esa-sceva/satcom-chunk-collection huggingface dataset:
# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 4 Experiments
### Performance Comparisons
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training–Test** & **Our Model** & **SVM** & **DT** & **RF** \\ \hline
50–50\% & 0.9688 (\#7) & 0.9160 (\#6) & 0.8850 (\#6) & 0.9247 (\#8) \\
60–40\% & 0.9684 (\#7) & 0.9190 (\#6) & 0.8923 (\#6) & 0.9275 (\#8) \\
70–30\% & 0.9698 (\#7) & 0.9170 (\#6) & 0.8925 (\#6) & 0.9250 (\#8) \\
80–20\% & 0.9691 (\#3) & 0.9141 (\#6) & 0.8793 (\#6) & 0.9188 (\#8) \\
90–10\% & 0.9810 (\#3) & 0.9193 (\#6) & 0.8812 (\#6) & 0.9238 (\#8) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: First experiment set: best performance comparison for different methods by F1 score. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training–Test** & **Our Model** & **SVM** & **DT** & **RF** \\ \hline
50–50\% & 0.9678 (\#3) & 0.9106 (\#6) & 0.8778 (\#6) & 0.9212 (\#8) \\
60–40\% & 0.9673 (\#3) & 0.9139 (\#6) & 0.8847 (\#6) & 0.9239 (\#8) \\
70–30\% & 0.9675 (\#7) & 0.9120 (\#6) & 0.8853 (\#6) & 0.9215 (\#8) \\
80–20\% & 0.9684 (\#3) & 0.9093 (\#6) & 0.8729 (\#6) & 0.9152 (\#8) \\
90–10\% & 0.9804 (\#3) & 0.9143 (\#6) & 0.8741 (\#6) & 0.9198 (\#8) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: First experiment set: best performance comparison for different methods by Accuracy. may not strictly match the user's criteria fed to the inference algorithm. Therefore, a higher recall for such a recommender or a predictor of a model will benefit an online social network. Additionally, of course, the model should maintain acceptable precision. Another reason for a higher recall comes from the fact that the dataset used for friend recommendation or trust relationship prediction tasks is usually incomplete. As previously discussed, part of an \"ideally complete\" dataset would be missing due to various actual reasons. Consequently, even though a model generates good prediction results with high precision, the results may still be far away from a \"real ideal.\" Thus, as a trade-off, a prediction result with a higher recall is acceptable. Taking the above considerations into account, one of the best models that fit in the trust relationship prediction tasks in this paper would achieve a higher recall while still maintaining an acceptable precision. From the results for the first experiment set, our proposed model had an average recall of 0.9327 in all 40 experiments; and for SVM, decision tree, and random forest in their respective 35 experiments, the average recalls were 0.8239, 0.7950, and 0.8382.
## 3 SSMA Broadcast Protocol
### SSMAb Characteristics  
Table 2 compares the proposed SSMAb and three approaches selected from each of the broadcast protocol categories qualitatively. Flooding and DPFNI [6] have merits in all features except for energy consumption. In RSBP [14], every node manages energy optimally in that it remains active only at sending and receiving broadcast slots, does not allow overhearing, and allows a node to rebroadcast a message only if it is not a leaf node. The SSMAb protocol has good characteristics in reliability of message delivery comparable to Flooding and energy management comparable to RSBP. It also can have lower end-to-end delay than RSBP since it allows concurrency in broadcasting. Furthermore, it can respond well against changes in topology since the _BSS_ schedule is topology-independent.
## 2 Materials and Methods
### Currents  
In order to study the currents along the water column, several \"Acoustic Doppler Current Profiler\" (ADCP) of the RD Instruments brand were deployed during the timeframe of the AQUIMAR project, for a minimum period of 3 months at 5-30 m depth between 2018 and 2020. Since Area E covers the entire southern coast of the Algarve, two points were established to cover the area (Figure 1)--one in the western Algarve (Lagos) and one in the eastern Algarve (Olhao). The same method was applied for Area B, where 2 points were defined to collect the data: one point in the north (Aveiro) and the other point in the south (Figueira da Foz). In each of the Areas A and D, one point was set up to represent each location: in Povoa do Varzim and Comporta, respectively. In Area C it was decided not to place any observation point due to the risk of equipment loss caused by travling activities.  
