esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Dynamics of an Electromagnetically Actuated
## 5 OAFM for Electromagnetically Actuated Cantilever Beam  
With the help of the following transformation,  
\[z(\tau)=Ae^{-0.5\tau}y(\tau) \tag{32}\]  
where \(A\) is defined in Equation (20), Equations (19) and (20) and can be rewritten as follows:  
\[\ddot{y}+p^{2}y+(k-8a_{0})A^{2}y^{3}e^{-c\tau}-12a_{0}A^{4}y^{5}e^{-2c\tau}-16 a_{0}A^{6}e^{-3c\tau}y^{7}=-\frac{f}{A}e^{0.5\tau}\cos\omega\tau \tag{33}\]  
Figure 3: The potential Vp given by (22) for \(a_{0}=0.001\) and \(K=1\) (red), \(K=1.5\) (yellow), and \(K=2\) (blue).  
\[y(0)=1;\dot{y}(0)=\frac{1}{2}c \tag{34}\]  
where \(p=\sqrt{1-4a_{0}-0.25c^{2}}\). The linear and nonlinear operators corresponding to Equation (33) are the following, respectively:  
\[l[y(\tau)]=\ddot{y}+p^{2}y;n[y(\tau)]=(k-8a_{0})A^{2}y^{3}e^{-c\tau}-12a_{0}A^{4 }y^{5}e^{-2c\tau}-16a_{0}A^{6}e^{-3c\tau}y^{7}+\frac{f}{A}e^{0.5c\tau}\cos \omega\tau \tag{35}\]  
The approximate solution of Equations (33) and (34) takes the following form:  
\[\overline{y}(\tau)=y_{0}(\tau)+y_{1}(\tau,C_{1},C_{2},\ldots,C_{n}) \tag{36}\]  
in which \(C_{i}\), \(i=1\), \(2\), \(\ldots\), \(n\) are unknown parameters at this moment. The initial approximation \(y_{0}(\tau)\) can be obtained from the linear differential Equations (5) and (6):  
\[\ddot{y}_{0}+p^{2}y_{0}=0;y_{0}(0)=1;\dot{y}_{0}(0)=\frac{1}{2}c \tag{37}\]  
the solution of which is the following (11):  
\[y_{0}(\tau)=\cos p\tau+\frac{c}{2p}\sin p\tau \tag{38}\]  
The nonlinear operator defined by Equation (35), calculated for the solution (38), becomes the following:  
\[\begin{array}{l}n[y_{0}(\tau)]=(k-\gamma a_{0})A^{2}e^{-c\tau}\Big{[}\Big{(} \frac{3}{4}+\frac{3c^{2}}{16p^{2}}\Big{)}\cos p\tau+\Big{(}\frac{3c}{8p}+\frac {3c^{3}}{32p^{3}}\Big{)}\sin p\tau+\Big{(}\frac{1}{4}-\frac{3c^{2}}{16p^{2}} \Big{)}\cos 3p+\Big{(}\frac{3c}{8p}+\frac{c^{3}}{32p^{3}}\Big{)}\sin 3p\tau \Big{]}-\\ -12a_{0}A^{4}e^{-2c\tau}\Big{[}\Big{(}\frac{5}{8}+\frac{5c^{2}}{10p^{2}}+ \frac{5c^{4}}{128p^{4}}\Big{)}\cos p\tau+\Big{(}\frac{5c}{16p}+\frac{5c^{3}} {32p^{3}}+\frac{5c^{5}}{256p^{3}}\Big{)}\sin p\tau+\Big{(}\frac{5}{6}-\frac{5 c^{2}}{32p^{2}}-\frac{15c^{4}}{256p^{4}}\Big{)}\cos 3p\tau+\\ +\Big{(}\frac{15c}{320p}+\frac{5c^{3}}{64p^{3}}-\frac{5c^{5}}{512p^{5}}\Big{)} \sin 3p\tau+\Big{(}\frac{16}{16}-\frac{5c^{2}}{32p^{2}}+\frac{c^{4}}{128p^{4}} \Big{)}\cos 5p\tau+\Big{(}\frac{5c}{32p}-\frac{5c^{3}}{512p^{3}}-\frac{c^{5}}{512 p^{5}}\Big{)}\sin 5p\tau\Big{]}-\\ -16a_{0}A^{6}e^{-3c\tau}\Big{[}\Big{(}\frac{5}{84}-\frac{105c^{2}}{256p^{2}}- \frac{105c^{4}}{1024p^{4}}+\frac{3c^{5}c^{6}}{4096p^{6}}\Big{)}\cos p\tau+ \Big{(}\frac{3c}{128p}+\frac{105c^{5}}{512p^{3}}+\frac{105c^{5}}{2048p^{5}}- \frac{35c^{5}}{8192p^{7}}\Big{)}\sin p\tau+\\ +\Big{(}\frac{21}{45}-\[\overline{z}=Ae^{-0.5 cr}[y_{0}(\tau)+y_{1}(\tau)]=Ae^{-\frac{z}{2}\tau}(\cos p\tau+ \frac{c}{2p}\sin p\tau)+C_{1}e^{-\frac{z}{5}\tau}(3\sin p\tau-\sin 3p\tau)+C_{2}e^{- \frac{z}{2}\tau}(5\sin p\tau-\sin 5p\tau) \tag{43}\]
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 4 Performance Evaluation
