esa-sceva/satcom-chunk-collection huggingface dataset:
# Full-Scale Measurements of Wind Characteristics on a High- Rise Building during Typhoon Sarika
## 2 Full-Scale Measurement Program
### The Ambient Condition of Experimental Site  
The measured experimental site is on the top floor of a high-rise building supported by four massive columns near the coast of the South China Sea. The measured building lies in the typhoon-prone region with a latitude of 20\({}^{\circ}\)02' N and a longitude of 110\({}^{\circ}\)35' E. The building is situated on the north coast of Haikou and is approximately 1.2 km from the shoreline. There are several low-rise buildings which are lower than 80 m around the measured building. Figure 2 illustrates the surrounding building environment near the experimental site. The terrain roughness in Chinese codes [13] is classified into four categories: A, B, C, and D, while it is grouped into five categories: 0, I, II, III, and IV in prEN 1991-1-4 (Eurocode, 2005). Category-A in Chinese Code corresponds to category-0 and I in the European Code, and Category-B, Category-C and Category-D correspond to the Category-II, Category-III and Category-IV, respectively. Considering that the Surrounding high-rise buildings are along specific wind directions of 30\({}^{\circ}\), 190\({}^{\circ}\), and 260\({}^{\circ}\), so the wind-field on the roof of the measured building will be susceptible to the severe interference, which is consistent with the category D of terrain roughness specified in the Chinese code. There are numerous low-rise buildings along wind directions of 100\({}^{\circ}\)-160\({}^{\circ}\), which is also consistent with the categories B or C of terrain roughness specified in the Chinese code. Because the wind direction of 315\({}^{\circ}\) is near the opening area in northwest of the sea, it corresponds to the category A of terrain roughness specified in the Chinese code. Considering that there are massive middle and high-rise buildings in China, it is more reasonable to classify the categories and types of the terrain roughness according to the Chinese codes.  
Figure 1: Track of typhoon Sarika and the ambient condition of experimental site.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 6 Experimental Evaluation
### Results  
In our experimental evaluation, we drew inspiration from the work of [PERSON] et al. [45]. Specifically, we benchmarked several state-of-the-art process discovery algorithms against the resulting baseline event log in order to assess the behavior of the Mixer PLC contained in the log and determine how well it matches the models produced by different methods. The considered methods were Heuristics Miner, Inductive Miner [46], ILP Miner [47], and DGMining with background knowledge [45]. We leveraged the PM4 Py [48] library for their implementation. To ensure consistency, default configurations were applied across all methods.
## 5 Experimental Results
### Imbalance Learning Performance Comparative Analysis
#### 5.4.5 Diversity  
Ensemble diversity is a property of an ensemble with respect to a set of data. It has been recognized as an important characteristic in classifier combination. Ensemble methods can effectively make use of diversity to reduce the variance-error without increasing the bias-error. In other words, ensemble learning is very effective, mainly due to the phenomenon that base classifiers have different \"biases\". Table 6 shows the ensemble diversity of the proposed method, original bagging, UnderBagging and SMOTEBagging. This table shows that, with respective to the traditional data sampling based methods, the margin guiding ensemble is not only more accurate for the classification of multi-class imbalanced data, but also leads to more ensemble diversity. Hence, the ability of the novel algorithm is demonstrated again.  
our method is slightly more computationally consuming, the time complexity of our method will decrease with the increase in imbalance ratio, because it is based on the under sampling technique.  
The space complexity of bagging and _UnderBagging_ are all \(O(ND)\) where \(D\) is the number of features. Although in the first step of the proposed approach, the margin values of the training set are computed by an ensemble, the space complexity is not increased, since the initial examples are not stored and the margin values of the training instances are not used for the base classifiers building. Hence, the space complexity of the proposed method is still linear and the same as that of _UnderBagging_.  
Figure 4: Bonferroni-Dunn (95% confidence level) for the comparative methods on all data sets (ensemble size = 100).
# Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems
## 3 Matrix Propagation Model for Optical Fibers
### Multi-Mode and Multi-Core Fibers  
Generalizing the model for both single- and multi-mode fibers, the transfer function characteristic, \(\mathbf{M}(\Omega)\), can be extended and take the form of a \(Z\times Z\) matrix, where \(Z\) is the number of all possible spatial degrees of freedom, and \(\mathbf{E}_{in}(\Omega)\) and \(\mathbf{E}_{out}(\Omega)\) are frequency-domain \(Z\)-dimensional input and output electrical field vectors. The term \"degrees of freedom\" includes both the two polarizations in a SMF (\(Z=2\)) and all possible spatial degrees of freedom in a FMF (\(Z=2P>2\), where \(P\) is the number of modes). The matrix \(\mathbf{\Lambda}^{(k)}(\Omega)\) can be expressed as  
Figure 3: Strong-coupling optical fiber model as the concatenation of shorter fiber sections.  
\[\mathbf{\Lambda}^{(k)}(\Omega)=\text{diag}\left[a_{1}(\Omega)\,,\,a_{2}(\Omega)\,, \,\cdots\,,\,a_{Z}(\Omega)\right], \tag{15}\]  
\[a_{d}(\Omega)=\exp\left\{\frac{g_{z}^{(k)}}{2}-j\tau_{z}^{(k)}\Omega-j\frac{D_{z }\lambda^{2}L_{sec}}{4\pi c}\Omega^{2}+j\frac{S_{z}\lambda^{4}L_{sec}}{24\pi^ {2}c^{2}}\Omega^{3}\right\},\,\,z\in\left[1,2,\cdots,Z\right], \tag{16}\]  
where \(\mathbf{g}^{(k)}=\left[g_{1}^{(k)},g_{2}^{(k)},\ldots,g_{Z}^{(k)}\right]\) is the uncoupled mode-dependent loss (MDL) vector, \(\boldsymbol{\tau}^{(k)}=\left[\tau_{1}{}^{(k)},\tau_{2}{}^{(k)},\ldots,\tau_{ Z}{}^{(k)}\right]\) is the group delay vector, related to the mode delay (MD), \(\mathbf{D}=\left[D_{1},D_{2},\ldots,D_{Z}\right]\) is the CD vector, and \(\mathbf{S}=\left[S_{1},S_{2},\ldots,S_{Z}\right]\) is the fiber dispersion slope vector.  
\(\mathbf{V}^{(k)}\) and \(\mathbf{U}^{(k)}\) are random \(Z\times Z\) unitary matrices generated by random Givens' rotation matrices [29]:  
\[\mathbf{V}^{(k)},\mathbf{U}^{(k)}\overset{\text{def}}{=}\prod_{i=1}^{Z-1} \prod_{k=i+1}^{Z}\mathbf{G}\left(i,k,\theta_{i,k},\phi_{i,k}\right), \tag{17}\]  
where each element \(g_{m,n}\) of \(\mathbf{G}\) is given by:  
\[g_{m,n}=\begin{cases}\cos\left(\theta_{i,k}\right),&\text{if }m=n=i\\ \cos\left(\theta_{i,k}\right)e^{i\phi_{i,k}},&\text{if }m=n=k\\ \sin\left(\theta_{i,k}\right)e^{i\phi_{i,k}},&\text{if }m=i\text{ and }n=k\\ -\sin\left(\theta_{i,k}\right),&\text{if }m=k\text{ and }n=i\\ 1,&\text{if }m=n\
eq i\text{ or }m=n\
eq k\\ 0,&\text{otherwise}.\end{cases} \tag{18}\]  
In the single-mode case, \(Z=2\), the rotation matrix from Equation (17) assumes the same form of Equation (9). The Givens' rotation matrices are basically rotations relative to an axis, therefore to generate the \(Z\times Z\) unitary matrices, we generate random rotations relative to all possible axes.
