esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Tamping Maintenance Cycle Prediction  
The concept of 'first hitting time' plays a crucial role in degradation analysis as it represents the moment when the system reaches a specified threshold. Building upon this notion, this study defines the tamping maintenance cycle of track segment \(k\) as the time interval during which the value of SDLL, \(X_{k}(t)\), deteriorates from the initial state \(X_{0,k}\) to the maintenance threshold \(X_{lim}\) for the first time:  
\[T_{k}=\inf\{\xi_{k}|X_{k}(\xi_{k})\geq X_{lim}\}=\inf\{\xi_{k}|\beta_{k} \Lambda(\xi_{k};\theta_{k})+\sigma_{k}B(\Lambda(\xi_{k};\theta_{k}))\geq X_{ lim}-X_{0,k}\}, \tag{2}\]  
where \(T_{k}\) is the tamping maintenance cycle. \(\xi_{k}\) is a random variable that represents the degradation time interval from the initial moment. The operator \(\inf\{\cdot\}\) denotes the inferior limit of a variable. As previously mentioned, the increment \(\Delta X_{k}\) follows a normal distribution. According to the principles of the Wiener process [41], \(\Lambda(\xi_{k};\theta_{k})\) follows an inverse Gaussian distribution with a location parameter of \((X_{lim}-X_{0,k})/\beta_{k}\) and a scale parameter of \((X_{lim}-X_{0,k})^{2}/\sigma_{k}^{2}\). Therefore, the probability density function \(f_{k}(\xi_{k})\) for the tamping maintenance cycle can be expressed as follows:  
\[f_{k}(\xi_{k})=\frac{X_{lim}-X_{0,k}}{\sqrt{2\pi\sigma_{k}^{2}[\Lambda(\xi_{k };\theta_{k})]^{3}}}\text{exp}^{-\frac{(X_{lim}-X_{0,k}-\beta_{k}\Lambda(\xi_ {k};\theta_{k}))^{2}}{2\sigma_{k}^{2}\Lambda(\xi_{k};\theta_{k})}}\frac{d \Lambda(\xi_{k};\theta_{k})}{d\xi_{k}}, \tag{3}\]  
Substituting \(\Lambda(\xi_{k};\theta_{k})=\xi_{k}^{\theta_{k}}\) into Equation (3) yields the following:\[f_{k}(\xi_{k})=\theta_{k}\frac{X_{lim}-X_{0,k}}{\sqrt{2\pi\sigma_{k}^{2}\left( \delta_{k}^{\theta_{k}}\right)^{3}}}\mathrm{exp}^{-\frac{\left(X_{lim}-X_{0,k}- \xi_{k}\delta_{k}^{\theta_{k}}\right)^{2}}{2\sigma_{k}^{2}\sigma_{k}^{2}}}\ \delta_{k}^{\theta_{k}-1}, \tag{4}\]  
In the field of probabilistic modeling for infrastructure and equipment deterioration prediction, the life expectancy calculated from the lifespan probability density function is typically used as the predicted result for the service life [39; 40; 41]. Following this approach, given the known probability density function \(f_{k}(\xi_{k})\) for the tamping maintenance cycle of track segment \(k\), the predicted value \(T_{k}\) for the tamping maintenance cycle can be calculated using Equation (5):  
\[T_{k}=\int_{0}^{\infty}\xi_{k}\cdot f_{k}(\xi_{k})d\xi_{k}, \tag{5}\]
# Prediction of Damage Level of Slab-Column Joints under Blast Load
## 6 Conclusions  
In this study, numerical analysis was performed using LS-DYNA for the behavior of a slab-column joint under an explosive load. The results of this study can be summarized as follows.  
1. ConWep's empirical values for shock wave parameters of a single member, such as a slab and a column, were compared with the pressure and impulse of a slab-column joint by numerical analysis. As a result, a region with a scaled distance of less than 0.1 m/kg\({}^{1/3}\) was defined as a joint region.
