esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Future Directions  
Wearable technologies are expected to continue evolving, driven by advancements in sensor technology, data analytics, and artificial intelligence. Over the past decade, there has been significant growth in the development and adoption of wearable devices, with a focus on enhancing accuracy, reliability, and usability. Key trends include the miniaturization of sensors, integration of multi-sensor platforms, and the use of machine learning algorithms for data interpretation.  
Over the next decade, based on current data trends, several key developments are projected:  
* Lab-on-a-chip (LOC) technology [90], which miniaturizes lab equipment into microscale devices, includes a new generation of wearable biosensors that directly interface with the human epidermis instead of rigid packages embedded in wrist-bands or bands. Wearable biosensors have been particularly suitable for point-of-care testing (POCT) [91] due to their distinctive characteristics of light weight, flexibility, and portability. As a result of POCT microfluidic devices, athletes have been able to analyze biofluids, including sweat, urine, interstitial fluid, saliva, tears, urine, blood, chloride, pH, lactate, glucose, cytokines, hormones, amino acids, and exogenous drugs without experiencing discomfort, pain, or needle phobia [22]. By overcoming these challenges, wearable biosensors will be fully commercialized and widely adopted in sports-related fields, dramatically changing state-of-the-art athletic monitoring and sports analytics.
* AI-Enhanced Wearables: Artificial intelligence will play a crucial role in improving the accuracy and interpretability of data from wearable devices. AI algorithms can analyze complex datasets, provide real-time feedback, and predict potential issues like overtraining or injury risks
* Virtual and Augmented Reality: Wearable devices integrated with VR and AR systems will revolutionize training methodologies. Athletes can simulate various training environments and scenarios, allowing for more effective skill development and performance analysis.  
Given the rapid pace of technological advancement, predictions of the future of wearable technologies in sports may need to be revised as new innovations emerge. The integration of AI and continuous improvements in sensor technology suggest that the most realistic forecasts will remain fluid, adapting to ongoing developments in the field.
## 4 Experimental Evaluation
### SI Cancellation by Adaptive Filter
#### 4.3.1 Constellation Plot  
To illustrate the performance of the AF-based digital SI cancellation, here we present constellation plots for the RX signal before and after SI cancellation with a SNR of 25 dB as examples in Figures 12 and 13, respectively.  
In Figure 12, the QPSK constellation points of the desired UL signal are denoted by red crosses, while the yellow points represent the constellation points of the RX signal containing both UL and SI signals. Conversely, in Figure 13, the red crosses represent the QPSK constellation points of the desired UL signal, and the yellow points represent the signal constellation points after undergoing essential signal synchronization and SI cancellation using AF.  
Analysis of the results in Figure 12 reveals a significant contamination of the desired UL signal by SI, which causes the constellation points to deviate significantly from their original positions (the locations of the red cross marks). This distortion makes it nearly impossible to recover the information carried by the UL signals.  
Conversely, examination of the results in Figure 13 demonstrates that, following necessary processing such as signal synchronization and AF-based SI cancellation, the impact of SI is substantially mitigated. As a result, the constellation points are restored to the vicinity of the desired signal's positions, offering a more favorable prospect for the system to recover information from the processed sienal.  
Figure 12: Constellation before AF processing.
## 3 Results and Discussion
### Uniaxial Compressive Tests  
Compressive strength of the concrete specimens after exposure to high temperatures and cooling to room temperature is measured through a uniaxial compression test.  
The compressive strength of concrete specimens subjected to high temperatures is shown in Figure 14 for non-cycling tests. The residual compressive strength of concrete and the loss ratio for different test cases is given in Table 3. The residual compressive strengths of the three specimens in each test case are close. The compressive strength of concrete slightly decreases from room temperature to 300 \({}^{\circ}\)C and is significantly reduced when the specimens heated up to 400 \({}^{\circ}\)C afterwards. The loss ratio in compressive strength of concrete subjected to 500 and 600 \({}^{\circ}\)C reaches 7.5% and 19.8%, respectively. The results show a similar trend with the previous studies [14].  
Figure 14: Compressive strength of specimens after non-cycling heating.  
Figure 13: Mass loss of the specimens after thermal cycling with lower temperature of 300 \({}^{\circ}\)C.  
