esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Correlation between Roof Displacement and Temperature Changes  
The roof displacement time series in the north-south and east-west directions and the roof temperature changes observed by the weather station were selected to compare and analyze their time series changes for determining the correlation between the displacement and temperature changes at the roof of the WGC. Given that the temperature data were collected from 12:40 on 5 July 2017 to 09:40 on 7 July 2017, the overlapping period data of the displacement time series derived by GB-RAR and temperature data were selected for comparative analysis. Figure 12 shows the comparison results between the temperature and displacement changes in the north-south and east-west directions during the study period.  
Figure 11: Displacement trajectory of the roof (from 00:00 to 24:00 on 6 July 2017).  
Figure 10: Displacement trajectory of the roof (from 5 July 2017 to 7 July 2017).  
As illustrated in Figure 12, the temperature ranged from 26.8 \({}^{\circ}\)C to 53.4 \({}^{\circ}\)C during the study period, and the temperature difference reached 26.6 \({}^{\circ}\)C. The temperature was relatively stable from 21:00 to 6:00 at night and reached the maximum value of the day around 15:00 every afternoon. The maximum displacement amplitudes of the roof monitored using GB-RAR were 18.84 and 15.94 mm in the north-south and east-west directions, respectively. We conducted correlation analysis to analyze the time series between displacement and temperature changes for determining their correlation. The correlation coefficient between the displacement changes in the north-south direction and the temperature changes is \(-0.56\), and that between the displacement changes in the east-west direction and the temperature changes is \(-0.65\). A certain negative correlation is found between the temperature and displacement changes in the two directions, and the correlation between the displacement changes in the east-west direction and the temperature changes is strong. Additionally, the correlation coefficient between the displacement changes in the north-south and east-west directions is 0.84, indicating the displacement changes between the north-south and east-west directions show a relatively strong correlation.  
Although the temperature changes have a certain influence on the displacement changes at the top of the super high-rise building, the construction vibration of the large tower crane on the top of the building, wind load, and other factors also have a great influence on the displacement changes at the top of the building because the WGC is under the construction stage. The displacement changes at the top of the building are caused by the joint action of temperature, construction vibration, wind load, and other factors. Therefore, a certain correlation is found between the temperature and displacement changes.
## 2 Design of sectionalizing Devices
### Criterion of Sensitivity Limits against Single-Phase SC  
The main criterion that should be used for determining the maximum distance at which it is possible to install SU is the criterion for ensuring the protection of power transmission lines from single-phase short circuits. This subsection is devoted to a short description of this criterion; a detailed description is presented in the work [38].  
According to this criterion, the length of power transmission line is compared with its maximum length, at which the sensitivity of a protective device to single-phase SC is ensured. The maximum length of transmission line \(L_{max.sens.}\) (km), at which the protection sensitivity is provided, is determined by:  
\[L_{max.sens.} = \frac{\frac{U_{Ph.}}{I_{sens.}}-\frac{Z_{T}}{3}}{\sqrt{R_{0 Ph}{}^ {2}+2{\cdot}R_{0 Ph}{\cdot}R_{0N}+R_{0N}{}^{2}+4X_{0 phN}{}^{2}}}, \tag{1}\]  
where  
\(U_{Ph}\) is phase voltage, V;  
\(Z_{T}\) is impedance of a transformer feeding the PTL, Ohm;  
\(R_{0 Ph}\) is specific active resistance of the phase wire, Ohm/km;  
\(R_{0N}\) is specific active resistance of the neutral wire, Ohm/km;  
\(X_{0 PhN}\) is specific inductive resistance of the phase-zero loop, Ohm/km;  
and \(I_{sens.}\) is current according to the sensitivity condition, A.  
If the length of PTL turns out to be higher than the allowed level, it is necessary to install SU at a distance less than the maximum level. This criterion must be considered when choosing places for SU allocation in a power transmission line, since it limits the choice of places according to other criteria.
## 4 Numerical Simulation Results and Analysis  
### Verification of Simulated Temperature of Grain Pile under Different Vertical Pressures  
To verify the accuracy of the numerical simulation and parameter values, the simulation data and experiment data under the same conditions were compared and analyzed. Figure 6 shows the comparison between the simulated and experimental values of the bulk  
Figure 4: Temperature difference of middle plane and near low-temperature boundary wall under different pressures.  
