esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Tunable Broadband Graphene-Based Metasurface with Amplitude-Phase Modulation
## 1 Introduction
Due to limitations in fabrication processes and other factors, application research pertaining to graphene in the microwave and millimeter wave range has been stagnant in its progression. [PERSON] et al. used graphene capacitor structure to construct graphene flexible surfaces to achieve dynamic electric field control of microwave absorption, reflection and transmission at 10 GHz [15]. [PERSON] et al. fabricated a dipole antenna with graphene as the radiation subject on cardboard, which achieved the radiation target and had better flexibility than metal [16]. Based on graphene, [PERSON] et al. designed a reflective-phase-encoded metasurface operating at 6-14 GHz to achieve theta = \(\pm\)60\({}^{\circ}\) beam steering and multibeam functionality [17]. Although some scholars have explored the application of graphene in the microwave band, the design of metasurfaces founded on graphene in the millimeter wave band is scarce. In this work, we propose an active and reflective graphene-based metasurface in the W-band. To achieve beam reconfigurability, the reflection amplitude and phase of each metasurface unit is modulated by varying the Fermi level of graphene. And the coding bandwidth rages cover 75-91.5 GHz and 99.3-115 GHz. Under the premise that the two coding units have a certain reflection phase difference, beam steering and multi-beam switching can be achieved at a fixed frequency through the elaborate arrangement of the amplitude coding pattern. In comparison to conventional phased-array antennas, the graphene metasurface has the advantages of simple structure and light weight.
# Point Cloud Repair Method via Convex Set Theory
## 1 Introduction
Then, the subunits without filling points were eliminated by using the neighborhood attribute information of boundary points to reduce the cost of subsequent calculation. Fine screening is used to accurately predict subunits that need new filling points. Therefore, we introduced a Markov random field (MRF) to transform the selection of subunits into the maximal a posteriori probability (MAP) of the random field label and then used the variational mean field to approximate MAP. Lastly, we added new points to the final filtered subunit, so that the specific subunit would become a convex set. We reconstructed the original features of the point-cloud surface by constructing a convex set of subunits located in the missing area. The division of the subunit set and the construction of the convex set were carried out iteratively. Each iteration made the subunit gradually move towards the direction of hole shrinkage and lastly repair the entire hole area. Our method could accurately infer the distribution of new filling points to ensure that the repaired shape was as consistent with the real surface shape as possible. The proposed method is not limited by the category of point clouds and could repair various categories of point clouds while maintaining the position of points in nonhole areas unchanged. The key contributions of this paper are as follows:(1) We introduce a Markov random field and transform the subunit selection problem into the maximal a posteriori of the random field label. By solving the maximal probability, we ensure that the new filling point is always located in the missing area. (2) We introduce and weaken the concept of a convex set, and redefine the problem of point-cloud repair into the problem of the composition of a convex set. The entire hole area is gradually repaired by constructing the specific subunit into a convex set.
# Numerical Study on Airfoil Aerodynamics in Proximity to
###### Abstract  
Wing-in-ground crafts will face waves with different amplitudes when flying over the ocean, and the high amplitude situations are especially lack of exploration. Hence, the aerodynamic characteristics of the NACA 4412 airfoil in proximity to the wavy water surface for various wave amplitudes are inspected in this paper. By solving Navier-Stokes equations, the lift coefficients of the airfoil when the angle of attack ranges from \(0^{\circ}\) to \(4^{\circ}\) are obtained. The results show that the fluctuation amplitudes of aerodynamic coefficients increase remarkably with successive increases in the wave amplitude and might threaten flight safety. The flow fields at \(0^{\circ}\) with low and high wave amplitudes are investigated. It is revealed that the upward movement of the water surface is the critical factor for the change of aerodynamics, and the mechanism varies with different wave amplitudes. Comparison of the flow fields at \(0^{\circ}\) and \(2^{\circ}\) further indicates that the influence of high amplitude waves depends on the distance between the leading edge of the airfoil and the water surface. This study discovers the reasons for the different aerodynamic characteristics under various wave amplitudes and angles of attack, and is of great value for the design of wing-in-ground crafts.  