###### Abstract  
Landslides pose a serious threat to the safety of human life and property in mountainous regions. Susceptibility assessment for landslides is critical in landslide management strategy. Recent studies indicate that the traditional assessment models in many previous studies commonly assume a fixed relationship between influencing factors and landslide occurrence within an area, resulting in an inadequate evaluation for the local landslides susceptibility. To address this issue, in this paper we propose a spatial proximity-based geographically weighted regression (S-GWR) model considering spatial non-stationarity of landslide data for assessing the landslide susceptibility. Spatial proximity is the basic input condition for the proposed S-GWR model. The challenge lies in defining the spatial proximity expression that shows the geographical features of landslides and therefore affects the model ability of S-GWR. Our solution chooses the slope unit as spatial adjacency, rather than the grid unit in DTM. The multicollinearity between landslide influencing factors is then eliminated through variance inflation factor (VIF) method and principal component analysis (PCA). The proposed model is subsequently validated by using data in Qingchuan County, southwestern China. Spatial non-stationary is identified for landslide data. A comparison with grid unit and four traditional evaluation models is conducted. Validation results using the area under the ROC (receiver operating characteristic) curve and success rate curve indicate that the spatial proximity-based GWR model with slope unit has the highest predictive accuracy (0.859 and 0.850 respectively).  
Keywords:landslide susceptibility assessment; geographically weighted regression; spatial non-stationary; spatial proximity; slope unit +
Footnote †: journal: Applied
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 4 Results and Discussion  
### Training the SentiChurn Model  
We used the proposed model [94] Given that an overlap exists between a churner and a non-churner, the threshold 'cut-off' must be defined. Usually, the threshold is set as fifty per cent. Any probability right of the threshold has the most specificity, while any probability left of the threshold has the most sensitivity, as shown in Figure 6.  
The dashed line in Figure 7 is the threshold. Any probability above the threshold means higher sensitivity, with more churners correctly predicted and better model performance, whereas any probability under the threshold means higher specificity, with more non-churners incorrectly predicted and worse model performance. The closer curve to the top left corner (0,1) denotes the better prediction power of the model. The ROC of the class' 'churner' and 'non-churner' is 0.97; this denotes the power of our prediction model performance.  
Figure 6: Threshold setting between churner and non-churner in the data set.  
Figure 7: ROC result for SentiChurn model.  
The classification report on Table 8 denotes the performance model, where the average metrics precision for both classes is 0.93, the average recall for both classes is 0.97, the average F1-score for both classes is 0.95, and the model accuracy is 95.8 per cent.  
In the confusion matrix (Figure 8), 13,611 non-churner customers were correctly predicted as non-churners by our model.  
Furthermore, 5549 churner customers were correctly predicted as churners by our model, 840 non-churner customers were predicted as churners by our model and no churners were predicted as a non-churner customer by our model.  
As for the normalised confusion matrix (Figure 9), 94% of non-churner customers were predicted correctly, 100% of churner customers were predicted correctly and 0.06% of non-churner customers were predicted incorrectly.  
The log loss score is 0.1, which means our model is fine. Figure 10 shows the probability distribution (x) with the log loss (y) and the distribution between the actual and predicted values.  
Figure 8: Confusion matrix of the SentiChum model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Non-Churner**} & **Precision** & **Recall** & **F1-Score** \\ \cline{2-4}  & **1.00** & **0.94** & **0.97** \\ \hline Churner & 0.87 & 1.00 & 0.93 \\ Macro average & 0.93 & 0.97 & 0.95 \\ Weighted average & 0.96 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Classification report.
