esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Coating Deposition  
The coatings were deposited by magnetron sputtering in direct current mode using a TaSi\({}_{2}\)-Ta\({}_{3}\)B\({}_{4}\)-(Ta,Zr)B\({}_{2}\) (70.8% Ta, 18.6%Si, 7.4% Zr, 2.9% B) ceramic target [30] with a diameter of 120 mm and a thickness of 6 mm. Coatings were manufactured using a UVN-2M vacuum installation [31]. The power supplied to the magnetron was maintained at 1 kW using a Pinnacle + 5 \(\times\) 5 power supply manufactured by Advanced Energy (Denver, CO, USA). The residual pressure was 0.005 Pa. The substrate temperature was no higher than 250 \({}^{\circ}\)C. The coating deposition rate was 160-180 nm/min. The coatings were deposited at an operating pressure of 0.1-0.2 Pa in Ar + C\({}_{2}\)H\({}_{4}\) and Ar + N\({}_{2}\) gas mixtures based on Ar (99.9995%), C\({}_{2}\)H\({}_{4}\) (99.95%) and N\({}_{2}\) (99.999%) gases. The gas flow was controlled by a three-channel gas supply system: the flow rate of argon was 25 sccm, and the flow rate of the reaction gases (C\({}_{2}\)H\({}_{4}\) or N\({}_{2}\)) was 5 sccm. The deposition time was 40 min.  
Plates of polycrystalline aluminum oxide (trademark VK-100-1) were used as substrates. The substrates were cleaned in isopropyl alcohol on an UZDN-2T unit at a frequency of 22 kHz for 5 min. Before deposition, the substrates were also cleaned in vacuum using an ion source (Ar+ ions, 2 keV) for 20 min.
## 5 Relationship between Contact Forces and Wear as well as New Insights
### Normal and Longitudinal Forces Do Not Exactly Follow Corrugation in Wavelength and Phase  
Figure 11 shows the normal contact force (\(F_{N}\)) and longitudinal contact force (\(F_{L}\)) when the rail is smooth and when corrugation is present. Although the wavelength of the modeled corrugation is constant (30 mm), the wavelengths of the resulting dynamic wheel-rail contact forces vary along the rail (Figure 11a). Specifically, \(F_{N}\) initially lags behind the corrugation, i.e., the peak of \(F_{N}\) appears to the left side of the corresponding corrugation peak. When approaching the sleeper, which has a width of 140 mm and is centered at 1.20 m, the force catches up and becomes in phase with the corrugation at approximately 1.1 m, i.e., near the edge of the sleeper. The force subsequently tends to lead the corrugation. A similar situation applies to \(F_{L}\) (Figure 11b). However, \(F_{N}\) leads \(F_{L}\) initially, and the two forces later tend to be in phase at approximately 1.23 m, with subsequent lagging of \(F_{N}\). Note that the wear is also strongest at approximately 1.23 m (according to Section 4.2).  
The PSD analysis in Figure 12a reveals that the main frequency components of \(F_{N}\) and \(F_{L}\) are 1257 Hz and 1226 Hz, respectively. The corresponding vertical and longitudinal rail vibration modes closest to the corrugation passing frequency of 1297 Hz are 1185 and 1291 Hz, which means that the normal and longitudinal contact forces do not exactly follow the excitation and have frequencies that are different from the natural vibration modes. Previous studies have either explicitly or implicitly assumed that the frequencies of the longitudinal contact force, the normal contact force and the vertical natural mode are the same as the corrugation passing frequency; however, the longitudinal mode has not been previously considered.  
Figure 11: Dynamic wheel-rail contact forces obtained for the corrugation in Figure 3 with \(\theta\) = 0 and P4 at 1.2 m (1.2 m is in the middle of a sleeper and 0.9 m is at the midpoint between two sleepers.). (**a**) Normal contact force. (**b**) Longitudinal contact force.  
Figure 12: Relationships between the PSD and frequencies of the contact forces and corrugation wavelength obtained for the corrugation in Figure 3b with \(\theta\) = 0, P4 at 1.2 m and A = 20 \(\upmu\)m. Note that in (**b**), \(f_{ corrugation}\) is not constant, but changes with the corrugation wavelength. Thus, although the PSD of \(F_{L}\) varies in a narrower band than \(F_{N}\) in (**a**), the opposite pattern is observed in (**b**). (**a**) PSD of \(F_{N}\) (upper) and \(F_{L}\) (lower). (**b**) Ratio of corrugation over contact force frequency.
