esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Finite Element Theory of Acoustic-Vibration Coupling
### Outer Acoustic Field  
To address such problems involving unbounded spatial domains in finite element analysis, an artificial absorbing boundary surface is introduced at a certain distance from the outer surface \(\Omega_{s}\) of the structure. This allows for the computation of the bounded acoustic domain V1 formed by the structure's outer surface \(\Omega_{s}\) and the artificial absorbing boundary surface \(\Omega_{e}\), as shown in Figure 2.  
A common type of absorbing boundary interface is the perfect match layer (PML), which is a virtual layer typically located at the edge of the simulation domain. It is implemented by introducing complex mathematical equations and difference equations [22]. At the artificial absorbing boundary interface \(\Omega_{e}\) within the bounded acoustic domain \(\mathrm{V}_{1}\) in finite element discretization, \"damping\" elements are employed to approximately simulate the absorption of outgoing acoustic waves without consideration for their frequency and incident angle. The purpose is to absorb incoming waves without reflection, preventing waves from reflecting back from the boundary of the simulation region, as shown in Figure 3.  
Following the same theory, the LMS SYSNOISE company has introduced the automatic match layer (AML) method. This method is similar to PML, as it is a virtual layer placed at the simulation domain's edge. Its purpose is to absorb waves and reduce reflections [23]. Different approaches are typically used, such as introducing absorption coefficients or adjusting model parameters. As shown in Figure 4, the absorbing layer and absorption coefficients in AML are automatically defined by physical models and  
Figure 2: Exterior sound and vibration coupling system with artificial absorption boundary.  
calculated frequencies. Consequently, compared to the PML method, the AML method reduces computational workload. Thus, with the decrease in computational workload, the predictive accuracy relying on absorbing elements has been enhanced. SYSNOISE is combined with the coupled finite element method to establish a finite element model for the considered structure. In a similar manner, the cylindrical shell and annular plate are set as the structural mesh components, described using shell elements. The air field, both internal and external acoustic fields represented by solid elements, is taken as the acoustic mesh components. Additionally, a porous core with absorbing fluid properties is included as part of the acoustic mesh. Coupling surfaces at the interaction boundaries between structural and acoustic components are used for calculating the vibration-acoustic coupling.
# Empowering Tourism Accessibility: A Digital Revolution in Pieria, Greece
## 2 Materials and Methods
### Olympus Riviera Digital Platform  
The Olympus Riviera Platform is a modern web application designed to provide seamless access to a wide range of geographic data, including POIs, obstacles, pavements, and other essential features related to urban landscapes. The platform is built using React for its frontend (Figure 4), offering users a dynamic and responsive experience while interacting with various map-based data. The design prioritizes ease of use and real-time interaction, ensuring that users can quickly retrieve, filter, and visualize data that are both accurate and comprehensive.  
At the backend, the platform communicates with a MongoDB database using Mongoose, a robust object data modeling (ODM) library for MongoDB and Node.js. Mongoose provides a straightforward way to define schemas, ensuring consistency in data structures, and supports complex queries and efficient filtering for retrieving data from the database [29; 30]. The Node.js server powering the platform exposes a REST API, serving as the primary communication channel between the React frontend and the database. This API enables the efficient fetching, filtering, and manipulation of POIs and other geographical data. By adhering to the RESTful architecture, the platform follows modern web communication standards, ensuring scalability and smooth interaction across the entire application stack [31]. This makes the system capable of serving an increasing number of users without compromising performance.  
One of the platform's key components is its data population tool, Seek & Go, which is responsible for populating the MongoDB database with real-world data. Seek & Go is equipped with middleware that exposes its own REST API, allowing users and administrators  
Figure 4: Filtering modal of the Olympus Riviera Platform frontend.  
to store and manage geographical features efficiently. The tool features a map-based interface for visual data editing, enabling users to pinpoint and modify locations with ease, thus ensuring data accuracy. This integration significantly enhances the precision and usability of geographic information within the system.  