2. The explosion created more pressure and impact on the slab than the column, thereby causing a larger displacement in the slab. In addition, it was observed that the damage of the member decreased sharply as the explosion position moved away from the member.
3. Even if the support rotation of the slab after the explosion was less than the limit of 2 degrees, it was observed that serious damage, such as spalling, occurred over a wide range of slab. Therefore, in addition to the support rotation and displacement, which are mainly used to evaluate blast-resistant performance, other evaluation factors are required.
4. Effective fracture volume was proposed as an evaluation factor for blast-resistant performance. Effective fracture volume was a good indication of the actual degree of damage to the member depending on the TNT weight and the explosive distance.
5. A prediction equation for the damage level of the slab-column joints through the TNT weight and the standoff distance of explosives was proposed. The reliability and accuracy of the proposed equation were verified through additional numerical analysis.  
Conceptualization, performing analysis, and writing--original draft, K.M.L. and J.H.L.; Writing--review and editing, D.G.Y., B.Y.L., and J.H.L.; supervision, J.H.L. All authors have read and agreed to the published version of the manuscript.  
The paper was supported by the research grant of the University of Suwon in 2017.  
The authors declare no conflict of interest.
# Industrial Metaverse: A Comprehensive Review, Environmental Impact, and Challenges
## 3 Industrial Metavere's Architecture, Roadmap, and Core Technologies Overview
### Edge-Computing  
There has been a rise in the popularity of the edge computing paradigm among academics and professionals. Through its ability to connect cloud computing facilities and services to end users, it serves as a facilitator for several emerging technologies, including the IoT, AR, and Vehicle-to-Vehicle (V2V) Communications. Edge computing can be thought of as an accessible platform close to the edge of the network or the data source. In order to meet user needs ranging from low-latency real-time applications to application optimization and much more, it provides networking, storage, compute, and edge intelligence [30]. With its decentralized architecture, edge computing is a cloud computing extension that offers benefits including reduced costs, lower bandwidth requirements, and lower processing power consumption. Edge computing is characterized in [31; 32] by a dense geographical distribution, mobility support, location awareness, proximity, and low latency. A computational resource black hole exists in the Metaverse. The IM will, thus, require higher computational capacity, and mobile users accessing the Metaverse will require considerably minimal latency to deliver real-time, advanced computational capabilities like AR/VR and speech recognition. In the Metaverse, edge computing contributes to low latency, high efficiency, as well as security. The introduction of edge computing in the Metaverse results in fog computing [33]. Given the similarities of the edge computing and fog computing paradigms, computational abilities are provisioned both in the local networks and the cloud, subsequently cutting down on the latency of and load on backbone networks.
## 3 Metabolic Responses to Different Antibiotic Classes
### Krebs Cycle and Glyoxylate Shunt: Carbon-Sparing Survival
Upon antibiotic exposure, suppression of the TCA cycle leads to redirection of metabolic flux through pathways such as the glyoxylate shunt and lactate fermentation. This shift facilitates the maintenance of redox balance and promotes a quiescent, drug-tolerant phenotype. These adaptations contribute to antibiotic tolerance and persistence by minimizing oxidative stress and metabolic activity. Image created using BioRender (accessed via web application, version as of May 2025), GraphPad Prism (version 10.1.2-2024), and PowerPoint (Microsoft 365, version 2404). The main central carbon metabolic pathways implicated in bacterial tolerance and persistence during antibiotic exposure are summarized in Table 5.  