## 4 Performance Analysis and Evaluation
### Analysis of Signaling Costs  
The state transition diagram in Figure 13 shows that there are no transient sets in the model, but only a single ergodic set with only one cyclic class. Hence, the properties of regular Markov chains can be exploited to analyze the behavior of the proposed model [16]. Let P be the regular transition probability matrix; then the steady state probability vector can be solved using the following equations:  
\[\pi P=\pi\;\;\text{and}\;\sum_{i=1}^{\pi}\pi_{i}=1 \tag{1}\]  
Here, m is the number of states and P is the fundamental matrix for the regular Markov chain. This is given by:  
\[Z=[Z_{{}_{\psi}}]=(I-P+A)^{{}^{-1}} \tag{2}\]  
where:  
(1) \(A\) is a limiting matrix determined by \(P\), and the powers of \(P^{n}\) approach the probability matrix \(A\);  
(2) Each row of \(A\) consists of the same probability vector \(\pi=\{\pi_{1}\), \(\pi_{2}\),... \(\pi_{n}\}\), _i.e._, \(A=\xi\pi\), where \(\xi\) is the column vector with all entries equal to 1; and  
(3) \(I\) is the identity matrix.  
The matrix \(Z\) can be used to study the behavior of the regular Markov chain, and, using this matrix, one can compute the mean number of times that the process is in a particular state.  
Let \(Y_{j}(k)\) be the number of times that a process is in state \(S_{j}\) in the first \(k\) steps; then \(M_{j}[y(k)]\), the mean number of times the process is in state \(S_{j}\) starting from state \(S_{i}\) is given by:  
\[M_{j}[y_{{}_{j}}^{{}^{(k)}}]\rightarrow(Z_{{}_{\psi}}-\pi_{i})+k\pi_{{}_{j}} \tag{3}\]  
The total number of boundary updates in \(k\) steps, starting from state \(S_{i}\), can be computed from the total number of times that the process is in the boundary states starting from state \(S_{j}\), the initial state. The average number of location updates (\(U_{bu}\)) in the analytical model, is given by:  
\[U_{{}_{bu}}=M_{i}[y_{{}_{1}}^{{}^{(k)}}]+M_{i}[y_{{}_{2}}^{{}^{(k)}}]+M_{i}[y _{{}_{3}}^{{}^{(k)}}]+M_{i}[y_{{}_{4}}^{{}^{(k)}}] \tag{4}\]  
so:  
\[U_{{}_{bu}}=\sum_{n=1}^{4}M_{i}[y_{{}_{n}}^{{}^{(k)}}] \tag{5}\]  
where 1, 2, 3 and 4 are the boundary states in the model.  
Generalizing:  
\[U_{{}_{bu}}=\sum_{n=1}^{\alpha}M_{i}[y_{{}_{n}}^{{}^{(k)}}] \tag{6}\]We can use the above equation to determine the number of binding update messages. Because we need to send a binding update message whenever the sensor node moves between IP-WSNs, each time a node enters a boundary state, a binding update message is generated. Therefore, we need to determine the expected number of times that the process enters into a boundary state within k steps.  
Thus, sensor nodes need to send \(U_{bu}\) binding update messages, given that the sensor node experiences a total of k transitions between SMAGs. Therefore, the ratio of intra-IP-WSN mobility is denoted as \(M_{\text{intra\_IP-WSN}}\) and is expressed by:  
\[M_{\text{intra\_IP-WSN}}= \left(k-U_{bu}\right)/\,k \tag{7}\]  
The ratio of inter-IP-WSN mobility is denoted as \(M_{\text{inter\_IP-WSN}}\) and is expressed by:  
\[M_{\text{int\_IP-IP-WSN}}= U_{bu}\,/\,k \tag{8}\]
## 4 Simulation Verification
### Voltage Sag Conditions  
A sudden voltage drop on the supply side is a common power-quality issue in electrical systems, typically caused by factors such as short-circuit faults, large power equipment startups, system overloads, line switching or tripping, and natural disasters. This issue significantly impacts the system's operational stability and the reliability of load equipment. To investigate the effects of sudden voltage drops on system performance, this chapter presents a simulation study, modeling a scenario where the supply-side voltage drops from 380 V to 266 V at 0.5 s and recovers to the rated value at 0.8 s. As shown in Figure 13:  
Under the condition of a sudden voltage drop on the supply side, the proposed control strategy significantly improves the stability of the DC-side voltage. Compared to traditional methods, this approach can suppress voltage fluctuations more quickly after the voltage drop, avoiding the larger fluctuations and recovery delays that may occur with traditional methods, while maintaining higher steady-state accuracy. Under this condition, the system's load current, compensation current, and grid current are shown in Figure 14:  
Figure 11: Current waveform under sudden load conditions using the AVA-LADRC method: (**a**) Load current; (**b**) Inject current; (**c**) Grid current.  
