esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Ocean-Based Climate Change Solutions Enhanced by DL
### Adaptation Strategies Supported by DL
#### 5.2.2 Improving Fisheries Management Leveraging DL
Sonar systems emit sound waves that travel through the water and bounce off fish and other objects. By analyzing the reflected signals, these systems can accurately identify fish species, measure their size, and estimate their abundance, providing valuable insights into marineecosystems. DL algorithms enhance the efficiency and accuracy of acoustic data processing, enabling precise and real-time monitoring of fish distribution [336,337]. For example, [PERSON] et al. [308] employed Mask R-CNN with ResNet-50 as the backbone to detect pelagic species, such as herring and juvenile salmon, from acoustic backscatter data, overcoming the time-consuming and error-prone process of manual analysis. By accurately analyzing the acoustic properties of the targets, the Mask R-CNN model achieved an mAP of 92.12, facilitating automated and efficient analysis of pelagic species. [PERSON] et al. [309] applied a modified U-Net model for pixel-level classification of fish species, focusing on herring and salmon in echogram images. The data, collected over two years along the coast of British Columbia, Canada, were obtained using an Acoustic Zooplankton and Fish Profiler operating at four different frequencies. The model achieved F\({}_{1}\) scores of 93% for herring and 87% for salmon, demonstrating its robustness even in noisy echogram data. DL can also be applied to predict fishing grounds by analyzing various environmental factors such as temperature, salinity, currents, and historical fisheries statistical data, enhancing fisheries productivity and reducing overfishing risks [338]. [PERSON] et al. [310] investigated the spatial and temporal variability of chub mackerel fishing grounds using fishery statistics, including the timing of fishing operations, catch amounts, and compositions, alongside ocean remote sensing data such as SST, chlorophyll concentration, and oxygen levels. Employing 3D CNN models, the study assessed changes in fishing grounds and observed a progressive northeastward shift. Similarly, [PERSON] et al. [311] used a U-Net model to predict fishing grounds for Ommastrephes bartramii in the Northwest Pacific, with SST as the input and central fishing ground locations as the output. Based on data from 1998 to 2020, their experiments tested 80 combinations of temporal and spatial scales. The results identified 15 days as the optimal time scale and 0.25\({}^{\circ}\times\)0.25\({}^{\circ}\) as the ideal spatial scale for their model. In summary, DL techniques are transformative tools for ocean climate change adaptation strategies, whether in marine renewable energy development, coastal protection, or fisheries management, and essential for mitigating climate change impacts and improving the resilience of coastal communities.
## 3 Experiment and Results
### In Vivo Magnetic Hyperthermia  
The expense of in vivo magnetic thermotherapy experiments is relatively costly. The most optimal conditions of WPI-Fe\({}_{3}\)O\({}_{4(8\mathrm{wt}\%)}\) to make a nanofluid for in vivo experiments in mouse models have been selected for the therapeutic characteristics of HepG2 tumor cells requiring instantaneous high temperatures to treat tumors. HepG2 cells were kept growing under the skin of mice; nanofluids (1.15 mg mL\({}^{-1}\)) made of by 100 mL modified superparamagnetic nanoparticles were injected into the tumor through soft tissue surfaces by curved needles, and an optical thermometer was placed in the tumor of the rectal area individual to monitor the temperature during the magnetic hyperthermia process. The mice were placed in the center of the AC electromagnetic coil and exposed to an AC magnetic field (\(f\) = 100 kHz\({}_{2}\)/H = 169 Oe T (13.62 kA m\({}^{-1}\)), \(H\cdot f\) = 1.36 \(\times\) 10\({}^{9}\) A m\({}^{-1}\) s\({}^{-1}\)) for magnetic hyperthermia. The temperature of HepG2 injected with WPI-Fe\({}_{3}\)O\({}_{4(85\mathrm{wt}\%)}\) nanofluids was rapidly increased and stably saturated at \(\approx\)46.5 \({}^{\circ}\)C during 180 s of hyperthermia, which is expected to be at a thermotherapy temperature (Figure 7a,b). After the magnetic hyperthermia, it can be clearly observed that tumors induced by HepG2 and treated by the WPI-modified superparamagnetic nanoparticles were significantly reduced, damaged by heat, and lost activity (Figure 7c,d). After 7 days of observation after the high-temperature magnetic hyperthermia, no physical damage caused by AC magnetic hyperthermia, tumor growth, and serious side effects was observed, thus indicating that AC magnetic hyperthermia applied to tumors was biologically safe for the mice injected (Figure 7e,f). To be clear, due to the imperfection of the experimental equipment and to ensure the integrity, safety, and accuracy of the experiment, we authorized the Korea Institute of Biomedical to conduct the magnetic hyperthermia experiment in vivo on mice and used high-energy thermal inductance (HETI) technology for periodic observation. In summary, the experimental results confirmed that the use of WPI-modified nanoparticles improved the effectiveness of magnetic hyperthermia in tumor therapy.  
