esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension
## 4 Simulation Analysis and Comparison
Figures 7a and 10a that the curve of the sprung mass deflection under active suspension with optimization LQR control is less pronounced than the curve of the passive suspension, active suspension with traditional LQR control, and active suspension, suggesting that the active suspension with the optimization LQR control performs better in terms of road holding than the other cases. Figures 8a and 11a show the tire dynamic load curve of the passive suspension, active suspension with traditional LQR control, active suspension with optimization LQR control, and the full-order state observer under case 1 and case 2. Moreover, it can be observed from Figures 8a and 11a that the curve of the tire dynamic load under active suspension with the optimization LQR control is smaller than the curve of the passive suspension, active suspension with traditional LQR control, and active suspension, which illustrates that the capability of active suspension under optimization LQR control is more stable over the other methods. In Figures 6b and 8b, the estimation error of the full-order state observer with the sprung mass acceleration, sprung mass deflection, and tire dynamic load under case 1 is presented. Moreover, Figures 9b and 11b show the estimation error of the full-order state observer with the sprung mass acceleration, sprung mass deflection, and tire dynamic load under case 2. It is obvious that the estimation error of the full-order state observer is too small, which influences the accuracy of the estimation. The output of the observer provides a satisfactory indication of the state of the system. From Figures 12-14 and Tables 2 and 3, it can be observed that the RMS of the sprung mass acceleration, deflection, and tire dynamic load under LQR control with optimization decrease by 22.7%, 53%, 68% in case 1, 74%, 71%, and 90% in case 2 compared with the LQR control without optimization. According to the above results, the sprung mass acceleration, sprung mass deflection, and tire dynamic load under the optimization LQR control method are obviously reduced, and the vibration of the suspension system is effectively suppressed compared with the other suspension systems; thus, the ride comfort and handling stability are ensured. Moreover, this illustrates that the suspension system can effectively maintain its own stability under the condition of receiving external disturbances. Therefore, the proposed controller in this paper can reduce suspension deflection and acceleration to a great extent, further demonstrating the viability of the proposed control method. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system. The hybrid control algorithm could overcome the shortcoming existing in the conventional LQR control.
# Numerical Simulation Analysis of Wellbore Stability in Weak Interlayer of Igneous Rock
## 3 Numerical Simulation Model for Wellbore Stability  
### Basic Assumptions and Physical Model  
To investigate wellbore stability in weak interlayer igneous formation, the following fundamental assumptions were made: the igneous rock behaves as an isotropic elastic-plastic material; he presence of weak planes are taken into account as transverse isotropy in terms of strength and deformability; the fluid within the borehole is considered incompressible; changes in formation and drilling fluid temperature do not affect the direction of the borehole [16; 17; 18]; and the stress distribution near the borehole adheres to Saint-Venant's principle.  
The model can theoretically be transformed into the two-dimensional plane for analysis [19]. Figure 4 presents an enhanced two-dimensional physical model of the igneous formation along with the applied load to the model [20]. The weak interlayer is depicted by the black lines [21]. The borehole trajectory is represented by a vertical borehole that traverses the entire square formation. The \(x\)-axis corresponds to the direction of maximum in situ stress, while the \(y\)-axis represents the direction of minimum in situ stress [22].
