esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effect of Slope of Equivalent Water Flow Path on the Subsurface Drainage of a PFC  
The effects of the slope of the equivalent water flow path (\(S_{R}\)) on PFC subsurface drainage were also investigated. The curves in Figure 6 illustrate the relationship between the slope of the equivalent water flow path (\(S_{R}\)) and the time for surface ponding (\(t_{p}\)), according to the different rainfall intensity values (\(I\)). The observation showed that the increase in \(S_{R}\) resulted in an increase of \(t_{p}\). For instance, for the PFC with \(L_{R}=10\) m and \(I=10\) mm/h, when \(S_{R}\) increased by 16 times (from 0.5% to 8%), \(t_{p}\) increased more than two times (from 81 to 189 min). This performance is consistent with that of [PERSON] et al. [8], [PERSON] and [PERSON] [16]. The results indicated that the PFC with a higher \(S_{R}\) exhibits a higher subsurface drainage capacity than the one with a lower \(S_{R}\). This rather contradictory result may be due to the speed of drainage. [PERSON] et al. [32] speculated that a higher slope could help increase the speed of drainage water in the PFC body and cause a lower water head in the PFC body.  
The varieties of slope \(S_{R}\) showed a lower impact on the subsurface drainage for the PFC with a longer \(L_{R}\). By way of illustration, at \(I=10\) mm/h, when the value of \(S_{R}\) increases two times (from 2% to 4%), for \(L_{R}=10\) m, \(t_{p}\) increased significantly by 10 min (from 86 to 96 min), but for \(L_{R}=15\), \(L_{R}=20\), and \(L_{R}=30\) m, this value increased by only 8 min, 7 min, and 3 min, respectively. Thus, it can be concluded that the subsurface drainage of the PFC with a longer \(L_{R}\) is less sensitive to the slope \(S_{R}\). It is of note that the change in \(S_{R}\) values had a significant effect on the results of \(t_{p}\). PFCs with different \(S_{R}\) provided a wide range of results for \(t_{p}\). Thus, it could be concluded that \(S_{R}\) had a high impact on the subsurface drainage of PFCs. Surprisingly, the results of \(t_{p}\) of PFCs with different geometric designs are almost close at high rainfall intensities. This indicates that the subsurface drainage of PFCs at high rainfall intensity has a similar response, regardless of the geometric designs.  
Figure 6: \(Cont.\)
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 5 Interference Reduction
### Proposed Data-to-Data Interference Reduction  
The data-to-data interference reduction is based on extrapolating the CSI. The delay component in (6) is represented by the function sinc and the Doppler component does not depend on the fractional part of the delay. This means that any fractional delay will produce sinc oscillations in the CIR, which will generate interference not only between pilots and data, but also between data carriers. Since the RP-OTFS pilot zone occupies all columns in the Doppler direction for \(2L\) pilot delays, and based on (6), we treat each particular column of the received DD grid as a separate one in the context of this algorithm. We also assume that the maximum reflection delay is \(L-2\), which is equivalent to the statement that there is no reflection, at least in the last two samples of the CIR.  
Based on the assumptions above, we can reconstruct the side lobe generating factor using the following formula, which was found experimentally:  
\[F(q)=A\cdot\text{sinc}(0.5+q), \tag{20}\]  
where \(q\) is the sample number (\(q=0\dots M-L-1\)) and \(A\) is a complex parameter that should be estimated. After receiving and estimating the channel (this process is described in the previous Section 4.3 on CIR estimation), we use the last two samples of CIR to estimate the complex parameter \(A\). Estimation of \(A\) is conducted separately for each column of the DD grid.  
The maximum delay, which could be estimated in \(\mathbf{H}\) in case of RP-OTFS, is \(L\). We will denote this estimate of part of \(\mathbf{H}\) as \(\mathbf{H}_{L}\). Then we append the reconstructed \(F(q)\) to \(\mathbf{H}_{L}\) and obtain the reconstructed CIR matrix. Practically, we extrapolate the estimated part of the \(\mathbf{H}\) matrix with the sinc function with the estimated parameter \(A\).  
