esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 8 Conclusions  
In conclusion, the fusion of cybersecurity technology with opportunistic networks in the proposed model represents a significant leap forward in the realm of wireless communications. The proposed two-layer approach, combining security protocols with an innovative routing protocol, successfully addresses the distinctive challenges posed by opportunistic networks.  
The first layer, anchored by the FT-OLSR protocol, showcases the effectiveness of employing fuzzy logic for trust computation and a Proof-of-Trust consensus algorithm. The decentralized trust evaluation and the isolation of misbehaving nodes contribute to a robust and secure network environment. The proposed system not only meets the distributed management, reliability, security, and scalability requirements but also streamlines the process, avoiding intricate calculations and conserving resources. The second layer introduces a novel routing protocol meticulously designed for opportunistic networks. The protocol's efficiency is demonstrated through distinct phases, optimizing message exchange, handling non-deliverable messages, and implementing smart criteria for message acceptance. This approach significantly improves key performance metrics, ensuring superior delivery probability, reduced overhead, and minimized communication latency.  
The extensive simulation results across various scenarios validate the model's performance and versatility. The proposed solution consistently outperforms established DTN routing algorithms under different conditions, emphasizing its adaptability to diverse network scenarios. Whether in a DakNet environment or a WiFi-Direct scenario, the model proves its robustness, offering superior delivery probability, low overhead, and reduced latency.  
In essence, this research paper paves the way for the integration of cybersecurity into opportunistic networks, offering a holistic solution that not only fortifies security but also optimizes routing for enhanced efficiency and sustainability. The proposed model emerges as a beacon in wireless communications, especially in scenarios where traditional networking infrastructures face limitations, ensuring seamless and timely information exchange in dynamic and resource-constrained settings. As the digital landscape evolves, the paradigm introduced here holds promise for shaping the future of wireless communication systems.  
Conceptualization, A.K. and B.Z.; methodology, A.K. and B.Z.; software, A.K. and B.Z.; validation, B.Z. and A.K.; formal analysis, B.Z. and A.K.; investigation, A.K. and B.Z.; resources, B.Z. and A.K.; data curation, A.K. and B.Z.; writing--original draft preparation, A.K. and B.Z.; writing--review and editing, A.K. and B.Z.; visualization, A.K. and B.Z.; super-vision, B.Z. and A.K.; project administration, B.Z. and A.K.; funding acquisition, B.Z. and A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 28: Latency WiFi-Direct case.
# A New Type Bionic Foldable Wing Design for High Maneuverable Unmanned Aerial Vehicle
## 3 Folding Wing Structure Design and Validation
### Aerodynamic Analysis of Folding Wing UAV
#### 3.3.3 Lateral Aerodynamic Characteristics  
As shown in Figure 23, when the angle of attack = 4\({}^{\circ}\), the rolling moment coefficients of two different wing attitudes vary with the sideslip angle. According to the lateral stability criterion [28], if:  
\[C_{l\beta}=\frac{\partial C_{l}}{\partial\beta}<0\]  
Figure 21: Polar curves of morphing wing UAV under different morphing attitudes and inlet velocities.  
Figure 22: The lift-to-drag ratios of fully unfolded and fully folded wings vary with angle of attack at three different inlet velocities.  
Then, the UAV has lateral static stability.  
where \(C_{l\beta}\) is the derivative of lateral static stability. As shown in Figure 23, a positive sideslip angle corresponds to a negative rolling moment coefficient, and a negative sideslip angle corresponds to a positive rolling moment coefficient, which meets the lateral stability condition. Therefore, when the UAV faces roll disturbance, it can maintain lateral stability.  
As shown in Figure 24, when the angle of attack = 4\({}^{\circ}\), the yaw moment coefficients of two different wing attitudes vary with the sideslip angle. According to the heading stability criterion [28], if:  
\[C_{n\beta}=\frac{\partial C_{n}}{\partial\beta}>0\]  
Then, the UAV has static stability of heading.  
where \(C_{n\beta}\) is the derivative of heading static stability. As shown in Figure 24, the positive sideslip angle corresponds to the positive heading moment coefficient, and the negative sideslip angle corresponds to the negative heading moment coefficient; therefore, the UAV meets the heading static stability condition. When the UAV is subjected to a brief asymmetric disturbance while in flight, a sideslip occurs. Heading stability enables the UAV to automatically eliminate the sideslip trend as soon as the interference disappears. Therefore, the UAV has the ability to resist asymmetric interference.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 3 Design Methodology, Layout and Circuit Simulation
### Layout and Circuit Simulation  
In this section, the layout, transistor dimensions, and post-layout simulations for each one of the circuits of the proposed battery-free temperature sensor prototype are presented to validate their functionality and performance. The sizes of the transistors  
Figure 11: Design flow chart of the proposed tag.  
were computed based on the operation region described in Section 2 as well as using Equations (2), (4) and (6).
