esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Surface and Interface Observation of the Fn-CaP Microchips Printed on the Dentin Substrate  
For a closer look at the surface and interface of the Fn-CaP microchips printed onto the dentin substrate, high-magnification SEM observation was performed. Figure 6 shows SEM images of the surfaces of the Fn-CaP microchips printed on the dentin substrate using LIFTOP. Although a few straight cracks were observed on the microchip surfaces in Figure 6a, most of the microchips maintained  
Figure 5: (**a**) Schematic image of the LIFTOP process for the human dentin substrate. (**b**,**c**) Confocal laser scanning microscopic images of (**b**) the Fn-CaP/C/PDMS/PET donor sample after LIFTOP and (**c**) the Fn-CaP microchips transferred on the human dentin substrate. (**d**) Laser pulse energy dependence of the diameters (average \(\pm\) standard deviation) of the holes on the donor sample after LIFTOP (red open circles) and of the transferred Fn-CaP microchips on the human dentin substrate (blue closed circles).  
a filmy shape. From the magnified image in Figure 6b, the dentin substrate had a non-flat surface with a number of scratches due to prior surface polishing. It should be mentioned that the surface of the Fn-CaP microchip was dented along the scratches on the dentin. In Figure 6c, the dents on the microchip along the scratches on the dentin can be seen more clearly.  
Finally, the interface between the transferred Fn-CaP microchip and the dentin substrate was evaluated by SEM using the cross-sectional sample prepared through the FIB processing. The SEM images in Figure 7a-c show the dentin substrate with the Fn-CaP microchip, (a) before, (b) during, and (c) after the FIB processing. A part of the area surrounded by the white dotted line in (b) was sliced with FIB. The sliced part was taken out for cross-sectional observation, as shown in (c). The cross-sectional SEM image of the sample is shown in Figure 7d. A white-colored top layer is a tungsten coat, which was applied onto the substrate in the FIB processing for surface protection. There were, for the most part, no obvious gaps at the interface between the microchip and the dentin substrate, suggesting good contact between them. Figure 7e shows a higher-magnification SEM image of the area surrounded by the white dotted line in (d), which confirms the presence of submicron pores in the film (near the interface) as well as the good contact at the film-dentin interface. These pores are related to the porous structure of the original donor film in Figure 2b. Thus, a donor film with a smoother surface can possibly lead to the formation of a smoother poreless interface.  
Figure 6: (a–d) SEM images of the Fn-CaP microchips transferred by LIFTOP on the dentin substrate.
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 2 Basic Principles of NoN Models
### Network of Networks
#### 2.1.2 Definition of Local Effectiveness  
In a realistic NoN assumed by the B-NoN and C-NoN models, arbitrary nodes cannot be locally effective unless there is a node that is also locally effective in the interconnected network. For example, in brain functional networks, the function of \"recognizing an image\" can be achieved only by the cooperation of neural cells in two regions, namely, the posterior occipital cortex (vision) and the anterior cingulate cortex (recognition) [13]. In the same manner, an electric power supply system is realized by the cooperation of nodes in a power network and a control communication network [12]. As such, the NoN models express the local effectiveness of node \(i\) by defining the variable \(\sigma_{i}\) as follows:  
\[\sigma_{i}=n_{i}\bigg{[}1-\prod_{j\in\mathcal{F}(i)}(1-n_{i})\bigg{]}, \tag{1}\]  
where \(\mathcal{F}(i)\) is the set of nodes connected to node \(i\) via internetwork links. For node \(i\) to be locally effective, Equation (1) requires (i) node \(i\) to exist and (ii) there to be at least one node connected to node \(i\) via internetwork links. If node \(i\) has no internetwork links, then the condition reduces to (i). For example, the node colored black in Figure 1b cannot become locally effective because both nodes connected via internetwork links are removed.
