esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Key Enabling Technologies
### System Deploy
#### 4.3.3 Network and Communication  
To ensure robust network connectivity in agricultural environments, the system integrates multiple network protocols. Message Queuing Telemetry Transport (MQTT) is used for lightweight, low-power communication between IoT sensors and cloud storage,  
Figure 16: Autonomous agricultural machinery control console.  
Figure 15: The demonstrative prototype AgriMach system.  
ensuring stable data transmission under low-bandwidth conditions. For high-speed data transfer, the system adopts Google Remote Procedure Call (gRPC) over HTTP/2, enabling efficient streaming communication between cloud servers, edge computing nodes, and autonomous machinery. Additionally, WebSocket technology is utilized to achieve real-time data synchronization between the web-based control panel and agricultural equipment, reducing remote operation response delays.  
In terms of network security and reliability, the system implements multi-layer encryption and authentication mechanisms. Data transmitted via NB-IoT and 5G are encrypted using AES-256, while devices authenticate access through OAuth 2.0-based identity verification, preventing unauthorized access. Furthermore, TLS 1.3 is employed for edge-to-cloud communication, ensuring data integrity and confidentiality. To enhance network robustness, the system incorporates an adaptive failover strategy: when the 5G connection is unstable, it automatically switches to LTE or Wi-Fi backup networks to maintain operational continuity.  
A cloud-based communication management system allows administrators to monitor network performance and dynamically adjust bandwidth allocation based on real-time workloads. The system also supports Quality of Service (QoS) priority management, ensuring that critical data--such as emergency stop signals and obstacle avoidance alerts--are assigned the highest priority, guaranteeing immediate processing even under network congestion.  
The system provides a web-based management platform, a mobile app, and API interfaces, supporting task scheduling, device management, and data analytics. The web platform is built using Spring Boot + Vue.js, integrating WebSocket for real-time data updates. The mobile app, developed with Flutter, supports both Android and iOS platforms. The API interfaces, implemented using Spring Cloud, follow a microservices architecture and RESTful API design, enabling remote invocation and third-party integration.  
By integrating NB-IoT and 5G dual-channel transmission, edge computing optimization, adaptive security measures, and intelligent bandwidth management, this network and communication architecture ensures high reliability, low latency, and efficient resource utilization, making it well-suited for large-scale autonomous agricultural operations.
## 3 Results  
We processed 100 PLR signals, extracting the set of 13 features from each of them. Due to the low number of samples, the repeated measurements of each patient have been considered independent signals. We tested if the PLR curves obtained from the 2 different protocols were leading to significantly different values of the features, using a Wilcoxon test. Observing that this was not the case, i.e., differences were never significant, we considered the features extracted from both protocols as two sets of values indexed by the same label (ON or Healthy).  
A preliminary statistical analysis was performed on the ON signals, verifying the starting assumption that unilateral ON should affect only one of the eyes of the patients, halving the number of available samples for classification. However, in this analysis we monitored the agreement of the features extracted from the ON patients, testing the presence of statistically significant evidence of discrepancies between the behavior of the two eyes. We applied the proposed classification pipeline aiming to classify the pathological eye of the ON population, obtaining not significantly different performances from random classification (\(p\)-value 0.63). The impossibility of correctly classifying as different the two eyes of ON patients, statistically confirmed the choice of using the PLR obtained by both eyes of ON patients altogether in our main classification task.  
The distribution of the metric scores obtained by the ridge classifier along the 200 simulations is shown in Figure 2. The set of features extracted from the PLR signals could correctly classify the PLR signals into ON vs. Healthy, with an accuracy of 0.76 \(\pm\) 0.02 and a corresponding MCC score of 0.52 \(\pm\) 0.04. Features ranking, with corresponding 95% bootstrapped confidence intervals are plotted in Figure 2c, showing that all the features except for \(R_{H}\), are significantly important for the prediction. The patient classification, done as described in Section 2.4, had an average accuracy of 83%, therefore outperforming the classification of the individual PLR curves. Despite the improvement in accuracy, we will not discuss in detail the patient classification, due to the low number of subjects. Indeed, our main analysis was done on the classification of the PLR signals, which gives more reliable results.
