esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Bias Pressures  
The geological section map of the project area shows that the terrain of the station excavation area fluctuates greatly. In some sections, the cover depth difference between the left and right sides of the station is large, causing the vertical pressure to be biased. Taking the entrance section as an example (Figure 11), a simplified diagram of the bias calculation is obtained based on the geological profile and the code for the design of railway tunnels [33]. The rock mass friction angle (\(\varphi_{c}\)) is taken as 23.52\({}^{\circ}\) when the GSI is 50, and the top plate friction angle (\(\theta\)) is taken as 14.11\({}^{\circ}\), which is 0.6 times \(\varphi_{c}\). According to the same method, the bias pressure on three typical bias sections is calculated (Table 6). The data shows that in the sections HMDK15 + 388, HMDK15 + 520 and HMDK15 + 560, the differences between the loads on the vault of the deeply buried side and the shallow buried side are 36.30 kPa, 13.21 kPa and 29.47 kPa, respectively, and those on the arch bottom are 53.12 kPa, 21.10 kPa and 41.24 kPa, respectively. With increasing cover depth, the load difference on both sides of the tunnel increases obviously. Such a large difference indicates that the bias pressure cannot be ignored during excavation. Asymmetrical deformation of the surrounding rock in the working face and an unsymmetrical stress state of the primary support structure easily occur during bias pressure tunnel excavation [34]. Therefore, the pre-reinforcement method of pipe shed grouting should be adopted to improve the unfavorable stress state of the lining under bias pressure [35]. Considering the large excavation section, the double-side heading method with double-side supports also can be adopted to ensure stability during the excavation of Guobo station [36].  
Figure 11: Geological section map and bias pressure calculation map of the entrance section.
## 1 Introduction
[PERSON] et al. [10] presented a robust design optimization method to investigate the manufacturing quality and operation reliability of superconducting magnetic energy storage in the design optimization stage. This paper deals with an enhanced robust design optimization (RDO) method and its application to the strength design problem of seat belt anchorage as related to the front crash safety of a multi-purpose vehicle. In the seat belt anchorage test, the seat frame could be in a state of fracture depending on its structure design. Thickness size variables of the structure members of the seat frame were considered as random design variables, including uncertainties such as manufacturing tolerance, which are an inevitable hazard in the field of automotive parts manufacturing. Strength performances were reviewed by finite element analysis (FEA), applying load and boundary conditions to accommodate the actual seat belt anchorage test, which are regulated in ECE and FMVSS. Probabilistic constraint functions were selected from the strength performances of the seat belt anchorage. The RDO problem was formulated such that the random design variables were determined by minimizing the seat frame weight subject to the probabilistic strength performance constraints evaluated in the reliability analyses, as well as the robustness of the three sigma (\(\sigma\)) level on side constraints. In this study, in order to ascertain the effects of the reliability analysis method on the probabilistic optimal design of the seat frame, both the mean value reliability method (MVRM) and the adaptive importance sampling method (AISM) were adopted in the RDO formulation. The RDO results, such as converged optimum design, numerical characteristics, and reliability probability, were compared according to the reliability analysis methods. The reliability probabilities from the reliability methods applied to the probabilistic optimum design were assessed by Monte Carlo simulation (MCS). In addition, deterministic optimization was carried out to review the characteristics of the probabilistic optimization results from the RDO. In the case of the RDO with the AISM, the structural reliability was fully satisfied for all the constraint functions, so the most reliable structure safety was guaranteed for the seat frame design. This paper is comprised of the following: Section 2 addresses the strength performances and explains the seat belt anchorage test, the fracture characteristics of the seat frame, and the structure analysis results from the FEA; Section 3 addresses the RDO with the MCS reliability analysis and describes the theoretical background and the enhanced RDO procedure; Section 4 addresses the results and discusses the characteristics of the robust optimal solutions according to the reliability analysis method; Section 5 presents the concluding remarks of this study.
