esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validations for Ship Stiffened Panel Strain Reconstruction  
### Test Setup  
1. Construction of Strain Monitoring System;  
The strain monitoring system for the stiffened ship panels, employing optical fiber sensors, comprised several key components: the stiffened ship panel itself, an RTS125+ distributed fiber optic sensor demodulator, an SI255 fiber grating sensor demodulator, DEW Soft dynamic strain gauge, a fixed platform, a hydraulic jack, and a computer, as illustrated in Figure 16a.  
Three strain reconstruction verification paths (Path 4, Path 5, and Path 6) were positioned on the outer side of the ship stiffened panel, corresponding to the verification paths (Path 1, Path 2, and Path 3) detailed in Section 2.1. Simultaneously, four loading points (Load C, Load D, Load E, and Load F) were situated on the outer side. Load C and Load D coincided with Path 4, while Load E and Load F aligned with Path 6, as depicted in Figure 16b. Moreover, an optical frequency domain reflective optical fiber sensor (OFDR sensor) was deployed along Path 4, Path 5, and Path 6 on the outer surface of the ship's stiffened panel [25]. This sensor was utilized to gather strain data along the \(x\)-axis of the mentioned verification paths in real-time, serving as a reference for subsequent evaluations of the strain reconstruction effectiveness.  
1. Test Cases;  
In the experimental setup, 'Case 3' was defined by fixing one end of the ship stiffened panel and applying two disparate force loads along the \(Z\)-direction at the Load D and Load F points using a jack. This induced bending and torsion deformation in the stiffened ship panel. Similarly, 'Case 4' was characterized by fixing both ends of the ship's stiffened panel and applying two unequal force loads along the \(Z\)-direction at the Load C and Load E points, resulting in bending and torsional deformation in the structure. This paper conducted experimental verification of the strain field reconstruction method for both 'Case 3' and 'Case 4'.  
Figure 16: (**a**) Strain monitoring system for the ship stiffened panel; (**b**) loading point and strain reconstruction verification path position on the outer side of the ship stiffened panel.
# Energy Efficiency Improvement in Reconfigurable Photovoltaic Systems: An Evaluation of Team Systems
## 6 Obtained Results
### Partially Sunny Day  
A partly sunny day is characterized as a day in which, owing to various factors, the amount of radiation fluctuates, causing the power delivered by the system to exceed or fall below thresholds multiple times. Consequently, on a partly sunny day, configurable systems perform more switching operations compared to sunny days.  
Figure 15 shows the power supply to the grid when a reconfigurable team system and a non-reconfigurable system are tested. Figure 15a shows the output power of the two inverters used in the reconfigurable team system, and Figure 15b shows the output power of the non-reconfigurable system (red trace) and the output power of the reconfigurable team system (blue trace).  
Figure 14: Performance of non-reconfigurable (fixed) and reconfigurable (team) systems on a cloudy day. (**a**) AC power delivered by the two inverters of the reconfigurable system; (**b**) AC power delivered by fixed and team systems.  
Figure 15: Performance of non-reconfigurable (fixed) and reconfigurable (team) systems on a partly sunny day. (**a**) AC power delivered by the two inverters of the reconfigurable system; and (**b**) AC power delivered by fixed and team systems.  
As depicted in Figure 15a, the switching action has occurred four times. Following the conditions, the second inverter is introduced to the circuit in two stages and subsequently removed. Additionally, Figure 15b presents a comparison of the two systems in terms of delivered AC power. The reconfigurable system demonstrates better performance in sections below the \(P_{\mathit{UID}}\) threshold.  
The power supply to the grid in this case is calculated and presented in Table 5. Although the difference in energy delivered between the two systems is not as substantial as on a cloudy day, it is still significant. However, the last column of the table reveals a higher power loss due to the higher number of switching actions.
# Overview of the Development of 3D-Printing Concrete: A Review
## 4 Key Preparation Technology
### Aggregate  
Aggregate affects the rheology, viscosity, stress characteristics before and after hardening and durability of concrete directly or indirectly from the internal structure [62]. The aggregate particle size has a great influence on the 3D printed concrete. When the aggregate particle size is too large, it will block the extrusion nozzle. On the contrary, if the aggregate particle size is too small, its specific surface area will increase, and the amount of paste increases that used to wrap the surface of the aggregate which will make the concrete easy to crack. When the ratio of the nozzle diameter to the maximum aggregate particle size is greater than 5, the printing material will not be blocked during the extrusion process. When the maximum particle size of the aggregate is less than 1/10 of the nozzle diameter, the cementitious material can be smoothly extruded through the nozzle [24; 25]. [PERSON] et al. [63] measured the fluidity, compressive strength, and flexural strength of mortar influenced by sand of different fineness. Within a certain range, as the fineness modulus of sand increases, the fluidity of mortar, 7 d compressive strength and flexural strength will be improved to varying degrees, as shown in Figure 12.
