esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Framework and Methods
### Analysis Process  
This research integrates the theories of uncertainty measurement and obstacle degree to assess and improve the resilience of subway stations in the context of flood disasters. The primary steps involved in this study are as follows:  
(1) Establish the evaluation framework for assessing the resilience of subway stations against flood disasters. This involves analyzing the underlying causes of flood events and the resilience mechanisms of subway stations, thereby delineating the resilience system pertinent to flood disasters and subsequently defining the evaluation framework.  
(2) Develop a classification standard for the resilience of subway stations in relation to flood disasters. This classification will be informed by the distinctive characteristics of flood disaster resilience observed in subway stations, alongside relevant standards and regulations.  
(3) Construct uncertainty measurement functions and matrices. Utilizing the established resilience classification standards, uncertain measurement functions will be developed, observed values will be incorporated, and a single-index uncertain measurement matrix will be created.  
(4) Determine the weights for the evaluation criteria. The measure of information entropy will be employed to ascertain the weights, culminating in the formation of a weight vector.  
(5) Calculate the comprehensive uncertainty measurement. A weighted calculation of the comprehensive uncertainty measurement will be performed using a multi-indicator measurement matrix.  
(6) Establish the measurement results. Categories will be determined based on established confidence criteria.  
(7) Identify critical obstacle factors through an analysis of obstacle degree.  
(8) Recommend strategies to enhance resilience.  
See Figure 2 for the specific analysis process.
## 4 Model Studies
### Results of the Simulated Rainfall Infiltration
#### 4.4.2 The Effect of Initial Saturation on Rainfall Infiltration  
Figure 11(a),b depict vertical profiles of saturation at section D-D\({}^{\prime}\) for an initial saturation of 60% and 15%, respectively. At the onset of the rainfall, it was also difficult for pore air to flow out through the slope surface because of the saturated ground surface. Therefore, the pores were occupied by pore air, and a somewhat unsaturated state was continued above the wetting front even during torrential rain. Figure 11(a),b demonstrate that the degree of saturation was closely related to the initial saturation. As the gas relative permeability coefficient decreased with the increased initial saturation (Figure 1), pore gas pressure increased gradually. Moreover, the increase of matric suction was closely correlated with increased pore gas pressure, while the water content of the soil surface with 60% initial saturation was much less than that with 15% initial saturation. In addition, water infiltration was greatly affected by pore gas pressure when the saturation of the soil surface was relatively low. When the initial saturation reached the saturation point, water infiltrated the  
Figure 11: Cloud picture of saturation distribution at the rainfall time of 2 h.  
Figure 10: Contour distribution pictures of three parameters at the rainfall time of 2 h.  
pore channel because the pore gas was relatively low. This also demonstrated that water infiltration was weakly affected by pore gas pressure.  
As the initial saturation increased, the content of pore gas decreased, and the channels for water infiltration and water relative permeability coefficient increased (Figure 1). Nitric suction increases with a decreasing initial saturation, resulting in an increase in water inflow. When the water relative permeability coefficient decreases, the water outflow decreases. As a result, a steep wetting front appears because of the difference between inflow and outflow intensity.
# Intelligent Recognition of Road Internal Void Using Ground-Penetrating Radar
## 3 Results
### Research on a Road Internal Void Image Enhancement Method Based on Improved Unet Model
#### 3.2.1 Unet Neural Network Model  
The Unet model, characterized by its symmetric encoder and decoder architecture, along with effective skip connections, is capable of deeply extracting and utilizing multi-level features, significantly enhancing its ability to process complex images. This makes the Unet model particularly well-suited for processing GPR images of road internals. Through end-to-end learning, the Unet can adaptively recover clear and precise structures from damaged images, effectively enhancing image quality and detail representation.  
The Unet network employs an encoder-decoder structure, which includes a contracting path and a symmetric expansive path, connected through a bottleneck layer. The network's architectural diagram is depicted in Figure 6.  
The primary function of the encoder is to capture the image's feature information and decrease its spatial dimensions. Simultaneously, it boosts the number of feature channels to augment the network's ability to represent features. Within the encoder, the input image undergoes four stages of downsampling, each containing two 3 \(\times\) 3 convolution layers, which are then succeeded by a 2 \(\times\) 2 max pooling layer, effectively extracting multi-scale features and capturing a broader range and more complex features.  
