esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Life-Cycle Costs (LCC)
### Capital Costs  
The capital costs of a railway tunnel mainly include the material and labour costs required for the project's construction. Unlike the construction of a normal high-speed railway, the construction of a railway tunnel requires the tunnel to be excavated and safely supported before the construction of the railway in the tunnel. Due to complex geological and geotechnical conditions, the majority of the capital costs are the construction of the tunnel itself rather than the subsequent railway construction. For this specific railway tunnel, the capital costs of the tunnel's construction are classified into four main portions based on the construction process of the project. The costs of each portion are primarily the material and labour costs of the construction process.  
For a rock tunnel, tunnel construction is gradually excavated and supported by III to V wall rocks. Generally, the excavation of the tunnel comes first and then the tunnel is temporarily supported by supporting structures followed by the assembly of tunnel lining as permanent support for the tunnel. Open cut and shed tunnels are also of great importance for safety within a rock tunnel. They are mainly constructed using concrete and rocks. The tunnel's entrances need to be refined to smoothly connect the tunnel to the outside railway sections. The costs of other ancillary work should also be included such as grouted rubble, concrete, the spoil area, and catenary. The construction of this high-speed railway tunnel took about two years to complete. The capital costs of the construction of the railway tunnel are summarised in Table 1. The information was obtained from the preliminary construction report [29]. The overall capital costs of this railway tunnel were about 24.25 million USD when the main machine was a tunnel boring machine. For simplicity, the total capital costs per year are reasonably assumed to be the same.  
After the construction of the tunnel, the railway infrastructure was constructed. The cross-section of a typical high-speed railway is shown in Figure 1. Railway components mainly include the superstructure and subgrade. The superstructure, including rail, sleeper, ballast, and sub-ballast, is the direct support of the high-speed train. Therefore, it is the most important part of the railway structure. Based on a report from the World Bank [30], railway construction costs per kilometre for a high-speed railway with a speed up to 350 km/h are exhibited in Table 2. It is clear that civil works for the ballast and subgrade contribute to over 70% of the railway's entire costs [31]. The construction cost of railway tunnels around the world is shown in Table 3 in which the railway construction costs are not included. It can be seen that the construction cost of this project is lower than other tunnels. However, it is to be noted that other examples are long tunnels compared to the tunnel in this study. Therefore, the construction cost is significantly lower.
# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 3 Mine Planning Using GISs
### Open-Pit Optimization
Consequently, planar regression algorithms based on principal component analysis provided the best results at the inter-ramp and bench-face levels. In addition, the optimal sampling window was \(21\times 21\) cells for the inter-ramp and \(9\times 9\) cells for the bench. Subsequent slope stability analysis relying on those assessed slope orientations would provide a more realistic geometry for potential slope instabilities in the design pit [20]. The methodology presented in this study is flexible and can be adopted according to the block size and pit shape of the mine.
## 3 Results and Discussion
### Stability Study of the Analytes
#### 3.3.2 Stability at 37 \({}^{\circ}\)C in Different Medias  
In order to study the release of the two APIs, dipyridamole and acetylsalicylic acid or its hydrolysis product, salicylic acid, into the gastrointestinal tract, two types of digestive fluids were used: Simulated Gastric (SGF) and Simulated Intestinal Fluid (SIF). In addition, PBS was used to study their permeability in Franz cells. Therefore, a stability study (within 3 h) of the three analytes at the three media (37 \({}^{\circ}\)C) followed to identify their degradation.  
Based on the graphs depicted in Figure 6, their relative stability was higher than 80%. The greatest losses occur, as expected, at acetylsalicylic acid in SIF and PBS, as these solutions are alkaline.  
Figure 5: Stability of acetylsalicylic acid, salicylic acid and dipyridamole at: ACN (dark blue), \(\mathrm{ACN}/\mathrm{H}_{2}\)O-30/70 (red), MeOH (green), \(\mathrm{MeOH}/\mathrm{H}_{2}\)O-50/50 (purple), H\({}_{2}\)O/FA 0.2% (pale blue), 0.1 N NaOH (orange), \(n=3\) and RSD \(<1.5\)%.