Figure 2: Mean monthly temperature (°C) of the water surface (up to 1 m deep) in the 5 study areas (**a**) 2018 and (**b**) 2019 (water temperature data based on satellite reanalysis data).
# Evaluation of IEEE802.15.4g for Environmental Observations
These were separated into two clusters covering one kilometer square area. The border router was located 3.5 km away of the closest router. The data rate was set to 50 kbps. The deployment showed that the radios can provide a single hop communication over that distance. By using a Line of Sight propagation model for a CC2420 radio (implementing O-QPSK PHY, 250 kbps at 2.4 GHz), they determined that for a solution using this radio would need at least 25 routing nodes. Through simulations and outdoor experimentation, [PERSON] et al. [18] investigate the feasibility of employing the Gaussian FSK PHY under multi-path conditions in a suburban area. With transmitter and receiver tuned at 413 MHz, with a BPSK modulation and a TX power of +10 dBm, the radio signal is received with a power level of \(-60\) dBm. Results show that for a frame length of 1500 B, PER \(\leq 10\%\) and considering a receiver sensitivity of \(-100\) dBm, the coverage area of the radio link is a few hundred meters. All these IEEE802.15.4g studies and experiments have something in common: they only consider a single radio setting. What is missing is a comparative performance evaluation of all possible radio settings of IEEE802.15.4g, with a particular focus on communication range, performance and limits for different scenarios. Through experimentation, we test all IEEE802.15.4g radio settings in different scenarios where a wide range of applications can take place. The contribution of this article is three-fold:  
1. We define a system to carry out range testing of different radio settings with automatic out-of-band synchronization and minimum human intervention. 2. We determine the best radio setting(s) to be used in the scenarios covered in this study, taking into account PDR, \"goodput\" (nominal data rate times the PDR), electric charge consumption and communication range. 3. We provide an executive summary of the regulation on sub-GHz band usage in Europe, the US and Japan. The remainder of the article is organized as follows. Section 2 details the materials used for the experiments, explains the main sub-GHz regulation, and describes the characteristics of the scenarios and PHYs tested. Section 3 shows the PDR for each scenario at different communication ranges. Section 4 determines the most fitting PHYs per scenario according to PDR value, throughput and power consumption. Section 5 concludes this article by presenting future work.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 2 Related Work
### Networking  
The International Electrotechnical Commission (IEC) 61162-3 (i.e., National Marine Electronics Association (NMEA) 2000) and 61162-450 networks are typically used onboard. The IEC 61162-460 network is also possible to procure more secure network. The NMEA 2000 network is structured in five layers, namely the physical layer, data link layer, network layer, application layer, and network management. Each component, together with capabilities and applications is described in [17]. Moreover, tools (e.g., Sail Soft NMEA Studio, Maretron N2K Analyzer, NMEA Reader, Maretron N2K Builder, Maretron N2 KMete) that can be used to design and analyze a NMEA network exist [18].  
Also, the International Telecommunication Union (ITU) issues technical standards for the interconnection of networks and technologies, and allocates global radio spectrum and satellite orbits [19]. The Recommendation ITU-R M.1371-5 [20] provides Automatic Identification System (AIS)-related specifications, and ITU-R M.823 [21] defines frequencies for the Differential Global Positioning System (DGPS) by region.
# Groundwater Detection Using the Pseudo-3D Resistivity Method: A History of Case Studies
## 2 Methods  
This pseudo-3D data array provides a faster and more efficient data acquisition system than 3D data array. Additionally, the pseudo-3D data array provides more detailed information than 2D data array. The pseudo-3D resistivity method aims to find a new balance between the exploration effect and the exploration efficiency, in which the layout of the power supply points and the exploration efficiency are similar to those of the 2D resistivity method, while the arrangement of the measurement points and the exploration effect are close to the 3D resistivity method.  
