esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Obstacles and Challenges in Integrating AR in Education
\begin{table}
\begin{tabular}{l c c} \hline \hline
**AR Technology and Educational** & **Frequency** & **Example** \\ \multicolumn{3}{l}{**Resources Obstacles and Challenges**} \\ \hline Cost of buying and maintaining AR & 10 & \begin{tabular}{c} _This kind of technology is too expensive;_ \\ _Currently AR is often very expensive;_ \\ \end{tabular} \\ \hline Lack of AR educational content and & & \
\begin{tabular}{c} _The obstacle is AR-based learning facilities which are still_ \\ _limited in their existence according to the subject matter_ \\ _and student characteristics;_ \\ \end{tabular} \\ \hline Lack of AR tools, immature technology & 4 &
\begin{tabular}{c} _The main problem is getting access to good tools;_ \\ _technological glitches;_ \\ \end{tabular} \\ \hline Complexity and difficulty to use AR & 3 & _Complexity of using the new technologies;_ \\ \hline Security concerns regarding AR & 3 & _Security concerns;_ \\ \hline Safety concerns regarding AR devices & 1 & _Students will not (be) harm(ed) from any devices;_ \\ \hline \hline \end{tabular}
\end{table} 
Table 12: AR technology and educational resources obstacles and challenges in integrating ARinE. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Teachers’ Obstacles and Challenges** & **Frequency** & **Example** \\ \hline Lack of teachers’ knowledge and skills & 13 & \begin{tabular}{c} _The ability and skills of teachers in using AR technology are also_ \\ _still limited;_ \\ \end{tabular} \\ \hline Lack of teachers’ digital skills & 3 & \begin{tabular}{c} _Teachers’ training and confidence with the technology is also a_ \\ _major concern;_ \\ \end{tabular} \\ \hline Lack of teachers’ time to find, learn, & 9 & \
\begin{tabular}{c} _The obvious obstacles and challenges are time, efforts, and_ \\ _required skills;_ \\ _Time it takes to find and learn how to use AR, fit within curriculum;_ \\ _Time to develop and integrate the AR in the lessons;_ \\ \end{tabular} \\ \hline Negative teachers’ attitude towards AR & 9 &
\begin{tabular}{c} _Teachers’ unwillingness, lack of motivation, disbelief in the potential_ \\ _of new technologies;_ \\ _Teachers are reluctant to learn new technologies;_ \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Students’ obstacles and challenges in integrating ARinE. studies. However, AR has been used for assessment in various subjects such as medical student laparoscopic skill [52] and structural biology [53]. As far as the school is concerned, the participants considered that the adoption of ARinE is prevented by the lack of the school's digital infrastructure and equipment, funding, directors' awareness and interest about AR as well as by the school's restrictive regulation (Table 16). Previous studies confirm the lack of schools' digital infrastructure, equipment, and funding [8; 9; 10; 11; 16; 40; 54] as well as the lack of institutional support [21].
# CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
## 1 Introduction  
Distributed blockchain technology, with its inherent decentralization, transparency, and immutability, has introduced a new paradigm of trust for the Internet. Relying on consensus mechanisms to ensure transaction integrity and data authenticity, blockchain has demonstrated broad application prospects across domains such as finance, the Internet of Things, and energy. However, alongside these advantages, blockchain networks are not immune to vulnerabilities. In particular, due to their heavy dependence on inter-node communication for consensus, the security of the network communication layer has emerged as a critical concern.  
Although blockchain was originally designed to resist single points of failure, its open and distributed nature renders it susceptible to attacks such as denial-of-service (DoS) and Sybil attacks. These threats not only disrupt normal transaction processing but may also compromise the overall stability of the network. To mitigate these risks, it is imperative to detect and respond to such attacks promptly and effectively to minimize their impact. Notably, while much attention has been given to the underlying technologies of computing power-based blockchain systems, relatively little research has been devoted to addressing threats at the network layer. Consequently, existing defense mechanisms are often inadequate against evolving threats.  
