esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response of Shield Tunnel with Double-Layer Lining
## 4 Result Analysis
### Structural Stress  
Extract the maximum stresses of the tunnel structure as shown in (Figure 7).  
From the figure, it can be observed that both the segmental lining and the secondary lining experience significant stresses under seismic loads, with higher stresses at the springing and spandrel. For the segmental lining, the stresses at the right spandrel, right springing, left spandrel, and left springing are 2.19 MPa, 2.61 MPa, 2.24 MPa, and 2.54 MPa, respectively. For the secondary lining, the stresses at the right spandrel, right springing, left spandrel, and left springing are 0.68 MPa, 0.87 MPa, 0.77 MPa, and 0.74 MPa, respectively. It is evident that both the segmental lining and the secondary lining exhibit relatively high stresses at their corresponding locations. This indicates that in the shield tunnel with overlapped double-layer lining, the load-transferring between the segmental lining and  
Figure 6: Acceleration amplification factor.  
Figure 7: Structural stress.  
the secondary lining is effective, allowing them to collectively withstand seismic loads as a cohesive structure.  
In terms of stress distribution, the stresses at all the positions of the segmental lining are greater than those at the corresponding positions of the secondary lining. The peak stress of the tunnel structure occurs in the right springing area, where the stress in the segmental lining is 2.61 MPa, approximately three times larger than that of the secondary lining. Thus, it can be inferred that under seismic loads, the segmental lining serves as the primary load-bearing structure, while the secondary lining acts as a reinforcing structure and shares a portion of the seismic load.
## 4 Experimental Results
### Overview of Model Performance and SHAP Analysis
#### 4.1.2 SHAP Analysis for XGBoost Model  
To better interpret the results of the XGBoost model, we utilized SHAP (Shapley Additive Explanations) values. SHAP is one of the methodologies used to interpret machine learning model predictions, providing a quantitative understanding of how each feature (independent variable) contributes to the model's prediction [33; 34]. SHAP values are based on the Shapley values from cooperative game theory and calculate the contribution of each feature for every data point. By doing so, SHAP enhances the interpretability of complex \"black-box\" models like XGBoost. A key advantage of SHAP is that it guarantees the fair distribution of contribution among the features. The higher the SHAP value for a particular feature, the more significant its impact on the prediction. Additionally, SHAP values can be either positive or negative: a positive SHAP value indicates that the feature  
Figure 3: Performance Evaluation of Models Based on RMSE.  
contributes to increasing the predicted value, while a negative SHAP value suggests that the feature decreases the predicted value.  
Figure 4 visually represents how each independent variable influences the dependent variable period (activity duration). The y-axis lists the independent variables, ordered by their importance, with the most influential variables at the top. The x-axis shows the SHAP value for each variable, representing the variable's impact on the prediction (participant activity duration). A positive SHAP value means the variable contributes to extending the predicted activity period, whereas a negative SHAP value suggests the variable shortens it.  
The color gradient in the graph represents the actual values of the independent variables. Here, purple represents high feature values, while yellow represents low feature values. For example, the act_level feature has a high SHAP value, meaning frequent activity has a significant positive effect on the predicted participant engagement period. On the other hand, features such as alone and commit_to_commit have smaller SHAP values, indicating a lesser influence on the prediction. By analyzing this graph, we can observe how different factors such as activity level, network centrality (k-core), and PR processing times contribute to predicting the length of participant engagement in the project.
