esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we present the ARounD (_Alternative-Route Definition_) communication scheme, that enables the definition of alternative communication paths for peer-to-peer message streams in IEEE 802.15.4/ZigBee-based cluster-tree WSNs. The ARounD communication scheme uses cluster-tree nodes during their inactive periods, in order avoid interferences with the cluster-tree network traffic. As a consequence, the ARounD communication scheme does not use the previously-defined cluster-tree paths, being able to improve several communication metrics such as: network congestion and end-to-end communication delays.  
Simulation results highlight that the use of the ARounD communication scheme can significantly decrease the end-to-end communication delay for source-to-destination data traffic, because it defines shorter inter-cluster paths between source and destination nodes and also because it is able to schedule such alternative paths in just one beacon interval. Furthermore, as source-to-destination messages do not contend for the wireless channel access with other messages, it avoids additional delays and dropped packets due to collisions. It is also shown that the ARounD communication scheme has just  
Figure 35: Packet Loss Rate for multiple many-to-one message streams, considering the ARounD Approach vs. Standard Approach under different traffic loads imposed by sensor nodes.  
Figure 36: Packet Loss Rate for multiple many-to-many message streams, considering the ARounD Approach vs. Standard Approach under different traffic loads imposed by sensor nodes.  
a negligible impact upon the cluster-tree communication and can be used to support the transfer of messages, guaranteeing limited QoS requirements such as bandwidth and softly-bounded delays.
# Plasmonic and Dielectric Metasurfaces: Design, Fabrication and Applications
## 3 Dielectric Metasurfaces
### OAM Beam Generation Using a Nanophotonic Dielectric Metasurface Array [123]
Figure 20: Schematic of eight dielectric rectangle resonators chosen to generate OAM beams, providing a phase shift from 0 to 2\(\pi\) and nearly constant amplitude. OAM beam generation using dielectric metasurface at 980 nm: We further design, fabricate and test the dielectric ellipse metasurface arrays at a wavelength of 980 nm. Figure 24 shows the experimental results of the fabricated dielectric ellipse metasurface arrays. Figure 24a plots the measured intensity profiles of the generated OAM beams of \(l\) = 1-4 using the fabricated metasurface arrays. Figure 24b exhibits the measured intensity profiles of generated OAM beams after being interfered by Gaussian beam. One can clearly see from Figure 24b that the number of helical arms in the distribution of the interference intensity is equal to the topological number \(l\) of the OAM beam, indicating the favorable results. Fabrication: The proposed rectangle and ellipse dielectric metasurfaces can be fabricated using the existing fabrication techniques on the SOI platform. Figure 25a shows the schematic diagram of the fabrication process of the proposed rectangle and ellipse dielectric metasurfaces: standard  
Figure 23: Simulated intensity and phase distributions of the generated OAM beams by the proposed dielectric ellipse metasurface array at a wavelength of 632.8 nm. Reproduced with permission from [123], Copyright The Optical Society, 2016. Figure 24: (**a**) Experimental intensities of the generated OAM beams of \(l\) = 1–4 by the proposed dielectric metasurface array at a wavelength of 980 nm; (**b**) experimental intensities of the generated OAM beams after being interfered with by the Gaussian beam with \(l\) = 0. Figure 22: Concept of OAM beam generation using the dielectric ellipse metasurface array. electron-beam lithography to form patterns followed by induced coupled plasma silicon etching. Figure 25b displays the scanning electron microscope (SEM) details of the top view of a fabricated dielectric ellipse metasurface array to generate the OAM beam.
