO-MALSTM to perform adaptive optimization of key strategy parameters. Specifically, IGWO-MALSTM was used to fine-tune the threshold values involved in calculating the breakout buy price, observation sell price, reversal sell  
Figure 10: SHAP summary plot showing feature importance and impact on model output.  
price, reversal buy price, observation buy price, and breakout sell price--aiming to enhance the overall profitability and robustness of the strategy.  
The backtesting results are summarized in Table 10 and further analyzed in the following discussion. According to the data, the original Rbreaker algorithm achieved the lowest maximum drawdown among all strategies, highlighting its strong risk control capability and ability to mitigate potential losses during adverse market conditions. When integrating the LSTM-Attention mechanism, the Rbreaker-LA strategy improved both the return rate and the Sharpe ratio, although this came with a slight increase in drawdown. The enhanced return reflects greater profitability, while the higher Sharpe ratio indicates improved risk-adjusted performance.  
Further improvements were observed with the Rbreaker-IGWO-MALSTM strategy. Although it incurred a slightly higher drawdown than the original Rbreaker, it achieved significant gains in both return and Sharpe ratio. This demonstrates that the integration of a robust optimization algorithm like IGWO-MALSTM can lead to superior performance by effectively balancing risk and reward. The findings also confirm that optimization algorithms such as IGWO-MALSTM are not only suitable for tuning hyperparameters in machine learning models, but can also be effectively applied to parameter tuning in financial trading strategies--offering high adaptability and practical value in quantitative investment.