esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Raman Spectrum Enhancement Module (RSEM)
#### 2.3.1 WGAN Network Structure
The generator network optimizes the network gradient through the generator's loss function, making the generated data distribution closer to that of the real data. The discriminator network optimizes the network gradient through the discriminator loss function, so that the discriminator's ability to identify false data is improved. After a sufficient number of confrontations between the generator and discriminator networks, the two are infinitely close to a Nash equilibrium. At this time, the data generated by the generator network is also infinitely close to the real data. In addition, the parameters of network training are also displayed in the lower right corner of the figure below. Compared with the original WGAN, we have made some improvements in order to better generate Raman spectrum data. The one dimensional convolution layer nn.Conv1d is used instead of the linear layer nn.Linear in the discriminator network, and the deconvolution layer nn.ConvTransposeld is used instead of the linear layer nn.Linear in the generator network. The one-dimensional convolution layer can adjust the information interaction between channels, not only so that the model can have strong abstraction capabilities, but also so that computational efficiency is ensured. In addition, Spectral Normalization is used instead of Batch Normalization in the network. Spectral Normalization can make the parameter matrix of the network satisfy the Lipschitz continuity condition, thus making WGAN training more stable [34].
# An Improved Galerkin Framework for Solving Unsteady High-Reynolds Navier-Stokes Equations
## 4 Numerical Experiments
### High-Reynolds-Number Challenge: Lid-Driven Cavity with a Stationary Cylinder at Re = 5000  
To demonstrate the full capability of the integrated framework in a truly challenging scenario, the unsteady flow in a square cavity of size \(\Omega=[0,1]\times[0,1]\) containing a stationary cylinder is now considered. The cylinder has a radius of \(r=0.15\) and is centered at \((0.5,0.5)\). The top boundary moves with velocity \(\mathbf{u}=(1,0)\), while all other external and internal (cylinder surface) boundaries are subject to no-slip conditions. The adaptive mesh refinement strategy ensures high-quality mesh generation, particularly around the curved cylinder boundary as illustrated in Figure 11, to accurately capture the complex flow features in this region. The kinematic viscosity is set to \(\
u=0.0002\), corresponding to a high Reynolds number of Re = 5000. This configuration is known to produce a highly  
Figure 10: Results at Re = 1000, \(T=10.0\) (\(30\times 30\) grids, initial 40 time steps). (**a**) Velocity field. (**b**) Pressure field.  
Figure 9: Results at Re = 1000, \(T=5.0\) (\(40\times 40\) grids, initial 40 time steps). (**a**) Velocity field. (**b**) Pressure field.  
complex and chaotic flow field, characterized by the interaction between the large primary vortex induced by the lid and the smaller, high-frequency vortices shed from the cylinder.  
Despite the chaotic nature of the flow and the presence of sharp, transient gradients, the pressure and velocity fields shown in Figure 12 remain smooth and free of spurious oscillations, a testament to the effectiveness of VMS. The simulation successfully captures the key physical phenomena, including the deformation of the primary vortex by the offset cylinder and the chaotic shedding of secondary vortices. This complex test case confirms that the integrated approach is robust and efficient, capable of tackling high-Reynolds-number flows with complex geometries and intricate physics, which would be computationally prohibitive with a uniformly refined mesh.  
To accurately resolve the flow dynamics around the curved cylinder boundary, a robust mesh generation strategy is employed. The initial mesh is constructed to conform to the geometry, and during the adaptive refinement process, local Delaunay re-triangulation is specifically utilized to maintain mesh quality and adapt to the curved interface. This ensures that elements near the cylinder surface are appropriately refined and well-shaped, preventing numerical artifacts and accurately capturing boundary layer phenomena. A detailed view of the refined mesh in this critical region is presented in Figure 11.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 4 Analytical Model
### Average Delay  
We derive the average delay \(E[D]\) for a COSB mechanism for a successfully transmitted data frame. The saturation average delay is defined as the average time between the time data frame at the head of its MAC queue ready for transmission and its successful reception at the destination.  
