esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A clinical trial was conducted on fourteen critically ill patients in an intensive care unit. Levetiracetam was administered intravenously either for prophylaxis or treatment of seizures. Blood samples were collected and plasma levels of levetiracetam were quantified. A population pharmacokinetic analysis was performed that included a number of patient-related somatometric characteristics and pathophysiological variables. A one-compartment model with intravenous infusion and first-order elimination kinetics was found to best describe the concentration-time data. Several renal function measures were estimated and evaluated in the modeling. Among them, CKD-EPI eGFR showed a statistically significant effect on levetiracetam clearance. With impaired renal function (especially at lower CKD-EPI eGFR values), levetiracetam clearance decreased. Simulations of dosing regimens revealed that even typically administered doses of levetiracetam may result in significantly increased concentrations and a higher risk of drug toxicity in patients with impaired renal function. Therefore, renal function should be considered when developing levetiracetam dosing regimens. Other measures describing disease severity, such as SOFA and APACHE II, had no effect on levetiracetam kinetics. Finally, when analyzing the association between APACHE II score and epileptic activity, it was found that patients with the lowest APACHE II score had a lower risk of experiencing epileptic seizures.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app12031208/s1](https://www.mdpi.com/article/10.3390/app12031208/s1)]([https://www.mdpi.com/article/10.3390/app12031208/s1](https://www.mdpi.com/article/10.3390/app12031208/s1)), Table S1: Previously published population pharmacokinetic modeling results for levetiracetam, Figure S1: Goodness-of-fit plots for the final best model (a. Observed vs. predicted by the model individual concentrations of levetiracetam, b. Individual Weighted Residuals (IWRES) vs. time, and c. Individual Weighted Residuals vs. concentration), Figure S2: Levetiracetam concentration vs. time plots for the 14 patients of the study, Figure S3: Visual predictive check plots for the literature models, Figure S4: Normalized prediction distribution errors (NPDE) vs. time and concentration for the literature models.  
Conceptualization, S.-L.M., N.M., A.K., D.K., H.S. and V.K.; methodology, S.-L.M., N.M. and V.K.; data curation, R.K. and V.K.; investigation, N.M., A.K., D.K., H.S.; writing original draft, S.-L.M., R.K. and V.K.; supervision, S.-L.M., N.M. and V.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Thorissio General Hospital of Elefsina (27 July 2020).  
Written informed consent was obtained from the patients to publish this paper.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 3 Proposed Three-Coil PT-WPT System
### Self-Oscillation Control Strategy for Achieving Constant Output  
In the context of the previous section's analysis, the three-coil PT-WPT system, characterized by negative resistance, functions within the PT-exact state. In this state, the output voltage and current are exclusively linked to the system's internal parameters. The equivalent AC output voltage, current, and power remain constant as the system stabilizes.  
Negative resistance can be achieved by sampling the loop current \(i_{T}\) within the resonant tank at the transmitting end. Figure 3 illustrates the negative-resistance control circuit and its implementation strategy. The current is sampled, captured, and transformed into a voltage signal by a current-sense transformer CU8965. It is then directed to the zero-crossing comparator AD8611 and dead time generation circuit to convert it into rectangular PWM signals. The isolated driver chip Silicon Labs Sl8271, serves as the driver for the full-bridge inverter circuit, amplifying the PWM signals from the comparator.  
Figure 4 shows the full-bridge inverter output waveform and the PWM signal governed by self-oscillation control. Ideally, disregarding the MOSFET switching dead time, the duty cycle width of the PWM signal of switches S1-S4 is 50%. The inverter output voltage \(u_{in}\) and its fundamental component \(u_{in-FHA}\) are in phase with the current \(i_{T}\). The effective values of the fundamental component of the inverter output voltage \(U_{in}\) and the inverter input voltage \(U_{d}\) are expressed as follows:  
\[U_{in}=\frac{2\sqrt{2}}{\pi}U_{d} \tag{13}\]  
Figure 2: Main circuit of the proposed WPT system with boost converter.