## 2 Materials and Method
### Causality Analysis Using Principal Component Regression  
PCR serves as a powerful methodological approach for establishing causality between soil properties and UCS in this study. Unlike traditional correlation analyses that simply identify associations, PCR enables researchers to infer causal relationships by addressing multicollinearity issues and isolating the principal factors that drive soil strength development over time. The PCR analysis incorporated key geotechnical properties such as void ratio, porosity, compression index, pre-consolidation stress and compressibility modulus. These properties were transformed into principal components that represent fundamental dimensions of soil behavior. The core PCR equation for causal analysis can be formally expressed as Equation (5). The time-indexed coefficients (\(\beta_{i,t}\)) are particularly critical for causal inference as they capture the evolving influence of each principal component across different curing periods. The stability or systematic change in these coefficients provides substantive evidence for causality rather than spurious correlation. Figure 4 illustrates the systematic methodology employed in the study, from material collection through experimental testing, multivariate statistical analysis, and ultimately to the optimization of stabilizer proportions based on the findings.  
To strengthen the causal interpretation, the PCR approach can be conceptualized within the [PERSON] causal model framework expressed in Equation (12).  
\[UCS_{i}(1)-UCS_{i}(0)=\tau_{i} \tag{12}\]  
where  
\(\text{UCS}_{i}(1)=\) potential outcome with treatment (e.g., reduced compressibility)  
\(\text{UCS}_{i}(0)=\) potential outcome without treatment  
\(\tau_{i}=\) causal effect for sample i  
By incorporating the principal components as instrumental variables that influence treatment assignment, PCR enables estimation of the average treatment effect (ATE) while controlling for potential confounders expressed in Equation (13).  
\[E[UCS_{i}(1)-UCS_{i}(0)]=E[\beta_{1,t}\times\Delta PC_{1}] \tag{13}\]  
where \(\Delta PC_{1}\) represents the change in the first principal component. This formulation explicitly links the PCR coefficient \(\beta_{1,t}\) to the causal effect of interest.  
Dynamic coefficient analysis, the time-varying coefficients for PC1 across different curing periods provide compelling evidence for a strengthening causal relationship at 7, 14 and 28 days are expressed as Equations (14)-(16).  
\[UCS_{7d}=\beta_{0,7d}-76.84\cdot PC_{1}+21.24\cdot PC_{2}+\varepsilon_{7d} \tag{14}\]  
\[UCS_{14d}=\beta_{0,14d}-139.96\cdot PC_{1}+18.60\cdot PC_{2}+\varepsilon_{14d} \tag{15}\]  
\[UCS_{28d}=\beta_{0,28d}-170.70\cdot PC_{1}+9.74\cdot PC_{2}+\varepsilon_{28d} \tag{16}\]
## 3 Results and Discussion
### Characterization of the Resins
#### 3.1.2 Fourier Transform Infrared Spectroscopy (FTIR)  
The FTIR spectra of activated/inactivated H-form and Na\({}^{+}\)-form resins were measured in the of 4000-600 cm\({}^{-1}\) wavenumber range (Figure 5B). The characteristic peaks for styrene-divinylbenzene linkages in the resin structure were observed in all resins. Peaks at 3022 cm\({}^{-1}\) and 2920 cm\({}^{-1}\) correspond to the C-H bonds in the benzene ring and -CH\({}_{2}\) in the cross-linked polystyrene matrix. The sharp peaks at 1638 cm\({}^{-1}\) and 1616 cm\({}^{-1}\) are representative of the C=C- bond vibrations of the styrene system [35]. Bands at 3471 cm\({}^{-1}\) and 3415 cm\({}^{-1}\) were characteristic of stretching vibrations of O-H bonds and were broader in the H-form resins. Peaks at 1497, 1442, 1406, and 1380 cm\({}^{-1}\) correspond to the stretching and bending of aromatic -CH- bonds of the ST-DVB, respectively [36]. The broad peak between 1166 and 1250 cm\({}^{-1}\) corresponds to the distinctive absorption peaks of the functional sulfonic acid groups (-SO\({}_{3}\)H) [37]. Furthermore, the peak band between 827 cm\({}^{-1}\) and 1114 cm\({}^{-1}\) may correspond to the benzene ring stretching vibrations induced by the resin's ST-DVB link matrix [35]. Signals in the H-form resins (both activated and inactivated) appeared to be less resolved, probably due to the higher water retention capacity, comparing to the Na\({}^{+}\)-form.