## 7 Conclusions  
A malicious PUE detector was proposed based on an SVM with Renyi entropy analysis of energy received for an MCRN. The system was examined in an SDR scenario by using USRP NI-2922 configured with GMSK and OFDM modulations, and the expected results were simulated with Monte  
Figure 12: Probability of detection with SVM vs. entropy for OFDM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SNR Value** & **Proposed SVM** & **Entropy [30]** & **Entropy [35]** & **Energy [36]** & **Energy [37]** \\ \hline \(-\)20 dB & 36\% & 4\% & 0\% & 0\% & 0\% \\ \(-\)18 dB & 82\% & 50\% & 0\% & 0\% & 0\% \\ \(-\)17.5 dB & 90\% & 85\% & 0\% & 0\% & 5\% \\ \(-\)15.5 dB & 92\% & 90\% & 0\% & 0\% & 22\% \\ \(-\)15 dB & 96\% & 94\% & 0\% & 0\% & 25\% \\ \(-\)10 dB & 99\% & 99\% & 83\% & 50\% & 60\% \\ \(-\)8.5 dB & 99\% & 99\% & 96\% & 90\% & 92\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Probability of detection vs. SNR values.  
Carlo experiments in AWGN channels. The results show that for the two modulations, the probability of detection increases when compared with other detection techniques, without the dependence of a threshold such as energy or entropy techniques by themselves and with better results in low SNR values.  
The results show that the SVM outperforms the energy detector method found in the literature by about 12 dB and the entropy method by about 3 dB. The influence of noise decreases due to the entropy calculation, which allows the system to detect PUE with lower SNR values. The system works in the two modulations, but it shows a better performance in OFDM systems where the threshold is hard to find, and it is dynamic in other detection techniques.  
The SDR experiments show that the SVM for detection can be implemented in MCRN, and the learning process does not take a significant amount of time for one completion. After that, the system can detect malicious PUE in real-time without an excessive complexity for calculations and with a high probability of detection under lower SNR values.  
Regarding future research, we suggest exploring the cooperative scenario in MCRN, where all users are in movement in an indoor scenario and an outdoor scenario.  
Methodology, software, and hardware: E.C.M.; experiment design: E.C.M., L.F.P.M., and J.E.O.T.; performed the experiments: E.C.M., L.F.P.M.; results analysis, review and editing of the paper: E.C.M., L.F.P.M. and J.E.O.T. All authors participated in the discussion and proofread the work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Colciencias, grant number Doctorado Nacional 757. The APC was funded by Universidad Distrial Francisco Jose de Caldas, CIDC.  
We express our gratitude to Colciencias, Universidad Distrial Francisco Jose de Caldas, Centro de Investigaciones y Desarrollo Cientifico (CIDC) and Universidad Nacional de Colombia for the founding of this Project.  
The authors declare no conflict of interest.
# Morphometric Analysis and Three-Dimensional Computed Tomography Reconstruction of Thai Distal Femur
## 4 Discussion
The distal femur of Thais was slightly different from that of the Chinese and Japanese populations as well [5, 6, 7, 8]. However, they were smaller than those of the Indian femur [30]. To analyze the relationship between each parameter, the morphometric data of distal femur in males and females were investigated using linear regression and correlation analysis. This is due to the statistically significant difference in morphology between males and females. Correlation pairs of morphometric parameters found differences between males and females. The high correlation coefficients occurred in medial anteroposterior width (MAP) and anteroposterior width (AP) both males and females. The correlation between each pairwise is a strong linear relationship if it falls within the range between 0.7 and 1.0. The equation that is a clear linear relation can be used to estimate the dependent variable precisely. For example, a linear regression equation is used to predict morphometric parameters in forensic anthropology. Using morphological data for the design of prosthetic knee, such as mediolateral (ML) and anteroposterior (AP), the size of Thais distal femur was compared to the commercially used prosthesis in Thailand. The results showed that there was a mismatch between the distal femur of Thai and the knee prosthesis, which is consistent with other studies [6, 23, 24, 30]. In the prosthesis design of a distal femoral component, at least six sizes should be available to cover Thais' anatomy, especially the ML and AP values. Femoral component overhang leads to soft tissue impingement, which causes painful irritation of the knee tendons and ligaments, while the undersized femoral component leads to increased blood loss through exposed cancellous bone, and the fit between a femoral component and a femur was also considered [39]. The limitation of this study is that the volunteers in this study were healthy and between 20 and 50 years old. The morphometric data from the normal femur may differ from the osteoarthritic femur. Moreover, most volunteers who participated in this study lived in Nakhon Ratchasima province. The difference in residence of volunteers may also affect the average values of morphometric data in the Thai population. Morphometric data of the distal femur were measured based on 3D models, the accuracy of which depends on the threshold value and mesh quality. In addition, measurements based on 3D models of the femur did not include cartilage thickness.