### Numerical Results
#### 4.3.2 Mobile Mesh Environment  
Having analyzed compute performance in static mesh conditions, we next explore the impact of adding mobility to the nodes on mesh dynamics. Figure 11 illustrates the fraction of satisfied Interests as well as mean full delay for different speeds of nodes, \(5\times 5\) mesh, enabled cache, and one and three producers. Observing the reported data, one may conclude that even slight mobility leads to drastic performance degradation in terms of satisfied fraction of Interest. The change between \(v=0\) m/s and \(v=1\) m/s is abrupt, decreasing the amount of satisfied Interest by 90% and 80% for \(M=3\) and \(M=1\), respectively. The major source of performance degradation is related to link breaks between end nodes, leading to the eventual loss of packets. It is important to note that the increase in the number of producers has a positive effect on the system's performance as more nodes have the requested service available.  
Notably, the mean full delay of delivered packets remains almost intact, with mobility coming into play. The rationale is that in mobile meshes, the considered compute service becomes opportunistic, and the Interests are fulfilled only when there is connectivity between nodes at the moment of request. The trade-off between delay and reliability is further illustrated in Figure 12. Please note that the considered characteristics improve in denser mesh conditions as the probability of the node being disconnected from the network decreases. However, these gains are further reduced by the increased interference level. It is important to note that the caching capability inherent in NDN technology does not significantly enhance compute service performance in dynamic meshes, although it is likely to be influenced by cache parameters such as size and freshness.  
Figure 10: Share of received compute results in static mesh.
# Virtualization Airborne Trusted General Computing Technology
###### Abstract  
Aircraft information service systems, such as airborne information systems, airborne integrated maintenance management systems, and cabin management systems, have greatly improved the ease of use and maintenance of civil aircraft. The current computing platforms used for accommodating these systems are unable to satisfy the multifaceted requirements of future information-based aircraft, such as energy conservation, emission reduction, high-performance computing, and information security protection, due to their high computing capacity, weight, and power consumption. Based on multi-core multi-threaded processors, a security hardware unit with microkernel virtualization technology and a virtualization airborne trusted general computing service architecture is proposed, and key technologies, including a high-performance processing and high-security hardware unit, virtualization management software unit, and virtualization security protection architecture were designed. By building a verification environment, the proposed platform was verified in terms of its application accommodation function, platform performance, and network security protection, for comparison with the existing platforms. The results showed that our method can fulfill the requirements of these technical indicators and is applicable, not only to new-generation civil aircraft, but also to unmanned aerial vehicles (UAVs) and emergency rescue aircraft with high-performance safety-critical computing needs.  