The combination of the Olympus Riviera Platform and Seek & Go provides a comprehensive solution for managing geographic data. The platform not only serves as an information hub for users exploring the Olympus Riviera region but also offers a powerful backend for data management. With its map-based editing tools and flexible database structure, the system is designed to accommodate continuous updates and future expansions, making it a valuable resource for both visitors and administrators.
## 1 Introduction
Some are machine learning algorithms using the features of Gabor filters [22], Haar [23], HOG [24], and CNN [25], or extracting features from the hand edge [26] or hand shape [7]. However, these features are not so intuitive to represent the hand gestures formed by different fingers configurations. If the customer wants to define a new class of gesture, such as pointing to the left or right using the index finger, many new training images need to be collected to retrain the model. Other algorithms take fingers as the features. The detection of fingers are based on ridge detection [27], convex hull [28], curvature [9], drawing a circle on the hand centroid [29; 30], or convex decomposition [31]. Nevertheless, the method in [31] is time-consuming, and the rest [9; 27; 28; 29; 30] might have difficulties when handling distorted fingers and images taken by the wearable camera. As for the subsequent classification, algorithms in [27; 30] are learning-based which require many training images for each class. Moreover, works in [9; 28; 29] used rule classifiers and zero training image, thus they lacked some adaptiveness for gestures with distortion and varying postures. Therefore, a balance should be kept between the convenience and robustness. The contribution of our work is threefold: a novel wearable camera system, an improvement on finger segmentation, and the recognition of fingers based on single template image. To our best knowledge, this is the first wearable camera system worn on the backside of the wrist for recognizinghand gestures (if the one using a smartphone [20] is not included). It is less intrusive than camera systems beneath the wrist [19] and doesn't require eye gaze as the one worn on the head [21]. We also innovated on the algorithms. A two-phase distance transformation method is proposed to segment fingers out of the palm. Then, the angles of the detected fingers with respect to the wrist are calculated. For those fingers not in sight, all their possible positions are predicted based on the known fingers and the template gesture. After that, these predicted gestures with a complete five fingers are compared to the template, and the one with a minimal error is the recognition result. The rest of the paper is arranged as follows: the architecture of the wearable hand gesture recognition system is described in Section 2. The algorithms of finger detection, finger angle prediction, and template matching are presented in Section 3 which are then evaluated on the dataset in Section 4. Furthermore, an application of robot control is given in Section 5. Finally, we discuss and conclude our work in Sections 6 and 7.
## 1 Introduction
The SR Ca\({}^{2+}\) release rate was higher during large depletions than during small ones, indicating that RyR refractoriness and recovery from inactivation are pivotal in the genesis of Ca\({}^{2+}\) alternans. [PERSON] et al. utilized a novel imaging approach to monitor intra-SR free Ca\({}^{2+}\) and transmembrane potential in intact hearts [14]. They found that RyR refractoriness is the first mechanism affected as heart rate increases, leading to the initial development of SR Ca\({}^{2+}\) release alternans. At higher heart rates, SR Ca\({}^{2+}\) load also begins to alternate, amplifying the magnitude of SR Ca\({}^{2+}\) release alternans and repolarization alternans. During ventricular fibrillation, a high SR Ca\({}^{2+}\) load coincides with nearly continuous RyR refractoriness, resulting in minimal SR Ca\({}^{2+}\) release despite the elevated load. The study by [PERSON] et al. examined the effects of SERCA inhibition on CRA [15]. They found that inhibiting SERCA with cyclopiazonic acid slowed SR Ca\({}^{2+}\) re-uptake and increased the magnitude of SR Ca\({}^{2+}\) and APD alternans. SERCA inhibition promoted the emergence of spatially discordant alternans, with SR Ca\({}^{2+}\) release alternans occurring before diastolic SR Ca\({}^{2+}\) load alternans. This suggests that the relative refractoriness of RyR