## 2 Numerical Model and Simulation Setup  
### CFD Model  
We used a Eulerian CFD model incorporating a tree drag parameterization scheme [7, 19]. The CFD model includes the momentum equation, mass conservation equation, and transport equation of pollutants. To parameterize subgrid-scale turbulence, the CFD model employs the prognostic equations of turbulent kinetic energy (TKE) and TKE dissipation rate. This model solves the Reynolds-averaged Navier-Stokes (RANS) equations assuming a three-dimensional, nonrotating, nonhydrostatic, and incompressible flow. A staggered grid system with a finite volume method and the semi-implicit method for pressure-linked equations (SIMPLE) algorithm [20] were adopted for the model. Wall functions suggested by [PERSON] and [PERSON] [21] were implemented to model the effects of the turbulent boundary layer. To parameterize the tree drag effect, tree drag terms (Equations (1)-(3)) were added into the equations of momentum, TKE, and TKE dissipation rate (\(\varepsilon\)):  
\[F_{tree,i}=-\rho\cdot n_{c}^{3}\cdot C_{d}\cdot LAD\cdot U_{i}\cdot|U|, \tag{1}\]  
\[F_{tree,k}=n_{c}^{3}\cdot C_{d}\cdot LAD\cdot|U|^{3}-4\cdot n_{c}^{3}\cdot C_{ d}\cdot k\cdot|U|, \tag{2}\]  
\[F_{tree,\varepsilon}=\frac{3}{2}\cdot\frac{\varepsilon}{k}\cdot n_{c}^{3} \cdot C_{d}\cdot LAD\cdot|U|^{3}-6\cdot n_{c}^{3}\cdot C_{d}\cdot\varepsilon \cdot|U|, \tag{3}\]  
where \(U_{i}\) are the \(i\)th Cartesian coordinates (i = 1, 2, 3) of the \(i\)th mean velocity component, and \(\rho\), \(k\), \(\varepsilon\), and \(|U|\) are the air density, TKE, TKE dissipation rate, and wind speed, respectively. \(LAD\), \(n_{c}\), and \(C_{d}\) indicate the fraction of the area covered by the vertical projection of leaves, leaf area density, and drag coefficient, respectively. For further details, see [PERSON] et al. [7, 19].
# Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis
## 3 Methodology
### Event Argument Entity and Relationship Labeling Strategies  
In order to construct event logic ontologies for fault diagnosis, we use Prot'eg'e prior to data labeling, as shown in Figure 1. Inspired by [24; 25; 26], the task of fault detection event argument entity recognition is converted to a sequence labeling task. Entities and relationships are predefined based on recommendations from experts in the field of mechanical fault detection. An entity follows the BIO pattern, in which B indicates the start of the entity, I indicates the middle of the entity, and O indicates none. The labeling process is as follows. Firstly, we build the ontology model based on the expert's advice. Based on the ontology model, 7 argument entities Attribute, Attribute value, Equipment, Sub equipment, Component, Part and Status value and 4 argument relations Lead To, Consist Of, Has Attributes, and Appear are defined. Appear, Lead To are event trigger words. Consist Of, Has Attributes are qualitative knowledge relations. In the second stage, we pre-label the data samples according to the labeling method. We discuss the solution when uncertain words appear, and update the labeling method. In the third stage, we divided into two groups for formal annotation and compared the annotation results to ensure the accuracy of the annotation. Figure 2a,b illustrate our labeling method with a sample sentence as an example. A sample sentence is as follows: The winding of the motor has become damp and the insulation has deteriorated causing the housing to be overcharged. Seven triples are contained in the sentence. Lead To, Consist Of, Has Attributes, and Appear are predefinedrelation categories. The relationship category is labeled as N if the entity does not have any relationships.