## 5 Discussion and Practical Tests
### Volatility spillover Effect: Intensity and Direction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Coefficient** & **S.D.** & **t-Statistic** & \(p\)**-Value** \\ \hline \(p_{11}\)\({}^{EIA}\) & 0.9729 & 0.0055 & 176.8909 & 0.0000 \\ \(p_{22}\)\({}^{EIA}\) & 0.9316 & 0.0141 & 66.0709 & 0.0000 \\ \(\mu\)\({}^{EIA}\) & 0.1138 & 0.0413 & 2.7554 & 0.0029 \\ \(\mu\)\({}^{EIA}\) & \(-\)0.0139 & 0.0093 & \(-\)1.4946 & 0.0675 \\ \(\omega\)\({}^{EIA}\) & 3.4121 & 0.1807 & 18.8827 & 0.0000 \\ \(a\)\({}^{EIA}\) & 0.0242 & 0.0172 & 1.4070 & 0.0797 \\ \(g_{2}\)\({}^{EIA}\) & 6.7405 & 0.4385 & 15.3717 & 0.0000 \\ \hline \multicolumn{5}{c}{_WTI Equation_} \\ \(p_{11}\)\({}^{WTI}\) & 0.9841 & 0.0039 & 252.3333 & 0.0000 \\ \(p_{22}\)\({}^{WTI}\) & 0.9309 & 0.0164 & 56.7622 & 0.0000 \\ \(\mu\)\({}^{WTI}\) & 0.0434 & 0.0361 & 1.2022 & 0.1146 \\ \(\varphi\)\({}^{WTI}\) & \(-\)0.0262 & 0.0153 & \(-\)1.7124 & 0.0434 \\ \(\omega\)\({}^{WTI}\) & 2.2234 & 0.1218 & 182.545 & 0.0000 \\ \({}^{WTI}\) & 0.1704 & 0.0286 & 5.9580 & 0.0000 \\ \(g_{2}\)\({}^{WTI}\) & 8.1736 & 0.8642 & 9.4580 & 0.0000 \\ \hline \multicolumn{5}{c}{_State-varying correlations_} \\ \(\rho_{1,1}\) & 0.1751 & 0.0255 & 6.8667 & 0.0000 \\ \(\rho_{2,1}\) & 0.1109 & 0.0391 & 2.8363 & 0.0023 \\ \(\rho_{1,2}\) & 0.2977 & 0.0556 & 5.3543 & 0.0000 \\ \(\rho_{2,2}\) & 0.3609 & 0.0637 & 5.6656 & 0.0000 \\ \(LR\) & \(\rho_{1,1}=\rho_{2,1}=\rho_{1,2}=\rho_{2,2}\) & 15.0222 *** & \\ \multicolumn{5}{c}{_Log-likelihood function_} \\ \hline \hline \end{tabular} Notes: This table presents the estimation results of the bivariate SWARCH model with state-varying correlations for EUA and WTI returns. To test the significance, we implement an identical correlation, i.e., \(\rho_{1,1}=\rho_{2,1}=\rho_{1,2}=\rho_{2,2}=\rho\), into the model and calculate the value of the log-likelihood function of the restricted model. Then, we use the difference between the two models (one with four-state correlations vs. one with an identical correlation) to develop the likelihood ratio (LR) statistic. The LR statistic follows a chi-square distribution with three (\(=4-1\)) degrees of freedom. *** denotes significance at the 1% level. See Table 1 for sample descriptions and data sources.  
\end{table} 
Table 5: Estimated results of the bivariate SWARCH model with state-varying correlations. We use the other two volatility states to conduct the second comparative analysis, which may detect the direction of the volatility spillover effect. As shown in Table 5, \(\rho_{2,1}\) is 0.1109 and \(\rho_{1,2}\) is 0.2977. The comparison shows that a higher carbon-energy correlation is observed when the energy market (proxied by WTI) faces an HV regime rather than the carbon market (proxied by EUA). This result implies that the volatility spillover direction is from the energy market to the carbon market (e.g., [PERSON] and [PERSON], 2017).
# Possibilities of Detecting Damage Due to Osmosis of GFRP Composites Used in Marine Applications
###### Abstract  
The marine composites market is driven by the increasing demand for lightweight, corrosion-resistant, and impact-resistant boats. Polymer matrix composites are currently the most popular composite material in marine applications. Fiberglass composites are practically the main type of fiber composites that are used extensively in marine applications. Due to the aggressive sea environment, composite structural elements of ships are exposed to damage due to the phenomenon of osmosis. This damage is also favored by defects that result from impacts and technological errors during the production of these elements. Non-destructive testing methods are necessary to detect damage in the internal structure of the composite. The paper presents a numerical analysis of the possibility of using vibrothermography in the detection of defects in glass-fiber reinforced laminates in marine applications. Numerical simulations have shown that the most favorable method for detecting defects will be acoustic waves. This is an unusual application because, as a rule, the range of ultrasonic waves is used in vibrothermography. In our further works, it is planned to verify numerical calculations through experimental research. The applicability of the terahertz technique was also assessed. During the experimental testing, all defects in the test sample of the glass-fiber reinforced composite were detected using this technique. The presented results indicate the applicability of the presented methods for the detection of defects in composites used in marine applications.  