## 2 Operation and Control
### Fault Detection and System Protection
#### 2.3.2 System Protection  
Relating to protection, [PERSON] et al. [58] present a new distributed parameter electrical circuit flashover paradigm of polluted insulators. As [PERSON] and [PERSON] [59] define, _\"(... ) adaptive protection is a basic principle of protection, this principle makes the relay can automatically adjust to various protection functions, or changes to more suitable for a given power system conditions (...)\"_. The reconfiguration and self-healing of the next generation SG (3G) is addressed in the study by [PERSON] et al. (2012) [60].  
The anomalies in the wind farms are detected by means of the information acquired through the supervisory control and data acquisition (SCADA) system, as reported by [PERSON] et al. [61]. The parameters of interest are selected by combining the information of the SCADA together with a backpropagation artificial neural network (ANN).  
[PERSON] et al. [62] offer four main contributions: QoS requirements for SG applications; the authors increase the existing functionalities in differentiated services code points (DSCPs), and they use priority queues for certain critical applications.
## 4 ITFWO for Various OPF Problems
### Basic OPF Solutions
#### 4.1.4 Type 4: Piecewise OPF Problem  
The fuel cost features for the network generators linked at first and second buses are modeled via a piecewise OPF problem because of various fuel types shown by:  
\[f_{i}(P_{Gi})=\sum_{k=1}^{n_{f}}\alpha_{i,k}+b_{i,k}P_{Gi}+c_{i,k}P_{Gi}^{2} \tag{43}\]  
where \(a_{i,k}\), \(c_{i,k}\), \(b_{i,k}\), are factors for the objective function of \(i\)th unit for \(k\)th fuel type; \(n_{f}\) is the number of fuel types for \(i\)th generator.  
The OPF problem for this type is modeled via Equation (44).  
\[\begin{split}& J_{2}=\sum\limits_{k=1}^{NG}\alpha_{i,k}+b_{i,k}P_{Gi}+c_{ i,k}P_{Gi}^{2}\ +\ \lambda_{V}\sum\limits_{i=1}^{NL}\left(\ VL_{i}-VL_{i}^{\text{lim}}\right)^{2}+ \ \lambda_{S}\sum\limits_{i=1}^{NTL}\left(\ S_{i}-S_{i}^{\text{lim}}\right)^{2}\\ &+\lambda_{Q}\sum\limits_{i=1}^{NC}\left(\ Q_{Gi}-Q_{Gi}^{\text{ lim}}\right)^{2}+\ \lambda_{P}(\ P_{G1}-P_{G1}^{\text{lim}})^{2}\end{split} \tag{44}\]  
Optimization results are in Table 1, which indicate that the objective function via the ITFWO is 646.4799 ($/h). The best objective function achieved by ITFWO is compared to the optimal solutions achieved via optimization methods in Table 5 such as IEP [87], LTLBO [38], FPA [80], MFO [80], SSA [105], SSO [22], MSA [80], MDE [93], GABC [106] and TFWO, and shows that ITFWO has the lowest objective function in comparison to the other methods. The convergence trends of the problem are given in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimizer** & **Fuel Cost (S/h)** & **Emission (t/h)** & **Power Losses (MW)** & **V.D. (p.u.)** \\ \hline IEP [87] & 649.312 & - & - & - \\ LTLBO [38] & 647.4315 & 0.2835 & 6.9347 & 0.8896 \\ FPA [80] & 651.3768 & 0.28083 & 7.2355 & 0.31259 \\ MDE [93] & 647.846 & - & 7.095 & - \\ GABC [106] & 647.03 & - & 6.8160 & 0.8010 \\ MFO [80] [
## 3 Neural-Network-Based Interference Cancellation in LIS Systems  
In this work, we propose a neural-network-based approach for interference cancellation in LISs, specifically targeting MRC and EGC receivers. Traditional iterative interference cancellation methods are sub-optimal [7]. On the other hand, when using ZF or MMSE receivers, significant computational complexity is required due to the repeated matrix inversions required at each channel frequency component. Our approach leverages neural networks to learn a mapping from received signals to transmitted signals, effectively bypassing the need for iterative interference cancellation. The neural network is trained to learn the relationship between the received signal, which is affected by interference, and the transmitted signal, represented by \(\mathbf{S}_{k}\). This allows the network to remove the interference without the need for direct channel matrix inversion or an iterative receiver.