The decoder primarily focuses on gradually upsampling the encoded feature maps back to the original image size and conducts feature learning through convolution layers, thereby enhancing feature extraction capabilities. The decoder architecture includes four upsampling stages. Each upsampling module contains two convolution layers and one transpose convolution layer, effectively increasing the spatial dimensions of the feature  
Figure 6: Unet structure diagram.  
maps and restoring their spatial resolution. After the upsampling module, a convolution layer is used to achieve two-channel feature maps, preserving the spatial dimensions of the feature layers while altering the channel count, mapping high-dimensional features to the required output dimensions for classifying and locating features and background in the target areas. Skip connections are a crucial component of the Unet structure. These connections directly tie feature maps from the encoder to matching feature maps in the decoder, helping to retain more contextual and positional information in the output and enhancing the restoration of image details through feature fusion.
## 2 Web Bearing Capacity Design of Cold-Formed Austenitic Stainless-Steel Channels
### ASCE Specification  
According to SEI/ASCE-8, the nominal web crippling strength (\(P_{n}\)) for cold-formed unlipped channels is determined using the following Equations (1)-(4). The SEI/ASCE-8 provides a set of equations to calculate the web bearing strength (\(P_{ASCE}\)) of unlipped channels under concentrated load or reaction for one solid web connecting the top and bottom flanges without any web openings.  
For unlipped channels under IOF loading:  
\[P_{n}=t^{2}C_{1}C_{2}C_{\theta}\bigg{(}538-0.74\frac{h}{t}\bigg{)}\times \bigg{(}1+0.007\frac{N}{t}\bigg{)}\times C_{t} \tag{1}\]  
For unlipped channels under EOF loading:  
\[P_{n}=t^{2}C_{3}C_{4}C_{\theta}\bigg{(}217-0.28\frac{h}{t}\bigg{)}\times \bigg{(}1+0.01\frac{N}{t}\bigg{)}\times C_{t} \tag{2}\]  
For unlipped channels under ITF loading:  
\[P_{n}=t^{2}C_{1}C_{2}C_{\theta}\bigg{(}771-2.26\frac{h}{t}\bigg{)}\times \bigg{(}1+0.0013\frac{N}{t}\bigg{)}\times C_{t} \tag{3}\]  
For unlipped channels under ETF loading:  
\[P_{n}=t^{2}C_{3}C_{4}C_{\theta}\bigg{(}224-0.57\frac{h}{t}\bigg{)}\times \bigg{(}1+0.01\frac{N}{t}\bigg{)}\times C_{t} \tag{4}\]  
where:  
_IOF_ is Interior-One-Flange,  
_EOF_ is End-One-Flange,  
_ITF_ is Interior-Two-Flange,  
_ETF_ is End-Two-Flange,  
\(h\) is the depth of the flat portion of the web measured along the plane of the web,  
\(P_{n}\) is the nominal localised bearing strength for unlipped channels,  
\(R\) is the inside bend radius,  
\(t\) is the web thickness,  
\(N\) is the actual length of the bearing.  
In the formulas:  
\[C_{t}=6.9\text{ for SI Units}\]  
\[C_{\theta}=0.7-0.3\bigg{(}\frac{\theta}{90}\bigg{)}^{2}\]  
\[\text{If }F_{y}/(91.5\text{ C}_{t})\ \leq\ 1.0,\text{ then }C_{1}=(1.22-0.22k)k\]  
\[\text{If }F_{y}/(91.5\text{ C}_{t})\ >\ 1.0,\text{ then }C_{1}=1.69\]  
\[C_{2}=(1.06-0.06\frac{R}{t})\leq 1.0\]\[C_{3}=(1.33-0.33k)k\qquad\quad\text{when}\qquad F_{y}/66.5\;C_{t}\;\leq\;1.0\]  
\[C_{3}=1.34\qquad\quad\quad\text{when}\qquad F_{y}/66.5\;C_{t}\;>\;1.0\]  
\[C_{4}=(1.15-0.15\,R/t)\leq 1.0\text{ but not less than }0.50\]  
\[k=\frac{F_{y}}{33C_{t}}\]  
\[m=t/0.075\text{ for SI Units}\]  
The above equations are applied within the following limits:  
\[N/t\leq 210\text{ and }N/h\leq 3.5\]  
\[R/t\leq 6\text{ for beams.}\]
## 6 Experimental Results
### Detection Validation
#### 6.3.2 Detection under Non-Zero Power Flow  
In the case of considering VC-VSI, both ZPF or NZPF conditions can make the island detection difficult. As has been mentioned before, this is due to the VC-VSI being the device that maintains the voltage at the PCC and not the resonant load (as is the case when CC-VSI are considered). Figure 22a-d shows the validity of the suggested AIDM also under NZPF scenarios. Figure 22a,b illustrates the detection trigger when the inverter was delivering less power than the resonant local load required. The detection times obtained were 77 and 70 ms, respectively. Finally, Figure 22c,d shows the detection trigger when the inverter exceeded the power required to feed the local load. In these last scenarios, the detections were achieved in 64 ms and 81 ms, respectively.  