According to [10],  
\[E[D]=E[B]\times\ E[Slot] \tag{27}\]  
where \(E[Slot]\) is the total length of slot time as given in Equation (24), that is,  
\[E[Slot]=(1-\ \gamma_{tr})\sigma+\gamma_{tr}\gamma_{s}T_{s}+\gamma_{tr}(1- \gamma_{s})T_{c}. \tag{28}\]  
\(E[B]\) is the average number of backoff slot times for a successful data frame transmission and is given by,  
\[E[B]=\sum_{b=0}^{m-1}(b+1)\beta^{b}\times\frac{W_{b}+1}{2}+m\beta^{m}\times \frac{W_{m}+1}{2},\,b\in(0,m) \tag{29}\]  
After some algebraic calculations, \(E[B]\) reduces to,  
\[E[B]=\frac{\left(W^{*}(1-\beta^{m})+\beta W^{*}(1-(2\beta)^{m})+1\right)}{2(1- 2\beta)(1-\beta)(1-\beta^{m})} \tag{30}\]
# Empirical Analysis of Automated Stock Trading Using Deep Reinforcement Learning
## 4 Agents for Automated Stock Trading  
We use a total of five actor-critic-based DRL algorithms for trading agents. They are A2C, DDPG, PPO, ensemble strategy, and Remake Ensemble. The ensemble strategy is a combination of the three actor-critic-based algorithms mentioned earlier. Remake Ensemble adds four more RL algorithms--ACKTR, SAC, TD3, and TRPO--to the ensemble strategy. All of these RL algorithms were implemented in code by using Stable Baselines ([[https://stable-baselines.readthedocs.io/en/master/index.html#](https://stable-baselines.readthedocs.io/en/master/index.html#)]([https://stable-baselines.readthedocs.io/en/master/index.html#](https://stable-baselines.readthedocs.io/en/master/index.html#))) and Equations (6)-(11) which are formulas for DRL algorithms are described in detail in the equation Appendix A. Figure 2 shows the stock trading mechanism. A technical indicator [41; 42; 43; 44; 45] is a mathematical calculation based on historic price, volume, or (in the case of futures contracts) open interest information that aims to forecast the financial market direction, and the technical indicators used in the model are as follows:  
* Moving Average Convergence-Divergence (MACD): calculated using closing price [41].
* Relative Strength Index (RSI): calculated using closing price [41].
* Commodity Channel Index (CCI): calculated using high, low, and closing prices [44].
* Average Directional Index (ADX): calculated using high, low, and closing prices [45].
## 7 Numerical Analysis
### Regulation Impact by DFIG Integrated through VSC-HVDC  
Besides the virtual inertia control of the VSC-HVDC, the DFIG participates in frequency regulation. With the governor delay \(T=0.4\) s, the DPF results under low, medium, and high wind speeds (\(8.5\) m/s, \(9.5\) m/s, \(11.5\) m/s respectively), are shown in Figure 8. The steady-state solution of the power flows is given in Table 2.  
Figure 8: DPFs under different wind speeds: (**a**) System frequency; (**b**) DC voltage; (**c**) Rotor speed of DFIG; (**d**) Captured power of WT; (**e**) Inertia of DFIG.  
With the inertia support from the DFIG, the transient drop rate of the frequency is reduced. Under the high wind speed, the rotor speed of the DFIG is fixed at the maximum value. No inertia is provided, leading to the comparatively weak regulation capability (see Figure 8a). The rotor speed of the WT is adjusted to reach the captured power to respond to frequency drop (see Figure 8d). The rotor speed is shown in Figure 8c, indicating that the kinetic energy changes during the regulation. Thus, the constant inertia based on the initial rotor speed overestimates the DFIG's inertia, and requires dynamic correction based on the change of the rotor speed (see Figure 8e).  
With the active power reserve of the DFIG for the primary regulation, the steady-state frequency is increased (see Figure 8a). The DFIG has a sufficient reserve for the frequency regulation under all wind speeds, as seen from the same steady-state frequency and the same increased active power output (see Table 2).  
