esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Models
The permutation feature importance is the decrease in the score for a model when a single feature value is randomly shuffled. As can be seen from this figure, the MLP and SVR models have the same three most important variables as the RF and GBR models, while the KNN model has only two of these three variables as the most important: the LWA particle density and the LWAC fixed density. This is consistent with the fact that the KNN models are worse than the MLP, SVR and tree-based models. In addition, the XGBoost models (and hence the WAE models) have more informative variables and outperform all other techniques discussed in this work. Regarding the computational cost, KNN and SVR required approximately 0.04 s for training and testing each model, while RF and GBR required between 0.08 and 0.12 s, and XGBoost required approximately 0.4 s. The WAE algorithm had the highest computational cost and complexity and required a mean time of 1.1 s; however, it obtained the best performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Parameters** & \(\mathbf{R^{2}}\) & **MAE** & **MAPE** & **RMSE** \\ \hline MLR (\({}^{\circ}\)) [19] & - & 0.766 & 3.396 & 18.86 & 4.332 \\ \hline ANN (\({}^{\circ}\)) [6] & 6 neurons & 0.825 & 2.897 & 15.85 & 3.745 \\ \hline RT (\({}^{\circ}\)) [19] & CHAID & 0.820 & 2.928 & 16.22 & 3.808 \\ \hline KNN (I) & Manhattan distance, \(K=5\) & 0.8038 & 3.0006 & 15.9617 & 3.964 \\ KNN (I) & Manhattan distance, \(K=7\) & 0.7930 & 3.1044 & 16.6643 & 4.0775 \\ \hline MLP (I) & 30 neurons, L-BFGS, ReLU, \(\alpha=0.1\) & 0.8290 & 2.8415 & 15.4097 & 3.7050 \\ MLP (I) & 40 neurons, L-BFGS, ReLU, \(\alpha=0.1\) & 0.8317 & 2.7956 & 15.0795 & 3.6765 \\ \hline RF (I) & \(md=5\), \(nt=100\), \(p=0.75\) & 0.8316 & 2.8207 & 15.7916 & 3.6784 \\ RF (I) & \(md=5\), \(nt=100\), \(p=0.9\) & 0.8343 & 2.7955 & 15.6373 & 3.6486 \\ RF (I) & \(md=5\), \(nt=100\), \(p=1\) & 0.8359 & 2.7808 & 15.5416 & 3.6303 \\ \hline GBR (I) & \(md=3\), \(nt=130\), \(p=0.75\) & 0.8762 & 2.4472 & 13.6766 & 3.1528 \\ GBR (I) & \(md=3\), \(nt=130\), \(p=0.9\) & 0.8765 & 2.4396 & 13.6751 & 3.1500 \\ GBR (I) & \(md=3\), \(nt=130\), \(p=1\) & 0.8732 & 2.4690 & 13.8063 & 3.1907 \\ \hline XGBoost (I) & \(md=3\), \(nt=130\), \(n=0.4\), \(m=0.6\), \(m=0.75\) & 0.8727 & 2.4375 & 13.6244 & 3.1402 \\ XGBoost (I) & \(md=3\), \(nt=130\), \(n=0.4\), \(m=0.001\),
## 1 Introduction  
This study explores the application of lichenometric analyses to unravel the chronology of bedrock fault-scarps, where other paleoseismological techniques, commonly used in alluvial and colluvial materials, are harsh to apply. Lichenometry is a dating technique normally applied to analyze the age of glacial moraines [1,2], landslides [3], and rockfall events related to coseismic shaking [4,5]. In most cases, lichenometric analyses are applied on exposed horizontal surfaces where lichens have the most favorable growing conditions, producing large _tallus_ sizes [6,7]. The pioneer works of [PERSON] on differential lichen colonization and weathering of prehistoric and recent rocky fault scarps [8,9] were the only ones preliminary exploring this method. More recently, an introductory study on the Sencelles Fault applies modern approaches to lichenometric analyses of horizontal surfaces to vertically exposed surfaces of bedrock fault scarps [10]. This work points to the large variability of lichen growth rates (LGR) related not only to the vertical or horizontal condition of the exposed surfaces but also to their geographical orientation, exposure to maritime areas, etc. Consequently, the obtained regional regression curves of LGR from the measurement of lichens in graves with inscribed dates in local cemeteries are sensitive to both position and orientation, producing differentially oriented lichen populations (DOLPs).  
