esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Comparison of Prediction Accuracy of Proposed Neural Network Model with Log-Distance Models  
Detailed prediction capabilities of all the log-distance models and the proposed model on measured path loss data are provided in the plotted graphs of Figures 21-23 in terms of MAE, RMSE and STD. The graphs show that the neural network model achieved the best predictions with marginal errors. The COST 213 (W/I) made the closest prediction to measured loss, but in terms of accuracy, the proposed neural network model achieved the best performance by 20%, 15% and 25%, respectively, across study sites. For example, while COST 213 (W/I) reached 3.34, 2.35 and 4.23 dB in terms of RMSE, the proposed neural network model attained 1.73, 2.11 and 1.45 dB across study sites. Generally, models which predict the path loss in the tested areas with RMSEs higher than the acceptable range of up to 6 dB are not selected as most suitable. However, such models could be further optimized for improved performance. The lower the RMSE value towards zero, the better the model. Regarding standard deviation error, COST 213 (W/I) achieved 1.73, 2.11 and 1.45 dB, while the proposed neural network model achieved 1.73, 2.11 and 1.45 dB, respectively. The poor predictions made by the log-distance-based models can be ascribed  
Figure 19: Mean prediction error statistics along with Data points in site 2.  
Figure 20: Mean prediction error statistics along with Data points in site 3.
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
###### Abstract
The long-term goal of this research is the downscaling of InSAR from satellites to drones, to provide long-term InSAR monitoring for natural hazards, geotechnical engineering and ageing infrastructure. The ongoing research and development is divided into four main stages:  
1. Hardware development for a drone radar system. 2. Software development for SAR imaging from the drone radar system. 3. Fieldwork for repeat-pass SAR data collection. 4. Software development for InSAR. Regarding the first two points, we have developed the hardware and software for a drone radar system utilising the DJI Matrice 600 Pro. The hardware includes a novel invention of radar horn antennas that are the first to be fabricated from Copper Clad Laminate (CCL) [24]. The CCL provides a lightweight and rigid dielectric substrate, where the internal surfaces are coated with a smooth 35 \(\upmu\)m thick copper layer to provide a boundary for the electromagnetic field. CCL is deemed preferable to copper sheets for achieving a lightweight component for drone deployment. The software includes a custom FMCW radar flowgraph built in the open-source GNU Radio Companion (GRC) and a Range Migration Algorithm (RMA) for SAR focusing. The purpose of this case study at Flint Hall Farm is to demonstrate the functionality of the developed drone radar system in acquiring SAR imagery that will be used in downstream InSAR processing for deformation monitoring of the landslide at Flint Hall Farm and other geotechnical hazards and infrastructure. The SAR image presented here is a novel achievement in a newly developing field of InSAR remote sensing. It is the first image made using the CCL antennas, which are themselves a novel invention, and it provides an improved spatial resolution compared to satellite SAR for feature identification. This study also demonstrates the use of corner reflectors as targets in landslide monitoring in rural locations. Ongoing and future work addresses points three and four, which includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslide at Flint Hall Farm and other geotechnical hazards and infrastructure. A discussion of the adapted InSAR processing chain for drone SAR data is provided in Section 5.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 3 Proposed Method
### Proposed Model
#### 3.4.3 Encoder Refinement  
The model's encoder is meticulously refined to capture intricate spatial hierarchies. Each encoder block is crafted with precision, incorporating convolutional layers, dropout regularization, and separable convolutions. The sequential application of attention mechanisms further refines the representation, allowing the model to learn and emphasize crucial features.
## 2 The IDIVS Concept and Application Domains
### IDIVS Input and Output  
The suggested minimal output produced and streamed by the IDIVS is assumed to be an ID and a virtual sensor value (numerical or categorical--holding information about an environment or object).  
Additional properties, mandatory or optional for the IDIVS, are shown in Figure 2. It also shows the input retrieved from sensors: ID, sensor value, and associated properties. Additionally, it may be useful that an IDIVS can set the timestamp of a virtual sensor value. Sensor data can be produced and consumed by the IDIVS regularly, irregularly, or on request, synchronously or asynchronously. Obviously, for the IDIVS to make use of machine learning based on user input in a supervised manner, it needs labelled data or equivalent input. The ML problem for this data fusion problem becomes to train the model,e.g., update the data fusion model such that the virtual sensor output value matches the user input, e.g., the correct labels.