With an increased output of DFIG, the frequency nadir is raised, leading to less DC voltage deviation (see Figure 8b), thus the inertia of the VSC-HVDC may be further increased.
## 3 Results and Discussion
### Ferric Reducing Antioxidant Power  
For FRAP, the UPG gelatins showed a significant increase in activity with respect to the CG. The highest activity was obtained for UPG-84 (732 uM TE) (\(p<0.05\)), while the other UPGs showed lower activity (from 244 to 385 uM TE), and no significant difference was found between these gelatins (\(p<0.05\)). This behavior might be attributed to the surface hydrophobicity and the random coil ratio of the gelatin.  
According to these results, the UPGs in this study had ferric reducing antioxidant power in the range from 244 to 732 TE/mg of gelatin, and these results are lower than those obtained for horse gram hydrolysates (\(\sim\)1100 uM TE/mg sample) [27] but higher than those obtained for seagrass collagen hydrolyses (\(\sim\)2-6 uM TE/g sample) [11]. Thus, the antioxidant activity of the UPG in this study is comparable to those obtained for gelatins that were subjected to chemical modifications due to the breaking of peptide bonds [11; 27]; in contrast, this study was limited to the interruption of intramolecular interactions due to the effect of cavitation [29].  
In general, from both antioxidant activity assays, it can be concluded that high-intensity ultrasound treatments improved the antioxidant properties of the gelatins by transferring an electron to stabilize the radicals. Further studies should be carried out for the antioxidant evaluation of the UPGs by hydrogen atom transfer to more comprehensively determine the antioxidant activity of these gelatins.
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
###### Abstract  
Driving warning systems are of great help in notifying emergencies. Based on the results of former studies as well as the multisensory integration effect (MIE), the current meta-analysis investigated the effectiveness of utilizing unimodal (i.e., auditory, visual, and tactile) and multimodal (i.e., bimodal and trimodal) driving warning systems in drivers' response time. Sixty eligible articles representing 308 individual studies were included in this meta-analysis. The results showed: First, both auditory warnings (pooled Hedges' \(g\) = 0.98, 95% CI: 0.34 to 1.61, \(p\) < 0.01) and tactile warnings (pooled Hedges' \(g\) = 0.77, 95% CI: 0.22 to 1.32, \(p\) < 0.01) were found to reduce the response time significantly compared to no warning, but visual warnings did not produce significant benefit; Second, tactile warnings outperformed the visual warnings (pooled Hedges' \(g\) = 0.74, 95% CI: 0.11 to 1.37, \(p\) < 0.05); Third, auditory-tactile bimodal warnings surpassed unimodal warnings (\(p\) < 0.05); Fourth, drivers' response time under trimodal warning conditions were shorter than that under bimodal warning conditions but not in a significant level. Overall, the results support multisensory redundant signal effect hypothesis in multimodal conditions. Current study provides a quantitative understanding of the effectiveness of driving warnings and could contribute to the design of related technologies.  
acident prevention systems; meta-analysis; response time; sensory modality; multisensory integration effect Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis. _Appl. Sci._**2025**, 15, 527. [[https://doi.org/10.3390/apjp15020527](https://doi.org/10.3390/apjp15020527)]([https://doi.org/10.3390/apjp15020527](https://doi.org/10.3390/apjp15020527))  
**Copyright**: 022 by the authors.  
Liconses MDPL, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://ceautivecommons.org/licenses/by/4.0](https://ceautivecommons.org/licenses/by/4.0)]([https://ceautivecommons.org/licenses/by/4.0](https://ceautivecommons.org/licenses/by/4.0))).
