esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Design Techniques for Two-Dimensional Leaky-Wave Antennas
## 4 Radially Periodic Leaky-Wave Antennas
### A Simple Technique for the Bloch Analysis
As shown in Figure 9b (for the two case studies \(N=6\) and \(N=7\)) and Figure 9c (for the two case studies \(N=15\) and \(N=16\)), this method perfectly evaluates the phase constant \(\beta\) regardless of the number of periods utilized in the analysis. This is not the case for the evaluation of the leakage constants as shown in Figure 9d,e, since the evaluation of \(\alpha\) is typically more difficult [94]. By adding unit cells in the CST simulation, indeed, the only effect is to introduce additional numerical noise. This is due to the fact that \(\alpha\) represents an  
Figure 8: The complex leaky radial wavenumber, \(k_{\rho}\), of the radially periodic LWA (represented on the **left**) can be approximately seen as the complex leaky longitudinal wavenumber \(k_{x}\) of an infinite, linear, metal strip grating with phase advance normal to the printed strips (as shown on the **right**) [7]. amplitude decay and, by considering high values of \(N\), the evaluation of this parameter is performed over distances at which most of the power has already been radiated by the equivalent 1-D periodic LWA implemented on CST. On the other hand, a small number of periods does not suitably represent the mutual coupling among unit cells. By observing the dispersion curves of the leaky phase and attenuation constants, the presence of the open-stopband phenomenon, which is related to the contradirectional coupling between two Floquet-Bloch harmonics, is clear [41]. When \(\beta\) tends to zero, indeed, the value of \(\alpha\) varies quickly, preventing the proper generation of a beam at broadside [41]: initially, it shows a sharply peaked behavior due to the accumulation of reactive energy, and then it drops to zero at the broadside frequency [3]. Although this aspect goes beyond the interest of this paper, it is worthwhile to point out that different techniques were proposed to mitigate or suppress the open stopband, thereby improving the near-field [96] and far-field radiating properties of 1-D and 2-D LWAs [97; 98].
## 4 The Proposed Method: SLP-E
### Protocol Execution Phase  
The actual protocol realization is covered in this phase. We consider two scenarios, namely, scenario-1 and scenario-2. If the source node is at a distance of one-fourth of the network radius as measured from the BS's position, it is considered as scenario-1; else, it is scenario-2. Since the nodes within the base station's range are most susceptible to an adversary who initiates the backtracking process from this location, this threshold value is set to apply particular security precautions to those nodes. In Figure 1, the pink color region represents scenario-1 and the blue color region represents scenario-2 of the network.  
Depending on where the source node's position (i.e., its position is in scenario-1 or scenario-2) is in the network, the SLP-E protocol adapts accordingly. This process improves privacy and assists in finding a balance between privacy and the network lifespan by making routing patterns more dynamic. The source node can therefore maintain its anonymity anywhere inside the network.  
Figure 1: SLP-E technique.
# Characteristic Analysis of Compact Spectrometer Based on Off-Axis Meta-Lens
## 3 Results and Discussion
### Influence of the Fabrication Error Analysis  
In this section, we give a numerical analysis how the imperfect implementation may affect the performance of the proposed device. To do so, we have assumed that any imperfection in fabrication may cause the phase distortion of the ideal device from Equation (1).  
Figure 6a gives the effective spectral range \(\Delta\lambda\), the spectral resolution \(\delta\lambda\) and the maximum intensity \(I_{\mathrm{m}}\) of the focal spot. The latter two are taken at the design wavelength. In the calculation, a random noise using the built-in function of the MATLAB is applied to the phase profile in Equation (1), whose value obeys a normal distribution \(\mathcal{N}\big{(}0,\ \sigma^{2}\big{)}\). Here, \(\sigma\) denotes the standard deviation. It is seen that the maximum intensity \(I_{\mathrm{m}}\) decreases when the value \(\sigma\) increases, indicating the weakened focusing efficiency at larger phase distortion. However, the effective spectral range and the spectral resolution at the design wavelength almost remain stable as \(\sigma\) changes because they are solely contributed by the zeroth diffraction order. These effects could be more clearly understood from the focusing pattern plotted in Figure 6b-d at \(\sigma=0^{\circ}\), \(90^{\circ}\) and \(120^{\circ}\), respectively. The spatial characteristics of the zeroth diffraction order are hardly influenced when the phase distortion gets more deteriorated. Its intensity decreases as the unwanted higher diffraction orders become stronger. For practice, we can expect that the proposed off-axis meta-lens can suffer a spatial phase distortion with the largest deviation less than \(48^{\circ}\) corresponding to a \(50\%\) amplitude reduction where the higher diffraction orders are thought weakly enough and have no influence on the performance of the spectrometer. This is relatively a large tolerance and allows the device to be precisely implemented.  