## 1 Introduction
[5], the authors designed an intelligent lighting system and enabled the visualization of the change in the illuminance level with the help of real-time data. In Ref. [27], the authors discussed the optimization of lighting conditions, camera height and colors by using parameters such as light type, light intensity, light source height and camera height in an adjustable way in citrus image processing. In Ref. [24], a study was conducted on the design and implementation of a low-cost fluorescent lamp lighting system for the autonomous robot workspace area. In Ref. [16], a study was conducted on automatic-lighting-method-based images' brightness quality analysis to achieve automatic control of illumination in the motion of a robot under dark conditions. In Ref. [28], a mobile robot was designed for real-time tracking of objects of different colors by using image processing techniques based on the principle of finding the center of gravity, and its properties were examined. In Ref. [29], in a Raspberry-Pi-based robot system designed for object tracking, the process of tracking the object--whose real position is determined by finding the centroid with color-based image processing using the open-source computer vision library (OpenCV)--was carried out. In a study on color-based object tracking based on moment calculation with image processing [14], simulation was performed with MATLAB/Simulink R2016a software, and tracking of the red ball was achieved with a six-axis industrial robot. In Ref. [30], a study was conducted to follow a 2D hexagonal object with a fuzzy logic controller robot using image processing. Within this investigation, an embedded system is devised, leveraging image processing methodologies to facilitate intelligent ambient lighting anchored in reference-color principles. This system is tailored for precise object detection and positioning within the realm of robotic handling applications. The evaluation of system performance revolves around scrutinizing the illuminance levels and power consumption parameters within a meticulously designed experimental framework. To achieve optimal illuminance levels without using any sensors, the LED-based lighting system's power through PWM is adjusted against red, green, blue and yellow (RGBY) objects with known positioning--an approach divergent from traditional methodologies.
## 3 Statistical Methods
### Testing Compliance with Statistical Distribution Laws
#### 3.1.4 Kolmogorov-Smirnov Test  
The Kolmogorov-Smirnov test compares the empirical cumulative distribution function (ECDF) to the theoretical distribution to determine if the data follow the specified distribution [22]. It evaluates the goodness of fit across the entire range of the data. The test statistic K-S is calculated as  
\[K-S=\max\lvert F_{n}(x)-F(x)\rvert \tag{4}\]  
where \(F_{n}(x)\) is the empirical cumulative distribution function, and \(F(x)\) is the theoretical cumulative distribution function. The statistic can be computed using software like MATLAB R2024a and Minitab 19. A large K-S value indicates that the data do not follow thetested distribution. Like the other tests, if the \(p\)-value is less than the significance level of 0.05, the null hypothesis is rejected.  
When a given dataset is provided to statistical software (e.g., Minitab, SPSS, R), the corresponding test statistic is calculated based on the statistical distribution model formulas within each software. In this study, the Shapiro-Wilk test approach (Section 3.1.1) was implemented using statistical software R 4.4.1 [31], and the other approaches were performed using Minitab 19 software [32]. Detailed information on this can be found by referring to each statistical software's manual.  
Based on a short survey on previous studies that performed detailed statistical analysis for high-voltage experiments, it seems that the application of the Kolmogorov-Smirnov [17] approach and the Ryan-Joiner method is rather limited to the Shapiro-Wilk [15; 16; 33; 34] method and the Anderson-Darling [15; 33; 34] method in high-voltage application studies.
## 3 Numerical Simulation
### Model Description  
In order to avoid the influence of the boundary effect on the calculation results, the size of the numerical model was set to the horizontal direction x = -70 to 70 m, longitudinal y = -60 to 60 m and vertical z = -40 to 40 m, respectively. The initial geostress was simulated based on the buried depth and fixed displacement method. As shown in Figure 4, the normal displacement constraints were applied to the edges of the model. Based on the strength test of the surrounding rock and the field geological  
Figure 3: Rockburst in drainage tunnel: (**a**) Tunnel boring machine (TBM) is buried and (**b**) 80 mm steel plate is damaged.  
Figure 2: Rockburst in auxiliary tunnels: (**a**) Extremely strong rockburst; (**b**) induced collapse and (**c**) support structure damage.  
survey data, the mechanical parameters of model were determined in Table 1. Mohr-Coulomb yield criterion was adopted for surrounding rock and support, that was the possible failure of the rock and support were considered in the calculation process.  