# Stochastic Model Predictive Control for Urban Traffic Networks
## 1 Introduction
[24] reported a stochastic optimization method to access a robust signal timing plan for arterial signal coordination, in which day-to-day demand variations were taken into account. Ref. [25] proposed a robust MPC model and a constrained mini-max approach to achieve optimal signal splits for urban traffic networks, in which uncertainties (e.g., inaccurate nominal state and demand prediction) were taken into account and assumed to be bounded. However, in their work, the objective function was defined to access the minimal cost under the largest possible model uncertainties. Accordingly, the optimal solution reported in [25] was calculated from a worst-case scenario formulation. Ref. [26] proposed a receding horizon parameterized control method for freeway networks. In this method, a scenario-based min-max scheme is designed to handle uncertainties. The authors in [27] proposed a novel MPC model for traffic signal control, whereby chance constraints are embedded into the model to prevent the arteries in the traffic network over-saturated in stressed load situations. Nevertheless, the uncertain model reported in [27] mainly focuses on preventing queue spillback on major trunk roads. Different from the aforementioned studies, by utilizing the store-and-forward model, we propose a stochastic MPC framework for traffic signal coordination in urban traffic networks. The objectives of the proposed stochastic MPC model are to prevent queue spill-back and to shorten the queue length of the whole network. One of the important features of our framework is that chance constrained programming [28] is used to handle uncertain traffic demands and stochastic disturbances on traffic networks. Furthermore, by embedding the stochastic simulation and neural networks (NNs) [29; 30] into a genetic algorithm (GA) [28; 31], we propose a hybrid intelligent approach to solve the uncertain optimization problem of the traffic network. The outline of this paper is organized as follows. In Section 2, we employ an improved store-and-forward traffic model to describe the traffic dynamics in urban traffic networks (UTN). In Section 3, a stochastic MPC model for UTN is proposed. In the proposed stochastic MPC model, uncertain traffic demands and disturbances are both taken into account and modeled by chance constrained programming. To avoid the spill-back congestion and reduce the queue length, chance constraints are imposed on the queue length of the road network. In Section 4, we propose a hybrid intelligent method to solve the stochastic MPC model. Section 5 reports the results of numerical simulation which aimed to verify the effectiveness of the proposed model and solution method. Finally, Section 6 concludes this paper with a summary of work.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 3 Proposed Framework
### MA Distributor Module
The trust that \(Z\) has in \(X\) can be calculated as:  
\[w_{X}^{Z}=(b_{lX}{}^{Z},d_{s}{}_{X}{}^{Z},u_{n}{}_{X}{}^{Z}) \tag{14}\]The trust that \(X\) has in \(Y\) can be calculated using  
\[w_{Y}^{X}=(b_{1Y}{}^{X},d_{sY}{}^{X},u_{nY}{}^{X}) \tag{15}\]  
The trust that \(Z\) has in \(Y\) is then:  
\[w_{Y}^{Z}=w_{X}^{Z}\oplus w_{Y}^{X}=\left((b_{1X}{}^{Z}b_{1Y}{}^{X}),(b_{1X}{}^ {Z}d_{sY}{}^{X}),(d_{sX}{}^{Z}+u_{nX}{}^{Z}+b_{lX}{}^{Z}u_{nY}{}^{X})\right) \tag{16}\]  
In the proposed model, the opinion about _prop_ (i.e., the propositions of nodes as trustworthy or untrustworthy or uncertain trust value) from _op_ (i.e., an opinion set) using \(M\) (i.e., a binomial variable as true or false) is then:  
\[M_{prop}^{op}=\left\{b_{prop},d_{prop},UM_{prop},autoM_{prop}\right\} \tag{17}\]  
where \(b_{prop}\), \(d_{prop}\), \(UM_{prop}\), and \(autoM_{prop}\) represent belief, disbelief, uncertainty, and the rate of atomicity, respectively. In particular, SL is used to compute the confidence between different network entities, where confident measures can be expressed as beliefs. It may also be used analogously to compute the reputation of nodes in sensor-based networks. Moreover, it can also be utilized for other applications. Algorithm 4 illustrates the proposed trust model for detecting and isolating malicious nodes. It uses the subjective logic framework (SLF) for assessing the trust value of a node. The parameters used in the trust computation and evaluation are initialized. First, the values of \(\alpha\) and \(\beta\) are set. These represent the maximum packet drop ratio (\(PDR\)) and the