###### Abstract
On the cellular side, three systems have been proposed by the 3 GPP, of which the narrowband Internet of Things (NB-IoT) is deemed to be optimal for mMTC applications, particularly by satisfying the following requirements set for the CIoT in [11; 12]:  
* Improve indoor coverage (e.g., in an apartment basement, or on indoor equipment that may be close to the ground floor), extending it by 20 dB when compared with commercially available legacy GPRS (non-EGPRS) devices, yielding a maximum coupling loss (MCL) of 164 dB;
* Support a massive number of low throughput devices (up to 40 per household, resulting in the number of households per cell as in [12]);
* Reduce complexity and improve power efficiency, allowing up to ten years of battery life in user equipment (UE) with a battery capacity of 5 Wh, even in locations with adverse coverage conditions, where up to a 20 dB coverage extension over a legacy GPRS might be needed;
* Have a latency tolerance of up to 10 s (but preferably not exceeding this figure).
## 4 Noisy Location with Local Differential Privacy
### Achieve Local Differential Privacy on Location
#### 4.2.2 Remapping Noisy Point to Worker's Location  
To generate the worker's noisy location, we then had to remap the noisy point generated by Algorithm 1 to the location of the worker. We can generate the worker's noisy location by following Algorithm 2.  
```
Input: worker's real location \(l_{0}\) Output: worker's noisy location \(l_{\theta}\)
01. \(\theta\) = random [0, 2\(\pi\))
02. \(p\) = random [0,1)
03. \(r=C_{\epsilon}^{-1}(p)\)
04. \(l_{\theta}\) = \(l_{0}\) + (\(r^{*}\) \(\cos\) (\(\theta\)), \(r^{*}\) \(sin\) (\(\theta\)))
05. return\(l_{\theta}\)
```  
**Algorithm 2** Worker's Noisy Location Generate Algorithm  
Intuitively, in Algorithm 2, we first generated the noisy point (\(r\), \(\theta\)) using Algorithm 1. Therefore, we could easily generate the worker's noisy location in step 04 after we generated the noisy point in steps 01 and 03.
## 4 Enhanced P&O Algorithm
### Enhanced P&O Mppt  
The proposed modification consists of specified systematic comparison sequences which lead to tracking the GMPP with a very simple process based on an adaptable step size P&O strategy; this algorithm comprises three procedures. The first step starts by initiating the tracking process with the measured \(V_{PV}\) and \(I_{PV}\), and if starting from zero, \(V_{max}\) and \(P_{max}\) are set to zero at the beginning of the process. By starting simulation of the PV array, \(I_{ph}\) and \(V_{ph}\) and the corresponding power \(P_{ph}\) are obtained for the \(Q_{th}\) instants. The second step is to make an auto-adjustment to the perturbation step size in order to improve the tracking response speed and reduce the oscillation around the MPP by using variable step size instead of a fixed step size. This idea was applied in [6] for MPPT under uniform conditions and is based on estimating the distance between the current power value and the MPP. This distance can be calculated based on the Pythagorean theorem, which is an essential relation in geometry among the three sides of a right-angled triangle. It states that the square of the hypotenuse is equal to the sum of the squares of the other two sides [6]. However, the parameters of this triangle are the voltage, the power, and the hypotenuse, where the value of hypotenuse indicates the distance between the current power value and the MPP; this means the possibility of estimating the best step using Equation (8).  
\[M=abs\left(\sqrt{dP^{2}+DV^{2}}\right) \tag{8}\]  
Therefore, the duty cycle in the adaptive step length P&O MPPT scheme becomes very dynamic and changes based on the power and voltage values. This concept is applied during the comparison process between the current power \(P_{pv}(Q)\) and the previous maximum power \(P_{\max}(Q-1)\) to track the actual maximal power \(P_{\max}(Q)\) and its voltage \(V_{\max}(Q)\). If the current power \(P_{pv}(Q)\) is higher than the previous maximum power \(P_{\max}(Q-1)\), then the actual maximum power \(P_{\max}(Q)\) is equal to the current power \(P_{pv}(Q)\); otherwise, the actual maximum power \(P_{\max}(Q)\) keeps its original value. In the final step, the value of \(V_{\max}\) corresponding to the resulting \(P_{\max}\) is updated for a new comparison between \(V_{PV}(Q)\) and \(V_{\max}(Q)\) to achieve the GMPP with accurate voltage and duty cycle by applying the P&O process with variable step size. The proposed algorithm is illustrated in detail in the flowchart in Figure 5. The proposed procedure is capable of GMMP tracking under any strength of partial shadowing with the presence of local and global maximum points, during initial conditions or fluctuating weather conditions, as validated and shown in the next section.  