Figure 5: Temperature gradient of bulk grain pile under different pressures.  
grain pile under different pressures at the height of 300 mm on the middle vertical surface of the test chamber. The simulated value and test value are essentially the same under different pressures. However, there are differences in the temperature of the grain pile measured at the same instant. The maximum temperature difference is 1.45 \({}^{\circ}\)C, and the relative error is 4.9%. This error is attributed to the difficulty in realizing the absolute heat insulation boundary condition in the test. There is a particular amount of heat exchange between the upper and lower ends of the temperature control boundary of the test device and the environment. In numerical simulation, the boundary conditions and parameters can be set to achieve an ideal heat insulation that makes the simulation value of the high-temperature boundary wall higher than the experimental value, and the simulation value of low-temperature boundary wall lower than the experimental value. In general, it is clear that there was a high agreement between the simulation results and the experimental data. In all cases, the coefficients of determination (R\({}^{2}\)) between the experimental and simulated temperatures were >0.95, as shown in Figure 6, indicating the good performance of the model in predicting temperature profiles of grain pile under different vertical pressures. Overall, the numerical results appropriately reflect the experimental law, thereby confirming the effectiveness and credibility of the numerical simulation method.
## 2 Augmentation of Li-Fi Wireless DMT Transmission Capacity Through Sparse Coded Mask Optimization
### Impact of the Masking Ratio on the Channel Capacity
#### 2.2.4 Additional Factors Influencing Masking Ratio  
* EVM Threshold
* As shown in our experimental plots, once \(r\) exceeds a certain level, the reconstruction's accuracy degrades, pushing EVM beyond forward error correction thresholds.
* Thus, there is an optimal or near-optimal range for \(r\) (around 75-85% in our testbed) where capacity is maximized while maintaining acceptable symbol quality.
* Number of Patches, Positional Encoding Overhead
* We also vary the number/size of patches (Figure 5 in the manuscript) and the batch size (Figure 6). These parameters can shift how effectively the model learns global vs. local structures.
* If patches are too large, we lose the fine-grained advantage of self-attention. If they are too small, overhead and complexity rise.
* Complex Modulation Formats
* Surprisingly, for 16-QAM (a more complex modulation format), we observed better reconstruction performance at higher masking ratios compared to QPSK. This is due to the presence of more detailed signal features.
* Future expansions can consider advanced modulation orders or other waveforms, further exploring how the masking ratio and overhead interplay to affect net capacity gains.  
In summary, the masked autoencoder technique enables true time-domain compression: a fraction \(r\) of the original signal is omitted, and in principle, we could fit \(\frac{1}{1-r}\) times more transmissions in the same interval. Practically, overhead, reconstruction fidelity, and EVM constraints diminish the raw gain factor, but we still achieve up to a 1.85\(\times\) capacity increase at an 85% masking ratio in a real Li-Fi environment. We will update our manuscript to provide these additional derivations and highlight the interplay between theoretical capacity improvements and practical constraints.  
For your information, Feedforward Neural Networks (FNNs) and Convolutional Neural Networks (CNNs) techniques can be used as an alternative to MAE techniques. However, they may exhibit the following limitations. FNNs process data in a fully connected manner; however, they do not effectively capture the sequential dependenciesbetween DMT patches. While FNNs could be used for final-stage regression tasks, they are not ideal for reconstructing missing patches. CNNs, on the other hand, are effective for processing spatially correlated data, and a 1-D CNN could be employed to reconstruct missing patches by convolving over-known adjacent patches. However, CNNs lack the ability to model long-range dependencies, which is crucial for accurately reconstructing masked patches over an extended DMT sequence. In contrast, the transformer-based MAE leverages self-attention mechanisms to capture long-range dependencies between masked and unmasked patches. Unlike CNNs, which focus on local information, MAE can infer missing patches using a global context, making it the most suitable choice for reconstructing masked DMT patches in this study, while CNNs and FNNs remain as potential alternatives.
