esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 2 Review of Different Aspects of Digital Twins
### Current Applications of Digital Twins in Public Bus Transportation
#### 2.3.1 Fleet Management
Future scenarios involving fleets consisting of alternatively powered vehicles, as well as strategic planning and efficient decision-making to optimize fleet performance, can lead to overall operational costs and sustainable choices [87]. Enhancing operational efficiency and improving the safety and monitoring of bus operation through the integration of digitalization in transportation systems was studied in Badalona, Spain. To understand the bus dynamics within the urban transportation network, a genetic algorithm was used to identify suitable configurations for simulating city traffic based on real data and bus schedule information. The authors were successful in developing a DT that is effective in mirroring the actual dynamics of the urban transportation system and can also adapt to possible anomalies [87]. A similar study was performed using the principle of a queuing system and modeling the concepts of a DT using Python programming language. The prediction of queue lengths and waiting times allows operators and management to prioritize vehicle fleet maintenance and repair activities in addition to resource planning and the fleet management structure. In the model, users can evaluate potential parameters of the enterprise (fleet) based on certain target indicator changes. It was concluded that the digital model serves as a great resource for forecasting activities, as well as developing business plans [88]. The authors of one study mentioned the concept of Automated Guided Vehicles (AGVs), where the research involved creating a virtual model of the physical AGV fleet in a controlled environment. The decision-making processes were improved by using optimization algorithms, and continuous improvement was achieved through feedback loops in the study. While the authors raised concerns about large-scale applications, they also concluded that extensive research should be conducted as DTs for AGVs have significant advantages over traditional fleet management techniques. The scalability in terms of learning-based resource management, particularly through deep reinforcement learning, was also discussed in another study focusing on complex resource management using the Internet of Vehicles (IoV). However, it was noted that DTs integrated with AI-based resource management are promising for highly dynamic networks [89]. By providing a data-driven approach to decision-making, transportation planners and operators can better allocate resources and implement policies. This can lead to the overall enhancement of public transport systems and provide better experience for passengers and increased trust in public transportation services. Furthermore, the DT model's adaptability makes it scalable and flexible, allowing it to be applied to different cities and transportation systems.
## 3 Automatic Intelligent Optimization
### Orthogonal Design Optimization
#### 3.2.1 Range and Sensitivity Analysis
Figure 10: Range analysis diagram. where \(k=1\), 2, 3, 4 represents the factors A, B, C, D, respectively, \(n=4\). The sum of the square \(SS_{E}\) and degree of freedom of the deviations \(f_{E}\) are defined as follows:  
\[ \begin{split} SS_{E}&=SS_{T}-\sum\limits_{k=1}^{n}SS_ {k}\\ f_{E}&=f_{T}-\sum\limits_{k=1}^{n}f_{k}\end{split}  \tag{19}\]  
The final \(F_{k}\) value for significance level is defined by the Equation (20). \[F_{k}=\frac{SS_{k}/f_{k}}{SS_{E}/f_{E}}. \tag{20}\]  
In general, the \(F\) value for significance level \(\alpha\) (usually \(\alpha=0.05\) or \(\alpha=0.1\)) should always be used as quantile points to judge if the factor \(k\) plays important role in effecting the results. Values of \(F_{\alpha}\) could be obtained from the \(F\) distribution table. Comparing \(F_{k}\) obtained by calculating the \(F\) value to \(F_{\alpha}\) from the \(F\)-table [32]: if \(F_{k}>F_{0.05}\), the factor \(k\) is considered to have a highly significant effect on the test result; if \(F_{k}>F_{0.1}\), the factor \(k\) is considered to have a significant influence on the test result; otherwise, it can be assumed that factor \(k\) has little effect on the results. The calculation results of ANOVA for all factors are shown in Table 7. From the common \(F\)-test table, \(F_{0.05}(2,2)=19\) and \(F_{0.1}(2,2)=9\) for the given degree of freedom shown in Table 7.  
Level \(\alpha=0.1\), that is \(F_{\alpha}\) (2, 2) = 9, was used to judge the significance of the factor in this study. It can be concluded from Table 7 that the factor C has significant influence to pump performance, and the factors' order of importance is C, A, B, and D, which is consistent with the trend of the range analysis results. By comparing the \(t_{i}\) value, it can be determined that the superior levels of each factor are A3, B2, C3, and D3. According to the sensitivity analysis, the final optimized combination is A3B2C3D3 (scheme 2), and the specific parameters are impeller outlet diameter \(D_{2}=46\) mm, blade outlet width \(b_{2}=3.2\) mm, blade number \(Z=6\), blade outlet angle \(\beta_{2}=35^{\circ}\). The impeller used this optimization scheme compares with the original scheme, while numerical simulations were carried out using the same CFD process.
