esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Pre-Ionized Plasma on the Dynamics of a Tin Laser-Triggered Discharge-Plasma
## 2 Experimental Apparatus
The increasing time and full width at half maximum (FWMH) of the current are 100 ns and 260 ns, respectively, and the typical current and voltage values are about 8 kA and 20 kV, respectively. The capacitance of the last loop is 120 nF, which delivers 24 J energy to the pre-ionized plasma. According to the experiments, no discharge will occur at \(\mathrm{d}t\) smaller than 0.68 us, which is mainly determined by the electrical gap distance, the laser irradiation, and the target material [23]. In fact, many experiments have been done by increasing \(T_{2}\), which is shown in Figure 1b. When \(T_{2}\) is large enough, the pre-ionized plasma will be generated after that the high voltage is loaded between the two electrodes. In this way, the high voltage will be maintained, which is similar to Figure 2A. The time interval was defined as being between the falling edge of the voltage and the onset of the current as \(\mathrm{d}t^{\prime}\), which is shown in Figure 2A. The experimental results show that \(\mathrm{d}t^{\prime}\) is increasing with the increasing of \(T_{2}\). However,  
Figure 1: Schematic of the experimental setup used for a laser-triggered discharge produced in tin plasma. (**a**) Schematic of the whole setup. (**b**) Photograph of the experimental setup from the side of the grazing incidence spectrometer of the vacuum chamber. (**c**) Time series diagram of the experimental setup. the laser-current delay dt (\(\sim\)10% of laser pulse to 10% of current) is almost 0.68 \(\upmu\)s under different \(T_{2}\). There are two peaks of the X-ray signals. The first peak is generated by the collision between the anode and the electrons, which is generated by the laser and accelerated by the electric field [22]. The second peak, which is corresponding to the dip in the current signal, is due to the Z-pinch formation by the current [14; 22]. By comparing the two X-ray signals, it can be found that there are much more smaller peaks for the second peak when dt is larger. Moreover, the pinch times, which are defined as the onset of the current and the dip of the current, are 116.4 ns and 105.2 ns for condition (A) and (B) respectively. Due to the expanding of the pre-ionized plasma, lower density of ions will be obtained when the laser-current delay is larger. In this way, the pre-ionized plasma will be pinched much more drastically and faster according to the Bennett equilibrium and the snow-plow model [24; 25]. Multiple pinches may occur when dt is larger as well [26]. Moreover, the FWHM of the X-ray signal is larger under larger dt, according to Figure 2.
## 4 Results and Discussion
### Data Augmentation Based on Improved Cycle GAN  
During the training of Cycle GAN, the parameter configuration shown in Table 2 was used: 200 epochs and a batch size of two. The learning rate for the generator was set to 0.0002, while the discriminator's learning rate was also 0.0002. The image size is  
Figure 10: Graph of datasets enhanced by rotate, flip, blur, lowlight enhancement, and partial labels.  
Figure 9: Partial categories of collected sewage graph based on camera.  
256 \(\times\) 256, the Optimizer used is Adam, and the Activation Function is LeakyReLU. The Lambda_cycle is set to 10.  
The evaluation metrics for assessing the performance of the improved Cycle GAN include the Frechet Inception Distance (FID) [40] and the Inception Score (IS) [41], which are used to determine whether the model converges during training. A smaller FID and a larger IS indicate better performance. After 120 epochs of training, the FID curve stabilizes, while the IS stabilizes after 140 epochs. The curves of FID and IS after 200 epochs of training are presented in Figure 11. It is observed during training that the quality of the generated images improves with the increase in epochs, eventually achieving the effect of sewage discharge under dim light at night, with an FID of 23.2 and an IS of 26.8. Figure 12 compares the results of Cycle GAN with and without the inclusion of residual modules and attention mechanisms. The results generated by the original Cycle GAN are shown in Figure 12a, while the results of the improved Cycle GAN are displayed in Figure 12b, demonstrating the superior quality of the night images generated by the improved model. A comparison of the generated images using the classic numbers of training epochs of 25, 50, 100, 150, and 200, with the original images, is illustrated in Figure 13:
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 4 Model Realization and Result Analysis
### Results and Analysis  
#### 4.2.1 Analysis of Training Dynamics and Convergence Patterns  
Figure 6 details the evolution of training and validation metrics throughout the iterative process. The upper plot displays accuracy progression: Training accuracy increases rapidly within the first 200 iterations. Validation accuracy closely follows this trend without significant deviation. Both curves demonstrate stable convergence, maintaining levels above 90% after 800 iterations with only marginal final discrepancy.  
