esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper introduces the RBFNN estimation-based adaptive SMC technique for PEMFC integrated with DC/DC buck converter power supply system. The model of the presented PEMFC power supply has been introduced, where the state and input disturbance of the plant are highly coupled. A Gaussian-based RBF neural network adaptive law with a backstepping SMC scheme is developed according to the defined Lyapunov functions to handle the parametric uncertainties and disturbance injections, such as buck converter parameter-varying and PEMFC operation point changing. The comparative study is carried out to regulate PEMFC power supply current/voltage on H-100 PEMFC supplied DC/DC buck converter with rapid control prototyping among traditional SMC, adaptive backstepping SMC, and RBF neural network. The simulation analysis and experimental results indicate that the proposed method can track the desired current/voltage with enhanced robustness; for instance, compared with the traditional adaptive law, even with the abrupt change of load occurring, about 3% overshoot was reduced in the case of inductor current control simulation, and up to 7.5% overshoot was reduced in output voltage regulation experimental test. The proposed control system can be applied in many industrial applications, such as automotive air conditioning and charging energy storage devices, such as batteries and super-capacitors, with the desired charging current or terminal voltage.  
Conceptualization, X.X. and H.H.; methodology, X.X., J.L. and H.H.; software, J.C.; validation, H.H., Y.C. and J.C.; formal analysis, X.X.; investigation, J.L. and Y.C.; resources, H.H.; data curation, X.X. and H.H.; writing--original draft preparation, X.X. and J.L.; writing--review and editing, X.X., J.L., Y.C. and H.H.; visualization, X.X.; supervision, J.L. and H.H.; project administration, H.H. and J.C.; funding acquisition, J.L. and H.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study did not involve humans.  
This study did not report any data.  
The authors declare no conflict of interest.  
Figure 11: Disturbance change when applying load step: (**a**) voltage control by adaptive backstepping SMC; (**b**) voltage control by RBFNN.
## 3 Results
### Secondary Outcomes  
Table 2 demonstrates the pre- and post-intervention values of cervical spine ROM, hand grip strength, and neck disability index.  
ANOVA of the ROM values revealed significant time effects on neck flexion, extension, lateral flexion, and rotation (\(p\) = 0.000), but a significant time \(\times\) group interaction effect was revealed only on neck extension, F (1, 48) = 4.846, \(p\) = 0.033, \(\eta\)2p = 0.092. Subsequent analysis showed that both interventions significantly increased neck extension ROM (\(p\) = 0.000), but post-intervention values were significantly higher in the PIR group vs. the SS group (\(p\) = 0.001, Cohen's d = 0.96 large effect size). There was also a group effect on ROM of neck flexion (F [1; 48] = 9.659, \(p\) = 0.003, \(\eta\)2p = 0.168), lateral flexion to symptomatic side (F [1; 48] = 4.241, \(p\) = 0.045, \(\eta\)2p = 0.081), and rotation to symptomatic side (F [1; 48] = 4.770, \(p\) = 0.034, \(\eta\)2p = 0.090).  
ANOVA of the hand grip strength revealed a significant effect of time (F [1; 48] = 19.360, \(p\) = 0.000, \(\eta\)2p = 0.380 for dominant hand; F [1; 48] = 29.670, \(p\) = 0.000, \(\eta\)2p = 0.382 for non-dominant hand), but no significant time \(\times\) group interaction was observed.  
ANOVA of the neck disability index values revealed a significant effect of time (F [1; 48] = 2025.000, \(p\) = 0.000, \(\eta\)2p = 0.878) and time \(\times\) group interaction (F [1; 48] = 158.760, \(p\) = 0.000, \(\eta\)2p = 0.360). Subsequent analysis showed that both interventions significantly improved neck disability (\(p\) = 0.000), but post-intervention values were significantly lower in the PIR group vs. the SS group (\(p\) = 0.004, [PERSON]'s d = 0.85 large effect size).  
As shown in Figure 3, the effect sizes of both interventions on outcome measures were quite similar. A large effect size was determined for almost all variables, except for rotation to the asymptomatic side (moderate effect size in both groups) and hand grip strength.  
Figure 3: Comparison of [PERSON]’s d effect size values between the groups. Abbreviations: NPRS, numeric pain rating scale; NDI, neck disability index; SS, self-stretching; PIR, post-isometric relaxation.
