esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Mathematical Model Development and Implementation
Additionally, the executable file is available in DataRepositoriUM ([[https://doi.org/10.34622/datarepositorium/2](https://doi.org/10.34622/datarepositorium/2) BUM2]([https://doi.org/10.34622/datarepositorium/2](https://doi.org/10.34622/datarepositorium/2) BUM2)), accessed on 28 October 2022). (due to its lower density in comparison with water, excess of hexadecane is considered to float, thus forming a free NAPL layer). For simplification, hydrocarbon concentration in the liquid phase refers to the mass of both dissolved and NAPL forms per unit of liquid volume and both are considered to be available for microorganisms growth;
2. Soil temperature was kept constant and equal to 20 \({}^{\circ}\)C;
3. Only values for alkanes were considered for all parameters and constants involved in the model processes;
4. For processes of stoichiometric calculations, hexadecane was chosen as a model compound, as alkanes of intermediate chain length are major constituents of petroleum fuels [17]. Thus, processes rates and model parameters were defined following the reactions presented in Table S1;
5. Microorganisms do not use any other source of organic matter besides hydrocarbons for growth;
6. The biomass of the hydrocarbon-degrading microorganisms follows growth kinetics according to the Monod equation;
7. To guarantee that all soluble iron does not leave the soil column with the water flow, and due to the fast transformation process of Fe\({}^{2+}\) and its unstable nature in the environment [31], it was assumed to be a process for iron oxidation in the presence of O\({}_{2}\). This process was considered a first order reaction for Fe\({}^{2+}\) and O\({}_{2}\) (Table 3). Simplifications (ii) and (iii) make the results presented here specific to saturated hydrocarbons but the entire model design applies to any hydrocarbon or mixture of hydrocarbons by simply changing the values of parameters, constants and processes rates. In addition, the present model is considered a wet soil mainly composed of fine sand and/or pumices [32]. However, soil properties can be easily changed by assuming new values for soil porosity and soil density. For the simulation of a real contamination, the model assumed an oil spill that resulted in a TPH concentration in the soil of 0.6 g/kg for an area of 1 m\({}^{2}\) and 2 m\({}^{2}\) soil column depth. In the model development, it was also considered the daily rainfall for the city of Braga (with an annual average of \(\sim\)3.75 L/d) as an additional flow of water to the soil column (
## 3 Method
### Evaluation for the Classification Model  
To inspect the accuracy of the classification model, we used a confusion matrix to calculate the accuracy metrics. A confusion matrix, also known as a contingency table or error matrix, is used to present the visualization effect of the classification algorithms' performance. Each column represents the predicted value, and each row presents the actual category. It consists of true positives (TP), true negatives (FN), false positives (FP) and false negatives (TN), which are defined in Table 2. Furthermore, the user accuracy (UA), producer accuracy (PA), overall accuracy (OA) and Kappa coefficient are all defined by the confusion matrix and specified in Table 2.
# Advanced Microbubbles as a Multifunctional Platform Combining Imaging and Therapy
## 2 Advanced Microbubbles for Imaging and Therapy
### Tumor Detection
These RGD-MBs showed significantly higher adhesion to \(\alpha\
u\beta 3\)-expressing endothelial cells compared to the controls. In vivo experiments verified that the quantitative acoustic intensity in Hep2-tumor xenografts showed significantly higher in small tumors (19.89 \(\pm\) 2.49) than those in medium (11.25 \(\pm\) 2.23) and large tumors (3.38 \(\pm\) 0.67).The results concluded that RGD-MBs enabled non-invasive in vivo visualization of tumor angiogenesis during tumor growth in Hep2 cancers [40]. In addition to the targeted microbubbles have showed above to assess the tumor angiogenesis, the other targeted UCAs applied in animal experiments have been summarized in Table 2.  