The lower loss curve reveals finer optimization dynamics. Training loss decreases sharply during the initial 200 iterations, while validation loss exhibits synchronous reduction. After 400 iterations, both losses enter a phase of gradual and stable decline, ultimately converging near 0.2. Although the final training/validation loss stabilizes around 0.2, this value does not indicate model deficiency: Its consistency with high accuracy convergence confirms the model has reached its optimization limit, with no compromise to practical predictive capability.  
The synchronous movement of curves--particularly the validation loss decreasing consistently with training loss without significant divergence--validates the effectiveness of regularization. Minor periodic fluctuations in validation metrics reflect normal sensitivity to data shuffling but demonstrate immediate recovery, indicating robust model behavior. Collectively, these patterns confirm an optimized learning process, exhibiting neither underfitting nor overfitting.
## 1 Introduction
Sorting operations carried out during the cultivation cycle generate food loss, such as fruits with defects caused by hail, those with spots or malformations, or those that are over-ripened; these fruits are left on the cultivation field at the end of the growing season. It is necessary to add the amount of fruit discarded or unsold by retailers and the by-products derived from industrial processing to these quantities. Therefore, we recovered and enhanced the pomace of pre-waste watermelons, obtaining a DF rich in bioactive components such as polyphenols, carotenoids, and vitamins, among other species that exert powerful antioxidant and anti-inflammatory effects [22]. Many studies have shown that the volatile fraction of watermelon originates from a combination of different classes of molecules, including esters, alcohols, organic acids, aldehydes, ketones, heterocyclic compounds, terpenoids, and volatile apocarotenoids [4, 6, 23]. The great variety of molecules that define watermelon aroma is the result of different biosynthetic or degradative metabolic pathways involving lipids, amino acids, and carotenoids, giving rise to a rich and complex VOCs population [3]. In this study, we analyzed the VOCs of a set of WDF samples by applying HS-SPME-GC-MS technique. The goal was to identify VOCs that could represent quality markers of the fiber and evaluate their stability over time. For this research, we chose the pre-waste watermelons of a particularly valuable cultivar, Gavina(r), in Italy covered by the trademark. To our knowledge, there is little evidence in literature regarding this uncommon and extremely peculiar watermelon cultivar. The analysis was performed immediately after obtaining the fiber (WDF_t0), and subsequently, the differences following storage were evaluated. We considered two aging steps: 30 days (WDF_t1) and 24 months (WDF_t2) of storage. The compositional stability and resistance of watermelon fibers to the aging processes can be evaluated both macroscopically, i.e., following the temporal evolution of the color point [24], and microscopically, through the variation of the VOCs fraction. It is well known that when \(\beta\)-carotene and lycopene break down, many different volatiles are released, indicating the loss of these valuable bioactive components [4; 25]. Furthermore, the degradation of these carotenoids, which are natural chromophores, involves variations in the color of the food containing them. The loss of these antioxidant nutrients leads to a decrease in the nutritional value of the WDF, on which the possible technological applications depend. The content of lycopene and \(\beta\)-carotene, which are beneficial to human health, can significantly determine their specific use in various production sectors, such as food. Moreover, the aroma of a product is particularly important in defining its possible applications, especially in the food and flavoring sectors. Therefore, this study aimed to obtain preliminary information to evaluate the possible technological applications of the sample according to its storage time.
## 6 Experimental Results
### Influence of Feature Selection on Prediction Performance  
First, we aimed to analyze how the feature selection affects the prediction performance of classifiers. We evaluated the classification accuracy using only selected features; a new subset of \(k\) features was obtained in a loop, where \(k\) = [50, 1560] and, in each iteration, \(k\) was incremented by 50. Then, parameter tuning for the classifier model (SVM, KNN, RFC) was done. The classification performance of the classifiers for each iteration is shown in Figure 5.  
For SVM and RFC, the accuracy increases steadily up to 400 features. After this point, the accuracy decreases and rises repeatedly, and the values usually do not exceed the limit of accuracy that was  
Figure 4: System design for pathological speech detection.  
Figure 3: Visualization of male samples, with 1560 features for each sample.  
reached with 400 features. On the other hand, the accuracy of the KNN classifier shows a decreasing tendency with an increasing number of features. This is probably due to the higher dimensionality of the data space since the KNN classifier is known to suffer from higher dimensions. Even though, in this case, the accuracy of KNN dropped in higher dimensional cases, this is not always the case, as can be seen, for example, in [29].
# Robust Model-Free Control for MMC Inverters in Cold Ironing Systems
## 1 Introduction  
The maritime transport is one of the most widely used means in global trade, accounting for around 80% of international exchanges. However, it is also a major source of pollutant emissions, particularly due to the use of auxiliary engines when ships are docked. In other words, for each kWh of electrical energy produced by onboard generators, a ship can emit up to 0.7 kg of greenhouse gases (GHGs) [1]. For example, a ship with a capacity of 8500 Twenty-Foot Equivalent Units (TEUs) can consume up to 12 tons of fuel daily, resulting in the emissions of over 37 tons of CO\({}_{2}\) per day [2]. These emissions include greenhouse gases (CO\({}_{2}\), SO\({}_{x}\), NO\({}_{x}\)) and fine particles, contributing to air pollution and the deterioration of air quality in port areas. This makes reducing pollutant emissions a priority issue for the maritime industry.  
