esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
This trend is further illustrated in Figure 6, where the mode magnitudes for the TM\({}_{11}\) and TE\({}_{11}\) modes are plotted vs. the phase constant (only the degenerate mode with the largest magnitude is plotted). In these figures, each curve contain 100 samples of the mode coefficients in the pass-band, and the band edges are omitted due to instabilities in the numerical result. It is observed that when the TE\({}_{11}\) mode begins to propagate, there is an apparent prevalence of this mode with respect to TM\({}_{11}\). Figure 4: Comparison of the obtained dispersion curves for the studied structures between the mode-matching formulation and CST. The dimensions are: \(r_{i}=2\,\)mm, \(r_{o}=4.7\,\)mm, \(r_{s}=4.7\,\)mm, \(\alpha=180^{\circ}\), and \(\delta=6\,\)mm. In the mode-matching formulation, 1 TEM, 17 TM, and 7 TEmodes are included. For \(\beta\gtrsim 14\,\mathrm{{}^{o}/mm}\), it can be noticed in Figure 4 that the propagation frequencies increase with the symmetry order, which is opposed to what happens in the range \(\beta\lesssim 14\,\mathrm{{}^{o}/mm}\). However, Figure 5b,c shows that the magnitude of the mode coefficients is similar in all the analyzed structures. To understand the difference in the response for \(\beta\gtrsim 14\,\mathrm{{}^{o}/mm}\), a more detailed analysis should be carried out on the impact of the relevant modes at these frequencies (TEM and TE\({}_{11}\)). In Figure 7,  
Figure 5: Magnitudes of the mode coefficients for different phases: (a) \(\beta=6\,\mathrm{{}^{o}/mm}\), (b) \(\beta=15\,\mathrm{{}^{o}/mm}\), and (c) \(\beta=27\,\mathrm{{}^{o}/mm}\). The dimensions are the same as in Figure 4.  
Figure 6: Magnitudes of the mode coefficients of the TM\({}_{11}\) and TE\({}_{11}\) modes. The dimensions and number of modes are the same as in Figure 4.  
the phase of the mode coefficients vs. \(\beta\) for the TEM and TE\({}_{11}\) modes is presented. It is observed that the phase of the TEM mode coefficient is similar in all the analyzed structures, as illustrated in Figure 7a. Furthermore, for \(\beta\gtrsim 14\,^{\circ}\)/mm, it is noted that the phases of the mode coefficient for the TEM and TE\({}_{11}\) are similar in the conventional periodic structure. However, as subsequent screens are rotated, the phase of the TE\({}_{11}\) mode coefficient changes, as illustrated in Figure 7b. In other words, the impact of the TE\({}_{11}\) mode varies with \(\xi\), and the difference is particularly large between the three twisted structures and the conventional periodic structure. This confirms the difference in the dispersion curves observed in Figure 4.
## 3 Results and Discussion
### Scenario 1 (S1)
#### 3.1.6 Comprehensive Evaluation of Feasibility  
According to the analysis of the above four indicators, in scenario 1, the reclamation scheme had a large impact on the EC of pollutants. Only scheme B was awarded the evaluation score, and the comprehensive evaluation index R was 9.5, while the other schemes were all 0 (Table 9). Therefore, only working condition HZ1 was feasible, and working conditions HZ2, HZ3, HZ4 and HZ5 were strictly restricted.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reclamation Scheme** & **NO\({}_{\text{X}}\)-N(t/a)** & **EC Change (\%)** & **R\({}_{\text{3}}\)** \\ \hline A & 5082 & - & - \\ B & 4887 & 3.84 & 10 \\ C & 4139 & 18.56 & 0 \\ D & 3855 & 24.12 & 0 \\ E & 3792 & 25.38 & 0 \\ F & 4021 & 20.88 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Feasibility evaluation in NOx-N environmental capacity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reclamation Scheme** & **PO4-P(t/a)** & **EC Change (\%)** & **R\({}_{\text{4}}\)** \\ \hline A & 421 & - & - \\ B & 410 & 2.58 & 10 \\ C & 373 & 11.49 & 0 \\ D & 321 & 23.87 & 0 \\ E & 345 & 18.16 & 0 \\ F & 332 & 21.11 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Feasibility evaluation in PO4-P environmental capacity.  