Figure 7: _Cont_.
## 3 Classifications of Routing Protocols in UAV Networks
### Routing Based on Network Structure
#### 3.1.3 Response-Based Routing Protocol  
Reactive routing protocol (RRP) is also known as on-demand routing protocol. RRP does not maintain routing information for all nodes in real time but builds a route only when the source node requests communication [27]. Therefore, it overcomes the overhead issues associated with proactive routing protocols. RRP saves bandwidth due to the absence of periodic updates. The main drawback of RRP is the high latency during the optimal path discovery phase. Popular reactive routing protocols are the Dynamic Source Routing (DSR) protocol and the Arbitrary On-Demand Distance Vector (AODV) protocol.  
DSR does not require a fixed network infrastructure but allows the network to self-organize and self-configure. In this protocol, each data packet contains a complete list of routing nodes, and all nodes that forward or receive the packet will store this routing information. Therefore, the system will still maintain stable performance even when the network structure changes or when the network nodes move. The AODV routing protocol is often used in UAV networks. During the route lookup process, the source node will search for the routing history established in the past. AODV will initiate a new route request process when no route exists. In the study [43], the authors compared and evaluated the performance of two prominent reactive routing protocols, DSR and AODV, based on the deployment of traffic monitoring applications over UAV networks. The implementation of both protocols was tested on a number of different traffic patterns and mobility and network loads. Both DSR and AODV was evaluated for performance based on factors such as packet delivery ratio, average end-to-end delay, normalized routing load, packet loss, and routing overhead. Based on experimental analysis using NS-2 under constant bit rate (CBR) and TCP traffic source conditions, both protocols provided significant performance improvements, but AODV outperformed DSR in almost every aspect.  
An improved AODV protocol was implemented by the authors in [44] to improve the performance of UAV networks. The authors proposed a multilayer energy-aware on-demand AODV protocol (noted as CLEA-AODV). The CLEA-AODV was divided into three parts: routing with the AODV protocol, Glow Swarm Optimization (GSO)-based cluster head selection, and cooperative medium access control (MAC). The cross-layer approach was implemented on the network layer and data layer, as shown in Figure 11.  
The key parameters considered to evaluate the performance of FANET are Packet Success Rate (PSR), Throughput (TP), End-to-End (E2E) Delay, and Packet Loss Rate (PDR). The simulated results using NS2 show that the CLEA-AODV protocol outperforms the standard AODV protocols in terms of PSR, TP, E2E delay, and PDR.
# From 5G to 6G--Challenges, Technologies, and Applications
## 4 Current Research
### Global Efforts  
There are currently many different endeavors by multiple research groups and centers to make B5G/6G networks a reality. In Table 5, a list of some of the inaugurated (or to-be inaugurated) projects geared toward the development and implementation of B5G/6G network elements is given. A clear trend among the projects' objectives can be observed, i.e., highlighting that \(THz\) bands and edge AI are the drivers of B5G/6G networks. The description of researchers' determination to bring the next generation of networks to life continues in Table 6, where some of the main research groups/centers experimenting on B5G/6G networks are presented. It is worthwhile emphasizing that many other projects, research centers, and groups exist that perform B5G research. Many of these groups are under governmental support, most notably the China, South Korea, Japan, Finland, and the EU. We expect many other governments to join the 6G race once 5G's massive global deployment is complete and proven to be stable.  
