esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Quantitative Analysis of ECTSA Method  
The quantitative measurement of delay and energy consumption is significantly reduced by the proposed ECTSA method. In current years, there has been a rise in attention on the MANET, and one of the most interesting applications is cross-layer design. The ECTSA, its various key indications, and its characteristics are presented in this work. Using cross-layer design as a foundation, a novel dynamic routing approach is suggested. In terms of Delay and Energy Consumption, the suggested ECTSA is compared with the existing Opposition Genetic-based Fish Swarm Optimization (OGFSO) [22]. The proposed and existing approaches are compared in Table 2, which clearly shows that the proposed ECTSA produces less delay (5 ms) and energy consumption (225 J), outperforming the existing OGFSO [22], which attains a delay of 7 ms and energy consumption of 300 J.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of Nodes & 50, 75, 100, 125 and 150 \\ Area & 1000 m \(\times\) 1000 m \\ Propagation Model & Two-Ray Ground \\ Antenna Model & Omni-Antenna \\ Traffic Model & CBR \\ MAC Protocol & IEEE 802.11b \\ Mobility model & Random Waypoint \\ Initial Energy & 10 Joules \\ Transmission Power & 0.8 watts \\ Receiving Power & 0.5 watts \\ Simulation time & 500 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Node Count**} & \multicolumn{3}{c}{**Delay (ms)**} & \multicolumn{3}{c}{**Energy Consumption (J)**} \\ \cline{2-5}  & **OGFSO [22]** & **Proposed** & **OGFSO [22]** & **Proposed** \\  & **ECTSA** & & **ECTSA** \\ \hline
0 & 0 & 0 & 0 & 0 \\
100 & 4 & 2.1 & 190 & 150 \\
200 & 4.2 & 2.7 & 200 & 178 \\
300 & 4.8 & 3.9 & 300 & 225 \\
400 & 5 & 4.2 & 140 & 100 \\
500 & 7 & 5 & 50 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation Analysis of Optimization Algorithms.
# Electrical Circuits Simulator in Null-Flux Electrodynamic Suspension Analysis
## 2 Unveiling the Inner Workings: Exploring the Principles and Model of an EDS System
### The Calculation of Mutual Inductance between SC and 8-Shaped Coils  
Equation (2) demonstrates that the forces developed in a null-flux EDS system result from the magnetic coupling between the magnetic field source and the null-flux coils. As the Maglev train moves, an interaction occurs between the onboard and outboard coils, expressed by variation in mutual inductance between the SC coil and the lower and upper loops of the 8-shaped coil. Traditional methods for calculating mutual inductance include the Newman formula, empirical formulae, and finite-element calculations [21; 29; 42]. In this work, the magnetic flux values, as functions of position, are determined by calculating the magnetic field generated by the SC coil through the Biot-Savart law, integrated over the area formed by the upper and lower loops of the 8-shaped coil [43]. For mathematical simplification, the SC coil was approximated to a rectangular shape with the same internal area as the actual coil, which had a racetrack shape, as shown in Figure 2. The flux of each side was determined separately, and the four fluxes were summed to obtain the total magnetic flux through the rectangular loop [44].  
Figure 4: (a) Specifications of the hardware used for numerical calculation, a desktop computer. (b) Specifications of the hardware used for the simulation of the EDS circuit, a laptop.  
