esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Complex Electromagnetic Environmental Effects Assessment  
In a broad sense, the study of electromagnetic environmental effects refers to research on the complex electromagnetic environment. Not only does it study a specific test object but also the whole electromagnetic environment. That is to say, various definitions of their effects belong to a branch of complex electromagnetic environmental research. The current research is mainly focused on quantitative assessment. According to our extensive literature review, we divide the study of electromagnetic environmental effects into two categories: the assessment of the complex objective electromagnetic environment for a specific environment and the assessment of the effects of the subjective electromagnetic environment for a certain type of equipment (object) [21]. The research architecture of electromagnetic environmental effects assessment is shown in Figure 1.  
The objective effects of the electromagnetic environment through an assessment keep a watchful eye on the quantitative evaluation of that environment's complexity in a specific space, time, and frequency band instead of the equipment's. However, the subjective assessment effects of the electromagnetic environment are closely related to a piece of equipment. The two types of assessments complement each other. The former usually grades the complexity of the electromagnetic environment according to the equipment's decreasing work efficiency after interference [22]. Simultaneously, the latter always adopts the methods of an objective effects assessment and then carries out deep research, innovation research, and integration research based on these methods. Although most current research focuses on the electromagnetic environmental effect assessment of subjective equipment, the objective effects assessment still plays a vital role in the development of electromagnetic-related research. To make it easier to evaluate, the electromagnetic environmental effects assessment systems involving previous research findings are developed throughout this review.
## 3 Test Results and Discussion
### Crack Pattern and Failure Modes
Flexural cracks penetrated further at a higher load, along with other flexural cracks in the deck slab as appeared in Figure 9c,d. However, a sudden diagonal tension crack appeared in the deck slab at a jacking load of 90 kN, respectively. Cracks propagated further till the barrier could not sustain a jacking load beyond 163.41 kN. Figure 8: Views of the crack pattern after the test specimen B-2 failed. (**a**) Shear crack on the right side of the barrier wall. (**b**) Shear crack on the left side of the barrier wall. (**c**) Rear side concrete crushing at the end of the shear crack on the back side of the barrier wall. Figure 9: Views of the diagonal tension crack at the barrier–deck overhang corner after the failure of the tested specimens B-3 and B-4. (**a**) Right side, B-3; (**b**) Left side, B-3; (**c**) Right side, B-4; (**d**) Left side, B-4. Specimen B-5 represents an interior location of a barrier wall connected to a non-deformable concrete slab using postinstalled, straight-end GFRP bars. Figure 10a,b show different views of the crack pattern of the tested barrier specimen after failure. In this specimen, the first visible crack was observed in the front side of the barrier wall at the barrier-deck junction at 30 kN jacking load, followed by a flexural crack at the intersection of the two tapered portions of the front face of the barrier wall at 70 kN jacking load. These flexural cracks penetrated through the barrier thickness with an increase in the jacking load. However, sudden concrete vertical splitting appeared approximately at the embedded GFRP bar location at a load of 140 kN, followed by signs of concrete breakout close to the end of the embedded bar in the slab at a jacking load of 159 kN. These concrete breakout cracks extended laterally in the slab and towards the back face of the barrier wall at higher loads till the barrier could not sustain a jacking load beyond 159.74 kN. Specim B-6 represents an end location of the barrier wall connected to a non-deformable concrete slab using postinstalled, straight-end GFRP bars. The amount of vertical reinforcement at the front face was doubled to represent the case of an end segment of the barrier wall as depicted in Figure 4b. Figure 10c,d show different views of the crack pattern of the tested barrier specimen after failure. The first visible crack was observed in the front side of the barrier wall at the barrier-deck junction at 20 kN jacking load, followed by a flexural crack at the intersection of the two tapered portions of the front face of the barrier wall at 60 kN jacking load. These flexural cracks penetrated through the barrier thickness with an increase in the jacking load. Nevertheless, a sudden shear crack appeared at the middle of the top tapered portion of the barrier wall at a jacking load of 180 kN and the specimens could not absorb the jacking load beyond 186.74 kN.