# Effective Modal Volume in Nanoscale Photonic and Plasmonic Near-Infrared Resonant Cavities
## 3 Results  
### Metallo-Dielectric Resonator  
We first present results on the evaluation of different \(V_{\mathit{eff}}\) expressions for the Ag-cladded metallo-dielectric cavity. As the dielectric (SiO\({}_{2}\)) cladding (200 nm) between cavity core (InGaAsP) and cavity shell (Ag) is thick enough, the cavity supports well confined photonic modes, and the electric fields outside the active region quickly converge to zero. Therefore, we anticipate minor variation in the resulting \(V_{\mathit{eff}}\) values with respect to change of simulation domain size (\(d_{\mathrm{air}}\)) compared to cavities supporting leaky plasmonic modes, such as those in Sections 3.2 and 3.3.  
A cross section of the metallo-dielectric cavity is shown in Figure 1a, where \(\mid\)E(**r**)\(\mid\) of the TE\({}_{012}\) mode is plotted. It is apparent that the optical mode mostly overlaps with the InGaAsP active region. Further, because the ratio of relative permittivity of InGaAsP to SiO\({}_{2}\) is \((n_{\mathrm{InGaAsP}}/n_{\mathrm{SiO2}})^{2}\approx(3.4/1.45)^{2}\) = 5.5, the concentration of energy in the active region can be found by scaling the color plot of Figure 1a by a magnification factor of 5.5. This will lead to a confinement factor as given by Equation (3) close to unity. The simulation domain for the metallo-dielectric cavity is shown in Figure 1b, where we use a surrounding air region of variable characteristic length, \(d_{\mathrm{air}}\). Figure 1c shows the \(V_{\mathit{eff}}\) values of the five \(V_{\mathit{eff}}\) expressions listed in Section 2 as a function of \(d_{\mathrm{air}}\), with the latter ranging from 795 nm to 3180 nm, or from about half of the upper photoluminescence (PL) boundary of InGaAsP to two times of it. For the metallo-dielectric cavity, among all five \(V_{\mathit{eff}}\) expressions, \(V_{\mathit{eff},1,2,3}\) expressions yield an identical result, and \(V_{\mathit{eff},4,5}\) expressions show a slightly smaller value due to the surface and PML integral terms added to the norm. The maximum difference between \(V_{\mathit{eff},1,2,3}\) and \(V_{\mathit{eff},4,5}\) is only 1.12%, which agrees with our analysis in Section 2. The dependence of \(V_{\mathit{eff}}\) on \(d_{\mathrm{air}}\) is seen to be negligible, and the small variation is most likely attributable to the different meshes that are generated upon changing \(d_{\mathrm{air}}\) and not related to any physical features of the modal profile.
# Pilot Sequence Allocation Schemes in Massive MIMO Systems Using Heuristic Approaches
## 1 Introduction
### Contributions  
Our study presented four new main contributions on the subject of Massive MIMO pilot sequence allocation problem:  
* The dataset as well as the scripts used to achieve the results are public (Available at github.com/evertonalex/utfpr-ppgi-pilotontamination, accessed on 16 May 2022);
* The heuristic parameters to the problem assessed were optimized;
* Six different heuristic approaches to the problem comparing it with the simplest solution were evaluated;
* Real scenarios under different parameters to establish the real impact of a pilot allocation scheme were simulated.  
Furthermore, we present the differences between our work and those presented in the previous section:  
* Two different mathematical models of the same practical problem were addressed;* Multiple versions and modifications in the PSO algorithm were tested to verify their performance;
* Using The minimum spectral efficiency per user as a performance parameter was used;
* Different scenario parameters were tested to verify the impacts of the pilot sequence schemes in macro, micro and femto-cells.