## 2 Microwave Liquid Crystal Technology  
In the last two decades, novel nematic LC mixtures have been specifically synthesized for microwaves. These anisotropic microwave Liquid Crystals (LCs) feature tunability in relative permittivity \(\tau_{LC}\) of about 25-30% with low dielectric losses in the range of tan\(\delta\approx 10^{-2}\) to \(10^{-3}\) above 10 GHz, decreasing slightly with frequency, which is different to all other materials above. Moreover, several radiation tests of these novel nematic LC mixtures for space qualification, including a total dose test, indicated no influence of radiation to microwave LCs [92, 165, 183]. Hence, they are also feasible for space applications. Moreover, these MLC components exhibit low frequency dispersion and extremely linear operation [184, 185, 186], i.e., very high power-handling capability. However, in contrast to traditional reconfigurable microwave components made of semiconductor materials, RF-MEMS, or ferroelectrics, MLC components might face some difficulties. (1) Due to their anisotropic nature, the orientation of LC molecules controlled by means of an electrostatic field might cause critical response times and might require complex biasing electrode networks. (2) Since LC is a fluid, it has to be infiltrated after processing via very small filling holes into some encapsulated packages such as flat panels for phased and reflect arrays or into long closed/sealed channels with respect to the wavelengths as for delay lines or into small cavities as for filters. This might complicate the preparation of the MLC components, but not necessarily when standard processes can be established such as the multi-layer low-temperature co-fired ceramic (LTCC) [187, 167] or the Liquid Crystal Display (LCD) technology [188]. During the experimental phase, it might be even beneficial, since the LC already being filled into a component can be exchanged by another one later for improvement or comparison. So far, it allows flexible handling. Thus, MLC technology definitely enables a low-cost, robust, and reliable mm-wave hardware solution for the large-scale manufacturing of compact and flat reconfigurable/tunable RF components, when standard processes such as LTCC or LCD technology can be applied for fabrication.
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 5 Simulation Results
### Simulation 1  
To showcase the performance of the designed AFSMFTC method in inner-loop tracking, the pitch angle is taken as an example. The validation is conducted using a unit step signal and a sinusoidal signal. Additionally, a comparison is made with the active disturbance rejection control (ADRC) method, incremental nonlinear dynamic inversion (INDI) method, and conventional PID method mentioned in the references [43; 44]. The simulation results are shown in Figure 6.  
From Figure 6, it can be observed that the AFSMFTC method designed in this study achieves the desired state faster compared to other methods. Additionally, it exhibits smaller overshoot during the tracking process, demonstrating excellent dynamic characteristics and robustness.
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 8 Conclusions  
In this paper, we summarize the side-channel and covert-channel vulnerabilities based on interconnects and outline the most advanced attacks and defense measures. We particularly discuss the three key characteristics of interconnects that are most easily exploited by side-channel and covert-channel attackers: lines contention, core distance, and spatial layout, focusing on their attack methods and separately discussing the countermeasures that have been proposed and their limitations. Finally, we analyze the future direction of interconnect side-channel.  
To date, there remains insufficient attention from both industry and academia on the security implications of interconnects. Traditional defense mechanisms against other microarchitectural side-channels are largely ineffective against interconnect-based attacks, making interconnects an increasingly important target for microarchitectural attacks. In the foreseeable future, the pursuit of processor performance will continue unabated. Whether through further improvements in chip fabrication processes or the emergence of super processors integrating more cores, this inevitably entails new interconnect architectures and the associated security vulnerabilities.  
Current research surveys on NoC security issues remain limited, and those available are either outdated [91] or lack comprehensive discussion on side-channel vulnerabilities [92]. This paper, being the first review focusing on processor interconnect side channels and covert channels, aims to summarize and reflect on existing attacks and defenses. Through this, we hope to elevate the academic focus on the security of processor and SoC interconnects, aiding future research endeavors. However, this survey is limited in scope; it discusses only a few impactful interconnect side-channel attacks, confines its discussion to the NoC of commercial processors, and treats interconnect side channels as a subset of microarchitectural side channels without delving into broader SoC interconnect security issues. As described in Section 7, interconnect side channels have significant potential to combine with other microarchitectural side-channel attacks or exploit other SoC security vulnerabilities, thereby amplifying threat levels or creating new attacks. We plan to address other SoC security issues based on or utilizing interconnect characteristics in future research.  
