esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section  
The calculations presented in this paper are based on a number of geometric concepts introduced previously: accessible surface, circular variance and mutual proximity. Here, they are introduced briefly.  
Accessible surface [21] is an extension of the Vander Waals (VdW) surface. It is defined by the center of a solvent-size sphere (in case of water of radius 1.4 A) as it is rolled around the VdW surfaces of the atoms in the target. Such a surface is generally smoother than the VdW surface as the solvent sphere does not fit into the crevices formed by the VdW surfaces of atoms.  
Circular Variance (CV), a concept introduced for the characterization of angular spread [22] has been found to be useful to characterize the extent a point is buried within a set of points [23]. The degree of buriedness indicator is calculated as the circular variance of vectors drawn from a test point to the points in the set: when the test point is way outside then these vectors are essentially parallel, so CV is near zero while when the test point is in the middle of the set then CV will be close to one:  
\[CV=1-\frac{|\sum_{i}\vec{r}_{i}-\vec{R}|}{\sum_{i}|\vec{r}_{i}-\vec{R}|}\]  
where \(\{\vec{r}_{i}\}\) is the set vectors pointing to the atoms of the protein and \(\vec{R}\) is the vector pointing to the test point.  
The concept of mutual proximity was used earlier to establish the contacts between a target and a docked ligand pose[24]. In this application, for each atom in a pair of proteins the one nearest to it from the other protein is established; whenever atom \(i_{1}\) is nearest to \(i_{2}\) and at the same time atom \(i_{2}\) is nearest to \(i_{1}\) the pair of atoms [\(i_{1}\), \(i_{2}\)] is considered a contact pair. Figure 1 shows a schematic demonstrating the relation between contact and mutual proximity.  
It was also found that a robust definition of surface atoms is obtained if atoms whose exposed accessible surface fraction exceeds 3% and whose CV calculated with respect to the rest of the protein atoms is less than 0.8 are selected. As the average distance between closest pair contact atoms is 5.0 A surface atoms within 4 A were considered part of the interface. This choice ensured that most, if not all, non-contact surface atoms were included in the interface. All statistics reported in this paper were calculated using these definitions. As the data involved crystal structures, only heavy atoms were considered.  
The test data set contained 1172 protein complexes, downloaded from the Protein Data Bank (PDB) [25]. The complexes selected were of high resolution and had no missing residues. For each complex, the PDB annotation for biological oligomers was used. When the complex contained more than two members, the pair with the largest number of contacts was retained. The PDB IDs of the 1172 complexes, together with the chain IDs used is provided as supporting information.
## 2 Numerical Approach  
The DES method based on the SST k-\(\omega\) turbulence model [14; 15; 16] was used in this study. Detached-Eddy Simulation (DES) is a hybrid large eddy simulation (LES)/RANS method. The concept of DES is to calculate the boundary layer by a RANS turbulence model and to switch the turbulence model into a LES mode in detached regions. Compared with using LES in the global computational domain, the DES method not only ensures the calculation accuracy, but also improves the calculation efficiency and saves the calculation cost. Because of the balance of computation resources and fidelity, the DES method had been widely applied in capturing vortex-shedding structures in turbomachines [17; 18; 19]. In the DES calculation, the LES model is activated in the region where the turbulence length predicted by the RANS model is larger than the local mesh scale. The turbulence length scale in an SST k-\(\omega\) model and in the DES model are shown in Equations (1) and (2), respectively:  
\[l_{\text{k-}\omega}=k^{0.5}/(\beta\times\omega), \tag{1}\]  
\[l_{\text{DES}}=\min(l_{\text{k-}\omega},C_{DES}\Delta). \tag{2}\]  
The dissipative term of the k-transport equation, the only modified term in the DES model, is transferred to Equation (3):  
\[D_{\text{DES}}^{k}=\rho k^{2/3}/l_{\text{DES}}. \tag{3}\]  
The commercial CFD software ANSYS CFX was used for the DES calculation. The advection term was discretized by the High Resolution Scheme, and the Second Order Backward Euler method was applied in the discretization of transient terms. According to the experimental results, the highest frequency of interest in this unsteady phenomenon of this stage would be less than 2500 Hz, so there must be more than 40 time steps in a blade computing pitch. In the unsteady simulation, every blade passing pitch was divided into 105 time steps with each time step including a maximum of 50 inner interaction steps. In this paper, total performance parameters and separation positions were dealt with time-averaged results recorded after a stable quasi-periodic flow state achieved. Fast Fourier Transformation analysis was based on the data collected in over 200 blade-passing pitch cycles.
