esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress in the Construction and Application of In Vitro Vascular Models
## 3 Application
### Blood-Brain Barrier  
The blood-brain barrier is a critical physiological barrier that protects the brain from external influences. It regulates the selective transport system between the brain and blood and metabolizes substances in the brain [118]. However, the blood-brain barrier also limits the effective treatment of brain-related diseases by preventing drugs from reaching the brain [119]. Therefore, the development of in vitro blood-brain barrier models is crucial.  
In vitro blood-brain barrier models are generally achieved by co-culturing multiple cell types, and cell co-culture is particularly critical to the maturation and stability of the blood-brain barrier model. [PERSON] et al. [120] (Figure 6) used soft lithography technology to fabricate microfluidic chips and incubated endothelial cells and primary astrocytes to form a blood-brain barrier model. The study demonstrated that the permeability of the blood-brain barrier model was consistent with in vivo data, allowing for continuous monitoring of drug permeability across the blood-brain barrier and reducing culture time. However, research indicates that reactive oxygen species can cause connexin dispersion and affect the stability of the model. Therefore, antioxidants can be added to increase the model's stability. However, as this model only contains two cell types and lacks pericytes, it still falls short of simulating the real blood-brain barrier. In another study, [PERSON] et al. [121] found that co-culturing endothelial cells, primary astrocytes, pericytes, and neurons in the blood-brain barrier model significantly enhanced the expression of trans-endothelial resistance and tight junction proteins, promoting blood-brain barrier formation by measuring transmembrane  
Figure 5: Schematic diagram of endothelial cell invasion: (**a**) schematic diagram depicting the invasion of human colonic microvascular endothelial cells (HCOCs) from the side channel into the central chamber in response to VEGF in the matrix; (**b**) formation of endothelial sprouts in a microfluidic device; (**c**) quantification of endothelial invasion. The asterisk (”) denotes statistical differences (\(p<0.01\)). Reprinted/adapted with permission from Ref. [115]. 2019, Amer Assoc Advancement Science.  
resistance. Co-culture also increased the expression of specific transporters, endothelial cell maturity, and model stability, allowing for distinguishing which new drug candidates are substrates for efflux transporters.  
However, the blood-brain barrier model requires co-culturing multiple cells simultaneously, resulting in a long culture cycle, complex procedures, and high costs. Furthermore, primary cells are preferred over biological immortal cell lines in this model as they better replicate the biological properties of cells in vivo.
## 5 Simulation Results and Discussion  
To illustrate the proposed model based fault diagnosis for current sensor in machine side converter, a MATLAB/SIMULINK (Version R2018a, MathWorks Inc., Natick, MA, USA) model is developed referring to the real laboratory prototype. The parameter is listed in Table 1. A field-oriented control combined with a space vector modulation is applied to control the rotor-side converter. Both the wind conversion system and fault diagnosis algorithm are implemented in the SIMULINK environment. Observer gains can be obtained by solving Equations (23) and (37) with MATLAB LMI tool or Yalmip tool box.  
Three types of sensor faults are designed to verify the performance:  
* **Type a**: gain error in phase a sensor, only 80% of the measured value fed to the controller,
* **Type b**: bias fault in phase b sensor, 4 \(A\) is added to the measured value,
* **Type c**: disconnection of phase c sensor, the measurement output becomes zero.  
**Type a** fault is modeled as multiplicative fault, **Type b** and **Type c** are additive faults. These faults are commonly presented in literature and practice applications.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Quantity** & **Value** & **Quantity** & **Value** \\ \hline Magnet steel & NdFeB permanent magnet & Insulation class & Class F \\ Protection & IP54 & Stator winding connection & Star connection \\ Rated voltage & 110 V & Rated frequency & 32.67 Hz \\ Stator resistance & 0.3667 \(\Omega\) & Rated power & 2.5 kW \\ Stator inductance & 3.29 mH & Rated speed & 335 r/min \\ Flux linkage & 0.283 Wb & DC-link voltage & 300 V \\ Generator inertia & 0.1133 Kg- m\({}^{2}\) & Grid inductance & 2 mH \\ Viscous damping & 0.008 N\(\cdot\)m\(\cdot\)s & Grid resistance & 0.19 \(\Omega\) \\ Pole pairs & 7 & Grid voltage & 110 V \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the surface-mounted permanent magnet synchronous generator.  