The simulations focused on a type of EDS system without cross-connections, using one 8-shaped coil and one SC coil. Wolfram(r)*s Mathematica software, version 12.1, performed the numerical calculations. The magnetic flux was calculated for a total distance of 32 m traveled by train, with 1-centimeter steps, totaling 3201 points, in the \(x\)-direction, from position \(-16\) to \(+16\) m. In the \(y\)-direction, the distance between the SC and 8-shaped coil centers was 0.185 meters, corresponding to the air gap shown on the Yamanashi test line. In the \(z\)-axis, the center of the SC coil was offset by 0.0366 meters below the center of the 8-shaped coil. This offset was the \(z_{0}\) position. The mutual inductance as a function of position was then calculated using Equation (3) [44]:  
\[M_{s,j}\bigg{[}x+\frac{\Delta x}{x}\bigg{]}=n*\frac{\phi_{j}[x+\Delta x]-\phi_ {j}[x]}{I}, \tag{3}\]  
where \(M_{s,j}\) represents the mutual inductance between the SC coil and the upper loop, \(j=1\), or lower loop, \(j=2\), of the 8-shaped coil; \(\phi_{j}\) denotes the magnetic flux passing through the upper loop, \(j=1\), or lower loop, \(j=2\), of the 8-shaped coil; \(n\) is the number of turns of one of the ground coil loops, and \(I\) is the magnetomotive force of the SC coil, generating 3200 points. The graph of the mutual inductance as a function of distance can be seen in Figure 5.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 1 Introduction
Thus, the main objectives of this paper are to:  
* Design a mobility model which can emulate the movement patterns of festival attendees and use it to test various opportunistic algorithms;
* Study already existing opportunistic routing solutions;
* Identify the key parameters that have the potential to increase the performance of an opportunistic algorithm in a music festival scenario;
* Create an opportunistic algorithm specifically tailored for high performance at crowded events;
* Study the impact of Bluetooth and Wi-Fi Direct on algorithm performance. Through experimental analysis, we identify three opportunistic algorithms that can work best at a music festival, namely binary Spray-and-Wait [4] and two custom-designed algorithms that we propose and present in Section 4. We test the solutions in an opportunistic network simulator using the traces generated by our mobility model, designed to mimic a festival crowd. The node movement is driven by social relationships or the node's interest in a particular area on the map (e.g., food court). The rest of the paper is structured as follows: Section 2 provides an overview of opportunistic networks, then presents two case studies: a music festival in Belgium and a street festival in Zurich. The proposed festival mobility model is presented in Section 3, while, in Section 4, we propose two opportunistic algorithm for a festival scenario. The test data and results are analyzed in Section 5. Finally, Section 6 outlines the main conclusions and identifies recommendations for further research.
# A Systematic Review of Responsible Artificial Intelligence Principles and Practice
## 7 Finding 5: Principles of Responsible AI
### Transparency and Explainability  
Transparency and explainability represent the importance of the decision making processes and reasoning in AI systems [58; 70; 71; 72; 73; 74; 75; 76; 77; 78]. Explainability and interpretability aims to build trust among users [79] while transparency ensures that decision making processes are understandable and accessible to stakeholders affected by AI outcomes [74]. Explainability focuses on the ability of AI systems to provide clear justifications for their decisions [80; 81] and helps stakeholders understand how AI systems operate. They enable the assessment of system reliability and support informed decisions about AI deployment. Explainability also provides insights into the decision making process [31; 82]. This includes the reasons for decisions, influencing factors, and available alternatives. Transparency requires information disclosure to ensure individuals know when they interact with AI systems [79; 83]and helps users understand how their personal information is used. For example, transparent biometric identification systems should inform users about the purpose and usage of collected biometric data [79]. Transparency and explainability contribute to responsible AI through multiple mechanisms [73; 77]. They enable the identification and mitigation of potential biases in AI systems [58; 76]. These principles support regulatory compliance and ethical governance by providing clear documentation of AI system behavior [74; 75]. They also enhance user autonomy by giving individuals meaningful control over their interactions with AI systems [70; 78]. In any system, proper documentation is essential for maintaining transparency [70; 71; 72; 78]. It requires clear content about system design, data sources, intended purposes, and operational requirements [79]. In business contexts, transparent AI systems should disclose their accuracy and robustness against potential risks, enabling organizations to implement appropriate safeguards [79]. Organizations developing AI systems must balance the level of transparency with security considerations [73; 81]. This includes protecting sensitive intellectual property while providing sufficient information for stakeholder trust and system accountability. The implementation of these principles requires standardized approaches across the AI development lifecycle [72; 74]. This includes clear documentation practices, consistent evaluation metrics, and established procedures for explaining AI decisions to different stakeholder groups [31; 80]. The integration of transparency and explainability in AI systems supports responsible innovation while maintaining public trust in AI technologies [75; 77].