## 2 Navigation Sensors and Techniques for Drones  
### Inertial Sensors  
Inertial sensors are effective in providing high-rate linear acceleration and angular acceleration to the users. For example, one of the drone flight controllers, Pixhawk 4, carries an ICM-20689 that supports up to 8000 Hz and 4000 Hz rate for gyroscope and accelerometer, respectively [3,4]. This information, coupled with the flight controller's feedback loop, allows drone attitude determination, stabilization, and to an extent, relative positioning. For positioning, the accelerations can be integrated over time to determine the relative change in velocity and position in the drone's body frame. When coupled with the magnetometer, all of the relative measurements will be in an east, north, up (ENU) inertial frame. Although the PVT information from the inertial sensors are very useful, especially for dead reckoning [5], the accuracies are dependent on the calibration and the drift of the sensors. For drones, such as the DJI S1000, which are used extensively for industry applications, such as agricultural and filmography applications, there are weight and power restrictions, such as a 15 min flight time and maximum takeoff weight of 11 kg [6]. Therefore, it is difficult to install high accuracy, geodetic survey grade inertial navigation sensors (INS) onboard the drones, as they are heavy, bulky, and require high power consumption. Consequently, although the low-cost INS on the drones can be used for stabilization and short-term relative positioning, an absolute positioning sensor is required for safe and accurate navigation.
## 3 Method
### Leaking Position Estimation  
The leaking positions are estimated by triangulating the corresponding trajectory lines, as shown in Figure 2c. A vertical line \(\mathbf{l}\in\mathbb{R}^{3}\) at column index \(c\) in an image is formulated as follows:  
\[\mathbf{l}=\begin{bmatrix}c\\ H\\ 1\end{bmatrix}\times\begin{bmatrix}c\\ 0\\ 1\end{bmatrix}, \tag{5}\]  
and a plane \(\mathbf{\pi}\in\mathbb{R}^{4}\) calculated from \(\mathbf{l}\) in the world coordinates is formulated as follows:  
\[\mathbf{\pi}=\mathbf{P}^{\top}\mathbf{l}, \tag{6}\]  
where \(\mathbf{P}\in\mathbb{R}^{3\times 4}\) is a camera projection matrix that transforms a point \(\mathbf{u}\in\mathbb{R}^{3}\) in the image coordinates into a point \(\mathbf{x}\in\mathbb{R}^{4}\) in the world coordinates.  
A line in a 3D space drawn by a waterdrop trajectory is calculated by intersecting two planes \(\mathbf{\pi}\) from the left images and \(\mathbf{\pi}^{\prime}\) from the right images, which is formulated as follows:  
\[\mathbf{L}=\begin{bmatrix}\mathbf{\pi}^{\top}\\ \mathbf{\pi}^{\prime\top}\end{bmatrix}, \tag{7}\]  
where \(\mathbf{L}\in\mathbb{R}^{2\times 4}\) is the null space and span representation of a line [18]. The final estimated leakage position from the top view \(\mathbf{x}_{\text{leak}}=[x_{\text{leak}},\ y_{\text{leak}}]\top\in\mathbb{R} ^{2}\) is calculated as follows:  
\[\mathbf{L}\begin{bmatrix}x_{\text{leak}}\\ y_{\text{leak}}\\ 0\\ 1\end{bmatrix}=\begin{bmatrix}0\\ 0\end{bmatrix}, \tag{8}\]  
where Equation (8) is well-posed because there are two equations with two unknown variables. It should be noted that the left and right camera projection matrices are defined such that the gravity direction is the \(z\)-axis in the world coordinates.
## 4 Results and Discussion
### Numerical Modelling Results and Comparison to Experimental Work  
Pressure-time histories and cumulative specific impulses calculated by CFD models for each gauge location are plotted in Figure 8 and overlaid with corresponding experimental traces measured in the 75% and 100% scale tests. CFD models showed good agreement with the experimental data, exhibiting similar waveforms at pressure gauge locations surrounding the corner for each geometric scale (Figure 8).  
In both the 75% and 100% scale tests, the CFD models underestimated the peak overpressure at gauge A. The underpredictions were approximately 45% and 40% for the 75% and 100% scale tests, respectively, when compared to experimental data. This resulted in absolute differences of 124 kPa for the 75% scale test and 103 kPa for the 100% scale test, as indicated in Tables 5 and 6. Apart from these differences, time of arrival and positive phase duration exhibited acceptable agreement between the CFD models and the experimental data. Furthermore, the specific impulses showed fair agreement, with absolute differences of up to 4 kPa.ms.  
