esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
A secure three-party key establishment technique for TMIS is presented to secure patients' medical prescriptions. It uses ECC for end-user anonymity, using the secure authentication and prescription safety (SAPS) protocol that can establish a secure connection between patients and a  
Figure 8: Probability of compromised intermediate device.  
Figure 9: Bytes revealed upon device compromise.  
doctor/nurse without revealing their secret identities. The proposed scheme achieves anonymity and un-traceability of the participants. The SAPS protocol has been analyzed by applying Rubin Logic to verify security, user anonymity, and un-traceability of participants during session key generation for secure information sharing between the doctor and the patient in TMIS. Public attacks and their countermeasures are analyzed at each level. For validation of SAPS, we have performed simulation using NS-2.35 to compare the performance of SAPS for storage, communication, and computation overheads, as compared to other methods to prove its suitability for the ubiquitous TMIS network. The storage cost reduction of the proposed scheme at the user side and at the server side is approximately 38% and 41%, respectively, as compared with the mean of the four latest existing techniques. The average computation overhead reductions at both the user side and the server side are 37% and 49%, respectively, as compared with the mean average of existing four schemes. Our future work will aim to analyze the impact of chaotic map-based keying for multi-party authentication between doctors and patients to get a medical prescription for common diseases without revealing either identities on an open network.  
This work was funded by the National Natural Science Foundation of China (61471035), and Fundamental Research Funds for the Central Universities (06105031, 06500010). In particular, it was supported by Cybernatics and Cyberspace International Science and Technology Cooperation Base.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data and overall proposed protocols, models, structure and flow of entire paper; [PERSON] and [PERSON] performed the formal modeling and verification of proposed scheme. [PERSON] contributed reagents/materials/analysis tools and also wrote the paper along with literature.  
The authors declare that there is no conflict of interest in this research.
# Analyzing the Biomechanical Characteristics of Ski Jumping Take-Off Phase Based on CFD
## 3 Results
### Drag Characteristics at Different Moments  
#### 3.2.1 Aerodynamic Characteristics at the Start Moment of the Take-Off Phase (SMT)  
As depicted in Figures 5 and 6, the static pressure experienced by athletes at the SMT is predominantly concentrated on the inner thighs and torso. As the fluid flows over the athlete's body surface, it generates relatively low-pressure zones in the flow area, leading to reduced static pressure on the outer sides of the athletes' lower limbs during take-off.  
The results indicate that the air drag experienced by various limbs of elite athletes varies based on the angle at the SMT. As shown in Table 3, during the initial stage of take-off, the drag on the left and right calves of Chinese athletes is significantly greater than that of NW elite athletes, measuring 13.26 N and 12.06 N, respectively. In contrast, the drag on the left and right calves of NW elite athletes is 2.08 N and 2.34 N, respectively. At the start moment of take-off, the drag on the left and right thighs of Chinese athletes is lower than that of NW elite athletes, measuring 0.02 N and 0.01 N, respectively, whereas the drag on the thighs of NW elite athletes is higher, with left and right drags of 2.88 N and 3.00 N, respectively. The discrepancy in drag borne by the trunk is even more pronounced: Chinese athletes experience a drag of 24.97 N on their trunk, compared to approximately 3.71 N for NW elite athletes. The total drag experienced by the athlete-ski system at the start moment of take-off for Chinese and NW elite athletes is 108.90 N and 55.40 N, respectively (Table 3).  
Figure 4: Coordination of limb joints in Chinese athletes during the take-off phase (CRP).  
Figure 5: Static pressure diagram of Chinese athletes at the start moment (SMT).  
Figure 6: Static pressure diagram of NW elite athletes at the start moment (SMT).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **LLD** & **RLD** & **LTD** & **RTD** & **TOD** & **TD** \\ \cline{2-7}  & **(N)** & **(N)** & **(N)** & **(N)** & **(N)** & **(N)** \\ \hline CA & 13.26 & 12.06 & 0.02 & 0.01 & 24.97 & 108.90 \\ \hline NWA & 2.08 & 2.34 & 2.88 & 3.00 & 3.71 & 55.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Drag characteristics at the start moment of the take-off phase (SMT).