## 1 Introduction
They constructed an empirical formula for the seismo-ionospheric disturbances relating the EQ magnitude and the epicenter distance to discuss the probability of EQ prediction but with much uncertainty. So far, it is well established that an ionospheric anomaly tends to appear about two weeks before an EQ but with no specified magnitude and distance. However, short-term EQ prediction is still one of the most challenging targets in earth science today [19]. There are only a few countries where EQ prediction, especially in the short-term, has really been put into practice. China is one of the most seismically active countries in the world. Destructive EQs pose a major threat to lives and property in almost all Chinese territory. There have been several large EQs on the Chinese mainland in the past 50 years, including the 1975 Haicheng \(M_{\text{S}}\) 7.3 EQ, the 1976 Tangshan \(M_{\text{S}}\) 7.8 EQ, the 2001 Kunlun Mountain \(M_{\text{S}}\) 8.1 EQ, and the 2008 Wenchuan \(M_{\text{S}}\) 8.0 EQ, which caused extensive damage, and 69,000 people lost their lives. Organized efforts of EQ prediction started immediately after the 1966 Xingtai \(M_{\text{S}}\) 6.6 EQ in Hebei Province, about 300 km southwest to Beijing. Since then, China has carried out extensive research programs on EQ prediction and application to prevent and mitigate EQ disasters [20]. The CSES satellite has been launched for more than four years, and effective payload data have been primarily put into EQ prediction service in China. So, in this paper, an introduction to the CSES satellite and its daily application are provided in Section 2. The records of the two examples of the 2021 Maduo \(M_{\text{S}}\) 7.4 EQ and the 2022 Menyuan \(M_{\text{S}}\) 6.9 EQ that occurred in Qinghai province will be described in detail, respectively, in Sections 3 and 4. The discussion and conclusions are provided in Sections 5 and 6, respectively.
## 3 Experimental Results and Discussion
### Influence of Mean Stress on Ratcheting Effect  
In order to investigate the effect of mean stress on ratcheting behavior, Figure 4 shows the uniaxial ratcheting behavior of SAF2507 stainless steel at stress amplitude of 300 MPa and mean stresses of 300 MPa, 350 MPa, and 400 MPa. The stress-strain hysteresis loops for different loading conditions are given in Figure 4a. Figure 4b shows that the ratcheting strain initially increased rapidly but then transitioned to a slow growth phase where the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & \(\sigma_{\text{m}}\)**/MPa** & \(\sigma_{\text{\alpha}}\)**/MPa** & \(N_{c}\) & \(\epsilon_{\text{h}}\)**/\%** \\ \hline Y1 & 300 & 300 & 200 & 0 \\ Y2 & 400 & 300 & 200 & 0 \\ Y3 & 500 & 300 & 200 & 0 \\ Y4 & 400 & 350 & 200 & 0 \\ Y5 & 400 & 400 & 200 & 0 \\ Y6 & 400 & 400 & 200 & 0.4 \\ Y7 & 400 & 400 & 200 & 0.8 \\ Y8 & 400 & 400 & 200 & 1.2 \\ \hline \end{tabular}
\end{table}
Table 2: Loading conditions of uniaxial ratcheting test.  
Figure 3: Test U3: (**a**) Stress control diagram, (**b**) stress–strain relationship, (**c**) ratcheting strain, (**d**) ratcheting strain rate.  
ratcheting strain rate stabilized. For a given stress amplitude, an elevation in mean stress led to an increase in ratcheting strain, [PERSON] [40] and [PERSON] [41] all discovered this result through ratcheting experiments on materials.
## 4 Proposed Trust-Based Optimized Reporting Scheme
### Trust Propagation
#### 4.3.1 Delta-Based Isolation Strategy  
In this subsection, the computation of delta values is introduced as a mechanism to investigate whether the trust value of a specific trustee is decreasing. The delta values are calculated based on the difference between the current trust value (T\({}_{\text{c}}\)) and the initial trust value (T\({}_{\text{i}}\)). In (8), the parameter \(\Delta_{\text{o}}\) represents the initial value of delta, which a source node (trustor) computes for any specific target node (trustee). In (9), \(\Delta_{1}\) denotes the secondary value of the delta, calculated using the primary delta (\(\Delta_{\text{o}}\)). Finally, the expression (10) illustrates this continuous process until the node is isolated. The purpose of computing this delta is to investigate whether the trust value of a specific trustee is decreasing. The value of delta depends solely on the current trust of the target node. If the current trust is less than the initial trust in (8) or the primary trust in (9), then the delta value is considered low, and vice versa. Once the delta value of a specific target node continuously decreases a fixed number of times (a predefined threshold), the node is reported to the root for isolation. Consequently, the node is isolated from the network, and its delta values are flushed out from all concerned source nodes in the network.