To check the efficiency of the data interference compensation method based on the sinc extrapolation, proposed above, we provide the results in the simulation Section 7.
## 2 Robot Control System
### Large-Scale Surface Orthogonal Motion Planning
#### 2.3.2. Waypoint Generation  
The surface orthogonal motion planning is inspired by the approach presented in [41], which addresses an inspection task of a landscape with an unmanned aerial vehicle (UAV). For this purpose, the landscape is rasterized into a grid. The grid resolution depends on the technical parameters of the sensor, the desired image resolution, and the desired overlaps of the individual images. The centers of the grid cells are shifted along the normal of the ground surface. This approach was adapted for the creation of 6D waypoints, representing surface orthogonal 6D poses of the radar sensor. Figure 8 shows the three steps of the implemented waypoint generation method, applied to the reconstructed surface of Figure 7. Figure 8a shows the centers of the grid cells. These are projected onto the reconstructed surface (see Figure 8b). The projected centers are shifted along the surface normal to the desired distance from the surface. The z-axis of the 6D waypoints are aligned with the negative surface normals (see Figure 8c).
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 2 Related Work
### Voxel-Based Methods  
The Voxel framework, as introduced by [5], initially employs an occupancy grid algorithm to represent the original point cloud as multiple 3D grids. Each grid corresponds to a voxel, and subsequent 3D convolutions are applied for feature extraction. [PERSON] proposed a hybrid network named PointGrid in the work of [13], which integrates both point and grid representations for efficient point cloud processing. This approach involves sampling a constant number of points in each embedded volumetric grid cell, thus allowing the network to utilize 3D convolutions to extract geometric details.  
In contrast, voxel-based methods follow a two-step process. Firstly, the original point cloud undergoes voxelization, thereby converting the unordered point cloud into an ordered structure. Subsequently, 3D convolution is applied for further processing. This approach is more direct and simpler, thus drawing inspiration directly from 2D convolutional neural networks. However, it comes with a significant computational cost, and due to the uniformity of each voxel postvoxelization, there is a loss of information regarding fine structures.
## 4 Experimental Results  
### Hardware Design of the Totem-Pole Bridgeless PFC Converter  
The overall structure of the totem-pole bridgeless PFC converter based on SiC devices is shown in Figure 13, which includes the main power topology, soft-start circuit, current and voltage sampling circuit, gate driving circuit, and controller. The main power topology consists of a PFC inductor, four power switches, and output bus capacitors. To ensure safety, the main power topology is electrically isolated from the control circuit.  
The main specifications of the totem-pole bridgeless PFC converter designed in this paper are shown in Table 1, where \(v_{in}=220\) VAC, \(v_{o}=400\) VDC, \(P_{o}=1600\) W, and \(f_{s}=100\) kHz. The SiC MOSFET is C3M0030090K from CREE, while the Si MOSFET is IPW65R045C7 from Infineon. The inductor is 350 uH, and the output capacitor is 7 \(\times\) 160 uF. The controller is TMS320F280049 from Texas Instruments.  
Figure 12: Simulation waveforms of PFC converter at 600 W input power. (**a**) Without feedforward compensation; (**b**) with the proposed VAFC.  
Figure 13: Overall block diagram of totem-pole bridgeless PFC converter.  
A prototype of the totem-pole bridgeless PFC is shown in Figure 14. It contains a main power board and a control board. The size of the main power board is 20 cm \(\times\) 10 cm, which includes an EMI filter, soft-start circuit, PFC inductor, SiC and Si switches, a gate driving circuit, and the high-voltage side of the sampling circuit. The size of the control board is 7 cm \(\times\) 4 cm, which mainly includes the peripheral circuit of the digital controller and the low-voltage side of the sampling circuit.