Note that even considering NZPF cases, the voltage magnitudes at the PCC (voltage and frequency) were practically unaltered. This is the key point of the need not to consider AIDM based on voltage or frequency displacements.  
Figure 21: Experimental results under zero power flow. (**a**) Null power reference zero and no local loads. (**b**) Inverter delivered power matching with the local resonant load consumption (4.6-kW/phase).  
Figure 22: Cont.
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 4 SINBAD-ARES Accelerator Design
### Linac Design  
The ARES linac will be constituted of two, 4.2 m long, 2.998 GHz traveling wave structures, each allowing a maximum beam energy gain of 75 MeV and powered by an independent RF station. The RF cavities will be surrounded by solenoids for the compensation of space charge driven beam defocusing. Each cavity will be fed by an independent RF station. The S-band linac allows for on-crest acceleration of the beam up to about 155 MeV.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**WP** & \(E_{gun}\) **[MV/m]** & **Laser RMS Spot [mm]** & **Laser RMS Length [ps]** & **Laser Trans/Long Shape** \\ \hline Cigar & 110 & 0.088 & 3 & FT/Gauss \\ Blow out & 90 & 0.2 & 0.076 & FT/Gauss \\ \hline \end{tabular}
\end{table}
Table 4: Laser and RF gun parameters used for obtaining the beam distributions shown in Figures 10 and 11. All the working points presented are obtained for a bunch charge of 20 pC. In the last column, the transverse and longitudinal shaping of the laser profile are specified. The following abbreviations are used: FT = Flat-Top, Gauss = Gaussian.  
Figure 11: Evolution of transverse emittance, longitudinal emittance and bunch length from the cathode to the linac entrance for the working-points represented in Figure 10.  
Figure 10: Examples of electron bunches (Q = 20 pC) produced in the ARES RF gun by using different settings of the photo-cathode laser. By using a cigar-like shaping of the photo-cathode laser the slice emittance and the slice energy spread of the beam is minimized, whereas by working in the blow-out regime the longitudinal phase space becomes linear.  
In order to operate bunch compression via velocity bunching or using the magnetic chicane downstream of the linac, the linac cavities are operated off-crest, therefore the final beam energy will be limited to about 100 MeV. Sufficient space at the linac exit has been reserved for a future energy upgrade and will temporarily host an experimental area [71], while the matching lattice will temporarily be used for beam diagnostics purposes.
## 3 Results and Discussion
### GPC Analysis  
The molecular weight of polymer is important information to understand its application [21]. Molecular weights of RBPs were detected by GPC-Multi-Angle Laser Light Scattering (MALLS) (Figure 3A) and GPC-differential refractive index (DRI) (Figure 3B)). The experimental results are shown in Table 2. The extraction temperature had a great influence on the molecular weight of RBPs. Both Mw and Mn increased with increased extraction temperature. According to Figure 3A,B, increasing the extraction temperature resulted in decreased RBP retention time (R\({}_{\text{T}}\)). Compared to RBP treated under lower extraction temperature (RBP30, Mw = 3.598 \(\times\) 10\({}^{4}\) g/mol), the molecular weight of RBP treated under higher extraction temperature (RBP80, Mw = 3.081 \(\times\) 10\({}^{5}\) g/mol) was larger by about 8.56-fold. It was inferred that the obvious increase in particle size under higher extraction temperature could decrease the porosity and capillary attraction of RBP and consequently enhance its physical entrapment.  