# Blockchain-based Multifactor Authentication for Future 6G Cellular Networks: A Systematic Review
###### Abstract  
There are continued advances in the internet and communication fields regarding the deployment of 5G-based applications. It is expected that by 2030, 6G applications will emerge as a continued evolution of the mobile network. Blockchain technology is one of the leading supporting technologies predicted to provide a secure and unique network to 6G-enabled devices, transactions, and applications. It is anticipated that the 6G mobile networks will be virtualized, have cloud-based systems, and aim to be the foundation for the Internet of Everything. However, along with the development of communication technologies, threats from malicious parties have become more sophisticated, making security a significant concern for the 6G era in the future. Despite enormous efforts by researchers to improve security and authentication protocols, systems still face novel intrusion and attacks. Recently, multifactor authentication techniques (MFA) have been deployed as potential solutions to attacks in blockchains. The 6G applications and the cellular network have specific vulnerabilities that need to be addressed using blockchain-based MFA technologies. The current paper is a systematic review that discusses the three technologies under consideration; then, several studies are reviewed that discuss MFA techniques in general and use blockchains as potential solutions to future security and authentication issues that may arise for 6G applications.  
Keywords:6G cellular network; blockchain technology; multifactor authentication technique; network security +
Footnote †: journal: Applied sciences
# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 4 Analysis of Predicted Results
### Experimental Analysis  
In this case study, all experiments are performed on the same hardware and software environment. Table 5 shows a comparison of the different structural conditions using the EMD-BiGRU algorithm. It should be seen that the proposed method achieves high accuracy and robustness. Specifically, the EMD-BiGRU achieves 0.0118%, 0.1084%, 0.0842%, and 95.81% in MSE, RMSE, MAE, and R2 for 10 points of Case 1. With increasing missing data, the R2 of Case 1 are 95.81% (10 points), 94.70% (15 points), 91.57% (20 points), respectively, which shows EMD-BiGRU has high accuracy. In addition, the proposed method has robustness due to the R2 value dropping slowly. For other structural conditions, Cases two, three, and four, the EDM-BiGRU has a similar result, and the R2 value is more than 0.9. Therefore, this result shows that the proposed method has high data recovery ability in civil structures.
## 3 The Proposed Method  
### Algorithm of the Proposed Method  
The PRE method consists of optimization and numerical simulation processes. First, the obtained vehicle vibration data and randomly assumed system parameters are substituted simultaneously into Equations (1) and (13). Since all terms of the left-hand side of Equation (1), which represents the equations of motion of the vehicle, become known by this operation, the right-hand side can be calculated to estimate input profiles \(\mathbf{u}(t)\). Thus, this process can be called the IEP (input estimation problem) of the vehicle. At the same time, Equation (12), which represents the equations of motion of the bridge, can be solved because the external forces generated by the vehicle accelerations are given. This process can be called DRS (dynamic response simulation) of the bridge. By solving these two processes numerically, the road profiles \(\mathbf{r}(t)\) can be estimated:  
\[\hat{R}_{i}(x_{i}(t))=\hat{r}_{i}(t) \tag{20}\]  
where \((\hat{\ })\) denotes the estimated value. This study assumes that \(\hat{R}_{1}(x)\): the estimated unevenness from the front wheel and \(\hat{R}_{2}(x)\): the estimated unevenness from the rear wheel match. However, it cannot be expected that the estimated road unevenness \(R_{1}(x)\) and \(R_{2}(x)\) match from the beginning of the processes because the system parameters are randomly assumed. Thus, the objective function to be minimized in the PRE method can be defined as the following formula:  
\[J=\int_{x_{0}}^{x_{1}}\{R_{1}(x)-R_{2}(x)\}^{2} \tag{21}\]  
The PRE method is a method to minimize this road unevenness error by repeating random-assuming of the mechanical parameters. The algorithm is shown in Figure 8.  