To address this gap, the present study aims to explore the detection of anomalous activities within Computing Power Blockchain Networks using deep learning andmachine learning techniques. Specifically, we propose an adaptive anomaly detection algorithm--CAA-RF--that integrates convolutional neural networks, random forests, and attention mechanisms to enhance detection accuracy and efficiency. By simulating representative attack scenarios in a lightweight computing power proof-of-stake blockchain environment, we validate the effectiveness of the proposed method. Compared with a variety of models, including DBNs, GANs, LSTMs, and VAEs, our approach achieves an F1 score of 0.9565 and a testing accuracy of up to 0.95674, demonstrating a significant advantage in F1 performance. This provides both theoretical and technical support for the development of more secure and reliable blockchain applications in the future.
## 5 Results and Discussions
### Experimental Results
#### 5.1.2 Forced Convection  
In the previous paragraph, the temperature was studied in natural convection conditions. In this paragraph, the influence of the position and of the current rate on the SCs temperature in forced convection is examined. For this purpose, tests were carried out with current rates between 20 A and 70 A. The speed value of the airflow is measured and constantly equal to 5.5 m/s in the tube and 0.69 m/s in the square section box where the SC module is placed.  
Effect of the Position  
In the Natural convection, the Supercapacitor in the center of the module is the most heated one. In Figure 11, the temperatures T3, T6, T9, T16, and T19 are compared. They are located on the different rows of the center line (L2--Figure 1) of the module (the most disadvantaged line). To facilitate comparison, the chosen SCs are located at the same position on the SC. When the position of the SCs is far from the air source, its temperature will be higher. In steady-state and \(I\) = 70 A, the difference between T6 and T3 is 3 \({}^{\circ}\)C. This difference will become much bigger with the last column, it has a value of 10 \({}^{\circ}\)C between T19 and T3.  
Figure 11: _Cont._  
The very slight difference between T17 and T9 is explained by the new configuration improvement. This trend is explained by the fact that the change from an aligned to a staggered arrangement between the row three and four brings an increase in the heat transfer coefficient which, despite the increase in air temperature, minimizes the increase in the temperature of the supercapacitor located on the fourth row.  
In forced convection conditions, the blown air that arrives in the last column will have higher temperatures due to the contact with all of the SCs. Therefore, the last column of SCs has the highest temperature.  
Effect of the Current  
Similar to natural convection, the temperature of the SCs increases with the current rate increases. The evolution of relative temperatures of the forced convection as a function of the current intensity is represented in Figure 12. The evolution is not linear and agrees with previous results [23; 26].  
Figure 11: Evolution of T3, T6, T9, T17, and T19 as a function of time.  
Figure 12: Relatives temperatures and deviation in forced convection.  
When comparing with Figure 10 (natural convection) a significant decrease is observed in the \(\Delta T_{rise}\) evolutions, but \(\Delta T_{max}\) (i.e., the difference between the highest and the lowest temperature augmentation) is higher than 5 \({}^{\circ}\)C if continuous current exceeds 39 A (in natural convection it becomes higher than 5 \({}^{\circ}\)C if current exceeds 30 A).