# Pentaband Dual-Polarized Antenna for Multiservice Wireless Applications
## 2 Proposed Antenna Configuration and Description
### Single-Port Antenna Element
The pentaband antenna design achieves impedance bandwidths of 9.5% (1.5-1.65 GHz), 15% (2.4-2.7 GHz), 15.6 % (5-5.85 GHz), 4 % (7.1-7.4), and 6 % (7.9-8.4) in 5 separate bands. The antenna also provides circular polarization (CP) at 2 distinct bands with an ARBW of 6.5% (1.5-1.6 GHz) and 15% (2.4-2.7 GHz). In Figure 4b, the axial ratio performance for the designated bands is shown. The pentaband antenna demonstrates stable and uniform CP across all 5 resonance bands with a maximum axial ratio of 3.3 dB. This suggests that the proposed design is suitable for a wide range of wireless communication applications that require high performance and versatility. Further, the CP behavior of the proposed antenna is analyzed using the surface current distribution, as shown in Figure 5. The figure shows the CP bands at 1.5 GHz and 2.6 GHz. Figure 4: Single element pentaband reflection coefficient of Ants #1 to #4. (**a**) Reflection coefficient (**b**) axial ratio. Figure 3: Step-wise design procedure for achieving multiband operation. (**a**) Ant #1, (**b**) Ant #2, (**c**) Ant #3, and (**d**) Ant #4. From the current distribution in Figure 5a, it is observed that the current on the top middle portion of the patch rotates in an anticlockwise direction over time, with different phases of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) at 1.5 GHz from left to right, respectively. This rotating current distribution generates a circular polarization in the radiated field. Additionally, the top left portion of the patch also exhibits current distribution in the same direction at 2.5 GHz, indicating that the antenna possesses CP characteristics at this frequency as well. Figure 5: Surface current distribution of proposed antenna at 1.5 GHz and 2.6 GHz. (**a**) 1.5 GHz, (**b**) 2.6 GHz. The CP performance of the proposed antenna is further analyzed in terms of the ARBW, as shown in Figure 4b. The ARBW is 6.5% (1.5-1.6 GHz) and 15% (2.4-2.7 GHz), indicating that the antenna provides CP at 2 distinct frequency bands. The ARBW refers to the range of frequencies over which the magnitude of the ratio of the major and minor axes of the ellipse that describes the polarization of the radiated field remains below a certain threshold, typically 3 dB.
## 3 Types of Underwater Communication Systems  
An underwater communication system can be wired, wireless [45; 46], or a combination of the two, depending on the applications and system requirements. To illustrate, Hybrid Remotely Operated Vehicles (HROVs) are operated both with tethered cables or wirelessly [47]. HROVs are vehicles used for underwater operations such as monitoring,exploration, and surveillance for scientific, military, recreational, and commercial purposes. Underwater Wired Communication (UWiC) is feasible via coaxial cables [48; 49], fibre-optic cables [50], and hybrid systems [51]. UWiC via coaxial cables is feasible using electrical conductors like copper and aluminium [48; 52]. Subsea cables are properly insulated with coatings designed to prevent corrosion from salinity and water ingress [53; 54; 55; 56].  
In [PERSON] et al. [46], three Underwater Wireless Communication (UWC) systems are identified, namely optical, EM, and acoustic. In contrast, [PERSON] et al. [5] identified four UWC technologies, specifically radio frequency (RF), Magnetic Induction (MI), Underwater Wireless Optical Communication (UWOC), and Underwater Acoustic Communication (UWAC). Each of these communication modalities have trade-offs, and therefore the implementation of these technologies is determined by factors such as costs, throughput capabilities, transmission ranges, latency (latency is the time it takes for a signal to travel a physical distance), and the device's longevity in aquatic conditions [5; 46].  