## 3 Results and Discussion
### Limitations on the Use of the SE Margin of the Ria de Arosa  
#### 3.2.1 Coastal Flood Risk  
The analysis of coastal flood risk using the Flood Hazard Index (FHI) method allowed for the identification of areas with higher and lower risk based on different time scenarios. The scenarios corresponding to 25 (Xa) and 100 (Xb) years present the highest danger [52], and these will be considered in the final map (Figure 7A).  
Urban areas located along the coastal environments (El Grove, Arosa, Cambados, Villanueva de Arosa, or Portonovo) present the highest risk of a coastal flooding event. These areas involve a high population density, which is significantly greater than in the rest of the study area, with high danger values (e.g., the tombolo of La Lanzada or the mouth of the Umia River).
## 2 Results
### Bactericidal Activity of LRM-CFS against P. aeruginosa Persisters from Biofilm  
To obtain _P. aeruginosa_ persisters from biofilm in CF-relevant conditions, 48 h old biofilms of PaCF1 and PaCF4 grown in ASM were exposed to concentrations of ciprofloxacin much higher than the MIC value (MIC: 0.25 \(\upmu\)g/mL). To this aim, mature biofilms of both strains were incubated with ciprofloxacin at 16, 32, and 64 \(\upmu\)g/mL in ASM, and the biofilm-associated viable bacteria count was evaluated after 8 and 24 h of incubation. As shown in Figure 3a,b, ciprofloxacin at all three concentrations tested caused a significant decrease in the CFU number of both _P. aeruginosa_ strains after 8 h of incubation, but the number of surviving bacteria remained almost stable at 24 h. The biphasic killing kinetic was suggestive of the successful selection of ciprofloxacin-tolerant bacteria. The activity of LRM-CFS diluted at 1:4, 1:6, and 1:8 was then tested on bacteria that had been previously exposed to ciprofloxacin at 32 \(\upmu\)g/mL (128 x MIC) for 24 h in ASM.  
Figure 3: Kinetics of PaCF1 (**a**) and PaCF4 (**b**) persister selection from biofilm in ASM. Bacteria were exposed to ciprofloxacin at concentrations of 16 \(\upmu\)g/mL, 32 \(\upmu\)g/mL, and 64 \(\upmu\)g/mL, and their viability was assessed at 8 h and 24 h time points by CFU counts. Killing activity of LRM-CFS diluted at ratios of 1:4, 1:6, and 1:8 was evaluated against persisters of PaCF1 (**c**) and PaCF4 (**d**) after 24 h of incubation in ASM. In (**c,d**)—CTRL: persisters selected with ciprofloxacin 32 \(\upmu\)g/mL from biofilm and incubated with MRSB pH 4; ciprofloxacin represents persisters selected with ciprofloxacin at 32 \(\upmu\)g/mL and further incubated with ciprofloxacin 32 \(\upmu\)g/mL. Results are shown as mean \(\pm\) standard error of the mean values (\(n=4\)). Statistical significance was evaluated by two-way ANOVA for (**a,b**) and one-way ANOVA for (**c,d**), followed by the Tukey–Kramer post hoc test. * \(p\) < 0.05, **,** \(p\) < 0.01, **,** \(p\) < 0.001.  
When tested at the dilution of 1:4, LRM-CFS caused approximately 2-log and 1-log reductions in the CFU number of PaCF1 and PaCF4, respectively, compared to the control. At the same concentration, around 1-log reduction in the CFU number of both _P. aeruginosa_ strains was observed, as compared to ciprofloxacin-treated samples. LRM-CFS at the dilutions of 1:6 and 1:8 caused a decrease of 1-log in the PaCF1 CFU number compared to the control (Figure 3c), whereas they resulted in being inactive against the PaCF4 strain.