# Comparison of Modern Deep Learning Models for Speaker Verification
## 3 Aim of Research  
The objective of this study is to identify the most effective state-of-the-art speaker embedding models for verifying speakers, with a special focus on international voices. We assess the models' ability to verify speakers using a limited set of short utterances, each no longer than 10 s and exclusively in Ukrainian. These utterances were collected from open sources that fall under the definition of \"in the wild\". The findings aim to provide a comprehensive guide for speaker verification and offer insights for selecting appropriate speaker embedding models in both research and commercial environments.
## 3 Basic Theory of Slope Characterization Models  
### Basic Guidelines for Ubiquitous-Joint Model of Layered Slopes  
Numerous scholars have modified and extended the ideal elastoplastic Mohr-Coulomb constitutive model based on specific research objects. The ubiquitous-joint model [35] is an extension of the Mohr-Coulomb model, wherein joint surfaces are added within the Mohr-Coulomb body. These joint surfaces are implicit joints, meaning they are not explicitly displayed in the slope model, and they also follow the Mohr-Coulomb yield criterion. The ubiquitous-joint model simultaneously considers the physical mechanical properties of both the rock matrix and joints.
# Ultra-High-Cycle Fatigue Life Prediction of Metallic Materials Based on Machine Learning
###### Abstract  
The fatigue life evaluation of metallic materials plays an important role in ensuring the safety and long service life of metal structures. To further improve the accuracy and efficiency of the ultra-high-cycle fatigue life prediction of metallic materials, a new prediction method using machine learning was proposed. The training database contained the ultra-high-cycle fatigue life of different metallic materials obtained from fatigue tests, and two fatigue life prediction models were constructed based on the gradient boosting (GB) and random forest (RF) algorithms. The mean square error and the coefficient of determination were applied to evaluate the performance of the two models, and their advantages and application scenarios were also discussed. The ultra-high-cycle fatigue life of GCr15 bearing steel was predicted by the constructed models. It was found that only one datapoint of the GB model exceeded the triple error band, and the RF model had higher stability. The network model coefficient of determination and mean square error for the GB and RF models were 0.78, 0.79 and 0.69, 3.79, respectively. Both models could predict the ultra-high-cycle fatigue life of metallic materials quickly and effectively.  
fatigue life prediction; machine learning; ultra-high-cycle fatigue; metallic materials 2023
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 5 Numerical Simulations
### Effect of the Association Probability  
In Figure 6, we plot the energy efficiency proportional fairness of total two-tier heterogeneous WPCNs with respect to the user association probability of tier 2 for different values of the transmit power of BSs. We obtain that with the increase of user association probability of tier 2, the average energy efficiency proportional fairness increases quickly and then remains unchanged. Due to the transmit power of BSs in tier 2 being less than that of BSs in tier 1, the energy efficiency proportional  
Figure 5: The energy efficiency proportional fairness of total two-tier heterogeneous WPCNs with respect to the density of BSs in tier 2 for different values of the tier-2 cell association bias B2.  
fairness utility function of tier 2 is larger than that of tier 1 from (7). Therefore, nodes associated with BSs in tier 2 will obtain a higher average energy efficiency proportional fairness. Meanwhile, the energy efficiency proportional fairness remains unchanged as the user association probability gets closer to 1 as shown in Figure 6. This is because when the association probability gets close to 1, the load of tier 2 is balanced such that the nodes connected with BSs in tier 1 will not transfer to BSs in tier 2. In this case, the energy efficiency proportional fairness is a constant. Furthermore, we note that the mean energy efficiency proportional fairness decreases with the transmit power of BSs in tier 2 increases for the same association probability. It can be explained from (7) that the energy efficiency proportional fairness increases with the reduction of transmit power. When the transmit power of BSs in tier 2 decreases, the energy efficiency proportional fairness in tier 2 increases and that of the total network also increases.