While UWiC systems are sizeable and difficult to scale out (see Figure 1), wireless technology is highly scalable and versatile [57]. Light is part of the EM spectrum and is highly malleable; it can be transmitted through fibre-optic cables or wirelessly by propagating it or sending it directly through water, as used in underwater Light Detection and Ranging (LiDAR) and UWOC systems [46; 50]. In contrast, acoustic and RF waves are uniquely used as wireless communication methods due to their ability to travel through water [58]. Acoustics is a preferred UWC system for long-distance communication underwater because sound waves travel well in the water medium [17; 59], while RF waves are used for shorter distances due to their limited range but faster data transmission in certain underwater conditions [60; 61; 62].
## 2 Materials and Methods  
This section briefly reviews the basic theory of the KCF algorithm and then elaborates how to improve the object tracking accuracy by adaptively fusing the features' responses and combining two filters. The KCF algorithm [12] is widely used in object tracking, and its ideas and methods have been presented in many papers, so it will not be elaborated in detail. The proposed method's architecture is illustrated in Figure 2. Initially, we extract the HOG and VGG features from the current frame T, select robust features from the VGG ones, fuse them in parallel with a specific proportion, and obtain the response through the trained correlation filter. Second, we use the designed fusion strategy to fuse the response patch of both filters. Third, we conduct occlusion detection and use the predicted result of the adaptive Kalman filter as the final tracking result if the object is occluded or obtain the object's subpixel location if it is not occluded. Finally, the noise covariance of the Kalman filter is adjusted according to the Euclidean distance between the center position of the object in the adjacent frames so that the predicted results can compensate and correct for the tracking results of the correlation filter, thus obtaining an accurate object position.
# RT-GAN: GAN Based Architecture for Precise Segmentation of Railway Tracks
## 4 Experiment
### Analysis  
Experimental results show that RT-GAN can obtain stronger track segmentation accuracy than other existing methods, both for unaugmented and augmented images. Upon careful analysis, these enhancements come from three main sources: (1) generator model, (2) adversarial training, and (3) explicit introduction of gradient information.  
First, the generator model is the foundation. The LSU and LSP used in RT-seg are able to extract image features at different scales effectively and use the optimal network structure obtained by heuristic search. In terms of results, RT-seg has more potential as a generator compared to other networks.  
Second, the adversarial training trained the generator network in a global direction. As the segmentation results show, before adversarial training, RT-seg misidentifies linear regions such as streetlights and eaves as rail tracks, which may be caused by the smaller weights of RT-seg's higher-level features (which have larger receptive fields) and larger weights of lower-level features (which have smaller receptive fields). In adversarial training, the track discriminator scores the segmentation result as a whole, forcing RT-seg to pay more attention to the global features of the track image, which results in a reduced mis-segmentation rate and an improved inference ability for the occluded targets of the the generator network.  
Finally, explicitly adding the gradient information of track targets in the form of loss function and discriminator inputs to the training and adversarial training of the semantic segmentation network enables RT-seg to notice track regions with distinct gradient features earlier and improve the semantic segmentation of track targets. This can be proved by the results of ablation experiments.