## 4 Discussion
Moreover, the elastic wave velocities predicted by this simplest model for water-saturated rocks may differ from the respective values found with the model containing pores and microcracks by 30%. Moreover, the difference is rather high within the whole porosity range. This may cause significant errors in the interpretation of field data. In addition to the RP model with pores and microcracks, two alternative models were considered. These models assumed that the elastic moduli of the host matrix were different from those of the calcite polycrystal. The reason for the difference can be attributed to various factors including the effect of grain boundaries. These models contained only one set of voids, namely, quasi-spherical pores visible with computed tomography. The models differed from each other only in the elastic moduli of the host matrix, which were inverted from the measured elastic wave velocities. Both the host matrices provided an acceptable solution to the inverse problem. The choice of the host matrix moduli did not have a significant effect on the prediction. It was shown that the velocities predicted by these models for water-saturated rock within the porosity range up to 20% did not significantly differ from those of the dual-porosity RP model. The difference was within 10% for both velocities _Vp_ and _Vs_. The following question arises: what is the benefit of incorporating the computed tomography results in RP modeling, irrespective of the fact that a large part of voids and possibly other heterogeneities are beyond the tomography resolution? The answer is as follows. These results give evidence for the existence of quasi-isometric pores and provide information on their shape. This helps to separate their effect from the other factors that control the effective elastic properties. This gives an opportunity to make the predictions based on the constructed RPM more reliable even without direct knowledge of other factors that control the effective elastic properties.
# GIS-Based Environmental Monitoring and Analysis
## 2 An Overview of Published Articles
A novel contribution of this research is the incorporation of the suitability map into an urban growth simulation, developed using a logistic regression (LR) model. This simulation projects urban expansion trends through 2027, enabling planners to assess potential development areas against established suitability criteria. The findings highlight spatial patterns in land suitability and expected urban growth, assisting policymakers in selecting appropriate development sites while preserving ecological balance. The study particularly emphasizes the necessity of considering multiple factors--including topography, accessibility, soil capacity, and geological characteristics--in the urban planning process.
## 2 Electrical Resistivity/Impedance Measurement: Is It Really that Simple?
### AC Measurement of Electrical Resistivity
#### 2.2.2 4-Electrode Configuration
The same measurement frequency was adopted by [PERSON] and [PERSON] [34], making the two external electrodes adhere to the external opposite prismatic specimen faces by means of gel, whereas the internal electrodes were brass or stainless knurled steel bars cast in concrete. Finally, [PERSON] et al. [31] performed electrical resistivity measurements on prismatic specimens containing blast furnace slag. The Wenner probe method, using a probe with 50 mm spacing and an AC excitation at 40 Hz, was exploited also within the Long-Term Bridge Performance (LTPB) Program of the Federal HighWay Administration (FHWA) [127], where the RABIT-CETM multifunctional Non-Destructive Evaluation (NDE) platform has been employed to enhance the assessment of bridge decks. The electrodes for the measurement of electrical resistivity were equipped with sponges, which were saturated with soapy water to assure electrical coupling between electrodes and concrete. The analysis of the data measured (which should be repeated five times at each location) enables to derive contour maps reporting the spatial distribution of the electrical resistivity values: areas with low resistivity describe highly corrosive environment, facilitating high corrosion rates. It is worth noting that, contrarily to AC 2-electrode measurement (Section 2.2.1), literature (Table 3) reports more documents involving concrete than paste or mortar specimens when addressing the 4-electrode configurations. To the best of the authors' knowledge, there are no specific reasons for this; a hypothesis could be that this is a simple costs-benefits issue, being paste/mortar specimens easier to be manufactured (and, often, smaller specimens are realized with respect to concrete ones, so that 2-electrode configuration appears more practical to be applied), being the cost of devices involved lower and being the overall test set-up easier to be prepared. Of course, the lower precision and accuracy of a 2-electrode test with respect to a 4-electrodes test still holds.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 4 Establishment of Performance Indicator Evaluation Model
### Implementation Steps of Grey-Target Decision Assessment Model
Finally, the performance of each operational process is ranked based on the calculated results according to the target distance. A smaller target distance indicates better performance.