The present work improves and completes the preliminary lichenometric analysis of rocky fault scarps on Mallorca Island [10]. The study uses data from 180 lichen specimens in three cemeteries close to the fault to be dated, two of them directly placed onto the proper fault trace. The obtained data are combined with previous unpublished fault-trench analyses to explore the relationships between the different lichen bands flooring the fault scarp and historical earthquakes on the island. The performed analysis allows us to identify recurrent displacements of the Sencelles Fault scarp based on differentially lichen colonization ribbons. Part of the youngest basal ribbon has been tentatively related to the 1851 CE Palma Earthquake (\(\leq\)VIII EMS Intensity) [10]. However, observed displacements cannot be truthfully cataloged as surface faulting events, but as secondary or sympathetic coseismic ground ruptures (_Sensu_ Serva and de Polo [11,12]). Additionally, the performed study also develops a methodological approach based on the lichen analysis of vertically exposed rocky surfaces for rapid and low-cost geochronological studies of presumably historic bedrock fault scarps. The performed lichenometric analysis can thus date limestone surfaces as old as 700-800 years, as has been proved in the semiarid SE Spain [7,13]. This methodology can replace or complement more expensive geochemical dating techniques of rocky surfaces based on the analysis of cosmogenic isotopes [14,15].
# FEM Analysis and Sensor-Based Measurement Scheme of Current Distribution for Grounding Electrode
## 4 Grounding Electrode Current Distribution Measurement  
### A \"Drainage Wire\"-Based Measurement Scheme  
It is impossible to directly measure the DC when it overflows into the soil. Therefore, it is necessary to use a carrier to lead the soil current to the medium, then measure the current in the medium, so that the current can be indirectly measured and monitored [21]. Therefore, this paper proposes a measurement scheme based on a \"drainage wire\" with a Hall sensor to capture and measure the weak current in the soil near the grounding electrode. We select a copper drain rod as the current carrier in soil, which can drain the current in soil by laying the \"drainage wire\" in the soil [22; 23; 24].  
According to [PERSON]'s law and Figure 12b, the current in the soil near the grounding electrode can be collected on the \"drainage wire\".  
\[dB=\frac{\mu_{0}ldl\sin\theta}{4\pi r^{2}} \tag{10}\]  
It can be deduced that the expression of the magnetic field \(B\) generated by a conductor of length \(L\) at a point P1 at a vertical distance \(R\) from its center is  
\[B=\frac{\mu_{0}I}{2\pi R}\frac{L}{\sqrt{L^{2}+4R^{2}}} \tag{11}\]  
Figure 11: Current density and potential distribution curves for the ring-shaped grounding electrode. (**a**) Potential distribution curves in the X and Y directions; (**b**) current density distribution curves in the X and Y directions.  
Figure 10: Current density and potential distribution curves for the cross-shaped grounding electrode. (**a**) Potential distribution curves in the X and Y directions; (**b**) current density distribution curves in the X and Y directions.  
where P1 is the measurement point, \(L\) is the length of the conductor, \(I\) is the current flowing through the conductor, and \(R\) is the vertical distance.
## 2 Background
### Narrowband IoT  
NB-IoT is a cellular technology based on long-term evolution (LTE) introduced by 3 GPP. NB-IoT reduces the LTE protocol stack functionalities to make it suitable for IoT applications. A brief comparison of NB-IoT with other LPWAN communication protocols aimed at comparable applications is in Table 1. Compared to protocols such as LoRaWANand Sigfox, NB-IoT works in LTE bands and does not require the use of a gateway. It provides extended coverage to delay-tolerant, low-cost, low-energy IoT applications, reaching good energy performance even in the worse-case, continuous transmission scenarios when compared to other communication protocols [17]. It represents a valuable choice for application fields that require massive deployments, good coverage and low data rates, including smart metering [33], smart grids [34], smart cities [35], healthcare [36; 37; 38], and industrial applications [39]. To the best of the authors' knowledge, it has still been seldom applied in home automation [40], mostly in home surveillance or metering applications [41; 42], and rarely using MQTT. Cellular connectivity provides opportunities for enterprises to embed additional services into home appliances [20]. Furthermore, NB-IoT will be used in the machine-to-machine communication use case in 5G networks and will coexist with other services in the 5G environment [43]. While some works exist on the application layer performance over NB-IoT [44; 45], this work mainly focuses on latency issues for the smart home use case, with the goal of ensuring a good user experience for the vocal interaction subsystem.