maximum \(MAC\) address per unit time, respectively. The model uses _Rank_ (i.e., the RPL-DODAG variable), \(PDR\), and \(\rho\) (i.e., the rate of change of \(MAC\) address per unit time) as trust parameters. The \(MAC\) address is divided by the time slot \(d\) (i.e., the time slot to check the \(MAC\) address) to obtain \(\rho\) (i.e., the trustworthiness of a node). All the other parameter values are set to 0 initially. Positive (\(p\)), negative (\(n\)), and uncertainty (\(u\)) values are calculated using these parameters in SLF. For each node, if \(PDR\) and \(\rho\) are higher than the relevant threshold (i.e., \(\alpha\) or \(\beta\)), the packet has been lost. This illustrates that the \(MAC\) value is always changing. As a result, the negative count, which was initially set to 0, is incremented; otherwise, the positive count is incremented. Next, the disbelief, uncertainty, and belief values are computed based on these negative and positive counts. If the belief value is greater than the threshold (i.e., 0.5), the node is trustworthy. If not and if the disbelief value exceeds a threshold (also 0.5), the node is malevolent (i.e., a sinkhole, Sybil, or rank node in our case). Therefore, if disbelief in a node is high, it is removed from the topology; otherwise, it is used for routing.
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 4 Functionality Comparisons and Security Analysis
### Efficiency Comparisons
#### 4.5.2 Unforgeability  
In this proposed scheme, we provide the signature definition of our sign-cryption scheme. From the above digital signature definition, we discuss the case where there exists a forger \(\mathcal{F}\) with the ability to forge a valid QR-signature on our scheme. We assume that there are some functions such that \(\mathcal{F}\) can make the hash query to the hash functions \(H_{1}(\cdot)\) and \(H_{2}(\cdot)\), symmetric encryption \(Enc_{pk_{R}}(\cdot)\) function and the signing function \(\mathit{Sig}(\cdot)\). After preparing these functions, \(\mathcal{F}\) can make its own query on these functions. \(\mathcal{F}\) can ask \(i\) times query, where \(i=1\sim l\) and \(l\) is the total number of IoT nodes. After the above \(q_{s}\) times query, if \(\mathcal{F}\) can output \(q_{s}+1\) signatures on our proposed scheme, we can use \(\mathcal{F}\) to break the RSA factoring problem.  
\[Adv^{Unf}_{\mathcal{F}^{Si_{S}(\cdot),H_{1}(\cdot),H_{2}(\cdot),k0_{1},E_{pk_{ R}}(\cdot)}}(\theta,t^{\prime})\leq\frac{1}{2^{l}\cdot q_{s}\cdot q_{e} \cdot q_{d}}+\epsilon^{\prime}.\]  
**Lemma 1**.: _First, we assume that there exists a secure digital signature function \(\mathit{Sig}(\cdot)\) and a secure hash function \(H_{1}(\cdot)\), which could be replaced with a random oracle \(RO_{1}\) and a secure hash function \(H_{2}\) in our proposed scheme. We also claim that our proposed scheme with the above unforgeability (Unf for short) satisfies the following situations. In other words, if our scheme is \((t^{\prime},\epsilon^{\prime})\) unforgeable, then_  
\[Adv^{Unf}_{\mathcal{F}^{Si_{S}(\cdot),H_{1}(\cdot),H_{2}(\cdot),k0_{1},E_{pk_{ R}}(\cdot)}}(\theta,t^{\prime})\leq\frac{1}{2^{l}\cdot q_{s}\cdot q_{e} \cdot q_{d}}+\epsilon^{\prime}.\]  
_where \(t^{\prime}\) is total experiment simulation time, including simulating \(l\) as an upper bound on the number of IoT devices, at most signature oracle \(q_{s}\) times query, at most encryption oracle \(q_{e}\) times query, at most decryption oracle \(q_{d}\) times query, and \(\epsilon^{\prime}\) has taken over the coin toss of our scheme._
# Impact of Groundtrack Pattern of a Single Pair Mission on the Gravity Recovery Quality
## 4 Discussion
In terms of regional investigation, the results are usually optimal for the \(\Omega=116^{\circ}\) case. In fact, this outcome is also expected, since that is the area where sub-cycle happens for the region targeted in this study (West-Africa). Nevertheless, for both global and regional study, the above statements are not always valid. When the maximum spherical degree and order of the solution goes beyond the Colombo-Nyquist rule (CNR), exceptions can be observed. A possible explanation for those exceptions could be the large spatial aliasing and the leakage from the surroundings which very likely deteriorates the impact of the sub-cycle. Obviously, more detailed studies should be directed in this way. The results of our study also emphasize the impact of the mission formation on the error, where the Pendulum formation usually outperforms the inline (GRACE-like) formation, as