esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision-Maker's Preference-Driven Dynamic Multi-Objective Optimization
## 5 Conclusions
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**DMOOP** & \(\mathbf{n_{t}}\) & \(\mathbf{\tau_{t}}\) & **PM** & **PPA** & **DPA** & **RSTFRA** & **PM** & **PPA** & **DPA** & **RSTFRA** \\ \hline fda1 & 10 & 4 & nNVD & 99.4333 & 98.1333 & **99.8000** & sNVD & 0.0297 & **0.0301** & 0.0296 \\ fda1 & 10 & 4 & nNVD & **99.5000** & 96.1667 & 96.0667 & sNVD & 0.0298 & **0.0308** & **0.0308** \\ fda1 & 10 & 4 & nNVD & **99.5000** & 94.3667 & 95.6000 & sNVD & 0.0298 & **0.0314** & 0.0310 \\ fda1 & 10 & 4 & nNVD & **99.0333** & 96.3000 & 96.8333 & sNVD & 0.0299 & **0.0308** & 0.0306 \\ \hline fda1 & 10 & 5 & nNVD & **100.0000** & **100.0000** & **100.0000** & sNVD & **0.0295** & **0.0295** & **0.0295** \\ fda1 & 10 & 5 & nNVD & **100.0000** & **100.0000** & **100.0000** & sNVD & **0.0295** & **0.0295** & **0.0295** \\ fda1 & 10 & 5 & nNVD & **100.0000** & 99.9667 & **100.0000** & sNVD & 0.0295 & 0.0295 & **0.0296** \\ fda1 & 10 & 5 & nNVD & **100.0000** & 100.0000** & **100.0000** & sNVD & 0.0295 & 0.0295 & **0.0295** \\ \hline fda1 & 10 & 2 & nNVD & 64.9667 & 64.9000 & **67.1333** & sNVD & 0.0457 & **0.0458** & 0.0441 \\ fda1 & 10 & 2 & nNVD & **67.4333** & 58.6667 & 57.6667 & sNVD & 0.0442 & 0.0506 & **0.0514** \\ fda1 & 10 & 2 & nNVD & **67.5667** & 58.7000 & 56.0000 & sNVD & 0.0404 & 0.0505 & **0.0532** \\ fda1 & 10 & 2 & nNVD & **67.7000** & 57.4667 & 57.7333 & sNVD & 0.0438 & **0.0518** & 0.0512 \\ \hline fda1 & 1 & 4 & nNVD & 26.2667 & **27.6333** & 25.6333 & sNVD & 0.0671 & 0.0648 & **0.0686** \\ fda1 & 1 & 4 & nNVD & 26.2333 & **26.5667** & 21.6333 & sNVD & 0.0719 & 0.0777 & **0.0853** \\ fda1 & 1 & 4 & nNVD & **26.1667** & 23.1333 & 22.7000 & sNVD & 0.0675 & 0.0773 & **0.0784** \\ fda1 & 1 & 4 & nNVD & 25.0000 & 25.3667 & **25.6000** & sNVD & 0.0735 & 0.0756 & **0.0769** \\ \hline fda1 & 1 & 5 & nNVD & **100.0000** & **100.0000** & **100.0000** & sNVD & **0.0296** & 0.0295 & 0.0295 \\ fda1 & 1 & 5 & nNVD & 71.4333 & 99.0333 & **100.0000** & sNVD & **0.0422** & 0.0301 & 0.0296 \\ fda1 & 1 & 5 & nNVD & **100.0000** & 98.9333 & 99.3000 & sNVD & 0.0296 & **0.0299** & 0.0298 \\ fda1 & 1 & 5 & nNVD & 76.2667 & **98.1667** & 95.7667 & sNVD & **0.0394** & 0.0303 & 0.0291 \\ \hline fda1 & 1 & 2 & nNVD & 66.0333 & 65.7000 & **66.2000** & sNVD & 0.0448 & **0.0453** & 0.0449 \\ fda1 & 1 & 2 & nNVD & 31.7000 & 30.2333 & **40.3000** & sNVD & 0.0973 & **0.1029** & 0.0726 \\ fda1 & 1 & 2 & nNVD & **62.6000** & 43.2333 & 43.4333 & sNVD & 0.04708 & **0.0690** & 0.0670 \\ fda1 & 1 & 2 & nNVD & **31.4667** & 31.3667 & 28.8667 & sNVD & 0.0981 & 0.1001 & **0.1089** \\ \hline \end{tabular}
\end{table} 
Table 3: nVD and sNVD for each DMOA for FDA1 with various frequency and severity of change.