Keywords:wing-in-ground craft; ground effect; wavy water surface; various wave amplitudes; aerodynamic characteristics; flow mechanism +
Footnote †: journal: Applied sciences
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 6 UAV Applications Areas
### UAVs for Automated Forest Restoration  
Another emerging research domain is to utilize UAVs for automated forest restoration (AFR). UAVs can be deployed to assist in several tasks needed for the implementation of forest restoration such as site survey, restoration strategy, site infrastructure, seeds supply, site management (fertilizing and weeding etc.), and biodiversity survey following restora  
Figure 11: UAV assistance on highway.  
tion interventions [107]. Existing available technologies such as positioning and imaging sensors help UAVs to perform certain tasks including rudimentary pre-restoration site surveys and monitor several aspect of biodiversity recovery. UAVs can monitor changes in climate, ecosystem composition, and functions of forests as well as supporting inspection of forest restoration [108]. High resolution cameras installed in UAVs can provide appropriate data on forest ecosystems to assist forest restoration projects. The adequate resolution of UAV cameras can support characterizing and analysis of forest areas due to their ease in acquiring data and versatility. Furthermore, optical sensors installed in UAVs are also being utilized to attain geometric features of forests e.g., canopy diameter and height [109]. Similarly, remote sensing operations from UAVs are also reliable and efficient substitutes for traditional forest measures.
## 4 Results and Discussion
### Lamb Wave and Bearing Estimation  
#### 4.4.1 Relationship between the Lamb Wave and \(x\)-Direction Peak Displacement  
The relationship between the \(x\)-direction peak displacements and frequencies at the three sites is shown in Figure 8a. Simultaneously, taking the frequency band width \(\Delta f=47.875\) Hz as the processing interval, the energy level of the acoustic sources recorded using the USR was calculated for the three sites, and the results are shown in Figure 8b.  
Figure 8: Displacement and energy level received at different sites: (**a**) peak displacement received by geophone and (**b**) energy level received by USR. The red symbols represent the peak displacements.  
As shown in Figure 8a, the peak displacement distribution structure shows a bimodal distribution at sites S1 and S2, while it shows a single peak distribution at site S3. In Figure 8b, the energy level received by the USR shows a single peak distribution at sites S1, S2 and S3, and there is no second peak at any site. By comparing Figure 8a to Figure 8b, it can be seen that the peak distribution structure is changed from the singlet structure to the bimodal structure for the same source at sites S1 and S2 but maintains the same singlet structure at site S3. Why is the peak distribution changed at site S1 or S2 but remains unchanged at site S3? This may be attributed to the Lamb wave. If an acoustic source is in the seawater beneath the ice, given an incident angle, certain frequencies of the Lamb modes can be excited [26]. When some Lamb modes of the ice plates were excited at sites S1 and S2, the horizontal displacement in the frequency band of the mode was higher, and the second peak was produced in the peak distribution structure. When the Lamb mode of the ice plate was not excited at site S3, the horizontal displacement in the frequency band of the mode was not higher, and there was no second peak in the displacement distribution structure. This idea will be further confirmed below.
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 3 Energy-Aware Grid-Based 3D-Obstacle Avoidance (EAOA)
### Frame Dimensions  
Figure 2(a) represents the 3D view. The (i, j) plane represents the area top view, (i, k) plane is parallel to the facing view.  
After locating the drone in the (i, j) plane, the planner will decide to range the minimum and maximum allowed heights for the drone (denoted \(h_{min}\) and \(h_{max}\), respectively). This range will specify the frame dimensions of the facing area. We denote \(F\) as the scanned front frame. We assume that the drone is located at point A, with height \(h_{ijk}\).  