The proposed method generates three fault isolation variables \(J_{a}\), \(J_{b}\),\(J_{c}\) and a detection flag \(f_{d}\). \(f_{a}\), \(f_{b}\) and \(f_{c}\) denote the isolation flags related to \(J_{a}\), \(J_{b}\) and \(J_{c}\). When the detection observer detects fault in the system, \(f_{d}\) changes from '0' to '1'. Only when the isolation variables \(J_{a}\), \(J_{b}\) and \(J_{c}\) exceed the defined thresholds will the corresponding isolation flags change from '0' to '1'.
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 4 Performance Analysis
### Exact BER of Conventional NOMA
\begin{split}\overline{\mathrm{P}_{1}(e)}=&\frac{1 }{2}\bigg{(}1-\sqrt{\frac{\overline{\gamma_{\mathrm{C}}}}{2+\overline{\gamma _{\mathrm{C}}}}}\bigg{)}+\frac{1}{8}\bigg{(}\sqrt{\frac{\overline{\gamma_{ \mathrm{A}_{1}}}}{2+\overline{\gamma_{\mathrm{A}_{1}}}}}\\ &-\sqrt{\frac{\overline{\gamma_{\mathrm{B}_{1}}}}{2+\overline{ \gamma_{\mathrm{B}_{1}}}}}+\sqrt{\frac{\overline{\gamma_{\mathrm{D}}}}{2+ \overline{\gamma_{\mathrm{D}}}}}-\sqrt{\frac{\overline{\gamma_{\mathrm{E}}}}{2+ \overline{\gamma_{\mathrm{E}}}}}\bigg{)}.\end{split}  \tag{32}\]
## 5 DT Technology in the Lifecycle of Transportation Infrastructure
### Construction and Implementation Phase
Additionally, tunnel fire accidents and other emergent issues are receiving increased attention. To expedite rescue operations, ref. [77] introduced an IoT-enabled DT system for smart tunnel fire safety management. Using deep learning models trained on Transformer networks and simulation datasets, the system predicts the real-time location and scale of fires, visualizes 3D fire scenarios, and supports evacuation, firefighting, and emergency rescue efforts. Regarding quality control, DT technology ensures full-process tracking and management of construction quality through real-time virtual mapping of the construction process. When combined with BIM, DTs compare detailed specifications from the design phase with actual operations during construction, thereby guaranteeing adherence to standards. Additionally, DTs track and inspect material quality by integrating supply chain data, ensuring that all materials meet regulatory standards [68]. In road construction, DTs monitor the production, transportation, and pouring of concrete, aligning with design requirements and minimizing the impact of material quality on construction [78]. The construction phase involves multiple teams and stakeholders, such as construction companies, supervisory units, and government departments. DTs provide real-time updates of virtual sites, allowing stakeholders to view progress and collaborate effectively. Construction teams can highlight issues on the shared platform, supervisory units can monitor compliance, and government departments can review key milestones to ensure that projects meet regulatory and quality standards.
## 3 Results and Discussion
### Solvatochromic Studies  
In the UV-Vis spectra of **6** (Figure 1a) and **7** (Figure 1b) recorded in dichloromethane, acetone, chloroform, diethyl ether, and ethyl acetate, intense Q-bands were considerably split into two components. The split was less marked for the other solvents, such as acetonitrile, toluene, THF, 1,4-dioxane, 1-butanol, DMF, and DMSO. Therefore, solvent effects on UV-Vis absorption spectra were studied in selected protic and aprotic solvents. In the Q-band region, depending on the applied solvent, the positions and intensities of the bands were influenced. For **6**, the largest red shift was noted in toluene, whereas the highest blue shift was observed in DMSO. For **7**, the largest red shift was noted in dichloromethane, and the highest blue shift was observed in 1,4-dioxane, DMF, and DMSO.  