In the face of these challenges, the International Maritime Organization (IMO) and the European Commission must implement measures to reduce these pollutant emissions in ports, as set out in regulations such as European Directive 2005/33/EC [3]. One of the solutions being considered to mitigate these impacts, shore-side electrification, also known as \"cold ironing\" or \"shore power\", stands out as a promising technology. This approach allows ships to connect to an onshore power supply, allowing them to operate without running their auxiliary engines. It represents a significant step towards cleaner and more sustainable ports. Shutting down the ship's generators can reduce combustion emissions by up to 90% during their stay in port [4]. Shore-side electrification involves connecting the ship to a land-based electrical network, as shown in Figure 1. Once a ship has docked, it no longer requires propulsion energy. However, several systems remain operational, including ventilation, heating and cooling systems, various auxiliary pumps, and certain control systems. These systems have a significant impact on energy consumption.  
Traditionally, this energy is provided by onboard generators, running on diesel and emitting greenhouse gases. However, by connecting ships to a land-based electrical network, their auxiliary engines can be shut down while their systems remain operational. This eliminates emissions during their stay at the port, leading to a significant reduction in environmental impact [5].  
As a result, powering these systems requires high power levels and voltages of 6.6 kV or 11 kV, depending on the IEC/ISO/IEEE 80005-1 [6] standard for certain types of ships. This high-power demand is associated with a significant number of transformers. In [7; 8; 9; 10; 11], shore power system topologies are classified into three groups: centralized distribution, decentralized distribution, and distributed DC, as shown in Figure 2.
## 5 Conclusions  
Our results showed that the application of BCs altered the hydrophysical properties of control sandy soil, as shown by the increased porosity P, available water content AWC, and reduced saturated hydraulic conductivity _Ks_. Statistical analysis revealed that P, AWC, and _Ks_ were affected by all three examined factors, while CA was only affected by the BC application rate and particle size. The addition of a higher application rate of 40 Mg/ha (compared to 20 Mg/ha) of water-repellent BCs to sandy soil caused a decrease in porosity (\(P\)) and an increase in plant available water (AWC), saturated hydraulic conductivity (_Ks_), and contact angle (CA). Treatments with applied smaller (<125 \(\upmu\)m) size fraction (compared to 125 \(\upmu\)m-2 mm fraction) of water-repellent BCs to sandy soil showed, on average, a decrease in \(P\) and _Ks_ but an increase in AWC and CA. The addition of water-repellent BCs produced at a higher (520 \({}^{\circ}\)C) pyrolysis temperature caused an increase in \(P\) and AWC and a decrease in _Ks_. The change in the CA parameter was not significant for this factor. Therefore, the effectiveness of biochar as a sandy soil amendment depends on its application rate, particle size, and pyrolysis temperature.  
By amending sandy soils with BCs, the amount of water required for irrigation can be reduced, which can help mitigate drought conditions and save water. Nevertheless, further investigations are needed to understand the effect of BC application under climate conditions with long hot and dry periods, which may promote soil water repellency.  
In addition to improving the hydrophysical properties of sandy soil, converting biomass into biochar and subsequently storing it in the soil is seen as a promising strategy for reducing atmospheric CO\({}_{2}\) concentrations and mitigating climate change by enhancing short-term carbon sequestration.  
Conceptualization, L.L., P.S., J.V. and R.V.; methodology, J.V. and P.S.; software, P.S.; validation, J.V. and P.S.; formal analysis, L.L., P.S., J.V. and R.V.; investigation, P.S. and J.V.; resources, J.V. and P.S.; data curation, P.S., J.V. and R.V.; writing--original draft preparation, L.L., J.V. and P.S.; writing--review and editing, L.L., P.S., J.V. and R.V.; visualization, P.S.; supervision, J.V., P.S. and L.L.; project administration, J.V. and P.S.; funding acquisition, J.V. and P.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Grant Agency project VEGA 2/0065/24 and by the Slovak Research and Development Agency project APVV-21-0089.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### Environmental and Functional Test  
In addition to functional testing during the integration of IDEASSat, the spacecraft also needed to comply with certification requirements imposed by funding agencies and the launch provider. IDEASSat was initially assembled in flatsat format to verify subsystem compatibility. The assembled flight model then underwent end-to-end communication tests, as well as vibration, sinusoidal burst quasi-static load, and thermal vacuum tests before delivery. Due to funding constraints, there was no engineering model for separate qualification and acceptance testing. Therefore, protoflight test standards were used for flight model verification [24].