Molecular weight distribution (D, Mw/Mn) was used to characterize polydispersion. The greater the value of D, the more dispersed the molecular weight [5]. According to Table 2, the extraction temperature had a great influence on the molecular weight distribution of RBFs. Molecular weight  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **RBP30** & **RBP40** & **RBP50** & **RBP60** & **RBP70** & **RBP80** \\ \hline MALLS R\({}_{\text{T}}\) & 8.35 & 8.34 & 8.07 & 7.66 & 7.56 & 7.48 \\ (min) & & & & & & \\ DRI R\({}_{\text{T}}\) & 8.78 & 8.69 & 8.48 & 8.27 & 8.15 & 8.1 \\ (min) & \(3.598\times 10^{4}\) & \(4.188\times 10^{4}\) & \(8.632\times 10^{4}\) & \(8.850\times 10^{4}\) & \(2.372\times 10^{5}\) & \(3.081\times 10^{5}\) \\ Mn (g/mol) & \(2.539\times 10^{4}\) & \(2.672\times 10^{4}\) & \(5.263\times 10^{4}\) & \(5.181\times 10^{4}\) & \(1.387\times 10^{5}\) & \(1.767\times 10^{5}\) \\ D & 1.417 & 1.567 & 1.64 & 1.708 & 1.711 & 1.744 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Molecular weight and polydispersity index of RBPs.  
Figure 3: Gel permeation chromatography (GPC) analysis of RBPs.  
(Mn) and D showed corresponding changes with Mw, which showed that RBP from RBF under high extraction temperature had broad polydispersity. Polysaccharides are polydispersed polymers in natural polymers. The properties and activities of polysaccharides are related to their molecular weight distribution.
## 1 Introduction  
Nonlinear optics, which emerged in the early 60s of the 20 th century, immediately after the invention of powerful lasers, had a significant influence on the formation of modern photonics. Studies in the field of nonlinear optics led to such discoveries as the generation of second and higher harmonics of laser radiation, parametric scattering, stimulated Raman scattering, wavefront reversal, self-focusing and self-defocusing of light beams, and multiphoton absorption. The development of nonlinear optics, in turn, had a significant impact on the development of lasers and laser technologies. This resulted in the emergence of lasers based on stimulated light scattering, tunable lasers with a wide range of wavelengths, pico- and femtosecond lasers, and terahertz radiation generators, which significantly expanded the instrumental capabilities for research in the field of nonlinear optics. Fantastic results obtained in the field of nanotechnology over the last two decades have provided scientists with new, unique materials, the study of nonlinear optical properties of which was crowned with the discovery of new natural phenomena. Thus, at present, nonlinear optics is still a burgeoning branch of physics.
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 5 Simulation Results
### Application to Knapsack

Table 2: _Cont._where the parameter \(\beta\) is used as the penalty coefficient and assigned the value equal to \(10^{10}\). Several standard databases for knapsack optimization problems exist to provide means for comparison between the algorithms which provide special values for \(p_{i}\)'s, \(C_{j}\)'s and \(r_{ij}\)'s. Among these database of multi knapsack optimization available at the website of Queen Mary University of London ([[http://www.eecs.qmul.ac.uk/~jdrake/mkp.html](http://www.eecs.qmul.ac.uk/~jdrake/mkp.html)]([http://www.eecs.qmul.ac.uk/~jdrake/mkp.html](http://www.eecs.qmul.ac.uk/~jdrake/mkp.html))) formerly hosted at University of Nottingham ([[http://www.cs.nott.ac.uk/~jqd/mkp/index.html](http://www.cs.nott.ac.uk/~jqd/mkp/index.html)]([http://www.cs.nott.ac.uk/~jqd/mkp/index.html](http://www.cs.nott.ac.uk/~jqd/mkp/index.html))) are selected for performance comparison of the proposed algorithms with several state-of-the-art binary optimization algorithms. The total number of populations are taken to be equal to 10 with maximum number of iterations selected to be equal to 1000. Table 3 presents the results obtained using the proposed optimization algorithm versus other optimization problems. The solutions are the optimization solutions to the function represented by (72) which are multiplied by _minus one_ to reflect the solutions to the original problem of (70) and (71). There exist several negative values in the table which show that in some cases, algorithms could not fulfil the constraints of optimization at least once. Such problem does not occur for the proposed XOR BGSA with repository. The best result obtained for each dimension is made bold faced to demonstrate its superior performance. The values used for optimization are collected from the website of Queen Mary University of London and their file names are presented for ease of reference. Problems have also been given a number for further discussions. As can be seen from Table 3. The proposed XOR BGSA with repository outperforms all other investigated optimization problems in almost all cases. The convergence property of the proposed approach versus previous optimization methods for optimizations number 7 to 12 are demonstrated in Figure 2. As can be seen from this figure, the convergence of the proposed algorithm towards the solution is well above other investigated algorithms. Although during the first few iterations, some of the algorithms rise faster towards optimal solutions, after 100 iterations, the proposed XOR BGSA speeds up and becomes the dominant one. It can be concluded from the figure that other than quality of solutions, the speed of the proposed approach is well above other investigated optimization methods.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 4 Simulation Results
### Data Set  
We utilize MATLAB R2021b to produce communication signal data for both training and testing sets. Assuming a NOMA-MIMO system with two users and one BS equipped with two antennas, the signal data spans from \(\text{SNR}=0\) dB to \(\text{SNR}=30\) dB in 1 dB increments. With a block number of 2000 and a batch size of 100, the overall training sample size amounts to 200,000.