# A Web Shell Detection Method Based on Multiview Feature Fusion
## 3 Methodology
### Modeling and Classification  
After data preprocessing, feature extraction, and selection, it is necessary to construct a corresponding model for malicious web shell judgment. Aiming at the classification goal, selecting the appropriate classifier in combination with the actual data features will help to improve the discriminative ability of the model. Considering the nonlinear characteristics of the web script feature boundary, the multidimensional characteristics of the features, and the dependency between the web shell features in various dimensions, in this paper, support vector machine (SVM) is chosen as the classifier for the following reasons:  
First, due to the complexity of web shell, many normal script files will also behave similarly to web shells, so malicious web shell is not linearly separable when using features to classify it. SVM has multiple kernel functions (e.g., polynomial kernel function and radial basis function) to handle nonlinear separable problems.  
Second, 16-dimensional (described in Section 4.3) and multidimensional feature vectors are totally extracted, and related work [36] has shown that SVM has achieved satisfactory results in multidimensional feature classification.
## 2 Methodology
### Numerical Solution and Model Verification  
The model was created in a 3D symmetric model by ANSYS. The model had three surface areas, and the axis of symmetry was on the XZ plane, as shown in Figure 2. These areas include water, scale, and tube. The water surface area had a dimension of 500 mm in length and 33.27 mm in width. Moreover, the scale and tube areas had a dimension of 500 mm in length and 1 mm in width. In addition, the mesh has been created in water, deposit layer, and tube wall domains via ANSYS meshing. A mesh independence test and validation are performed to exclude the influence of the mesh on the simulation calculations. The boundary conditions for the simulation are shown in Table 2.  
Figure 3: The maximum allowable stress and yield strength based on ASME code section II Part D for carbon steel seamless pipe (SA210 C) edited from [28].  
Figure 2: Boundary conditions on the simulated domain.  
The Reynolds number, convection heat transfer coefficient, and Nusselt number at a fully developed area were calculated as follows:  
\[Q=\ddot{q}\cdot A_{OS}=\dot{m}\cdot C_{p}\cdot(T_{O}-T_{i}) \tag{11}\]  
\[h_{\infty}=\frac{\ddot{q}}{T_{s}-T_{O}} \tag{12}\]  
\[Nu_{\infty}=\frac{h_{\infty}\cdot D_{i}}{k} \tag{13}\]  
\[Re=\frac{4\cdot\dot{m}}{\pi\cdot D_{i}\cdot\mu} \tag{14}\]  
\[PR=\frac{C_{p\cdot\mu}}{k} \tag{15}\]  
\[Nu_{\infty}=0.0214\cdot\left(Re^{0.8}-100\right)\cdot Pr^{0.4} \tag{16}\]
# Evaluating the Effect of Intensity Standardisation
## 3 Results
### Quality of Intensity Standardisation  
We measured the quality of standardisation by determining whether histograms of baseline and follow-up scans were similar, in terms of the KL divergence, after standardising intensities or not. The measured KL distance is shown in Figure 3. In most cases, standardising intensities between baseline and follow-up scans resulted in significantly lower KL divergence values (\(p<0.001\)), except when using WhiteStripe. The situation may be explained by the large variation in the left-most tails of the resulting histograms of intensities (See Appendix A). This variation results in a higher mismatch between histograms of intensities and, thus, in higher KL divergence.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 4 Adaptability of the Formulation
### Transition to Quantum Field Equations  
The Wigner-Weyl transform provides a rigorous bridge between classical and quantum mechanics by mapping quantum operators to phase-space functions. For any operator \(\hat{A}\), its Wigner-Weyl transform is defined as:  
\[A_{W}(q,p)=\int dy\,e^{-ipy/\hbar}\langle q+y/2|\hat{A}|q-y/2\rangle \tag{67}\]  
where \(A_{W}(q,p)\) is the Wigner function counterpart. This transformation preserves the algebraic structure through the Moyal product:  
\[A_{W}\star B_{W}=A_{W}e^{i\hbar(\frac{\partial}{\partial q}\frac{\partial}{ \partial p}-\frac{\partial}{\partial p}\frac{\partial}{\partial q})/2}B_{W} \tag{68}\]  
Key features:  
* \(\star\)-product reduces to an ordinary product when \(\hbar\to 0\);
* Expectation values become phase-space integrals: \(\langle\hat{A}\rangle=\int A_{W}(q,p)W(q,p)dqdp\);
* This ensures that \([\hat{A},\hat{B}]\to i\hbar\{A_{W},B_{W}\}_{\mathrm{PB}}\) correspondence.  
