esa-sceva/satcom-chunk-collection huggingface dataset:
# Audio-Visual Action Recognition Using Transformer Fusion Network
## 5 Discussion  
In this study, we propose a novel approach for enhancing action recognition by integrating visual and audio information from videos using a transformer structure. While our model exhibits promising capabilities, there are inherent limitations and areas for future exploration. Firstly, the model employs a shared feature extraction network for both audio and visual data. This design was primarily driven by the aim to maintain network efficiency; however, it may impose limitations on the model's ability to distinctly capture modality-specific features. For instance, separate feature extractors, such as 3D CNNs [9; 38] for visual temporal features, and VGGish [39] or audio spectrogram transformers (ASTs) [40] for audio, can potentially yield better recognition performance. Secondly, our work focused on the fusion of visual and audio data, and we evaluated its performance. Recently, with the rise of multi-modal learning, various large-scale audio-visual datasets like Epic-soududs [41] have emerged. It will be beneficial to conduct future experiments on these large-scale datasets to verify our model's performance. Moreover, there is a growing trend of attempting multi-modal learning with various modalities, such as text or data from different sensors. It is necessary to explore whether our structure can be beneficial in these contexts as well. Our structure, designed for visual and audio data, uses three modal fusion modules. However, with the addition of other modal data, additional modal fusion modules may be required. This can lead to an increase in model size or may not be optimal structurally. To address this, an improved modal fusion module capable of accommodating more modalities is needed.
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 5 Results
### Effect of Group Movement on Evacuation Time
#### 5.5.1 Effect of Group Size on Evacuation Time  
The age of group members has a significant effect on evacuation efficiency (Figure 13). The middle-aged group showed higher efficiency in the evacuation process. Although the elderly group is more adaptable in responding to emergencies, their ability to evacuate is limited by their mobility and response capacity. The juvenile group, with slower mobility and prone to fear and panic in emergencies, often needs the accompaniment and guidance of adults in the evacuation process in order to evacuate safely.  
With the same age composition, evacuation time increases with group size, especially for groups with weaker evacuation abilities. Two-person groups tend to evacuate faster and more efficiently, with greater overall flexibility. Three-person groups, due to their increased size, the more complex relationships between members, and the need for coordination, have correspondingly more configurations in which the group is in motion, as illustrated in Figure 14, and are more susceptible to blockages during evacuation, thus resulting in slower overall movement.  
Figure 12: Evacuation times for various group types at different slopes.  
Figure 13: Effect of group size on evacuation time.
## 2 Materials and Methods
### Damage Size Classification (S-Class)  
Damage localization and sizing is then the next fundamental step that the UGW-based SHM must accomplish. The SHM system is expected to provide reliable and consistent information concerning the location of the damage on the structure and, potentially, information about its severity (in-plane dimensions, e.g., size). This information is pivotal in enhancing the awareness of the maintenance/repair operation to be performed. Again, a ML approach was used. In order to experimentally replicate a few damage configurations without increasing material costs and waste, damages were simulated through an added mass placed on the surface of the plate. Three wooden cuboids, representing three distinct classes, were considered for the experiments: 20 mm \(\times\) 25 mm, 20 mm \(\times\) 35 mm, and 20 mm \(\times\) 45 mm sized (Figure 8a), hereinafter referred to as S-Class 1, S-Class 2, and S-Class 3, respectively. The cuboids have a thickness of 10 mm, and, prior to testing, a mass of 0.3 kg was applied on each cuboid to guarantee contact with the plate.  