## 5 Results and Discussion
### Strain Distribution
#### 5.3.2 The Influence of Pipeline Diameter and Wall Thickness  
Figure 18a,b present the strain-time (\(\varepsilon\)-\(t\)) curves at the mid-span of pipelines considering different values of \(D\) (89 mm, 114 mm, and 139 mm) and \(T\) (2.5 mm, 3.0 mm, and 3.5 mm), respectively. From Figure 18, it can be seen that the \(\varepsilon\)-\(t\) curves depicted from FEA agreed well with those from EXP, which indicates that the developed FEA model is reasonable to predict the \(\varepsilon\)-\(t\) curves of buried pipelines under impact loads. Furthermore, similar to the above experimental results, the effect of \(D\) on \(\varepsilon\) was more obvious than that of \(T\) on \(\varepsilon\). Moreover, \(\varepsilon\) increased with the increase of \(D\), while it decreased with the increase of \(T\). The main reason is that the longitudinal stiffness became small with the increase of  
Figure 16: The influence of impact height (\(H\)) on strain: (**a**) comparisons of strain–time (\(\varepsilon\)–\(t\)) curves between FEA and EXP results under different \(H\); (**b**) comparisons of the peak strain (\(\varepsilon_{\max}\)) among FEA, EXP, and THE results under different H.  
Figure 17: The influence of impact height (\(H\)) on strain distribution: (**a**) \(H\) = 0.5 m; (**b**) \(H\) = 1.0 m; (**c**) \(H\) = 1.5 m; (**d**) \(H\) = 2.0 m.  
\(D\), and the pipeline mainly underwent longitudinal bending deformation under the vertical impact load. In addition, a thick wall is helpful to improve the whole rigidity of pipeline. Therefore, a small diameter and a thick wall can improve the ability of the pipeline to resist the impact load.  
Figure 19 plots the influence of diameter (\(D\)) and wall thickness (\(T\)) on the strain distribution of specimens. The maximum strain region increased with the increase of \(D\), whereas it decreased with the increase of \(T\), which is in good agreement with the experimental results.  
Figure 19: _Cont._  
Figure 18: Comparisons of strain–time (\(\varepsilon\)–\(t\)) curves between FEA and EXP results under different diameter (\(D\)) and wall thickness (\(T\)): (**a**) \(D\); (**b**) \(T\).
Nevertheless, the time-averaged array gain is a meaningful indicator of the typical EMF exposure induced by a BS. As array antennas adapt their radiation patterns to the environment, it is essential to include this inherently dynamic attribute in the modeling. In this article, we compare codebook beamforming and the aforementioned Massive MIMO transmission precoding schemes in terms of the maximum time-averaged antenna array gain they yield to distill conclusions about their effect on the EMF exposure in the serviced area. This problem was first addressed in [6] for a single antenna array performing codebook-based beamforming. Several analytically-defined DOD distributions, acting as a proxy for the distributions of users inside the serviced area (i.e., uniform, cosine in elevation, and azimuth) were analyzed. All UEs were modeled in free space, thus having the LOS to the BS (no environment model was included). An analytic network utilization model was implemented to determine the number of simultaneously active UEs. The results showed around 6 dB reduction for the 95 th percentile of the maximum time-average BS gain as compared to the theoretical maximum, for high system utilization values. In [7], the approach of the authors of [6] was extended to a BS capable of MRT precoding (dubbed eigen-beamforming [7] or conjugate beamforming [8]). The 3 GPP statistical model [9] was used with an urban environment, a single 64-element BS, and both indoor and outdoor UEs. Depending on the number of simultaneously served UEs, the 95 th percentile of the compliance distance (which is proportional to the maximum time-averaged BS gain [5]) constituted 30% to 50% of the theoretical maximum. This contribution builds upon and extends the approach proposed in [6; 7] to more realistic precoding schemes and a more advanced Ray-Tracing (RT) channel model. The RT modeling yields _spatially consistent_ channels, which depends on the environment geometry and UEs locations. For the MU-MIMO systems one important implication is a realistic UE DOD distribution, which governs the beam directions. The magnitude of the inter-UE channel correlation depending on the distance between the UEs is captured in the RT modeling--a factor that greatly impacts the BS pattern when using interference-canceling precoding schemes such as ZF. It has been shown that the RT method reproduces key parameters of measured Massive MIMO channels [10], whereas the state-of-the-art statistical model (WINNER-II) tends to underestimate the amount of correlation in the channels of closely spaced UEs [11]. Various other approaches to 5G channel modeling have been proposed in the literature and we direct an interested reader to the recent overview articles in [12; 13].