The lower temperature, i.e., 25 \({}^{\circ}\)C or 300 \({}^{\circ}\)C, in the thermal cycling tests may influence the loss in compressive strength of concrete. After exposure to five thermal cycles between 500 \({}^{\circ}\)C and 300 \({}^{\circ}\)C, the loss ratio in compressive strength is 15.0%. In contrast, the loss ratio is greater and reaches 27.4% after thermal cycles between 500 \({}^{\circ}\)C and 25 \({}^{\circ}\)C. A similar trend is observed when the maximal temperature is elevated up to 600 \({}^{\circ}\)C, and the loss ratios are 39.8% and 32.7% for tests 600-25-5 and 600-300-5, respectively. It is recognized micro-cracks in the interface transition zones caused by different thermal responses between aggregates and cement matrices dominate the decline of compressive strength of concrete at high temperatures. During cooling the specimens, expanded aggregates appear to shrink, further propagating the micro-cracks in the interface transition zones. More intensive micro-cracks in concrete may be induced by cooling to 25 \({}^{\circ}\)C in the thermal cycling and thus there is more loss in compressive strength compared to that cooling to 300 \({}^{\circ}\)C.
## 6 Analysis of Vibration Characteristics of Some Components of Converter Transformer
### Analysis of Winding Vibration Characteristics  
The acceleration distribution cloud diagram of the converter-transformer winding under the action of the inrush current is shown in Figure 13. From the experimental results, it can be seen that as the current increases, the vibration acceleration of the winding also increases, and the acceleration of the winding reaches the maximum when the current reaches the peak. The maximum vibration amplitude of the upper and lower ends of the winding can reach 5.99 \(\times\) 10\({}^{3}\) m/s\({}^{2}\) when the inrush current reaches the peak, that is, 10 ms, while the vibration acceleration in the middle of the winding is small, only 583 m/s\({}^{2}\). For the same horizontal plane, the vibration acceleration of the middle part of the upper and lower ends of the winding is about 42% larger than that of the two sides of the winding, while the vibration acceleration of the two sides of the winding in the middle region is about 44% larger than that of the middle part of the winding.  
Figure 13: Vibration distribution of converter-transformer windings.  
In the Figure 14, 1-9 are the positions of different measuring points in the winding. According to the point position in the figure, the acceleration characteristics at different point positions were obtained and the time-frequency conversion was performed to obtain the content of different frequencies of acceleration at each point of the winding, as shown in Figure 15. It can be seen from the simulation results that the acceleration ratio of each frequency at the points (1, 2, 3, 7, 8, 9 points) where the winding acceleration is greatly affected by the inrush current is consistent with the distribution of the input inrush current, but the DC component in the middle area of the winding (4, 5, 6 points) has a larger ratio and a smaller second harmonic content.
## 4 Smart Contract Vulnerability Detection Tools
### Static Analysis  
Software vulnerabilities can be found using a technique called static analysis, which examines the code directly. Static analysis is a tool that may be used to find vulnerabilities in smart contracts. The following steps are typically involved in the static analysis procedure used to find vulnerabilities in smart contracts:  
1. Code scan: To find potential flaws, static analysis tools scan smart contract codes. Lexical, syntactic, and semantic analysis of the code is often required for this;
2. Detection of defects: After the scan is finished, the static analysis tool searches the code for errors such as integer overflows, array overruns, reentrant attacks, etc.;
3. Vulnerability classification: The vulnerabilities are categorized so that programmers may find and address the issue right away.  
Analysis feedback: Static analysis tools offer developers analysis feedback, which may include potential flaws, proposed fixes, and potential repercussions.  
Static analysis can be automated to find vulnerabilities, which increases detection efficiency and accuracy when compared to manual code inspection. Static analysis techniques can increase the thoroughness of detection by looking through all potential code pathways to find undetected flaws. Static analysis does, however, have significant drawbacks, such as the potential to disclose some vulnerabilities incorrectly or to miss some vulnerabilities. Static analysis is therefore typically used in conjunction with other detection techniques to produce improved vulnerability detection findings.  
Slither [4] is a static analysis framework that uses a variety of well-known program analysis techniques, such as data flow and contaminant tracing, to further refine information. Solidity smart contracts are named as SlithIR's intermediate expressions. Slither has four key use cases: assisting with code audits, enhancing user comprehensions, discovering code optimization automatically, and finding vulnerabilities automatically.
## 3 Methodology  
Both pre-stack and post-stack seismic volumes provided by Equinor [28], along with a suite of logs from 13 wells, core data, and well reports, are incorporated into this work. The study began by performing quality control (QC) assessments on seismic data. This involves essential checks such as assessing the following: fold coverage, stacking quality, amplitude and phase spectra, geometry, signal-to-noise ratio, data consistency, migration effectiveness, well-tie correlation, sample rate, and dominant frequency. These checks are crucial for ensuring the seismic data's reliability, accuracy, and suitability, facilitating improved interpretation and inversion results by addressing issues related to subsurface sampling, signal clarity, and frequency content. The seismic data have a sample rate of 4 ms and a Nyquist frequency of 125 Hz. The well-seismic tie correlation shows an average correlation coefficient of about 0.82.  