This formalism allows classical entropy gradients (\(T\
abla\overline{S}\)) to naturally generalize to quantum operators (\(T\hat{S}\)).
# Interest Forwarding in Named Data Networking Using Reinforcement Learning
## 7 Conclusions and Further Work  
This paper has addressed Information Centric Networks in the framework of Named Data Networking (NDN). We have proposed an adaptive forwarding strategy, NDNFS-RLRNN for the NDN architecture which employs an online learning algorithm, reinforcement learning using the random neural network, to forward Interest packets. Our proposed approach is dynamic and does not persist on the links put forward by the routing protocol, so that it may better recognize the role of the forwarding plane to take advantage of the in-network caching capability of the NDN architecture.  
In our tests, we compare NDNFS-RLRNN with two other forwarding strategies: one that restricts forwarding and probing of Interests to the interfaces suggested by the routing protocol and another that broadcasts each Interest. We also considered three different caching policies from the literature to evaluate the forwarding strategies under different caching behaviours. We consider two ubiquitous caching policies exhibiting different cache eviction rate properties and a policy that bases its caching decisions on the network topological information.  
Figure 7: Network load performance of NDNFS-RLRNN (\(p=0.3\)), ASF and NRR for different uniform content popularity distributions, under: (**a**) LCE caching policy, (**b**) _Betw_ caching policy and (**c**) _ProbCache_ caching policy. Content popularity is modelled according to the Mandelbrot-Zipf (MZipf) distribution with the settings: \(p=5.0,\alpha=0.0,0.3,0.6,0.91.2\). Each CR is equipped with a cache size of 0.4% of the content catalogue. The results reported are the average of 20 randomized simulation runs.  
Our results suggest that when in-network caching is effective, NDNFS-RLRNN strategy can achieve better delivery than a forwarding strategy that persists on the existing static routing layer preferences and a more efficient performance than a nearest replica forwarding strategy that floods requests. Furthermore, the results also indicate that for ubiquitous caching policies, NDNFS-RLRNN's performance can be significantly improved by increasing its exploration of the network. For a policy that caches contents at specific nodes based on the network topology, we see that the rate of improvement in terms of exploiting in-network caching reduces as the probing frequency of NDNFS-RLRNN increases. However, the results also show that even with a low probing frequency, NDNFS-RLRNN produces the best performance compared with the other forwarding strategies under this policy.  
Future work will focus on evaluating our approach for congested systems, and also in the presence of denial of service attacks. Such evaluations can also lead to improvements in the adaptive on-line policy and result in a better evaluation of the computational overhead of our adaptive on-line approach.
## 3 Algorithm Design and Analysis
### Ms-SCTP Method
#### 3.1.1 Model Description  
SCTP could establish multiple paths between two communication nodes, each of which could be used for data transmission. The SCTP multi-path transmission model is shown in Figure 6. After the SCTP was established, multiple communication paths were established between the two nodes, and data could be transmitted on multiple streams. Multi-path communication required multiple interfaces, which could be heterogeneous network interfaces. During the transmission, if an error occurred in the  
Figure 5: Compare the throughput variance between SCTP and TCP.  
stream, the data in other streams could be transmitted normally. In addition, erroneous data could be retransmitted in the same stream or in other streams. The multi-objective optimization problem selected a candidate path. SCTP managed these multiple streams in a unified manner. In the process of data transmission and reception, in order to ensure the order of data, each data block in SCTP was equipped with a sequence number. The sequence number was maintained by a stream sequence number (SSN) and a transport sequence number (TSN). Data loss and data sequence in the SCTP scheme were detected through TSN and SSN.