# Review on the Recent Numerical Studies of Liquid Atomization
## 4 Particle Tracking
### Mathematical Description  
The motion of the gas phase is still in the Eulerian framework, while the particle motion is solved by the integral of [PERSON]'s law, which can be written as:  
\[m_{p}\frac{du_{p}}{dt}=F_{d}+F_{g}+F_{x} \tag{11}\]  
where \(m_{p}\) is the parcel mass, \(u_{p}\) is the velocity, \(F_{d}\) the drag force, \(F_{g}\) the gravitational force, and \(F_{x}\) represents the other force and are usually neglected. For instance, the magnitudes of the longitudinal forces such as Basset force, pressure gradient force, and virtual mass force are at least two orders less than the drag force. The Magnus force acting on rotating spheres and lift force working on high tangential zone are also not the case for the parcels in atomization flow field and thus can be ignored. The drag force can be expressed by:  
\[F_{d}=\frac{1}{2}C_{d}A_{p}\rho(u-u_{p})\left|u-u_{p}\right| \tag{12}\]  
where \(C_{d}\) is the drag coefficient and \(A_{p}\) is the windward area.  
The Lagrangian tracking framework resolves droplet trajectories at the resolved scale (>50 \(\mu\)m) yet cannot intrinsically capture sub-particle interfacial instabilities that govern breakup processes. This necessitates the introduction of breakup models to close the momentum exchange term, where the droplet acceleration is modulated by competing forces.  
\[\frac{du_{d}}{dt}=\underbrace{\frac{u_{g}-u_{d}}{\tau_{d}}}_{Drag}+\underbrace {C_{\text{We}}\frac{\sigma}{\rho_{d}\tau_{d}^{2}}\
abla n}_{\text{Surface tension}}+\underbrace{B(We,Oh,\hat{\eta})}_{Breakup-induced\ acceleration} \tag{13}\]  
Here, the breakup term B is parameterized through the Taylor analogy breakup (TAB) or Kelvin-Helmholtz/Rayleigh-Taylor (KH-RT) models, which inject SGS interface instability effects into resolved-scale motion via: Weber number (dictates dropletdeformation thresholds), Ohnesorge number (controls viscosity-dominated breakup delay), axis-switching parameter (tracks preferential breakup orientations from local shear spectra). This multiscale coupling enables particle tracking methods to reproduce experimentally observed spray granulometry within 12% error.  
Turbulence models of RANS, LES, and DNS are all competent for particle tracking method. Since particle tracking usually covers a much wider range of atomization than interface tracking, RANS models are more often used as it saves computer resources.
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 2 Adversarial Samples and Adversarial Attack Strategies
### Adversarial Example (AE)
#### 2.1.2 Characteristics of Adversarial Examples  
Adversarial samples have three basic characteristics [59], i.e., transferability, regularization effect, and adversarial instability.  
* **Transferability.** When constructing adversarial samples for an attack against one target model \(M_{1}\), it is unnecessary to obtain the architecture or parameters of model \(M_{1}\) if the adversary has knowledge of \(M_{2}\), as long as model \(M_{2}\) is trained to perform the task that model \(M_{1}\) performs as well.
* **Adv**s**arial **instability.** After physical transformation, such as translation and rotation, it is easy to lose its own adversarial for adversarial samples. In this case, adversarial samples will be correctly classified by target models.
* **Regularization effect.** Adversarial training [15] is a regularization method that can reveal the defects of models and improve the robustness of samples. Compared to other regularization methods, the cost of constructing adversarial samples is expensive.