Figure 4: Schematic representation of bacterial metabolic adaptation in response to antibiotic exposure. Upon antibiotic pressure, glucose catabolism is rerouted from the tricarboxylic acid (TCA) cycle toward alternative pathways. Pyruvate, the central metabolic node, is diverted to lactate fermentation, contributing to redox imbalance and ATP generation via substrate-level phosphorylation. Concurrently, suppression of the TCA cycle—mediated by the reduced activity of enzymes such as succinate dehydrogenase (Sdh)—leads to the accumulation of acetyl-CoA. This promotes activation of the glyoxylate shunt, driven by isocitrate lyase (Icl), enabling carbon conservation and supporting bacterial persistence. These metabolic changes facilitate survival under antibiotic-induced stress by reducing reactive oxygen species production and preserving redox homeostasis. Image created using BioRender (accessed via web application, version as of May 2025), GraphPad Prism (version 10.1.2-2024), and PowerPoint (Microsoft 365, version 2404). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pathway** & **Antibiotic Association** & **Functional Outcome** & **References** \\ \hline Glyoxylate Cycle & \(\beta\)-lactams, Quinolones & Carbon conservation, reduced ROS, support for cell wall regeneration & [5; 6; 7; 23; 40; 41; 51; 52] \\ \hline Tricarboxylic Acid (TCA) Cycle & \(\beta\)-lactams, Quinolones & Downregulation lowers ROS and slows growth & [6; 42; 43; 50] \\ \hline Fermentation Pathways & \(\beta\)-lactams, Aminoglycos & Decreased oxidative stress, energy conservation, growth rate modulation & [6; 7; 12; 42; 43] \\ \hline Pentose Phosphate & Fosfomycin, Anthetabolites & NADPH production, support for anabolic metabolism & [66; 67; 68] \\ \hline Anaplerotic Flux (PEP–OAA Axis) & Glycopeptides for cell wall biosynthesis & [56] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Role of central carbon metabolism in antibiotic tolerance and persistence.
## 3 Results  
### Performance Metrics and CR-ICC  
Table 1 displays the findings of the binomial logistic regression analysis conducted at both the team and player levels. Significant differences were observed in the CR-ICC, Pts, Eff A, and balance variables concerning the team's success in the set (\(p>0.001\)). These significant differences were also evident when considering individual player performance. However, no notable differences were found in the Eff R variable (\(p=0.576\) for the team and \(p=0.866\) for the players). The fit for each variable was subsequently assessed using the AIC, BIC, and Nagelkerke R2 coefficient (Table 2). The results indicated that all variables demonstrated a better fit at the team level, displaying lower AIC and BIC values and higher Nagelkerke coefficients compared to the individual results for each player. Notably, the variables Pts (R\({}^{2}\)N = 0.714), Eff A (R\({}^{2}\)N = 0.690), and CR-ICC (R\({}^{2}\)N = 0.547) stood out with high Nagelkerke R\({}^{2}\)N values, suggesting that they could account for 71.4%, 69%, and 54,7%, respectively, in explaining a team's set victory. The CR-ICC coefficient yielded an R\({}^{2}\)N value of 0.547. In contrast, Eff R exhibited a considerably low R\({}^{2}\)N value of 0.006, indicating its limited explanatory power of only 6% in relation to set victory. Table 2 displays the optimal cutoff points for each variable, which maximize the values of sensitivity and specificity. In addition, the table includes the area under the ROC curve (AUC) values, which represent the probability for each model. Figure 2 illustrates the ROC curves, showcasing the best predictions achieved for the variables of Pts (AUC--93.9%), Eff A (AUC--93.0%). The critical coefficient CR-ICC also showed significant predictive value (AUC--87.1%). However, Eff R exhibited low predictive capacity (AUC--56.0%).
# Performance Prediction of Composite Marine Propeller in Non-Cavitating and Cavitating Flow
## 2 Methodology and Results
### Coupled CFD-FEM FSI Analysis
#### 2.4.2. Cavitating Flow  
The deformed shapes of the composite propellers (models 1 and 5) in the cavitating flow were also predicted and compared with the rigid propeller. The test conditions were the same as for the non-cavitating flow, except for the cavitation number (non-cavitating flow: \(\sigma\) = 6.57, cavitating flow: \(\sigma\) = 1.2). The pitch distribution changes of the composite propellers in the cavitating flow showed no significant difference compared to the non-cavitating flow, as shown in Figures 8 and 9. The shape parameters of the rigid and composite propellers for the non-cavitating and cavitating flows in a radial direction are compared in Figure 12.  