## 4 Discussion
### Challenges and Outlook  
In the present study, the model could not be calibrated and validated for multiple years, or a full year. This was due to the lack of in situ measurements during the dry season that hampered the calculation of the annual GPP and the modeling of interseason variation of GPP. Moreover, about half of the total available Sentienl-2 data images had to be removed from the original dataset because the cloud coverage was higher than 30% of the total area of the tale. With the increased availability of both in situ measurements and satellite data in the future, it will be possible to fine-tune the model and study not only interseason but also interannual variation in GPP. This was, however, not the main objective of this study that aimed at assessing the capability of Sentinel-2 data for spatiotemporal upscaling of flux tower GPP measurements. Furthermore, the application of the workflow in different regions and different ecosystems may require further development of the tool and will make the upscaling methodology robust.  
Further analysis to estimate the effect of the flux partitioning method in the calculation of the ground base GPP may need to be carried out. Although the night-time method of flux partitioning proposed by [PERSON] et al. [93] was generally accurate, further research could investigate the day-time method of flux partitioning suggested by [PERSON] et al. [94] or the night-time data-based method proposed by [PERSON] et al. [95]. Using multiple flux partitioning approaches would allow the evaluation of the robustness of the GPP prediction method [96]. Moreover, an uncertainty analysis could also be implemented to better understand the accuracy of the in-situ GPP estimation and its impact on the workflow [97].  
Additionally, further analysis to investigate residual noise in the VI time series may be required. In the current study, the noise in the time series was reduced by removing cloudy pixels, water pixels and pixels not belonging to vegetation (e.g., water bodies, snow, temporal floods, or other temporal phenomena). However other noises arising from surface-viewing geometries or sun sensors can still be hindered [98]. Noise-reduction algorithms such as the Savitzky-Golay filtering [99], the asymmetric Gaussian function fitting [100], the double logistic function fitting [101], or the best index slope extraction (BISE) method [102], might be also implemented in the methodology to increase the accuracies of the derived VIs.
###### Abstract  
This study outlines the applicability of four metaheuristic algorithms, namely, whale optimization algorithm (WOA), league champion optimization (LCA), moth-flame optimization (MFO), and ant colony optimization (ACO), for performance improvement of an artificial neural network (ANN) in analyzing the bearing capacity of footings settled on two-layered soils. To this end, the models estimate the stability/failure of the system by taking into consideration soil key factors. The complexity of each network is optimized through a sensitivity analysis process. The performance of the ensembles is compared with a typical ANN to evaluate the efficiency of the applied optimizers. It was shown that the incorporation of the WOA, LCA, MFO, and ACO algorithms resulted in 14.49%, 13.41%, 18.30%, and 35.75% reductions in the prediction error of the ANN, respectively. Moreover, a ranking system is developed to compare the efficiency of the used models. The results revealed that the ACO-ANN performs most accurately, followed by the MFO-ANN, WOA-ANN, and LCA-ANN. Lastly, the outcomes demonstrated that the ACO-ANN can be a promising alternative to traditional methods used for analyzing the bearing capacity of two-layered soils.  
bearing capacity analysis; artificial neural network; metaheuristic algorithms +
Footnote †: journal: Applied Sciences
# A Novel Disturbance Rejection Control of Roll Channel for Small Air-to-Surface Missiles
## 3 Controller Design Base on Extended State Observer  
### Extended State Observer  
Suppose that the nonlinear uncertain object affected by unknown disturbance is:  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2},\\ \dot{x}_{2}=x_{3},\\ \vdots\\ \dot{x}_{n}=f(x_{1},x_{2},\ldots,x_{n},w(t),t)+bu,\\ y=x_{1},\end{array}\right. \tag{13}\]  
In which, \(f(\cdot)\) is the dynamics equation of the system, \(u(t)  
where \(e_{1}=y-\dot{y}=x_{1}-\hat{x}_{1}\), \(\beta_{i}\) is the observer gain. \(\hat{x}_{i}\) represents the estimation of state \(x_{i}\) and \(i=1,2,\ldots,n\) is the system state variables. \(\hat{x}_{n+1}\) is an estimate of the compound error.  