composites; vibrothermography; terahertz radiation; non-destructive testing Article
# A Next-Generation Core Network Architecture for Mobile Networks
## 5 Basic Operations
### VoLTE Call Flow
#### 5.2.2 Scenario with eeNodeBs  
Figure 15 shows a VoLTE call setup message flow when using an eeNodeB. In this case, almost everything remains the same as with the legacy eNodeB. The only difference is in the way the re-direction between SN and VCN is performed: this no longer involves the UE (see Section 6.2). At the lower layers, however, the use of an eeNodeB and the consequent absence of all bearers (except for the Radio bearer) significantly improve latency, as we do not have all the IP-in-IP tunnels typical of a bearer-based architecture.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 2 Preliminaries
### Cooperative Control with an Event-Triggered Communication Scheme  
An event-triggered communication scheme is proposed to lower the communication burden. The event-triggered time sequence is generated iteratively by the following formula.  
\[t^{i}_{s+1}=\inf\{t|t>t^{i}_{s^{\prime}},g_{i}(t)>0\} \tag{16}\]  
where \(g_{i}(t)\) is described by:  
\[g_{i}(t)=\|M\|\|\alpha(e_{xi}(t))+\beta(e_{vi}(t))\|+a_{i0}\|\alpha(e_{xi0}(t) )+\beta(e_{vi0}(t))\|-\gamma(\|\alpha y_{i}(t^{i}_{s})\|+\|\beta z_{i}(t^{i}_{ s})\|) \tag{17}\]  
with:  
\[e_{xi}(t) = x_{i}(t^{i}_{s})-x_{i}(t)\] \[e_{vi}(t) = v_{i}(t^{i}_{s})-v_{i}(t)\] \[e_{xi0}(t) = x_{0}(t^{i}_{s})-x_{0}(t)\] \[e_{xi0}(t) = v_{0}(t^{i}_{s})-v_{0}(t)\] \[y_{i}(t^{i}_{s}) = \sum\limits_{j=0}^{n}a_{ij}(x_{j}(t^{i}_{s})-x_{i}(t^{i}_{s}))\] \[z_{i}(t^{i}_{s}) = \sum\limits_{j=0}^{n}a_{ij}(v_{j}(t^{j}_{s})-v_{j}(t^{i}_{s}))\]  
where \(M=L_{G_{n}}+diag\{a_{10},\cdots,a_{n0}\}\); \(\alpha>0\), \(\beta>0\), \(\gamma>0\) are the positive constants; Since \(y_{i}(t^{i}_{s})\) and \(z_{i}(t^{i}_{s})\) only are calculated at the event-triggered time, the proposed event-triggered scheme can reduce communication burdens. The event-triggered communication condition (16) has one main feature, that is whether or not the states of robots should be transmitted is determined by the errors \(y_{i}(t^{i}_{s})\), \(z_{i}(t^{i}_{s})\) between the states of its neighbors at the latest event time and the latest transmitted states and the errors \(e_{xi}(t),e_{vi}(t),e_{xi0}(t),e_{xi0}(t)\) between the current states and the latest transmitted states.  
**Remark 2**.: _It is worth mentioning that the control input is updated when \(g_{i}(t)>0\), that is the condition of the event triggering. At the same time, the new state of the \(i\)-th robot will be sent to the other robots that have communication links with the \(i\)-th robot. Besides, if the above inequality does not hold, the \(i\)-th robot does not need to send information to others while the values of \(y_{i}(t_{s}^{i})\) and \(z_{i}(t_{s}^{i})\) will not be changed. Hence, the communication resources are saved._  
According to the proposed event-triggered communication scheme, the controller of the \(i\)-th robot is designed by:  
\[u_{i}(t)=\sum\limits_{j=0}^{n}a_{ij}(\alpha(x_{j}(t_{s}^{j})-x_{i}(t_{s}^{i})) +\beta(v_{j}(t_{s}^{j})-v_{i}(t_{s}^{i}))) \tag{18}\]  
where \(a_{ij}\) is the element of the adjacency matrix \(A\); \(x_{i}(t_{s}^{i})\) and \(x_{j}(t_{s}^{j})\) are the positions of the \(i\)-th and the \(j\)-th robots at the event-triggering time, respectively; \(v_{i}(t_{s}^{i})\) and \(v_{j}(t_{s}^{j})\) are the velocities of the \(i\)-th and the \(j\)-th robots at the event-triggering time, respectively. It should be pointed out that the control input in (18) is determined by the position errors and velocity errors between the \(j\)-th robot and the \(i\)-th robot at the event-triggering time.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 2 State of the Art
### UWB-Based Positioning Systems  
The harmonized European standard \"ETSI EN 302 065-1 Part 2\" [48] describes the electromagnetic requirements for UWB location-tracking devices. (Similarly, the FCC has also introduced its own electromagnetic limitations for UWB valid across the United States [49].) UWB equipment is categorized there according to the operational frequency range and the type of tracking it performs. In this context, three different systems are defined:  
* LT1 Systems: General location tracking of people and objects in the 6 GHz to 9 GHz region.