# Stents in Congenital Heart Disease: State of the Art and Future Scenarios
## 3 Congenital Heart Disease  
The term CHD indicates a structural or functional abnormality of the heart or intraducic great vessels arising during pregnancy. CHD may occur in many forms affecting different parts of the heart, which can be generally classified into three main categories [30]: (i) cyanotic heart disease, including transposition of the great arteries, tetralogy of Fallot, tricuspid atresia, pulmonary atresia, double outlet right ventricle, and total anomalous pulmonary venous connection; (ii) left-sided obstructive lesions, among which are hypoplastic left heart syndrome (HLHS), mitral and aortic stenosis, aortic coarctation (CoA), and interrupted aortic arch; (iii) septation defects, which may involve the atria, the ventricles, or the central part of the heart. Other functionless abnormalities of the great veins or the branches of the aortic arch as well as congenital arrhythmias are generally not included in this classification. CHD represents the most common congenital malformation diagnosed in newborn babies, with an incidence of about 6 of every 1000 live births for severe and moderate forms of CHD, which increases up to 75 of every 1000 live births including also minor forms [31]. Although morbidity may vary according to the severity of the specific defect form, CHD is retained as the leading cause of mortality related to congenital defects overall [32]. However, the improvements in diagnostic and therapeutic procedures in the last decades have strongly increased life expectancy, leading to a consistent demographic shift, with an expected increase in the population of adults with CHD [33]. Since critical CHD forms are not compatible with life, medical intervention is frequently required soon after birth [34], relying on the combination of open-heart surgery in multiple steps with percutaneous transcatheter interventions such as stenting.  
Given the inherent complexity of the different CHD forms and the associated medical interventions, a clear comprehension of the evolution and distribution of blood flow in the various districts during intrauterine life is fundamental for understanding how congenital cardiac malformations affect both fetal growth and adaptation at birth. Therefore, in the following section, the characteristic features of fetal circulation are analyzed in comparison to adult circulation, providing a clear picture of how specific CHD forms can be managed during fetal life.
# Applying Reinforcement Learning for AMR's Docking and Obstacle Avoidance Behavior Control
## 5 Conclusions  
The main contribution of this study is the use of AprilTag and DQN reinforcement learning to replace traditional docking algorithms. In general robot docking control, rules, features, or landmarks are used along with tuning parameters for optimal control. However, if the robot's parameters are not correctly adjusted, docking may fail, or recognition may be inaccurate. Therefore, we utilized the DQN machine learning method to enable human-like control behavior, allowing the robot to perform docking more accurately and naturally. However, DQN reinforcement learning requires a large amount of training data, which is time-consuming and costly to collect in real-world scenarios. To overcome this challenge, we applied the Gazebo robot simulation environment as a sandbox for pretraining. Relative pose information was obtained from AprilTag and laser data, and DQN was used for autonomous training in path planning. By configuring environment size, speed, and angular velocity, the robot was able to learn autonomously using the DQN method. Experimental results demonstrated that setting 50 neurons was sufficient to achieve docking and obstacle avoidance with excellent performance. Although DQN performed well in our study, we acknowledge that other reinforcement learning (RL) methods, such as Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Deep Deterministic Policy Gradient (DDPG), were not compared [24; 25; 26]. While DQN is suitable for discrete action spaces and provides computational efficiency, future work will explore a comparative study to assess the trade-offs between DQN and other RL approaches. This will provide a more comprehensive understanding of the effectiveness of different RL techniques in autonomous mobile robot navigation and docking.  