Next, we conduct a thorough quantitative examination of the well-log and core data. After that, a set of physical relations is calibrated to ascertain the viability of obtaining petrophysical parameters from the seismic inversion results. This calibration involves porosity, saturation, and mineralogy estimations. The well logs are also analyzed to determine the most significant reservoir zones of interest (Zol). These zones are then analyzed for lithology and fluid saturation with the help of petrophysical models.  
To calibrate a reservoir-dependent rock physics model appropriate for the Volve field, rock physics diagnostics (RPD) are then conducted. After that, using the rock models created during the RPD stage, we perform perturbational modeling to create potential reservoir scenarios. Finally, we evaluate the seismic responses and amplitude versus offset (AVO) signatures of the different rock physics perturbations. Synthetic gathers are generated for each well based on different model scenarios.  
The second part of this work entails changes in the reflection angle by incorporating rock physics modeling and the synthetic seismic analysis results. Lastly, we perform seismic inversion and apply machine learning to predict clay volume, porosity, and water saturation. These results indicate that the optimal hydrocarbon production areas correlate reasonably well with the actual production values.
# Computer Vision for Fire Detection on UAVs--From Software to Hardware
## 3 Literature Review
### Research Early Statistics  
The query was executed on 25 March 2021 and 72 documents emerged. All the papers were reviewed in order to obtain the necessary information and early statistics came in place. First of all, the usage of the VOSviewer ([[https://www.vosviewer.com/](https://www.vosviewer.com/)]([https://www.vosviewer.com/](https://www.vosviewer.com/)) (accessed on 31 July 2021)) software tool was proposed. It is a tool for constructing and visualizing bibliographic couplings. These couplings are presented in Figure 3, according to the country of origin. The 7 th most referred countries filter was applied and eight of them are visible. This clustering technique shows the country impact for publications related to fire detection using UAV's [17].  
More specifically, the cluster connections shows Canada has a huge impact. According to Figure 4, whose data were taken from the platform Statista ([[https://www.statista.com/](https://www.statista.com/)]([https://www.statista.com/](https://www.statista.com/))  
Figure 2: Adopted PRISMA flow diagram 2020.  
(accessed on 31 July 2021)), this great impact on Canada is due to the great forest fires issue Canada is facing for decades. The average number of wildfires in Canada is 6704 fires per year, bringing the total number of wildfires in the last two decades to 134,082. Figure 5, whose data were also taken from Statista, shows the number of burned areas by forest fires. It is clear that Canada has been facing a major fire problem for many years and its contribution to solving this problem through the UAVs and Computer Vision is huge. In addition, Greece, Cyprus and Spain are suffering from deforestation through wildfires.  
Moreover, based on Figure 3, Canada, France, China, Greece and Australia are bibliographically neighboring. This fact shows that between these countries, there is a strong citation relationship by referencing common papers from countries that participate in the same cluster. Conversely, USA and Austria are showing two different approaches to UAV fire detection, setting the papers apart from what is published in the other countries [18].  
Finally, the number of publications was analyzed via Scopus, in order to reveal any trend in the last decade. Figure 6 shows an exponential increase from 2016 until year 2019. This increase is due to the integration of deep learning through CNN models in the field of Computer Vision and, when compared to classical algorithms, it is observed that their implementation is an easier process. In 2020, there was a slight decrease which continues to decrease further in 2021. The exponential growth until the year 2019 is due to the development of Computer Vision and more specifically Machine Learning methods. The years 2020 and 2021 are pandemic years due to the COVID-19 disease. Moreover, the research was done in the first trimester of 2021, so there were fewer publications.  
Figure 4: Number of forest fires in Canada from 2000 to 2019.  
Figure 3: Top-7 bibliographic couplings between countries.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 2 Background
### Federated Learning  
FL is a distributed machine learning framework designed to enable collaborative model training across multiple parties without requiring centralized access to raw data [8]. This approach primarily addresses the challenges faced by centralized training methods, including concerns related to data privacy, data silos, and high communication costs [9]. Within the FL framework, each participating client performs model training locally and only transmits intermediate results, such as model parameters or gradients, to a central server. The server then aggregates these updates to refine the global model.  
This decentralized learning paradigm effectively preserves the privacy of network traffic data, as raw data remain on local devices and are never shared externally, thereby mitigating the risk of data leakage. Moreover, FL requires the transmission of only lightweight model updates (e.g., gradient or weight changes), which substantially reduces the communication overhead and network burden associated with data sharing.
# Self-Adjustment Energy Efficient Redeployment Protocol for Underwater Sensor Networks
## 9 Conclusions  
Given the large number of underwater sensor network applications and the increasing interest in exploiting and monitoring seas and oceans, much new research has focused on this type of network. One of the most important research fields is network deployment. The target of the deployment solutions is to increase area coverage, which is usually not ensured by the initial random network deployment. In addition, maintaining network connectivity and minimizing energy consumption as much as possible were also the objectives of any deployment protocol.  