Good agreement was observed between the CFD models and experiments for gauges B1-B3. Calculated peak overpressures at gauges B1-B3 were all within 10% of experimental mean values, representing small absolute differences of 1-3 kPa. Calculated times of arrival were within 5% of experimental mean values, and positive specific impulses had low absolute differences of less than 1 kPa.ms.  
Figure 8: CFD models pressure histories and cumulative specific impulse vs. experimental measurements at each gauge for the (**a**) 75% and (**b**) 100% scale tests.  
Given inevitable experimental variability, the numerically modelled pressure histories and positive phase blast wave parameters demonstrated acceptable agreement with test results.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 3 Y-Network Using MBLT Algorithm
### Decoding Process of MBLT Algorithm  
As shown in the previous section, the encoding process of the MBLT codes is similar to regular LT codes. As a result, the shaping of the right degree distribution is intact. Hence, the BP algorithm can still be used, as shown in Algorithm 2. However, the MBLT focuses on the degree-one encoded symbols, which are essential to kick-start the decoding process and keep the complexity at the lowest. Under these circumstances, the BP algorithm in the MBLT case will be much faster and more effective than the regular LT codes.
## 4 Computational Experiments and Analysis
### Variation in the Composite Scattering Coefficient with Different Vehicle Placement Configurations  
As shown in Figures 10 and 11, schematic diagrams of the two vehicle parking configurations are presented. The calculated composite scattering coefficients corresponding to these configurations, as well as the angular distribution of the scattering coefficient for the vehicle shown in Figure 4, are illustrated in Figure 12.  
As shown in Figure 11, the angular distribution curves of the scattering coefficient exhibit a sharp peak at angle \(\theta_{s}=-60^{\circ}\) for the vehicle under the first parking configuration, while a peak appears at angle \(\theta_{s}=-30^{\circ}\) for the vehicle shown in Figure 11. This indicates that the observed peaks are primarily caused by electromagnetic wave reflection from the sloped left side of the rough-surfaced vehicle. The varying inclination angles on the left side lead to irregular effects on the scattering coefficient.  
Figure 11: The second vehicle parking configuration.  
Figure 9: Variation in the composite scattering coefficient with incident frequency.  
Variation in the Composite Scattering Coefficient with the Root Mean Square (RMS) Height of the Vehicle Surface  
Figure 13 shows the variation in the composite scattering coefficient with surface height fluctuations of the vehicle. The root mean square (RMS) values of the surface height fluctuations are \(\delta_{1}=0.01\lambda\), \(\delta_{2}=0.05\lambda\), and \(\delta_{3}=0.1\lambda\), respectively.  
As shown in Figure 13, the composite scattering coefficient exhibits an enhanced scattering effect in the specular reflection direction \(\sigma\) and a sharp peak at \(\theta_{s}=-60^{\circ}\). Overall, the composite scattering coefficient increases with the rise in the root mean square (RMS) height of the surface fluctuations. This is because greater RMS values correspond to more pronounced surface irregularities, which lead to increased scattering in incoherent directions and reduced scattering in the coherent (specular) direction.
## 5 Discussion
### Advantages and Disadvantages of the Approach  
The advantages of the approach, first of all, consist of the high accuracy of the estimates obtained and their high resilience, which consists of the ability to keep the result of the assessment around the real value when varying the initial data in a fairly wide range. This advantage is determined by the nature of performing mathematical calculations, which are based on stochastic networks and focus on obtaining the distribution function of a random variable, which is the time of the attack. Approaches to stochastic modeling based on Markov models have the same accuracy. However, Markov models, as a rule, are associated with the need to form a state graph, and subsequently, solve a system of linear equations. With a large number of nodes in the network, the dimension of such a graph, and consequently, such an equation system will be extremely large, which will significantly complicate their application by large computer networks. At the same time, the proposed approach has fairly good scalability.  