Surface water spraying and borehole water injection in surrounding rock are common methods for rock soften. However, the effect of these methods is closely related to the water absorption of surrounding rock. The surrounding rock of Jinping tunnels is mainly marble, which has a water absorption of less than 0.1%. There was a very limited role to play through surface water spraying and borehole water injection in surrounding rock in this project, therefore, it was not necessary to do more research. Based on the phenomenon of rockburst in auxiliary tunnels, it had the characteristics of fracture induction and dynamic impact damage, and this kind of rockburst had the uncertainty of the dynamic source (microseismic source) and damage location. It is advisable to take measures to release the energy of the microseismic source and make the high stress transfer to the deeper surrounding rock. In the strong rockburst area, this transfer could be achieved by active measures like stress release hole and advanced stress relief blasting.
## 4 Case Study
### Specifications  
The considered simulation scenario is composed of a small voltage network of an existing university campus in Portugal, represented in Figure 3. The considered microgrid has 21 buses, 20 loads, 20 photovoltaic systems, 4 wind generators, 7 storage systems, 2 capacitors, 300 electrical vehicles and 1 external supplier. In addition, this is a future scenario, relative to a typical day of the winter of 2050, based on a study presented in [70]. For demonstration purposes, this study contains the results of the network manager and only one of the loads.  
Figure 3: Case study Low Voltage distribution network. Image adapted from [70].  
The reference load in this study is Load 15. It is, in reality, a laboratory that aims to simulate the energy consumption of a house, illustrated in Figure 4, by monitoring the usage of several appliances and loads, namely HVAC, lights and sockets, among others. The simulated smart house has a bedroom, a hall, a bathroom, a kitchen and a living room. The remaining loads are simulated through agents, which use historical data of the energy consumption of the respective buildings of the campus.
## 3 Results  
### Physico-Chemical Parameters of Deep Groundwater  
Groundwater from the 37.9 m deep piezometer has been monitored since April 2018, and physico-chemical data have been collected monthly. These data (as well as data collected from other points) have been used to characterize the hydrogeological characteristics of the lake, which have been published in previous studies [34; 35; 36]. Basically, between April 2018 and November 2019 the average values for physico-chemical parameters of groundwater collected from the 39.8 m  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Gene**} & \multirow{2}{*}{**Primer**} & \multirow{2}{*}{**Nucleotide Sequence (5\({}^{\prime}\)\(\rightarrow\)3\({}^{\prime}\))**} & **Annealing** & \multirow{2}{*}{**Reference**} \\  & & & **Temperature (\({}^{\circ}\)C)** & \\ \hline \multirow{3}{*}{\(atzA\)} & atzA\_F & CCA TGT GAA CCA GAT CCT & & \\  & atzA\_R & TGA AGC GTC CAC ATT ACC & & 58 & [29] \\  & atzB\_F & TCA CCGG GGG ATG TCG GGC & & \\  & atzB\_R & CTC TCC CGC ATG GCA TCG GGG & & \\  & atzC\_F & GCT CAC ATG GAG CTA CTC CA & & \\  & atzC\_R & GTA CCA TAT CAC GGT TTG CCA & & \\  & atzD\_F & TCC CACC CTG ACA TCA ACA ACA AC & 60 & [30] \\  & atzD\_R & GGCG TCG GGT TTG ATG ATG & & \\  & atzE\_F & GAG CCT CT CTG TCC GTA GAT CG & 60 & [30] \\  & atzE\_R & GAT GGC GTG TAC GCT TTA CC & & \\  & atzF\_F & ACC AGC CCT TGA ATC ATC AG & & \\  & atzF\_R & TAT TGT CCC GAT ACC CAA CG & & \\  & trzN\_F & CAC CAG CAC CTG TAC GAA GG & & \\  & trzN\_R & GAT TCG AAC CAT TCC AAA CG & 57 & [31] \\  & trzD\_F & CCT CGC GTT CAA GGT CTA CT & & \\  & trzD\_R & TCG AAG GCA TA ATG CAT TG & 60 & [32] \\  & & 27F & AGA CIT TGA TCC TGG CTC AG & & \\
16S rRNA & 1492R & GGG TTA CCT TGT TAC GAC TT & 53 & [33] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sequence and conditions of primer pairs used in this study.  