### Contributions of This Article  
In order to respond to the aforementioned discussions, this study aims to propose an unsupervised anomaly detection method for rail fasteners that can detect minor anomalies in complex environment. The main contributions can be summarized as follows.  
1. A knowledge-distilled generative adversarial network for unsupervised rail fastener anomaly detection is proposed. During training, the teacher-student framework collaboratively learns normal sample distributions, and the teacher network generates high-fidelity normal images as supervisory signals, while the student network minimizes reconstruction errors through joint knowledge distillation and adversarial training. For inference, the student generator produces significant feature discrepancies when encountering anomalous samples due to reconstruction failure, enabling the discriminator to calculate anomaly scores without labeled defects. The core innovation is the distillation-adversarial co-optimization framework, which achieves precise anomaly localization using only normal samples. The framework is specifically designed for railway operation and maintenance scenarios to address the critical challenge of anomalous data scarcity. Compared to conventional approaches, it achieves a substantial reduction in dependence on manual inspection processes.
2. A multi-scale attention coupling feature enhancement mechanism (MSAC) is proposed to effectively address complex interference in railway environments such as illumination variations and foreign object occlusions. This mechanism integrates feature maps from three encoder levels through skip connections within the teacher network. This architecture synergistically represents hierarchical semantic information while mitigating feature degradation caused by downsampling in encoder-decoder structures. Furthermore, the mechanism incorporates both channel and positional information, enabling it to emphasize key channel features and significantly suppress background noise.
3. An enhanced anomaly discriminator is proposed, incorporating an enhanced joint pyramid upsampling module to preserve fine-grained details through multi-scale feature map fusion within the student network. The proposed discriminator enhancement improves cross-level feature representation capability, thereby significantly boosting detection sensitivity for small-scale anomalies. This design enables maintenance personnel to identify incipient anomalies prior to their escalation into critical failures, thereby facilitating the implementation of predictive maintenance protocols. The remainder of this paper is organized as follows: Section 2 details the proposed model framework and its underlying principles. Section 3 presents the experimental results and analysis. Finally, Section 4 concludes the article.
## 4 Experiments and Results
### Prediction
#### 4.2.5 Individual Models  
We train and validate the classifiers using the same CWA\({}_{U}\) for this experiment. The results in Table 5 reveal a consistent trend across all models: an incremental improvement in F1-score as CWA\({}_{U}\) increases from 20% to 80%. This result aligns with expectations, as higher CWA\({}_{U}\) values capture more comprehensive student grade and behavioral data.  
Tables 4 and 5 show that the baseline outperforms the machine learning models when trained solely on engagement or behavior features. Moreover, combining engagement with behavior or performance features does not substantially improve F1-scores, suggesting that engagement alone is less predictive of whether a student will earn a participation certificate, which aligns with the feature importance analysis presented in Section 4.3.  