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 4 Performance Evaluation
### Network Density  
#### 4.3.1 Urban Scenarios  
The obtained simulation results of different maps and different traffic routes in urban scenarios have been analyzed and elaborated. We observe that the network density, the map and the traffic route are the three main parameters which impact significantly the performance of IVC protocol. In general, a lower network density leads to more disconnected networks, which decreases the overall performance of the IVC protocols: low PDR, high delay and high number of transmissions (network overhead). On the contrary, a higher network density enables the IVC protocols to achieve better overall performance. However, the geographic and traffic environment could also affect significantly the performance of IVC protocols, _i.e._, the complex road topology, high-rises and the deviated distribution of vehicles. Therefore, to implement a high performance IVC protocol, it is important to consider the abovementioned elements.
# Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
###### Abstract  
The demand for real-time 4K video streaming has introduced technical challenges due to the high bandwidth, low latency, and minimal jitter required for high-quality user experience. Traditional single-path networks often fail to meet these requirements, especially under network congestion and packet loss conditions, which degrade video quality and disrupt streaming stability. This study evaluates Multipath tunnel- Generic Routing Encapsulation (MPT-GRE), a technology designed to address these challenges by enabling simultaneous data transmission across multiple network paths. By aggregating bandwidth and adapting dynamically to network conditions, MPT-GRE enhances resilience, maintains quality during network disruptions, and offers throughput nearly equal to the sum of its physical paths' throughput. This feature ensures that even if one path fails, the technology seamlessly continues streaming through the remaining path, significantly reducing interruptions. We measured key video quality metrics to assess MPT-GRE's performance: Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Our results confirm that the MPT-GRE tunnel effectively improves SSIM, PSNR, and reduces MSE compared to single-path streaming, offering a more stable, high-quality viewing experience. Our results indicate that analyzing the SSIM, MSE, and PSNR values for 4K video streaming using the MPT tunnel demonstrates a significant performance improvement compared to a single path. The improvement percentages of the SSIM and PSNR values for the MPT tunnel are (29.05% and 29.04%) higher than that of the single path, while MSE is reduced by 81.17% compared to the single path.  
Keywords:MPT-GRE; streaming; tunnel; 4K; SSIM; MSE; PSNR +
Footnote †: journal: Electronics Letters
## 1 Introduction
### Research Gaps  
To date, in response to these challenges, an extensive array of research initiatives have been undertaken with the aim of developing sophisticated techniques for the reconstruction of missing data within LST datasets [49; 58; 59; 60; 61; 62]. A number of methods have been developed, which can be generally divided into three types according to the sources of reference information: (1) spatial information, (2) multitemporal observations, and (3) other complementary data, for example, from ground meteorological stations [59]. However, techniques that rely on comparing cloudy-sky pixels to nearby clear-sky ones are effective only in images with minimal cloudiness [62; 63]. Although meteorological station data can provide the temperature of the point where the station is located, accurate and precise heat wave analysis requires continuous temperature distribution [41; 64]. To address these gaps, an improved method for reconstructing LST in cloud-covered areas has been proposed. This method employs a linking model that integrates MODIS-LST with other surface variables, such as surface topography and land cover conditions, through a machine learning algorithm known as random forest (RF) regression [49]. Owing to its robust predictive performance and capability to process complex, non-linear data, this approach not only enables accurate predictions of various extreme climate phenomena but also facilitates a comprehensive evaluation of the significance of different temporal and spatial features in predicting LST.