## 6 Evaluation and Discussion
### Security Analysis  
The effectiveness of NDN-BDA against adversary nodes was tested in various scenarios in this section. It was assumed that the adversary \(adv\) can be any vehicle functioning as a producer or consumer in the network.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 5 Case Study and Result
### Generality Verification Based on the IEEE 69-Node System
Simultaneously, the node voltage deviation reduced from 44.2 p.u. to 26.3 p.u., a decrease of about 40.2%, significantly improving overall voltage stability. From the trends in network loss changes and the dynamic fluctuations in voltage at various nodes, it is clear that Scheme 3 demonstrates superior network loss control across most periods. In terms of voltage, the node voltages are more balanced, with a significantly reduced fluctuation range compared to the previous two schemes, indicating improved system operating characteristics. Based on a comprehensive analysis of the experimental results, we found the following. In the IEEE 33-bus system, the proposed method reduced total active power loss from 1320.4 kWh to 729.3 kWh (a 44.8% improvement), and voltage deviation from 25.1 p.u. to 16.1 p.u. (a 35.9% improvement). In the IEEE 69-bus system, power loss decreased from 1412.4 kWh to 846.7 kWh (a 40.1% reduction), while voltage deviation was reduced from 44.2 p.u. to 26.3 p.u. (a 40.5% improvement). Figure 16: Voltage levels of each node in scheme 2 based on the IEEE 69-bus system. Figure 17: Voltage levels of each node in scheme 3 based on the IEEE 69-bus system. These results show that the proposed method achieves consistent improvements in both systems, and that its advantage becomes even more significant in larger and more complex networks. It can be concluded that the proposed dynamic reconstruction method for active distribution networks, based on the improved EO algorithm, demonstrates strong adaptability and optimization performance in both the IEEE 33-node and IEEE 69-node examples. This validates the applicability and superiority of the proposed method across distribution networks of different scales and highlights its significant potential for broader implementation. Although formal statistical significance tests were not explicitly performed, the robustness and stability of the proposed method were assessed through multiple experimental runs with varied initializations and parameter perturbations (e.g., load profiles and algorithm settings). The method consistently exhibited stable convergence and performance, which indirectly confirms its reliability under a variety of practical conditions.
# Removal of Phosphate from Aqueous Solution by
## 3 Results and Discussion
### Adsorption Experiments
#### 3.2.4 Influence of Solution pH on Phosphate Adsorption Capacity  
The pH level in the solution is an important factor in determining the adsorption potential of different pollutants [57]. In this work, the effects of different adsorbents on phosphate adsorption at pH values from 3 to 11 were investigated. As displayed in Figure 6, the pH of a solution affected phosphate removal. The adsorption capacity of PZC 7:3 was raised from 0.85 to 1.01 mg/g in the pH range of 3-7, which is contrary to the studies that reported that adsorption remained unchanged or decreased in the pH range of 4-8 [58; 59]. In acidic environments, phosphorus mainly exists in H\({}_{2}\)PO\({}_{4}\)\({}^{-}\), and H\({}_{2}\)PO\({}_{4}\)\({}^{-}\) can be adsorbed on PZC by electrostatic attraction or interact with the hydroxyl groups on the surface of PZC via ligand exchange [60; 61]. When the pH was between 7 and 11, the adsorption capacity of PZC 7:3 for phosphate was found in the range of 1.01-1.06 mg/g. The calcium in zeolite can precipitate with HPO\({}_{4}\)\({}^{2-}\) or PO\({}_{4}\)\({}^{3-}\)[8].  
Figure 5: The removal rate of phosphate in solution of differential materials. (**a**) Influence of preparation methods on phosphate removal. (**b**) Influence of different ratios (biochar:zeolite) on phosphate removal. Different colors refer to various materials and results in this section.
## 4 Applications  
The technology platform as it is presented in this paper has been used for several measurement campaigns including the following monitoring tasks:  
* the identification of natural frequencies of cantilevered balcony plates using a meshed sensor network controlled by the smartphone application,
* the identification of vertical and rotational natural frequencies of a dynamic wind tunnel rig using RPi and external sensors,
* the identification of natural frequencies of a pole structure for model updating,
* the determination of prestressing forces of external tendons in a highway bridge [15],
* the identification of stay cable forces at Queensferrry Crossing bridge,
* the determination of peak velocities from acceleration measurements at a bridge pier during blast operation, and
* the identification of modal properties of a simply supported steel beam.  
In most of these measurement tasks, the natural frequencies needed to be identified. Using a high-quality reference measurement system in addition to the presented mobile measurement system, very good agreement in the identified natural frequencies was found, highlighting the accuracy and validity of vibration measurements taken by the mobile measurement system.  
It should be noted that the presented measurement system was mostly applied for short-term vibration measurements. Utilizing a standard rechargeable power bank battery with a capacity of 10.000 mAh, in-situ measurements of up to 20 h can be performed. Continuous longer term measurements would require an extended power supply, and in the current hardware configuration the measurement system is neither water-proof nor dust-proof. However, RPi and sensor hardware can be integrated in different enclosures to ensure a certain ingress protection (IP) grade depending on the operational conditions. Attachment to the structure can then be faciliated by a bolted connection. In a previous study, a system composed of RPi and highly accurate MEMS-based inclination sensors, integrated in a polycarbonate enclosure, was employed to monitor a long span roof beam of an industrial plant subjected to snow load [29; 30]. During winter, the inclination was measured continuously, and the height of the snow layer was determined from measured data. A wired connection was used since power and network access was available. The system was configured to store large amounts of measurement data on external USB storage media. Additionally, the obtained data were periodically synchronized with a remote file server, and system health information, such as CPU temperature and load, was sent to a central server.  