Conceptualization, C.-C.L.; methodology, C.-C.L.; software, B.-J.Y.; validation, B.-J.Y.; formal analysis, B.-J.Y.; investigation, B.-J.Y.; resources, B.-J.Y.; data curation, B.-J.Y.; writing--original draft preparation, B.-J.Y.; writing--review and editing, B.-J.Y. and C.-J.L.; visualization, B.-J.Y. and C.-J.L.; supervision, B.-J.Y. and C.-J.L.; project administration, C.-C.L. All authors have read and agreed to the published version of the manuscript.  
The authors conducted this study without financial support from external organizations or funding agencies.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
We acknowledge [PERSON] and [PERSON] for their assistance with the experiments.  
The authors declare no conflicts of interest.  
Figure 23: Robot docking completed via DQN.
## 3 Constitutive Model of Rock Mass Under THMC Coupling
### Constitutive Model Under THMC Coupling with Governing Equations
#### Hydraulic Behavior (H)  
The hydraulic field in the rock mass is driven by the flow of water through pores and fractures, typically described by [PERSON]'s law. Pore pressure \(p\) influences mechanical behavior (effective stress) and interacts with chemical processes such as mineral dissolution or precipitation.  
The governing equation is as follows:  
\[q=-k\
abla p \tag{4}\]  
In Equation (4), \(q\) is the Darcy velocity (fluid flux), \(k\) represents the permeability of the rock, and \(\
abla p\) is the pressure gradient.  
The coupling with mechanical fields occurs through changes in pore pressure affecting the effective stress:  
\[\sigma^{\prime}=\sigma-\alpha p \tag{5}\]  
where \(\sigma^{\prime}\) is the effective stress, \(\alpha\) is the Biot coefficient, and \(p\) is the pore pressure.  
The mass conservation of fluid in the porous medium is described as follows:  
\[\frac{\partial\theta}{\partial t}+\
abla.p=Q \tag{6}\]where \(\theta\) is the porosity, \(t\) is time, and Q is the source or sink term (e.g., fluid injection or withdrawal).
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards
### MDP Model for Expected Total Rewards
#### 3.2.4 State Transition Probability  
After action \(a_{i}\) is selected, the system states will migrate from \(s_{i}\) to \(s_{i+1}\), which can be expressed as \(s_{i}\triangleq\left\langle{{{h_{{\rm{S}R}^{i}}},{{h_{{\rm{S}D}^{i}}},{{h_{{ \rm{R}D}^{i}}}},{{\epsilon}(i)}}}}\right\rangle\to s_{i+1}\triangleq\left\langle {{{h_{{\rm{S}R}^{i+1}}},{{h_{{\rm{S}D}^{i+1}}},{{h_{{\rm{R}D}^{i+1}}}},{{ \epsilon}(i+1)}}}}\right\rangle\). Since the value of channel fading is equal probability, the state transition probability is:  
\[p[{{s_{i+1}}}]{{s_{i}}},{{a_{i}}}]=\frac{1}{{K^{3}}}p[{{\varepsilon_{i+1}}}]{ {\varepsilon_{i}}},{{a_{i}}}]. \tag{32}\]  
We assume that \(\varepsilon(i)\) and \(\varepsilon(i+1)\) are in the \(M_{1}\) and \(M_{2}\) levels of the battery, which should satisfy the Equation: \(\frac{{{M_{2}}{{E_{\max}}}}}{M}\leq\frac{{{M_{1}}{{E_{\max}}}}}{M}-{{a_{i}}}* \frac{{{T}}}{2}+{E_{H}^{i}}{{E_{e}}}<\frac{{{({M_{2}}+1){{E_{\max}}}}}}{M}\). As mentioned in Section 2.2, the energy harvesting process obeys the Poisson distribution with mean \(\lambda\). Therefore, Equation (32) can be further given as:  
\[p[{{s_{i+1}}}]{{s_{i}}},{{a_{i}}}]=\frac{1}{{K^{3}}}\sum\limits_{n ={{n_{1}}}}^{{{n_{2}}}}\frac{{\lambda^{n}}}{{n!}}{{e^{-\lambda}}}, \tag{33}\]  
where \(n_{1}\) and \(n_{2}\) is given as \(n_{1}=\left\lceil{\frac{{{M_{2}}{{E_{\max}}}}}{M}+{{a_{i}}*\frac{{{T}}}{2}} \frac{{{M_{1}}{{E_{\max}}}}}{M}}\right\rceil\) and \(n_{2}=\left\lceil{\frac{{{({M_{2}}+1){{E_{\max}}}}}}{M}+{{a_{i}}-{{M_{1}}} \frac{{{E_{\max}}}}}{M}}\right\rceil-1\), respectively. \(\lceil x\rceil\) is the function that rounds the variable x up.