Ca\({}^{2+}\) release is a primary factor governing the onset of intracellular Ca\({}^{2+}\) alternans. The role of Ca\({}^{2+}\) is considered substantial in the development of CRA; however, studies on the mechanisms of CRA have not been able to identify the major factors for CRA generation or conduct a detailed analysis of the interrelationships among them. The objective of this manuscript was to develop a method to identify the main Ca2+-related factors leading to CRA. A whole-ventricular model with MI was constructed to induce CRA. After obtaining the Ca2+-related subcellular parameters with cellular transmembrane potential in cardiomyocytes with and without alternans, statistical tests and machine learning-based feature selection methods were integrated to investigate the main Ca2+-related factors in priority of importance that affect CRA, facilitating a deeper understanding of the electrical mechanism of tachycardia induced by MI and providing new perspectives for research methods in cardiac electrophysiology.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 4 Field-Test Results and Discussion
### Tracking Domain Analysis  
The PM beamforming was performed from the beginning of the data and MPDR was performed after 10 s. The interference was added to the data after 15 s. Tracking performance of the low elevation PRN 4 is now considered. The tracking performance is shown in Figure 12. MPDR beamforming applied on clean data from 10 to 15 s showed nearly 6 dB of gain with respect to the single antenna. In the presence of interference, MPDR was able to maintain the gain. However, the gain obtained was higher in the presence of interference and was observed to be around 12 dB with respect to the  
Figure 11: Field data collection setup and sky plot of visible GPS satellites.  
single antenna case. The C/N\({}_{0}\) gain observed with PM in the presence of interference was about 7 dB. The carrier Doppler errors were computed by taking the difference between the reference antenna Doppler with clean data without interference and beamformed data in the presence of interference. The Doppler RMS errors, PLI, and C/N\({}_{0}\) gain for different PRNs are shown in Table 6. These values are computed from the tracking results in the presence of interference. For all the PRNs, PLL was able to maintain lock even in the presence of interference.
# Guineensine: Isolation, Synthesis, and Biological Activity
## 3 Total Synthesis and Reactions
### Total Synthesis
#### 3.1.4 The Initial [PERSON] et al. Approach (2019)  
The most recent synthetic approaches to **1** were reported by [PERSON] et al. in their impressive publication in 2019, along with the preparation of a library of guineensine analogues to perform structure-activity relationship studies on their endocannabinoid uptake inhibition properties [57].  
As described earlier, their initial attempt to replicate the Julia-Kocienski olefination and base their total synthesis on this reaction was unsuccessful (see Section 3.1.3). Followingthis, they achieved the synthesis of \(\mathbf{1}\) in seven steps, starting from the commercially available 3-nonyn-1-ol \(\mathbf{51}\). First, \(\mathbf{51}\) was subjected to an alkyne zipper reaction [62] to obtain alcohol \(\mathbf{52}\), which contained the triple bond in the terminal carbons. After Swern oxidation with oxalyl chloride, the intermediate aldehyde \(\mathbf{53}\) reacted with phosphonate \(\mathbf{39}\) to give dienoate \(\mathbf{54}\) via a Horner-Wadsworth-Emmons (HWE) defination reaction. Dienoate \(\mathbf{54}\) was easily transformed to the corresponding isobutylamide \(\mathbf{56}\), thus creating the linker and head group of the target molecule. Iodide \(\mathbf{57}\) was then prepared in good yield by Pd-catalyzed hydrocarbonylation and treatment with iodine, and, finally, the desired molecule \(\mathbf{1}\) was constructed by a Suzuki cross-coupling reaction with boronic acid \(\mathbf{58}\), using thallium (I) ethoxide as an additive (Scheme 4).  
The diastereoisomeric ratio was well managed throughout the synthetic route to finally obtain the pure compound. However, in most reactions, the products had to be purified by one or more flash chromatographic runs. Moreover, the final product was provided in pure form only by preparative reverse-phase HPLC. This process was not acceptable by the authors from a total synthesis point of view, even if from a medicinal chemistry perspective it would be totally acceptable, since it ultimately leads to pure final products.