Keywords:airborne general computing service (AGC); microkernel virtualization; virtualization security protection (VSP); trusted computing +
Footnote †: journal: Applied
## 4 Results and Discussion
### Design of the Multi-Agent System Architecture (MAS)
#### 4.2.3 Modeling Communication and Coordination Between Agents  
To ensure efficiency, a structured communication model was designed with three levels:  
1. Hierarchical (EOO coordinates all entities).
2. Direct interaction between specialized entities.
3. Negotiation based on priorities for the distribution of resources.  
Efficient communication between entities ensures coordinated and adaptable management to operational changes in the terminal (Table 4).  
The orchestrator coordinates and exchanges information with all specialized agents, which also interact with each other based on operational dependencies. The hierarchical layout reflects the functional relationships and data flows necessary for coordinated terminal operations (Figure 3).  
The architecture of the model would be:  
1. Hierarchical Architecture (Hierarchical): the system has an orchestrating agent (EOO) that manages and coordinates the other agents, which resembles the hierarchical structure of the image. Decision-making flows from the top down, with each agent specializing in specific tasks.
2. Router + Aggregator Pattern: The EOO acts as a router that receives data from different agents and redistributes it according to operational needs. It also works as an aggregator, collecting information from multiple sources (IoT sensors, optimization algorithms, traffic data) to generate coordinated decisions.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Entity** & **Send Data To** & **Receive Data From** \\ \hline EOO (Orchestrator) & ALL & ALL \\ \hline EDV (Vehicle Distribution) & EOO, ENR & EOO \\ \hline ENR (Navigation and Routing) & EOO, ECF & EDV, EOO \\ \hline ECF (Flow Control) & EOO, ECE & ENR, EOO \\ \hline ECE (Shipping Coordination) & EOO, ESO & ECF, EOO \\ \hline ESO (Monitoring and Optimization) & EOO & ALL \\ \hline \hline \end{tabular}
\end{table}
Table 4: Communication between entities.  
Figure 3: Communication flow between intelligent agents within the proposed MAS architecture. Source: Own elaboration.  
Shared Database with Different Tools: The model is based on the integration of optimization tools, planning algorithms, and operational databases, which fits with the pattern of a shared database used by different agents.  
The multi-agent system is a combination of a hierarchical architecture with elements of router, aggregator, and shared database. The orchestrator agent centralizes decision-making, but specialized agents interact with different tools to optimize traffic, space allocation, and boarding.
## 4 Discussion of Results
The current model assumes sequential stage processing. Parallelization of stages (e.g., categorization and weight assignment) could improve efficiency but would necessitate extensions to the GERT formalism. These limitations suggest that the model is best suited for systems where processing delays are tolerable (e.g., cultural heritage recommendations) rather than ultra-low-latency applications. Future work could explore hybrid architectures combining GERT with queueing theory for high-load scenarios. To assess the robustness of the developed GERT-based models under parametric uncertainty, a sensitivity analysis was conducted by systematically varying the input parameters. Specifically, the transition probabilities and processing intensities for each stage were independently perturbed by \(\pm\)10%, \(\pm\)20%, and \(\pm\)30% from their baseline values. For each perturbation level, the composite model was recalculated, and the resulting probability density functions were analyzed to extract the following key statistics: expected processing time, standard deviation, and 95% quantiles. The results showed that although absolute values of these metrics shifted within a reasonable range (e.g., the expected processing time changed by no more than \(\pm\)1.5 units), the overall shape of the density functions and the relative dynamics between stages remained consistent. This confirms that the system demonstrates stable behavior under moderate parameter fluctuations and validates the use of heuristic parameter estimation in contexts where precise measurements are not available. A representative visualization of the sensitivity experiment is presented in Figure 7, highlighting the minor impact on distribution shape across variations.