Figure 12: THD of phase A current at different times under sudden load conditions: (**a**) 0.3 s; (**b**) 0.4 s.  
By analyzing the data in Figure 14, it is evident that under the condition of a sudden voltage drop on the supply side, the proposed improved control method still effectively maintains the system's stable operation. It prevents the adverse effects of severe voltage fluctuations on system performance, while effectively suppressing high-frequency noise and harmonics in the current waveform. This results in a significant reduction in the THD of the current. This optimization ensures a smoother recovery process after the voltage drop, enhancing the overall dynamic response and power quality. The THD of the phase A current at 0.6 s and 0.9 s is shown in Figure 15.  
The improved control method can significantly reduce the harmonic distortion rate, greatly improving the system's power quality and enhancing the operational efficiency and stability of the equipment. The reduction in harmonics minimizes energy losses, equipment overheating, and the risk of failures caused by excessive harmonics, thereby extending the lifespan of the system and equipment. Furthermore, the optimized control method enhances the smoothness of the current and voltage waveforms, reduces the system's  
Figure 14: Current waveform under voltage sag conditions using the AVA-LADRC method: (**a**) Load current; (**b**) Inject current; (**c**) Grid current.  
Figure 13: DC-side voltage waveform under voltage sag states.  
pollution to the grid, and complies with power quality standards, thereby strengthening the system's reliability.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 2 Materials and Methods
### System Dynamics  
The ROV yaw dynamics model asserted for this controller ignores Coriolis forces and assumes decoupled motion for calculating drag forces, and takes the form of Equation (1) with variable definitions given in Table 1. In order to estimate the coefficients, Equation (1) must be rewritten in \"regression form\", i.e., in the form \(y=\Phi\Theta\), where \(\Phi\) is a matrix of known quantities and \(\Theta\) is a vector the coefficients to estimate. This can be done by parameterizing the system in accordance with Equation (2).  
\[\begin{split} A_{0}u_{\psi}=& N_{ib}\dot{w}-r(N_{r}+N_{ rr}|r|)-\dot{p}(I_{xx}-N_{\dot{p}})-\dot{q}(I_{zy}-N_{\dot{q}})-\sin(\theta)(gg_{y}m- Vb_{y}g\rho)+\dot{u}(N_{\dot{u}}-g_{y}m)+\\ &\dot{v}(N_{\dot{u}}+g_{x}m)+\dot{r}(I_{z}+N_{\dot{r}})-\cos( \theta)\sin(\phi)(gg_{x}m-Vb_{x}g\rho)\end{split} \tag{1}\]  
Figure 4: Deterministic artificial intelligence topology.  
\[\Phi^{T}=\left[\begin{array}{c}-\dot{p}\\ \dot{p}\\ -\dot{q}\\ \dot{q}\\ \dot{r}\\ \dot{u}\\ \dot{v}\\ \dot{w}\\ (-g\sin(\theta)-u)\\ (\vartheta-g\cos(\theta)\sin(\phi))\\ g\sin(\theta)\\ g\cos(\theta)\sin(\phi)\\ -r\\ -|r|r\end{array}\right]\quad\Theta=\left[\begin{array}{c}I_{zx}\\ N_{p}\\ I_{zy}\\ I_{z}\\ N_{t}\\ N_{u}\\ N_{w}\\ gym\\ y_{x}m\\ Vb_{y}\rho\\ N_{r}\rho\\ N_{r}\end{array}\right] \tag{2}\]  
Then the optimal estimates for \(\Theta\), \(\dot{\Theta}\), can be computed using recursive least squares (RLS) as follows in Equation (3) [34] (which identically form the basis of comparative benchmark adaptive techniques [35]), and the readers will recognize Equation (3) as the feedback portion of the classical linear-optimal state estimator, the Kalman Filter [36; 37]. Thus, deterministic artificial intelligence is akin a dual representation of a Kalman Filter applied to control rather than state estimation, where the limitation of linear or linearized feedforward is released and replaced with any form of coupled, nonlinear dynamics demanded by the governing physics.  