## 3 Results and Discussion
### X-ray Diffraction Analysis (XRD)  
In Figure 15, the XRD patterns of the representative specimens RS, C5S0, C45S10, and C15S2 are presented. According to [PERSON] et al. [70] as well as [PERSON] and [PERSON] [54], the improvement of the mechanical performance of modified cement-based materials is affected  
Figure 14: Morphology of LWCM: (**a**) RS; (**b**) C15S2; (**c**) C5S0; (**d**) C45S10.  
by both the physical interaction between nano additives and cementitious matrix, and also the chemical transformation during the hydration reaction. The objective of the experiment was to reveal the influence of NS and CNTs on the transformation of chemical compositions owing to the hydration process. The chemical identification of the peaks was carried out via XRD analysis software HighScore Plus 3.0 and relevant literature sources [54; 67; 71; 72; 73].  
Based on the curves in Figure 15, three characteristic peaks of minerals were identified, indicating the formation of CH, calcium aluminum silicate hydrate (CASH), and CaCO\({}_{3}\) in LWCM. In contrast with the pattern of the reference sample, the intensity of CH and CASH peaks exhibited a slight increase for specimen C5S0 and C15S2, but no remarkable change was observed, and no occurrence of new peaks was identified. This indicates that although the presence of CNTs and NS can facilitate cement hydration, the degree is limited. Considering the evolution of compressive and flexural curves, it can be concluded that the effect of physical interaction due to CNTs and NS that fill the voids and bridge the cracks is greater in the cement matrix than that in chemical transformation. However, when the content of CNTs and NS was increased to 0.45% and 1.0%, respectively, for sample C45S10, peaks of hydration products CASH at around 31\({}^{\circ}\), 33\({}^{\circ}\), and 43\({}^{\circ}\), as well as CH at approximately 63\({}^{\circ}\), disappeared on the pattern. Further, the intensity of the remaining peaks that belong to CH and CASH all displayed different extents of decrease, showing the decline of the hydration reaction and formation of hydration products. This is in accordance with the results obtained in TG analysis and testifies to the adverse effects of excessive addition of CNTs and NS which hinders cement hydration. The examination of XRD only determines the existence of crystal phases, notwithstanding, according to [PERSON] [67], at the 20 degree in the range from 28.7 to 35.0, the amorphous background in XRD patterns can demonstrate the presence of amorphous CSH. From the patterns acquired in XRD tests, it can be seen that a large amount of amorphous CSH was yielded during the hydration process for all four samples, and, qualitatively, specimens C15S2 and C5S0 were linked to greater intensity of amorphous CSH compared to that of RS and C45S10.
## 6 Numerical Example
### The Effect of Parameters  
As we saw in Section 3, all of the AOs proposed in this study depend on the two restrictions \(\mathcal{N}\) and q. We observe the changeability effect of parameters (\(\mathcal{N}\) and q) on the ranking order of our proposed AOs.
## 1 Introduction
The RMNCE approach is then applied to develop a RMNCE-based tightly-coupled (RMNCE-TC) architecture, including a new RMNCE-based satellite selection algorithm and a RMNCE-based adaptive unscented Kalman filter (RMNCE-UKF). Additionally, the altitude aiding algorithm [27; 28; 29] is adopted to augment the external measurements for aiding the navigation solutions. The main contributions of this research are summarized below:  
1. A novel RMNCE approach is put forward and proved mathematically. The main advantage of the RMNCE approach is that the noise estimate is only based on measurements and therefore can be isolated from the state estimation error. 2. A novel satellite selection approach is proposed by considering the measurement noise variance of different satellites, which takes both GDOP and the online estimated measurement noise into account to select an optimal satellite combination. Herein, the observation quality of the GNSS measurements can be well monitored and the differences in accuracy of different measurements can be fully considered. 3. An AKF scheme is designed and applied to UKF [30; 31]. The RMNCE-UKF ensures that the measurement noise estimate is uncorrelated to the state estimate, and correspondingly avoids the risk of filter divergence and self-oscillation. Moreover, a new **R** expansion strategy, which can be regarded as an alternative approach to Receiver Autonomous Integrity Monitoring (RAIM) and other detection algorithms, is designed to avoid the negative effects of outlying observations. The remainder of the paper is organized as follows: Section 2 introduces the proposed RMNCE theory and provides a mathematical proof; Section 3 illustrates the proposed satellite selection algorithm; Section 4 gives the adaptive RMNCE-UKF; Section 5 presents an overview of the proposed RMNCE-TC architecture; Section 6 presents both simulation and practical test results verifying the overall system performance, and, finally, Section 7 presents the conclusions of the work.