Microsized bubbles can only be used for intravascular imaging, but nano-scaled bubbles can be carried out of vasculature for tumor tissue imaging. [PERSON] prepared biocompatible chitosan-VC lipid nanobubbles (NBs) to conjugate Cy5.5 as ultrasound-fluorescence dual-modal contrast agents for tumor imaging in mouse tumor model injected with murine liver cancer cells H22. Their sizes range between 400 and 800 nm, and both in vitro and in vivo studies indicated that they had high accumulation in tumors [41]. [PERSON] et al. prepared CaCO\({}_{3}\) nanoparticles as ultrasound enhanced-contrast agents (UECAs) for tumor ultrasound imaging. In vivo experiment showed this nanosized UECA had a significant US contrast enhancement at the tumor site and was excreted out quickly [42] (Figure 3). [PERSON] fabricated NBs (478.2 \(\pm\) 29.7 nm in diameter, PDI 0.164 \(\pm\) 0.044) via a thin-film hydration method, and successfully attached the NBs with biotinylated anti-ErbB2 antibody, which has high affinity and specificity for the HER2 overexpressed in some breast cancer tumors [43]. It was found that these NB-affibody conjugates were promising candidates for HER2-positive tumors and applicable for quantitative diagnosis. A novel kind of nanoparticle containing folate-PEG-chitosan and perfluorooctyl bromide cores was prepared [44]. Not only excellent stability and biocompatibility were confirmed, but also an increased amount of targeted nanoparticles was observed accumulated in hepatocellular carcinoma cells. Figure 2: Ultrasound images of MAT B III tumors in CPS (Contrast Pulse Sequencing)mode. Green outline implied tumor; red outline implied nontumoral vasculature. (**A**) Before injection of the VEGFR2-targeted microbubbles (BR55); (**B**) At 20 s after injection of BR55 (peak intensity), the tumor vasculature and nontumoral vasculature shows hyperechoic; (**C**) At 10 min after injection of BR55, only the VEGFR2-targeted microbubbles are adherent to the tumor vasculature; (**D**) At 11 min, the microbubbles in the tumor were cleared and residual circulating bubbles were waiting for refilling of the tumor.
# In Vitro Genotoxicity Assessment from the _Glycyrrhiza_ New Variety Extract
## Materials and Methods
### In Vitro AMES Test  
WG extract's mutagenic potential was assessed by bacterial reverse mutation assay consistent with OECD TG471 [23]. Four histidine auxotroph strains of _Salmonella typhimurium_ (TA100, TA1535, TA98 and TA1537) and a tryptophan auxotroph strain of _Escherichia coli_ (WP2 _uvrA_) were chosen according to the previous studies [24; 25]. The strains were purchased from Molecular Toxicology Inc. (Boone, NC, USA). The mutagenic activity of WG water extract was evaluated with or without S9 mix, which is an external metabolic activation system, from the rat livers using direct plate incorporation. Dose ranges were established using the five test strains with or without S9 mix in three plates per dose, according to the results of a range-finding test performed on the WG water extract. The highest dose administered in this study was 5000 \(\mu\)g/plate for all test strains, and five-serial diluted concentrations (0, 50, 150, 500, 1500 and 5000 \(\mu\)g/plate) were tested during the main study. A negative control (distilled sterile water) and positive controls for each strain with or without S9 mix were included. Positive controls with S9 mix were as follows: SA (0.5 \(\mu\)g/plate) for TA100 and TA1535, 2-NF (2 \(\mu\)g/plate) for TA98, 4 NQO (0.5 \(\mu\)g/plate) for WP2 _uvrA_, and ICR-191 (0.5 \(\mu\)g/plate) for TA1537. Positive controls without S9 mix were as follows: 2-AA (1 \(\mu\)g/plate) for TA100 and TA1537, (2 \(\mu\)g/plate) for TA1535 and (6 \(\mu\)g/plate) for WP2 _uvrA_, and B[a]P (1 \(\mu\)g/plate) for TA98. For the plating assay, 0.5 mL of S9 mix (or sodium-phosphate buffer, pH 7.4 for non-activation plates), 0.1 mL of bacterial culture (approximately 0.5 \(\times\) 10\({}^{8}\) cells), and 0.1 mL of WG water extract were mixed with 2.0 mL of overlay agar. The contents were well mixed, then poured into the surface on a plate, which was contained with minimal agar. Plates were inverted after the overlay agar was solidified, then incubated at 37 \(\pm\) 2 \({}^{\circ}\)C for 50 \(\pm\) 2 h. The revertant colonies were counted by macrography. Results are presented as the number of revertant colonies, and the increase factor was calculated by dividing the number of colonies in the treated plates with the number of colonies in the negative control plates. Experiments were conducted using triplicate plates for each concentration.