## 2 Literature Review and Theoretical Framework
### HMD-VR Simulators in Maritime Engineering Training and Education  
In MET institutions, conventional semi-immersive and non-immersive Maritime Engine Room Simulators (MERSs) have been considered effective learning tools for educating and training maritime engraining students and officers for almost half a century [19; 50]. Due to their numerous educational and training benefits, such as enhancing knowledge comprehension, improving information assimilation, and developing incident management and stress-coping skills in a risk-free environment [51; 52; 53; 54], their use in maritime training has been regulated by the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), 1978, as amended [55; 56]. However, given their relatively high costs, restricted immersive experiences and dependence on physical infrastructure and training personnel [50; 56], recently, there has been a growing interest in the gradual integration of fully immersive VR technologies [17; 32; 41]. One notable example is the HMD-VR engine room simulator, developed by the Faculty of Maritime Studies at the University of Rijeka, Croatia [6; 42]. Representing a new generation of fully immersive HMD-VR technology, this engine room simulator is expected to enhance maritime engineering training by providing students with time- and space-independent, interactive, experiential, and personalised learning sessions. Its key feature lies in its ability to enable students to simultaneously execute complex diagnostic procedures within various training scenarios that would otherwise be challenging, costly, or hazardous to replicate in real onboard settings [42].  
Despite the growing interest of the scientific community in understanding the potential of HMD-VR technology in maritime higher education and training programmes, studiesthat have specifically investigated maritime engineering students and their experiences with fully immersive VR technology are still relatively scarce.  
A pilot study [6] surveyed maritime engineering students during preliminary trials of the HMD-VR engine room simulator. It found that students who had never used the HMD-VR technology showed an optimistic standpoint when asked about the potential of implementing HMD-VR technology in maritime higher education curricula. Furthermore, the findings indicated that students perceived HMD-VR as more beneficial for improving learning effectiveness than traditional MERS.  
Based on the work of [32], which explored the relationship between perceived ease of use (PEU), learning benefits and satisfaction among maritime engineering students who engaged in HMD-VR engine room simulator trials. Their study revealed that students who found the simulator more intuitive and user-friendly exhibited more positive attitudes toward its learning benefits, expressed greater intention to use it in the future and reported higher satisfaction with the overall VR experience.
## 4 Stochastic Differential Power Control Game
### State Space Model  
The variables in terms of system dynamics include the remaining battery energy of the MTC node along with the experienced interference and channel gain. These variables will help in the formulation of the state space model for the proposed SDG power control. An MTC user transmits only for a finite amount of time due to limited battery energy. The consumed power is related to the available battery energy for node \(k\) at time t as \(E_{k}(t)\) by a generic relation:  
\[dE_{k}(t)=\ -p_{k}(t)dt, \tag{16}\]  
The total interference experienced by an MTC node considering the intra-domain and inter-domain interference behavior as already defined in Equations (1) and (2) is represented as:  
\[I_{k}(t)=i_{k}(t)+\ I_{k}^{\prime}(t)=\sum_{i=1,\ i\
eq k}^{K}p_{i}(t)G_{i,k}(t )+\sum_{j=1}^{J}P_{j}(t)G_{j,k}(t). \tag{17}\]  
The interference state can now be shown as:  
\[dI_{k}(t)=\ \frac{\partial}{\partial_{t}}I_{k}(t) \tag{18}\]  
Hence, the state space for the considered SDG scenario is defined as:  
\[S_{k}(t)=[\ E_{k}(t),\ I_{k}(t)]. \tag{19}\]  
Here, the remaining energy, as well as the interference caused by the k\({}_{\text{th}}\) MTC node, is considered as variables of the state space, which are acquired as the proposed differential game is solved using mean field approximation. During transmission, a device has a certain cost associated with the operation as described in the proposed state dynamics. The available energy at all devices continues to decrease during operation, so selection of the transmitting power for each device affects the battery life.