Figure 11: Sound pressure levels of rigid and composite propellers (model 5) in a non-cavitating flow.  
Figure 10: Sound pressure level of rigid and composite propellers (model 1) in a non-cavitating flow.
## 4 Discussion
### Comparison with the Results of Previous Studies  
Some previous studies compared the effectiveness of selection criteria for various datasets as mentioned in the Introduction [26; 27]. The compared selection criteria were not the same with this study, and the MS situation on physiological data was not considered in the previous studies. However, the results of previous studies were analyzed together with the results of this research to identify the difference in the effectiveness of each selection criterion for physiological data and data without ISV. The relative ranking of selection criteria for classification accuracy was investigated in this analysis; the results of ranking for all problems were averaged, and the relative ranking was obtained based on comparisons with the averaged values (Table 7). KSC was the most effective in previous studies, but it is less advantageous than DBTC and GACV for physiological data. DBTC was relatively superior in the previous studies, and it was still ranked high in this study. The effectiveness of DBTC was more robust for ISV, and yielded consistent performance compared with KSC. XAB and GACV were not as good as KSC and DBTC as reported in previous studies, but the ranking of GACV was higher than KSC, and it was the same with DBTC for physiological data. Nevertheless, the variation of its effectiveness was severe according to a classification problem, and the averaged classification accuracy was lower than KSC as indicated in the results in Table 2. Therefore, it is difficult to conclude that GACV is superior to KSC for physiological data, and a more rigorous comparison using big-sized datasets will be needed.
# Childhood Uveitic Glaucoma: Complex Management in a Fragile Population
## 2 Classification and Clinical Features of UG in Childhood
In 70-80% of cases, it appears as unilateral focal necrotizing retinoconoroiditis with focal vitritis. The anterior segment might be secondarily involved, presenting as granulomatous anterior uveitis and high intraocular pressure. Ocular toxoplasmosis more commonly results from the reactivation of congenital disease, where new active satellite lesions appear next to an atrophic scar with hyperpigmented borders. The absence of scarring lesions suggests acquired disease. Congenital toxoplasmosis occurs due to the passage of T.gondii by the transplacental route. The transmission risk is highest during late pregnancy, but most severe forms occur during the first trimester with the classic triad of retinoconoroiditis, cerebral calcifications and seizures [25; 26]. Other forms of posterior uveitis are infrequent in childhood. Vogt-Koyanagi-Harada (VKH) is a multisystem inflammatory autoimmune rare disease affecting the eyes, ears, brain, skin and hair [27; 28]. It is more frequent in ethnicities with higher pigmentation, such as Asians, Middle Easterners, Hispanics, and Native Americans. The incidence of VKH in the pediatric population is ethnicity-dependent, varying between 0.5 and 3% of all pediatric uveitis [29]. The diagnosis is usually delayed in children compared to adults, so associated ocular complications, including glaucoma, occur more frequently. Therefore, visual outcomes in VKH are usually worse in the pediatric population [30]. Another entity associated with posterior uveitis in children is Behcet's disease (BD). BD is a multisystemic vasculitis that is less common in pediatric patients than adults, and it is characterized by recurrent mucocutaneous ulcers affecting the oral cavity and the genital area. Pediatric BD onset is around 10-15 years of age, and ocular involvement usually occurs within 2-3 months. It includes bilateral recurrent panuveitis with retinal vasculitis and persisting vitritis. Iridocyclitis, episcleritis, retinitis, retinal hemorrhages, optic nerve edema, and cystoid macular edema can also be found [31]. Data on the relative risk of developing glaucoma in pediatric patients according to the type of uveitis [4] are limited and extremely heterogeneous (Table 1).