## 3 Construction of the Basic Network
### Road Sections  
The vulnerability assessment model for road sections, grounded in the theory of complex networks, seeks to identify critical units within the highway network and evaluate their susceptibility during emergencies. In alignment with the demand coverage optimization calculation method referenced in the objective function in Section 2.4, this section further elaborates on the road section vulnerability assessment model from both static and dynamic perspectives. The analysis is categorized into the following two scenarios: node failure and edge failure [27].  
(1) A Model for Identifying Vulnerabilities Related to Node Failures  
\[\mathrm{V_{Nj}}=\mathrm{P_{Nj}}\times\mathrm{Z_{Nj}} \tag{24}\]  
Among the variables, \(\mathrm{V_{Nj}}\) represents the vulnerability identification value associated with the failure of node j, \(\mathrm{P_{Nj}}\) denotes the probability of failure for node j failure, and \(\mathrm{Z_{Nj}}\) signifies the comprehensive measure value index of node j failure.  
(2) A Model for Identifying Vulnerabilities Related to Edge Failures  
\[\mathrm{V_{Lj}}=\mathrm{P_{Lj}}\times\mathrm{Z_{Lj}} \tag{25}\]  
Correspondingly, \(\mathrm{V_{Lj}}\) represents the vulnerability identification value associated with the failure of edge j, \(\mathrm{P_{Lj}}\) is the probability of failure for edge j, and \(\mathrm{Z_{Lj}}\) is the comprehensive measure value index pertaining to the failure of edge j.
## 3 Results
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Baseline**} & \multicolumn{4}{c}{**Follow-Up**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Combined (n = 118)**} & \multicolumn{2}{c}{**TMD-Patients (n = 62)**} & \multicolumn{2}{c}{**TMD-Free (n = 56)**} & \multicolumn{2}{c}{**Combined (n = 100)**} & \multicolumn{2}{c}{**TMD-Patients (n = 54)**} & \multicolumn{2}{c}{**TMD-Free (n = 50)**} \\ \hline Age (years) \({}^{\star}\) & & 24.5 & (19.0) & 27.0 & (0.0) & 24.0 & (12.5) & 25.5 & (19.5) & 30.5 & (33.0) & 24 & (13.0) \\ \hline \multirow{2}{*}{Sex \({}^{\dagger}\)} & Male & 37 & (31) & 13 & (21) & 24 & (43) & 34 & (33) & 12 & (22) & 22 & (44) \\ \cline{2-13}  & Female & 81 & (69) & 49 & (79) & 32 & (57) & 70 & (67) & 42 & (78) & 28 & (56) \\ \hline \multirow{2}{*}{Ethnicity \({}^{\dagger\dagger}\)} & Mori & 4 & (3) & 0 & (0) & 4 & (7) & 4 & (4) & 0 & (0) & 4 & (8) \\ \cline{2-13}  & European & 81 & (69) & 56 & (09) & 25 & (45) & 71 & (68) & 48 & (89) & 23 & (46) \\ \cline{2-13}  & Asian & 30 & (25) & 6 & (10) & 24 & (43) & 26 & (25) & 6 & (11) & 20 & (40) \\ \cline{2-13}  & Other & 3 & (3) & 0 & (0) & 3 & (5) & 3 & (3) & 0 & (0) & 3 & (6) \\ \hline Height (cm) \({}^{\ddagger}\) & & 168.5 & (10.5) & 166.7 & (10.4) & 170.5 & (10.4) & 168.5 & (10.5) & 166.4 & (10.6) & 170.8 & (10.0) \\ \hline Weight (kg) \({}^{\ddagger}\) & & 69.3 & (1.26) & 67.4 & (1.27) & 71.4 & (1.24) & 69.1 & (1.28) & 67.4 & (1.28) & 70.9 & (1.24) \\ \hline TMD Subtype & & & & & & & & & & & & \\ \hline \multirow{2}{*}{TMD Subtype} & & & & & & & & & & & & \\ \cline{2-13}  & Disc & & 17 & (27) & & & & & & & & \\ \cline{2-13}  & Muscle & & 20 & (32) & & & & & & & & \\ \hline Both & & 25 & (40) & & & & & & & & & \\ \hline \hline \end{tabular}   
* Median (QThere was evidence that the jaw-opening forces in the three TMD subtypes changed differently from each other (\(p<0.001\)), with smaller improvements in the disc-displacement group (27.8% higher forces, 95% CI 9.8-48.8%, \(p=0.002\)) compared to the myofasical-pain group (93.5%, 70.0-120.2%, \(p<0.001\)) and disc-displacement-and-myofasical-pain combined group (91.1%, 70.4-114.2%, \(p<0.001\)) subtypes (Figure 4). At follow-up, there was still evidence of lower-jaw-opening forces in all three subtypes compared to TMD-free participants (all pairwise \(p\leq 0.003\)) (Figure 4). Figure 3: Spaghetti plot illustrating jaw-opening forces of TMD and TMD-free group at baseline and after 6-month follow-up. Red line indicates the mean increase in the jaw-opening forces. Figure 2: Receiver operating characteristics (ROCs) curve for forces at baseline.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 2 Related Works