## 5 Performance Evaluation
### Indoor Random Deployment Scenario  
In this simulation, the deployment scenario follows the 3 GPP's indoor coexistence evaluation scenario [22], as shown in Figure 16. Each of two operators deploys four WLANs in a single-floor apartment building. Devices are evenly allocated to the WLANs. The devices of each WLAN are randomly distributed around its serving AP. In Phase 1, all APs are legacy APs (i.e., AP SLDs) working in different primary channels as shown in the figure, while in Phase 2, the APs function as AP MLDs that can work on both links, reserving their primary links only for Sync-PL operation. The system parameters follow those for the simulation scenario [23] and evaluation methodology [24] used for IEEE 802.11 ax. The indoor path loss model of the IEEE 802.11 ax simulation scenario is used. We consider the transmission bit rates of IEEE 802.11 ac, which range from 6.5 (MCS 0) to 78 (MCS 9) Mbps. Packet errors are generated based on the packet error rate (PER) curves provided in the evaluation methodology for IEEE 802.11 ax. The MCS of a link is taken to be the highest bit rate with a PER lower than 10% at the signal-to-noise ratio (SNR) of the link [25; 26]. Acknowledgment frames are transmitted at the lowest transmission bit rate (6.5 Mbps). The noise figure is 7 dB, and the noise floor is \(-\)94 dBm. The simulation parameters are listed in Table 1.  
Figure 23 shows the per-device throughput performance for an increasing number of total devices. The overall trends are similar to those observed in the single-spot deploymentscenario. Sync-FT and its coexistence solutions achieve better performance than the legacy-only case. Sync-PL shows almost the same throughput performance as that achieved with legacy devices only. As the number of devices increases, the throughput gain of Sync-FT also increases. In contrast, the performance degradation of the legacy devices is more severe than in the single-spot deployment scenario. Sync-FT-Repick+Comp still shows the best coexistence performance among the proposed solutions. Similar trends of \(\gamma_{t}\) are also observed in Figure 24.  
Figure 22: Values of the new metric \(\gamma_{t}\) in the single-spot deployment scenario. (**a**) With STR capability, (**b**) Without STR capability.  
Figure 23: Throughput with an increasing number of devices in the indoor random deployment scenario (left: MLDs, right: legacy devices). (**a**) With STR capability, (**b**) Without STR capability.  
Figure 24: _Cont._
# Selected Plants as Sources of Natural and Active Ingredients for Cosmetics of the Future
## 3 Centella asiatic
### Biological Activity  
Cosmetics containing C. _asiatica_ extract (INCI: _Centella Asiatica Extract_) assist in smoothing facial expression wrinkles and preventing the formation of new ones, counteracting the appearance of stretch marks, and reducing scars [50,51]. The bioactive ingredients found in the plant (Section 3.3) prevent excessive accumulation of fat in cells, thus exhibiting anticellulite properties, improving skin elasticity, and consequently resulting in a slimming and firming effect. Moreover, the antioxidant properties of C. _asiatica_ components contribute to delaying the skin aging process. By stimulating the peripheral circulation, the _C. asiatica_ extract strengthens hair, accelerates its growth, and inhibits its loss [47,48,50].  
Madecassoside present in _C. asiatica_ extracts protects skin against lipid peroxidation, as well as intensifies synthesis of collagen while stimulating angiogenesis. It also enhances the infiltration of inflammatory cells and supports re-epithelialization to facilitate nearly complete wound closure in burned skin.  
Asiaticaside has similar activity to madecassoside. It exhibits antibacterial and anti-inflammatory properties, thereby supporting the treatment of deep, slow-healing wounds and burns, and accelerates the scar reduction process [47,50]. The activity in wound healing process is associated with promoting the angiogenesis (e.g., by stimulation of vascular  
Figure 7: The most important bioactive ingredients from _Centalla asiatica_[42,48].  
endothelial growth factor production) and also with suppressing apoptosis and increasing cellular proliferation. Additionally, by increasing the level of type I collagen (through T\(\beta\)RI kinase-independent Smad signaling pathway) it triggers an anti-wrinkle response [50].  
Furthermore, the bioactive components of this plant positively affect sleep quality. Therapeutic effects have been observed in gastrointestinal diseases (including stomach ulcers, duodenal ulcers, and inflammatory bowel disease) as well as a positive impact on the cardiovascular system (including lowering arterial blood pressure and reducing symptoms of venous insufficiency in the lower limbs) [52; 53].
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Large Intelligent Surfaces
#### 2.2.2 LIS-Aided Communications with Decreased User Interference  
A LIS system is made up of several tiny LIS units, with various benefits over a centralized implementation. To begin with, it is possible to maintain the area of each LIS surface unit at a tiny level that enables variable implementations and configurations [28]. Second, LIS units can be inserted, removed, or changed without dramatically altering the system design. In contrast, with a relatively tiny surface area, those electromagnetic waves do not overlap each other. Therefore, each LIS unit is capable of preventing inter-user interference [16].  