## 3 Results and Discussion
### The Effect of the Heating Rate on the Pore Pressure
#### 3.5.2 The Effect of the Heating Rate on the Pore Pressure of the Air-Dried Specimens  
The evolution of the pore pressure at a depth of 10 mm from the heated surface of the air-dried specimens at a target temperature of 600 \({}^{\circ}\)C is shown in Figure 22a. The maximum pore pressure generated by the LWAC slab at a heating rate of 10 \({}^{\circ}\)C/min was larger than that at 5 \({}^{\circ}\)C/min, that is, it increased from 96 KPa to 122 KPa. A similar trend was presented for the NWC slab, that is, it increased from 214 KPa to 363 KPa. Moreover, the NWC slab had a sudden pressure rise regardless of whether the heating rate was 5 or 10 \({}^{\circ}\)C/min, but this was especially pronounced when the heating rate was 10 \({}^{\circ}\)C/min. The pressure curves shows that a faster heating rate led to a faster pressure rise, as was the case for the LWAC slab. This trend was also observed at 30 and 50 mm from the heated surface (as shown in Figure 22b,c). Based on the above results, a higher heating rate would generate a larger pore pressure inside the concrete slab.  
The evolution of the pore pressure when the target temperature was 800 \({}^{\circ}\)C, at a depth of 10 mm from the heated surface of the air-dried specimens, is shown in Figure 23a. It can be seen that both the LWAC and NWC slabs had larger pore pressures when the heating rate was 10 \({}^{\circ}\)C/min, and the pressure curves showed a steep rise. Examining the specimen shows that many cracks occurred at 10 mm from the heated surface. However, when the heating rate was 5 \({}^{\circ}\)C/min, the pressure curve was relatively flat, and the pressure value was also lower. At a distance of 30 mm from the heated surface (as shown in Figure 23b),  
Figure 21: Effect of the heating rate on the pore pressure of the oven-dried specimens performed at a target temperature of 800 \({}^{\circ}\)C: (**a**) 10; (**b**) 30; (**c**) 50 mm depths.  
Figure 22: Effect of the heating rate on the pore pressure of the air-dried specimens performed at a target temperature of 600 \({}^{\circ}\)C: (**a**) 10; (**b**) 30; (**c**) 50 mm depths.  
when the heating rate was 10 \({}^{\circ}\)C/min, the pore pressures of both the LWAC and NWC slabs increased rapidly. However, because more cracks generated at 10 mm, the internal steam escaped more easily; thus, the pore pressure was lower than that at a heating rate of 5 \({}^{\circ}\)C/min. This phenomenon was also observed at 50 mm from the heated surface, as shown in Figure 23c. The above results show that when the specimen is in an air-dried state, due to the large water content and high steam pressure during heating, cracks might be more likely to occur inside the specimen.
# Optimal Power Allocation with Hybrid Relaying Based on the Channel Condition
## 2 System Model
The AF scheme which simply amplifies the received signal does not require CSI. The AF-DF scheme is adequate when the communication link between the source and the first relay is good, with simple implementation by amplifying the received signal, and has high throughput performance when a communication link between the first relay and the second relay is not good. Also, the DF-AF scheme has high throughput performance when the communication link between the source and the first relay is not good and is adequate when the communication link between the first relay and the second relay is good, with simple implementation. In this paper, both AF-DF and DF-AF system models are represented for general analysis of hybrid relay network in various channel conditions.
###### Abstract  
This study explores the potential of graph neural networks (GNNs) in analyzing brain networks of children and adolescents exposed to trauma, addressing limitations in traditional neuroimaging approaches. MRI-based brain data from trauma-exposed and control groups were modeled as whole-brain networks using regions-of-interest (ROIs), with GNNs applied to capture complex, non-linear connectivity patterns. Results revealed that the trauma-exposed group exhibited simplified network structures with higher importance in regions associated with cognitive and emotional regulation, such as the posterior cerebellum. In contrast, the control group demonstrated richer connectivity patterns, emphasizing regions related to motor and visual processing, such as the Right Lingual Gyrus. Compared to traditional \(t\)-test results highlighting regional density differences, the GNN approach uncovered deeper, network-level insights into the relationships between brain regions. These findings demonstrate the utility of GNNs in advancing neuroimaging research, offering new perspectives on trauma's impact on brain connectivity and paving the way for future applications in understanding neural mechanisms and interventions.  