When engagement and behavior features are combined, the model's performance becomes comparable to the baseline. Although these two features are not directly related to student performance, they provide valuable insights. These insights enable more accurate predictions regarding participation certificate achievement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **CWA\({}_{U}\)** & **F1-Score (\(\mu\pm\sigma\))** \\ \hline \multirow{4}{*}{Baseline} & 20\% & 0.63 \(\pm\) 0.06 \\  & 40\% & 0.70 \(\pm\) 0.08 \\  & 60\% & 0.72 \(\pm\) 0.06 \\  & 80\% & 0.73 \(\pm\) 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance results (F1-score) of baseline at different CWA\({}_{U}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**CWA\({}_{U}\)**} & \multicolumn{5}{c}{**F1-Score (\(\mu\pm\sigma\))**} \\ \cline{3-6}  & & **E** & **B** & **E + B** & **E + P** & **B + P** & **E + B + P** \\ \hline \multirow{4}{*}{LR} & 20\% &  
By incorporating performance-related features, we observe an improvement, highlighting the role of engagement and behavior in predictive modeling. The combination of behavior and performance features, which are linked to evaluative activities, leads to better prediction performance. This is expected, given their close association with student grades. The similarity between the results from behavior + performance and the three-feature combination underscores the direct impact of behavior and performance on performance outcomes.  
While LR, RF, and SVM models show comparable results, RF performs the best. However, RF is computationally the most intensive--the mean and standard deviation of RF's execution time for nested cross-validation was \(655.57\pm 54.34\) s, while other models required \(2.94\pm 0.31\) s; these times may vary based on system hardware and configuration. LR is the least computationally expensive model, and it predicts student performance early, achieving an F1-score of 0.73 at just 20% \(\text{CWA}_{U}\). This insight can help educators identify at-risk students early, allowing them to implement targeted interventions.
## 2 Materials and Methods
### Statistical Analysis  
Values are reported as mean (M) \(\pm\) standard deviation (SD). Statistical significance was established at \(p\) < 0.05. The distribution of each variable was examined using the Kolmogorov-Smirnov normality test. Variance and sphericity assumptions were checkedwith the Levene and the Mauchly tests. Differences in match RPE according to playing positions and player role were determined by one-way ANOVA followed by the Dwass-Steel-Critchlow-Fligner test or by a paired _t_-test, respectively. Differences in well-being status according to playing positions or player role and time (MD+1 and MD+2) were assessed through mixed two-way ANOVA followed by the Bonferroni post hoc test (match day as repeated measure and playing positions or player role as independent measure). Furthermore, partial eta-squared (\(\eta p^{2}\)) was calculated for group effects with the following interpretation: >0.01 small, >0.06 moderate, and >0.14 large [27]. Effect size (ES) values were calculated using Cohen's d and were interpreted using the thresholds proposed by [PERSON] et al. [28], as follows: ES > 0.2 as small, ES > 0.6 as moderate, ES > 1.2 as large, and ES > 2.0 as very large [28]. The statistical analyses were performed using SPSS software version 26.0 (IBM Corp., Armonk, NY, USA).
## 2 Heliostat Modelling Algorithm  
The overall algorithm to simulate a single heliostat is depicted in Figure 1. First, the sun position needs to be calculated for a given location and instant of time (as explained in detail in our previous work [18]). Then, the heliostat orientation must be determined, using one of the various mechanical models available. After that, the heliostat orientation needs to be transformed to the horizontal referential, to handle the simulation of the sun's rays and their reflection in the mirror, before going back to the real orientation.  
The mirror curvature plays a significant role in the way the sun's rays interact with each heliostat mirror. The heliostats are totally independent of each other, so, for example, the front-row heliostats might have a different curvature or mechanical rotation axes than the heliostats of the back row.  
Then, it is necessary to check for possible shading and blocking ray interceptions, due to other neighbor heliostats in the field. Finally, the rays must be collected in the target to allow all sorts of image and data analysis. Apart from these core functionalities to simulate the heliostats at work, the software must also provide functionality to construct the heliostat field and define its mode of operation.  
In our computer package, the core routines that are specifically related to heliostats amount to approximately 1900 lines of C code. This does not include, for example, image and data analysis, mathematical operations, and other general infrastructure.