To evaluate the solvatochromic effects, the correlation between the refractive indices of solvents and Q-band shifts was tested [24; 34; 35]. The \(1/F\) value is a rational function of the solvent's refractive index (\(n\)), which is calculated from the equation:  
\[F=\frac{n^{2}-1}{2n^{2}+1} \tag{1}\]  
The wavelength (\(\lambda_{\text{max}}\) in nm), representing the maximum of the Q-band, was plotted against \(1/F\). For tribenzoporphyrazine derivative **6**, the linear correlation characterized by the correlation index (R\({}^{2}\)) of 0.86 (Figure 2a) was calculated for 8 out of 12 solvents, such as dichloromethane, acetone, chloroform, diethyl ether, ethyl acetate, acetonitrile, toluene, and THF. For macrocycle **7**, the linear correlation index (R\({}^{2}\)) of 0.82 (Figure 2b) was found for the following solvents: diethyl ether, ethyl acetate, THF, 1,4-dioxane, DMF, and DMSO. Notably, the Q-band wavelengths of **6** and **7** in selected solvents were also correlated with the dipole moments. For tribenzoporphyrazine **6**, the linear correlation index (R\({}^{2}\)) of 0.91 (Figure 3a) was noted for 6 out of 12 solvents: dichloromethane, acetone, chloroform, toluene, THF, and DMSO. The correlation between the dipole moment of the solvent and the \(\lambda_{\text{max}}\) of the Q-band for compound **7** was calculated as a value of 0.94 (Figure 3b) for solvents such as acetone, ethyl acetate, acetonitrile, toluene, DMF, and DMSO. The outcomes of the solvatochromic study performed for **6** and **7** might suggest that the red shift of the Q-band can be the result of the coordinating strength of the solvent or solvation. The amount of data considered for both plots (Q-band vs. refractive index and Q-band vs. dipole moment) is related to the best R\({}^{2}\). Adding more solvents decreased the significance of the obtained results. The data used for calculations, i.e., the absorption maxima of **6** and **7** as well as the values of the refractive indices (\(n_{D}\)) and dipole moments (\(\mu\)) in the applied solvents
# Differential Evolution and Fuzzy-Logic-Based Predictive Algorithm for V2G Charging Stations
## 4 Discussion  
From the curves shown and discussed in the previous section, one can see that no single fixed value of \(K_{4}\) in the cost function would deliver a reasonable performance in all scenarios. Furthermore, not even a linear relationship between any of the inputs, PV generation, or energy cost and \(K_{4}\) would suffice. Our approach implicitly defined a nonlinear relationship that took into account both inputs, adapting \(K_{4}\) and finally attaining a greater cost reduction throughout the day. As a result, the fuzzy logic component was central to the V2G control algorithm.  
The main strength of the proposed algorithm is its generality, in that charging stations of different sizes could be tackled with it. Additionally, operational constraints associated with them could be incorporated, tailoring the algorithm to the specific V2G system at hand. Clearly, one should understand that the more constraints are imposed on the optimization model, the higher the final operational cost will be. The generality of the presented investigation aimed at exploring how the two uncertain inputs (PV generation and electric tariff) could be handled by the fuzzy logic block at a high level of abstraction, without the interference of additional low-level constraints. Yet, the interplay among all these components is regarded as being interesting and will be the focus of future work.
# Study on the Propulsion Characteristics of a Flapping Flat-Plate Pumping Device
## 4 Results
### Experimental Validation  
To verify the accuracy of the numerical simulations for different flapping modes of the flat-plate hydrofoil, physical prototypes were fabricated based on the previously defined parameters and tested in the experimental flume at Zhejiang Hengze Ecological Technology Co., Ltd., Hangzhou, Zhejiang Province, China. The physical tests were conducted in a rectangular flume with dimensions of 5 m \(\times\) 20 m \(\times\) 2 m. Importantly, the inlet velocity was set to 0 m/s, meaning there was no external forced flow in the system. Instead, the flow was generated entirely by the motion of the flapping hydrofoil, with the system relying on the thrust and lift forces induced by the hydrofoil's oscillations to drive the flow through the channel. To minimize the influence of the free surface during testing, the flow velocity sensor was positioned at the same depth as the midsection of the hydrofoil. The flapping flat-plate hydrofoil device and the crank-slider mechanism were placed at the inlet, as shown in Figure 18.  