# Physical, Thermal, and Mechanical Characterization of PMMA Foils Fabricated by Solution Casting
## 3 Results  
### Surface Features of PMMA Powder, Foils, and As-Cast Foils  
The surface features of PMMA powder and foils were prepared by the solution casting method and investigated with the microscope shown in Figure 2a-d. The PMMA powder shows a hollow structure. The commercial foil of PMMA shows transparent surface film without any inhomogeneities. However, in the sample prepared from 18 mL, the solution shows undissolved powder particles on the surface of the film. This indicates that complete solubility of powder within the solution was not achieved, and, as a consequence, there was some remaining undissolved powder left behind in the solution. The foils generated from the 24 mL solution showed a smoother, transparent surface without any inhomogeneities.  
The uniformity of the surface features is investigated by examining the surface area in a three-dimensional image profile. The photographic image of the foils with corresponding 3D images was examined. The images are shown in Figure 3a-d.  
The surface thickness and uniformity are well-marked from the 3D image profile. The PMMA foils prepared from 18 mL solution showed uneven surface thickness, which confirmed the height of the image profile, whereas the foil prepared from 24 mL solution shows uniform surface thickness with the confirmed image of the surface depth profile.
# Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin
## 4 Case Study of Seasonal Variation in Light-Use Efficiency and SIF across the Amazon Basin
### Data Retrieval
#### 4.1.1 Photochemical Reflectance Index
We approximated shadow effects by computing the monthly average PRI (Equation (3)) for both geometry views: \(\text{PRI}_{\text{forward}}\) (\(\text{PRI}_{\text{f}}\)) and \(\text{PRI}_{\text{backward}}\) (\(\text{PRI}_{\text{b}}\)). By averaging the monthly difference (\(\text{PRI}_{\text{diff}}\)) between the two geometry views, we reduced noise and obtained reasonable estimates of variation in photosynthetic efficiency between the wet and dry seasons between the years 2000 and 2012. Figure 3: (**a**) The area, highlighted in red, was sampled with MODIS imagery; (**b**) MODIS datasets required to calculate monthly values of Photochemical Reflectance Index (PRI)consisted of 156 mosaics for Band 12 (546 to 556 nm) and Band 11 (526 to 536 nm) for each viewing angle for the years specified in the text.
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 4 Application Research and Method Comparison
### Feasibility Verification of the Prediction Model  
To further verify the prediction effect of the optimal combination prediction model obtained in Section 4.5, the data from 101 to 120 weeks were used as the validation set and substituted into the model for prediction. The prediction results are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Number**} & \multicolumn{4}{c}{**Evaluation Index**} \\ \cline{2-5}  & **MAPE (\%)** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline Model 1 & 0.2763 & 0.9469 & 0.6396 & 0.7261 \\ Model 2 & 0.1883 & 0.0377 & 0.0469 & 0.9891 \\ Model 3 & 0.2565 & 0.3728 & 0.5914 & 0.8922 \\ Model 4 & 0.6082 & 2.4631 & 1.4048 & 0.2875 \\ Combined model & 0.1685 & 0.0371 & 0.0384 & 0.9893 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation index values of different models for cumulative displacement.  
Figure 12: Cumulative displacement prediction results of the combined model.  
It can be observed from Figure 13 that the two curves produced by the actual value of the validation set and the predicted value of the model were relatively close. The corresponding evaluation index values were MAPE = 0.3493%, MSE = 1.1635, RMSE = 0.9001, and R\({}^{2}\) = 0.9895. This further verifies that the combined model had a good prediction effect, thus offering a solution problem in the field of landslide displacement prediction.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 3 Pressure Support Ventilation (PSV)
### Parametric Modeling Techniques  
The parametric models refer to the techniques that need some parameters to be specified before becoming eligible for making predictions [55]. A number of parametric modelling approaches, including autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA), have been proposed for the analysis of RR intervals [56]. AR, MA, and ARMA models have been used to analyze RR intervals of a small duration (e.g., 5 s) [57]. This may be attributed to the stationary nature of the small-duration RR intervals. The stationarity of the RR intervals is usually verified using techniques like Augmented Dickey-Fuller (ADF). On the other hand, models like ARIMA have been suggested for the relatively longer RR intervals, which are nonstationary [56].