Methodology, J.Y.; Formal analysis, D.L.; Investigation, X.W.; Writing--original draft, J.Z.; Writing--review & editing, P.Q. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
# Phytocannabinoids Biosynthesis in Angiosperms, Fungi, and Liverworts and Their Versatile Role
## 3 Biosynthesis of Phytocannabinoids
### Application of Biotechnological Approaches to Phytocannabinoids Production in Heterologous Hosts  
Phytocannabinoids can be generated in different heterologous hosts such as fungi, bacteria, and plants with the help of biotechnological techniques. Several investigations reported that in vitro culture of phytocannabinoids biosynthesis in _C. sativa_, with the help of explants and micropropagation, is a widely used biotechnological approach for phytocannabinoid production. Moreover, apart from _C. sativa_, _Nicotiana benthamiana_ also emerged as the favorable heterologous host for the production of phytocannabinoids. It exhibits the production of several proteins and bioactive compounds and has glandular trichomes that help overcome cell death, autotoxicity, and cell damage caused due to intermediates produced during phytocannabinoids biosynthesis. In recent research, it was found that a major biotechnological tool for phytocannabinoids production and inducing genetic modification is established via the micropropagation technique. Additionally, cell suspension culture, hairy root, and adventitious root culture also produce a small quantity of cannabinoids. In _Saccharomyces cerevisiae_, galactose produces phytocannabinoids such as CBG, CBDA, \(\Delta^{9}\)-THCA, and minor phytocannabinoids such as cannabidivarinic acid (CBDVA) and \(\Delta^{9}\)-tetrahydrocannabidivarinic acid (\(\Delta^{9}\)-THCVA) [46; 47; 48; 49; 50; 51].
## 2 The Phase-Locked Loop and Normalized Fokker-Planck Equation
### Modulo-\(2\pi\) Phase-Error PDF  
A common form of partial solution to Equation (7) is the modulo-\(2\pi\) probability [1; 2; 3; 4; 10; 11; 12; 13]  
\[P(\phi,\tau)=\sum_{k=-\infty}^{\infty}p(\phi+k2\pi,\tau) \tag{17}\]  
where the capital letter P is used to denote the modulo-\(2\pi\) PDF while little letter \(p\) denotes the unbounded nonmodulo-\(2\pi\) PDF, respectively. In the steady state, \(p(\phi,\tau)\) is spread over all \(\phi\) since cyclic slipping event (lock on at \(k2\pi\)) have occurred. The relation between these two PDF's can be expressed as  
\[P(-\pi<\phi<\pi,\tau)=p(\phi,\tau)\text{modulo }2\pi \tag{18}\]  
which shows that \(P(\phi,\tau)\) has a period of \(2\pi\) rad. This is like the original function except that each \(2\pi\)-wide region contains an identical distribution and P begins with impulses at \(k2\pi\) for all \(k\). As P diffuses with time, the probability that the phase will jump out of the primary interval \(-\pi<\phi<\pi\) is the same as the probability that it will jump into that interval. As time increases, \(p(\phi,\tau)\) diffuses so the ultimate value approaches zero everywhere. Consider only the interval \(-\pi<\phi<\pi\) and set the boundary and normalized conditions, respectively:  
\[P(\pi,\tau)=P(-\pi,\tau);\;\int_{-\pi}^{\pi}P(\phi,\tau)d\phi=1 \tag{19}\]Hence the modulo-\(2\pi\) PDF describing the phase error is obtained.
## 4 Simulation and Performance Analysis
### Simulation Environment  
The NS-2.34 network simulator has been used to simulate the EGR algorithm. An accurate mobility model and a highway traffic environment are produced using the \"mobility model generator for vehicular networks\" (MOVE) [30]. An open source, highly portable, microscopic, and continuous traffic modeling tool called simulation of urban mobility (SUMO) serves as the foundation for MOVE [31]. The majority of the key elements of the vehicular traffic environment, including the number of roads, the number of lanes, the quantity of intersections, the number of traffic lights in each intersection, the speed of vehicles, the probability that a vehicle will turn left or right at a specific intersection, etc., are set up and implemented using a road map editor and a vehicle movement editor. NS-2 has directly utilized the mobility trace produced by MOVE with SUMO's support. To evaluate EGR, two types of vehicle traffic environments--high-speed and sparsely connected vehicles--are taken into account. The three EGR modules, namely geometrical localization for GPS outage, link quality, and residual link lifetime, have been implemented [32; 33; 34]. This investigation focuses on determining if the three EGR modules reduce routing overheads, one-hop link disconnection, and increased throughput with increased vehicle velocity and vehicle density in a highway traffic environment. The simulation region consists of a set of two horizontal and two vertical highways that intersect, creating sixteen junction locations spaced equally apart. Every road has two lanes. Simulation parameters used for the experiment are shown in Table 1. After configuring the network and traffic flow with the parameter values that were taken into consideration, the simulations were run. Locations sending vehicles, destination vehicle, and geocasting region are arbitrarily chosen for each simulation run from one of two predetermined locations that are the same for all ten simulation runs. Each value used in the results analysis was obtained from an average of ten independent simulation runs.