Figure 7: The Fourier’s power spectra of the vehicle vibration accelerations for each case: Scenario 1, Scenario 6, Scenario 9, and Scenario 12: (**a**) \(\tilde{z}_{s1}\); (**b**) \(\bar{z}_{s2}\); (**c**) \(\tilde{z}_{u1}\); (**d**) \(\tilde{z}_{u2}\). The differences between the obtained vibrations of the focused scenarios are tiny, even in the frequency domain.
# Objective Prediction of Human Visual Acuity Using Image Quality Metrics
## 2 Materials and Methods
### The Point Spread Function and the Image of the Eye
Implementing regression fits requires compliance with a series of assumptions to reach conclusive results. These are the homoscedasticity, normality, and independence of residuals. The homoscedasticity hypothesis establishes that the variability of residuals is independent of the explanatory variables. Failure to comply with this condition may result in the fitted parameters varying according to sample size. Regarding normality, residuals should follow a normal distribution with a zero average. Last but not least, the Durbin-Watson (_DW_) [32] statistic can be used to verify the independence of residuals, \(r_{i}:\)  
\[DW=\frac{\sum_{i=2}^{n}(r_{i}-r_{i-1})^{2}}{\sum_{i=1}^{n}r_{i}^{2}};, \tag{7}\]  
where \(n\) is the number of eyes. The null hypothesis (no statistical evidence that residuals are positively self-correlate) is rejected if the \(DW\) value is less than a lower critical value \(DW_{(L,\alpha)}\), where \(\alpha\) represents the significance level (in this work, \(\alpha=0.05\)). If \(DW\) is higher than an upper \(\text{DW}_{(\text{U},\alpha)}\) critical value, it is accepted that there is no correlation. In intermediate cases, the test is not conclusive. If the value \((4-DW)\) is less than \(\text{DW}_{(\text{L},\alpha)}\), there is statistical evidence that residuals negatively self-correlate. If the value \((4-DW)\) is higher than \(DW_{(U,\alpha)}\), there is no statistical evidence for a negative self-autocorrelation [33]. Besides fulfilling the above assumptions, it is necessary to establish a criterion to select the metric model that best predicts VA. [PERSON] et al. [34] introduced an objective index (_WI_) to evaluate the performance of an estimation model. In general, the more accurate the model is, the bigger \(R\) is and the smaller the mean of the squares of residuals. The _WI_ index is defined as:  
\[WI=\frac{R}{\frac{1}{n}\sum_{i=1}^{n}r_{i}^{2}}, \tag{8}\]  
Another descriptive statistic, which measures the dispersion of a dataset and can be used to compare models' performance, is the quartile coefficient of dispersion (\(QCD\)) [35]. It is defined by:  
\[\overline{QCD}=\frac{1}{n}\sum_{i=1}^{n}\left(\frac{Q_{3}-Q_{1}}{Q_{3}+Q_{1}} \right)_{i}, \tag{9}\]  
where \(Q_{1}\) and \(Q_{3}\) are the first and third quartiles of the dataset, which consists of the results of each metric of every eye over the set of 49 images. The higher this coefficient is, the wider the data variability is. Therefore, a model with a low \(\overline{QCD}\) is expected.
# Design of Magnetic Concrete for Inductive Power Transfer System in Rail Applications
###### Abstract  
Inductive power transfer (IPT) systems are transforming railway infrastructure by enabling efficient, wireless energy transmission for electric locomotives equipped with Liion batteries. This technology eliminates the need for overhead power lines and third rails, offering financial and operational advantages over conventional electric propulsion systems. Despite its potential, IPT deployment in rail applications faces significant challenges, including the fragility of materials (i.e., ferrite and Litz wires), thermal management during high-power transfers, and electromagnetic interference (EMI) on the transmitter side. This study discusses several factors affecting IPT efficiency and introduces magnetic concrete as a durable and cost-effective material solution for IPT systems. Magnetic concrete combines soft ferrite powder with water and coarse aggregates to enhance magnetic functionality while maintaining structural strength comparable to conventional concrete. Its durability and optimized magnetic properties promote consistent power transfer efficiency, making it a viable alternative to traditional ferrite cores. A comparative study has been performed on non-magnetic and magnetic concrete (with 33% ferrite powder) using both permeability tests and finite element analysis (FEA). The FEA includes both thermal and electromagnetic simulations using Ansys Maxwell (v.16), revealing that magnetic concrete can improve temperature management and EMI mitigation, and the findings underscore its potential to revolutionize IPT technology by overcoming the limitations of traditional materials and enhancing durability, cost-efficiency, and power transfer efficiency. By addressing the challenges of fragility, thermal management, and shielding of the unique coil topology design presented, this study lays the groundwork for improving IPT infrastructure in sustainable and efficient rail transport systems.  