For each cuboid, 180 tests were performed, varying its position over the plate. Each test consisted of two phases: excitation of the S-C PZT and acquisition of the signals from the remaining 4 sensors (S-i, i = 2, 3, 4, 5). More specifically, the plate is assumed to be divided into the four equal zones previously studied, according to the R-classification delineated in Figure 7b. Within each zone, 45 equally spaced damage locations were analyzed. As shown in Figure 8b, the investigated flaw spots were uniformly distributed: the external damages of each region were positioned at a distance \(\Delta\)L = 25 mm from the region boundaries (both external boundary of the plate and internal borderline), for a total of 24 distinct damage positions, while consecutive spots were arranged at a mutual distance of \(\Delta\)x = 16.67 mm. The total squared area covered by the damage spots is 100 mm \(\times\) 100 mm, whereas the areas around the sensors were avoided to prevent the wooden blocks from interacting with the sensors. Hence, 540 tests were performed.
## 6 Conclusions  
This review has explored the key perspectives and research challenges in wireless communication hardware, focusing on the evolving landscape of next-generation networks beyond 5G and towards 6G. The seamless integration of terrestrial and non-terrestrial networks, including satellite and HAPS, presents a fundamental shift in communication infrastructure, enabling global connectivity and ultra-reliable low-latency communications. Advancements in mmWave and sub-terahertz technologies, massive MIMO, beamforming networks, and RIS are essential to overcoming the limitations of current networks and ensuring enhanced performance.  
Additionally, challenges such as high power consumption, spectrum efficiency, and thermal management must be addressed to develop more sustainable and energy-efficient wireless systems. The deployment of AI and ML in optimizing beamforming, digital twin technology for network orchestration, and WPT solutions for IoT networks further underscore the importance of interdisciplinary innovation. As we move towards the 6G era, it is evident that collaboration among industry stakeholders, researchers, and policy makers is crucial for realizing the full potential of these emerging technologies. Future research should focus on bridging the gap between theoretical advancements and practical implementations, ensuring that next-generation wireless networks are not only faster and more reliable but also energy-efficient, scalable, and seamlessly integrated into our digital society.  
Conceptualization, G.A.K.; methodology, D.G.A. and T.N.F.K.; validation T.M.E.; formal analysis, D.G.A. and C.L.Z.; investigation, D.G.A., T.N.F.K. and T.M.E.; resources, D.G.A. and T.M.E.; data curation, C.L.Z.; writing--original draft preparation, D.G.A., T.M.E. and T.N.F.K.; writing--review and editing, G.A.K. and C.L.Z.; visualization, D.G.A. and T.M.E.; supervision, G.A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.
## 2 Methodology
### Predictive Performance Quantifiers  
This study suggests choosing RMSE (root mean square error), nMAPE (normal-ized mean absolute percentage error), and \(R^{2}\) (coefficient of determination) as quantitative indicators of prediction performance in order to assess the prediction performance of the prediction algorithm at various levels. In Equations (10)-(12), the precise formulas for the aforementioned metrics are displayed.  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(y_{m}^{i}-y_{p}^{i}\right)^{ 2}} \tag{10}\]  
\[nMAPE=\frac{1}{n}\sum\limits_{i=1}^{n}\left|\left(\frac{y_{m}^{i}-y_{p}^{i}}{ \left|y_{m}^{i}\right|_{\max}}\right)\right| \tag{11}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(y_{m}^{i}-y_{p}^{i}\right)^{2}}{\sum \limits_{i=1}^{n}\left(y_{m}^{i}-\overline{y}_{m}\right)^{2}} \tag{12}\]  
In the above equation, \(n\) is the total number of data samples, \(y_{m}^{i}\) and \(y_{p}^{i}\) are the measured and predicted values of \(i\), and \(\overline{y_{m}}^{-}\) is the average of \(y_{m}\). In these metrics, RMSE is the square root of the mean of the squared difference between the measured and predicted values. The smaller the RMSE, the closer the prediction result is to the actual value and the more accurate the prediction algorithm is. \(nMAPE\), as an improvement of \(MAPE\), is guaranteed that the denominator is not zero. For \(nMAPE\), the smaller it is, the better the prediction performance is represented. Additionally, \(R^{2}\) indicates how well the predicted value fits the actual value. The closer \(R^{2}\) is to 1, the better the predicted value fits the actual value.