The development of strategies has been looked at in terms of massive MIMO, LIS, and RS-based systems, with a focus on making interference-suppressing uplink detection algorithms based on ZF, MRC, EGC, and MMSE [29].
# Event-Based Path-Planning and Path-Following
## 2 Problem Formulation
### Sonar Model  
We assume that the AUV is equipped with a multi-beam forward looking sonar (FLS) installed onboard the AUV in the XOY plane in order to detect obstacles in the heading direction. The data produced by the FLS can be presented as a set of pairs \((\alpha_{i},\rho_{i})\), where \(\alpha_{i}\) is the signed angle counted from the AUV's heading direction to the beam direction, \(\rho_{i}\) is the distance to an obstacle in the beam direction, \(i=\overline{1,N_{b}}\), \(N_{b}\) is the number of beams. Here and further, a signed angle is assumed to be positive if it is counted clockwise (when viewed from above), and negative in the other case. If no obstacles are detected in the direction \(i\) or \(\rho_{i}\) is greater than the detection range \(\rho_{\max}\) then \(\rho_{i}=\infty\). For certainty, we take \(\alpha_{i}\leq 0\) for the left beams (beams that are on the left from the AUV's heading direction) and \(\alpha_{i}>0\) for the right beams. For further purposes, define the following subsets of beams: \(I_{L}=\{i:\alpha_{i}\leq 0\}\), \(I_{R}=\{i:\alpha_{i}>0\}\), \(I_{A}=\{i:|\alpha_{i}|\leq\overline{\alpha}_{a}\}\), \(\overline{\alpha}_{a}>0\). They indicate the presence of obstacles on the left, right, and ahead of the AUV. Based on the FLS data, one can form a situational (local) model of underwater environment as a sequence of 2D points \(\mathcal{Q}=(Q_{1},Q_{2},\ldots,Q_{N_{b}})\).
# Protein Dynamics in F-like Bacterial Conjugation
## 3 Structures Involved in Pilin Processing, Pilus Extension, and Retraction
### Pilus Assembly and Extension Proteins  
F pili extension/retraction is different from non-conjugative type IV pili, the best studied models of the type II secretion systems, in several key regards. Fluorescence microscopy imaging has shown F pili rotate throughout the process of pilus extension while type IV pili do not, indicating that the molecular motors involved in the F pilus remain stationary leading to pili rotation [70; 71]. Furthermore, type IV pili encode two different hexameric adenosine triphosphatases (ATPases), PilF (for extension) and PilT (for retraction), in contrast to the F-like T4 SS family in which retraction is considered to occur in an energy-independent manner; TraC and TraD are proposed to supply energy to the system for pilus extension and transferosome transport cooperatively [15; 71].  
This intricate process of assembling TraA pili monitors requires the aid of many proteins, including TraB, -C, -E, -F, -G, -H, -K, -L, -U, -V, -W, and TrbC [11]. Of these proteins, TraF, -H, -U, and -W are specific to the F T4 SS, all others have structural and functional homologs [14]. Mutations in these _tra_ and _trb_ genes cause various disruptions in the assembly of the pilus, from alterations in length, to completely inhibiting the formation of a pilus. Although these proteins are known to be involved in the assembly and/or extension of the pilus, the structure and function of each is largely unknown.  
The pilus is composed of mature TraA subunits, which assemble into a helical structure with a length of approximately 20 \(\upmu\)m, an outer diameter of 8-10 nm, and an inner lumen of 2-3 nm in diameter [11; 72]. The structure of the pilus has been determined by scanning transmission electron microscopy (STEM), where the subunits were found to be mainly \(\alpha\)-helical and the pilus exhibited a 5-fold rotational symmetry [58; 73]. A separate cryo-EM structure provided a higher resolution image of the five-start helical structure composed of \(\sim\)12.8 TraA pilins per helical turn (Figure 3) [16].