## 1 Introduction
In particular, we exploited the resting state fMRI data of a single subject to construct three functional connectivity networks and explore the topological organization of their macro-scale architectures by means of a modularity analysis. We investigated the topological properties of the most important nodes of the networks to detect stable hubs. Here, the SYNC hub nodes represent regions with a high level of synchronization over the entire time interval with different functional sub-systems. Such nodes differ from the hubs of correlation and coherence networks as statistical correlation is related to the average synchronization level over the observation period, while spectral coherence can identify the frequencies that drive the linear association between the two time courses. One of the principal objectives of this work is to verify whether an overlap of these nodes exists with respect to those identified in the Pearson and coherence networks in order to better elucidate the synchronization index method in functional connectivity analysis. We stress that the main focus of this exploratory analysis is a detailed comparison of the three network topologies concerning their modular organization and the roles of the regions acting as hubs in such modular configurations. Recent precision functional mapping has shown that individual brain organization is qualitatively different from group average estimates and that individuals can exhibit distinct brain network topologies [34; 35]. For this reason, we have chosen a single subject-based analysis. Indeed, the individual analysis address a targeted comparison of the regions included in each module and the hub roles between the three functional networks obtained respectively with SYNC metric, Pearson statistical correlation and spectral coherence. We also tested the reliability of the results by analyzing ten scans of the same subjects. The rest of this paper is structured as follows. In Section 2 a description of the data is provided. In Section 3.1 the analysis of synchronization of dynamic processes in the phase space is described with an illustrative example of the proposed metric. Section 3.2 is devoted to both topological and modularity analysis of fMRI connectivity matrices. In Section 4 the results of the analysis for the SYNC, the Pearson and coherence networks are presented with a discussion on the modular organization and the most important nodes of the three networks and a comparison between them. Finally, the conclusion of the work is presented in Section 5.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Routing Protocol
#### 4.1.1 [PERSON] et al. [12] proposed a \(Q\)-learning-based multiobjective optimization routing protocol (QMR) where end-to-end delay and energy consumption are optimized simultaneously. They also dynamically changed the \(Q\)-learning parameters such as learning rate, discount factor, and \(\epsilon\)-value for exploration and exploitation. QMR consists of routing neighbor discovery, \(Q\)-learning algorithm, routing decision, and penalty mechanism. Initially, the QMR collects the geographic locations of their neighbors using a global positioning system (GPS) and sends HELLO packets to start the route discovery process. Each HELLO packet contains the node's geo-location, energy, mobility model, queuing delay, and discount factor. Nodes start to maintain and update their neighbor table upon receiving the HELLO packets. A neighbor table contains the arrival time, learning rate, MAC delay, and \(Q\)-value along with the information of the HELLO packet [12].  
Figure 10: Multihop routing in FANET.  
After route discovery, QMR selects a neighbor to forward the data packet using \(Q\)-learning. The \(Q\)-learning algorithm considers energy consumption, link stability, one-hop delay, and neighbor relationships to select the next hop for data forwarding. The learning rate of the algorithm is an exponential adaptive function that depends on the one-hop delay. The discount factor varies with the velocity of the neighbor. For faster neighbors, the discount factor is low, and vice versa. Moreover, the trade-off between the exploration and exploitation depends on the actual velocity of the data packet traveling over a link, link quality, and neighbor relationship [12].  
By incorporating all the variables, the source node computes \(k\)-weighted \(Q\)-values and forms a \(Q\)-table, where \(k\) represents the link quality and neighbor relationship. Then, the source node selects the link with the maximum \(k\)-weighted \(Q\)-value to forward the data and obtains maximum reward [12]. If there is no neighbor with a nonzero \(k\)-weighted \(Q\)-value, then the source node receives the minimum reward for all neighbors, updates the neighbor table, and searches for new neighbors using route discovery [12].