One of the main characteristics of the underwater environment is the dynamism of this environment due to the water currents. In this research, we first studied the impact of these water currents on the deployment protocol. We showed that if this impact is not considered during the design of the protocol, the results of this protocol in a real environment will be different from the simulated results. Then, we proposed a self-adjustment deployment protocol that takes into account the water currents during the deployment. The idea of the proposed protocol is the same as that used by boat navigation to adjust their direction to reach the target destination in the presence of water currents. In the second step, we proposed a solution to reduce the energy consumed during the deployment by reducing the distance traveled by nodes to reach their last positions. The idea is to take advantage of the water currents by favoring the node displacements in the direction of the water currents and avoiding as much as possible displacements against the current. Simulation results show that the proposed solution reduces by 41% the distance traveled by sensor nodes while maintaining a very good coverage rate of 97%.  
In future work, we will consider a more realistic underwater environment by introducing obstacles in the network area. Moreover, we will study the non-uniform deployment in which redundant nodes are considered near the sink to balance the load and hence avoid the quick energy depletion of these nodes.  
This research was funded by the Vice Presidency for Graduate Studies, Research, and Business (GRB) at Dar Al-Hekma University, Jeddah, under grant no. (RFC/21-22/004). The author, therefore, acknowledges with thanks GBR for technical and financial support.  
No data were used to support this study.  
The authors declare no conflict of interest.
## 1 Introduction
In this pursuit, the highlights of the present study can be summarized as follows:  
1. An active FTC scheme that can simultaneously adapt to the UAV control system with actuator faults and model uncertainty is proposed. The FTC scheme is resistant to wind disturbance and capable of FTC under time-varying faults. Compared with the ESO in earlier studies [30], our control scheme further optimizes the parameter design. Instead of setting parameters as in conventional methods, there is only a need for the adaptive adjustment of the uncertain terms and disturbance parameters. This leads to the improvement in the system's robustness. 2. We propose an RBF neural network for predicting the time series of aircraft state parameters, which can rapidly estimate the fault data and uncertain parameter values. Fault data were used for training the weight parameters of the neural network. The trained model was then employed to estimate the testing data of larger fault values. Through iterative training, the estimate accuracy of the neural network for fault data was improved, thereby improving the fault-tolerant performance. 3. Rather than the model-based fault observers, the optimized RBF neural network was used in this study to estimate the parameter values in the face of changing parameters of nonlinear terms. The optimized ADRC controller was applied for the overall stability control of the system, thus achieving a rapid and accurate estimate of the actuator fault values with the proposed FTAC. The remaining contents of the present study are organized as follows: Section 2 introduces the dynamic model, fault model, and wind gust disturbance model of the multi-rotor aircraft; Section 3 provides an overall design of an ADRC controller and an RBF neural network; Section 4 describes the design of the FTAC scheme as well as the FTC law;Section 5 presents the simulation results, which verify the efficacy of the proposed FTAC scheme. In the end, our general conclusions and an outlook for future work are provided.
## 4 Analysis of Numerical Results
### Influence of the Tunnel Spacing  
Five working conditions of the tunnel spacing (7.2 m, 10.2 m, 13.2 m, 16.2 m and 19.2 m) were selected for the simulation of stepwise excavation, as shown in Table 2.  
With the increase in tunnel spacing, the lateral ground settlement curve changes from V-shape to W-shape, the ground settlement range expands and the settlement trough becomes shallow, as shown in Figure 13. The maximum settlement was shifted from the right tunnel which was excavated first to the centerline of the two tunnels. As the tunnel spacing increases, the settlement of the strata above the tunnel gradually decreases, as  
Figure 11: Lateral variation curve of ground settlement for different tunnel depths at 10 m depth in excavation direction.  
Figure 12: Variation curve of displacement at different monitoring points with the tunnel depth.  
shown in Figure 14. When the two tunnels are close together, the area of superimposed ground settlement caused by the twin-tunnel construction is larger, forming a V-shaped settlement curve, and the maximum settlement is larger than the maximum settlement at the surface of the single-lane tunnel excavation. This is due to the fact that the ground settlement curve of the twin tunnels is obtained by superimposing the ground settlement caused by the excavation of two single-lane tunnels, so the settlement values are larger than those caused by the construction of a single tunnel. As the tunnel spacing becomes larger, the area of superimposed ground settlement caused by the twin-tunnel construction becomes smaller, forming a W-shape settlement curve with more obvious characteristics. The twin tunnels are considered to be small spaced construction when the settlement curve is V-shaped, and the degree of mutual disturbance during excavation is strong. Meanwhile, the twin tunnel is considered a large spacing construction when the settlement curve is W-shaped, and the mutual disturbance is weak at this time.