expected through its more isotropic sampling behavior. The spatial covariance illustrations of this research work for the GRACE-like formation depict the symmetry of the spatial covariance around the selected points for the cases \(L_{max}=32\), 64, and CNR, although in the case of \(L_{max}=64\), the spatial covariance graph center is shifted a bit, although for the \(\Omega=116^{\circ}\) (where sub-cycle happens) the shift is less noticeable than for \(\Omega=0^{\circ}\). For the Pendulum formation, the graphs of \(L_{max}=32\) and CNR feature symmetric patterns, but the graph centers are shifted towards left where it is more predominant for \(L_{max}=32\). That is not the case for maximum spherical harmonic coefficient 64. It is interesting to say that for the MCNR recovery, the scenario (a) significantly outperforms the scenario (b) in terms of the symmetry. Regarding the orbit parity impact on the gravity recovery, one should notice that the parity state (odd or even) is usually meaningful for the full repeat cycle. However, the evolution of the groundtrack pattern before the full cycle might be sophisticated and differ in each case; hence it is more reasonable to see the impact by its association to the groundtrack pattern distribution at a specific time span. That means, for the recovery time-spans shorter than the full-repeat cycle, instead of talking about the parity impact on the gravity recovery, it makes more sense to study the groundtrack pattern at that specific time interval and look for its potential association with the recovery quality. This issue will be addressed in our future works in more detail.
## 5 Discussion
### Future Directions and Challenges  
A key objective of this research is to develop a biofeedback system that supports a proactive approach to injury prevention. By identifying and correcting biomechanical deficiencies before they lead to injuries, the system could align with a preventive care model that shifts the focus from reactive treatment to early intervention. If effective, this strategy has the potential to reduce healthcare costs associated with sports injuries while improving overall athletic performance. Providing athletes and coaches with real-time data on movement inefficiencies may support modern training methodologies that prioritize injury prevention and long-term physical well-being.  
However, several technical challenges must be addressed during development to ensure the system's reliability and usability. One potential limitation is the reliance on Bluetooth communication, which may be susceptible to interference or bandwidth constraints in environments with high device density. Future iterations of the DAid(r) system may need to explore alternative communication technologies, such as ultra-wideband (UWB) or advancements in Bluetooth protocols, to improve data transmission robustness.  
Expanding the application of this biofeedback system beyond soccer to other sports, such as basketball, tennis, or track and field, could further increase its impact. Future research should explore the feasibility of monitoring additional biomechanical parameters, including joint angles, muscle activation levels, and fatigue metrics, to provide sport-specific feedback. Additionally, integrating personalized biofeedback tailored to an athlete's unique biomechanics, injury history, and learning pace could enhance user engagement and compliance, making interventions more effective at an individual level.  
Another important area for future exploration is the potential for remote monitoring through cloud-based platforms. This capability could allow healthcare providers and coaches to oversee training sessions in real time or analyze recorded data for longitudinal tracking. By making advanced biofeedback tools accessible to remote or underserved areas, this approach could expand the reach of injury prevention technologies to a broader population of athletes.  
This study establishes a concept for future evaluation of the feasibility and effectiveness of a smart socks-based biofeedback system in sports injury prevention and performance optimization. By combining wearable technology, real-time data analysis, and mixed reality feedback, this research contributes to the advancement of technology-driven injury prevention strategies. Addressing key challenges related to system connectivity, personalization, and sport-specific applications will be critical in refining the system and realizing its full potential in sports science and rehabilitation.