## 5 Performance Evaluation  
### Performance Evaluation Methods  
We calculate the positioning error using Euclidean distance, as defined by the following:  
\[Error=\sqrt{\left(x_{p}-x_{a}\right)^{2}+\left(y_{p}-y_{a}\right)^{2}} \tag{29}\]  
where \(\left(x_{p},y_{p}\right)\) is the predicted position, and \(\left(x_{a},y_{a}\right)\) is the actual position of objects. A determination coefficient is usually utilized to measure the fitting degree of a regression model and expressed as \(R^{2}\). It can be calculated using the following equation:  
\[R^{2}=1-\frac{RSS}{TSS} \tag{30}\]  
where \(RSS\) (residual sum of squares) represents the sum of squares of the difference between model predictions and actual observations. \(TSS\) (total sum of squares) represents the sum of squares differences between predicted variables and its meaning. The value of \(R^{2}\) ranges from 0 to 1. The closer it is to 1, the better the model fits the data. In contrast, the closer it is to 0, the worse the model fits the data. Figure 8 shows the determination coefficients of LRA, LBP_CNNs, and LBP_FFT_CNNs under 10 random distances. The results reveal that LRA achieves an \(R^{2}\) of 0.9867, demonstrating robust performance but falling short compared to deep learning-based models. LBP_CNNs, with a \(R^{2}\) of 0.9934, exhibits superior capability in capturing intricate data patterns. Notably, the LBP_FFT_CNNs model attains the highest \(R^{2}\) of 0.9949, underscoring the efficacy of integrating Fourier-transformed features into convolutional neural networks.
## 3 Results
### Normality Model Comparison  
An additional validation of the results is presented. A normality model using the same input data is trained and utilized to make health predictions of the generators. Details on how to build a normality model are available in Reference [4]. The value of the generator stator temperature is predicted by a ridge regression model and the prediction error is used as a metric for the generator status. Details on the algorithm can be found in Reference [24], the decision of using this algorithm is dictated by its capacity to deal with multicollinearity in the inputs. The results of the normality model are presented in Table 7.  
One can see that while the normality model yields reasonable results, it scores lower overall values for the tracked indicator when compared to the presented methodology. In particular, it should be noted that the number of FPs is more than double the proposed solution and the total number of TPs is lower. The only case in which the normality model performed better is WF7, where two additional TPs are found.  
The presented results were obtained using a large sample of real data. The sample is extremely heterogeneous since it represents four different turbine brands, and the windfarms are located in different geographical locations (Poland, Spain and United Kingdom), characterized by very different climates and wind conditions. Such results are rare in the literature, as many algorithms have been tested either in laboratories or in a reduced sample of turbines.  
In Section 4, the detailed analysis of windfarm 5 is proposed. This one was chosen since it has a high prevalence of failures of the generator and two predictions were classified as FN, so it is useful to analyze them in detail to determine the reason why the alarms were raised.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & **Brand** & **Location** & **TP** & **FN** & **FP** & **TN** & **Accuracy** & **Kappa** & **Sensitivity** & **Specificity** & **Precision** & **F1** \\ \hline
**WF1** & Vestas & Spain & 4 & 0 & 2 & 5 & 82\% & 65\% & 100\% & 71\% & 67\% & 80\% \\
**WF2** & Vestas & Spain & 1 & 3 & 0 & 5 & 67\% & 27\% & 25\% & 100\% & 100\% & 40\% \\
**WF3** & Siemens & Poland & 1 & 0 & 12 & 5 & 33\% & 4\% & 100\% & 29\% & 8\% & 14\% \\
**WF4** & Siemens & Poland & 4 & 2 & 6 & 3 & 47\% & 0\% & 67\% & 33\% & 40\% & 50\% \\
**WF5** & Sevionion & Poland & 0 & 5 & 2 & 6 & 46\% & \(-\)28\% & 0\% & 75\% & 0\% & NA \\
**WF6** & Senvion & Poland & 2 & 2 & 5 & 13 & 68\% & 17\% & 50\% & 72\% & 29\% & 36\% \\
**WF7** & Nordex & UK & 5 & 1 & 6 & 14 & 73\% & 41\% & 83\% & 70\% & 45\% & 59\% \\ \hline
**TOTAL** & & & 17 & 13 & 33 & 51 & 60\% & 14\% & 57\% & 61\% & 34\% & 43\% \\ \hline \end{tabular}
\end{table}
Table 7: Normality model results. WF stands for Windfarm, TP True Positive, FN False Negative, FP False Positive and TN True Negative.  