# A Study on the Movement Characteristics of Fuel in the Fuel Tank during the Maneuvering Process
## 2 Research Object and Numerical Method
### Numerical Calculation Method and Validation
\(f_{i,d}\) can be defined as  
\[f_{i,d}=f_{d}(X_{i})=\big{(}\frac{\partial f}{\partial X^{d}}\big{)}_{i} \tag{4}\]  
By multiplying both sides of the equation by the function \(W(X-X_{i})\) and integrating it in space \(\Omega\), we obtain the following equation:  
\[\int_{\Omega}f(X)W(X-X_{i})dX=f(X_{i})\int_{\Omega}W(X-X_{i})dX+f_{i,d}\int_{ \Omega}\Big{(}X^{d}-X_{i}^{d}\Big{)}W(X-X_{i})dX+r(X-X_{i})^{2} \tag{5}\]  
An assumption made here is that point \(X_{i}\) is strongly influenced by nearby points, while distant points have a weaker influence on it. Thus, a function \(W(X-X_{i})\) can be defined as \(W(X-X_{i},h)\), where h is a length that characterizes the impact space distance. The density of the point set within the computational domain affects the distribution layout of the points and the size of the smoothing length. The density of any point can be obtained by integrating the density of the neighboring particles around that point:  
\[\rho_{i}=\sum_{j=1}^{N}m_{j}W_{ij} \tag{6}\]  
The spatial distribution of the point set within the computational domain dynamically changes over time, and the number and positions of the points supported by point \(i\) are reiterated at each time step. During the calculation, other points within the supporting domain of point \(i\) exert an effect on it through a smoothing function, which is used for the integration interpolation of the central point \(i\). To verify the credibility of the FPM in accurately simulating fuel movement, a simulation was conducted to analyze the movement of a simple elongated cylindrical fuel tank under excitation. The simulation results were then compared with experimental findings presented in reference [21], as depicted in Figure 2. The comparison demonstrates a close agreement between the shape of the free liquid surface predicted by the FPM and the experimental results. Under excitation, the free liquid surface exhibits a rightward movement and is constrained by the right boundary, resulting in a smooth surface with a higher left side and a lower right side. As the excitation continues, the liquid on the right side experiences a certain degree of compression and flips along the upper boundary towards the left. This flipping process causes collisions and localized disruptions at the edge of the free liquid surface, leading to the formation of splashing. Based on the excellent agreement between the simulation results and the reference experimental data, this method can be applied in engineering computational analysis.
## 1 Introduction
### Main Contributions and Limitations of the Proposed Methodology  
The main contributions of the proposed solution are the following:  
* Enable the lock screen bypass, regardless of the OS version;
* Prove the effectiveness of the LLDA-ISPCF methodology on devices with File-Based Encryption;
* Enable data acquisition from a wide range of mobile devices, regardless the manufacturer;
* Enable device data acquisition using specialized, proprietary and open-source tools while preserving data integrity;
* Allow the root user to be enabled without the risk of losing user data, which allows physical extraction on models whose acquisition process is only possible with the rooted device;
* Allow the acquisition and analysis of data by any capable forensic tool, observing the compatibility of the analysis tool with the format generated by the acquisition tool.  
Regarding the limitation of the methodology, it is considered that--to the best of the authors' knowledge and until the elaboration of this work--devices that use Universal Flash Storage (UFS) main memory or that have hardware-supported encryption are not compatible with the tools employed in the proposed methodology. That future updates to the firmware by the manufacturers may make the methodology unfeasible. Also considered is the fact that Special Firmware, such as the Combination Firmware used on Samsung devices may not be available for a specific model or manufacturer, and its use combined with ISP requires a specific study of the device from which the data will be acquired to bypass the lock screen mechanism.
## 3 Results and Discussion
### Indoor Air Quality
More specifically, as presented in Figure 9, the findings from the Presentation Room show that during June 2020, just after the re-opening of the museum under the strict health protocols due to the COVID-19 pandemic, the TVOC concentration was at its peak (16 ppm). Similarly, the TVOC levels in the Dentistry Hall were considerably high; they did not surpass any of the proposed limits. Figure 8: Total volatile organic compounds (TVOC) concentration in the museum premises for the entire measurement period. \({}^{***}\)\({}^{**}\) symbol is used for the outliers. Figure 7: Carbon dioxide levels in every hall of the museum for the entire monitoring process. Furthermore, the investigation of the influence of ambient air quality levels to indoor conditions is also of great importance. For this reason, Figures 10 and 11 present the indoor to outdoor ratio (I/O) results for CO\({}_{2}\) and TVOC during the entire experimental period at different experimental points. From Figure 10, it is clear that the CO\({}_{2}\) I/O ratios are very close to 1 for all of the examined rooms and especially during the warm months, when doors and windows were frequently open. Thus, external CO\({}_{2}\) levels seem to have an impact on the internal environment, as the indoor CO\({}_{2}\) concentrations were at relatively low levels, as mentioned above. I/O ratios \(>\) 1 are related to high occupancy levels within the examined rooms. Figure 10: Indoor to outdoor (I/O) ratio of CO\({}_{2}\) levels within the museum during the entire experimental period. Figure 9: TVOC levels in every area of the museum throughout the entire measurement period. An opposite behavior is observed for the case study of TVOC. Figure 11 illustrates that during all of the experimental months, the majority of TVOC I/O ratios were found to be >1 in most rooms, even during summer. Ambient levels of TVOC did not demonstrate a serious impact on the microenvironment of the building, as internal emissions of TVOC are frequently related to indoor sources, such as furnishing, wooden materials, coatings, paints, and the daily use of detergents. These TVOC sources were also reported in the research of [PERSON] et al. [40].