###### Abstract  
In this paper, a data-centric communication framework is proposed for multicast routable generic object-oriented substation event (GOOSE) messages (MRGM) over the wide area network (WAN) for effective substation-to-substation (SS2 SS) and substation to control center (SS2 CC) communications. In this structure, the IEC 61850 GOOSE message is transmitted over the WAN using the data distribution service (DDS) as a fast, reliable, and secure data-centric communication middleware. The main feature of this framework is its multicast capability, where several authorized subscribers can receive a published message simultaneously. This can significantly improve the system monitoring and control of the protection systems in modern smart grids, where intelligent schemes can be applied. The effectiveness of the proposed platform, in terms of total end-to-end delay between participants, is evaluated through experimental results obtained from the actual hardware-based test setup developed at the Florida International University (FIU) smart grid testbed. The results demonstrate that the latency between sending and receiving a GOOSE message among participants is within its maximum time span defined by the IEC 61850-90-5 working group for communications over the WAN.  
IEC 61850; routable communication; multicasting; GOOSE message; data distribution service; data-centric middleware +
Footnote †: journal: Applied  
Article
## 5 Experimental Results
### Simulation Studies  
This section presents our simulation results. We used the dynamic models of the IEEE 39-bus and IEEE 162-bus test systems and a reduced real power system in China to demonstrate that our algorithm can minimize the impact of cascading outages with the aid of the proper post-separation actions.
## 4 Discussion  
### Effect of Station Spacing on Fault Imaging  
The magnetotelluric inversion results (Figure 2a-d) highlight the effect of station spacing on detecting low-resistance fault zones. The station spacings tested range from 200 m to 800 m, and the results consistently show that even at depths of up to 3 km, the linear structure of the faults can be effectively captured. These findings demonstrate the robustness of the employed inversion method across various station spacings, particularly in delineating fault zones spanning shallow to deep subsurface layers.  
However, it is evident that smaller station spacings (200 m and 400 m) provide superior accuracy in defining fault geometries and structures compared to larger spacings (600 m and 800 m). In Scenario 1, fault imaging is more precise when the station spacing is reduced to 200 m or 400 m. In these cases, the spatial locations of the three faults, especially F1 and F3, closely align with their actual positions in the geological model, and their depths are well-matched in the inversion results.  
In contrast, when the station spacing increases to 600 m or 800 m, the inversion results begin to diverge from the actual fault positions. Notably, the F1 fault exhibits significant positional deviations, suggesting that larger station spacings reduce the reliability of imaging complex fault geometries. This trend is also apparent in Scenario 2, where the 800 m  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **Survey Length** & **Frequency** & **No. of Air** & **No. of Vertical** & **RMS Misfit** & **RMS Misfit** \\
**Spacing (m)** & **(km)** & **Band (Hz)** & **Layers** & **Cell** & **(Scenario 1)** & **(Scenario 2)** \\ \hline
200 & 14 & 0.001–1000 & 8 & 25 & 0.82 & 1.51 \\
400 & 14 & 0.001–1000 & 8 & 25 & 1.27 & 1.38 \\
600 & 14 & 0.001–1000 & 8 & 25 & 1.42 & 1.50 \\
800 & 14 & 0.001–1000 & 8 & 25 & 1.64 & 1.59 \\
400 & 12 & 0.001–1000 & 8 & 25 & 1.29 & 1.40 \\
400 & 10 & 0.001–1000 & 8 & 25 & 1.26 & 1.44 \\
400 & 8 & 0.001–1000 & 8 & 25 & 1.26 & 1.49 \\
400 & 14 & 0.01–100 & 8 & 25 & 1.41 & 1.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mesh parameters used for inversion of synthetic data.  
station spacing causes the F3 fault to show a broad influence area with unclear boundaries, indicating a loss of resolution.  
Additionally, the F2 fault proves challenging to image across all station spacings and scenarios. Regardless of the acquisition parameters, the inversion consistently fails to accurately resolve the depth and position of the F2 fault, suggesting that this fault may either require a denser dataset or be subject to limitations inherent in the inversion algorithm or the characteristics of the fault zone itself.  
Figure 2: Inversion resistivity profile of different station spacings. (**a**) Station spacing is 200 m. (**b**) Station spacing is 400 m. (**c**) Station spacing is 600 m. (**d**) Station spacing is 800 m.