Figure 18: (**a**) Crank-slider drive mechanism; (**b**) Synchronous belt drive mechanism.  
The experiment employed a JD-LS6C Doppler velocimeter to measure flow velocity at the outlet of the test flume, with a measurement range of 0.02-5.00 m/s and an accuracy of 2% F.S. Flow velocity was measured at five different points along the outlet cross-section of the test flume, and the average value was used to ensure data accuracy. The flapping hydrofoil device was operated for 80 cycles to allow the flow field to stabilize before data acquisition was initiated.  
Figure 19 presents the velocity-frequency curves for the flapping flat-plate hydrofoils of the two devices under different frequencies.  
As shown in Figure 19, all four curves exhibit a linear increasing trend with flapping frequency, indicating a positive correlation between frequency and flow velocity enhancement. At the same frequency, both the simulated and experimental flow velocities of the simple harmonic motion are consistently higher than those of the quasi-harmonic motion. The simulation trends show good agreement with the experimental results. This further confirms that simple harmonic motion is more effective in enhancing pumping performance.
## 5 Analysis and Results
### Correspondence Analysis of Landscape Values and Land Use Types  
Correspondence Analysis was used to analyze the relationship between the perceived value of the landscape and the objective elements of the landscape in spatial distribution, and to help explain the spatial characteristics of compatibility. The land use type map was overlayed with the Kernel Density raster layer, and the regional statistical tools in ArcGIS software were used to count the density values of six landscape values in three land use types. SPSS software was used to carry out correspondence analysis and generate scatter diagrams of the correspondence analysis.  
According to the regulatory detailed planning and urban design scheme, the research analyzes three land use types: public space, commercial land and residential land, and analyzes their spatial distribution relationship with six landscape values. Using the correspondence analysis method, the corresponding relationship between the six types of landscape values and the three types of land use involved in this study was studied. The dimension was selected as two, the row variable (landscape value) and column variable (land use type) were set, then, SPSS was used to obtain the coordinate values in the two dimensions, and the corresponding analysis chart (Figure 4) was drawn. According to the distance between the scatter points and the origin and the distance between the scatter points, we can see that there are many corresponding relationships between the six landscape values and land use types, but each has its own emphasis.  
Among the six landscape values, leisure value and social value are located in the first quadrant. It can be seen that the places with higher leisure value perceived by residents are closely related to commercial land in land use types. Artistic values and cultural values are located in the second quadrant, which is mainly related to public space. In addition, the historical value is distributed in the third quadrant, which corresponds to public space as well as artistic value and cultural value, reflecting that these three landscape values are closely related to public space. The environmental value is distributed in the fourth quadrant, and the corresponding land use type is residential land, which reflects the increasing demand from residents for the environmental quality of living space, and a good environment is beneficial to living space.
# Multiellipsoidal Mapping Algorithm
## 4 Experiments
### Object Mapping  
In experiment 1, as shown in Figure 3, the point cloud (left) is composed of 10,916 three-dimensional points and the multiellipsoidal map (right) contains 150 ellipsoids.  
Similarly, in experiment 2, shown in Figure 4, a map of a tree of 31,049 points is adapted with 400 ellipsoids. Notably, the ellipsoids that are on the floor of the approximation are projected onto the 2D plane defined by the x and y axes of the figure.  
Figure 3: Experiment 1.  
In Figure 5, the results of experiment 3 are shown. This is an example of a person with opened arms. As can be seen, concave areas are represented accurately by several ellipsoids. The point cloud has 40,883 points, and the multiellipsoidal map contains only 350 ellipsoids.  
In experiment 4 in Figure 6, a man is standing with his back toward the observer. The point cloud is formed by 37,142 points, and the multiellipsoidal map contains 350 ellipsoids.  
Figure 4: Experiment 2.  
Figure 5: Experiment 3.  
In Figure 7, another human figure is considered, in this case, a man with open arms sitting in a chair. The point cloud is formed by 42,272 points, and the multiellipsoidal map has 350 ellipsoids.  