## 3 Spatial Enhanced Detection Algorithm  
The complex background seriously interferes with the signals of weak targets in the airspace. It is difficult to suppress the background effectively in the detection of low signal-to-clutter ratio targets in complex scenes, which tends to lead to false alarms, and too strong background suppression often leads to partial loss of the target signal. In this chapter, we examine methods to enhance the target signal while effectively suppressing the background. Based on the coupling characteristics of dim targets and complex environments, this chapter introduces an optical imaging mechanism into spatial-domain processing and combines the difference between target and background characteristics in the imaging process to build a single-frame processing method for signal enhancement and the background suppression of low signal-to-clutter ratio targets in complex scenes [23]. Inspired by the observation uncertainty caused by the spatial component changes in different elements in a space, this section proposes a Local Uncertainty Measurement based on the principle of component consistency to suppress complex scenes and detect small targets in complex backgrounds. On the basis of suppressing background clutter, the target signal is better retained and enhanced. The experimental results show that the proposed algorithm achieves a better detection performance in complex scenarios and has an excellent real-time performance.
## 3 Results and Discussion
### TEM Analysis  
The purpose of the TEM analysis was to look for possible subsurface defects, which might be formed during polishing [31] and could decrease the LIDT of the crystals. The TEM image of mechanically polished and plasma-unprocessed crystal (Figure 3a) clearly shows a nonhomogeneous subsurface damage layer which extends horizontally through the whole TEM image and reaches a maximum depth of \(\sim\)250-300 nm.  
Most of these defects within smaller depths were removed after a plasma etching of 100 nm (Figure 3b), but several accumulations were still present in the \(\sim\)100-300 nm depth range. Figure 3c shows a plasma-etched crystal to a 250 nm depth, which did not have any damaged subsurface. It is worth to mention that the thin dark band just beneath the surface was due to a possible curvature of FIB thinned lamella and the resulting increased scatter of the source electrons. In Figure 4a-c, the TEM images show closer views of the same samples, and Figure 4d additionally shows the plasma-etched sample to a 400 nm depth, which also did not exhibit any damaged layer.  
Figure 3: TEM images of (**a**) plasma-unprocessed crystal, (**b**) plasma-etched to 100 nm and (**c**) plasma-etched to a 250 nm depth. The biggest accumulations of subsurface defects are marked by red arrows.  
Several studies have revealed that ion bombardment might induce the amorphization of the subsurface of a crystalline material. For example, the exposure of a Si surface for 30 s to Ar\({}^{+}\) ions of 150 eV energies led to an amorphous subsurface layer with a thickness of 5 nm [32]. Modelling the impact of 3 \(\times\) 10\({}^{14}\) ion/cm\({}^{2}\) Ar\({}^{+}\) with 500 eV energy onto a Si surface demonstrated the formation of a damaged amorphous subsurface layer with a thickness of 3 nm [33]. It was also interesting to trace whether the presented oxygen plasma processing did not induce additional structural damages to the Co\({}^{2+}\):MgAl\({}_{2}\)O\({}_{4}\) crystal subsurface, especially for longer duration etchings to 250 nm and 400 nm depths. In Figure 5, a high-resolution TEM image and electron diffractogram (inset) show a crystalline cubic structure, which is present immediately below the surface of the plasma-unprocessed Co:MALO crystal.  
The crystallinity of the subsurface remained unchanged in all plasma-etched samples (Figure 5a-c).
## 2 Theoretical Background and Hypotheses Development
### Response (R): Continuance Usage Intention (CII)  
In this study, the continuance usage intention component represents the behavioral outcome shaped by users' cognitive and affective evaluations during their interactionswith MOCCA. Continuance usage intention refers to a user's intention to continue using a technology or system beyond the initial adoption phase, emphasizing the sustained use of the technology after the user has gained experience [88]. It reflects the extent to which users believe they will continue using MOCCA.  
As an emerging AI-based technology, social robots are being developed rapidly, offering intelligent services. However, AI-powered technologies often face trust-related challenges, as trust is particularly critical during the adoption stages and plays a key role in shaping users' perceptions of novel technologies [89; 90]. Since trust shapes individuals' willingness to rely on AI-driven systems, it serves as a crucial determinant of continuance usage intention in MOCCA. Previous studies suggest that trust plays a central role in influencing the continuous use of AI-driven robots [72; 90]. Trust is also essential for ensuring the long-term success and adoption of such technologies [91]. Users are more likely to continue engaging with MOCCA when they trust its reliable capabilities, ethical behavior, and security measures.  