## 3 Study Area and Datasets  
The Sichuan-Yunnan region is located in the area where the Eurasian plate and the Indian plate collide with each other and squeeze strongly. It covers the Sichuan-Yunnan diamond block, the southern Yunnan block, the western Yunnan block, and the eastern part of the Bayanhar block. This region consists of main faults like the Longmen Mountain fault, Anning River fault, Zemu River fault, Xiaojiang River fault, Honghe River fault, and Xiaojinhe River fault, and is the most significant area for strong earthquakes in western China [36]. The area is about 865,000 square kilometers, and is twice as large as California and almost three times larger than Italy. The seismicity in the Sichuan-Yunnan region is at a high level. Up to now, more than 30 earthquakes larger than magnitude 7.0 have occurred, and these earthquakes have caused significant casualties and property losses. For example, the great Wenchuan earthquake killed more than 69,000 people and caused 852.3 billion Yuan direct economic losses [37]. After a destructive earthquake occurs, a reconnaissance team will be sent to conduct field investigation and produce an MSI map that reflects the scope and degree of the ground impact caused by the earthquake. These MSI maps are very valuable for earthquake emergency response and post-disaster rehabilitation. To measure the ground motions caused by earthquakes, there are now 400 permanent, strong ground motion observation stations mounted in this region: 224 stations in Sichuan Province and 176 in Yunnan Province. These stations have recorded high-quality, strong ground motions in the past few years [38].  
The MSI for a station in an earthquake is determined by adding the location of the station to the MSI map and determining which isoseismic line encircles it. Nine moderate-to-large earthquakes that have both investigated MSI maps and ground motion records are analyzed in this study. The locations of these earthquakes, as well as the spatial distribution of the strong motion stations, are displayed in Figure 1. There are 106 different strong motions stations, and some stations record more than one earthquake event. The detailed information of the nine earthquakes and the numbers of strong motion records are shown in Table 1. The surface magnitude (Ms), which is used in China to measure earthquake magnitude, of these earthquakes varies from 5.8 to 8.0, and the depths are from 5 km to 33 km. The epicentral distances of stations are from 6 km to 312 km, and the MSIs are from VI to IX. In total, 125 pairs of ground motion records and MSIs are used to analyze the relationship. The complete information of the 125 sets of data on MSIs, station names, site conditions, and epicentral distances is shown in Supplementary Materials Table S
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Blockchain and GDPR: An In-Depth Analysis
#### 5.3.4 Developing a Global Ethical and Regulatory Framework for AI in Healthcare  
In the realm of AI healthcare applications, the intersection of ethical and regulatory concerns is of paramount importance. The urgency of developing a comprehensive global legal and regulatory framework tailored explicitly for AI in healthcare is underscored by the author of [1]. Such a framework must be adaptable to the rapid advancements in AI technology and ensure that these technologies are utilized ethically and securely within healthcare settings. This adaptability is critical due to the unique challenges posed by AI that traditional regulatory measures may not fully address. The framework should be robust and adaptable, capable of evolving alongside technological advancements while safeguarding ethical norms to ensure the beneficial application of AI in healthcare.  
Furthermore, the proposal [2] for establishing a data governance panel in AI healthcare applications highlights the need to mitigate biases in AI systems. The panel, comprising diverse stakeholders including patient representatives, clinical experts, ethicists, and AI specialists, would play a vital role. Their collective responsibilities would involve reviewing and monitoring the datasets used for training AI systems, ensuring that these datasets encompass a variety of demographic groups, clinical scenarios, and health conditions to avoid biases that could result in skewed or unfair outcomes in healthcare delivery. Additionally, the panel would be responsible for the ongoing monitoring of AI systems post-deployment, continuously assessing their performance and ensuring their fairness and alignment with the evolving needs and demographics of the patient population. The collective insights from [1; 36; 37; 38] emphasize the imperative balance between rapid technological advancements in AI and the ethical considerations guiding their application in healthcare. This balance is essential for respecting patient privacy and agency, addressing potential biases, and upholding human dignity and integrity in patient care. A comprehensive ethical and regulatory framework is thus needed to tackle issues such as decision-making autonomy, algorithmic discrimination, data privacy, and security, considering the unique challenges posed by AI, such as the need for explainability and the potential impacts on human dignity and integrity.
## 2 System Architecture
### Patrol Robot  
The patrol robot comprises of a processor, wheeled robot, and stereo vision system (SVS). An ASUS K401D notebook running Windows 7 was used as a processor. USB-connected ASUS Xtion pro live was used as an SVS to provide color images and depth information. Owing to that the depth information provided by SVS is based on its active infrared sensor, and the sensor is very sensitive to sun ray, the proposed system in this research just can be used indoors. The wheeled robot was built by integrating a KOBUKI robot, a product that is designed for programmable control without default functions, and an RFID system using an Arduino Uno R3 controller. Depth information of the environment captured by the SVS is analyzed by the processor to find a suitable path. The direction of movement is transformed into commands controlling the robot. The SVS continues monitoring the environment in order to make adjustments to the path in real-time. The cliff sensor, wheel drop sensor, and bumpers of the KOBUKI are also used to modify the patrol path. The SVS also seeks out inspection points and detects intruders. The robot is programmed to approach the inspection points and follow intruders until they disappear. All of this information is transmitted to a database via Wi-Fi. The structure of the proposed robot is presented in Figure 2.  