# Scale-Model Experiment on the Delayed Failure Mechanism of Deep Tunnels in Brittle Rock
###### Abstract  
Rock bursts usually happen during the hours or days after tunnel excavation, even in an unsupported opening where no collapses occur. To investigate the mechanism of those delayed failures in brittle rock tunnels, this paper showcases the performed scale-model test based upon the Jinping II headrace tunnelling project. The model test was conducted in a particularly designed loading apparatus; the scale-model is composed of a similar material for the deep brittle rock. The tunnel in the scale-model is excavated by a specially made drilling tool. The failure mode of the deep circle tunnel under isotropic and anisotropic geostress were obtained; the delay failure time was recorded, and the accompanying stresses and strains changing were monitored. Under isotropic geostress the failure shape has a smooth circle boundary, failure process totally costs 56 h. While under anisotropic geostress a dog-eared breakdown was found, the failure process amounted to 72 h. The time-to-failure was evaluated by delay failure theory, and the evaluation equation was implemented into in FEM code. Numerical simulations have been performed to simulate the failure time and failure mode. The numerical results of failure time and failure mode mainly match the scale-model testing results.  
deep tunnel; hard rock; scale-model testing; delayed failure; time-to-failure
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 4 Health Indicator Construction
### Feature Postprocessing  
Feature postprocessing techniques construct a suitable health indicator to predict RUL built upon various statistical metrics in Table 1. For reliable RUL estimations, a health indicator requires to be observable and correlated with the system degradation process over time. The feature importance ranking and PCA identify the health indicator and threshold values for the RUL prediction.  
We first select main features out of all available features in Table 1 to build a reliable RUL prediction model. A suitable condition indicator has a consistent positive or negative behavior as a  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Statistical Features**} \\ \hline Time domain & mean, standard deviation, RMS, maximum-to-minimum difference, skewness, kurtosis, \\  & kurtosis, energy, signal median absolute deviation, crest factor and shape factor \\ \hline Frequency domain & mean frequency, skewness, and kurtosis of the power spectrum and \\  & mean and standard deviation of the local maxima of the power spectrum \\ \hline Time–frequency domain & mean, standard deviation, skewness and kurtosis of \\  & the conditional spectral moment with different orders between 2 and 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical features of time domain, frequency domain, and time–frequency domain.  
system gets closer to failure. We use the monotonicity as the feature selection metric to quantify the importance of the features [42]. The monotonicity of \(i\)th feature is defined as  
\[M_{i}=\frac{|N_{p}(\delta_{i})-N_{n}(\delta_{i})|}{N-1} \tag{1}\]  
where  
\[\delta_{i}=x_{i}(t)-x_{i}(t-1),\forall t\in\{1,\dots,N\},\]  
with \(x_{i}(t)\)\(i\)th feature signal of sampling sequence \(t\), \(N\) is the number of measured samples, and \(\delta_{i}\) the difference of \(i\)th feature signal \(x_{i}\) over sampling sequences. \(N_{p}\) (respectively, \(N_{n}\)) is the number of positive (respectively, negative) values of \(\delta_{i}\) for all training data. The monotonicity evaluates the feature importance score on a scale ranging from 0 to 1. A higher ranked feature tracks the degradation process more reliably and, hence, is more suitable to train the RUL prediction model. Features with large importance score are selected for feature postprocessing based on the training data in offline.  
Once we identify the main features, PCA extracts the health indicator by reducing the feature dimension. PCA essentially reduces a system of a large number of features into a few principal components using a linear transformation while maintaining most of the variability from the feature set [30,31]. Note that the mean and the standard deviation obtained from training data are used to normalize the features of the entire datasets. After PCA is applied to the feature set of the training data, we select the specific principal component out of multiple principal components as the health indicator if it increases as the machine degrades.