# Hardware Acceleration of Tsunami Wave Propagation Modeling in the Southern Part of Japan
## 3 Numerical Results
### Numerical Results
When the Calculator is based on a Virtex-6 microchip, the results are obtained in 39.5 s, while in the case of a VC709 microchip, one needs only 7.85 s. So, the achieved acceleration compared to the tsunami travel time is nearly 127 times faster, and the achieved performance gain as compared to a regular PC is by approximately 380 times (see Table 1). Figures 6 and 9 show that for such locations of the initial sea surface displacement (sources _S0-c_ and _ST-a_), the largest tsunami wave heights (5+ m) are obtained at Shikoku Island, while the coast of Kii Peninsula is practically safe as the expected tsunami wave heights do not exceed 1 m.  
As follows from the numerical results, the given position of initial sea surface displacement results in an almost 8 m tsunami wave at a certain part of Shikoku Island (Figure 9). At the same time, the height of the tsunami wave is less than 1 m along the entire coastline of Kii Peninsula (see also points with horizontal index 1340-200 in Figure 11A). This is less than the height of the wave going toward the coast orthogonal to the source's long axis direction. Figure 10 presents the computed maximal heights of a tsunami wave from source _S3-b_ with profile given in Figure 5, which is positioned at the Tonankai zone (the east-most source location among all regarded). The numerical simulation shows that the new (compared to the result given in Figure 9) position of the initial surface displacement of the same shape results in 6 m high tsunamis at Kii Peninsula, while all the Shikoku Island and the Kyushu Island coasts are practically safe with wave heights not exceeding 0.5 m.  
Figure 11A-D shows the computed distributions of tsunami wave maxima along the shore for all 12 model sources considered. We arranged the numerical experiments according to the same shape of initial sea surface displacement (given in Figure 5), varying the position of such model source along the shore and the distance of the model source from the shore. The colors of the wave maxima correspond to this distance. In Figure 11 we display in one picture the wave maxima distributions from three model sources located at different distances from the shore. Pictures A, B, C, and D of Figure 11 present the tsunami height distributions according to shore variation of the model source position. Figure 10: Distribution of wave height maxima as the result of a tsunami wave generated by the source _S3-b_ (see Figure 3).
# Tunable Mid-Infrared Graphene Plasmonic Cross-Shaped Resonator for Demultiplexing Application
###### Abstract  
In this study, a tunable graphene plasmonic filter and a two-channel demultiplexer are proposed, simulated, and analyzed in the mid-infrared (MIR) region. We discuss the optical transmission spectra of the proposed cross-shaped resonator and the two-channel demultiplexer. The transmission spectra of the proposed MIR resonator are tunable by change of its dimensional parameters and the Fermi energy of the graphene. Our proposed structures have a single mode in the wavelength range of 5-12 \(\upmu\)m. The minimum full width at half maximum (FWHM) and the maximum transmission ratio of the proposed resonator respectively reached 220 nm and 55%. Simulations are performed by use of three-dimensional finite-difference time-domain (3D-FDTD) method. Coupled mode theory (CMT) is used to investigate the structure theoretically. The numerical and the theoretical results are in good agreement. The performance of the proposed two-channel demultiplexer is investigated based on its crosstalk. The minimum value of crosstalk reaches \(-48.30\) dB. Our proposed structures are capable of providing sub-wavelength confinement of light waves, useful in applications in MIR region.  
graphene; plasmonics; Fermi energy; resonator; demultiplexer Article
## 3 Results and Discussion
### Comparative Experiments and Analysis
#### 3.5.3 Comparison with Deep Learning Models  
Table 6 presents a comparison of ECA-DenseNet with deep learning models such as ResNet50 [35], MobileNetV3 [36], InceptionV3 [37], GhostNet [38], and GoogLeNet [39] using classification accuracy-related metrics. The accuracy of ECA-DenseNet is 99.42%, its Kappa value is 99.01%, and its MCC value is 99.11%. It outperforms other deep-learning models, being 3.32% higher than InceptionV3 and 1.7% higher than ResNet50, which are the top-performing models among them.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Kappa (\%)** & **MCC (\%)** \\ \hline
**ECA-DenseNet** & **99.42** & **99.01** & **99.11** \\ XGboost & 80.71 & 75.28 & 76.79 \\ SVM & 91.42 & 90.89 & 91.12 \\ BP & 72.74 & 70.44 & 70.44 \\ RF & 84.92 & 83.17 & 83.77 \\ GBM & 87.22 & 86.42 & 86.42 \\ PLS-DA & 82.22 & 83.41 & 87.14 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of ECA-DenseNet and other machine learning models.  