According to the results of the comprehensive feasibility evaluation, in this evaluation, scheme B was acceptable, and schemes C, D, E and F were not feasible. Therefore, the suitable scale of reclamation in Haizhou Bay was 18 km\({}^{2}\) (Table 10).
# A Study on the Pantograph Slide Wear Model Based on Energy Dissipation
## 4 Development of a Pantograph Slide Wear Model  
### Rationality Assumptions for Model Establishment  
This paper proposes rationality assumptions for the pantograph-catenary current-carrying friction-wear model by integrating engineering practice and research validity, as follows.  
1. Material Homogeneity Assumption: The pantograph slide is assumed to exhibit a uniform composition and microstructure during frictional processes, eliminating localized defects that could induce abnormal wear.
2. Environmental Stability Assumption: The ambient parameters (temperature: 20-25 \({}^{\circ}\)C; relative humidity: 40-60% RH) are assumed to remain constant throughout the experiments to exclude interference from environmental fluctuations and other undefined variables.
3. Scope Limitation Statement: The model does not account for critical factors such as arc discharge and thermal expansion effects, which may significantly influence the system behavior and prediction accuracy under harsh conditions (e.g., high voltage or elevated temperatures).
4. Wear-Energy Linear Relationship: During the steady-state wear phase, the wear volume is assumed to be linearly proportional to the dissipated energy from electrical friction, with a positive correlation [25].  
Figure 2: Curve of pantograph–catenary current-carrying friction dissipation energy and wear volume.
# Monte Carlo Analysis of the Intensification Factor of Design Response Spectra for Hoisted Loads
## 4 Effect of Damping on the Linear Response Spectrum  
Unlike the stiffness and the mass, the damping, which appears in Equation (6), is a parameter that cannot be chosen easily by design or that is determined by the operational parameters' weight and length. At the same time, the damping is a key driver of the maximum acceleration experienced with harmonic inputs, even during the transient.  
The typical RS is given at a fixed damping values with a formula of Eurocode 8 [4] to scale the RS values when the damping differs from the baseline value, often 5%:  
\[\sqrt{\frac{10}{5+100\zeta}} \tag{10}\]  
Figure 3: Schematic representation of a crane.  
Metal structures and systems have typical \(\zeta\) damping values ranging from 2% to 5%. There is a significant uncertainty in the damping values as it is clearly shown by [17]. The hoist crane is supposed to be part of a structure, often in metal; and, thus, it requires a damping scaling factor suitable to such lower damping values. To study this aspect, we ran a dedicated Monte Carlo analysis and quantitatively estimated the damping scaling factor in conditions equivalent to the ones used in simulating the crane.  
The procedure of Section 2 generates a large number of random seismic events. For each of these events, the RS is calculated with a set of damping values that differ from the one used for the time history generation. For each of these RS curves, the average acceleration in the plateau bandwidth [6 Hz, 30 Hz] is collected and plotted as shown in Figure 4. The set of random RS generated in this way is shown in Figure 5, where the boundaries for each damping ratio are given by the standard deviation of all the relevant results.  
Figure 4: Average scaling factor of the RS values as measured in the plateau bandwidth [6 Hz, 30 Hz] compared with the [4] scaling law and the one proposed here.  
Figure 5: Average and standard deviation at each frequency of the RS curves calculated with different damping values.  
From Figure 4, it is clear that the scaling formula given by Eurocode 8 is not a good fit below 2%, as already highlighted by [18]. We propose here an alternative formula based on our numerical results:  
\[\sqrt{\frac{5.5}{0.5+100\zeta}} \tag{11}\]  
Our alternative scaling rule is also shown in Figure 4. This formula is based solely on the definition of shock response spectra. Eurocode 8 is calibrated for higher damping values, which are usually of interest for large structures during seismic events and for ground response spectra.