## 5 Results and Discussion  
### Strain Response of the PM-PCF Sagnac Interferometer  
Firstly, the strain response of the PM-PCF Sagnac interferometer was investigated over the range of 0-1500 \(\mu\varepsilon\) in steps of 250 \(\mu\varepsilon\) through the use of translation stages in order to prove the sensing idea. The spectra of the interferometers are presented in Figure 4 as well as the wavelength shift as a function of elongation.  
An interferometer fringe at 1546.3 nm was selected to analyze shifts due to elongation of the PM-PCF. A linear response to strain is observed, which is in agreement with the literature. The sensitivity of the PM-PCF Sagnac loop is determined to be approximately 0.98 pm/\(\mu\varepsilon\).
# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 7 Protocols Analysis  
WSN is composed of battery-driven nodes where computational power is quite limited in terms of memory and CPU processing capacity [38]. These resources are required to process the number of overhead packets which depend on the routing protocol complexity. Accordingly, these limited resources might be insufficient to process such time-consuming  
Figure 19: Energy Consumption vs. mobility speed.  
Figure 20: RTT vs. Speed of nodes.  
Figure 18: Throughput vs. Bandwidth.  
routing algorithms. For example, LEACH is a clustering-based mechanism that divides the network into clusters and selects a cluster head for each set of nodes and this, in turn, exploits a lot of energy particularly when the CPU has a low speed and hence spends an extended processing time. In addition, the cluster head might be quite far from the sink and this would use an extra amount of energy of nodes which could result in fast energy depletion. On the other hand, AODV and DSR are single path mechanisms that trigger the tight calculation of sensor resources for every route disconnect produced by link or node failure through the route discovery phase and this occurs frequently with the high mobility speed of sensors. This intensive route calculation also shortens the lifetime of nodes. In addition, DSR is a link-state protocol where all available routes in the network should be stored in the memory of each node. This would have another burden on the processing capacity which restricts the network scalability. Furthermore, EPAR, as a developed version of DSR, selects the routes based on the highest residual energy nodes; however, it is a single path scheme as well so it has the same problems as the DSR method. Therefore, we can understand that using such complex protocols will affect negatively the lifetime of nodes particularly with restricted processing resources even if the protocol selects the route based on the available energy as EPAR does. On the contrary, GA-AOMDV selects the routes based on the energy and also provides multipath routes and this, in turn, would save a lot of energy as shown in the results particularly with the high mobility speed of nodes. In addition, GA-AOMDV is a distance-vector mechanism that does not require a large memory as link-state-based protocols need.
## 3 Results
### The Comparison and Modeling of FLDAS and GLDAS ET and SSM Products
Therefore, additional investigations are conducted in this region to compare FLDAS and GLDAS's coherency with satellite products to improve their performance through machine learning modeling. Scatterplots are applied to compare the satellite products with FLDAS, GLDAS and the proposed model. The results are presented in Figures 12 and 13 for SSM and ET, respectively. It is noted that both FLDAS and GLDAS have undesired results with near-zero R\({}^{2}\) and high RMSE, \(\left|\psi\right|\) and \(\psi\). The proposed models in this study remarkably improve the results with a very good correlation coefficient R\({}^{2}\) (\(>\)0.8) and lower RMSE and bias. Figure 12: Comparisons of NASA-USDA SSM versus SSM values obtained from FLDAS, GLDAS and the proposed model using testing data in Egypt. The solid black and red lines represent the one to one line and regression line, respectively.