When it comes to 6G development between governments, a few concerns arise, as summarized in [185], published in _Nature Electronics_. The author argued that the technological war between China and the rest of the world, especially the U.S., has led to sovereign concerns about governing 5G technologies. This is understood in the context of national security, where China very recently has been accused of infiltrating other governments using their communication equipment. This has necessitated the fact that other governments have started developing their own technologies that would create--as the the author explains--a geographically fragmented 5G that would prevent the creation of a global 6G. The author suggested three solutions to resolve this issue, one being risk management by handing over most of the development of the critical network infrastructures to private sector service providers, where cybersecurity legislation largely exists for such operators to maintain cyber flexibility. The other two solutions are exemplified by creating strategic partnerships between entities that have mutual commitments and trust each other and practicing common-good methods globally. Other unpopular opinions are whether a real 6G network development movement will take place anytime soon or not at all, based on current technological limitations, how incomplete 5G is, or simply that 6G will be an enhanced version of 5G networks.  
Lastly, in Figure 8, we look at the timeline of some of the 6G research activities in the last few years and give our insight into 6G's evolution over the next few years. We include some of the main standardization organizations, prominent technology companies, and giant mobile manufacturers.  
Figure 8: The envisioned timeline for 6G.
## 1 Introduction
### Related Work
#### 1.1.1 Fault Detection in Pipelines
The following considerations can be made regarding the analysis performed using the different methods of detecting faults in pipelines:  
* Most hardware-based methods focus on the use of sensors and systems, which in some cases involve an additional cost of installation. In addition, thinking about the possible application to the ship's firefighting system, it can be observed that many of these methods are not recommended for use in marine environments due to the problems that can be generated in systems that work with seawater.
## 4 Control Strategy
### Auto-Tuning Method  
MATLAB provides several methods for tuning PID controllers, including graphical methods, automated tuning tools, and manual tuning options. M provides several graphical tools for analysing the response of a closed-loop system and determining the appropriate PID parameters. The most common graphical methods include the step response method, which involves plotting the system's step response and adjusting the PID parameters until the response meets the desired specifications, and the frequency response method, which involves analysing the system's Bode plot and adjusting the PID parameters to achieve the desired gain and phase margins. Moreover, MATLAB provides several automated tuning tools for PID controllers, which use various algorithms to determine the optimal PID parameters based on the system's response to a test input. The most common automated tuning tools in MATLAB include the PID Tuner, which uses the internal model control (IMC) method to optimize the PID parameters.  
IMC is a method that can be used to optimize the parameters of a PID controller. The IMC method is a model-based control technique that involves using a mathematical model of the process being controlled to design the controller. The first step in the IMC method is to identify a mathematical model of the process being controlled. This model should accurately describe the dynamics of the process over a wide range of operating conditions. Once the process model has been identified, the next step is to design the controller using the IMC method. The controller is designed by first selecting a suitable set of performance specifications, such as the desired closed-loop response time, overshoot, and settling time. Then, the controller parameters are selected based on the model of the process and the performance specifications. The final step in the IMC method is to tune the controller to ensure that it meets the desired performance specifications. This involves adjusting the controller parameters until the desired closed-loop response is achieved.  
The IMC method can be used to optimize the parameters of a PID controller by incorporating the process model into the controller design. The process model can be used to design a controller that is better suited to the dynamics of the process, resulting in better performance and stability. By using the IMC method to optimize the PID parameters, it is possible to achieve better control of the process, reduce variability, and improve product quality.