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 3 Proposed Method
### Data Acquisition  
Currently there are some databases that can be used for sleepiness detection such as NTHU-DDD, YawDDD, MRL Eye, UTA-RLDD, etc. This proposal uses the night-time yawning-microsleep-eyeblink-driver distraction (NITYMED) database, a database recently made available containing videos of males and females in a real night driving environment manifesting drowsiness symptoms through their eyes and mouth. NITYMED consists of 130 videos in mp4 format at 25 fps in 1080p (FullHD) and 720p (HD) resolutions. Compared to the other databases, NITYMED is more realistic for the purpose of this work, which justifies its use.
## 4 Results
### Zircon Hf Isotope
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Spot**} & **Age** & \multirow{2}{*}{\({}^{176}\)Hf/\({}^{177}\)Hf} & \multirow{2}{*}{\(\pm\)(\(\pm\)**\(\sigma\)**)} & **12\%** & **14\%** & **17\%** & **16\%** & **17\%** & **16\%** & **17\%** & **16\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** & **17\%** \\  & **(Ma)** & & & & & & & & & & & & & & & \\ \hline
1 & 216 & 0.282094 & 0.000016 & 0.000553 & 0.000002 & 0.026018 & 0.282092 & \(-\)24.0 & \(-\)19.3 & 0.6 & 1612 & 3420 & 22 & 2473 \\
2 & 206 & 0.282042 & 0.000016 & 0.000127 & 0.000001 & 0.005597 & 0.282041 & \(-\)25.8 & \(-\)21.3 & 0.6 & 1665 & 3593 & 21 & 2592 \\
3 & 213 & 0.282078 & 0.000021 & 0.001187 & 0.000043 & 0.055077 & 0.282073 & \(-\)24.6 & \(-\)20.1 & 0.7 & 1661 & 3486 & 29 & 2516 \\
4 & 218 & 0.282129 & 0.000021 & 0.000207 & 0.000099 & 0.079955 & 0.282121 & \(-\)22.7 & \(-\)18.3 & 0.7 & 1626 & 3324 & 29 & 2407 \\
5 & 215 & 0.282080 & 0.000016 & 0.000358 & 0.000008 & 0.014805 & 0.282078 & \(-\)24.5 & \(-\)19.8 & 0.6 & 1623 & 3464 & 21 & 2504 \\
6 & 219 & 0.282056 & 0.00015 & 0.000135 & 0.00002 & 0.005020 & 0.082055 & \(-\)25.3 & \(-\)20.6 & 0.5 & 1646 & 3532 & 21 & 2553 \\
7 & 213 & 0.282032 & 0.000016 & 0.000228 & 0.000005 & 0.009181 & 0.282031 & \(-\)26.2 & \(-\)21.6 & 0.6 & 1683 & 3616 & 22 & 2611 \\
8 & 221 & 0.282002 & 0.000016 & 0.000366 & 0.00011 & 0.013612 & 0.282001 & \(-\)27.2 & \(-\)22.4 & 0.6 & 1729 & 3669 & 25 & 2672 \\
9 & 214 & 0.282032 & 0.000016 & 0.000362 & 0.00007 & 0.03017 & 0.282001 & \(-\)27.2 & \(-\)22.6 & 0.6 & 1726 & 3706 & 21 & 2675 \\
10 & 225 & 0.282064 & 0.000019 & 0.000670 & 0.000011 & 0.028462 & 0.282061 & \(-\)25.0 & \(-\)20.2 & 0.7 & 1657 & 3502 & 27 & 2534 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LA–MC–ICP–MS zircon Lu–Hf isotopic data of the biotite schist. rims was \(-14.0\)\(\pm\) 0.4 (n = 6) (Figure 7). Additionally, the T\({}_{\text{DM}}\)\({}^{\text{C}}\)(Hf) ages for these zircon rims fell within the range of 2176 Ma to 2092 Ma. All the Lu-Hf isotope analysis results obtained from both zircon cores and rims are summarized in Table 6, with the reported errors representing the 2\(\sigma\) of the mean.