Considering the possible disadvantages of the proposed approach, it should first of all be said that the need to perform a certain number of calculations, which may seem  
Figure 31: Graph of accuracy and recall of model training.  
unnecessarily large, force us to abandon the use of this method and use other, easier mathematical approaches. Such simpler approaches include a number of approaches based on the formation of attack graphs and the use of security metrics that take into account expert knowledge [68; 69]. However, the accuracy of estimates with these approaches depends on the expert's qualifications. In the proposed method, dependence on expert knowledge is minimized. On the other hand, the need to perform a certain number of mathematical calculations in the proposed approach may frighten users due to the lack of appropriate software and tools (frameworks) at a professional or commercial level. The creation of such frameworks is currently not a big problem. If we assume that such tools have appeared on the security software market, then the proposed approach significantly outperforms approaches based on expert knowledge.
## 1 Introduction
### PCM Contribution to Sustainability
investigated PCM contributions in thermal load modulation through numerical simulations, revealing a potential 70% reduction in wall-related heat gain during peak hours without a significant cumulative increase [43]. [PERSON] and [PERSON] reviewed the life cycle assessment (LCA) of PCMs for building applications, concluding that PCMs are more environmentally friendly compared to conventional thermal insulating materials [44]. Incorporating PCMs into building envelopes proves highly effective in enhancing energy efficiency, notably reducing cooling and heating loads, particularly during peak demand periods. [PERSON] and [PERSON] conducted a comparison between a traditional building envelope and one with PCM integration, revealing a 20% overall thermal efficiency improvement [45]. The PCM-incorporated envelope demonstrated even thermal load regulation, reducing heat loss during PCM solidification by 29%. Similarly, [PERSON] and [PERSON] investigated PCM-integrated building envelopes in NEOM, Saudi Arabia, resulting in an impressive 11,681 kWh reduction in annual energy consumption compared to a reference building with conventional insulation [46]. In addition to PCMs, the optimization of energy efficiency in building infrastructure can be effectively accomplished through the implementation of the nearly zero energy buildings (NZEBs) concept [47; 48], distinguished by their superior energy performance and substantially reduced overall energy consumption. Concurrently, the integration of the technology transfer paradigm, operationalized within the framework of the triple-helix model involving governmental bodies, the construction sector, and academic institutions, emerges as a pivotal strategy [49; 50]. This collaborative approach facilitates the seamless exchange and application of advanced technologies, fostering a synergistic environment for innovation and sustainable energy practices within the construction domain [51; 52]. This paper extends beyond existing studies by covering a wide geographical scope, analyzing diverse climatic regions across Jordan rather than limiting the focus to specific areas in Amman. This comprehensive approach facilitates a detailed analysis of PCM applications in various environmental conditions. A critical component of the study is the careful selection of PCM materials and a thorough analysis of their performance, offering new insights into the effectiveness of PCMs in different climates. Additionally, the study encompasses a comparative analysis with regions beyond Jordan, notably including extended simulations for Oradea, Romania. This inclusion provides a global perspective and validates the broader applicability of the findings. The research also places a strong emphasis on sustainability by focusing on eco-friendly PCM materials, contributing significantly to the conversation on sustainable building practices.
## 4 Discussion
[PERSON] et al., in their 2018 study, analyzed airway resistance during expiration in OSA patients compared to a control group [32]. Expiratory resistance (EXR) in the OSA group was significantly higher (\(p\) = 0.04). Moreover, the authors observed a statistically significant negative correlation between EXR and the minimum cross-sectional area of the oropharynx (\(r\) = \(-\)0.41, \(p\) = 0.02), and between EXR and the oropharynx volume (\(r\) = \(-\)0.48, \(p\) = 0.01) in OSA patients. [PERSON] et al. studied changes in the upper airway cross-sectional area of patients with varying degrees of severity of OSA. Their findings include significantly lower values (narrower cross-sectional area) at the level of uvula in expiration, lower positioning of the hyoid bone and thicker soft palate in patients with severe OSA compared to patients with mild/moderate OSA (\(p\) < 0.05) and the control group [33]. According to [PERSON] et al. each millimeter of the mandibular advancement during bimaxillary surgery led to a significant increase in the oropharyngeal volume, while every millimeter downwards reposition resulted in a statistically significant increase in the nasopharyngeal volume [34]. The reliability of upper airway volume analysis based on CBCT studies was investigated by [PERSON] et al. UAV measurements based on CBCT scans showed high intracaxaminer and interexaminer reliability (93% and 73%, respectively) [35]. Additionally, our findings are similar with those of [PERSON] et al., who described minimal retropalatal area in concordance with higher STOP-Bang scores, whose questionnaire is also a validated screening tool to identify OSA patients. They concluded that CBCT images where the minimal retropalatal area is smaller than 110 mm\({}^{2}\) might indicate patients who are at risk for developing OSA. Hence, airway measurements obtained from CBCT images and STOP-Bang questionnaire scores may be used to increase referrals to a sleep lab [36]. The present study showed statistically significant differences in oxygen blood saturation, which is in agreement with [PERSON]'s study, which noted a difference in saturation between OSA patients (94.6 \(\pm\) 2.3) and the control group (87.7 \(\pm\) 7.1%) [30]. The limitation of our study is the fact that it based on questionnaire-reported sleep disorders without confirmation of OSA diagnosis using polysomnography. The sensitivity of BQ ranges from 56% to 84%, specificity 38-59% [32]. For logistical reasons, we also measured in the study group the daytime SpO2 level instead of night-time SpO2, which seems to be more reliable. The other limitation may be associated with UAV measurement. Even though the Amira software was used to determine UAV, the landmarks were plotted in CBCT manually.