## 1 Introduction
Therefore, analyzing the characteristics of the interference from the power converter station equipment to UAVs is not only of great significance for the safe use of inspection UAVs, but also has considerable value as a reference for other intelligent equipment operating in complex electromagnetic environments. This article will take the converter equipment inspection drone as an example, a sensor to analyze the electromagnetic interference characteristics of the power converter station in the surrounding environment. The article will warn of the impact of electromagnetic emissions on the communication circuits and the control of the drone. It will discuss the methods of electromagnetic protection and show the impact of the complex electromagnetic environment on UAVs or other electronic equipment.
## 4 Results and Discussion
### Comparison with Related State-of-the-Art Work  
A comparison between this automatic epileptic seizure detection method and the existing state-of-the-art works from 2014 to 2017 is provided in Table 9. The previously reported methods [8; 9; 11; 16; 26] were tested with the identified EEG segment, thus making the comparison more feasible and providing additional perspective. All the recognition results for these methods [8; 9; 11; 16; 26] were copied from their reports. The recognition results of this method include the averages of the five classifiers by setting the TTTR to 7:3.  
Taking dataset A versus dataset E and dataset B versus dataset E as two examples, the maximum recognition accuracy of the presented method was as high as 100%, whereas some others also produced results as high as 100%. From Table 9, the recognition accuracy obtained in classifying healthy and seizure classes (dataset A versus dataset E and dataset B versus dataset E) was as high as 100%. The seizure-free and seizure classes (dataset C versus dataset E and dataset D versus dataset E) obtained by this proposed method was at least as high as 99.83%, but the existing methods could only reach a maximum accuracy of 99.82%.  
When considering all five of the EEG datasets, the classification task problems were datasets A, B, C, and D, versus dataset E; datasets A and B versus dataset C and dataset D versus dataset E; and dataset A versus dataset B versus dataset C versus dataset D versus dataset E. Fortunately, this proposed method can be utilized to achieve high accuracies, of 99.46%, 97.85%, and 97.43%, respectively, and the other methods were not as accurate.
## 6 Conclusions  
In this paper, we propose CPCROK to preserve vehicle identity and location privacy in low-density VANET scenarios, without imposing an obvious increase in transmission overhead. In CPCROK, three modules cooperate to generate highly authentic fake beacons, forming a single fake trajectory. This trajectory is authentic enough to deceive attackers, even in low-density VANET scenarios with limited online knowledge. The evaluation results demonstrate that CPCROK can reduce the success rate of attacks by approximately 90% compared to the plain mix-zone approach, and by 60% compared to the state-of-the-art fake beacon-based mix-zone approach. Additionally, CPCROK significantly reduces the additional transmission overhead by generating fewer fake beacons. Specifically, while the FB scheme requires three fake traces and the AFB scheme requires six, CPCROK only necessitates one to achieve the same protection level, resulting in a transmission overhead reduction of 67% compared to the FB scheme and 83% compared to the AFB scheme.  
In future work, we will evaluate CPCROK against other pseudonym-changing schemes. We also plan to train and evaluate CPCROK with other datasets, such as NGSIM and HighD. Finally, we plan to extend our threat model to consider other attack strategies (e.g., deep learning-based trackers) and develop a pseudonym-changing mechanism that can adapt to different attack scenarios.  
Conceptualization, J.W.; methodology, J.W. and Y.S.; software, J.W. and H.L.; writing--original draft preparation, J.W.; writing--review and editing, A.M. and D.G.; visualization, J.W. and H.L.; supervision, Y.S. and C.P.; validation, A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 9: The minimum number of fake trajectories required to achieve various success rates for fake beacon schemes.