# The Long and Winding Road to Cardiac Regeneration
## 4 Biomaterials
### Natural Biomaterials
#### 4.2.2 Fibrin  
Fibrin deserves special mention due to its remarkable properties and effectiveness in cardiac regeneration. Fibrin originates from fibrinogen, a protein involved in the blood clotting process.  
It forms a fibrillar network with a unique three-dimensional structure, offering mechanical support and promoting cell adhesion and migration.  
Fibrin acts as a provisional scaffold that facilitates the attachment and proliferation of cells involved in cardiac repair processes. Fibrin also interacts with growth factors and cytokines, influencing cellular behavior and promoting tissue healing [234].  
In cardiac tissue engineering, fibrin is commonly utilized as a bioactive scaffold. It can be formed into gels or hydrogels, providing a supportive environment for cell growth and tissue regeneration. Fibrin-based scaffolds have shown potential in enhancing cardiac function, promoting angiogenesis, and improving tissue remodeling [235].  
Numerous studies have investigated the use of fibrin in cardiac tissue engineering. Researchers have explored its application in creating cardiac patches, injectable matrices, and tissue-engineered constructs demonstrating the ability of fibrin to support cell viability, enhance neovascularization, and improve contractile function in the heart [236].
## 1 Introduction
Afterwards, Section 5 introduces and provides a formal definition of the concept of effective SNR along with mathematical expressions for its statistical distribution under both homogeneous and heterogeneous SNR scenarios, considering an ideal modelling approach as well as an average modelling approach. Based on the developed effective SNR model, analytical results for the performance of CA as a diversity technique in terms of the ergodic and secrecy capacities are presented in Sections 6 and 7, respectively. Numerical results are then presented and discussed in Section 8. A summarised discussion of the main results and findings of this study is presented in Section 9. Finally, Section 10 summarises and concludes this work.
## 8 Conclusions  
Traditional wired industrial networking technologies have numerous drawbacks. They lack flexibility, face reliability issues (due to wear and tear) and are expensive to deploy and maintain. Wireless technology, however, through its increased pervasiveness, can introduce a completely new range of industrial applications as it has the potential to provide fine-grained, flexible, robust, low-cost and low-maintenance monitoring and control. While present-day wireless technologies have taken a step in the right direction, they still have severe limitations, especially when real-time, reliable distributed control operations are concerned. This article presents an overview of current wireless technologies and academic researches and their deficiencies, and describes some key research issues that still need to be addressed in order to successfully extend the use of wireless technologies to the industrial monitoring and control sector.
# Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network
## 3 Proposed Model
### State and Action  
The proposed model is a traffic signal control system that controls the vehicles by optimizing the order in which a green light occurs at multiple intersections. Because multiple intersections are involved, it is necessary to recognize the status of the surrounding intersections. According to Figure 5, the lane entering the intersection is defined as \(l_{n}\). Therefore, there are a total of eight incoming lanes, from \(l_{1}\) to \(l_{8}\), at a 4-way intersection. There are also eight lanes in the direction leaving the intersection, from \(n_{1}\) to \(n_{8}\).  
The information on the target intersection is determined based on the number of vehicles currently waiting at the intersection. That is, the queue length information (\(ql\)) in each lane of the target intersection is required. In Figure 5, the queue length of lane \(l_{1}\) is defined as \(ql_{1}\). In addition, the situation of the surrounding intersection is determined by the traffic load in each lane of the surrounding intersection, where the vehicle exits from the target intersection. Traffic load information of the surrounding intersection is required for a vehicle to exit the target intersection. This is because the number of vehicles that exit from the target intersection should be less than the number of vehicles that can be accommodated by the surrounding intersection. The lane in the direction of the exit from the target intersection is the incoming lane of the surrounding intersection, and the traffic load of lane \(n_{1}\) is defined as \(tl_{1}\). As shown in Figure 5, there are two directions on the road: one is a straight lane capable of turning right and the other is a left lane. Thus, state (\(s_{t}\)) is defined as in (1):  
\[s_{t}=\big{[}ql_{1},ql_{2},ql_{3},ql_{4},ql_{5},ql_{6},ql_{7},ql_{8},tl_{1}, tl_{2},tl_{3},tl_{4},tl_{5},tl_{6},tl_{7},tl_{8}\big{]}. \tag{1}\]  
The proposed model determines the order of the green signal based on the intersection. The possible movement signal at the 4-way intersection is shown in Figure 6. The action of the MDP indicates the phase of the green signal. The phase of the intersection can vary  
Figure 5: A 4-way target intersection.  