The focus of this paper, however, is on short-term vibration measurements taken by the proposed wireless sensor network. In order to show its practicality and to study the performance during field tests, the last three monitoring tasks of the above presented list will be discussed here in detail.
## 5 Discussions and Conclusions  
### Integrated Discussion of Results  
This paper develops a coordinated decision-making model for WPPs and DR aggregators participating in the energy and frequency regulation markets. The objective of the developed model is to optimize the bidding strategies for market participants. Nucleolus- and Shapley-value-based methods are deployed to fairly distribute the combined revenues to each player. In addition, to demonstrate the effectiveness of the proposed trading structure, different trading schemes, which include coordinated and uncoordinated bidding strategies, are presented and compared in the case studies. The key findings of this paper can be summarized as follows:  
1. A cooperative framework is proposed for wind power and DR resources, aiming to explore their complementary characteristics in the provision of frequency regulation. Furthermore, leveraging these complementary attributes, an optimal bidding strategy is developed for WPPs and DR aggregators. Notably, the coalition formed between WPP 1 and the DRA results in a significant increase in total profit by 5.0%, when compared to a scenario where the DRA operates without the participation of wind power producers;
2. CVaR is deployed to control the revenue volatility associated with uncertainties. The results of the case study demonstrate that as \(\beta\) increases, the CVaR rises and the market revenue declines. Specifically, in Case S2 (where \(\beta\) ranges from 0 to 0.5), CVaR increases by 6.7%, resulting in a revenue reduction from USD 55,534.5 to USD 54,029. Similarly, in Case S4, CVaR rises by 11.4%, leading to a 3.47% decline in revenue;
3. The coordinated bidding strategy can bring higher benefits for both WPPs and DR aggregators. The results show that the proposed trading scheme results in the highest market benefits in both two-player and three-player games. In addition, the regulation-related profits become a larger fraction of the overall profits. Specifically, in Case S6, the coalition achieved market profits with 46.54% from frequency regulation and 5.9%  
Figure 9: Expected profit and CVaR for different \(\beta\), (**a**) expected profit versus CVaR for Case 1, (**b**) expected profit versus CVaR for Case 2, (**c**) expected profit versus CVaR for Case 3.  
from energy imbalance, highlighting the significant contribution of regulation-related activities to the total earnings.
# Action of Antimicrobial Peptides against Bacterial Biofilms
## 4 Future Considerations  
Treating bacterial infections caused by biofilm-producing microorganisms is a troublesome task and a major challenge for health care systems. Antibiotic therapy or antibiotic releasing products are not adequate to control biofilm related infections, particularly due to the emergence of antibiotic resistant infections. Currently, there is no clear answer for the management and prevention of these infections. Use of very high concentrations of antibiotics in attempts to disrupt or prevent biofilm formation can be associated with cytotoxicity and poor prognosis. Hence, finding an alternative class of drugs to address biofilm-related infections represents a promising strategy. AMPs have broad-spectrumanticriboial activity and are generally immune to development of bacterial resistance [45; 133] and can work synergistically with first line antibiotics. AMPs have several promising characteristics that can be used to inhibit biofilms. However, there is limited information on the interaction of AMPs with biofilm components. More research is needed to understand their precise mechanisms of action such as inhibiting QS signals that restrict biofilm formation and interfere with signaling pathways involved in the synthesis of EPS. Molecular modelling approaches may provide insights on action of AMPs on biofilms. AMP-AMP and AMP-drug combinations that can induce biofilm matrix degradation could be the potential areas of future anti-biofilm research.  
In conclusion, this review found that AMPs have a variety of active anti-biofilm mechanisms that could be exploited for clinical applications to eradicate biofilms. It is clear that AMPs have high potential for further development as an active anti-biofilm agent, particularly in the high-risk environments such as hospital settings. AMPs could be used as a stand-alone therapy or in combination with other antimicrobials to eradicate biofilms. Further in vivo investigations are warranted to better understand the complex host environment that may affect their efficacy by reducing their activity and stability. Moreover, the role of immunomodulatory activities must be evaluated in complex biofilm environment in vivo.  
This research was funded by Australian Research Council (ARC) discovery project funding scheme (project number DP160101664).  
The first author received PhD scholarship from Higher Education Commission (HEC) of Pakistan and the University of New South Wales, Australia.  
The authors declare no conflict of interest.