# A Web-Based Application for Smart City Data Analysis and Visualization
## 3 Methodology: System Architecture and Design
### Data Analysis  
After preprocessing, the cleaned and structured datasets are analyzed through statistical aggregation and advanced visualization techniques. The system's analytical layer summarizes sensor data at different temporal resolutions, allowing users to explore patterns and fluctuations over time. This involves grouping the data by relevant time intervals, such as hours or days, and computing key statistical descriptors including minimum, maximum, average, median, and standard deviation. These summaries supported both exploratory data analysis and the development of time-aware visual outputs. Afterwards, moving averages are applied to highlight temporal trends and reduce noise in highly granular datasets, helping to reveal underlying behavioral patterns that short-term fluctuations might otherwise obscure. When data density is too high for efficient rendering, downsampling strategies are utilized to retain trend integrity while optimizing for readability and performance, particularly in browser-based visualization environments.
# Quantum Image Steganography Schemes for Data Hiding: A Survey
## 3 Quantum Image Steganography Schemes
### Quantum EMD Protocol
& \multirow{6}{*}{Researchers increased LSB’s security by embedding it by a modulo value rather than in order.} & Low robustness: Using primarily LSBq makes it easy for intruders to detect and extract the steganographic image. \\  & & & Implemented QRMW image model to increase the LSB method’s capacity. \\ \hline \multirow{6}{*}{Turtle Shell-Based Matrix [15]} & \multirow{6}{*}{Embeds the secret message’s qubits into an image based on a turtle-shell pattern.} & \multirow{6}{*}{Embeds the secret message’s qubits into an image based on a RGB pixels to detect which RGB pair the qubits should be embedded in each pixel, increasing the quality of the image.} \\ \cline{1-
# Low Power Blockchained E-Vote Platform for University Environment
## 3 Proposed Approach
### General System  
As shown in Figure 2, taking the Tunisian university system as an example, we consider that the Ministry of Higher Education and Scientific Research is linked to the different universities (Sfax, Tunis, Gabes etc.). In this figure, we represent four universities that are connected to the ministry by a public Blockchain. The connection is carried out via internet (Wifi, 4G, etc.).  
Each university relates to its different faculties, institutions and schools. They are named (H.S 1, H.S 2, etc.). These institutions relate to the same public blockchain. However, each H.S uses a private blockchain. This is called the Quorum Blockchain.  
Thus, let us take the case of an institution with seven departments. Each department has three levels, each with four groups. The total will be 84 groups, each with about 30 students. Each group represents a node of our Blockchain. So, the students who are part of this group will be able to vote for their representative. This vote will be counted, and the person voting will not be able to change or re-vote as soon as the vote is counted. In order to know if the student is part of this Blockchain node, the identification is realized using the barcode of their student card.  
A mobile application will be created to allow the identification of the student and then to make it possible for them to choose the candidate they want and then cast their vote.
## 2 Experiments and Methods  
### Verification Test of Xanthium sibiricum's Corrosion Inhibition Effect on Reinforcing Steel  
#### 2.1.1 Experimental Materials  
HRB400 grade reinforcing steel bars with a diameter of 20 mm were selected. These bars were cut into small segments approximately 200 mm in length. The segments were then immersed in a citric acid solution for one week. After removal, they were polished with sandpaper until the surface was smooth and shiny. Finally, the segments were cleaned with alcohol, dried in an oven, and set aside for later use. The cement, sand, aggregates, mineral admixtures, and chemical additives used for mixing concrete were all sourced from Ningbo Hengzhu Building Materials Co., Ltd. in Zhejiang Province (Ningbo, China). Additionally, 500 g of _Xanthium sibiricum_ extract, sodium chloride, and epoxy resin adhesive were prepared.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 4 Experiments, Results, and Ablation Study
### Experimental Settings
#### 4.1.4 Baseline Methods  
We compared our work with traditional registration methods, which extract 3D features using techniques such as Point-to-Point ICP [1], Point-to-Plane ICP [27], FPFH [8], SIFT [48], SuperPoint [49], and FCGF [9], followed by geometric transformation estimation through RANSAC [28]. Additionally, we compared our work with learning-based registration methods, including supervised methods like DGR [14], 3D MV Reg [50], and REGTR [13], as well as unsupervised methods such as UR&R [21], BYOC [22], LLT [23], and PointMBF [24]. The results of these methods are based on previous work [21; 22; 23; 24].