Figure 5: Two practical configurations for the actual application prospects: (**a**) The focal line along the \(u\)-axis as a function of wavelength for the configuration with \(f=30\) mm, \(D=2\) mm, \(\alpha=15^{\circ}\) and \(\beta=-89^{\circ}\) at \(\lambda_{\mathrm{d}}=1550\) nm; (**b**) The focal line along the \(u\)-axis as a function of wavelength for the configuration with \(f=30\) mm, \(D=6\) mm, \(\alpha=45^{\circ}\) and \(\beta=-65^{\circ}\) at \(\lambda_{\mathrm{d}}=850\) nm; (**c**) Spectral resolution \(\delta\lambda\) as a function of wavelength of the configuration in (**a**); (**d**) Spectral resolution \(\delta\lambda\) as a function of wavelength of the configuration in (**b**).
# Sulfonation Treatment of Polyether-Ether-Ketone for Dental Implant Uses
## 3 Results
### Sulfonated PEEKs' Hydrophilic Properties with Various Sulfonation Times  
An important factor to consider when conducting research on the hydrophilic qualities of a material's surface is the contact angle. Table 2 and Figure 5 present the findings of the investigation on the water contact angles. In each group, the average water contact angles of sulfonated PEEKs were lower than that of PEEK (90.00). For example, the water average contact angles of 5 s sulfonated PEEK were 87.227 and 90.00, and the average water contact angles of 30 s sulfonated PEEK were 81.467 and 75.212. The untreated PEEK exhibited a contact angle that was greater than that of the other groups, which suggests that sulfonation enhances PEEK's hydrophilicity.  
Figure 4: SE: EDX mapping of sulfonated PEEKs with 120 s sulfonation and H\({}_{2}\)O hydrothermal post-treatment procedures, showing the distribution of the SE S, O, and C elements.  
Figure 5: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Etching Times** & **Angles Obtained** \\ \hline PEEK (control) & & 90 \\ Sulphonated PEEK (5 s) & 5 s & 90.00 \\ Sulphonated PEEK (10 s) & 10 s & 90.00 \\ Sulphonated PEEK (30 s) & 30 s & 87.22 \\ Sulphonated PEEK (60 s) & 60 s & 81.467 \\ Sulphonated PEEK (120 s) & 120 s & 75.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The etching times and angles obtained.  
As the result of the current study, the significance of chemical processes on the polymer was emphasized. Furthermore, the complexity of interaction and surface topography were also considered.  
The operation steps of the sulfonated surface modification method are relatively simple and can form a porous structure conducive to cell growth on the PEEK surface, introduce sulfonic acid groups with biological activity, and improve PEEK's antibacterial and osteogenic capabilities. However, the concentrated sulfuric acid and other modified reagents are dangerous to handle; so, the experiment has certain limitations.  
Figure 5: (**A**): EDX mapping of PEEK, showing the distribution of the O and C elements only. (**B**) Water contact angle measurements. (**C**) Water contact angle’s mean values, with Turkey’s HSD significance.  
PEEK can overcome some of the limitations of metal implants, such as stress shielding and metal allergy. PEEK is currently used as an orthopedic implant in clinical practice and has achieved good results.
## 3 Results
### Soil Compaction  
As the soils studied are used in road construction, the compaction references of the different soils were determined by standard and modified Proctor tests. Figure 7 shows the density variation as a function of moisture content after compaction for the four studied soils (Figure 7A-D). These curves can be used to determine the maximum dry density and the corresponding water content (also known as the optimum water content).  