## 3 Influence of Inertia on Frequency Regulation
### Inertia Coefficient Analysis
#### 3.2.2 \(\zeta\) = 1 Case  
For the case of \(\zeta\) = 1, the system frequency response under a step-up load change can be expressed in the \(s\)-domain as  
\[f_{r}(s)=\frac{G_{P_{L}\rightarrow\omega}(s)}{s}=G_{0}\Bigg{\{}\frac{1}{\left( s+\omega_{n}\right)^{2}}+\frac{z_{1}}{\omega_{n}{}^{2}}\Bigg{[}\frac{1}{s}- \frac{s+\omega_{n}}{\left(s+\omega_{n}\right)^{2}}-\frac{\omega_{n}}{\left(s +\omega_{n}\right)^{2}}\Bigg{]}\Bigg{\}} \tag{16}\]  
Taking the inverse Laplace transform of (16), the time-domain expression of frequency response can be derived and represented as  
\[f_{r}(t)=L^{-1}[f_{r}(s)]=G_{0}\Bigg{[}\frac{z_{1}}{\omega_{n}{}^{2}}-\frac{z _{1}}{\omega_{n}{}^{2}}e^{-\omega_{n}t}+\left(1-\frac{z_{1}}{\omega_{n}} \right)te^{-\omega_{n}t}\Bigg{]} \tag{17}\]  
The expression of the steady-state frequency remains unchanged, which is listed as  
\[f_{r_{\infty}\infty}=G_{0}\frac{z_{1}}{\omega_{n}{}^{2}}=\frac{R}{DR+1} \tag{18}\]  
Figure 7: Variations of important performance indices versus inertia coefficient \(H\) for the case of \(\zeta<1\).  
The expression of ROCOF can be derived by differentiating (17) as  
\[\frac{df_{r}(t)}{dt}=G_{0}\big{[}e^{-\omega_{n}t}-\omega_{n}te^{-\omega_{n}t}+z_{ 1}te^{-\omega_{n}t}\big{]} \tag{19}\]  
It can be concluded from (19) that \(df_{r}(t)/dt<0\) is always satisfied for the case of \(\zeta=1\), and the relevant proof is detailed in Appendix A. This conclusion implies that the frequency nadir, peak time, and frequency overshoot will no longer exist for the case of \(\zeta=1\). At time \(t=0\) s, (19) changes into the following equation:  
\[\frac{df_{r}(t)}{dt}\Big{|}_{t=0s}=G_{0}=-\frac{1}{2H} \tag{20}\]  
Equation (20) indicate that the ROCOF at \(t=0\) s for the case of \(\zeta=1\) is only dependent on \(H\). Moreover, the settling time can be expressed as  
\[\Big{|}\frac{f_{r}(t_{s})-f_{r,\infty}}{f_{r,\infty}}\Big{|}=2\%\Rightarrow \bigg{(}1+\omega_{n}-\frac{\omega_{n}}{z_{1}}\bigg{)}t_{s}=2\%e^{\omega_{n}t_ {s}} \tag{21}\]  
However, since (21) is a transcendental equation, its analytical solution does not exist. Alternatively, the numeric solution of \(t_{s}\) should be employed to measure the system dynamics.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 1 Introduction
### Background and Motivation
Therefore, for the system where only part of the data has higher security requirements, there will be more computing overhead and energy consumption. To solve this problem, this paper proposes two client-aware negotiation methods: one based on renegotiation and the other based on redirection. The CAN-SEAT strategy aims at reducing the cost of the existing secure transmission protocols. It can be incorporated into many practical systems. The CAN-SEAT strategy is only described as an example built on the HTTPS protocol and can be deployed with other protocols depending on the specific implementation of the protocols.