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 1 Introduction
* A Q-learning algorithm, where the training data are obtained directly from a simulated dynamic traffic environment, allowing for a more realistic representation of state transitions by observing channel busy ratio (CBR) values for different transmission rates and vehicle densities. * A reward function is defined, combining CBR and transmission rate, to keep the channel load under a target threshold while maximizing the transmission rate for congestion control. * Our simulation results demonstrate that the proposed Q-learning approach is successful in maintaining the desired channel load under various dynamic traffic scenarios and exhibits a lower Beacon Error Rate (BER) compared to existing methods. The structure of this paper is outlined as follows:  
In Section 2, we conduct a review of current approaches for congestion control, with a specific emphasis on recent machine learning (ML)-based techniques. In Section 3, we present our formulation of congestion control as an MDP and introduce our proposed Q-learning-based algorithm for congestion control. In Section 4, we demonstrate the application of our framework with a concrete example and discuss the results obtained. Finally, in Section 5, we provide a conclusion and suggest directions for future work.
# 1-Bit Reconfigurable Transmitarray Antenna with Out-of-Band RCS Reduction +
## 2 Design Concept  
The key step to designing a low-RCS 1-bit RTA is constructing two RCS reduction bands in the lower and upper sides of the antenna's operating band while maintaining high-gain radiation and beam scanning in the middle band. Then, the antenna can be better protected from being captured by enemy radar. The entire structure consists of two layers separated by an air cavity, which is the AFST layer and the RTA layer, as described in Figure 1. In addition, a horn antenna is located under the RTA layer at a specific distance to provide spherical incident waves to the RTA layer. The designed low-RCS RTA exhibits various frequency responses in the three frequency bands, and its different components offer different functionalities. Each component of the integrated structure must work in cooperation with one another to create three artificial bands with distinct frequency responses.  
Concretely, the AFST layer must have the two following functions. On the one hand, it needs to be able to absorb the incident waves around the out-of-band frequencies \(f_{L}\) and \(f_{H}\) and have dual-polarization characteristics. On the other hand, the incident wave around \(f_{M}\) can transmit the AFST layer with low insertion loss to ensure the in-band high-gain radiation characteristic. Furthermore, the operating frequency band of the 1-bitRTA layer should be consistent with the transmitting frequency \(f_{M}\) of the AFST layer to minimize the loss of radiation gain. In addition, the 1-bit RTA plays a crucial role in regulating the phase of the incident wave from the feed horn antenna, thereby achieving high-gain radiation performance and beam scanning. Meanwhile, the RTA layer should provide complete reflection characteristics similar to PEC around \(f_{L}\) and \(f_{H}\) to ensure the out-of-band RCS reduction.
## 7 Conclusions and Future Work  
This paper presented a simulative performance assessment of LoRaWAN networks in which the EDs use the LBT AFA MAC protocol in compliance with the ETSI regulations. The methodology of this assessment has general validity, as no limiting assumptions are made, but all the involved parameters (e.g., traffic generation patterns, payload size, EDs mobility patterns, channel model, and the LoRa parameters) are configurable depending on the application. In the network configuration phase, the designer can follow the methodology presented in this work to select, given the number of EDs, the most suitable combination of the MAC parameters to fulfill the application requirements.  
The paper presented the results obtained in several scenarios with a varying number of LoRaWAN EDs and different configurations of the MAC parameters. These parameters remain fixed during the simulation. It would be interesting to evaluate the effects on the performance of varying such parameters during the simulations, therefore further investigations will address how to dynamically change the MAC parameters in the EDs exploiting the SDN paradigm. Future work will also deal with the combination of the assessed LBT AFA with a backoff algorithm able to provide prioritized channel access to time-critical messages.  