## 3 Proposed Framework
### Reference View Extension  
For view synthesis, the virtual viewpoint has a rotation and translation relationship with the reference viewpoint, as shown in Figure 3. In Figure 3a, the virtual view has only horizontal translation relative to the reference view, while the rotation and translation between the two views are both included in Figure 3b. Assume that the intrinsic matrix of the virtual camera is the same as the reference camera. It can be seen in Figure 3, as the virtual view moves, its imaging range will exceed the reference view. No pixels in the reference image are projected into these areas, so that holes will be created, i.e., OFRs. OFRs are not caused by the occlusion between the foreground and background, so it is difficult to confirm whether these regions belong to the background, especially for the depth image with multiple depth levels. Most of the previous methods use background pixels to fill the OFRs together with disocclusions, which is not suitable and easy to make the inconsistent content to be selected. In this paper, different inpainting methods based on [PERSON]'s algorithm are used to fill the OFRs and disocclusions. To deal with the OFRs, we extend the reference image and depth image. Pixels in the neighbor source regions are applied to the filling of the extended regions. This can preserve local consistency of texture in reference image and depth image. The enlargement of the image size after extension means that the imaging range of the reference view is enlarged, so that pixels can be projected into the OFRs. The extension of the reference view involves two steps: Calculation of the extended region and extended region filling.
## 5 Performance Analysis and Simulation
### Comparison of the Performance Simulation
#### 5.3.1 Comparison of Safety Period
As described in Section 3.3.2, if the SoN continues requesting the SN to help determine the PNCS when there is no PN available in the PNCS, better security performance will be obtained. **Experiment 5:** The impact of \(M\) on the safety period of the proposed schemes  
In order to further study the impact of \(M\) on the safety period of the proposed schemes in the case of replacing the PN, we compare the safety period of the two schemes of RSPS and RDPS. The average safety period is obtained by repeating the experiment 100 times, as shown in Figure 14.  
Figure 10.0 Comparison of the safety period of RDPS scheme with different \(n\). From Figure 14, we can see that the security performance of our proposed RDPS scheme is superior to the RSPS scheme, and the average safety period of the two schemes increases with \(M\). This is because for an attacker, he needs to trace to the PN so as to find the SoN. In fact, the value of M determines the size of the PNCS. The larger the value of M, the more PNs are available for the SoN, and the higher the security is. On the other hand, no matter how big the value of \(M\) is, the SoN only select one PN from the PNCS for use at a time, after a PN is used for a while, the SoN can replace the PN with another one. The SoN does not use \(M\) PNs simultaneously. Moreover, after the PN is replaced, the data packet transmission path will also change, and the attacker can no longer overhear the data packets and will roll back to the SN along the previous path. More PNs can bring more diversities and uncertainties in routing which will increase the difficulty for the attacker to track back and provide longer safety period for the true source node.
# New Insights into the Biology of the Emerging Tembusu Virus
## 2 Tembusu Virus
### Genomic Organization and Replication
To complete its cellular cycle, TMUV has to (1) translate its genomic RNA into viral proteins, (2) replicate its viral RNA genome, (3) encapsidate its genome, (4) assemble the genome into an immature virion in the endoplasmic reticulum, (5) mature the virus particles in the Golgi apparatus and (6) be secreted as infective virus particles in the extracellular space by exocytosis. Although these steps are not well known for TMUV, they do not vary among flaviviruses and have been extensively described elsewhere [14; 15]. TMUV translation and replication occur in the endoplasmic reticulum (ER). Different cellular mechanisms might likely facilitate the replication, evasion and propagation of the virus, and notwithstanding recent progress [16], the exact mechanisms remain to be determined. Moreover, given the importance of the UTR regions in flavivirus replication, a comprehensive analysis of the \(5^{\prime}\) and \(3^{\prime}\) untranslated regions and their functions in the TMUV cycle needs to be elucidated as well. Together, the identification and characterization of host factors interacting with viral RNA and proteins are crucial for a better understanding of TMUV replication. TMUV presents a typical flavivirus structured \(3^{\prime}\)UTR, which plays an important role in the virus's fitness [17]. During replication, this region leads to the formation of an RNA molecule called subgenomic flavivirus RNA (sfRNA). This highly structured RNA fragment has a critical role in viral replication and pathogenesis [18]. Thus, despite the current lack of direct evidence, sfRNA from TMUV is highly likely to have an effect on TMUV replication and pathogenesis in vertebrate hosts and vectors. It is also possible that sfRNA evolution drives modification in the viral pathogenesis pattern and thus the host range [19].