# Wavelength Selection for Satellite Quantum Key Distribution
## Appendix A Phase Screen Model  
Turbulence is a statistical process. To account for random fluctuations in the refractive index, we use the modified von Karman refractive power spectral density (PSD) function as it is the simplest model that includes both the inner \(l_{0}\) and outer \(L_{0}\) scales [52; 71]:  
\[\Phi_{\phi}(k)=0.49r_{0}{}^{-5/3}\frac{\exp(-k^{2}/k_{m}^{2})}{(k^{2}+k_{0}^{2 })^{11/6}} \tag{1}\]  
where \(k_{m}=5.92/l_{0}\), \(K_{0}=2\pi/L_{0}\) and \(r_{0}\) is the fried parameter for each region given by  
\[r_{0}=(0.423k^{2}sec(\zeta)\int_{h-}^{h+}c_{n}^{2}(h)\,dh)^{-3/5} \tag{2}\]  
where \(k\) is the wavenumber. The empirical Coulman-Vernin profile [72]  
\[L_{0}(h)=\frac{4}{1+(\frac{h-8500}{200})} \tag{3}\]is used to compliment the Hufnagel Valley model,where \(l_{0}=0.005L_{0}\). The scintillation index (\(\sigma_{I}^{2}\)) is defined as the normalized variance of irradiance fluctuations and is used here to quantify the strength of atmospheric turbulence:  
\[\sigma_{I}^{2}=\frac{\left\langle I(x_{0},y_{0})^{2}\right\rangle}{\left\langle I (x_{0},y_{0})\right\rangle^{2}}-1 \tag{10}\]  
where \(I(x_{0},y_{0})\) is the received irradiance at point \(x_{0},y_{0}\). The beam is initialized using a Gaussian function and is represented by a uniform 2D square grid (of size 1024 \(\times\) 1024) of complex numbers corresponding to the value of the electromagnetic field at that point in space [54]. Moreover, it is propagated using a discrete Fourier transform (DFT) algorithm. The phase screens are produced by performing fast Fourier transform (FFT) over a 2D uniform grid of Gaussian white noise, with variance being given by Equation (10). To ensure that the condition of weak turbulence is maintained across each phase screen, the Rytov parameter is kept constant for each region of turbulence using the following equation [73]:  
\[r_{R}^{2}=1.23k^{7/6}\int_{h-}^{h+}c_{n}^{2}(h)(h-h^{-})^{11/6}dt=b \tag{11}\]  
where \(b\) is set to 0.2, in accordance with reference [54]. The effect of turbulence can be considered negligible above 20 km, so Equation (11) results in the total atmospheric turbulence spread across 17 phase screens. Software used in these simulations was developed in MATLAB v2021a [52] and run on [PERSON], Ireland's high-performance computer provided by the Irish Centre for High-End Computing (ICHEC). [PERSON] efficiently executed multiple iterations of the Monte Carlo simulation to generate 10,000 individual turbulent paths, enabling ensemble statistic analysis to characterize the propagation path and determine the impact of atmospheric turbulence.
# A Novel Approach of Unit Conversion in the Lattice Boltzmann Method
## 3 Results and Discussions
### One-Dimensional Examples
#### 3.1.1 1D Diffusion  
In this example, an insulated rod of copper whose left and right ends are maintained at constant temperatures of 298 K and 373 K, respectively. The rod has length \(L=2\) m and uniform heat generation \(q=100\) kW/m\({}^{3}\). The properties of the copper in the SI unit system are listed in Table 4. The problem is governed by the following equation  
\[\rho C_{p}\frac{\partial T}{\partial t}=k\frac{\partial^{2}T}{\partial x^{2} }+q. \tag{72}\]  
where \(C_{p}\) is the specific heat at constant pressure and \(k\) is the thermal conductivity. The analytical solution of the steady-state temperature distribution is given as Ref. [23]:  
\[T=\bigg{[}\frac{T_{c}-T_{h}}{L}+\frac{q}{2k}(L-x)\bigg{]}x+T_{h}. \tag{73}\]  
where \(T_{c}\) and \(T_{h}\) are the cold and hot temperatures, respectively. In LBM simulation, the following data are used:  
\[\tau_{e}=1, \tag{74}\]  
and  
\[S_{i}=w_{i}\frac{q}{\rho C_{p}}. \tag{75}\]  
Using the previously mentioned steps in Section 2.2, each quantity can be mapped from the physical-scale to the dimensionless-scale after the reference variables of the physical-scale are defined. The current example is characterized by stability only. This gives more flexibility in choosing the length reference variable for the lattice-scale. Based on that \(\tau_{e}=1\) and \(x_{o(LS)}=10\,lu\) are selected. By setting \(\rho_{(LS)}=\Theta_{o(LS)}=1\) and manipulating Equations (48)-(50), all of the reference variables of the lattice-scale are defined. Follow Equations (56)-(62), each quantity is mapped from a dimensionless-scale to the lattice-scale. Table 4 shows that all quantities of the current example are mapped from the physical-scale to the lattice-scale using the present approach. Table 4 lists the reference variables of the physical  