Table 4 presents the references of the compaction materials obtained during the soil compaction test to characterise their water status.  
Unlike other soils, the MTI soil did not have compaction curves with a well-defined maximum dry density and optimum moisture content. As shown in Figure 7c, the moisture-density curve of this soil is relatively flat with little change in dry density over a wide range of moisture content. For standard Proctor compaction, the maximum dry density was obtained at moisture content close to zero (1.70%), while no noticeable change in dry density was noted for modified Proctor compaction. This behaviour is typical for poorly graded sands (coefficient of uniformity C\({}_{\text{u}}\) < 6 [40]. The higher compaction energy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **BE42** & **Cabalé** & **Morne Coco** \\ \hline Maximum dry density for standard Proctor test (g/m\({}^{3}\)) & 1.48 & 1.58 & 1.70 \\ Optimum moisture content (OMC) for standard Proctor test (\%) & 27.0 & 27.0 & 20.1 \\ Maximum dry density of modified Proctor test (g/m\({}^{3}\)) & 1.65 & 1.63 & 1.91 \\ Optimum moisture content (OMC) for modified Proctor test (\%) & 19.3 & 24.5 & 14 \\ Compaction improvement with modified energy & 11\% & 3\% & 12\% \\ Absolute density (Mg/m\({}^{3}\)) & 2.66 & 2.85 & 2.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compaction references.  
Figure 7: Standard and modified Proctor compaction curves (**(A)**: Morne coco, **(B)**: BE42, **(C)**: MTI and **(D)**: Cabalé soils).  
in the modified Proctor test significantly improved the compactness of the material by 3% to 12% and reduced the optimum moisture content (OMC) across the material. However, this variation remained negligible for the MTI soil which is insensitive to water.  
The Morne Coco soil has the highest dry density among the soils studied, while the Cabale soil has the highest true density. In the case of the Morne Coco soil, the granular arrangement is better, so a greater mass of soil is present in the same volume (Table 4).
# Research Progress on Conducting Polymer-Based Biomedical Applications
## 4 Current Stage and Challenges
constructed poly(3,4-ethylenedioxythiophene) (PEDOT) nanotubes using nanofibers of poly(lactide-co-glycolide) (PLGA) as a template for a precisely controlled release of dexamethasone [145]. PEDOT was electrochemically deposited around the electrospun nanofibers  
Figure 10: Photografting of poly(lactic acid) (PLA) films with maleic anhydride (MA) (top) and acrylic acid (AA) (bottom), followed by subsequent coupling reactions. Adapted with permission from [142]. Copyright 2012 American Chemical Society. of PLGA. Subsequently, PLGA can be dissolved, resulting in hollow nanotubes of PEDOT. Figure 11a compares the release of dexamethasone from the PLGA nanofiber and the PEDOT-coated PLGA nanofiber. The layer of PEDOT around the PLGA nanofibers slows down the release of dexamethasone. Furthermore, the drug release can be controlled more precisely by actively actuating the PEDOT nanotubes loaded with the drug with an electric field. As depicted in Figure 11b, upon the application of a positive voltage, PEDOT shrinks as electrons are inserted into the chains and counterions are discharged into the solution. This contraction of the nanotubes allows the release of the drug at the ends of the nanotubes. In Figure 11c, the cumulative mass releases of dexamethasone from the PLGA nanofibers and the PEDOT-coated PLGA nanofibers with and without electrical stimulation are shown. By the electrochemical deposition of PEDOT around the PLGA nanofibers, a substantial decrease in the amount of dexamethasone released was observed. In addition, when the electrical stimulation of 1 V was applied at specific points, the amount of the drug release increased significantly. SEM images of the PEDOT nanotubes after dissolution of the PLGA nanofibers in the core in Figure 11d-f reveal the hollow morphology of PEDOT as well as the well-defined internal and external texture. The diameters of the nanotubes varied from 100 to 600 nm, whereas the average wall thickness of PEDOT ranged from 50 to 100 nm.
# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 2 Related Works
Static approaches that rely on variable suspension systems and road profile sensors (RPS) cannot achieve rapid control processes or respond quickly to sudden changes in road conditions [37; 38]. Moreover, implementing real-time stability adjustments by integrating various systems, like machine learning-based predictions and sensor fusion, presents significant challenges [39; 40]. Another critical aspect that can be improved is responsiveness, which involves accurately detecting and adapting to terrain irregularities without relying on prior modeling data. Our system uses the GY-521 sensor, which is effective in low-traction environments such as icy or mountainous roads, to provide real-time stability. At the same time, the YOLOv8 model predicts surface conditions, which enhances control and reduces risks. Our proposed approach outperforms existing solutions by integrating a robust hardware-software framework that guarantees real-time anomaly detection and vehicle stability. In contrast to previous works, which typically concentrate on either software-driven methods or hardware-based implementations, our system seamlessly merges both for enhanced performance. Many current solutions neglect to incorporate real-time sensor data processing or take advantage of advanced deep learning models like YOLOv8, which restricts their adaptability to changing road conditions. Furthermore, implementing GY-521 acceleration sensing and intelligent anomaly detection ensures superior road assessment and stabilization. By addressing the shortcomings of previous approaches, our method offers a more reliable, efficient, and flexible solution for real-world applications. While previous studies have focused on specific elements like sensor-based systems or posture manipulation to prevent damage, our solution offers a more comprehensive approach by accommodating lateral balance and ensuring overall stability across various surfaces, including slippery and irregular terrains. These features deliver comfort and safety that rival current vehicle technologies, addressing immediate and long-standing vehicle stabilization challenges. In summary, our real-time stabilization system presents an innovative and scalable solution to the problem of uneven roads, enhancing safety, comfort, and efficiency in intelligent transportation technology.
## 3 Suggested Framework for Asset Digitalization and Management
### Asset Models for Digitalization and Management
#### 3.2.3 The Asset Monitoring Model  
An Asset Monitoring Model incorporates signal integration and a robust Extract, Transform, Load (ETL) process to transform raw signals into meaningful asset-related information. This process is carried out by advanced systems that utilize sensors distributed throughout an Internet of Things (IoT) network. The network consists of sensors, nodes, and cloud computing infrastructure. Nodes aggregate sensor data, facilitate efficient communication within the network, and transmit the collected information to cloud platforms and applications for further analysis and processing. The following aspects are crucial in designing an efficient and meaningful IoT network for asset management and monitoring (Figure 4):  
* The network topological design. Physical aspects of node placement and connectivity. Nodes capture signals using sensors with different sampling frequencies, and it is essential to identify variables related to the asset to manage. Each node will have a number of sensors, each with a distinct sampling frequency.
* The logical grouping. The conceptual organization of sensors to ensure effective data analysis and interpretation. The identification of variables related to the asset to manage.
* The server hosting essential services. For the processing, utilization, and delivery of information to end users.  
Figure 4: Scheme for defining indicators of the digitized asset based on sensors and nodes from the IoT network.  
3.2.4. The Intelligent Asset Management Models  
These modes are selected for the specific management of a given asset, while ensuring sustainability with minimal effort in data handling. This is addressed by a framework that connects different data models, structurally correlating data tables akin to human reasoning. This supports decision-making processes with specific asset data models. Asset management models are embedded into the Intelligent Asset Management Platform App (IAMP App), catering to decision-making processes such as [13] Reliability-Centered Maintenance (RCM), Condition-Based Maintenance (CBM), Life Cycle Costing (LCC), etc.  
These apps configure data models based on their analysis scope and can interact with simulation and AI tools, supplementing database records with results. These tools also interact with or provide Business Intelligence Apps (BI Apps) to generate tailored information presentations.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 6 Discussion
### Graphic Guidance  
Though the questionnaire did not report a decisive answer in favor of MR graphic overlay against paper-based instructions for assembly operations, the participants in most cases noted that using extended virtuality technologies gave them more confidence in what component to select and how to position it, compared to paper instructions, and that it required less effort and concentration to understand. On the other hand, with paper instructions, there is no possibility for overlaid graphics to block the visual over the product, or to distract the operator from performing specific tasks.  