Summarizing these five experiments, it is observed that a good approximation and representation of the objects are obtained even when a small number of ellipsoids is used. Table 2 shows the memory cost. Consider a four-byte floating-point number; then, the memory cost of the cloud point (three floating-point numbers for each point) and of the multiellipsoidal map (six floating-point numbers for each ellipsoid) is calculated. Finally, a percentage cost of each map is shown, where it is easy to observe that an information-rich map with low memory cost has been obtained.  
Figure 6: Experiment 4.  
Figure 7: Experiment 5.
# Magnetoelectric Composites: Engineering for Tunable Filters and Energy Harvesting Applications
## 3 Results
Figure 6: Complex impedance plots in temperature range from 200 to 300 \({}^{\circ}\)C for multiferroic composite samples: P20-F80 (**a**), P40-F60 (**b**), P60-F40 (**c**), P80-F20 (**d**), P85-F15 (**e**), and P90-F10 (**f**). The Arrhenius law can express the activation energy of conductivity [55],  
\[\sigma_{\text{AC}}=\sigma_{0}*\exp(-E_{\text{a}}/k_{\text{B}}*T) \tag{2}\]  
where \(\sigma_{\text{AC}}\), \(\sigma_{0}\), \(E_{\text{a}}\), \(T\), and \(k_{\text{B}}\), are conductivity, activation energy, temperature, and Boltzmann constant, respectively. The recorded data were fitted with the above equation, and the resulting values of activation energies are shown in Figure 8. For the pure PZT material, the value of activation energy was taken from theoretical calculations presented in the work of [PERSON], [PERSON], and [PERSON] [56]. Figure 8a shows the activation energy values for the studied P-F multiferroic samples and grain and grain boundary conductivity contributions (other for the grains and grain boundaries). In the 20% of ferrite phase content sample (P80-F20 sample), there is a significant difference in the level of activation energies which means that the ferrite conductive phase is mainly located in grain boundaries. Consequently, the grain contribution to the complex AC response is lower than those of the results for the P-F composite samples with higher than 20% of ferrite content (i.e., P60-F40, P40-F60, and P20-F80 samples), where the grain and grain boundary contributions are almost equal. Figure 8b demonstrates that our multiferroic tunable bandpass filters are very precise and efficient, which could have many applications in energy harvesting and tunable bandpass filters. Figure 8: Dependence of the activation-energy values (**a**) and compositional tuning (**b**) with the ferrite concentration for obtained P-F composite samples. Figure 7: The normalized imaginary parts \(Z^{\prime\prime}/Z^{\prime\prime}_{\text{max}}\) of impedance as a function of frequency in temperature range from 200 to 300 \({}^{\circ}\)C for multiferroic composite samples: P20-F80 (**a**), P40-F60 (**b**), P60-F40 (**c**), P80-F20 (**d**), P85-F15 (**e**), and P90-F10 (**f**).
# Molecular Dynamics-Based Study of Graphene/Asphal Mechanism of Interaction
## 2 Models Establishment and Simulation Methods
### Molecular Dynamics Simulation Methods
#### 2.2.5 Relative Concentration of Asphalt Molecules (RDF)  
The self-healing of asphalt materials involves the initial interfacial interaction and subsequent diffusion of asphalt molecules to the microcrack interface. The distribution state of asphalt molecules during the healing process can directly reflect the healing characteristics of asphalt [15]. The relative concentration (\(C_{i}\)) can qualitatively analyze the spatial density distribution of molecules in the mixture system and characterize the distribution state of asphalt molecules during the self-healing process [23]. The relative concentration of selected molecules was calculated directly in the \"Analysis\" function of the \"Forcite\" module of the Materials Studio 2020 software, as shown in Equation (4).  
\[C_{i}=(N_{i}/V_{i})/(N_{t}/V_{t}) \tag{4}\]  
where \(C_{i}\) is the relative concentration of i molecules in the selected space system; \(N_{i}\) is the number of i molecules in the selected spatial system; \(V_{i}\) is the volume of molecules in the selected space system; \(N_{t}\) is the total number of molecules in the simulated space system; \(V_{t}\) is the total molecular volume of the simulated system.