Figure 5: Flowchart of the proposed algorithm.
# Influence of Source Shape on Semi-Airborne Transient Electromagnetic Surveys
## 2 Methods
### SATEM Model  
To investigate the influence of the source geometry on SATEM responses, we established a numerical model with the transmitter-receiver configuration illustrated in Figure 2. The background medium consists of a homogeneous half-space with a resistivity of 50 \(\Omega\cdot\)m. A conductive 3D anomalous body with dimensions of 400 m \(\times\) 600 m \(\times\) 200 m and resistivity of 1 \(\Omega\cdot\)m is embedded at a depth of 400 m, positioned 500 m laterally from the source.  
Two distinct transmitter configurations were examined: a straight-line source (depicted by the blue line) with a total length of 2.5 km and a curved source (depicted by the red line) with a total length of 2.8 km. Both sources share identical endpoint locations; however, the curved source incorporates a significant bend in its central section. The airborne receivers (shown in green) were positioned at an elevation of 20 m above the ground surface to measure the induced electromagnetic responses.  
Initially, we computed the SATEM responses for the homogeneous 1D half-space model using different source configurations, without incorporating the conductive 3D anomalous body, to establish baseline responses. Then, we calculated the SATEM responses for the 3D model using sources with varying geometries, as illustrated in Figure 2. Subsequently, we conducted a comprehensive analysis to evaluate the influence of the source geometry on the observed electromagnetic data in this modeling scenario.
###### Abstract  
This study aims to improve the shear performance of reinforced concrete (RC) beams by utilizing the favorable tensile and shear deformation capabilities of high-strength, high-toughness epoxy mortar. This study investigates the effect of reinforcement layer thickness on the shear failure modes, bearing capacity, and deformation capacity of beams through static tests on three specimens reinforced with thin layers of high-strength, high-toughness epoxy mortar and one unreinforced beam. The results show that reinforcing RC beams with thin layers of high-strength, high-toughness epoxy mortar can significantly enhance its shear bearing capacity and deformation capacity. The reinforcement layer of epoxy mortar can partially exert the shear resistance provided by the stirrups. The thicker the reinforcement layer, the more significant the improvement in the shear bearing capacity and deformation capacity of the strengthened beam. The epoxy mortar layer bonds well with the concrete, but delamination between the cover concrete and the core concrete leads to failure of the reinforcement layer, meaning that shear bearing capacity does not increase linearly with the thickness of the epoxy mortar layer. Based on the experimental results, a shear bearing capacity calculation formula for RC beams reinforced with thin layers of high-strength, high-toughness epoxy mortar is proposed, which matches the experimental results well.  
epoxy mortar; strengthening; high toughness; RC beams; shear performance; deformation capacity +
Footnote †: journal: applied sciences  
Article
## 3 Distance Vector-Hop (DV-Hop) and Differential Evolution (DE) Algorithms
### Differential Evolution Algorithm
#### 3.2.4 Selection  
DE uses the greedy operation to select between \(x_{i}^{t}\) and \(u_{i}^{t+1}\), and generate the next generation of the individual \(x_{i}^{t+1}\):  
\[x_{i}^{t+1}=\left\{\begin{array}{ll}u_{i}^{t+1}\,&if\ \ \ f\left(u_{i}^{t+1} \right)>f\left(x_{i}^{t}\right)\\ x_{i}^{t},&otherwise\end{array}\right. \tag{12}\]  
where \(f(\cdot)\) is the objective function. Given that the optimization problem in this paper is in minimization form, the selection condition is \(f\left(u_{i}^{t+1}\right)\leq f\left(x_{i}^{t}\right)\). The overall flow chart of the DE algorithm is shown in Figure 1.  
Figure 1: Flow chart of the differential evolution (DE) algorithm.
## 4 Discussion
### Significance and Superiority of Our Work
#### 4.2.3 Real-Time Focus and Practicality  
Many studies focus on offline or academic IDS development, often failing to address the real-time requirements of IoT networks. These systems are unsuitable for practical deployment due to high latency, resource usage, and inability to adapt to dynamic environments. However, our proposed IDS is specifically designed for real-time operation, ensuring practical usability in real-world IoT networks.