Reference [31] proves that the tracking purpose of the above formula can be achieved for properly selected nonlinear functions \(g_{1}(x),\ldots,g_{n}(x),g_{n+1}(x)\).  
\[\hat{x}_{1}\to x_{1},\ldots,\hat{x}_{n}\to x_{n},\hat{x}_{n+1}\to x_{n+1} \tag{17}\]  
Through Equation (17), we can obtain:  
\[\hat{x}_{n+1}\to x_{n+1}=d\cdot\Delta f+\Delta bu \tag{18}\]  
Although \(d\cdot\Delta f\) and \(\Delta bu\) are unknown, real-time interference of system operation can still be estimated through ESO. Therefore, the selection of \(g_{i}\) is extremely critical. Currently, most of the following non-smooth functions with linear regions are adopted [32]:  
\[g_{i}(e)=\text{fal}(e,\alpha,\delta)=\begin{cases}e/\delta^{1-\alpha}&|e|> \delta,\\ |e|^{\alpha}\text{sgn}(e)&|e|\leq\delta.\end{cases} \tag{19}\]  
In general, let the nonlinear functions \(g_{1}(x),\ldots,g_{n}(x),g_{n+1}(x)\) is the same, where \(\beta_{i}\) and \(\delta\) are constants greater than zero. The size of \(\beta_{i}\) has a great influence on the controller performance, and is the ESO parameter to be designed. When \(\alpha<1\), this function has characteristics: small error, large gain; large error, small gain.
# Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM-FNN
## 4 Model Construction  
### Model Workflow  
The frame of the intelligent ROP prediction model established in this paper is shown in Figure 6. According to the drilling characteristics of ultra-deep wells, the key influencing factors are analyzed and the model input parameters are preliminarily determined. The characteristic parameters are analyzed by cross relation number, the input parameters are optimized, and the characteristic dimension is reduced. The original data were transformed into time series data, and the drilling rate prediction model was built by LSTM and FNN in a series. By optimizing the super-parameters of the model, the optimal prediction model was obtained.
## 6 Conclusions  
In this paper, we studied the SER performance of AF-based HSTS with a dominant co-channel interferer at the relay. The performance analysis framework was developed by deriving new analytical expressions for the average SER of commonly-used modulation techniques: M-PSK, M-QAM and M-PAM. Simple asymptotic expressions for the corresponding exact analytical expressions were also presented. The presented analyses showed that CCI causes significant degradation in the SER performance of the considered HSTS. Our comprehensive analysis was proven to be useful in understanding how interference characteristics at the relay can degrade the overall system performance, depending on different system and network parameters. Future research may include different channel models for the satellite-terrestrial link, the effects of the fading parameters of the network channels and the performance of other forwarding schemes at the relay.  
This research work is supported by the Important National Science and Technology Specific Project of China under Grant No. 2016 ZX03001022-006, the National Natural Science Foundation of China under Grant Nos. 91438113, 61402283, 61573242, 61401501 and 61304225, the National Science and Technology Major Project under Grant No. GFZ0X0301010708, and the Shanghai Science and Technology Committee under Grant Nos. 15511105100 and 16 DZ1100402.  
Figure 8: Average SER/asymptotic SER versus SNR for BPSK with CCI in average shadowing.  
Figure 9: Average SER/asymptotic SER versus SNR for QPSK with CCI in average shadowing.  
**Author Contributions:** The original idea was proposed by [PERSON] and [PERSON]. The further data analysis was realized by [PERSON] under the supervision of [PERSON] and [PERSON]. [PERSON] wrote the manuscript, and all of the authors participated in amending the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 3 Materials and Methods
### Experimental Setup  
The experimental setup evaluated the performance of the ViTGNN framework for detecting SARS-CoV-2 from CT scan images. The dataset was divided into training, validation, and test sets (80:10:10). ViTGNN used Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to extract graph-based features from CT scans, which were then processed by Vision Transformers (ViT) for high-level feature classification. This hybrid approach ensured that the model leveraged the strengths of both architectures for improved diagnostic accuracy. Training was carried out on a system with the following specifications:128.0 GiB memory, an AMD Ryzen Threadripper 3960X 24-core processor \(\times\) 48, NVIDIA Corporation GA102 [GeForce RTX 3090] graphics, 2.9 TB disk capacity, Ubuntu 22.04.5 LTS (64-bit) operating system, and GNOME version 42.9.  