Figure 4: Schematic diagram of D1Q3, D2Q5, and D2Q9 lattice models. scale and lattice-scale, the scaling units between SI and lattice systems, and the transforming properties between scales. The values with \({}^{\star}\) represent those values are assumed. Figure 5 shows the temperature distribution along the rod. The steady-state condition is achieved when the difference between two successive iterations of temperature is less than \(10^{-9}\). Figure 5a compares the LBM predictions with analytical solution (i.e., Equation (73)) at the lattice-scale. Figure 5b compares the LBM predictions with the analytical solution at the physical-scale. It can be concluded that excellent agreements between the numerical and analytical solutions at both scales can be achieved.
## 7 Conclusions  
This research presents a hydrogen-IES low-carbon economic dispatch method under tiered carbon pricing, using two demand response scenarios, in order to address the hydrogen demand of an industrial park. The research purpose of using P2G to produce green hydrogen to replace part of gray hydrogen as a hydrogen source to balance carbon emission reduction and economy and to absorb a large amount of renewable energy has been realized. Based on the examination of the cases, the following conclusions are derived:  
By employing P2G technology to produce green hydrogen, it becomes possible to substitute a portion of gray hydrogen as the hydrogen source. This approach enables the full exploitation of the advantages of high green hydrogen pricing and low-carbon emissions while also making use of low gray hydrogen costs and large carbon emissions. This leads to a substantial drop in carbon emissions from the system, resulting in lower carbon costs and wind curtailment costs. As a result, the overall economic efficiency of the system is improved.  
Various forms of demand response contribute to the reduction of peak electricity consumption and the optimization of load management. Price-based demand response has a direct effect on power prices, and in the short term, it is more readily accepted by users compared to incentive-based responses. The impact of price-based demand response on the load is more significant, resulting in a higher potential for energy savings.  
Integrating CCS into the IES leads to higher purchase energy costs and operational costs as a result of its high energy consumption characteristics. Nevertheless, the remarkable carbon reduction impact of CCS substantially decreases the expenses associated with carbon trading, resulting in a further decrease in overall costs.  
Writing--original draft preparation, B.S.; methodology, R.W.; supervision, M.W. ([PERSON]); software, M.W. ([PERSON]); conceptualization, Q.Z.; writing--review and editing, Y.L.; resources, H.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (no. 62073196, 62133008); the Natural Science Foundation of Shandong Province (no. ZR2019 ZD09); and the Key Research and Development Project of Shandong Province (2019 ZZY010903). This research was supported by the Taishan Industrial Experts Program (no. tscx202312127) and the State Grid Science and Technology Project (5100-202116567A-0-5-SF).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company State Grid Shandong Integrated Energy Services Co., Ltd. and author [PERSON] was employed by the company Shandong Zhengchen Technology Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Combination of Robust Algorithm and Head-Tracking for a Feedforward Active Headrest
## 2 Design of the Optimal Filter Coefficients of Active Headrest with Traditional Algorithm
### Head-Tracking System
Considering that ANC has a certain noise reduction effect at the error microphones when the secondary paths change little, we divide the area where the human head may move into 25 regions, marked as No. 1-No. 25, as shown in Figure 2. The size of each region is 8 cm \(\times\) 5 cm. When the center of the head is located in one of the divided regions, the optimal filter coefficient that is precalculated when the head is at the center of the current region is retrieved.