Figure 7: Scatter plot for multi-performance comparison of ECA-DenseNet and other machine learning models.  
Training time is a critical indicator for measuring model training efficiency, which increases with the number of model parameters [40]. A comparison of training efficiency among deep learning models in this study is shown in Table 7. ECA-DenseNet has a parameter scale of 28,774 KB, which is approximately 69.1% smaller than the largest parameter scale of InceptionV3 (92,968 KB). Additionally, its training time is 310 s, approximately 66.3% shorter than InceptionV3's 921 s. Even when compared to GhostNet, the model with the smallest parameter scale, ECA-DenseNet only experiences a training time increase of about 187.0% despite a parameter scale increase of approximately 309.2%. This demonstrates that ECA-DenseNet not only achieves superior classification performance but also maintains high training efficiency through lightweight design, striking an optimal balance between the model complexity and computational cost.  
To visualize the comparative data more intuitively, a radar chart was plotted across five comprehensive dimensions. Among them, accuracy, MCC, and Kappa were normalized in the positive direction (higher values indicate better performance), while Training Time and Parameters were reverse-normalized (lower values indicate stronger capabilities), as shown in Figure 8. This chart vividly demonstrates the performance differences among ECA-DenseNet and five other deep learning models, highlighting ECA-DenseNet's balanced superiority in accuracy, generalization ability (MCC/Kappa), and computational efficiency (training time/parameters). Specifically, ECA-DenseNet exhibits the largest radial coverage in the radar chart, indicating its dominance across all evaluated metrics and confirming its effectiveness as a lightweight and high-performance model for spectral data classification.
## 1 Introduction
In addition, VOC trends can be correlated with physiological parameters (e.g., growth profile and possibly active pathways) and serve as indicators of agent effectuality. Such an experimental approach was previously explored for the investigation of bacterial volatiles generated by salivary bacteria [20; 21]. The headspace of bacteria incubated with AgNPs at a fixed concentration was assessed at different time points, in order to obtain a qualitative snapshot of the current VOC emissions--the results suggested bacteria inhibition due to noticed alterations in the production of metabolic markers [20]. The fold-change of bacterial VOCs after AgNPs-treatment was correlated with paired alterations in protein profiles; the observed relationships were modeled to predict the effectiveness of the interaction bacteria-agent [21]. _Proteus mirabilis, Enterococcus faecalis, Klebsiella pneumoniae_ and _Escherichia coli_ have been reported as the most commonly identified microorganisms in pressure ulcer wounds [22; 23]. Pressure ulcer isolates from _Proteus_ and _Klebsiella_ species have demonstrated multidrug resistance [24]. In parallel, _E. faecalis, K. pneumoniae_ and _E. coli_ colonizing such types of wounds were noticed to be multi-resistant to different antibiotics [23; 25]. Such a context justifies a particular interest in the stress-driven metabolic behavior of these bacterial species. The aim of the present study was to map and elucidate alterations in the metabolism of _E. coli, E. faecalis, K. pneumoniae_ and _P. mirabilis_ isolated from pressure ulcers and cultivated in medium supplemented with biosynthesized silver nanoparticles (bio-AgNPs). For this purpose, volatile metabolites emitted by cultures were comprehensively analyzed, using HS-SPME/GC-MS (headspace solid-phase microextraction/gas chromatography coupled with mass spectrometry) as analytical platform. Information regarding metabolic response of bacteria to bio-AgNPs can contribute to the elucidation of agent's action mechanism for specific pathogens. This approach can aid the development of novel methods for assessment of bacteria susceptibility and provide a deeper understanding on resistance mechanisms.
# Energy Bus-Based Matrix Modeling and Optimal Scheduling for Integrated Energy Systems
###### Abstract  
Integrated energy systems (IESs) can easily accommodate renewable energy resources (RESs) and improve the utilization efficiency of fossil energy by integrating various energy production, conversion, and storage technologies. However, the coupled multi-energy flows and the uncertainty of RESs bring challenges regarding optimal scheduling. Therefore, this study proposes an energy bus-based matrix-modeling method and a coordinated scheduling strategy for the IES. The matrix-modeling method can be used to formulate the steady- and transient-state balances of the multi-energy flows, and the transient model can clearly express the multi-time-scale characteristics of the different energy flows. The model parameters are fitted with data from experiments and the literature. To address the inherent randomness of the RESs and loads, a coordinated scheduling strategy is designed that contains two components: day-ahead optimization and rolling optimization. Day-ahead optimization uses the system steady-state model and multiple scenarios from the RES and load forecast data to minimize the operation cost while rolling optimization is based on the system's transient-state model and aims to achieve the optimal real-time scheduling of the energy flows. Finally, a case study is conducted to verify the advantages and effectiveness of the proposed model and optimization method. The results show that stochastic optimization reduces the total daily cost by 1.48% compared to deterministic optimization when considering the prediction errors associated with the RESs and loads, highlighting the stronger adaptability of stochastic optimization to prediction errors. Moreover, rolling optimization based on the system's transient-state model can reduce the errors between day-ahead scheduling and rolling correction.  