## 5 Discussion  
Based on the simulation and analysis results, we can provide a concise analysis and discussion, as follows:  
1. As the number of tasks assigned to edge servers increases, the likelihood of violating precedence constraints also increases, resulting in unpredictable wait times due to the existence of tasks with dependencies. However, our proposed Petri-net-based constraint amending method can efficiently obtain feasible solutions even for large-scale scenarios with 1600 satellites and approximately 900 tasks within a short time frame of 0.12 s. This highlights the effectiveness and efficiency of our proposed method.
2. Compared to IMPSOQ and MCHO, our algorithm is more effective in generating solutions closer to the Pareto front for all 18 instances, as indicated by the dominance rate indicator. This is because our proposed constraint amending method does not restrict the solution space and can efficiently repair precedence constraints in any randomly generated solution. In contrast, IMPSOQ and MCHO use a queue-based method and the UpwardRank method, respectively, to calculate feasible task sequences under the  
Figure 12: Variation trend of _aNS_, _aHV_, and _aDR_ for each algorithm in different instances. (**a**) Average number of nondominated solutions (_aNS_); (**b**) average hypervolume (_aHV_); (**c**) average dominance rate (_aDR_).  
current task parameters, which not only increases computational cost but also affects the quality of the assignment solutions.
3. Our proposed algorithm is superior to others in achieving a balance between the convergence and diversity of non-dominated solutions. This is demonstrated by the hypervolume indicator, which evaluates the strength of non-dominated solutions. We achieved this balance by implementing our SDRS method, which evaluates both the convergence and diversity of non-dominated solutions, resulting in better solutions overall. In contrast, other algorithms such as NSGA-II and IMPSQQ rely on crowding distance measures and grid-based methods, respectively, to filter solutions after obtaining a non-dominated solution set. This approach can eliminate some dominated solutions that may have good diversity. Additionally, MCHO optimizes multiple populations for different objectives, but the population used for diversity evaluation only employs normalized linear aggregation, which can compromise the effectiveness of balancing diversity.
###### Abstract  
This paper presents a case study on integrating Agile Systems Engineering methodologies in the preliminary design phase of satellite systems, focusing on the \({}^{3}\)ColStar satellite mission. Through Model-Based Systems Engineering (MBSE), technical consistency was rigorously managed across various architectural documents, ensuring coherency and minimizing errors. Furthermore, the preliminary design was developed, with the implementation of the Arcadia Method, supported by the Capella modeling tool. This allowed the digitalization of the system, which was represented by models that contain requirements, architecture, and interfaces between the different parts of the system. At the same time, the preliminary design process was streamlined and completed within an accelerated time frame of 4 months, with weekly sprints driving progress based on the scrum methodology. This case study highlights the effectiveness of Agile Systems Engineering principles to improve the team communication accuracy, communication, and efficiency of satellite systems preliminary design, providing valuable insights for future missions.  
Agile Systems Engineering; small satellite; MBSE; Arcadia; Capella; preliminary design
# QoE Modeling on Split Features with Distributed Deep Learning
## 6 Results
### Vertical Federated Learning (VFL)
#### 6.2.1 Isolated vs. vFL  
For the experiments with the _meanBitrate_ feature, when the split index was set to 2, the \(p\)-value between G0 and \(vFL\) was \(3\times 10^{-6}\) (+100% accuracy change), while \(p\)-value was \(0.575\) (\(p>>0.05\)), which indicates again that G1 does not benefit from \(vFL\) potentially due to the existence of the strong feature, _meanBitrate_. When the split index was set to 4, the \(p\)-value was \(1.1\times 10^{-5}\) for the comparison between G0 and \(vFL\) (\(+23\)% accuracy change); while there was no statistically significant difference between the accuracy distribution of G1 and \(vFL\) (\(p=0.50\)). When the split index was set to 6, the \(p\)-value was \(3.4\times 10^{-8}\) for the comparison between G0 and \(vFL\) (+18% accuracy change), while it was \(0.2\) (\(p>0.05\)) for the comparison between G1 and \(vFL\). The reason here was again due to the fact that G1 had _meanBitrate_ feature which is alone enough to get on-par accuracy as \(vFL\). In overall, G0 clearly benefited from the federation as \(vFL\) results were much better.  