Keywords:inductive power transfer (IPT); magnetic concrete; thermal management; electromagnetic interference (EMI); railway application
## 2 Methodology
### Texture Detection in Spatial Domain
#### 2.1.1 FHOG Feature Extraction  
The traditional HOG method firstly divides the image into many cells (e.g., size \(8\times 8\) pixels), and then calculates the histogram of nine unsigned gradient directions for each cell [13]. However, this method considers two regions separated at 180 degrees to be identical, which is not accurate to describe floating objects with 360-degree rotation directions. To guarantee the ability of feature description, the HOG method extracts texture features by sliding image blocks with a small stride. Therefore, the dimension of final feature descriptor is very high, which leads to high time complexity and over-fitting, especially in the case of small sample training.  
As shown in Figure 1, an improved HOG named FHOG is adopted. First, the nine cells in a sliding image block are divided into four groups. In each group, an 18-dimensional signed HOG eigenvector and a 9-dimensional unsigned HOG eigenvector are combined into a \(4\times 27\) matrix. Then, columns of the matrix are added up to get a \(1\times 27\) vector, while rows of the matrix are added up to get a \(4\times 1\) vector. Finally, through connecting the two vectors, a 31-dimensional FHOG eigenvector (describing a block with nine cells) is obtained. Note that when describing the same block with nine cells, traditional HOG method needs an 81-dimensional descriptor, which is 50 dimensions larger than that of FHOG. More importantly, FHOG not only improves the speed of feature extraction, but also has a better feature description ability.
## 2 Theoretical Foundation and Sybil Attack Landscape
### Targeted Networks and Vulnerabilities  
Sybil attacks target diverse networks of four types: (1) OSN platforms such as Twitter connect millions worldwide. An attack, as publicized by NBC [26], can impact masses and even nations. (2) Online store platforms such as Amazon can end up hosting fake product reviews, inflating a product's image to drive sales; tactics like such often involve payment for generating fake reviews. (3) Vehicular ad hoc networks (VANETs) are a subset of mobile ad hoc networks (MANETs), where VANETs enable vehicular communication protocols like vehicle-to-vehicle (V2V), vehicle-to-roadside (V2R), and vehicle-to-infrastructure (V2I) connections. VANETs Sybil attacks vary, including those that impersonate by fabricating MAC addresses or certificates when attackers infiltrate by breaching sensitive data or disrupting vehicle traffic. Moreover, (4) wireless sensor networks (WSNs) include weather-related networks for environment tracking, disaster management, and industrial automation. Sybil attacks are apparent through nodes simulating sensors to transmit false data, overloading networks with malware, or deploying data spoofing or resource exhaustion tactics.
## 4 Deep Reinforcement Learning-Based Approach for Solving the Problem
### Reinforcement Learning Key Elements  
For the system model to be converted into reinforcement learning equivalents, the state, actions, and reward function must be defined, which represent the key elements of RL. In our environment, where multiple users are performing an intensive-application on an MEC system, the key elements of RL are specified as follows:  
* State: In our study, the computational requirements for the intensive application can be utilized to define the state space \(S\) as follows \(s_{t}=\{(\alpha_{i},\beta_{i},\Gamma_{i})_{t}|i\in K\}\)
* Action: The offloading decision can be utilized to specify the action space \(A\), in which selecting an action \(a_{t}=\{(y_{i})_{t}|i\in K\}\) based on the \(s_{t}\) can follow the policy \(\pi(a_{t}|s_{t})\).
* Reward: The reward value is given by the objective function according to (Equation (11)) as part of our problem formulation. Therefore, the objective function value at time \(t\) can be calculated using policy \(\pi(a_{t}|s_{t})\) depending on the state \(s_{t}\) and after selecting an action \(a_{t}\). Afterward, the same procedure is continuously repeated with the time index increasing as \(t=1,2,\ldots,T\). As a result, based on the findings of this study, the total reward \(r_{t}\) is minimized using a policy \(\pi\) that can be defined as \(\lim_{T\rightarrow\infty}\frac{1}{t}\sum_{t=0}^{T}r_{t}\), where \(r_{t}\) denotes the \(\omega_{i}\) in Equation (11).