## 3 Results and Discussion
### Kinetic Analysis  
The shrink core model is often used to described the kinetic behaviors of leaching processes [6; 28; 29; 30; 31], wherein a reaction between solid and fluid reactants occurs on the outer surface of the solid. The typical three kinetic models [32; 33; 34] are represented as followed:  
(1) if diffusion through a liquid was the controlling step  
\[K_{1}t=\eta \tag{3}\]  
(2) if diffusion through a product layer was the controlling step  
\[K_{2}t=1-2/3\eta-(1-\eta)^{2/3} \tag{4}\]  
(3) if a surface chemical reaction was the controlling step  
\[K_{3}t=1-(1-\eta)^{1/3} \tag{5}\]  
which, \(\eta\) is the leaching efficiency, \(K_{1}\) is the apparent rate constant for diffusion through the fluid film, min\({}^{-1}\); \(K_{2}\) is the apparent constant for the diffusion through the product layer, min\({}^{-1}\); \(K_{3}\) is the apparent rate constant for surface chemical reaction, min\({}^{-1}\); \(t\) is the reaction time, min.  
The apparent rate constants obtained from the plots and correlation coefficients of each experimental parameter are provided in Table 2. The results indicated that the regression coefficient of surface chemical reaction was maximum among the three kinetic models, indicating that surface chemical reaction was the rate-controlling step. Therefore, Equation (5) was used to express the kinetic behavior of leaching process following the shrinking core model.  
Figure 8: Effect of liquid-to-solid ratio on the leaching efficiency of vanadium.  
The leaching reaction rates of vanadium and chromium at different temperatures were fitted (Figure 9), and the value of \(K_{3}\) was obtained, where \(K_{3}\)isthe reaction rate constant corresponding to the slopes of the straight lines. Then, the specific apparent activation energy could be calculated based on the Arrhenius equations, the results are shown in Figure 10.  
\[\ln K=\ln A-\mathit{Ea}/(\mathit{RT}) \tag{6}\]  
where \(\mathit{Ea}\) is the apparent activation energy, \(A\) is the pre-exponential factor, and \(\mathit{R}\) is the molar gas constant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Parameter**} & \multicolumn{2}{c}{**Diffusion through**} & \multicolumn{2}{c}{**Diffusion through**} & \multirow{3}{*}{**Surface Chemical Reaction**} \\  & \multicolumn{2}{c}{**the Liquid Film**} & & & \\ \cline{2-5}  & \(\eta\) & \multicolumn{2}{c}{**\(\mathbf{1-2/3\eta-(1-\eta)^{2/3}}\)**} & \multicolumn{2}{c}{\(\mathbf{1-(1-\eta)^{1/3}}\)} \\ \cline{2-5}  & \(\boldsymbol{K_{1}(\min^{-1})}\) & \(\mathbf{R^{2}}\) & \(\boldsymbol{K_{2}(\min^{-1})}\) & \(\mathbf{R^{2}}\) & \(\boldsymbol{K_{3}(\min^{-  
The apparent activation energies of vanadium and chromium leaching out were calculated as 19.93 kJ\(\cdot\)mol\({}^{-1}\) and 21.26 kJ\(\cdot\)mol\({}^{-1}\), respectively. The low value of apparent activation energy indicated that vanadium and chromium were easy to leach out in acidic medium. And, the vanadium was easier than chromium to leach out within the addition of MnO\({}_{2}\).
## 2 Proposed Method
### Missing Value Imputation
#### 2.2.3 GRUD-WGAN Model  
The GRUD and the WGAN models are combined to establish the GRUD-WGAN model, as shown in Figure 4. The GRUD model is adopted to be the essential component of both the generator and the discriminator of the WGAN framework. Specifically, the generator is constructed with a GRUD layer and a linear layer. The incomplete input sequence \(X_{t}\) at time \(t\) with length \(l\) is fed into the GRUD model, and \(n\)-dimensional hidden state vectors are outputted. The linear layer maps these hidden state vectors to a reconstructed sequence \(\widetilde{X}_{t}\) of the same shape as the input sequence. Then, the complete output sequence \(\widehat{X}_{t}\) is calculated according to the mask code matrix \(M_{t}\), expressed as:  
\[\widehat{X}_{t}=M_{t}\odot X_{t}+(1-M_{t})\odot\widetilde{X}_{t} \tag{13}\]  
Figure 4: The framework of the proposed GRUD-WGAN.  
The discriminator is also built by a GRUD layer and a linear layer. The GRUD is adopted to accept the incomplete sequences of the real data set or the complete imputed data set produced by the generator. The linear layer is used to map the hidden state vectors to a single value, which indicates the Wasserstein distance.  