### Lightweight Remote-Sensing Image SR  
Inspired by the success of lightweight natural image SR, lightweight RSISR has received increasing attention. [PERSON] et al. [21] first proposed LGCNet, which aims to enhance super-resolution performance by combining local and global contrast features. FeNet [46] achieved a good balance between computational cost and reconstruction accuracy by using Lightweight Lattice Blocks (LLB) as nonlinear extraction modules and utilizing a nested structure. CTN [48] reduced the network's parameters by using lightweight convolutions instead of traditional 3 \(\times\) 3 convolutions and generated SR images by alternating feature extraction and enhancement. [PERSON] et al. [41] proposed an Attention-Based Multi-level Feature Fusion Network (AMFFN), which ensures efficient SR reconstruction through information distillation and attention-based multi-level feature fusion. [PERSON] et al. [28] proposed a Saliency-Aware Dynamic Routing Network (SalDRN), which employs networks of different depths to handle various regions of RSIs to tackle SR challenges of varying difficulty. Distance Attention Residual Network (DARN) [56] utilizes Distance Attention Blocks (DABs) to efficiently leverage shallow features, effectively mitigating the loss of detailed features during the extraction process of deep CNNs. [PERSON] et al. [57] proposed a Stepwise Fusion Mechanism (SFM) to integrate features retained after progressive distillation, effectively addressing the issue of insufficient information flow caused by channel separation during feature distillation. [PERSON] et al. [27] proposed a Hybrid Attention-Based U-Shaped Network (HAUNet) to effectively explore multi-scale features and enhance global feature representation through hybrid convolution-based attention. To address the issue of losing low-weight background feature information, [PERSON] et al. [58] employed a large-kernel attention mechanism and a multi-scale mechanism to generate background feature weights, thereby increasing attention to neglected information. [PERSON] et al. [32] proposed a high-frequency and low-frequency separation reconstruction strategy, allowing the network to improve the reconstruction details of high-frequency components while maintaining lower model parameters. Unlike methods that focus on developing lightweight network architectures or modules, our approach emphasizes designing more efficient and lightweight feature fusion enhancement structures and focuses on alleviating the burden on convolution layers. Our strategy facilitates comprehensive feature representation using fewer parameters and Multiply-Add Operations (Multi-Adds).
# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 3 Materials and Methods
### Performance Evaluation  
In this section, we briefly overview each metric used to measure performance. Model evaluation metrics play a crucial role in analyzing performance, providing an objective basis for measuring model efficiency and accuracy in text classification tasks. Metrics such as accuracy, precision, recall, and F1 score help determine how well models distinguish between correct classes and minimize errors. These metrics not only facilitate comparison between different models but also enable continuous optimization to ensure the best solution for the specific application.  
Machine learning models are evaluated using various metrics, with accuracy, precision, recall, and F1 score being among the most commonly used. These metrics provide a detailed assessment of a model's ability to make correct predictions while minimizing errors [31]:  
* Accuracy: Accuracy represents the proportion of correct predictions out of the total predictions made by the model. It is suitable when dataset classes are balanced but may be misleading in cases of significant class imbalance.
* Precision: Precision measures the proportion of correctly predicted positive instances out of all positive predictions. It is particularly important when the cost of false positives is high, such as in fraud detection systems.
* Recall: Recall, or sensitivity, measures the proportion of correctly predicted positive instances out of the total actual positive instances. This is critical when it is important to identify all positive cases, as in medical diagnoses.
* F1 score: The F1 score is the harmonic mean of precision and recall, providing a balanced measure, especially useful when there is class imbalance.  
Cross-validation is a fundamental technique for evaluating a machine learning model's performance. The most common method is k-fold cross-validation, where the dataset is divided into k subsets, and the model is trained and validated on various combinations of folds. This process helps reduce variance in performance estimates and provides a more robust evaluation [32].