The pseudo-3D array uses the transmitter electrodes in a line. Figure 1 shows a schematic diagram of the field arrangement of the pseudo-3D resistivity method for different shapes. As shown in Figure 1, the \"Red Crosses\" are the location of the transmitter electrodes. The \"Black Circles\" are the location of the receiver electrodes. As shown in Figure 1a,b, from A\({}_{-13}\) to A\({}_{13}\), the distance is 2.6 times the width of square 6. The infinitely powered electrode is located at position 1. The transmitter equipment is located at position 3. We assume that the coordinates \((-a,\,-b)\) and \((a,\,b)\) are at the corners of square 6. The coordinates of the transmitter electrodes \((x,y)\) are given as:  
\[(x,y)=\left\{\begin{array}{c}(x,0);\,when\,\,|x|\,\,\leq\,a\\ \left(a+2^{i},\,0\right)\,;\,when\,\,a<x\,\,\leq\,(3a+1)\\ \left(-a-2^{i},\,0\right)\,;\,when-\,(3a+1)\,\,\leq\,x<-a\end{array}\right. \tag{1}\]  
where \(x,y,\,a,\,i\) are integers.  
Figure 1: The observation system of pseudo\(-3\)D resistivity surveying, (**a**) inline shape, (**b**) crisscross shape, (**c**) “#” shape, and (**d**) “*” shape.  
The neighbor distance is c in the horizontal direction (\(X\)); and d in the vertical direction (\(Y\)). When the distance of the transmitter line is 2.6 times the width of square 6, the coordinate origin is assumed to be the center of the measurement area.  
The coordinates of the transmitter are given as:  
\[(X_{c},\,Y_{c})=(x\times c,y\times d)=(x\times c,\,0) \tag{2}\]  
The pseudo-3D array can measure the data using the pole-pole and pole-dipole methods.  
We mainly apply the inline pseudo-3D resistivity method to groundwater detection and dam leakage detection. Based on the characteristics of the exploration targets and topography, the pseudo-3D resistivity method uses the transmitters to power the underground through the electrodes, then, the receivers simultaneously receive the electric field signals from all the measurement locations. The receivers obtain the apparent resistivity and the induced electric parameters when each power supply point of the corresponding shape is obtained, which completes the exploration.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 4 Secure and Efficient Routing Protocol for Jellyfish Attack Prevention in V2X (SecE-V2X)
### Honesty Metrics  
The main impact of Jellyfish attacks on a network is that they have a significant influence on throughput and end-to-end delay. Regarding the effects of these attacks, we choose the appropriate metric for every Jellyfish category. In the proposed protocol, three Honesty metrics are used: packet delay, packet loss rate, and packet reordering. By computing these metrics, our proposed protocol mitigates the Jellyfish nodes from participating in the routing process. In the subsections that follow, these metrics are described in detail.
###### Abstract
Controller parameters are calculated by means of optimisation (solution of two nonlinear algebraic equations). Most of the mentioned methods require the exact process model, they are limited to a few IP models, or the calculation of controller parameters requires optimisation. One of the more sophisticated tuning approaches is the magnitude optimum (MO) method [57; 58; 59]. The MO method can be applied to various processes frequently encountered in chemical and processing industries. Tracking and disturbance-rejection closed-loop responses are usually fast without oscillations [60]. The applicability of the MO method has been extended by using a nonparametric approach in the time-domain instead of using explicit parametric identification of the process. The extension is based on multiple integrations of process input and output signals, and is hence called the magnitude optimum multiple integration (MOMI) method [61]. The MOMI method extracts the required information from a simple time-domain experiment while retaining all advantages of the MO method [60]. Since the original MO method was developed for stable (non-integrating) processes, the MOMI method cannot be used for IP. Namely, when using a one-degree-of-freedom (1-DOF) controller structure [62], MO criteria result in integrating gain of the PI(D) controller equal to zero. Namely, proportional (P) and PD controllers are not capable of rejecting input disturbances. However, in the paper, we show that MO criteria can be met by using 2-DOF PI controllers, similar to the case when optimising disturbance-rejection performance [60]. A 2-DOF controller implements reference weighting factor \(b\). The advantages of an extension of the MOMI tuning method to integrating processes are a tuning formula in closed form for an IP of arbitrary order with delay, and the selection of the process data in the time-domain (given by the process open- or closed-loop time responses) or in the frequency domain (given by the arbitrary-order process transfer function). Moreover, the frequency- and time-domain approaches are equivalent, and the latter does not introduce any errors in the calculation of controller parameters (according to MO criteria). Reference weighting factor \(b\) allows users to emphasize either disturbance-rejection or reference following in a continuous matter. Besides the mentioned advantages, the extension provides efficient closed-loop control, while PI controller parameters calculation is still based on simple algebraic expressions, making it suitable for less demanding hardware, like slower PLC controllers. The content of this paper is organised as follows. First, the MOMI tuning method for IP is presented. In Section 3, the stability and robustness of the MOMI tuning method are evaluated. Section 4 provides examples on several different process models and compares the proposed method to other tuning methods made for IP. Real-time experiments in the time-domain on a charge-amplifier drift-compensation system, a laboratory plant, on an industrial autoclave, and on a solid-oxide fuel-cell temperature control are outlined in Section 5.