The training environment used the Adam optimizer with a learning rate of 0.0001. The model was trained for 50 epochs with a batch size of 32, incorporating early stopping to prevent overfitting. Image preprocessing techniques such as CLAHE, Gaussian blurring, and image augmentation enhance image quality and improve feature detection. The implementation was carried out using PyTorch v2.6.0 and PyTorch Lightning v2.3.1, with additional libraries such as NumPy v1.26.4, Pandas v2.2.1, and Matplotlib v3.8.3. ViTGNN performance was assessed using key metrics such as accuracy, precision, recall, F1-score, and AUC. The results demonstrate the ability of the model to effectively detect SARS-CoV-2 from CT scans, highlighting its potential for medical diagnostics and its broader applicability to other healthcare challenges involving complex imaging data.
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 4 Proposed Algorithm  
In this section, we formulate the dynamic UAV-deployment problem based on route prediction. The goal is to predict the sailing trajectory of a user ship by a reinforcement learning algorithm and dynamically deploy UAVs to maximize user coverage based on the sailing trajectory under constrained energy consumption, latency, and computation. We propose a dynamic UAV-deployment scheme for changing user dynamics that combines deep reinforcement learning algorithms for route prediction as well as for UAV deployment.
# Fatigue Resistance Characterization of Warm Asphalt Rubber by Multiple Approaches
## 1 Introduction
Among different WMA additives, commercial organic additives (Sasobit, Licomont, and Asphaltran) were found to enhance rutting resistance while foaming additives (Aspha-min) exerted an indistinct effect [16; 17]. One chemical additive, Evotherm-DAT, was reported to negatively affect rutting resistance [18] while another chemical additive used in Brazil showed limited influence on rutting performance [19]. In terms of fatigue behavior, both the asphalt rubber binder and mixture showed superior performance compared to the conventional ones [20]. However, for fatigue characterization, the author's previous study showed that the ITFT results of WAR mixtures (AR with organic additive performed worse) did not correlated well with the Superpave fatigue test results of their corresponding WAR binders (AR with wax additive performed best) [13]. One potential explanation for this is that the G*sin\(\delta\) value cannot precisely characterize the fatigue property, since this parameter is based on linear viscoelastic properties but not cracking resistance [21]. Meanwhile, the inconsistency may be ascribed to the complicated influence of various WMA additives on asphalt-aggregate adhesion. To this end, the objective of this study is to gain a more comprehensive understanding of the fatigue behavior of WAR with different WMA additives. A series of fatigue tests were conducted on WAR binder, mortar, and mixture specimens. The results of different fatigue tests were summarized and analyzed. This paper is also expected to provide reference to select fatigue-evaluating approaches for rubberized asphalt materials.
# Analysis and Optimization of Output Low-Pass Filter
## 4 Design Trade-Offs
### Filter Design Steps  
The filter design procedure is given below. The design can be realized quickly and optimally with the help of this procedure and the flow diagram in Figure 17. The filter parameters can be quickly calculated by means of the flow diagram using Equations (1)-(8). This systematic approach provides guidance for optimum filter design within the framework of specified standards.  
1. Select the switching frequency for the inverter.
2. Decide which filter configuration to use.
3. Determine the normalized capacitor value to maintain the leading power factor standard (\(Cn\leq 0.12\) pu).
4. Utilize the contour plot of \(P_{d}\) and THD vs. quality factor and cutoff frequency to establish a target for power loss and THD, ensuring THD and \(P_{d}\leq 5\%\).
5. Calculate the value of the inductor to obtain the required cutoff frequency.
6. Calculate the damping resistance to obtain the desired quality factor.  
The design methodology and simple calculation procedure, as seen in Figure 17, offer a more comprehensive approach compared to existing filter design techniques. Although trial-and-error methods can be useful for initial exploration, they lack the systematic framework for optimizing filter parameters across all relevant constraints, including those related to THD, power factor, and power loss [40]. Similarly, graphical and enumeration methods may not fully account for the complex trade-offs between different filter components  
Figure 16: Inverter circuit simulation using PSIM including PV array, DC link inductor, inverter (waveshaper and unfolding circuit), low-pass output filter, grid model, and control blocks (feedforward).  
and operating conditions [16]. The proposed method, however, directly addresses these trade-offs, as evidenced by the contour plots (Figures 14 and 15) showing the relationship between filter parameters, quality factor, and performance metrics. This systematic approach offers not only efficient parameter calculation but also valuable design insights unavailable through less structured methods.