## 3 Scientometric Analysis Results and Discussion  
### Annual Publications and Subject Area of Articles  
To identify the most significant study subjects, the Scopus analyzer was used to scan the Scopus database. According to the statistics, the top four disciplines, based on quantity of papers, were geology (19%), engineering (18%), environmental research ecology (17%), and meteorology (17%). Atmospheric science accounted for 14% of all documents, followed by physical science (11%) and water resources (9%); geochemistry and geophysics accounted for up to 12% of all papers, as shown in Figure 2. These areas accounted for approximately 75% of the estimated number of Scopus articles that have been reviewed. The frequency with which journal publications and literature reviews are used in a document was investigated; 73% of articles published in journals were for preliminary or continuing research, while 27% were for evaluating previous work.  
Figure 1: Steps of landslide scientometric analysis; CSV: comma separated values.  
Figure 2: Subject areas which contribute to rainfall-induced landslide analysis.  
The model depicts the trend in research area publication from 2000 to 2020, which is represented by the graph in Figure 3. From 2000 to 2010, the number of publications on the topic of rainfall-induced landslides grew, and then a significant increase in the number of publications occurred in 2014, followed by an increase in the number of publications from 2015 to 2020. Although a significant increase was seen in recent years, the real amount may be somewhat less. It is interesting to observe the fresh and innovative approaches to the issue which have been used, with specialists concentrating their efforts on predicting rainfall-induced landslides and monitoring methods.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 5 Experiments and Discussion
### Deployment
#### 5.1.3 Data Collection Phase  
In the experiment process, selected measurement nodes served for real time network monitoring and full packet capture, respectively. The metrics of network monitoring include throughput, end-to-end delay, and packet loss ratio. In the meantime, all the received and forwarded data packets are captured at the designated nodes. On the basis of these metrics and captured packets, researchers can evaluate network performances among different simulation scenarios.
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 1 Introduction
### State-of-the-Art Review  
There are multiple optimization methodologies to address the problem regarding the optimal placement and sizing of renewable energy resources in electrical networks. These methodologies include particle swarm optimization (PSO) [10], the vortex search algorithm (VSA) [11], the improved Harris hawks optimizer [12], teaching learning-based optimization [13], genetic algorithms [14], the Smalling area technique [15], population-based incremental learning [16], and mathematics-based approaches in the general algebraic modeling system (GAMS) [17]. The main characteristic of the optimization methodologies above is that they focus only on minimizing power losses in order to solve the problem regarding renewable energy integration (placement/sizing) in electrical networks. Power losses are minimized considering only the demand condition, which does not occur in a real electrical system, since the said condition is continuously changing throughout the day.  
On the other hand, optimization methodologies based on metaheuristic techniques that use a master-slave strategy have been considered to address the problem of optimal PV integration. In [11], a discrete-continuous version of the VSA technique was implemented for optimal PV integration in electrical distribution networks while considering daily load and generation profiles. In [18], the problem regarding optimal PV integration in DC networks to reduce greenhouse gas emissions was solved via the GAMS software. In [19], the optimal integration of wind power generation in electrical distribution networks was studied while including the reactive power capability and wind speed curves. Wind power integration was implemented in the GAMS software by proposing an MINLP model. In [20], a hybrid metaheuristic technique to size and locate the distributed generation was presented. This hybrid technique combined PSO and gravitational search techniques, which minimize the total energy losses in a distribution network and simultaneously maximize the profit of its distributed generation. Ref. [21] presented optimal PV integration in distribution networks using mixed-integer second-order cone programming. Second-order cone programming guarantees the global optimum of a relaxed model while reducing the processing times and eliminating the standard deviation. However, this integration only includes the minimization of energy losses in the electrical network.