depending on the structure of the intersection. At a 4-way intersection, the action (\(a_{t}\)) is defined as in (2).  
\[a_{t}=\left[p_{1},p_{2},p_{3},p_{4}\right] \tag{2}\]
## 5 Numerical Simulation Analyses of the Dynamic Evolution Process of Overlying Rock Strata
### Analysis of the Development Process of Water-Conducting Fissure Zones  
As the working face advances to 40 m, the central part of the goat mainly experiences tensile stress, while compressive stress appears at both ends of the goat. Subsequently, rock layers fracture and delaminate, with the height of the water-conducting fracture reaching 11 m. When the working face advances to 100 m, there is a significant change in the stress distribution of the surrounding rock, but no obvious change in direction. At this stage, the elevation of the water-conducting fracture zone increases to 39 m. By the time the working face reaches 160 m, the central part of the goat mainly exhibits horizontal compressive stress, while tensile stress occurs at the sides of the goat. The water-conducting fracture zone at the top of the goat develops to 132 m. Subsequently, the height of the water-conducting fracture zone increases slowly.  
As the working face advances to 220 m, the squeezing and stretching stress on the overlying strata of the goat gradually increases. At this point, the height of the water-conducting fracture zone has increased to 175 m. As the working face advances to 240 m, the height of the water-conducting fracture zone increases to 202 m. After that, with the extension of the mining distance, the height of the water-conducting fracture zone no longer increases.  
Based on the above analysis, before the working face advances to 100 m, and though the extent of overlying rock damage gradually increases, the height of the fracture zone remains relatively low. During the advancement of the working face from 100 m to 160 m, the height of the fracture zone rapidly rises, jumping from 39 m to 132 m, marking the first abrupt development process of the water-conducting fracture zone height. Subsequently, the height of the water-conducting fracture zone continues to increase slowly. A cantilever beam structure is formed above the goat, suppressing the upward expansion of the fractures. When the mining face advances from 220 m to 240 m, the height of the mining face fracture zone rapidly increases, jumping from 175 m to 202 m, marking the second abrupt development process of the water-conducting fracture zone height, after which the height of the water-conducting fracture zone no longer increases.  
Comparing the results of numerical modeling with the fracture zone range obtained from physical model testing, there is good consistency between the physical simulation and numerical modeling results regarding the range and evolution patterns of the fracture zone. The physical simulation visually demonstrated the formation process and spatial extent of the fracture zone, while the numerical modeling verified the physical simulation results by simulating the dynamic changes in the rock layers at different advancement distances.
## 5 Efficiency Analysis
### Signing Authority (SA) Performance  
We examine the performance for the Signing Authority SA, since SA is involved in each message exchanged via the secure communication protocol. For the simulation Simulink from Matlab was used, were message requests follow a Poisson distribution. A FIFO queue is implemented. For each requested signature, the SA must repeat the following computations.  
Computing \(e(K_{1},\hat{\rho})\cdot e(K_{2},\hat{t})^{-1}\) requires 2 pairings (20.752 ms), a multiplication (\(4.1\times 10^{-3}\) ms) and computing the inverse of an element (0.151 ms). To verify \(\mathcal{R}\), the SA must compute an exponent \(g^{\mathcal{R}}\) which according to [58] can be approximated with 120 multiplications (0.473 ms), a hash function (\(6.3\times 10^{-3}\) ms) and an exponent \(K_{1}^{C^{\prime}}\) (9.036 ms). To verify \(t_{cur}||rand\) one subtraction \(current\)\(time-t_{cur}\) (\(1\times 10^{-3}\) ms) to ensure message freshness and a hash function on some information relevant with time \(t_{cur}\) to acquire randomness \(rand\). The overall computation cost for a single message equals to 31.618 ms, as summarized in Table 4.  