To make full use of the available data to accelerate convergence, the reconstruction error \(L_{r}\) is added to the loss function of the generator, given by:  
\[L_{r}=\left\|\left(\tilde{X}_{t}-X_{t}\right)\odot M_{t}\right\|_{2} \tag{14}\]  
\[L_{G}=-D(G(X_{t}))+L_{r} \tag{15}\]  
when the training process converges, a generator that receives incomplete sequences and outputs complete sequences can be obtained.
## 2 Related Works
### Hybrid Optimization and Anomaly Detection in Sensor Networks  
Hybrid approaches that combine optimization techniques with machine learning have been explored to enhance anomaly detection accuracy and efficiency. The authors in [16] proposed a hybrid AI optimization method (AHGFFA) for MANET-IoT sensor networks, integrating unsupervised learning for threat detection with a secure group-based routing algorithm. Their work effectively mitigates risks like Blackhole and Grayhole attacks, improving network security. However, their system relies on predefined trust thresholds, making it less adaptable to new attack patterns.  
Similarly, the authors in [17] introduced a convolutional Autoencoder combined with Bi-Directional Gated Recurrent Units (GRUs) for monitoring heat pump anomalies in industrial systems. Their work shows promising results, particularly in detecting anomalies without labeled data. However, challenges such as noisy labels and limited generalization to different sensor types remain significant. Unlike these methods, our proposed approach focuses on real-time adaptability and low-latency classification, making it more suitable for high-risk environments like forest fire monitoring.
## 5 Discussion
### Comparative Security of DVS and RGB Sensors  
Our comparative analysis of DVS and RGB sensor security reveals important distinctions between these technologies. Traditional RGB cameras, while providing rich visual information, demonstrated significant vulnerability to noise-based attacks that severely degraded perception capabilities. In contrast, the event-based nature of DVS technology offers potential security advantages through its high dynamic range and reduced data redundancy [28].  
The complementary characteristics of these sensor types suggest that a fusion approach could enhance overall system security [61]. RGB cameras excel in static scene understanding and color perception, while DVS sensors offer superior performance in high-dynamic-range scenarios and rapid motion detection. By integrating data from both sensor types and implementing cross-validation mechanisms, autonomous vehicles could potentially detect inconsistencies that indicate sensor manipulation [47].  
However, it is important to note that our study used DVS cameras exclusively for research purposes, without integration into the vehicle's driving decision model. Future work should explore the practical challenges of incorporating DVS technology into production autonomous vehicles, including calibration requirements, computational overhead, and integration with existing perception pipelines [62].
## 4 Discussion
### Influence of Crack Inclination  
Taking the orientation angle of 0\({}^{\circ}\) as an example, the stress intensity factor at the crack tip was analyzed for crack inclination ranging from 0\({}^{\circ}\) to 90\({}^{\circ}\), and the stress intensity factor was obtained, as shown in Figure 14.  
In the mean value model, the stress intensity factor is symmetrically distributed with a symmetry axis of 45\({}^{\circ}\). In contrast, in the ice-rock model, the stress intensity factor at the crack tip lacks symmetry. Meanwhile, the stress intensity factor at the left end of the crack is significantly higher than that at the right end. This indicates that the crack expansion rate will be higher at the left end compared to the right end.  
The crack propagation patterns for crack inclination angles of 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\), with a crack position angle of 0\({}^{\circ}\), are shown in Figure 15.  
Figure 12: Stress intensity factor at different crack orientation angles.  
When the crack position angle is \(0^{\circ}\), the crack always extends horizontally. However, the difference is that when the crack inclination angle is \(90^{\circ}\), the new cracks at both ends of the crack always expand horizontally away from the ice. Furthermore, for cracks at different angles, the left end of the crack will expand horizontally towards the ice, and the right end of the crack will expand horizontally away from the ice.