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## Appendix B Additions Required for Binary Mailman Codebook  
Let \(\mathbf{B}_{N\times K}\) denote the binary mailman matrix with \(N\) rows and \(K\) columns. It can be decomposed recursively as  
\[\mathbf{B}_{N\times K}=\left[\begin{array}{cc}\mathbf{B}_{(N-1)\times\frac{ K}{2}}&\mathbf{B}_{(N-1)\times\frac{K}{2}}\\ \mathbf{0}_{1\times\frac{K}{2}}&\mathbf{1}_{1\times\frac{K}{2}}\end{array} \right]. \tag{3}\]  
_Wide Matrices:_ Following [38], we decompose \(h\) into its first half \(h_{1}\) and its second half \(h_{2}\) such that we get  
\[\mathbf{B}_{N\times K}h=\left[\begin{array}{c}\mathbf{B}_{(N-1)\times\frac{ K}{2}}(h_{1}+h_{2})\\ \mathbf{1}_{1\times\frac{K}{2}}h_{2}\end{array}\right]. \tag{4}\]  
Let \(c_{\mathrm{w}}(N)\) denote the number of additions to compute the product \(\mathbf{B}_{N\times K}h\). The recursion (4), implies  
\[c_{\mathrm{w}}(N) =\frac{K}{2}+c_{\mathrm{w}}(N-1)+\frac{K}{2}-1 \tag{5}\] \[=c_{\mathrm{w}}(N-1)+2^{N}-1. \tag{6}\]These are \(\frac{K}{2}\) additions for \(h_{1}+h_{2}\), \(c_{\rm w}(N-1)\) additions for the matrix-vector product, and \(\frac{K}{2}-1\) additions to sum the components of \(h_{2}\). Note that \(\mathbf{B}_{1\times 2}h=h_{2}\). Thus, \(c_{\rm w}(1)=0\) and \(c_{\rm w}(N)=2^{N+1}-5=2K-5\) for \(N>1\).  
_Tall Matrices:_: In this case, we decompose \(h\) into its first \(N-1\) components collected in \(h_{1}\) and the last component denoted by \(h_{2}\). We obtain  
\[\mathbf{B}_{N\times K}^{\sf T}h=\left[\begin{array}{c}\mathbf{B}_{(N-1) \times\frac{K}{2}}^{\sf T}h_{1}\\ \mathbf{B}_{(N-1)\times\frac{K}{2}}^{\sf T}h_{1}+\mathbf{1}_{\frac{K}{2}\times 1 }h_{2}\end{array}\right]. \tag{10}\]  
Let \(c_{\rm t}(N)\) denote the number of additions to compute the product \(\mathbf{B}_{N\times K}^{\sf T}h\). The recursion (10), implies  
\[c_{\rm t}(N)=c_{\rm t}(N-1)+2^{N-1}. \tag{11}\]  
These are \(c_{\rm t}(N-1)\) additions for the matrix-vector product, and \(\frac{K}{2}=2^{N-1}\) additions to add \(h_{2}\). Note that \(\mathbf{B}_{1\times 2}^{\sf T}h=[0\quad h_{2}]^{\sf T}\). Thus, \(c_{\rm t}(1)=0\) and \(c_{\rm t}(N)=2^{N}-2=K-2\) for \(N>1\).
## 4 Real-Time Prediction
### Application of the Real-Time Prediction Method  
The real-time prediction method based on the KF-FFT-SVM model makes landslide predictions according to the principle that the mechanical vibrations of the landslide slope failure are recorded in the displacement data. In this study, the Beidou receivers had the ability to measure the displacement of the landslide at the frequency of every 10 min with an accuracy of 3 mm, which was given by the manufacturer. The raw displacement data are shown in Figure 11, and it is clear that there are random errors from the Beidou receivers as the displacement curves are not continuous in their rise in several periods. Thus, the raw data were pre-processed, and the 30 min statistics of the raw displacement data are given in Figure 12. Supposing 30 min as the unit time, then the KF method was used to filter the random error and to make a prediction of the displacement for the next 30 min. FFT, here, was used to find the precursory vibration frequency of the displacement acceleration before the landslide slope failure. When the precursory vibration frequency was found, we could generate the training data from the BD1, BD2, and BD3 and train the SVM by these data. The trained SVM was tested by the data obtained by BD4 as shown in Figure 15.  