###### Abstract  
This paper proposes an intelligent reflecting surface (IRS)-assisted energy efficiency optimization algorithm to address the problem of energy efficiency (EE) degradation in high-speed rail communication systems caused by line-of-sight link blockages between base stations and trains. The joint optimization of base station beamforming and IRS phase shifts is formulated as a variable-coupled energy efficiency maximization problem, subject to the base station's transmission power and the IRS unit's modulus constraints. This is known to be an NP-hard problem, making it challenging to obtain the global optimal solution. To tackle the issue of optimization variable coupling, an alternating optimization is employed to decompose the original problem into two sub-problems: base station beamforming and IRS phase-shift optimization. The Dinkelbach method is utilized to convert the fractional objective function into a difference form; then, the successive convex approximation (SCA) algorithm is applied to transform non-convex constraints into convex ones, which are solved using CVX. The Riemann conjugate gradient (RCG) algorithm can effectively solve the difficult unit module constraint. Finally, an alternating iterative strategy is employed to converge to a suboptimal solution. Our simulation results demonstrate that the proposed algorithm significantly enhances system efficiency with low computational complexity. Specifically, when the number of IRS reflecting elements is 64, the system's EE is improved by approximately 12.41%, 35.26%, and 37.96% compared to the semi-definite relaxation algorithm, the random phase shift approach, and no IRS scheme, respectively.  
IRS; high-speed rail communication; energy efficiency; beamforming; alternating optimization
# Track-Bridge Interaction of CWR on Chinese Large-Span Bridge of High-Speed Railway
## 2 Research Model of Track-Bridge Interaction
### Research Model Proposed by Scholars
In 2017, [PERSON] used the nonlinear resistance model to divide the CWR into several sections, established the balance differential equations, and obtained the additional force of track-bridge expansion and contraction. The calculation results are in good agreement with the finite element model. At the same time, the method is suitable for the calculation of the expansion force of any span railway simply supported beam and continuous beam [29]. In 2019, [PERSON] and [PERSON] put forward a new change mode of fastener longitudinal resistance considering the loading history and carried out experimental verification based on [PERSON]'s friction model. Compared with the previous [PERSON]'s model and the double-spring model, the mechanical behavior of this model is more consistent with the measured results. According to the new model, based on the Ritz method and the principle of minimum potential energy, the track-bridge interaction analysis algorithm considering the load history was derived [30]. In 2020, [PERSON] et al. proposed an effective simplified model of the CRTS-II ballastless track structure. Compared with the bridge model recommended by China's seismic design code, the simplified track model can accurately simulate the longitudinal restraint effect of the track structure on the bridge, with high calculation efficiency and significantly improved calculation accuracy [31].
## 3 Results and Discussion  
### pH, Zeta Potential, and Average Particle Diameter of Film-Forming Solutions  
The pH, zeta potential and average particle diameter of the ozone-treated film-forming solutions (FFSs) are given in Table 1. The pH values of the FFSs ranged from 5.71 to 5.96. Ozone treatment increased the FFSs' pH values (\(p<0.05\)). No significant differences were found among samples with O-10 and O-30 (\(p\geq 0.05\)). Previous research revealed that the pH of FFSs influences films' tensile strength, colour, transparency, and water vapour permeability [33; 34]. The stability of films can be affected by the pH of solutions of whey protein films plasticised with glycerol. When pH falls below the isoelectric point, film formation deteriorates since the SH group's reactivity decreases significantly [35]. Previous research reported that films at low pH and high pH are characterised by more brittle and rubber-like film formation, respectively [36; 37].  
The zeta potential is calculated by measuring the response of charged particles to an electric field. When subjected to a continuous electric field, particles move at a constant velocity. Using this velocity, the charge and the zeta potential can be calculated [38]. While some samples showed statistically significant differences in the determined zeta potential values (\(p<0.05\)), it is essential to note that the results were numerically very similar. The results indicate that the zeta potential values for all the samples were negative, suggesting that the particles' surfaces carried a negative charge. [PERSON] et al. [39] reported negative zeta potentials of WPC samples and suggested that the protein composition and applied treatments may influence these potentials.  
The average particle diameters of the FFSs varied from 593.0 to 727.5 nm, where ozone treatment considerably affected them (\(p<0.05\)). The average particle diameters of the O-10 and O-30 samples were lower than the control, whereas those of O-5 were higher than the control. The larger particle diameter of O-5 might be due to the lower zeta potential. [PERSON] et al. [5] reported that the average particle diameters of FFSs from whey powders at various demineralisation ratios ranged from 1569.95 to 2989.25 nm. The smaller average particle size could indicate the increased molecular interactions [40], and therefore increased film strength, which is in agreement with the results found in this work.