In Figure 2(b), the frame is represented by the rectangle BCDE of area a \(\times\) b, where a and b represent the length and width, respectively. We aim to determine the frame dimensions. The length \(a=(h_{max}-h_{min})\). We assume the facing view is captured with a camera angle \(\phi=62^{\circ}\), similarly to the work of [PERSON] et al. [24]. Authors found that any object detected out of this area angle will not cause any danger to the UAV.. Point M is the projection of A. Both points represent actually the centers of two consecutive grid-cells in the plane (i, j). To calculate the length (BE) and width (BC) of the scanned frame, we have to find the dimensions of the rectangular pyramid (ABCDE). Let us take the isosceles triangle (AFG) that has angle \(\phi\), and height h = AM. The side AF (denoted \(R\)) can be computed from Equation (1), since AM is known and equal to the length of the top-view grid cell:  
\[R=\frac{h}{cos(\frac{\phi}{2})}. \tag{1}\]  
Hence, the length b of the frame \(F\) can be computed as follows:  
\[b=2\times R\times sin(\frac{\phi}{2}). \tag{2}\]  
Now that the frame dimensions are known, the next step is to transform \(F\) to a grid-based form denoted \(\Gamma\). The dimensions of each cell in \(\Gamma\) fit to the height and width of the drone. Hence, we denote \(v_{ij}\) a cell located in row i and column j in the grid \(\Gamma\) and has dimensions L\(\times\) W (L for length and W for width).
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
###### Abstract  
This paper describes some of the challenges that need to be addressed in order to develop collaborative spectrum-sharing systems. The importance of these challenges stems from the assumption that rules for spectrum sharing can change after the deployment of radio networks and that the whole system must be able to adapt to them. To address such a requirement, we used a policy-based approach in which transmissions are controlled by a policy interpreter system, and the policies can be modified during system operation. Our primary goal was to develop a prototype of such a system. In this paper, we outline the implementation of policy interpretation, automatic generation of transmission opportunities in case a request for transmission is denied by the policy reasoner, and the generation of rendezvous channels for the synchronization of otherwise asynchronously running software-defined radios.  
spectrum sharing; collaborative spectrum sharing; policy-based sharing; transmission opportunities; rendezvous channels; policy interpreter
## 1 Introduction
TA has been developed by measuring the distribution of grayscale levels in the region of interest (ROI) delineated in the image, thus distinguishing lesions from healthy tissues [18; 19; 20; 21; 22]. Therefore, data from TA in combination with clinical information can indicate and validate diagnostic hypotheses [23]. One of the most frequent methods for extracting texture parameters from grayscale images is the co-occurrence matrix (GLCM) [24], which can detect subtle changes in images [25; 26; 27]. In recent years, computerized analyses of morphology and texture have been used to aid in the diagnosis of various pathologies [21; 22; 23; 24]. Automated patterns applied to medical image analysis for lesion recognition have high accuracy and are recognized as imaging biomarkers [28]. The objective of this study was to compare CT and MRI images of TMJs affected by the flattening and erosion of the cortical bone using TA to detect condylar bone marrow changes in these joints. By applying TA to compare CT and MRI images of TMJs affected by flattening and erosion of the cortical bone, we aim to enhance the detection of subtle condylar bone marrow changes that are often challenging to identify through visual inspection alone. This approach has the potential to improve early diagnosis of TMJ degeneration, enabling more timely interventions. This research also seeks to establish quantitative imaging biomarkers specific to TMJ degeneration, which could help standardize assessment criteria across the field. By focusing on these specific conditions, this study may provide new insights into the progression of TMJ degeneration, aiding clinicians in distinguishing between various stages of the condition. Furthermore, the findings from this study could inform the development of more targeted treatment strategies and contribute to the advancement of computer-aided diagnostic tools in TMJ imaging, potentially enhancing the accuracy, efficiency, and early detection of conde changes in clinical practice.