* LT2 Systems: Person and object tracking and industrial applications at well-defined locations in the 3.1 GHz to 4.8 GHz region.  
* LAES Systems: Staff tracking in location-tracking applications for emergency and disaster situations (LAES) in the 3.1 GHz to 4.8 GHz region. Licences may be required.  
Some power and time transmission limitations that currently apply to LT2 systems are somehow alleviated in LAES systems, due to the highly critical and transient character of operations conducted at the scene of an emergency:  
* The maximum allowed value of the mean and peak effective isotropic radiated power (e.i.r.p.) is 20 dBm higher in LAES systems in the band defined between 3.4 and 4.2 GHz.
* The duty cycle of both LT2 and LAES systems is limited to a maximum of 5% per second. However, a duty cycle limited to a maximum of 1.5% per minute and a maximum \(T_{on}\) duration of 25 ms may also apply to LT2 transmitters.  
While it is assumed that any IPS working under LAES regulations should perform better than in LT1 or LT2 systems, to the best of our knowledge there are no commercial or scientific studies that corroborate this premise. In fact, the single scientific work specifically addressing LAES systems elaborates on the design of their first silicon integrated power amplifier [50].  
Regardless of the operational frequency range, most UWB-based positioning systems determine the position of an object (from now on called _tag_) through a two-step approach. First, a set of rangings between the tag and a group of devices with well-known positions (from now on called _anchors_) are obtained from the analysis of the ToA, the TDoA, the TWR, and/or the PDoA. Second, the position of the tag is estimated using a geometric multi-lateration or a fingerprinting technique [51]. Alternatively, the angles obtained from the analysis of the AoA can be used in combination with a multiangulation tecnique.  
Different entities of the IPS can run the localization algorithm: an external cloud, the fixed anchors, or the tag itself. While this task has been traditionally delegated to the cloud due to its greater computing capabilities, the growing performance of modern low-power microcontrollers allows it to be performed in the tag itself. One key advantage of this option is the latency reduction for a mobile tag to obtain its position, thus providing timely trajectory tracking [52].
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 3 Propagation Mechanism and Signal Processing of Guided Waves in Anchor Bolts
### Propagation of Guided Waves in Anchor Bolts with Corrosive Defects  
When guided waves penetrate the corroded area on the surface of an anchor bolt, the reductions in the cross-sectional area and material damage in that region result in energy focusing and scattering, leading to changes in the distribution of energy density. Additionally, the propagation velocity and direction of guided waves within the rod are influenced by factors such as the properties of the medium and the geometric shape. Consequently, the change in the propagation path caused by corrosive defects also affects the velocity and direction of the wave propagation. The propagation of guided waves in anchor bolts with corrosive defects is illustrated in Figure 7.  
Figure 6: Group velocity dispersion curve of 20 mm ribbed anchor bolts.