# Wireless Power Transfer Approaches for Medical Implants: A Review
## 2 Different Approaches for a Wireless Power Transfer System
### Inductive-Based Wireless Power Transfer
As the frequency increases, the electromagnetic wavelength becomes more commensurate with the coil dimension and the space between the coils. In this stance, the radiative and non-radiative components are part of the electromagnetic waves. Biological tissue also creates significant problems for the propagation of electromagnetic fields and dilutes the electrical field, thus affecting the efficiency of the inductive link [33]. According to [PERSON]'s induction law, increasing the size of coils and the number of turns boosts inductive link efficiency [33]. When the transmitting coil and the receiving coil have the same size, the maximum coupling is achievable. Although, in practice, the implanted coil is significantly smaller than the transmitting coil [70]. Mainly, the inductive-based WPT system is used for medical devices such as brain and spinal cord stimulators. [PERSON] et al. [8] have developed a stimulator that occupies an area of 5 mm \(\times\) 7.5 mm and operates at the resonant frequency of 198 MHz while having a 14 cm distance from the transmitter, which is located outside of the body. The stimulator gets the energy that has already been stored by a switched capacitor and releases the energy as an output stimulus once the voltage reaches a threshold. The control unit utilizes positive feedback to trigger the circuit, so no stimulation control circuit block is needed. An in vivo experiment was performed to demonstrate the performance of the stimulator. Two electromyography (EMG) recording electrodes were implanted into the gastrocnemius muscle of a rat while the ground electrode was attached to the skin. A free-floating neural implant, which is insensitive to the location, is provided as an inductive link in [10] for wireless energy transmission. The authors have created prototypes of floating implants for precise measurements. The system works with a power transfer efficiency of 2.4% at 60 MHz and provides 1.3 mW power to the implant 14-18 mm away from the transmitter. Their coil link is stable against the lateral and angular misalignments of the floating implants if the coils continue to have the high-Q resonator. The extra heat produced by the resonator coil also does not exceed safety limits. Recent works of the inductive WPT scheme are evaluated and presented in Table 2. The panel consists of printed and 3D coils. Printed coils maintain acceptable performance under lateral malalignment and are reliable for implants [4].
# Model Experiment Study on Vertical Bearing Characteristics of Large-Diameter Ring Piles
## 3 Analysis of Model Experimental Results
### Analysis of Settlement Law of Pile Top and Pile Bottom in Model Experiment
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Project**} & **No. 1 Solid Pile** & **No. 5 Ring Pile** \\ \hline Ultimate bearing capacity & \multicolumn{2}{c}{50 kN} & 40 kN \\ \cline{2-3} Safety factor & \multicolumn{2}{c}{2} \\ \cline{2-3} Characteristic value of & \multicolumn{2}{c}{25 kN} & 20 kN \\ \cline{2-3} Safety reserve & \multicolumn{2}{c}{25 kN (100\%)} & 20 kN (100\%) \\ \cline{2-3} Pile safety reserve & \multicolumn{2}{c}{25 kN (100\%)} & 15 kN (75\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of characteristic values of ultimate bearing capacity. As shown in Table 3, considering the most unfavorable situation, Ring Pile No. 5 was subjected to the load of the characteristic value of the bearing capacity of Solid Pile No. 1 and still had 75% safety reserve.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 5 Simulation
### Simulation Results  
#### 5.2.1 Case 1: Normal Operation  
The meaning AUV performability, \(\Pr(A_{1})\), \(\Pr(A_{2})\), and \(1-\Pr(A_{3})\) is as shown in Equation (17).  
\[\Pr(A_{1}) =P(\{\omega|Y_{S}(\omega)\geq A_{1}\}) \tag{17}\] \[\Pr(A_{2}) =P(\{\omega|A_{1}>Y_{S}(\omega)\geq A_{2}\})\] \[1-\Pr(A_{3}) =1-P(\{\omega|Y_{S}(\omega)\geq A_{3}\})\]  
\(\Pr(A_{1})\) denotes the probability of the mission being completed even in the event of a system failure, while \(\Pr(A_{2})\) denotes the potential for the mission to be completed while in a safety critical state, meaning that in the event of a failure, the mission can no longer be completed. The likelihood that a safety critical defect or several failures that the system cannot handle will prevent the system from carrying out its mission is represented by \(1-\Pr(A_{3})\). The results of the performability analysis using two methods over a 50 h period are shown graphically in Figure 4.  