# Matching and Control Optimisation of Variable-Geometry Turbochargers for Hydrogen Fuel Cell Systems
## 2 Materials and Methods
### One-Dimensional System Model  
The 1D simulation model used for this work was developed based on the simulation model described in previous work by the authors [24], in which the air path design was adapted to use a VGT. The base model was developed in GT-Suite (v2022, Gamma Technologies, Westmont, IL, USA), and PEMFC calibration was performed using manufacturer data. As shown in Figure 5, this model follows a similar structure to the reduced model in the FCM, but each component is modelled at a higher fidelity using 1D computational fluid dynamics (CFD), enabling the response of the fuel cell to varying pressure and flow rates to be evaluated. As with the FCM, the mass flow rate is controlled using the electric motor, and the back pressure is controlled using the state-based control scheme shown in Figure 4. For more information about this model, including its validation, please refer to the earlier publication [24].  
Operating Point Optimisation  
To ensure optimal operating conditions for both sets of turbines, numerical optimisation within GT-Suite was conducted for the OER and turbine inlet pressures for both turbine geometries independently, with the aim of maximising net system efficiency at each current density operating point. Net system efficiency is defined in Equation (16).  
\[J_{i}=\eta_{net,elec,i}=\frac{P_{FC,i}-P_{EM,i}}{\dot{m}_{H,j}\cdot LHV} \tag{16}\]  
Figure 5: One-dimensional GT-Suite model diagram.  
Optimisation was performed using the accelerated genetic algorithm (A-GA), which is a modified NSGA-III global search algorithm that incorporates fast metamodelling between each generation. Initial optimisation was performed for each current density across the full range of the compressor map using a population size of 10, for 15 generations. The crossover rate was 1, with a mutation rate of 0.5, crossover rate distribution index of 15, and mutation rate distribution index of 20. This was then refined within \(\pm\)20% of the initial operating point locations, using a population size of 20 for 25 generations, with a crossover rate of 1, mutation rate of 1, crossover rate distribution index of 15, and mutation rate distribution index of 10. The results of the second-stage optimisation were also used to generate the contour plots shown in the sensitivity study results.
## 4 Results and Analysis
### Objective Function Analysis  
In this section, the results obtained for the Otsu and Kapur entropy objective functions are analyzed. Based on Figures 7-9, it can be noted that the COSGO algorithm performed  
Figure 6: The segmented images with different algorithms.  
better. In Figure 7, which is based on the PSNR results, the Otsu function obtained superior results over the COSGO algorithm and WOA. In the case of the Kapur function, the GWO and SCSO algorithms obtained better results, respectively. Based on the level of the images, the COSGO algorithm with the Otsu function at level 2, the SCSO algorithm with the Otsu function at level 3, the COSGO algorithm with the Otsu function at level 4, and the COSGO algorithm with the Otsu function at level 5 obtained promising results in the PSNR.  
Figure 8: SSIM analysis for multiple threshold levels (levels 2–5) using Otsu and Kapur methods with various metaheuristic algorithms. COSGO again shows superior structural similarity performance, particularly under the Otsu thresholding strategy, indicating its effectiveness in preserving structural details.  