We assume that the server processes messages at a constant time of 32 ms per message. Since authorities are equipped with sufficient computational power we assume a scenario  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Processing Incoming Message** & **Time in ms** \\ \hline Compute \(e(K_{1},\hat{\rho})\cdot e(K_{2},\hat{t})^{-1}\) & 20.903 \\ \hline Binary Search (5000 random shorted list) & 0.011 \\ \hline Decrypt \(enc_{SA}(\overline{m},t_{cur}||rand||\mathcal{R})\) & 0.615 \\ \hline Verify \(\mathcal{R}\) & 9.515 \\ \hline Verify \(t_{cur}||rand\) & 0.007 \\ \hline Sign & 0.466 \\ \hline Encrypt \((r,s)\) & 0.111 \\ \hline Total time Request & 31.628 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Processing of incoming messages by the SA.  
where 10 servers are available in parallel. In Table 5 we summarize the performance of the SA for 50 up to 250 incoming messages per second. AM stands for Average Messages per second, PDM stands for Poisson Distribution Mean, AQL stands for Average Queue Length, AW for Average Wait time in seconds and MP for the total amount of Messages. Processed in 1 h. From our results it is shown that for the examined setup, the SA server can handle 200 messages per second with less than 1 sec delay. Figure 4a,b demonstrate the average wait time and the average queue length for the SA.
## 4 Simulation Results
### Results of Pattern 2  
In this section, the results of ROA capability in MPPT solving in PSC in pattern 2 is presented and its capability is also compared with that of the PSO. The PV configuration is 2S, with radiations of 1000 and 700 W/m\({}^{2}\). Curves for the power, voltage, and current of PV system as well as the duty cycle in MPPT solving are illustrated in Figure 10. Furthermore, ROA performance is compared with the MRFO and PSO methods in these figures. The maximum extracted power of the PV system obtained in pattern 2 were 90.52 W, 90.47 W, and 99.45 W from a GMP equal to 90.54 W using the ROA, MRFO, and PSO methods, respectively. These figures demonstrate that the ROA has better capability in obtaining more PV power with less steady-state fluctuations, and more tracking efficiency and speed  
Figure 9: **(a)** PV power, **(b)** PV voltage, **(c)** PV current, **(d)** Duty cycle in pattern 1 using different methods.  
than the MRFO and PSO. Thus, the ROA is superior to the MRFO and PSO in MPPT solving in pattern 2, in partial shading condition with higher global power.  
Figure 10: (**a**) PV power, (**b**) PV voltage, (**c**) PV current, (**d**) Duty cycle in pattern 2 using different methods.  
In Table 5, the numerical results of ROA implementation in pattern 2 solving in terms of PV output power, tracking efficiency, and speed for different methods are presented and also compared with the MRFO and PSO. In Table 5, the tracking efficiency of the ROA, MRFO, and PSO are given as 99.97%, 99.92%, and 99.90%, and the tracking rate is at 1.08 S, 2.25 S, and 2.78 S, respectively. These prove the superior capability of the ROA in MPPT solving in pattern 2, and in achieving more power and efficiency with a better tracking rate. Therefore, the proposed ROA is more efficient compared to the MRFO and PSO.
# Satellite Requirements for Observation of Close Proximity Celestial Bodies +
## 2 Methodology for Orbit and Communication Resources Selection  
### Constellation Design  
There are several methodologies for satellite constellation design, and among those, are designed by [PERSON] [4] and [PERSON] [5] for circular inclined orbits. Those well known design strategies result in the [PERSON] and Rider constellations, respectively, which have been used for constellation design on Earth, such as Starlink and One Web.  
A Walker constellation is described by the notation \(T/P/F\), where \(T\) is the total number of satellites in the constellation, \(P\) is the quantity of orbital planes, \(F\) is the relative phasing parameter, which indicates the right ascension of the ascending node (RAAN) separation between orbital planes. For this methodology, the number of satellites is the same for each plane, \(S=T/P\), the RAAN distance between orbital planes is the same for each orbit, and satellites are evenly spaced in each orbital plane through their true anomaly.