For the experiments without the _meanBitrate_ feature, when the split index was set to 2, the \(p\)-value of the Dunn post hoc test between G0 and \(vFL\) was \(5\times 10^{-4}\) indicating that G0 significantly benefited from \(vFL\) (+94% accuracy change) as expected, since G0 had only 2 features that were rather content-related, TI and SI. The \(p\)-value between G1 and \(vFL\) was 0.10 which indicates that the F1-score distribution of the two are on-par. When the split index was set to 4, the \(p\)-values of the Dunn post hoc test were again less than 0.05; \(p=0.04\) between G0 and \(vFL\) (+19% accuracy change); \(p=2\times 10^{-5}\) between G1 and \(vFL\) (+24% accuracy change). When the split index was set to 6, the \(p\)-values of the Dunn post hoc test were both less than 0.05; with \(p=8\times 10^{-3}\) between G0 and \(vFL\) (+13% accuracy change); and \(p=2\times 10^{-6}\) between G1 and \(vFL\) (+26% accuracy change). Therefore, in the latter two cases, the \(vFL\) F1-score was better in a statistical significance sense than both isolated group accuracies, dominating over the feature set in G0 with respect to the importance to the estimation model.  
In overall, the above results show that the \(vFL\) approach has statistically significant performance that is superior to the _Isolated_ local models with an average of 26% accuracy improvement. As such, it is suitable for datasets where the whole feature set does not contain a very important feature for the estimator.
## 2 Key Steps in a FRB Search Pipeline
### Rejection of Radio Frequency Interference
#### 2.1.6 Spectral Kurtosis  
Tests of statistical moments for a certain distribution can serve as a powerful metric for identifying RFI. Sources in nature typically produce electric fields that are Gaussian, and any extreme non-Gaussianity that rises from observed RFI sources leads to deviations in the statistical properties of radio measurements. Spectral Kurtosis (SK) is a statistical parameter that can be used to differentiate between Gaussian noise and non-Gaussian features in the dataset. Kurtosis is the fourth moment of a normal variable. It characterises the sharpness of the Gaussian distribution [13]. Typically, stationary broadband astrophysical signals have Gaussian-like kurtosis. The presence of RFI will significantly affect the kurtosis of the data, thus providing astronomers with a way to filter RFI without affecting the signal of interest. From the definition of kurtosis, one can derive the spectral kurtosis estimator,  
\[\hat{SK}=\frac{XYc+1}{X-1}\left(\frac{X\cdot A_{2}}{A_{1}^{2}}-1\right), \tag{6}\]  
Figure 2: A dynamic spectrum at 1.4 GHz taken with the MeerKAT telescope with a single pulse from a PSR J1226-3223. The left panel shows the spectrum before any RFI excision and the right panel shows the spectrum after running IQRM on the data. The figure has been adopted from Figure 3 in [11].  
where \(X\) is the number of spectral values that are accumulated, \(Y\) is the total number of spectra that are used to estimate this value, \(c\) is a shape parameter of the response of the detector that is empirically derived, and  
\[\begin{split} A_{1}&=\sum_{j=1}^{X}\left(\sum_{i=1}^{ Y}I_{i}\right)_{j}\\ A_{2}&=\sum_{j=1}^{X}\left(\sum_{i=1}^{Y}I_{i} \right)_{j}^{2}\end{split} \tag{7}\]  
are the first and second statistical moments of the data. For Gaussian noise, the distribution of \(\hat{S}\)K is predictable and any deviations from it can be identified up to a certain threshold and confidence and are removed from the data. Several studies have assessed the effectiveness of spectral kurtosis in searches for radio transients [14; 15].