## 5 Results Analysis and Discussion
### Computational Efficiency and Deployment Considerations  
Training efficiency: Our experimental results show that the optimal LSTM-DNN(64,1,2) configuration demonstrates favorable training characteristics, with training times averaging approximately 10.5 min per model instance based on our experimental records. Larger configurations with 256 or 512 LSTM units were more prone to convergence issues and required substantially longer training times.  
Model configuration trade-offs: The research finding that smaller network configurations consistently outperform larger ones has important implications for practical deployment. Specifically, models with 64 LSTM units showed better stability and performance compared to configurations with 256 or 512 units, which frequently experienced non-convergence issues. This contrasts with the general assumption that larger networks yield better performance and suggests that resource-efficient models may be more suitable for practical applications.  
Deployment feasibility: Shallow learning models such as linear regression and random forest require minimal computational resources for both training and inference, making them suitable for resource-constrained environments. The multi-run training strategy, while improving model reliability, increases computational cost by requiring five model instances per configuration, which may be a consideration for resource-limited deployments.
# Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability
## 1 Introduction
Although this approach provides a predictable and visual method, disregarding ignored positions and postures may also lead to an incomplete reachability analysis. Third, the geometric calculation-based approach can be summarized in the following steps. The hand position and solution space of the shoulder joints' position are determined, then, several random shoulder joint positions are selected in this space. Next, the arm pose is calculated on the basis of constraints, such as joint angle, collision, and hand and shoulder joint positions. The maintenance spot is reachable if a set of solutions can be obtained, otherwise it is unreachable. This approach has a low dependence on the person, but it does not consider all arm poses because it can only obtain and evaluate limited solutions. It is also a local area-oriented analysis method. From the view of kinematics, human arms, especially the hand, are the most flexible segments, owing to a series of joints, including the humerus, ulna, radius, carpi, metacarpi, phalanges, and corresponding muscles. Hence, the arms and hands, with a high participation, played a vital role in maintenance, wherein almost all maintenance activities rely on arms and hands. Consequently, the focus on arms and hands is functional and effective for the maintenance reachability analysis. On the basis of the analysis above, avoiding the poor timeliness of the physical prototype-based way is the first issue to be resolved in obtaining a predictable and objective result in the maintenance reachability analysis. Decreasing the subjectivity of the virtual prototype-based method is another issue that needs to be addressed. In addition, accuracy and efficiency should also be concerns. Consequently, by adopting the virtual prototype-based approach for a predictable analysis, as shown in Figure 1, this study proposes an objective and quantitative methodology to implement the maintenance reachability analysis. First, unlike the traditional methods that started from the human body, the presented methodology starts from the maintenance spot where the human hand is attached. Second, to enhance objectivity, an original global data sequence set, including the wrist, elbow, and shoulder joints under the constraints of kinematics, is generated, in which a data sequence represents an arm motion by processing the spatial data based on the kinematic relationships among the joints of the human arm, using the position and rotation data of each joint. Third, in terms of accuracy, the surrounding objects are also represented by their geometric data. Each data sequence is analyzed to judge whether collision occurs between the arm segments and surrounding objects. In this filtering process, the data sequence is retained if the aforementioned collision does not occur. In terms of efficiency, owing to the large amount of global data sequences, the efficiency of the interval selection in the collision calculation is also taken into consideration in this methodology.