Figure 1: System architecture of autonomous patrolling robot.
## 2 Related Work
### Spatiotemporal Models and Machine Learning for Traffic Prediction and Scheduling  
To address the challenges of latency variability and bandwidth fluctuations in satellite networks, spatiotemporal models and machine-learning-based methods have become key research directions. Spatiotemporal models integrate the temporal and spatial characteristics of networks, enabling dynamic resource allocation adjustments to changing conditions. Machine learning approaches analyze historical data to predict future traffic demands, optimizing scheduling and resource management [16; 17; 18]. For instance, [PERSON] et al. [16] employed spatiotemporal data fusion techniques to achieve precise traffic predictions, significantly enhancing resource allocation. Advanced techniques like deep learning and reinforcement learning have shown substantial improvements in network throughput and latency reduction. For example, [PERSON] et al. [19] demonstrated that reinforcement learning can dynamically optimize resource scheduling, greatly reducing latency. Similarly, Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) networks have been applied to model traffic fluctuations, enabling dynamic bandwidth allocation [20; 21; 22]. In [21], LSTM networks effectively captured traffic patterns, supporting real-time optimization. Reinforcement learning has also been used for real-time traffic distribution and scheduling, with [23] showcasing significant gains in network utilization through cross-path distribution.  
Despite these advances, machine learning and spatiotemporal models heavily rely on historical data, limiting their real-time adaptability. Their generalization and responsiveness to sudden traffic changes remain critical challenges. Innovative approaches are needed to address these bottlenecks, ensuring both predictive accuracy and adaptability in dynamic satellite network environments.
## 4 Results and Discussions  
This section discusses two main issues: the performance of the monitoring messaging methods implemented in the testing platform and the evaluation of a complex transmission system using GNU radio. As previously explained, the testing and evaluating communication protocols at the PHY layer generate many monitoring messages that must be handled in real-time. These messages' size generation and handling are essential and will be assessed in the developed platform. The aim of testing an example transmission system is to demonstrate the capabilities of the proposed platform.
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 2 5G System
### Network Interfaces  
The mentioned network interfaces refer to the connections between different software modules within the 5 GC. In general, each of these connections follows specific protocols and standards. In the core context, four interfaces stand out--\(N3\), \(N4\), \(N6\), and \(N9\)--each with its standardized functions, while other interfaces are not covered in this context.  
* **N3--**Communication interface between NR and UPF. This interface carries the data encapsulated for routing [8].
* **N4--**The control exerted by the SMF over the UPF is carried out through this interface, with all their communications taking place exclusively through it [8].  
* **N6--**The UPF connects to external data networks through this interface. Data no longer undergo the encapsulation process at this stage, although implementing a Virtual Private Network (VPN) is feasible [8].
* **N9--**Within the core infrastructure of 5G, it is possible to deploy UPFs in a sequential configuration. These UPFs are interconnected through the \(N9\) network interface, and scenarios involving mobility are examples that use this approach [8].
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 4 Experimental Results and Discussion
### Received Data Analysis  
Figure 8 shows the total amount of data received by the sink node over time. This experiment was conducted in a BER 0.15 environment with 200 nodes. It can be seen that the network with RS(68, 32) achieves a higher instantaneous data collection rate than RS(36, 32) owing to its larger parity length, but it is impossible to collect data stably because the node is out of power for long periods. On the contrary, the data collection rate of the network with RS(36, 32) is more consistent than that of RS(68, 32), owing to the lower blackout time. However, the total amount of data collection is the lowest, because the error correction rate is too low to prevent the retransmission of broken data. Compared with RS(68, 32) and RS(36, 32), two-level RS and EA-RS collect data with higher rates most of the time. In particular, EA-RS achieves a higher amount of data collection than two-level RS, because it controls the parity length more finely and precisely.  
Figure 7: Blackout for varying number of nodes during the entire time (BER 0.15).  
In addition, we experimented with a different number of nodes in the same environment. Figure 9 shows the data collection results for the sink node according to the number of nodes. The amount of collected data for the EA-RS method is the highest in every network size, and the difference increases as the number of nodes increases.