Figure 4: Performability in scenario case 1.  
As a result of the AUV functioning flawlessly over the entire operating duration, it is evident that \(\Pr(A_{1})\) and \(\Pr(A_{2})\) remain high. The number of parts used rises and \(\Pr(A_{1})\) somewhat falls when the AUV completes its exploration mission in 1.5 h. It is evident that as the AUV completes the mission and the recovery and charging phases, its performability \(\Pr(A_{1})\) rises. Compared to the MDD-based approach, the proposed method more clearly illustrates the differences between each phase.
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 1 Introduction
### Related Work
Their approach uses fuzzy control to adaptively adjust AUV trajectories in response to real-time VoI evaluations and energy constraints. Multi-AUV coordination hasalso demonstrated benefits in this regard. The authors in [15] introduce an integrated sensing and communication model in which AUVs strategically navigate overlapping sensor communication zones to reduce energy consumption and enhance network throughput. Their framework incorporates dynamic path adjustments and reinforcement learning for real-time obstacle avoidance and trajectory optimization. Similarly, ref. [11] presents a routing protocol based on MARL, enabling nodes to make cooperative routing decisions that consider both VoI and residual energy, thereby reducing latency and improving energy efficiency. In [36], the authors apply a VoI-to-energy optimization model to develop distributed, adaptive path planning strategies that respond to dynamic ocean currents and support effective coordination among multiple AUVs. Beyond multi-AUV cooperation, recent research has explored the integration of AUVs with USVs to enhance underwater communication and mission performance. For instance, ref. [17] proposes a joint navigation framework in which a USV maintains acoustic connectivity with a mobile AUV while avoiding collisions. The study in [19] further demonstrates that communication performance can be improved by dynamically adjusting the USV's position to strengthen the acoustic link quality. In a related effort, the authors in [18] employ optimal control techniques to plan USV trajectories that enhance network connectivity, underscoring the advantages of integrated AUV-USV systems for reliable underwater operations. In a further development, ref. [20] introduces a localization-aware MPC-based path planning strategy, where a USV coordinates a team of AUVs to improve energy efficiency, exploration coverage, and localization accuracy under acoustic communication constraints.
## 3 Results
### Comparison of the Models  
Using RFECV, we selected ten features to develop the Logistic Regression, SGDClassifier, XGBoost, and RVM models. The only feature in common with the Bayesian Network  
Figure 5: Posterior probabilities scenarios of having recurrent anxiety symptomatology when combining the features of the BN model for 1 feature (**A**), 2 features (**B**), and 3 or 4 features (**C**). Category 3: Nearly every day for gad2, gad4, gad6, and a PHQ-9 score of 20 or higher for the depression group. Category 0: Not at all for gad2, gad4, gad6, and a PHQ-9 score between 5 and 9 for the depression group.  
model was gad2. The remaining nine features are different and are described in Table 6 in the Appendix A.  
Table 6 presents the AUC metric of the three models as well as the proposed BN model. The first four columns (Min, Mean, Max, SD) correspond to the 100 evaluations using nested cross-validation for the three models and the 100 evaluations of the repeated cross-validation in our approach. The last column indicates the AUC on the test data, which utilised the same data split for all five models, as discussed in Section 2.6.  
From Table 6, it is observable that all four models outperformed the Bayesian network in terms of Mean AUC, with values of 0.897, 0.883, 0.884, and 0.894 for Logistic Regression, SGDClassifier, XGBoost, and RVM, respectively, compared with 0.836 for the Bayesian network. Additionally, the standard deviations of the three models are lower than that of the Bayesian network, with values of 0.037, 0.050, 0.039, and 0.035 for Logistic Regression, SGDClassifier, XGBoost, and RVM, respectively, compared with 0.056 for the Bayesian network.  
However, upon evaluating the performance of the AUC metric on the test data, the Bayesian network exhibited an AUC of 0.821, while Logistic Regression, SGDClassifier, XGBoost, and RVM exhibited AUC values of 0.814, 0.842, 0.799, and 0.831, respectively. Given the cross-validation results on the training data, these test results should not be interpreted as meaning that the BN is superior, but they do show that the performance of the latter is not substantially inferior.