## 2 Implementation of CBFCL in PV Systems
In the literature, traditional control methods are widely used in the protection system against the fault current of the grid-connected PVPP system. The ML algorithm provides an intelligent way to overcome the variables that harm the PV panel system. At the same time, traditional protection techniques struggle to reduce the causes that can cause damage during the fault time. In this study, the output voltage value (\(V_{dc}\)) of the PV panel system is set to 481. Figure 3: (**a**) Design of a capacitive bridge fault current limiter, and (**b**) flowchart of proposed control algorithm. Figure 2: Design of DC chopper. The machine learning method used in this study is the supervised learning approach. Supervised learning is trained with labeled data and makes predictions for new inputs. This study uses the Classification Learner Toolbox tool in MATLAB 2022b to test and compare the performance of several classification algorithms, including Tree, SVM, KNN, and Ensemble. Each of the classification methods has different classifier functions. The SVM algorithm has Coarse Gaussian, Medium Gaussian, Fine Gaussian, Cubic, Quadratic, and Linear classifier functions. The ML model is developed to classify different operating conditions of the grid-connected PVPP system. The input of the model is defined as the DC-link voltage, and the output is defined as the switching signal of the semiconductor. During the training process, the model is trained using the labeled dataset, and the obtained predictions are compared with the actual target values, and their accuracy is analyzed. To evaluate the performance of the model, accuracy, precision, recall, and F1-score are calculated. Each metric is obtained using the following formulas:  
\[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}\times 100 \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100 \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100 \tag{3}\]  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\times 100 \tag{4}\]  
The DC-link voltage value is measured in real time and defined as the input of the ML algorithm. The ML algorithm minimizes and predicts the oscillations and faults of the power grid, thus improving the stability of the system. Also, the output of the ML approach is described as the switching signal of the semiconductor switching element in CBFCL. The ML algorithm detects and generates output signals when there is a fluctuation in the \(V_{dc}\) voltage value [34].
# On-Orbit Polarization Calibration for Multichannel Polarimetric Camera
## 2 The Multichannel Polarimetric Camera and Main Polarization Elements
### Main Polarization Parameters of the Polarimetric Camera
A new calibration model and measurement scheme are proposed.
# Understanding Consumer Acceptance for Blockchain-Based Digital Payment Systems in Bhutan
## 3 Theoretical Framework and Hypothesis Development
### Facilitating Conditions  
Facilitating conditions (FCs) are the resources, infrastructure, and support mechanisms needed to use a technology [53; 93]. It demonstrates the need for an enabling environment in which users receive tools and support to work properly with emerging technologies [111]. It emphasizes the need for robust infrastructure, such as secure Internet connections, access to devices, and transparent regulatory environments, to overcome adoption barriers [112; 113]. In Bhutan, a nation with a relatively underdeveloped digital infrastructure, favorable conditions are crucial for the implementation of blockchain. Reliable Internet access in the countryside, poor access to digital technology, and regulatory uncertainties are the major barriers [51]. State-sponsored projects to build connections, regulations, and technical support are vital to a blockchain-friendly ecosystem [114]. Expanding access to the Internet in rural areas and subsidizing devices could drastically reduce user barriers to entry [115; 116]. Education and training provide users with the ability to use blockchain [117]. Public documentation, practical workshops, and public education campaigns can close knowledge gaps and create user acceptance [118; 119]. Technology providers and community leaders can further strengthen the infrastructure to make consumers feel empowered to use and embrace blockchain in digital payments and business processes [14; 120]. By tackling these infrastructure- and resource-related barriers, Bhutan can create a platform that supports the adoption of blockchain at scale. Stimulating conditions not only eliminate technical and logistical barriers but create trust and readiness of consumers so that blockchain can seamlessly blend into the financial and business ecosystems of the country. Therefore, we propose the following hypothesis:  
**H4**: _Facilitating conditions positively influence consumer acceptance of blockchain-based digital payments in Bhutan._
# Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning
## 2 Problem Statement and Model Setup
### Predictive Frameworks
#### 2.2.1 Model M1: \(k\)NN  
Consider a training dataset \(S_{N}\) and a new observation with feature vector \(x_{0}\). We predict the output value of the new observation by calculating the average value of the \(k\)-nearest training points to \(x_{0}\) under the defined distance metric (e.g., Euclidean distance). Mathematically, the output value of the new observation using \(k\)NN could be defined as  
**Model M1**:  
\[f^{k\text{NN}}(x_{0})=\frac{1}{k}\sum_{i\in\mathcal{N}_{k}(x_{0})}y_{i}, \tag{10}\]  
where \(\mathcal{N}_{k}(x_{0})=\left\{i=1,\dots,N:\sum_{j=1}^{N}\mathbb{I}\big{[}\|x_ {0}-x_{i}\|\geq\big{\|}x_{0}-x_{j}\big{\|}\big{]}\leq k\right\}\) is the neighborhood set of the \(k\)-nearest data points to \(x_{0}\). Here, \(\|\cdot\|\) denotes the Euclidean norm. \(k\) is a hyperparameter, and the optimal value \(k^{*}\) can be determined by validation.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.9 Practical Challenges in AI-Based IM
Community-driven initiatives such as standardized key performance indicators for AI explainability in critical applications (e.g., URLLC and aerial/NTN), open testbeds, and joint development of interoperable EAPP for RICs can accelerate the path toward scalable, trustworthy B5G deployments. Pursuing these directions will not only enhance IM in B5G networks but also contribute to the foundational technologies required for future 6G networks.