Figure 3: ROC curve of the entire dataset, using a unique threshold value for all the windfarms. The point defined by the intersection of the dashed lines is the result reported in Table 6.
# Dynamic Monitoring of the Water Flowing Fractured Zone during the Mining Process under a River
## 3 Materials and Methods
### Curve Fitting and Numerical Modeling Method
The flow of water in the WFFZ obeys the Ahmed-Sunada (Forchheimer) relation, and the equations of motion, continuity and state are as follows [29]:  
\[\rho C_{a}\frac{\partial u}{\partial t}-\beta\rho\mathbf{uu}+\frac{\eta}{k}\mathbf{u}+ \
abla p=f \tag{7}\]  
\[\frac{\partial(\varnothing\rho)}{\partial t}+\
abla\cdot(\rho\mathbf{u})=\rho q \tag{8}\]  
\[\varnothing=\varnothing_{0}[1+c_{\varnothing}(p-p_{0})] \tag{9}\]  
where \(C_{a}\) is the acceleration coefficient, \(\beta\) is the non-Darcy factor, \(q\) is the source intensity, \(p_{0}\) is the initial pressure (Pa), \(\varnothing_{0}\) is the initial porosity, \(c_{\varnothing}\) is pore compressibility, and \(\
abla\) is the divergence operator. The flow of water in the roadway is mainly free flow, obeying the Navier-Stokes equation [29]:  
\[\left\{ \begin{array}{c}\rho\frac{\partial u}{\partial t}-\eta\
abla\cdot( \
abla\mathbf{u}+\
abla u^{T})+\rho\mathbf{u}\
abla\mathbf{u}+\
abla p=f\\ -\
abla\cdot\mathbf{u}=0\end{array} \right. \tag{10}\]  
The initial flow velocity of the model was 0 m/s, and the initial pressure was atmospheric pressure (the relative pressure was 0 Pa). The stable water inflow boundaries were set on the top of the aquifer at \(t\) = 0 s, and the inflow velocity was 1.0 \(\times\) 10\({}^{-3}\) m/s. The water inflow was removed at \(t\) = 4000 s in order to simulate the water flow mechanisms under limited water content. The outlet boundaries were set on both sides of the roadway; the relative pressure was 0 Pa. Other boundaries of the model were set as no-slip walls. According to the lithology of the overlying strata of the 8101 longwall face and the physical parameters of water, the parameters were set as shown in Table 1.
## 2 Data and Processing  
### Selection of a Solar Power Plant for Analysis  
Insolation intensity is a key determinant in selecting the site for a solar power plant [17], and it fluctuates with location and timing [18]. To select the solar power plant for our analyses, first, the areas with high insolation intensity in Korea, which could be suitable for installing solar power plants, were found. The data analyses regarding the average insolation of the 14 sites for the past 20 years (1988-2007) showed that Mokpo and Jinju, located on the southwest coast of the Korean Peninsula, had the highest insolation in  
Figure 1: Monthly insolation (MJ/m\({}^{2}\)) averaged for the 9 years from 2011 to 2019.  
Korea [4]. Second, the solar power plants were investigated, which were installed in areas with the highest insolation. Then, the Samcheonpo solar power plants were chosen, which are operated by the Korea Southeast Power Co. because the data on solar power generation and the meteorological variables needed for further analyses could be secured. The white star in the red cone in Figure 2 shows the location of the Samcheonpo solar power plants in Goseong-gun, Gyeongsangnam-do, Korea.  
As shown in Table 1, there are five units with a facility capacity of 0.1 MW in the Samcheonpo solar plants. Among these five units, unit #1 was chosen as the plant for our analyses and, thus, we gathered the data on solar power generation provided by the Korea Southeast Power Co. in Sacheon, Gyeongsangnam-do, Korea through the official government portal for public information release.