# Exploring the Impact of Virtual Reality on Painting Appreciation
## 3 Results and Discussion
### Correlations among Measurements  
The results of the Pearson's correlation analysis are presented in Table 2. Except for the absence of a significant correlation between the viewing duration and other variables, all other variables showed significant positive correlations (\(p<0.05\) and above). To improve the readability, items with high correlations (\(0.69<r<0.99\)) are indicated with a light purple background, those with moderate correlations (\(0.39<r<0.7\)) with a light red background,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subjective Questionnaire (1-5 Points)**} & \multicolumn{2}{c}{**Physical Paintings**} & \multicolumn{2}{c}{**Virtual Paintings**} & \multirow{2}{*}{**Significance**} \\ \cline{2-2} \cline{4-5}  & & & & & \\ \cline{2-2} \cline{4-5}  & & & & & \\ \cline{2-2} \cline{4-5}  & & & & & \\ \hline
1. Pleasure & 4.3  & 3.9  & 3.4  & 3.6  & *** \\
2. Relaxed & 4.4  & 4.3  & 3.9  & 3.2  & *** \\
3. Calmness & 4.3  & 3.8  & 3.3  & *** \\
4. Having access to all works I like & 4.4  & 4.4  & 3.8  & *** \\
5. Originality & 3.9 & 3.7 & 3.7 & 3.6 & \\
6. Attractive & 4.1  & 3.7  & 3.6  & * \\
7. Valuable & 4.1  & 3.7  & 3.6  & 3.4  & *** \\
8. Meaning of cloud & 4.3  & 3.7  & 3.6  & 3.7  & *** \\
9. Title encouragement & 3.9  & 3.8  & 3.7  & 3.3  & ** \\
10. Hang it in my house & 4.0  & 3.6  & 3.5  & 3.6  & ** \\
11. Preference & 3.9  & 3.6  & 3.6  & ** \\ \hline Viewing duration (s) & 137.3  & 182.4  & 138.1  & 157.4  & *** \\ \hline \hline \end{tabular}  
* \(p<0.05\), ** \(p<0.01\), and *** \(p<0.001\). [ENDFOOTNOTE] & 1 and those with low correlations (r < 0.4) with a light blue background. There was a significant positive correlation between \"pleasure\", \"relaxed\", and \"calmness\" moods, indicating that they influenced each other.  
The results also show that there were moderate positive correlations between the three moods and \"the work is attractive\" and \"the work is valuable\" items, indicating that the positive emotions generated while viewing also allowed for inducing a positive affirmation for the painting in the viewers. Simultaneously, the paintings imparted higher senses of value and were more attractive. Additionally, the degrees of preference for the paintings were significantly positively correlated with all items, except \"viewing duration\". Among them, pleasant emotions while viewing; originality, value, and attractiveness of the paintings; and the willingness to hang them at home were highly relevant. These findings suggest that enhancing these feelings can increase the fondness for a painting. Additionally, no significant positive correlation between viewing duration and preference indicates that the participants did not necessarily look longer at a painting because they liked it, and the viewing duration may be longer owing to factors such as their inability to understand its content or evaluation of the painting skills.