## 3 Results and Discussions
### Simulation of the Solidification of Radioactive Wastes Using SRC
#### 3.5.1. Maximum Packing Capacity of SRC for Radioactive Wastes  
The maximum packing capacities for the waste soil and metal waste are shown in Figure 10. As expected, the solidification of the coarse aggregate and metal waste resulted in a higher compressive strength than that of the coarse aggregate and waste soil. The compressive strength of SRC containing waste soil (5 wt%) and coarse aggregate (30 wt%) was investigated to meet the disposal criterion after 28 days of curing. In contrast, 7 wt% metal waste and 30 wt% coarse aggregate exhibited compressive strengths that satisfied the standard. However, the compressive strength decreased with an increasing portion of metal waste from those higher than 5 wt%, which is likely attributed to its weak binding capacity with SRC and the formation of pore spaces due to the excessive amount of metal waste. Therefore, a suitable packing amount of metal waste of 5 wt% is recommended, even though 7 wt% satisfied the compressive strength criterion.
## 2 Materials and Methods  
The workflow of the used methodology for crop yield potential prediction using machine learning, which was based on phenological modeling using vegetation indices from Sentinel-2 images, consisted of five fundamental steps: (1) acquiring of ground truth crop yield samples in a four-year study period (2019-2022); (2) calculating seven vegetation indices from Sentinel-2 images per sample, with a focus on evaluating novel saturation-resistant indices; (3) phenological modeling based on all seven evaluated vegetation indices;(4) predicting crop yield potential for maize and soybean with machine learning; and (5) assessing the accuracy of the predicted crop yield potential (Figure 1).
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Meteorological Data  
The meteorological data used to estimate NPP in this study were the monthly mean temperature, total precipitation, and total sunshine duration data from 2000 to 2020. These data were obtained from 92 meteorological stations, including 39 stations in Inner Mongolia and 53 stations in surrounding provinces within 100 km. The original meteorological data were downloaded from the China Meteorological Data Service Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 28 January 2021). Missing data were filled by the average data of the same period before and after the year. Gaussian kriging interpolation was performed to interpolate the monthly meteorological data to a spatial resolution of 250 m based on the same projection as the remote sensing images on the GEE platform.
## 1 Introduction  
In recent years, technological advancements have made systems in engineering fields increasingly complex, such as spacecraft, electronics, communication, and networking. The increasing complexity of systems necessitates higher performance, such as greater reliability, reduced mass, lower unavailability, and reduced costs. Additionally, it introduces more uncertainty due to the growing number of design parameters, some of which cannot yet be precisely specified or may change during development [1; 2; 3; 4]. Consequently, engineers aim to identify designs with both optimal performance and high robustness. High robustness implies that designs are less sensitive to parameter fluctuations. Hence, considering these criteria simultaneously is essential in designing complex systems.  
Traditionally, multi-performance optimization aims to find points that optimize multiple performance metrics simultaneously. However, performance metrics often conflict, leading to multiple non-dominated Pareto-optimal solutions. This implies that no other solutions dominate these solutions. Theoretically, these solutions are crucial in multi-objective optimization. Practically, though, they are sensitive to variable perturbations [5; 6; 7].