# A Study on Cyber Target Importance Quantification and Ranking Algorithm
## 2 Related Works
### Cyber Target Development and Cyber Target Prioritization Process
#### 2.3.2 Cyber Target Prioritization Process  
The priorities of military targets are selected by integrating the target nomination list (TNL) recommended by the component forces according to the goals of the joint force  
Figure 3: Cyber-Targeting Process.  
commander and reviewing them in relation to the commander's objectives and operational point of view. Prioritization of the CTL is also normally applied according to the \"Cyber Force Operation Guidelines\" according to the commander's objectives.  
When examining the priority selection items for the component military target recommendations of the military operations, as shown in Table 1, the target recommendation priority criteria can be divided into three items.  
The US JP 3-60 [4] presents special considerations for prioritizing military operations targets. To summarize, it is organized into three types, as shown in Table 2. The following items are considered when calculating a target's priority:  
1. Adequacy for the achievement of the commander's objectives (compliance with commander's objectives and operating guidelines: Criteria Item 1, Special Consideration Item 1);
2. The importance of targets (relative importance between targets in cyberspace: Criteria Item 2);
3. Target suitability (Special Consideration Items 2, 3).  
Based on the above, the cyber target prioritization process was designed as shown in Figure 4. The priority analysis items are selected based on the commander's objectives, importance of the target, and suitability of the target, and can be added or excluded, if necessary, by the commander. A cyber target prioritization item is applied in consideration of the characteristics of the CO, and by quantifying and aggregating them, cyber targets can be prioritized. The cyber priority target list becomes a CPTL, which is utilized in the third stage of targeting (capability analysis) and operational planning.  
To give priority to the CTL, this study proposes a method for calculating and prioritizing the importance score for each target to quantify its importance among the cyber target priority analysis items.
## 1 Introduction  
The integration of distributed generation (DG) and the emergence of microgrids (urgirds) are reshaping the conventional operation of distribution systems. These technologies introduce new levels of flexibility and control, enabling more effective management of power flows [1, 2]. As the penetration of renewable energy sources grows, along with advancements in energy storage and demand-side management, the traditional centralized approach to power distribution is evolving into a more decentralized and dynamic model [3, 4].  
Microgrids, typically comprising local DG units, energy storage, and controllable loads, can operate autonomously or in coordination with the main grid [5]. When connected to the larger distribution network, the aggregation of multiple microgrids at specific buses allows system operators to dynamically adjust power flows, enhancing both local optimization and overall network efficiency and stability [6, 7, 8].  
Historically, power adjustment within distribution systems has been primarily used for contingency management, reacting to unexpected events to maintain stability [9, 10]. However, with the increasing complexity and variability of modern power systems, there is a growing need for proactive strategies that leverage the flexibility of microgrids and distributed resources to optimize network performance under normal operating conditions [11; 12]. This shift from reactive to proactive management requires new methodologies capable of dynamically reconfiguring the network and optimizing power flows to minimize operational costs, reduce losses, and maintain voltage stability.  
In this context, this work introduces the concept of flexible power buses. These buses, representing aggregated microgrids or flexible resources, can dynamically adjust active and reactive power within predefined ranges. By integrating flexible power buses into the distribution network and applying network reconfiguration, the system can more effectively respond to varying demand and generation conditions, ultimately enhancing overall performance and resilience.
# Modularity-Based Incremental Label Propagation Algorithm for Community Detection
## 1 Introduction
[19] proposed a modularity-specialized LPA (LPAm) which uses modularity as an objective function for optimization, so that the results of community detection are always in the direction of increasing modularity. However, one obvious defect of this algorithm is that it is easy to fall into a local optimal and it may misclassify the community of nodes. Based on LPA and LPA, [PERSON] et al. [20] proposed another improved LPA, called LPAMP, which first optimizes the modularity to obtain a coarse clustering of nodes and then performs label propagation. LPAMP can reduce the randomness of the LPA and improve the stability and accuracy of the community detection results, but it is slow and cannot be used for large networks. To solve the instability problem of LPA mentioned above, a modularity-based incremental LPA (MILPA), is proposed in this paper. First, the node degree and the node membership are introduced to determine initial community of nodes. After all nodes have initial labels, we define an objective function based on modularity gain to guide the label updating of nodes. Experiments on both synthetic networks and real-world networks show that the proposed MILPA greatly reduces the randomness of the label updating process and has stable and great performance in community detection.