## 4 Results
The confusion matrix shown in Figure 9 was generated using the optimized Random Forest model, which was trained using 5-fold cross-validation during hyperparameter tuning. However, these results specifically reflect the performance on the held-out test set, derived from an 80/20 split after shuffling the dataset. This test set was not used during model training or validation, ensuring that the confusion matrix truly represents the model's generalization performance. Each row represents the true class, and each column represents the predicted class, with the values in the matrix indicating the number of occurrences. The diagonal elements (e.g., 29 for Class 0, 69 for Class 3, and 73 for Class 5) represent the correctly classified instances, indicating good model performance on these classes. However, several misclassifications are evident, especially for certain classes like Class 4 (15 correctly classified and 29 misclassified), Class 2 (46 correctly classified and 5 misclassified), and Class 7 (only 1 predicted correctly). These misclassifications suggest that the model struggles more with certain fault categories, possibly due to class imbalance or the subtle differences between classes. Additionally, the model performs better with larger classes, such as Class 5, which has a large number of samples, compared to smaller classes with fewer samples, leading to potential bias toward the majority class. The distribution of the labeled data across the eight fault categories is as follows: Class 3 (vibration impulses) contains 474 samples. Class 5 (rotation frequency and harmonics) has 452 samples. Class 2 (wideband vibration impulses) has 263 samples, and Class 4 (high-frequency vibrations) includes 231 samples. In comparison, Class 0 (ultrasonic level) consists of 180 samples, while Class 1, Class 7, and Class 6 are severely underrepresented with only 12, 14, and 5 samples, respectively. This class imbalance is an important fac  
Figure 8: Training and validation accuracy scores. tor that affects model performance, particularly for minority classes, and explains the misclassifications observed in the confusion matrix.
# Grouting Process Simulation Based on 3D Fracture Network Considering Fluid-Structure Interaction
###### Abstract  
Grouting has always been the main engineering measure of ground improvement and foundation remediation of hydraulic structures. Due to complex geological conditions and the interactions between the grout and the fractured rock mass, which poses a serious challenge to the grouting diffusion mechanism analysis, fracture grouting has been a research hotspot for a long time. In order to throw light on the grout diffusion process in the fractured rock mass and the influence of grout on the fracture network, and to achieve more realistic grouting numerical simulation, in this paper a grouting process simulation approach considering fluid-structure interaction is developed based on the 3D fractured network model. Firstly, the relationship between fracture apertures and trace lengths is used to obtain a more realistic value of fracture aperture; then a more reliable model is established; subsequently, based on the 3D fracture network model, different numerical models are established to calculate fluid dynamics (grout) and structure deformation (fractured rock mass), and the results are exchanged at the fluid-structure interface to realize the grouting process simulation using two-way fluid-structure interaction method. Finally, the approach is applied to analyze the grouting performance of a hydropower station X, and the results show that the grouting simulation considering fluid-structure interaction are more realistic and can simultaneously reveal the diffusion of grout and the deformation of fracture, which indicates that it is necessary to consider the effect of fluid-structure interaction in grouting simulation. The results can provide more valuable information for grouting construction.  