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 4 Finite Blocklength Analysis
### Overlay Allocation
Moreover, the improvement reduces as well with the number of available time slots, going from a peak \(87.5\%\) increase in rate \(\mathsf{R}_{\text{b}}\) for the overlay allocation with respect to the orthogonal one in the asymptotic setting, to a \(48.7\%\) improvement for \(\mathsf{M}=100\) and \(32.7\%\) for \(\mathsf{M}=25\). The plot also confirms that, in practical finite-length setups, there exist values of \(\mathsf{R}_{\text{a}}\) for which an orthogonal allocation is convenient (i.e., the ratio falls below 1). Nonetheless, such a region drastically reduces by increasing the number of slots available over a frame. Finally, the effect of the number of channel uses appears to have a minor impact. Taking the lead from this, we explore in Figure 5b the configuration \(\rho_{\text{a}}/\sigma^{2}=10\) dB, \(\rho_{\text{b}}/\sigma^{2}=0\) dB, focusing only on \(n_{s}=100\). Increasing the SNR enjoyed by the QoS-constrained service drastically increases the advantage perceived by the IoT traffic when an overlay allocation is adopted. Rate exceeding a six-fold improvement for the IoT traffic can be achieved in the asymptotic ergodic setting, while, even for as few as 25 slots, more than a two-fold increase in \(\mathsf{R}_{\text{b}}\) is expected when in overlay allocation. Interestingly, the region where the orthogonal allocation is superior for the equal SNR scenario appears to vanish in this scenario. \[\mathsf{R}_{\text{a}}^{*}(\mathsf{P}_{e},\mathbf{u})\cong\frac{1}{\mathsf{M} }\mathcal{C}_{\text{a,ov}}(\mathbf{u})-\sqrt{\frac{\mathcal{V}_{\text{a,ov}} (\mathbf{u})}{n_{s}\mathsf{M}^{2}}}Q^{-1}(\mathsf{P}_{e}) \tag{24}\]0 Finite length results for orthogonal and overlay allocations. We fix \(\rho_{\text{a}}/\sigma^{2}=\rho_{\text{b}}/\sigma^{2}=0\) dB. The number of time slots are in the set \(\mathsf{M}\in\{25,100,200\}\), the number of channel uses per time slot are \(n_{s}\in\{100,1000\}\) and the target average codeword error probability for service \(\mathcal{F}_{\text{a}}\) is \(10^{-3}\). The asymptotic ergodic rate region for the both orthogonal and overlay allocations are also provided for reference.
## 4 Modification of Simple Models  
Considering the directivity of the 3 GPP antenna pattern (see Figure 3), we modified the Gaussian and Sinc pattern models. The beam patterns for the non-modified and modified Gaussian and Sinc antenna for directions \(\Phi_{0}=0^{\circ}\), \(\Phi_{0}=30^{\circ}\), and \(\Phi_{0}=60^{\circ}\) in the azimuth plane are illustrated in Figures 5 and 6, respectively.  
To assess the difference between the 3 GPP and Gaussian and Sinc (non-modified and modified) antennas, we calculated the RMSE based on Equation (11). For the analyzed case, \(g_{1}((\theta,\phi),\Phi_{0})\) and \(g_{2}((\theta,\phi),\Phi_{0})\) are the 3 GPP pattern and non-modified or modified models. The results are depicted in Figure 7.  
The graphs in Figure 7 show that the proposed modification of the simple models al-lows for a reduction in the RMSE by about 5 dB compared to the non-modified models. We can see that the difference in RMSE between the same type of non-modified and modified pattern models for small values of the beam directions (i.e., scanning angles). The RMSE differences were about 4 dB or 6.5 dB for the Gaussian and Sinc, respectively. These differences resulted from a slight difference (\(\Delta G\)) in directivity between the non-modified models and the 3 GPP pattern for \(\Phi_{0}=0^{\circ}\) (see Table 1). The values of \(\Delta G\) are considered in the modified models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antenna Pattern** & \
\begin{tabular}{c} **Mean Execution Time for** \\ **Non-Modified Models (ms)** \\ \end{tabular} &
\begin{tabular}{c} **Mean Execution Time for** \\ **Modified Models (ms)** \\ \end{tabular} \\ \hline Gaussian & 0.179 & 0.196 \\ Cosine & 0.252 & 0.266 \\ Sinc & 0.291 & 0.304 \\
3 GPP & 92.935 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean time for determining antenna patterns.  
Figure 5: Patterns of non-modified and modified Gaussian antennas in the azimuth plane for different directions (\(\Phi_{0}=0^{\circ}\), \(30^{\circ}\), and \(60^{\circ}\)).  
In our opinion, analyzing the pattern mapping error (i.e., RMSE) is not the best approach when evaluating simple models against real ones. From the interference evaluation viewpoint, we proposed assessing the effectiveness of simple pattern models based on SIR errors, which is presented in the remainder of this paper. On the other hand, slight differences in the directivity of the patterns caused significant changes in the RMSE and SIR error. This clearly shows a crucial role of this parameter, which we used in our modification.