## 2 Materials and Methods
### Antioxidant Capacity Measurements  
#### 2.3.1 DPPH\(\bullet\) Radical Scavenging Assay  
The experimental method was based on the protocol described by [PERSON] et al. with some modifications [18]. The FAA tea powder was prepared by freeze-drying fresh tea leaves, followed by grinding and sieving through a 100-mesh sieve to obtain a fine powder. Specifically, 0.1 g of FAA tea powder was added to 5 mL of anhydrous ethanol. Although the tea powder did not completely dissolve, the mixture was vigorously vortexed to ensure homogeneity, followed by centrifugation at 10,000 r/min for 15 min [19]. Then, 0.5 mL of supernatant was mixed with 2 mL of a 0.2 \(\times\) 10\({}^{-5}\) mol/L DPPH\(\bullet\) free radical solution and incubated in the dark for 30 min at 25 \({}^{\circ}\)C. The absorbance of the resulting solution was measured at 515 nm using a UV-VIS spectrophotometer. DPPH\(\bullet\) free-radical scavenging activity (%) is measured based on the following equation.  
\[\text{Scavenging activity (\%)}=[1-(\text{A}_{\text{sample}}/\text{A}_{0})] \times 100 \tag{1}\]  
where A\({}_{\text{sample}}\) and A\({}_{0}\) are the absorbance measured at 515 nm for the sample and control, respectively. IC\({}_{50}\) refers to the concentration of effective phenols at 50% removal of DPPH\(\bullet\) free radicals.
## 2 Materials and Methods
An rf magnetic field resonant with the Larmor frequency and perpendicular to the bias field creates atomic coherences within the F = 4 manifold that couples to the linear polarisation of the second laser beam, called the probe beam, through the Faraday effect [22]. The probe beam propagates perpendicularly to the pump beam and bias field and is 2.75 GHz red detuned from the 6 \({}^{2}\)S\({}_{1/2}\) F = 3\(\rightarrow\) 6 \({}^{2}\)P\({}_{3/2}\) F\({}^{\prime}\) = 2 transition. The modulation of the probe beam's polarisation is detected by a polarimeter, consisting of a polarising beam splitter whose outputs are coupled to a balanced photodetector. This signal is monitored with a lock-in amplifier, that is referenced to the frequency of the primary and secondary rf magnetic fields. The quadrature outputs (\(X\) and \(Y\)) of the lock-in can be used to calculate the amplitude of the signal (\(R=\sqrt{X^{2}+Y^{2}}\)) and its phase (\(\phi=\arctan[Y/X]\)), which is directly proportional to the amplitude and phase of the rf field. Magnetometer sensitivity is limited by atomic projection noise and quantum back action at the level of 50 fTHz\({}^{-1/2}\)[21]. The bandwidth of the sensor is limited by the atomic response rate at 40 Hz. The primary rf field is generated by a coil with a length of 9 mm, with a winding thickness of 0.5 mm wound around a ferrite core of 2 mm diameter and 15 mm length. The coil is located in between the object and the vapour cell to represent a realistic measurement geometry. The axis of the coil is directed along the bias magnetic field and centred with  
Figure 2: (**a**) Components of the radio-frequency atomic magnetometer setup. (**b**) Caesium 6 \({}^{2}\)S\({}_{1/2}\)\(\rightarrow\) 6 \({}^{2}\)P\({}_{1/2}\) transition (D1 line) and 6 \({}^{2}\)S\({}_{1/2}\)\(\rightarrow\) 6 \({}^{2}\)P\({}_{3/2}\) transition (D2 line) energy structure. The arrows represent the frequency of the laser beams used in the experiments. Pump beam (solid blue line) along with spin-exchange collisions create a population imbalance among the Zeeman sublevels of the F = 4 ground state manifold. The signal is created by polarization rotation of the probe beam (dotted red line). the cell to minimise the contribution of the primary field in the measurement [4]. In this configuration, the sensor is most sensitive to the presence of the secondary field generated by the presence of the object, i.e., zero signal recorded when there is no object [10]. The amplitude of the primary rf field generated by the coils varies with the operating frequency due to the frequency-dependent impedance of the coil, and frequency dependence of amplifiers used in the electronics. The data presented and discussed in this manuscript has been calibrated against this variation.