## 5 Operational Descriptions of the Future Distribution Networks with the Sundom Smart Grid
### Energy Management Use Cases
#### 5.1.3 Energy Management Use Cases of the Intelligent Microgrid Network Phase
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**HL-UC Name** & **Events in the LV and MV Distribution Networks** \\ \hline Electricity supply & \\ \hline Electricity consumption & \\ \hline \multirow{6}{*}{Load control} & LV: The controllable loads used for the DR programs are passive and dynamic load types, from which several independent aggregators can collect various controllable loads: electric heating systems, water boilers, ventilation, various customers’ BESS, and the SS-BESS. \\ \cline{2-3}  & MV: Load control within a microgrid is as in the previous evolution phase. In this phase, a single microgrid can be considered a controllable load to be controlled to reduce or eliminate its demand even by intentional islanding. Load control by a microgrid set can be optimized for the DSO’s congestion management or the TSO’s ASs. \\ \hline Electricity generation & LV: Several independent aggregators can collect power from various generation units: customers’ BESS (as a generation), PV units, and the SS-BESS. \\ \cline{2-3}  & MV: Grid-connected microgrids can be operated to supply electricity to the utility grid. \\ \hline \multirow{6}{*}{Generation control} & LV: MV: A grid-connected microgrid can be operated as a flexible generation unit. \\ \cline{2-3}  & Volt/Var control \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Energy management high-level use cases in the intelligent microgrid network phase. Further, considering the operational scenarios of the various flexible resources from the different voltage levels, a responsive generation control PUC could be developed for the electricity generation HL-UC. Similarly, a responsive demand control PUC could be developed for the electricity consumption HL-UC. In these circumstances, these PUCs could be combined and named the flexibility control PUC. It would be worth exploring whether the flexibility control could be a new concept level building block presenting a new HL-UC for future power systems' energy management. Like previous chapters, we can derive UCs for all evolution phases, but in the following, the focus is only on the microgrid phase, as presented in Figure 1.
# Evaluation of Emotional Satisfaction Using Questionnaires in Voice-Based Human-AI Interaction
## 3 Results
### Exploratory Factor Analysis  
The emotional scores collected using 30 pairs of KWs were analyzed using EFA. The analysis was conducted using varimax rotation and principal component extraction. From the scree plot, it was confirmed that the KWs were structured by two factors. Factor 1 includes ten KWs, and factor 2 includes nine KWs, as shown in Table 3.  
Table 3 summarizes the two factors that reduced using factor analysis and the KWs belonging to those factors. It is evident that 10 and 9 KWs, respectively, were aggregated as two factors, and 65% of the total KWs explained the emotion in the interaction situation. Based on the factor score of the KWs, which belong to the two factors, the names of the factors were determined based on the opinions of three Kansei engineering experts. The name of the suggested factor was set as the emotion that can be expressed on behalf of the feeling that occurs when interacting with the VIS. The first axis was named \"pleasurability\" because the cluster of emotions that occur when interacting can be referred to as pleasant and lighthearted with no difficulty. The second axis was called \"reliability\" because the cluster of emotions that arise refers to communication skills, such as the ability to speak clearly and respond to VIS interactions. To check the difference in emotions according to the design parameters of the VIS around the derived emotion axis, the user's emotional scores were mapped, and ANOVA was used to evaluate the difference in emotional satisfaction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kansei Word** & **Factor 1** & **Kansei Word** & **Factor 2** \\ \hline Natural–Abrupt & 0.804 & Lighthearted–Bothersome & 0.789 \\ Bracing–Unpleasant & 0.783 & Interesting–Indifferent & 0.768 \\ Reassured–Concerned & 0.751 & Hopeful–Btoken hearted & 0.744 \\ Refreshing–Uncomfortable & 0.739 & Detailed–Sloppy & 0.744 \\ Spontaneous–Awkward & 0.690 & Friendly–Picky & 0.734 \\ Reliable–Unreliable & 0.679 & Feel Unburdened–Stuffy & 0.728 \\ Good–Irritated & 0.677 & Satisfied–Insufficient & 0.685 \\ Concentrated–Dejected & 0.674 & Suitable–Inappropriate & 0.680 \\ Glad–Perplexed & 0.658 & Pleasant–Boring & 0.655 \\ Attractive–Banal & 0.646 & & \\ Variance & 10.253 & Variance & 9.233 \\ \% Variance & 0.342 & \% Variance & 0.308 \\ \hline \multicolumn{3}{c}{Pleasurability} & Reliability \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kansei word cluster obtained using factor analysis.