Keywords:grouting; fracture network modeling; numerical simulation; fluid-structure interaction +
Footnote †: journal: Applied Sciences
## 6 Evaluation and Analysis
### Performance Evaluation on the Total Number of Secret Keys Produced  
Figure 7 demonstrates the total number of secret keys produced (\(TK\)) versus the number of requests per minute on different topologies, where our proposed algorithms show much higher value than the benchmark algorithms. From Figure 7a, we can see that the \(TK\) for the MS-TAR algorithm is 11.8 times larger than that for the Benchmark-LFU. The reason is that the topology abstraction-based routing scheme can fully utilize the time to produce and inject keys into the QKPs without wastage. Meanwhile, it establishes the quantum link for each pair of sites with the lowest distance to generate the highest key rate. We can observe that the relationship of \(TK\) for benchmark algorithms is the same with that of \(SP\) for benchmark algorithms. This is because the keys in the benchmark algorithms are produced based on the requirements of the requests. Hence, the higher the number of successful requests for the benchmark algorithm, the more keys will be  
Figure 6: Average number of relaying hops versus number of requests per minute on the (**a**) 6-plane Star topology, (**b**) 6-plane Delta topology, (**c**) 7-plane Star topology and (**d**) 7-plane Delta topology.  
produced throughout the entire process. In addition, the LF-TAR and MS-TAR algorithms can produce more keys than the GF-TAR algorithm, which can be attributed to the extra keys produced by GEO-based QKD in the former two algorithms. It can also be observed that the _TK_ of MS-TAR is higher than that of LF-TAR. This is due to the fact that the GEOs are linked to the closest sites for achieving a higher key rate in the MS-TAR algorithm. However, in the LF-TAR algorithm, the GEO-based QKD is used as an alternative plan to produce keys for the requirements of the requests which are not fulfilled by LEO-based QKD. The time resource wastage of the GEO satellites for QKD is the major reason for a relatively lower _TK_ of the LF-TAR algorithm compared to that of the MS-TAR algorithm.
# Transit Traffic Filtration Algorithm from Cleaned Matched License Plate Data
## 3 Materials and Methods  
To overcome the basic assumptions and relative merits associated with current algorithms to classify transit travel times, a new method is proposed based on the statistical analysis of measured travel times in the collected origin-destination pairings. It is vital to highlight here that the proposed method is not aiming at true travel time estimation but rather filtration of the travels with intermediate stops between origin-destination pairings for the purpose of transit traffic counting and analysis. To ensure a clear representation of the steps performed, this section is divided into three parts, first illustrating the overall  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Assumptions** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Percentile} & \multirow{3}{*}{20\% of observations classified as outliers} & \multirow{3}{*}{Simple and easy to use} & Algorithm incorrectly classifies 20\% of data as outliers without regard to their validity \\ \hline \multirow{3}{*}{Mean absolute deviation} & Observations are classified as outliers if they fall outside a validity range of 3 MAD & Simple and easy to use & Algorithm classifies all values outside the validity range as outliers without regard to their validity \\ \hline \multirow{3}{*}{[PERSON], [PERSON] algorithm [8]} & Algorithm uses time intervals; validity range is based on observations in previous time interval & \multirow{3}{*}{Takes into account the relationship between neighboring time intervals} & Hard to apply, high number of user-defined parameters, does not react to abrupt changes in travel times \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [7]} & Vehicles with valid travel time observation will tend not to be overtaken, and if on multilane road then travel times will not be greatly dissimilar to overtaking vehicles & \multirow{3}{*}{Simple and easy to use} & Depends on tolerance time parameter, which can be obtained from known distribution of valid travel times \\ \hline \multirow{3}{*}{[PERSON] algorithm [15]} & Algorithm uses time intervals; validity range is based on observations in previous time interval & \multirow{3}{*}{Takes into consideration the relationship between neighboring time intervals, could tract abrupt changes} & Hard to calibrate due to a large number of user-defined parameters \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of the most prominent transit traffic filtration algorithms found in the literature.  
workflow of method development and then describing the proposed method with a focus on mathematical and statistical operations involved in the algorithm. Lastly, the final subsection is dedicated to a description of the verification methods of the proposed method.