## 5 Architecture Proposal Operation and Fault Tolerance
### Protocol Messages  
In order to achieve the proper operation of the architecture, 14 messages have been designed and developed. We have used 4 Bytes for _clusterID_, _nodeID_, 1 and d parameters and the node position, and 2 bytes for the type of message and the CM type. All are fixed size messages except the backup message that depends on the number of neighbors (although it is sent using incremental updates, there could be several new neighbors) and it is only sent when changes take place. Bandwidth cost in bytes for each message is shown in Figure 15 (1 = \"Discovery\", 2 = \"Discovery ACK\", 3 = \"H connect\", 4 = \"Welcome H\", 5 = \"Keepalive H\", 6 = \"M connect\", 7 = \"Welcome M\", 8 = \"Keepalive M\", 9 = \"CM request\", 10 = \"CM connect\", 11 = \"Welcome CM\", 12 = \"Keepalive MM\", 13 = \"backup CH\", 14 = \"H replace\"). The messages' size is based on MAC layer in 802.11 and TCP/IP headers. The sum of these headers is 70 bytes.  
Figure 14: Protocol operation when a CH leaves the network.  
Figure 13: Protocol operation when a CM leaves the network.
# Epigenetic Signatures of Dental Stem Cells: Insights into DNA Methylation and Noncoding RNAs
## 5 Limitations and Conclusions  
This study presents a few limitations inherent to its in silico and retrospective design. The analyses were conducted on publicly available DNA methylation datasets and do not include functional validation or longitudinal assessment of methylation changes during differentiation. Consequently, the associations between DNA methylation, gene expression, and stem cell fate remain correlative. While the integrative network and enrichment analyses revealed biologically plausible candidates, the regulatory roles of many identified long noncoding RNAs (IncRNAs) remain hypothetical and require experimental confirmation. The constructed gene-IncRNA network was sparse, reflecting the conservative nature of the filtering pipeline, which prioritized robustness and reproducibility. However, sparse topologies may be more susceptible to stochastic associations, and centrality metrics should be interpreted with caution. Although several hub genes such as _PAX6_, _BMP7_, and _JAG1_ are well-established regulators of odontogenesis, the functional involvement of newly identified nodes remains to be experimentally validated. Future studies should aim to improve network resolution by integrating larger and more diverse methylomic or transcriptomic datasets, including time-course analyses under defined inductive protocols. Another limitation is that this study focused exclusively on dental-derived stem cells, without comparison to non-dental stem cells or fully differentiated dental tissues, limiting the broader generalizability of the identified methylation signatures to odontogenic identity. Moreover, probes on sex chromosomes were excluded to avoid confounding effects from X-inactivation and the lack of Y chromosome homology; as a result, potential sex-specific epigenetic differences were not addressed. Finally, although age-related variation is a known determinant of DNA methylation, this analysis was designed to characterize cell-type-specific patterns under standardized culture conditions, rather than inter-individual variability. Future research should explore the influence of age, sex, and differentiation status more explicitly to enhance the biological interpretability and translational relevance. In summary, this work substantially extends the scope of existing studies by delivering a functionally annotated epigenomic map of dental stem cells and highlighting the role of noncoding RNA regulation in odontogenic differentiation. This integrated approach provides a valuable resource for future research in regenerative dentistry and orthodontics. Regarding the latter, this study can certainly shed light on crucial aspects of the biology of tooth movement and its common adverse effects, such as root resorption, in relation to craniofacial development and, ultimately, individual variability in treatment outcomes.
## 4 Method Analysis and Control Strategy Development
### Motor Rotary Velocity Regulation Control Algorithm
#### 4.1.3 PID-Bang-Bang-Fuzzy Compound Intelligent Algorithm  
Fuzzy control is an intelligent control algorithm that is suitable for objects that cannot be accurately modeled and nonlinear complex systems. It is a control algorithm with broad application prospects. This study further investigated combining the advantages of the fuzzy control algorithm with the PID-bang-bang compound control algorithm to form a PID-bang-bang-fuzzy compound intelligent algorithm. A schematic of the control principle of the motor rotary velocity control system, based on the PID-bang-bang-fuzzy compound intelligent algorithm, is shown in Figure 8. When the error between the real-time value and the target value was greater than threshold value \(\varepsilon\), to quickly eliminate the large error and improve the convergence speed, the system applied the bang-bang controller. When the error was less than threshold value \(\varepsilon\), to ensure the control accuracy of the system in the steady state, the PID controller was used, with the fuzzy algorithm used to calculate the PID parameters. In this way, the online self-tuning of the PID controller parameters could be realized, and the performance of the PID controller could be further improved to better adapt to changes in the motor rotary velocity control system parameters and working conditions.  