## 3 Result
### Systemic Diseases and Carotid Artery Calcification  
In the CAC (+) group, 16% were hypertensive, and 8% were diabetic. In the CAC (\(-\)) group, 6.7% were hypertensive, 10.5% were diabetic, and 6.1% patients were having both  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Subcategory**} & \multicolumn{2}{c}{**Age (Years)**} \\ \cline{3-4}  & & \(\leq\)**60** & \(\geq\)**61** \\  & & **(_n =_ 362)** & **(_n =_ 138)** \\ \hline \multirow{2}{*}{Gender} & Male & 191 (52.76\%) & 85 (61.5\%) \\ \cline{2-4}  & Female & 171 (47.23\%) & 53 (38.4\%) \\ \hline \multirow{2}{*}{Calcification} & Present & 18 (4.97\%) & 7 (5.08\%) \\ \cline{2-4}  & Absent & 344 (95.03\%) & 131 (94.92\%) \\ \hline \multirow{4}{*}{Systemic diseases} & Diabetes & 32 (8.83\%) & 20 (14.4\%) \\ \cline{2-4}  & Hypertension & 21 (5.80\%) & 15 (10.86\%) \\ \cline{2-4}  & Combination of hypertension and diabetes & 13 (3.59\%) & 16 (11.59\%) \\ \hline \multirow{2}{*}{Smoker} & Yes & 51 (14.08\%) & 21 (15.21\%) \\ \cline{2-4}  & No & 311 (85.91\%) & 117 (84.78\%) \\ \hline \multirow{2}{*}{Percentage of alveolar bone loss} & \multirow{2}{*}{Mean \(\pm\) SD} & \multirow{2}{*}{39.6 \(\pm\) 15.4} & \multirow{2}{*}{44.1 \(\pm\) 15.4} \\ \cline{2-4}  & & & \\ \hline Number of missing teeth & \multirow{2}{*}{Mean \(\pm\) SD} & \multirow{2}{*}{4.6 \(\pm\) 5.0} & \multirow{2}{*}{7.2 \(\pm\) 6.7} \\ \hline \multirow{2}{*}{Periodontal risk} & Low & 43 (11.87\%) & 22 (15.94\%) \\ \cline{2-4}  & Medium & 204 (56.35\%) & 66 (47.82\%) \\ \cline{2-4}  & High & 115 (31.76\%) & 50 (36.23\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of variables according to age group.  
diabetes and hypertension. There was no significant association found between systemic diseases and calcification (Table 3). However, when these systemic diseases were compared according to the age group, they were statistically significant with increasing age (Table 4).
## 3 Results and Discussion
### Volatile Compound Analysis of Black Tea Under Different Shaking Combinations
#### 3.2.2 PLS-DA Analysis of Volatile Compounds  
The partial least squares discriminant analysis (PLS-DA) was shown in Figure 3. The model parameters (R2X(cum) = 0.934, R2Y(cum) = 0.986, Q2(cum) = 0.925) indicated high credibility and predictive ability. Principal component 1 (57.1%) and principal component 2 (9.2%) effectively separated and predicted black tea samples under different shaking combinations (Figure 3A). Apparently, the tea samples were clearly classified into five groups, with CK on the far left and YLS on the far right showed the largest difference, which validated the results of sensory evaluation. HTS is just next to CK, and they are both on the left side of the horizontal axis. HYS approaches the origin and S is positioned between HYS and YLS. These results confirm that different shaking combinations significantly affect the composition and content of volatile components in black tea.  
According to the value of variable importance factor (VIP > 1, \(p\) < 0.05), thirty-one compounds were identified as key components differentiating black teas under different  
Figure 3: PLS-DA analysis of main components in black teas under different shaking combinations. (**A**) Score plot showing the distribution of samples based on the first two principal components. Each point represents a sample, and the ellipses indicate confidence intervals for each group. (**B**) VIP (Variable Importance in Projection) plot displaying the key volatile compounds (VIP scores > 1.0) contributing to the separation of groups with. (**C**) Biplot illustrating the relationship between samples (points) and volatile compounds (vectors) in the PLS-DA model. Treatments: CK represents indoor natural withering, S represents shaking, YLS represents yellow light plus shaking, HTS represents high temperature plus shaking, and HYS represents high temperature with yellow light plus shaking.  
shaking combinations, with their contents significantly promoted in YLS (Figure 3B). The biplot (Figure 3C) further illustrated the contributions of these volatiles to classification, with most of the key differentiating compounds are in the right quadrant near S and YLS. It was noted that 3-methyl-butanal, (E)-2-hexenal and 2-pentyl-furan were closer to YLS, indicating their significant influence on aroma differentiation.