## 1 Introduction
As a result, to make full use of the detail diversity of SAR images, to suppress speckle noise, and to improve the problem of small interclass differences between SAR images, a three-branch multi-scale feature fusion network called MFN for SAR image classification was proposed in this paper. The MFN model combines the advantages of both CNN and Swin Transformer architectures. Inspired by the ConvNeXt and Swin Transformer models, MFN comprises three branches: ConvNeXt-SimAM, Swin Transformer, and multi-scale feature fusion. The multi-scale feature fusion branch can thoroughly mine the shallow detailed features and advanced semantic information from different levels and fuse the local features and global features of SAR images through the feature fusion unit (FFU) to enhance the representation ability of the SAR target classification model. Finally, the Bayesian hyperparameter optimization algorithm was used to optimize the model's hyperparameters to improve the classification performance of the model. The main contributions of this paper are as follows. 1. Parallel local and global feature branches were designed to fully explore the local and global features at different levels. In addition, the SimAM attention mechanism was added to the local feature module to enhance the learning ability of network features. This enhancement allowed the network to focus more on the texture detailsof SAR images within the limited feature information and to effectively suppress speckle noise. 2. The multi-scale feature fusion branch was constructed, comprising an FFU and a down-sampling module. This branch adaptively fuses local detailed features and global semantic information of SAR images from different scales. 3. A three-branch multi-scale feature fusion model based on a Bayesian hyperparameter optimization algorithm was proposed, which fully combined the advantages of ConvNeXt and Swin Transformer and achieved excellent results on the MSTAR dataset with fewer parameters and higher recognition accuracy. The rest of this article is arranged as follows. Section 2 introduces the related work. Section 3 introduces the design idea and network structure of Bayes-MFN. In Section 4, we present the experimental results of Bayes-MFN from different angles and compare them with the traditional models. Finally, Section 5 summarizes the study and provides an outlook for future work.
###### Abstract  
Yeast-derived biosurfactants may substitute or complement chemical surfactants as green reagents to extract petroleum hydrocarbons from contaminated soil. The effectiveness of contaminant clean-up by sophorolipids was tested on kerosene-contaminated soil with reference to traditional synthetic surfactants. The sophorolipids produced by the yeast _Candida bombicola_ CB 2107, cultivated with the carbon sources 10 g/L glucose and 10 g/L rapeseed oil, were most effective in contaminant removal. This biosurfactant revealed a critical micelle concentration of 108 mg/L which was close to that of Triton X-100 (103 mg/L), the synthetic surfactant considered as reference. It outperformed Triton X-100 in reducing kerosene concentrations (C10-C40) in contaminated soils. In a soil initially containing 1080 mg/kg of C10-C40, the concentration was reduced to 350 mg/kg using the biosurfactant, and to 670 mg/kg using Triton-X. In the soil with initial concentration of 472 mg/kg, concentrations were reduced to 285 and 300 mg/kg for biosurfactant and Triton X-100, respectively. Sophorolipids have the potential to replace synthetic surfactants. Properties and performance of the biosurfactants, however, strongly differ depending on the yeast and the growing conditions during production.  
petroleum hydrocarbons; yeast-derived biosurfactant; bioremediation +
Footnote †: journal: Applied Physics Letters
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 2 Research Foundation
### Characteristics of Deep-Sea Shadow-Zone Channels  
When a sound source and hydrophone are positioned near the deep-sea surface, distinct convergence zones and shadow zones can be observed. In convergence zones, the intensity of the sound field can exceed that of a spherical spreading sound field by approximately 25 dB, as explained by normal mode theory [36]. Between these convergence zones, sound waves experience a significantly greater propagation loss than in spherical spreading conditions, forming a region known as the acoustic shadow zone, as illustrated in Figure 1.  
Despite its name, the shadow zone is not entirely devoid of sound waves. Due to environmental factors such as surface waveguides and mesoscale oceanic phenomena,  
Figure 1: Propagation characteristics of the acoustic field in deep-sea channels.  
a fraction of the sound energy can enter the shadow zone through scattering and diffraction. However, this energy is relatively weak, highly susceptible to hydrological variations, and characterized by significant uncertainty. Additionally, sound waves can enter the shadow zone through seabed reflections, typically propagating along four primary paths: seabed reflection, seabed-surface reflection, surface-seabed reflection, and surface-seabed-surface reflection.  
As the distance between the transmitter-receiver, the depths of the sound source and hydrophone, and the seabed depth vary, the multipath time delays among these different propagation paths also change. Furthermore, sound waves entering the shadow zone via reflection are significantly affected by the material composition of seabed sediments and seabed morphology, leading to additional uncertainties in signal propagation.  
In summary, the low SNR at the receiver remains a major challenge for communication within the shadow zone.