# Electromagnetically Induced Transparency in All-Dielectric U-Shaped Silicon Metamaterials
###### Abstract  
An analogy of electromagnetically induced transparency (EIT) based on all-dielectric metamaterial is theoretically demonstrated in this paper. The U-shaped Silicon-based metamaterial unit cell comprises a dipole antenna supported by one horizontal nanoscale bar and a quadrupolar antenna supported by two vertical nanoscale bars. The near-field coupling between the two antennas and the reduction of absorption loss lead to a narrow EIT-like transmission window with a high quality-factor of 130, which exhibits a refractive index sensitivity with a figure-of-merit of 29. The group delay of 0.75 ps and the group index of 2035 are obtained in the transmission window. Due to these unique optical properties, the proposed metamaterial structure can find many applications including slow-light devices, optical sensors, enhancement of non-linear processes, and storage of quantum information.  
electromagnetically induced transparency; all-dielectric metamaterial; near-field coupling; destructive interference; slow-light +
Footnote †: journal: Applied Sciences
# Improved Homotopy Perturbation Method for Geometrically Nonlinear Analysis of Space Trusses
## 4 Results and Computational Examples
### Example 1: 24-Member Space Truss  
Figure 2 shows the first example of a three-dimensional space truss with two axes of a Cartesian coordinate system for which the \(z\)-axis represents the out-of-plane dimensions. As it is shown in Figure 2, a concentrated load is applied to the top of the dome-shaped space truss. The space truss consists of 24 members and 13 nodes in total, leading to 21 degrees of freedom. Based on the previously studied models, the modulus of elasticity \(E\) is 6895 kN/cm\({}^{2}\), and area A is 6.452 cm\({}^{2}\) and \(\Delta\)P is considered to be 0.4448 kN.  
Subsequently, the 3D truss is analyzed with the proposed PM method in this article and conventional Newton-Raphson. Figure 3 shows the displacement curve obtained separately based on each method and the computational cost for each method is summarized in Table 1. It is concluded that the PM method is able to precisely predict the behavior, while it reduces the computational cost by 10.9% and the number of iterations by 26.8% compared to classic Newton-Raphson.
# Experimental Damage Identification of a Model Reticulated Shell
## 2 Methodologies  
### The Auto-Regressive Time Series Model  
In a health monitoring system, a series of original acceleration data can be obtained from sensors placed on various locations of a structure with a certain sampling frequency. These data constitute the time series. The initial acceleration data should be pre-processed to remove the mean and the trend. After pre-processing, the acceleration time histories are compared to those time histories from different loading states (i.e., sizes, directions, and positions of loads) and different environmental cases. Assuming the signals before and after the damage are linear stationary, the AR model is established as follows:  
\[X_{t}=\varphi_{1}X_{t-1}+\varphi_{2}X_{t-2}+\cdots+\varphi_{p}X_{t-p}+a_{t} \tag{1}\]  
where \(X_{t}\) is the pre-processed acceleration signal, \(\varphi_{i}\) is the \(i\)th AR coefficients, \(p\) is the order of the AR model, and \(a_{t}\) is the residual term [38].  
In Equation (1), the optimal model order is determined using the Akaike information criteria (AIC) and the AR coefficients are determined using the least squares method. Structural damages can cause changes to the acceleration responses, based on which the identification of damage can be achieved using the difference in AR model coefficients between the damage and the undamaged states of the structure. Using the first four AR coefficients, a damage-sensitive feature, \(DS_{i}\) is defined as follows:  
\[DS_{i}=\varphi_{if}-\varphi_{id} \tag{2}\]where \(DS_{i}\) is the \(i\)th damage-sensitive feature, \(\phi_{if}\) and \(\phi_{id}\) are the \(i\)th AR coefficients for damage and undamaged state, respectively. If the value of \(DS_{i}\) is zero, the structure is in the undamaged state. If not, the structure is in the damaged state.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### Suf  
The authors of [38] developed the SURF algorithm, which is a quick and robust approach for feature detection and extraction in the images. The major attraction of the SURF method is its ability to calculate operators fast by applying box filters, enabling its use for real-time applications. For interest point identification, it utilizes Hessian matrix approximation because of its high performance in terms of computing time and accuracy. There are two phases of creating SURF description. The first stage is to establish a repeatable orientation using data from a circular region around the key-point. The SURF descriptor is then extracted from a square area that is aligned to the specified orientation.