Figure 7: Comparative PSNR analysis across different threshold levels (levels 2–5) using Otsu and Kapur methods with four algorithms. COSGO consistently outperforms other methods, especially under the Otsu-based segmentation, indicating its robustness in preserving image quality.  
In Figure 8, the Structural Similarity Index Measure (SSIM) values were analyzed for different metaheuristic algorithms in combination with two distinct objective functions: Otsu and Kapur entropy. The results reveal that the proposed COSGO algorithm, when used with the Otsu objective function, generally exhibits strong performance in terms of structural similarity. Notably, at the multi-level thresholding level of 5, COSGO demonstrates superior performance with both Otsu and Kapur entropy compared to the other algorithms. This indicates that the COSGO algorithm is more capable of preserving structural information, such as luminance, contrast, and texture consistency, particularly at higher threshold levels. The effectiveness of COSGO under these conditions highlights its potential for more accurate and visually coherent image segmentation.  
In Figure 9, the Feature Similarity Index Measure (FSIM) values obtained from various algorithms are presented and analyzed. The results indicate that the proposed COSGO algorithm demonstrates superior performance compared to other competing methods. Particularly, when integrated with the Otsu objective function, COSGO achieves the highest FSIM values across multiple test images. This suggests that COSGO is more effective in preserving low-level visual features, such as texture and edge information, during the segmentation process. The enhanced FSIM scores further confirm that the proposed approach maintains a high degree of similarity between the segmented and original images, thus contributing to improved perceptual quality and segmentation accuracy.
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
## 5 Conclusions  
This paper proposed an unequal error protection (UEP) algorithm based on the FEC mechanism in order to improve scalable video streaming quality. It first proposed a low complexity performance metric, block recovery rate (BRR) using a hierarchical coding structure of scalable video and packet loss rate. Then, it developed the FEC assignment algorithm to maximize the value of BRR. The simulation results showed that the proposed algorithm outperforms previous UEP algorithms by 1 to 2 dB for variable channel condition. In addition, in the implemented three-screen TV system, the proposed scalable video layer-switching method selects appropriate layers to be sent according to ABW by RSSI-based feedback.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2015R1C1A1A02037743), and this research was also supported by the Gachon University research fund of 2016 (GCU-2016-0055).  
[PERSON] and [PERSON] provide the proposed methods together. [PERSON] performed the experiment and analyzed the data using H.264 SVC, and [PERSON] did the same work using H.265/HEVC and its scalable extension (SHVC). Also, [PERSON] and [PERSON] wrote the paper together.  
The authors declare no conflict of interest.
# Software-Defined Networking: Categories, Analysis, and Future Directions
### SDN Advantages
Tree exploration discovery protocol (TEDP) outperforms the OpenFlow protocol and standard link layer discovery protocol (LLDP) in terms of the reduction of extra packets for topology discovery. The tree exploration discovery protocol (TEDP) leverages the topology graph mapping by using the probe packets that are responsible for signaling the SDN controller to a single forwarding device. As a result, the discovery mechanism is optimized in SDN compared to LLDP, which introduced load in IP networks [61]. * Load Balancing Support in Future Networking: Research work comprises enabling the SDN infrastructure among the multiple NFV nodes and service function chaining aiming to reduce the delay of state migration of VNF. Concretely, the objectives are to cope with the limited resource capabilities of NFV nodes, meeting the desired QoS by infrastructure, and ensuring the least end-to-end delay for computational states migration by confining the OpenFlow-enabled devices' capabilities in 5G core networks [62]. * Fault Localization: In SDN, centralized management is used for localizing the failure and reconfiguration help to localize faults. It handles failures in a proactive fashion based on the prediction of service unavailability [63]. * Programmable Reachability Optimization: SDN handles firewall problems in term of conflicting rules automatically. Similar research works offer improvements with the least computational overhead in reachability optimization and conflict debugging problems [64]. * Support for Cellular 6G Network: Future networks such as 6G require SDN-enabled softwarization and management for remote and machine learning application decision-aware re-configurations in network resources [65; 66; 67]. * Security-Aware Communication in Future Autonomous Networks: Softwarized policy implementation architecture can enhance the security among autonomous systems that have the least human interactions, and consequently, it can mitigate the security risk of inter-domain communication [68; 69].