The fuzzy controller is the core part of the fuzzy control system, with its design based on experience and effective fuzzy control rules. In the PID-bang-bang-fuzzy compound intelligent algorithm, the fuzzy controller was mainly used to adjust Kp, Ki, and Kd. The input of the fuzzy controller was the error of the rotary velocity (e) and the change rate of the error of the rotary velocity (ec). The output was the variation of the proportional coefficient (\(\Delta\)Kp), integral coefficient (\(\Delta\)Ki), and differential coefficient (\(\Delta\)Kd). The following fuzzy subsets of e, ec, \(\Delta\)Kp, \(\Delta\)Ki, and \(\Delta\)Kd were selected: [NB, NM, NS, ZO, PS, PM, PB], which represented negative big, negative middle, negative small, zero, positive small, positive middle, and positive big, respectively.  
The triangle membership function is simple and convenient. At the same time, it does not occupy too much memory in the calculation process. In this design, the membership function types of inputs e and ec, as well as outputs \(\Delta\)Kp, \(\Delta\)Ki, and \(\Delta\)Kd were all triangles. The domains of the inputs and outputs were (\(-3.3\)). The membership functions are shown in Figure 9. In order to improve the adjustment speed of fuzzy PID, triangular membership function is used. The fuzzy rule surfaces of the output variables are shown in Figure 10.
# A Reward Population-Based Differential Genetic Harmony Search Algorithm
## 3 Improved Harmony Search Algorithm
### Differential Genetic Harmony Search Algorithm (DEGA-HS)
#### 3.2.3 Flow of DEGA-HS Algorithm  
As stated above, the mutation strategy of the differential mutation algorithm has the advantage of overcoming premature convergence and degeneration. Therefore, we considered introducing the mutation strategy of DE into the HS algorithm to overcome the premature convergence of HS, thus improving the convergence rate. At the same time, there is excessively high randomness in the generation of a new harmony in the general harmony search algorithm. High randomness brings both advantages and disadvantages. To balance this contradiction, the Partheno-genetic algorithm is also introduced.  
The flow of the improved DEGA algorithm is as follows.  
Step 1. Initialization of parameters.  
In the new algorithm, the original tone pitch adjusting bandwidth (BW) will be canceled. Therefore, only the harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjusting rate (PAR), and maximum times of creation (\(T_{max}\)) will be initialized.  
Step 2. Random initialization of harmony memory.  
Step 3. Generation of a new harmony.  
For the general harmony search algorithm, there are mainly three operations to generate a new harmony at this step: memory consideration, pitch adjustment, and random selection. However, the operations of the proposed algorithm differ from them. The flow is as follows.  
First, a random number \(rand(1)\) is generated between (0,1). If \(rand(1)\) is smaller than or equal to HMCR, the mutation strategy of \"DE/best/1/bin\" is used to generate a new harmony. Namely, \(x_{j}^{new}=x_{j}^{best}+F(x_{j}^{k}-x_{j}^{l})\). Then, a second random number \(rand(2)\) is generated between (0,1). If \(rand(2)\) is smaller than or equal to PAR, the mutation strategy of \"DE/rand/1/bin\" is used. Namely, \(x_{j}^{new}=x_{j}^{rand}+F(x_{j}^{k}-x_{j}^{l})\). Otherwise, no adjustment is made.  
If \(rand(1)\) is greater than HMCR, a new harmony is generated within the search boundary. Next, a third random number \(rand(3)\) is generated between (0,1). If \(rand(3)\) is smaller than or equal to PAR, a new harmony is generated according to the operation of the Partheno-genetic operation. Otherwise, no adjustment is made.  
Step 4. Update of harmony memory.  
The newly generated harmony is assessed. If it is better than the poorest harmony in the harmony memory, substitution is made.  
Step 5. Judgment on termination.  
Check whether current times of creation have reached the set maximum times of creation. If no, Steps 3-4 should be repeated until the maximum times of creation are reached.