graph neural networks (GNNs); brain structure; childhood trauma; MRI data analysis; neuroimaging; pilot study 2020 IEEEexample:BSTcontrol
# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 3 A Method for Multi-Source Data Fusion Based on Video Frame Interpolation
### Self-Supervised Training Strategy Based on Transfer Learning
This self-supervised frame interpolation process not only reduces the dependence on annotated data but also enhances the model's ability to capture the high-dynamic motion characteristics of the melt pool by learning the intrinsic relationships between frame sequences. To verify whether the video frame interpolation model has successfully learned the motion characteristics of the melt pool, this study performs 2\(\times\) interpolation on infrared videos with a frame rate reduced to 0.5\(\times\) and compares the interpolated frames with the actual frames. The experimental results demonstrate that the model not only effectively fills the information gaps between frames but also exhibits a high degree of accuracy and reliability in capturing dynamic characteristics, as detailed in Section 4.1. To enhance the image fusion model's ability to capture and understand the high-dynamic motion characteristics of the melt pool, we propose a transfer learning-based image fusion model training strategy. Specifically, the image fusion model is first pre-trained on Dataset 3, which consists of heterogeneous image pairs from the source infrared and visible-light videos that correspond in time, to establish a basic understanding and capturing ability of the melt pool motion characteristics. Subsequently, using transfer learning methods, the pre-trained model is transferred to Dataset 4, which consists of heterogeneous image pairs from the interpolated-frame infrared and visible-light videos that correspond in time, for further training, thereby further enhancing its ability to capture and understand the high-dynamic characteristics of the melt pool. This training strategy allows the model  
Figure 6: The training process of the MTVFI model. to extract low-dynamic and high-dynamic features from two types of video sources and effectively combine these features to generate an integrated image representation. The above training is based on the selected computer, which runs on a Unix system with a GTX 3090 GPU and 24 GB of video memory, implemented in Python 3.7 based on the PyTorch (version 1.8) open-source framework and configured with CUDA 10.2, cuDNN 7.6.5, and other related environments to support the use of the GPU.
## 2 Related Works
### Reputation in V2V Communications  
The V2V reputation system is categorized into centralized and decentralized models. The centralized approach, pioneered by [3], revolves around a scheme that centrally distributes, updates, and stores vehicles' reputation scores. That study introduces a reputation announcement scheme for VANETs using Time Threshold to assess message reliability. The researchers in [22] recently proposed a centralized system for highways and urban roads, relying on a central Trusted Authority to calculate feedback scores from various vehicles and update the target's reputation. Moreover, Ref. [23] proposed an incentive provision method where the RSU updates the sender's reputation score based on observed actions validated by vehicles.  
Conversely, distributed reputation systems operate without infrastructure dependence. In this model, vehicles autonomously collect, maintain, and update reputation scores in an ad hoc manner. The authors in [24] developed a node reputation system to evaluate the reliability of vehicles and their messages. They grouped vehicles with similar mobility patterns that are close to each other into platoons to minimize propagation overhead. The authors in [25] introduced a framework for self-organized vehicles that filters out malicious vehicles based on standard scores.
## 2 Decoupling Techniques (DTs) in Ultra-Wideband MIMO Antennas (UMAs)
### Decoupling Structure (DS)
In reference [58], the MIMO antenna was composed of four identical rhombic-shaped monopole radiators arranged orthogonally, as shown in Figure 4i. To eliminate Wi-MAX and WLAN bands from the UWB range, the antenna radiators were loaded with elliptical CSRR structures, and to achieve high isolation, a plus-shaped DS was added. In [59], the MIMO antenna consisted of four AEs, each composed of a microstrip feed line and a square-slotted ground plane. A circular stub protruded from the ground plane strip to achieve circular polarization, as shown in Figure 4j. Also, a rectangular-shaped strip was integrated into the antenna ground surface to achieve good isolation between antenna ports.