Conceptualization, L.L. and L.L.B.; methodology, L.L., L.L.B. and G.P.; investigation, L.L., L.L.B., G.P., A.P. and M.P.; software, L.L., A.P. and M.P.; writing--original draft preparation, L.L., L.L.B. and G.P.; writing--review and editing, L.L., L.L.B. and G.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Catania through the PIA.CE.RI. 2020/2022 project, program title \"Linea 2-Progetto interdipartimentale SIS-RENEW\" and \"Linea 2D-Piano di incentiti per la ricerca di Ateneo\".  
Not applicable.  
The data relevant to this paper could be made available upon request to the corresponding author.  
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
## 2 Related Work  
### Skeleton-Based Action Recognition  
Traditional approaches to human action recognition, which rely on skeleton data, typically involve the manual extraction of human action features [13; 14; 15; 16; 17; 18]. The process of manual feature-based methods is quite complicated, and the performance is unsatisfactory. As deep learning has gradually become the mainstream method in computer vision, attempts have been made to utilize RNN and CNN to recognize and classify human actions using skeletal data. RNN-based approaches typically utilize skeleton data from human activities as a collection of time series [19; 20; 21], whereas CNN-based approaches initially transform the skeleton data from human actions into a pseudo-picture. RNN-based approaches prioritize the temporal aspects of human actions, whereas CNN-based approaches prioritize the spatial aspects of human actions [22; 23; 24]. Due to its strong parallel computing capability, CNN is extensively employed in skeleton-based human activity recognition. Nevertheless, research has indicated that both RNN and CNN are insufficient for accurately representing the intricate structure of human action skeleton data [4]. Skeleton data falls within the category of non-Euclidean data, whereas RNN and CNN are usually used to process Euclidean data. This makes it difficult for RNN or CNN to obtain useful features from skeleton data.  
GCNs are very good at dealing with non-Euclidean data. [PERSON] et al. [5] developed the ST-GCN and used it to recognize human actions based on skeletons. The ST-GCN blends the GCN and TCN, which can pull out the spatio-temporal features of human action skeleton data at the same time. For the first time, this method has shown great promise in the area of skeleton-based human action recognition. One problem with ST-GCN is that it cannot fully catch the complex dynamic changes in actions because it uses a fixed graph structure. Subsequently, [PERSON] et al. [6] merged the two-stream architecture and GCN to produce a two-stream adaptive graph convolutional network (2s-AGCN). The 2s-AGCN learns the graph topology by constructing a parameterized adjacency matrix so that the network can adaptively adjust the graph structure according to the input data. While this approach enhances the model's ability to accurately recognize, it also introduces a substantial amount of computational parameters. However, [PERSON] et al. [25] proposed the Shift-GCN by combining Shift convolution with ST-GCN. Shift-GCN greatly reduces the computational cost while maintaining a good recognition accuracy. Similarly, [PERSON] et al. [26] processed the skeleton data of human actions to obtain three information streams of human actions: joints, bones, and speed. They fused these information streams and used a composite scaling strategy to propose an efficient GCN with high accuracy and a small number of trainable parameters.
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 2 Precoding Schemes
### Non-Linear Precoding Schemes
#### 2.2.2 Vector Perturbation Precoding  
Vector perturbation works similarly to THP, as the transmitted symbols are \"perturbed\" and moved from the original to a new constellation of points. On the receiver side, a diagonal matrix is used to scale the received signal, which is then reconstructed through a modulo operation.
## 3 Results and Discussion
### Llme-SPME Method Optimization
#### 3.1.1 Selection of Organic Solvent  
Additional organic solvents are usually avoided when applying SPME because the SPME fiber may become saturated with the solvent (e.g., hexane) instead of the analyte of interest. In our study, it was found that the extraction efficiency could be significantly improved with the addition of minute amounts (_i.e._, microliters) of organic solvent. Several commonly used organic solvents in liquid-liquid extraction were selected including pentane, hexane, benzene, chloroform, ethyl ether, and ethyl acetate. A series of 10-mL TCE water samples containing 20 \(\upmu\)L each of organic solvent were prepared. For ethyl ether and ethyl acetate, another set of samples with 500 \(\upmu\)L of each in 10 mL aqueous TCE standards were prepared for comparison because their solubilities in water are relatively high. The extraction temperature at 25 \({}^{\circ}\)C and extraction time at 15 min were used as LLME-SPME extraction conditions. The responses obtained from the different solutions are given in Figure 1A. The response was significantly higher with the addition of 20 \(\upmu\)L of hexane.  