integrated energy system; matrix model; optimal scheduling; day-ahead optimization; rolling optimization
# Three-Dimensional Broad Learning Gravity Data Inversion Using Single-Anomaly Training Samples
## 1 Introduction
### Network Complexity Selection  
There are three hyperparameters for the BL network, namely, \(N_{C}\) (the number of neurons in each group of mapped features), \(N_{F}\) (the number of groups of mapped features), and \(N_{E}\) (the number of groups of enhancement nodes). Different combinations of these three parameters result in varying network complexities, each reflecting distinct functional mapping capabilities. Therefore, selecting an appropriate mapping complexity (i.e., a suitable combination of hyperparameters) is essential for the BL gravity data inversion.  
A grid search is employed to select the most suitable hyperparameter values for \(N_{C}\), \(N_{F}\), and \(N_{E}\) within the ranges [20:20:200] \(\times\) [20:20:200] \(\times\) [50:50:500] ([\(s\):\(ie\)] represents a range starting from \(s\), ending at \(e\), with an interval of \(i\)). In this study, 30,000 samples from the training dataset are used to train the BL networks, while 3000 samples from the valida  
Figure 2: Generation of an input–output training sample pair based on forward modeling of gravity data. (**a**) An output training sample with a single anomaly. (**b**) Gravimeter deployment points. (**c**) Forward modeling of gravity data. (**d**) An input training sample.  
tion dataset are employed to optimize the network complexity for 3D gravity inversion. Specifically, the objective function defined in Equation (2) is used to quantify the inversion performance for each sample in the validation dataset. The optimal network complexity is identified as the one that yields the minimum mean objective function value across all samples in the validation dataset.
## 5 Conclusions  
(1) Zeolite can significantly increase the permeability and adsorption capabilities of the stone chip-bentonite-zeolite mixture. The permeability coefficient was reduced from \(10^{-7}\) cm/s to \(10^{-8}\) cm/s by incorporating 12.50% zeolite. XRD results showed that without zeolite incorporation, ion exchange occurred between sodium-based bentonite and Zn\({}^{2+}\), which increased the layer spacing and reduced the impermeability performance. The addition of zeolite also adsorbed Zn\({}^{2+}\) for ion exchange, which reduced the ion exchange of sodium-based bentonite and weakened the increase of layer spacing, as shown by the amount of bentonite swelling increasing, thus increasing the permeability performance of the mixed soil.  
(2) Zeolite has a high specific surface area, which contributes to its high adsorption capacity. As zeolite is added to bentonite, it will share a portion of the negative effects of pollutants on bentonite, enhancing the overall adsorption capacity of the mixed soil. At the same concentration, the absolute value of zeta potential falls as zeolite admixture increases.  
(3) The Langmuir model and the Freundlich model can be used to describe the isothermal adsorption characteristics of Zn\({}^{2+}\) by zeolite doping mixture; according to the Langmuir model, the maximum adsorption number of pollutants adsorbed by the mixture at different zeolite doping can be predicted; according to the Freundlich model, the adsorption is mainly chemisorption, and ionic interchange occurs.  
Conceptualization, S.X., Y.F., X.X. and Z.W.; software, Y.F., J.W., J.L. and W.W.; validation, Y.F., W.W. and J.W.; formal analysis, Y.F., J.W. and J.L.; investigation, Y.F., J.W. and J.L.; resources, S.X., X.X. and Z.W.; data curation, W.W., J.W. and Y.F.; writing--original draft preparation, Y.F., S.X. and X.X; supervision, S.X., X.X. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the Zhejiang Provincial Natural Science Foundation (LY20E080022), and the National Natural Science Foundation (52178363, 5207867).  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.