# Real-Time 3D Reconstruction Method for Holographic Telepresence
## 2 Real-Time 3D Reconstruction Method
### Surface Generation  
Next, the surface generation from point cloud data that has been extracted is the process where the unorganized points in the point cloud were connected and generated a set of triangles that closely approximates a surface of interest. The Marching Cube algorithm takes voxel data and extracts an isosurface, a polygonal mesh surface representation. Each voxel has a value, and the marching cube algorithm attempts to create a mesh for the surface at a specified iso value. When it comes to an isosurface, the implicit function \(f(x,y,z)=c\) is generally what defines it. This means that all 3D points in the volume part of a c-surface satisfy the previous criterion. When we use Marching Cubes, the 3D volume is divided into voxel squares of equal size. The predefined triangular pattern is possible. A triangular pattern is applied to voxels that intersect the isosurface to approximate the course of the isosurface within the cell.  
In this research, discrete point cloud data are processed using the techniques presented in [PERSON] et al. (2017). We decided to use this algorithm because it would provide insight into the fundamental concepts behind several existing surface reconstruction methods. It was possible to reduce the algorithm's complexity by using a set of points on an unknown surface with their normals. The Marching Cube algorithm has been proven by [PERSON] et al. (2017), who claim that it is very compact and easily manages the data which has and can be a benefit for telepresence, which requires immediate transmission as well as fast and compact data structures to allow for reconstructing and providing a virtual 3D model in real time to remote users. [PERSON] et al. (2018) agreed that the enhancement needed to produce a fast and inexpensive 3D data acquisition system for multiple Kinect sensors.  
The modification of the Marching Cube algorithm is based on (2016). In Algorithm 2, the step in number 7 is used a looping foreach voxel recalculating and generating process. The cube vertices are insufficient to determine the correct surface triangulation. The original marching cube does a lookup into one of eight different cases as in the yellow frames in Figure 3a that shows as the 4 th, 6 th 7 th, and 10 th and 11 th cubes in the modified lookup table. Modifying the marching cube to triangulate the implicit surface allows the possibility of the prescribing boundaries to increase, and it becomes sufficient. Extended marching cubes then look at each voxel's boundaries and do a lookup into one of 14 different cases in blue frames, as shown in Figure 3b. The mesh associated with the looked-up case is added in place of the voxel. After all voxels have been processed, the result is a set of mesh triangles that approximate the mesh from which the point cloud was created.
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 4 Proposed System for the EEG-based Preference Detection
### Preference Modeling and Ground Truth  
To set the true preferences (ground truth table), we used the DEAP self-assessment reports to identify the preference states using a nine-point Likert scale for valence dimension. In this study, we considered the valence dimension as a preference indicator to align with the target preference state: pleasant and unpleasant. Moreover, we considered EEG trials that had at least two different valence levels--low and high. The valence levels are classified as follows: (1) low valence if the valence rating is between 1 and 5; and (2) high valence if the valence rating is between 6 and 9. The presence of a low or high valence is an indicator of an unpleasant or pleasant preference state, respectively.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Affective Model** & Dimensional emotion model (Valence-Arousal-Dominance) \\ \hline
**Stimuli** & Visual- and audio-based stimuli (1-min music video) \\ \hline
**Participants** & 32 participants aged 19 to 37 years \\ \hline
**Trials** & 1280 trials (40 trials for each subject) \\ \hline
**EEG device** & 32 EEG channels of the Biosemi Active Two system. The EEG data stream was collected using 32 Ag/AgCl electrodes, which were arranged in accordance with the 10-20 international system. \\ \hline
**Experimental Protocol** & Each participant watched and rated his or her emotional responses to 40 music videos on scales of arousal, valence, and dominance using self-assessment manikins (SAM). Participants also reported their liking of and familiarity with the videos. \\ \hline
**DEAP Database** & Different datasets that are publicly available in the DEAP database include recorded signal data, frontal face videos for a subset of participants, stimuli-volunteers’ self-reported data, and subjects’ self-assessments. \\ \hline \hline \end{tabular}
\end{table}
Table 7: DEAP dataset description.