The trained KF-FFT-SVM model can make online predictions. The displacement data obtained by the FDMS acts as the input for the KF-FFT-SVM model. The outcome of the KF-FFT-SVM model is the precursory warning of a landslide. If the outcome of the KF-FFT-SVM model is \"Positive\", the early warning will be triggered. However, if the outcome is \"Negative\", there will be no early warning. In this study, the sample period of the data was 30 min; therefore, the KF could predict the landslide displacement in the next 30 min. This indicates that the KF-FFT-SVM model can forecast landslide slope failure at least 30 min in advance. In practice, the parameters of KF-FFT-SVM model are pre-set; however, the model can be retrained, and the parameters can be adjusted dynamically.
# SDR-Enabled Multichannel Real-Time Measurement System for In Situ EMF Exposure Evaluation
## 2 Materials and Methods
### Receiver Section
\[E\left(\frac{V}{m}\right)=10^{\ast}\left(\frac{P+113.2-20*lg(f)}{20}\right) \tag{3}\]  
where P is the CP reading in dBm, and f represents the channel central frequency in MHz. Afterwards, the total electric field strength can be computed as:  
\[E_{t}=\sqrt{Ex^{2}+Ey^{2}+Ez^{2}} \tag{4}\]  
where Ex, Ey, and Ez represent the time averaged electric field strengths (root mean square--RMS values) on the orthogonal directions. If measurements are performed in far field condition, the knowledge of the E/H field strength solely is sufficient for calculating the power density (PD) by applying Equation (5) [3]:  
\[PD=\frac{\left|E^{2}\right|}{\zeta_{0}}=\zeta_{0}\left|H^{2}\right| \tag{5}\]  
where \(\zeta_{0}\) is the characteristic impedance of free space. The SDR measurement system was compared to solutions enabled using three SAs and a high bandwidth multichannel DO. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Communication** & \multirow{2}{*}{**Scenario**} & **Channel’s Central** & **Channel’s Bandwidth** \\  & **Standard** & & **Frequency (MHz)** & **(ChBw) (MHz)** \\ \hline
1 & LTE-A & File upload & 1750 & 20 \\ \hline
2 & LTE-A & Streaming & 1750 & 20 \\ \hline
3 & LTE-A & Speed test & 1750 & 20 \\ \hline
4 & IEEE 802.11 ax & TCP Download & 5220 & 20 \\ \hline
5 & IEEE 802.11 ax & TCP Upload & 5220 & 20 \\ \hline
6 & IEEE 802.11 ax & TCP Upload & 5230 & 40 \\ \hline
7 & IEEE 802.11 ax & TCP Upload & 5210 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental scenarios considered. The SA model R&S FSL 13 was used for setting up the SA-enabled measurement system. Since simultaneous multichannel measurement is required, the system must use 3 SAs. Each E-field probe was connected to one SA which provided CP measurements. The 3 SAs were triggered by an external signal and the data were automatically retrieved via a custom-made Python application [42]. The experimental setup used for the SA measurement system is presented in Figure 5b. The third measurement system was based on a 4-channel DO model R&S RTO6024 and is presented in Figure 7a. Each of the 3 E-field probes was connected to a dedicated DO input channel. The oscilloscope has a bandwidth of 6 GHz and a maximum sample rate of 20 G samples/s with a vertical resolution of up to 16 bits. When multiple channels are enabled, the oscilloscope bandwidth reduces to 4 GHz. The DO enables multi-channel spectrum analysis by performing fast Fourier transform (FFT) on the measured time samples. CP measurement option is also available. Remote measurements with the DO were performed via a Python application that also allowed automatic data retrieval. The measured CP values were saved to a file and further processed. In Figure 7b, we present a screenshot of the oscilloscope during the simultaneous measurements of the CP on all three channels.