## 5 Discussion
Nevertheless, this indicates that the redundancy reduction based on 14 spectral indices applied in our case study do not increase classification accuracy. Additionally, when comparing the achieved results with some presented in the literature (Table 3), it can be concluded that in general the accuracy of automatic detection of cultivated plant species based on the Sentinel-2A multitemporal images was around the level of 85%. However, the application of other sensors such as Landsat may help to increase accuracy. However, as presented by [PERSON] et al., (2018)[14], this mostly depends on the weather conditions. Therefore, alternative methods are needed to integrate SAR and optical data for crop mapping. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Indexes** & **Sensors** & **No of Images** & **Method** & _OA [\%]_ & **Kappa** & **No. of Training Samples** \\ \hline
[15] & NDVI & Sentinel-2A & 16 & SVM classification & 82 & 0.64 & 54\% (140)-Jansi 38\% (76-Chitrakoot) 38\% (76-Chitrakoot) 38\% (104-Parna) \\ \hline
[14] & NDVI + spectral bands & Landsat-7 and -8, Sentinel-2A, and RapidEye & 36 (2015) and RapidEye & Binary fuzzy c-means clustering & 89.49 (2015) 77.19 (2016) & - & - \\ \hline
[16] & 91 various spectral indexes & Sentinel-2A & 1 & Random forest & 93.1\% & - & 50\% for training and 25\% for hyperparameter tuning \\ \hline  & NDVI & & & & & & \\  & Perpendicular Moisture (PMI), Normalized Differential & & & & & \\  & Senescent Vegetation & & & & & \\  & Index (NDSVI); Normalized Difference Residue Index (NDRI); and Normalized Differential Village Index (NDTI) & Sentinel-2 & 8 & 95.92–97.85\% & 0.93–0.96 & 0.10\% of sample data \\ \hline  & NDVI, IECI, MCNARI, MTCI, NDI45, NDVI, PSSRa, REIP, S2 REP, SAVI, and WDVI & Sentinel-2 & 14 & Random forest & 86 & 0.81 & 5000 points located within 34.6\% ground truth polygons \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification results in different studies.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Threat Management
#### 3.4.2 Threats Addressed  
The \"threats handled\" attribute plays a critical role in quantifying the effectiveness of privacy-preserving techniques in addressing data breaches and ensuring optimal privacy levels. With the continuous expansion of cloud computing services, addressing vulnerabilities and weaknesses in privacy systems has become a complex and ongoing challenge, without any absolute guarantees [59]. Protecting personal information is a fundamental aspect of privacy-protecting approach design, allowing designers to mitigate system weaknesses and vulnerabilities. However, to truly enhance privacy measures, designers need to develop new technological approaches that mitigate the overall impact of privacy risks and make privacy-protecting approaches more resilient to a wide range of threats [22].  
Privacy-preserving techniques typically incorporate running method algorithms that can handle various forms of attacks. These attacks include known plaintext and MITM attacks. By employing these algorithms, privacy-preserving techniques can effectively mitigate the risks posed by these threats and ensure the privacy and safekeeping of susceptible data.  
It may be essential for designers to stay updated with the evolving landscape of privacy threats and continuously improve the capabilities of privacy-preserving techniques. By addressing a comprehensive set of threats and vulnerabilities, designers can bolster the resilience and effectiveness of privacy-preserving techniques, providing robust protection for users' sensitive information in cloud computing environments.
## 8 Conclusions and Future Work
### Further Research Directions  
Future work will concentrate on further reducing the computational overhead associated with both encryption and decryption, by adopting lightweight cryptographic primitives and exploring the use of hardware acceleration. These optimisations aim to enhance the practicality of the proposed scheme for large-scale IoT deployments. In addition, we plan to refine fine-grained access control mechanisms by supporting dynamic attribute revocation, context-aware access policies, and adaptive access strategies. Improvements to multi-authority key management will also be investigated, with the goal of streamlining key distribution and enhancing scalability in collaborative environments.  
To evaluate real-world performance, the scheme will be implemented on representative IoT hardware platforms--such as Raspberry Pi and edge computing devices--providing insights into its operational feasibility under constrained conditions. In parallel, ongoing research will focus on the development and integration of quantum-resistant ABE schemes, ensuring that future iterations of the framework remain secure in the face of advancing quantum capabilities.
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 6 Discussion  
### Sentinel-2 for Damage Detection  
In this work we have used multi-spectral, optical satellite images from the Sentinel-2 constellation to detect damage to coconut trees. We point out that our estimates correspond to a \"cumulative net loss\" of species-specific canopy cover. Consequently, our method can detect a wide range of changes to coconut trees, but cannot differentiate precisely between a small number of completely denuded or unrooted trees and a larger number of trees with only slightly thinned out canopies. We use four months of data after the typhoon to compensate for the high-cloud coverage. We further alleviate the problem of erroneous predictions by explicitly modelling uncertainty and using only high-confidence estimates, while maintaining a good recall (>80% overall and >98% in areas with coconut plantations).  
Although not having 100% recall could lead to a slight underestimation of the damages, we did not observe a systematic effect in our validation areas. It appears that, by retaining only sufficiently reliable estimates, we obtain a map that still contains the large majority of changes, but is a lot less affected by noise, as demonstrated by the clearly reduced MAE in our validation areas.