deep piezometer were \(7.45\pm 0.16\) for pH, \(16.57\pm 2.17\,^{\circ}\)C for temperature, \(2.17\pm 1.00\) mg/L for dissolved oxygen, and \(23.22\pm 0.09\%\) for oxygen saturation, \(28.61\pm 38.85\) mV for Eh, \(2315.50\pm 172.02\)\(\mu\)S/cm for EC, and \(1436.44\pm 140.23\) mg/L for TDS. Concerning atrazine concentrations varied from below the detection limit up to \(224.43\) ng/L (July 2019). Thus, deep groundwater is circumvented, brackish, and near to reducing redox conditions.
## 5 Simulation Results and Analysis
### Algorithm Comparison and Analysis
#### 5.2.2 Comparison of Algorithms  
For the parameter calibration work in Section 5.2.1, this section compares and analyzes the three proposed algorithms. Taking configuration 4 of Table 5 as an example, three algorithms were used to optimize the energy flow between system components, obtaining the output curves of each component and the real-time battery decay curve of lithium-ion batteries. The comparison results are shown in Figures 12-14.  
Figure 12: Power curve comparison of three algorithms.  
Figure 13: The power discretization analysis of three algorithms.  
From Figures 12 and 13, it can be seen that rule-based algorithm prioritizes the charging of the ESS, which leads to repeated charging and discharging of a portion of the electricity, accelerating battery degradation and increasing system operating costs. Based on the GA, there is a slight improvement in this situation. However, based on the charging and discharging curve of lithium-ion battery in DP, it is proved that the ESS is in the constant current discharge stage in most stages and maintains constant current charging when the available charging capacity of the lithium-ion battery is insufficient. And from the power differentiation analysis, it can be seen that the charging and discharging rate of batteries based on DP is much lower than the other two algorithms, and the discharge is more frequent. This charging and discharging mode can fully reserve the available charging and discharging space of the battery, increase the redundant photovoltaic power generation it consumes, and have a small impact on the battery degradation. Meanwhile, when the system requires a continuous and large amount of charging or discharging, the ESS can make accurate responses. Based on the above conclusions, the various indicators of the three algorithms are compared, as shown in Table 6.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 3 Results
### Evaluation and Comparison
#### 3.2.1 Statistical Methods for Evaluation of the Synthetic Data
Across all datasets, the Conv1D-WGAN-GP model performs strongly, often achieving values well within the excellent thresholds (<0.05 for the mean and <0.1 for the std, as per Table 1). For instance, in the ERGAN dataset, it achieves a mean diff. of 0.0048 and a STD diff. of 0.0153, indicating near-perfect reproduction of average and variability. Similarly, low values are observed for Industrial (0.0124, 0.0084) and Pig Farm (0.0096, 0.0081), reinforcing the model's ability to preserve both the load level and its temporal spread, regardless of the dataset size. In contrast, models like Conv1D-GAN show elevated mean and standard deviation differences, with mean values reaching 0.1142 (Pig Farm) and STD differences as high as 0.0510, indicating a tendency to inject both bias and excessive variability, which may reduce realism. Meanwhile, Vanilla-GAN shows inconsistent behavior, occasionally maintaining a low mean but with suboptimal variability (e.g., Household STD of 0.0343--just at the tolerable edge). While the Conv1D-WGAN-GP model generally achieves the lowest Mean and STD differences across datasets, it is worth noting that in the Pig Farm dataset, the  
Figure 10: Comparison between the mean and STD metrics of the investigated algorithms using the ERGAN (**a**), Industrial (**b**), Household (**c**), and Pig Farm (**d**) datasets. Vanilla-GAN model achieves a slightly lower Mean diff. (0.0092 vs. 0.0096 for the Conv1D-WGAN-GP) and a reasonably close STD diff. (0.0161 vs. 0.0081). This indicates that Vanilla-GAN is capable of capturing central tendencies effectively, at least in simpler statistical terms. However, when viewed alongside other metrics, such as the higher Real-vs-Synthetic Distinguishability Score (0.5517 for Vanilla-GAN vs. 0.5034 for Conv1D-WGAN-GP) and less overlap in t-SNE visualizations, the Conv1D-WGAN-GP still demonstrates superior realism and generalization. Nonetheless, this highlights that no single metric should be used in isolation, and even simpler GANs like Vanilla-GAN can perform well in specific scenarios. Overall, the Conv1D-WGAN-GP stands out by consistently preserving both the average load and its natural fluctuation across diverse settings, which is essential for reliable downstream tasks such as load forecasting and anomaly detection. The JSD metric (Figure 11) offers insight into the overall similarity between the real and synthetic data distributions, with lower values indicating higher fidelity. Across the datasets, Conv1D-WGAN-GP consistently achieves the lowest JSD scores, such as 0.0062 (Industrial) and 0.0078 (Fig Farm), placing it firmly within the excellent range (<0.05). This suggests that the model effectively preserves the global statistical structure of the original data. In contrast, models like Conv1D-GAN often exhibit much higher JSD values, for example, 0.0565 (Fig Farm), indicating greater divergence and less realistic generation.