The volume effect of hexane was also investigated and results are given in Figure 1B. The optimum volume for hexane was 20 \(\upmu\)L. Therefore, 20 \(\upmu\)L hexane was used in this study.  
The putative mechanism for the enhanced SPME efficiency by LLME is that the LLME enriches the TCE from aqueous solution into an organic film on the solution surface. When organic solvents with the densities less than 1 g cm\({}^{-3}\) (e.g., hexane) are selected, the organic film resides at the solution/headspace interface. The TCE enriched film has a greater mass transfer efficiency to the headspace. Solvents denser than water such as chloroform did not provide an enhancement of the SPME efficiency, because the denser TCE enriched organic film was not in contact with the headspace even with stirring. Compared with hexane, pentane has better volatility but did not improve the extraction efficiency significantly. The reason could be that the fiber was saturated with pentane that displaced any TCE on the fiber.  
Other than density and volatility, the selection of organic solvent should also consider the retention properties of the organic solvent. The retention index (RI) can be used as a criterion to select an organic solvent. The RI of the analytes should be larger than the RI of the organic solvent so that the solvent  
Figure 1: Effect of different extraction solvent for liquid–liquid microextraction assisted solid phase microextraction (LLME–SPME) (**A**) and effect of hexane volume for LLME–SPME with 95% confidence intervals (_n_ of 3) (**B**). The extraction temperature was 25 \({}^{\circ}\)C and extraction time was 15 min.  
delay period will not include any analyte peaks. Table 1 listed the RIs for TCE and the organic solvents tested in our study. In this study, the solvent peak of hexane can be fully separated from the TCE peak with respect to retention time.
# A Design for an Internet Router with a Digital Optical Data Plane
## 6 Optical Packet Multiplexing
The results also clearly demonstrate the difference between backward and forward shift approaches and show that forward shift is much more efficient, especially for smaller buffer regions. Figure 17, right, shows the difference between backward and forward shifting under input loads varying from 10%-100% compared to VOQ iSLIP [23]. It compares one, two, and four large IP packets' worth of buffering and shows that forward shifting is much more efficient under all loads than backward shifting assuming the same amount of buffering. It also shows that forward shift achieves at least 95% of the efficiency of VOQ iSLIP under all loads. Figure 16: Our proposed packet merging architecture (**left**), composed of a _lookahead_ region, a configurable switched delay line _shift_ region, and a passive optical coupler. Packets in the _lookahead_ region are batch scheduled with shifts in the _shift_ region configured to avoid overlap (**right**).
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Neural Network Models
#### 2.1.2 Input and Output Parameters
According to the well-known vertical ion drift equation [13],  
\[W=U\cos(\theta-D)\cos I\sin I \tag{2}\]  
where, \(D\) and \(I\) are the magnetic declination and inclination, respectively. \(W\) is the vertical ion drift velocity, \(U\) is the horizontal wind velocity and \(\theta\) is the geographic azimuthangle. We adopt magnetic declination \(D\) and magnetic inclination \(I\) as two input factors of our models. In this study, we concentrate on the forecasting of ionospheric foF2, and the output parameter of our models is the foF2 value at the corresponding time.
## 3 System Description
### Network Architecture  
A UWSN described in [4; 21] is considered, where sensor nodes are uniformly distributed in two-dimensional plane \(A\) denoted in Figure 1 at a depth \(D\) of a given three-dimensional region _R_\({}^{3}\). These sensor nodes are considered static as they are anchored to the ocean floor and there is a surface buoy, which acts as a sink located at position **P0**. The AUV is operating at a constant depth _D_\({}_{\mbox{\tiny{AUV}}}\) < \(D\) with an average velocity \(v\).  