Another important factor influencing continuance usage intention is the emotional connection users develop with MOCCA, facilitated by parasocial relationships in HRI. Prior studies have shown that when users perceive robots as relatable entities with human-like emotional traits and responses, they are more likely to participate in long-term interactions with the robot [92; 93; 94]. Parasocial interactions can encourage users to form stronger emotional bonds, which, in turn, increase their intention to continue using MOCCA.  
MOCCA's key features, such as its human-like traits, ability to act autonomously, emotional responsiveness, and privacy protections, help build trust and foster parasocial interactions. These features allow MOCCA to be perceived as a relatable, trustworthy, and dependable partner. As users continue to experience these features, they are more likely to maintain their trust and involvement with MOCCA, supporting their intention to continue using MOCCA.  
Thus, we propose the following hypotheses:  
**Hypothesis 10**: _Parasocial interaction has a positive effect on the continuance usage intention of MOCCA._  
**Hypothesis 11**: _Trust has a positive effect on the continuance usage intention of MOCCA._
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 2 Explicative Head-Mounted Fault Display System Design
### User Interface Design  
The user interface should be designed with a human-centered approach to encourage the maintenance operators to interact with computers in an intuitive, human-like manner. Then, the main objective is to create an enjoyable experience for both novice and expert users, making the interface feel natural to the user rather than being inherently \"natural\". In this context, the literature highlights several advanced human-machine interfaces (HMIs) for monitoring and controlling factory machinery. An illustrative example is shown in Figure 2. This interface employs various technologies to enhance human-machine interaction, thereby improving efficiency and user experience in industrial settings. Several key applications of these technologies can be identified. (i) Human-centered approach: utilizing touch, voice recognition, or natural language processing to create intuitive interfaces [28]. (ii) Multimodal interaction methods: combining touch, voice, and gesture for a seamless user experience [29; 30]. (iii) Adaptive systems: designing AI and machine learning-based systems that learn user preferences and behaviors to provide personalized interactions and predictive functionalities [31]. These technologies collectively enhance the usability, accessibility, and efficiency of human-machine interactions, making them more intuitive and effective for various applications [2; 32; 33].  
Figure 1: Real-time head-mounted fault system architecture [27].  
Figure 2: Example of the human–machine interface view both on the computer and the headset systems.  
In the last few years, one has witnessed an explosion in VR hardware and applications. VR experiences range from the mundane to the wondrous, their complexity and utility varying greatly [24; 27]. In this paper, one proposes to develop an xHMFD system, integrating an explainable decision-making system. Its primary function is remotely monitoring machines and generating health performance indicators, allowing managers to easily understand the system's processes and operations. Secondly, it supports the decision-making process regarding the type of maintenance required in specific situations and increases user interaction impact, the user's proficiency, and their ability to stay focused on the issue.  
To meet these requirements, the interface is designed to present information clearly and intuitively, enabling the operator to maintain the system easily and efficiently. This involves creating visual representations of data, such as 3D models of the wind turbine, text boxes and charts displaying performance measurements, and alerts highlighting potential issues. The system will integrate also some interactive elements that allow users to manipulate the display, zoom into specific components, and last but not least, access detailed explanations about an abnormal situation.
An analysis of inelastic neutron scattering measurements in the paraelectric phase at temperatures just above the transition has suggested that the incommensurate phase forms from the condensation of a pair of TO and TA phonon modes, leading to a structural instability that distorts the structure at the wavevector of these two modes [17]. The wavevector of the distortion depends only weakly on temperature over the stability range of the phase [17; 18]. Similarly to STO, the 1.76 eV bandgap [19] of SPSe freezes out electronic degrees of freedom from the phase transition under normal thermal conditions, so that the driving mechanism of the equilibrium transition involves predominantly anharmonic interactions between vibrational modes. Here we focus on the spatiotemporal evolution of the above bandgap photoinduced phase transition, and associated transport effects, which characterize the phase transition on a nanosecond timescale. Time-resolved X-ray diffraction gives direct access to the order parameter via the intensity of superlattice reflections, which are a direct result of a coupling of phonon modes. Based on the timescales of changes in the order parameter under different experimental conditions, we can narrow down whether the phase transition propagates as a strain wave, through ballistic or diffusive phonons, or another process. This helps to understand the energy flow in the system after strong photo-excitation.