## 4 Simulation and Analysis
### Simulation Results
#### 4.2.1 Path I  
The reference trajectory of a circle was Equation (33), the initial status \(\xi_{start}\) was \(\left[0,0,0\right]^{\mathrm{T}}\), and the range of weight ratio \(q\) was \(q\in\big{(}\begin{array}{cc}13&50\end{array}\big{)}\). The simulation comparison was conducted between a traditional MPC (the weight ratio \(q\) is 25) and MPC based on ANFIS.  
\[\left\{\begin{array}{c}X=2.5*\sin(\frac{2*\mathrm{pi}t}{30})\\ Y=3.5-2.5*\cos(\frac{2*\mathrm{pi}t}{30})\\ \varphi=0.5\end{array}\right. \tag{33}\]  
As shown in Figures 9-14, after stabilization, the  
Figure 14: \(\Delta\varphi\) with disturbance.  
Figure 12: \(\Delta\varphi\) without disturbance.  
Figure 13: \(\Delta l\) with disturbance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\Delta l\) & \(\Delta\varphi\) \\ \hline traditional MPC & 6.887\% & 5.737\% \\ MPC based on ANFIS & 2.826\% & 1.555\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum fluctuation difference without disturbance.  
Compared with the traditional MPC without disturbance, MPC based on ANFIS reduced the maximum fluctuation difference of \(\Delta l\) by 58.966%, and the maximum fluctuation difference of \(\Delta\varphi\) was reduced by 72.895%. The stability of \(\Delta l\) increased by 67.668% and the stability of \(\Delta\varphi\) was increased by 61.369%. All the data showed that the error curve of the proposed algorithm was flatter and more stable compared with the traditional MPC without disturbance.  
The external disturbance applied to the \(X\) direction at \(t\) was 10 s, and the disturbance was 0.2 m. After stabilization, the results are shown in Tables 6 and 7.  
Compared with the traditional MPC, MPC based on ANFIS reduced the maximum fluctuation difference of \(\Delta l\) by 57.673%, and the maximum fluctuation difference of \(\Delta\varphi\) was reduced by 72.328%. The stability of \(\Delta l\) was increased by 66.109%, and the stability of \(\Delta\varphi\) increased by 59.899%. The error curve was much flatter and stabilized faster compared to the traditional MPC with disturbance. The experiment showed the effectiveness and superiority of the MPC based on ANFIS.
## 2 Internet of Vehicles and Smart Cities
### Architecture
#### 2.2.2 Four-Layer Architecture  
The four-layer IoV system architecture, presented by CISCO [16], is divided as follows:  
* _End-point layer_, which is responsible for the vehicles (and sensors), V2V communications, and software.
* _Infrastructure layer_, which handles all communication technologies used by entities.
* _Operation layer_, responsible for policy enforcement and flow-based management of network providers.
* _Service layer_ that handles the services offered by the cloud infrastructure that is connected to the network.  
Figure 1: V2X communication modes. In order: vehicle-to-vehicle (V2V), vehicle-to-sensor (V2S), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), vehicle-to-roadside unit (V2R).