Figure 1: Network architecture.  
The plane \(A\) is partitioned into several regions named clusters in order to gather data in a more efficient way. For this purpose, the AUV first computes the Voronoi generator point [22] cluster, and then executes a network partitioning tour (NPT) to broadcast the Voronoi generator points which are used by sensor nodes to identify the cluster to which they belong.  
After clustering, member nodes (MNs) of each cluster select a delegate node called a cluster head (CH) that is responsible for the following tasks:  
1. To further divide a cluster into several subclusters.
2. To select a primary data-gathering node named a path-node (PN) in every subcluster, which relays the gathered data to AUV.
3. To disseminate the list of PNs to share throughout the cluster.  
After subclustering, the AUV executes another tour, called a data-gathering tour (DGT), to visit every PN of which addresses are informed by the CH. The detail of this procedure is given in Section 4.
## 2 Simulation Framework
### Simulation Setup
Another way to generate free surface waves in OpenFOAM is by means of the _waves2 foam_ toolbox; this library includes the relaxation zone technique to prevent the reflection of transmitted waves [31]. In this study, _waves2 foam_ toolbox was not used, and only those libraries included in OpenFOAM v1912 were employed. The tested wave properties included one amplitude only of \(a=0.5\) m (\(H=2a\)) and wave periods for short and long waves (\(T=4\) s and \(T=8\) s, with wave steepness values of \(H/\lambda=0.0527\) and \(H/\lambda=0.0238\), respectively). In this way, waves \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Buoy** & \(R(\mathbf{m})\) & \(L_{b}(\mathbf{m})\) & \(R/L_{b}\) \\ \hline \(R_{1}\) & 0.10 & 1.5 & 0.067 \\ \(R_{2}\) & 0.25 & 2.0 & 0.125 \\ \(R_{3}\) & 0.50 & 3.0 & 0.167 \\ \(R_{4}\) & 1.00 & 4.0 & 0.250 \\ \(R_{5}\) & 1.50 & 6.0 & 0.250 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tested values for the buoy’s radius \({}^{\text{\textregistered}}\) and its arm length (\(L_{b}\)). Figure 2: (**a**) Buoy with radius \(R\); \(B\) is the characteristic width (1 m) and \(L_{b}\) is the arm length, while \(y\) is the rotation axis; (**b**) Top view of the buoy. with lower and higher theoretical powers were tested (see subsequent section). During the initial conditions, when time step t = 0, the still water level (SWL) coincided with \(z=0\) and the seabed had a constant water depth of \(h=3.0\) m along the \(XY\) plane. The time step was initially set as 0.01 s and, to conserve the numerical stability, an automatic adjustment of the time step was allowed based on the Courant number, rather than adopting a fixed time step. In the _alpha.water_ file, in the initial field for boundary conditions, _zeroGradient_ was applied at the outlet, sides, buoy, and bottom, to avoid surface tension effects between the walls and water-air interface. Meanwhile, at the inlet and top, it was defined by _waveAlpha_ and _inletOutlet_, respectively, with a value of 0. In the velocity file (U), the inlet and outlet boundaries were set up as _waveVelocity_ type and the buoy patch was set as _movingWallVelocity_, whereas the top boundary was set as _pressureInletOutletVelocity_ and the rest of the patches were set to _fixedValue uniform_ at (0, 0, 0). In the case of pressure, all patches were set to _fixedFluxPressure_ with a value of 0, except at the top, which was set as _totalPressure_ (p0), which was evaluated by the model at every time step. Finally, only the buoy path was set as 'calculated', whereas the other patches were set as _fixedValue uniform_ at (0, 0, 0) in the _pointDisplacement_ file. This process is summarized in Figure 3.  
Regarding the computational demand, all simulations were run on a 64-bit system with a memory of 31.3 GiB (processor intel(r)Core(tm)i7-6950X CPU @ 3.00 GHz \(\times\) 20). To ensure consistency in the results of the numerical simulations, 100 wave periods were run for each individual test. The longest simulation lasted 3488 h using the computer resources available.