\[\dot{\Theta}(t) =\dot{\Theta}(t-1)+K(t)(\tau(t)-\Phi^{T}(t)\dot{\Theta}(t-1) \tag{3}\] \[K(t) =P(t)\Phi(t)=P(t-1)\Phi(t)(I+\Phi^{T}(t)P(t-1)\Phi(t))^{-1}\] \[P(t) =(I-K(t)\Phi^{T}(t))P(t-1)\]
## 3 Case Studies
### Overview of Installation Sites
#### 3.1.2 Solar PV and Biogas Power Plant at DEI Agra  
The rooftop solar PV (Figure 3a) with battery backup and a pilot Biogas plant to harness agricultural and livestock waste at DEI Agra (Figure 3b). This region has significant solar irradiance as well as organic waste generation, creating conducive climatic conditions for anaerobic digestion. Solar power plant system comprises a photovoltaic slanted-roof setup with multi-Si panels, and lithium-ion (Li-ion) battery for energy storage, and an inverter, aiming to provide a sustainable electricity solution.
# Resistance of Concretes to External Chlorides in the Presence and Absence of Sulfphates: A Review
## 3 Resistance of Concrete to External Chloride in the Presence of Sulphate  
While the majority of work has focused on understanding chloride binding in isolation from other anions, this is unrealistic in natural environments. Of key importance is the co-existence of chlorides and sulphates, such as found in sea water. Some studies have investigated the effects of the concomitant attack of chlorides and sulphates on cement systems [123; 124; 125], with mixed findings. Both chlorides and sulphates react with hydrated aluminate phases to form Friedel's salt and ettringite, respectively. However, in combination, sulphates are preferentially bound to aluminates, leading to reduced chloride binding [126; 71; 127]. Yet, many factors, including ion concentrations [128; 129], cation type [5; 73; 130], supplementary cementitious materials (SCMs) or type of cement [17; 127; 131], temperature [132; 133], exposure conditions (wetting and drying versus static submersion) [134; 135], etc., can affect the dynamics of chloride and sulphate interactions. These factors are discussed further below.
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
## 4 Analytical Model and Performance Analysis
### Performance Analysis
#### 4.4.3 Throughput Analysis
\begin{split} S_{data}&=\left\{\begin{array}{ll} \frac{G_{1}E[L_{data}]}{T_{SYNC}},&G_{1}\leq G_{2}\\ \frac{G_{2}E[L_{data}]}{T_{SYNC}},&G_{1}>G_{2}\\ \end{array}\right.\\ &=\left\{\begin{array}{ll}\frac{\gamma(P_{busy}P_{s,suc}^{v})E[L_{data}]}{ T_{virt}},&G_{1}\leq G_{2}\\ \frac{(1-\gamma)N_{sch}E[L_{data}]}{T_{data}},&G_{1}>G_{2}\\ \end{array}\right.\end{split}  \tag{55}\]  
Let \(S_{data}^{1609.4}\) be the throughput of non-safety packets transmitted on the SCHs in the IEEE 1609.4 standard. Due to the fixed channel switch between CCHI and SCHI defined in the IEEE 1609.4 standard, during a synchronization interval, only half of \(T_{SYNC}\) is used to transmit the non-safety packets, and thus \(S_{data}^{1609.4}\) can be expressed as Equation (55) when \(\gamma\) is equal to 1/2.