# In-Vivo Quantification of Knee Deep-Flexion in Physiological Loading Condition trough Dynamic MRI
## 2 Materials and Methods
### Identification of the Optimal Scanning Planes and Registration Accuracy  
In order to provide a reference motion to test the registration algorithm performance, five additional static scans (3D HYCE) were also taken in weight-bearing configuration, setting the flexion angle, by means of the rig, approximately at \(0^{\circ}\), \(15^{\circ}\), \(45^{\circ}\), \(75^{\circ}\), and \(90^{\circ}\) (Figure 4). Bones were segmented from all the scans, and the femur, tibia, and fibula from non-weight-bearing MRI were registered to the corresponding bones on each scan through an Iterative Closest Point (ICP) algorithm developed in Matlab (Figure 4). Since the anatomical reference frames were defined on the non-weight-bearing MRI, as noted above, in this way, it was possible to define the rototranslational matrixes describing the relative pose of the anatomical reference systems of the femur and tibia at the five static scans. The femoro-tibial motion was derived by parametrizing the rototranslational matrixes, using the center of the femoral reference system to track the translations and the ZXY cardiac angle sequence to represent the rotations [17].  
To reduce the scanning time, the number of DMRI planes acquired for each exam was limited to two. To determine which plane combinations would allow for the optimal motion reconstruction, DMRI was simulated from the five static weight-bearing scans of one volunteer by resampling the original MRI in different planes (Figure 5). The set of tested plane pairs is reported in Table 1. For each combination of planes and each static scan, the registration algorithm was run, and the positional and orientational accuracies were evaluated as the mean absolute error (MAE) between the reconstructed and measured tibio-femoral motion. Once the combination resulting in the smallest rotational and translational MAE was identified, plane orientation was further adjusted to minimize the chance of out-of-plane motion during knee flexion. Accuracy was also tested for these optimal planes to ensure no quality loss in bone registration.  
Once the optimal scanning planes were determined, DMRI results were simulated from the static scans for all the three volunteers, as described above, and the registration algorithm was run. With respect to the real dynamic imaging described below, the bone spatial pose is known in this case, thus facilitating an accurate validation of the registration algorithm. The overall rotational and translational algorithm accuracies were then defined as the MAE between reconstructed and measured motion, as well as averaged on the three volunteers.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 6 Conclusions  
In this paper, a comprehensive survey was conducted on the use of quadcopters in smart agriculture. Among all UAV types, quadcopters were selected due to their multiple features and capabilities, such as flexibility, low cost, ease of use, high maneuverability, ability to hover, and many more cited advantages.  
An examination of the types of quadcopters specifically used in smart agriculture was carried out, categorizing them into two groups: commercial and custom-built quadcopters. Each type was described with its relevant specifications, and examples were provided for each, showcasing their wide applicability in smart agriculture across different countries.  
Subsequently, a discussion was presented on the various applications that quadcopters can perform in this field, such as planting, irrigation, mapping, and monitoring. This analysis demonstrated how quadcopters are essential tools in every phase of the agricultural cycle. Additionally, we carefully presented in Table 14 key examples along with their limitations, highlighting the significance of these findings and offering valuable suggestions for future advancements in the field.  
Furthermore, the modelling of quadcopters was explored in detail, covering aspects from configurations to deriving mathematical equations. Kinematics, dynamics, and aerodynamics equations were provided to describe the motion and behavior of quadcopters.  
Finally, the challenges encountered in this field were identified, along with proactive measures and future directions proposed for continuous improvement.  
To sum up, although the use of UAVs in smart agriculture has been discussed in other works, to the best of our knowledge, none have particularly explored the most popular among them, \"quadcopters\", and their particular use in smart agriculture. Therefore, this paper provides researchers and innovators working with quadcopters in smart agriculture with a deep dive into the different aspects of these UAVs, including types, applications, and modelling techniques, paving the way for future advancements in the rapidly growing field of smart agriculture.  
**Author Contributions:** Conceptualization, K.K.; writing--original draft preparation, K.K.; writing--review and editing, A.M., M.A.-U., B.C. and M.K.; visualization, K.K.; supervision, A.M., M.K., B.C. and M.A.-U.; project administration, A.M. and M.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.