As it is now assuming more and more importance in the field of assembly training and guidance, advantages and drawbacks of MR guidance applications are well highlighted in the participants' comments. MR provides understandable guidance, gives confidence to novice operators and is less complicated and requires less effort to be interpreted. The largest drawback of graphic guidance lies in the implicit element of distraction that is introduced. Subjects reported that the graphics occluded their view on real-world objects, as well as graphics occluding other graphics. For example, some subjects reported that it was difficult to see the holes for the label screws under the projected label graphic. This issue could be reduced through better positioning and visibility logic for graphics, which requires more developing and computational effort.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 4 Simulation
### Performance Metrics  
This paper simulates single-source DOA estimation and used the root mean square error (RMSE) to evaluate model performance:  
\[\text{RMSE}=\sqrt{\frac{1}{LN}\sum_{i=0}^{L-1}\sum_{n=1}^{N}\lvert\Delta\theta _{i,n}\rvert^{2}} \tag{13}\]  
where \(N=1000\) represents the number of test samples.
## 5 Adaptive Load Vectors
### The Adaptive Modal Pushover Analysis (AMPA)  
The Adaptive modal pushover analysis (AMPA) method was introduced by [70] in their paper titled \"Adaptive Spectra-Based Pushover Procedure for Seismic Evaluation of Structures\". [PERSON] and [PERSON]'s adaptive modal pushover analysis addresses the limitations of the traditional modal pushover analysis by iteratively updating the modal load pattern based on the deformed shape of the structure at each step of the analysis. This adaptive process allows for better consideration of higher-mode effects, nonlinear behavior, and changes in structural characteristics under seismic loads, leading to a more accurate assessment of the structure's performance.  
Figure 6: Illustration of the adaptive pushover procedure [38; 69].  
The adaptive modal pushover analysis (AMPA) procedure can be summarized as follows:  
1. Create a mathematical model: develop a comprehensive mathematical model of the structure.
2. Define nonlinear relationships: establish the nonlinear force-deformation relationships of the structural components. This includes the initial stiffness, yield moment, and post-yield stiffness, or an encompassing force-deformation envelope.
3. Compute the damped elastic spectrum: calculate the damped elastic response spectrum for site-specific ground motions, considering appropriate damping constants, such as 5% for reinforced concrete structures.
4. Perform an eigenvalue analysis: Conduct an eigenvalue analysis to determine periods and eigenvalues. Compute the modal participation factors using: \[\Gamma_{j}=\frac{1}{g}{\sum_{i=1}^{i=N}W_{i}\varpi_{ij}},\] (44)
5. Calculate the story forces for modes: compute the forces at each story level for each mode using: \[\Gamma_{j}=\frac{1}{g}{\sum_{i=1}^{i=N}W_{i}\varpi_{ij}},\] (45)
6. Compute and combine the base shears: calculate the modal base shears \(V_{j}\) and combine them using the SRSS method to compute the building base shear \(V\): \[V_{j}=\sum_{i=1}^{i=N}F_{ij},\] (46) \[V=\sqrt{\sum_{j=1}^{N}V_{j}^{2}},\] (47)
7. Scale story forces: uniformly scale the story forces by a scale factor \(S_{n}\), expressed as: \[V_{j}=S_{n}V_{j},\] (48) where \[S_{n}=\frac{V_{B}}{N_{S}V},\] (49)
8. Perform a static analysis for each mode: Conduct a static analysis using scaled incremental story forces for each mode independently. Ensure a bidirectional analysis for modes other than the fundamental mode.
9. Compute element responses: calculate the element forces, displacements, story drifts, and member rotations using the SRSS combination, updating the results of the previous step accordingly.
10. Check and update for yielding: At the end of each step, compare the member forces. If yielding is detected, recompute the member and global stiffness matrices and return to Step 4.
11. Iterative analysis: repeat the process until the maximum base shear or global drift exceeds a predetermined limit.
## 3 Results on ML/DL-Based Channel Estimation  
This section outlines ML/DL techniques for channel estimation in UWA communication. First, Section 3.1 introduces the models, optimizers, and training examples. Second, Section 3.2 details system features like bandwidth, Tx-Rx distance, modulation schemes, subcarriers, and CP. Subsequently, Section 3.3 covers metrics such as training loss, BER, and complexity. Finally, Sections 3.4 and 3.5 discuss key findings in single- and multi-carrier systems.