esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
## 4 Experimental Results
### Calibration of the Panoramic Camera  
In this paper, the multi-camera system was fixed on an octagonal structure to ensure the constant relationships among cameras. A two-step method is conducted to calibrate the internal parameters of each camera and ROPs (relative orientation parameters). Firstly, more than 30 images of a high-precision chessboard were taken with each camera at different poses and positions, as shown in Figure 8. The inner orientation elements and the distortion coefficients of each camera were calibrated by [PERSON]'s calibration method [PERSON] _et al._ (2018). The interior orientation elements for each camera are shown in Table 1.  
Secondly, a professional three-dimensional calibrate field was used to calibrate the ROPs. Ten groups of images were taken at different poses and positions, where each group of images are captured at the same time, as shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Cam** & \(f_{x}\) & \(f_{y}\) & \(c_{x}\) & \(c_{y}\) & \(k_{1}\) & \(k_{2}\) \\ \hline
1 & 2.2782 & 2.4294 & 1.3259 & 0.9473 & 0.0281 & 0.0098 \\
2 & 2.2684 & 2.4194 & 1.1535 & 1.0435 & 0.0255 & 0.0036 \\
3 & 2.2729 & 2.4238 & 1.3974 & 1.0620 & 0.0257 & 0.0045 \\
4 & 2.2759 & 2.4256 & 1.2523 & 1.0469 & 0.0285 & \(-\)0.0028 \\
5 & 2.2601 & 2.4104 & 1.2447 & 9.4271 & 0.02718 & 0.0095 \\
6 & 2.2744 & 2.4254 & 1.1629 & 1.0865 & 0.0253 & 0.0013 \\
7 & 2.2761 & 2.4268 & 1.2567 & 1.0027 & 0.0253 & 0.0089 \\
8 & 2.2731 & 2.4244 & 1.5161 & 1.0635 & 0.0306 & \(-\)0.0094 \\ \hline \end{tabular}
\end{table}
Table 1: Inner orientation elements of each camera.  
Figure 8: Chessboard-images captured from one camera forcalibration.  
The external parameters of the \(i^{th}\) camera at the \(k^{th}\) exposure epoch were calibrated based on the traditional PnP method [33]. Let the first camera be the master camera, while the other seven cameras are slave cameras. Let \(R_{i,k}\) and \(t_{i,k}\) represent the rotation matrix and position of the \(i^{th}\) camera relative to the world coordinate system at the \(k\)th exposure epoch, where \(k\) = 1, 2, \(\ldots\), \(K\). In this experiment, the value of \(K\) is 10. The ROPs between the master camera and any slave camera can be computed by Equations (16) and (17). The optimum relative orientation parameters can be seen in Table 2.  
\[R^{\prime}_{i}=\underset{R^{\prime}_{i}\in SO_{3}}{\mathrm{argmin}}\sum_{k=0}^ {K}\left\|R^{\prime}_{i}-R_{1,k}(R_{i,k})^{T}\right\| \tag{16}\]  
\[t^{\prime}_{i}=\frac{1}{K}\sum_{k=0}^{K}(t_{i,k}-R_{1,k}t_{i,k}) \tag{17}\]
## 3 Results and Discussion
### Antifungal Tests
flavus_[46; 47], these results of AA.AgNPs and SD.AgNPs were considered as a similar effect. The silver nanoparticles of the American company MTI (Aladdin, WY, USA) could inhibit the fungus _A. flavus_ effectively at the concentration of 150-200 ppm [46]. Differently, silver nanoparticles Biopure-AgNP (NanoComposix, San Diego, CA, USA) could show their successful inhibition at 80 ppm [47]. Figure 6: The growing diameters of _Aspergillus niger_ (**a**), _Aspergillus flavus_ (**b**) and _Fusarium oxysporum_ (**c**) on various agar dishes including the PDA dishes (control sample), the PDA dishes containing AA.ext and SD.ext and the PDA dishes containing 20 ppm AA.AgNPs and SD.AgNPs. Figure 6c described the fungal diameters of _F. oxysporum_, this strain was also not impacted by AA.ext and SD.ext, which was proven by the same diameter growth between PDA and two extract dishes. When _F. oxysporum_ was treated with AA.AgNP20, AA.AgNP40, SD.AgNP20 and SD.AgNP40, its mycelium diameters were also decreased as a function of the silver nanoparticle concentration. In the study of [PERSON] et al., _F. oxysporum_ was treated with commercial AgNPs (Nano-Kolid Sp. z o. o, Warszawa, Poland), AgNPs showed the decrease of the mycelium zone significantly at 15, 25 and 35 ppm [48]. Through this value, the AA. AgNPs and SD. AgNPs could be said that they achieved a similar antifungal capacity with commercial AgNPs. When compared to two aforementioned strains, _F. oxysporum_ exhibited a weaker ability in contact with silver nanoparticles. After 96 h, _F. oxysporum_ diameters were 51.8 and 30.3 mm when contacting AA.AgNP20 and AA.AgNP40 and 48.7 and 34.2 mm when contacting SD.AgNP20 and SD.AgNP40. These values were significantly less than that of _A. flavus_ and _A. niger_ on similar media and conditions. Altogether, the biosynthesized silver nanoparticles of either AA.AgNPs or SD.AgNPs revealed their effectively antifungal ability against three strains of _A. flavus_, _F. oxysporum_ and _A. niger_. In addition, the strong points of plant extract-mediated silver nanoparticles were confirmed to inherently possess an antibacterial activity and superior biocompatibility [49] due to nontoxic and naturally reducing and stabilizing plant molecules. However, the overall biocompatibility of AgNPs was mainly governed by concentrations. Using the rat aortic ring model, green AgNPs of 50-300 ppm were demonstrated as an antiangiogenesis agents for biomedical applications [50]. [PERSON] et al. utilized green AgNPs to treat the infected rats through oral administration at the AgNPs concentration of 16 ppm [51]. There were no side effects caused by green AgNPs. Taken together, the biosynthesized AgNPs were considered as biocompatible agents at working doses.
# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 2 Triboelectric Nanogenerator  
### Working Mechanism of Triboelectric Nanogenerator  
The working principle of the TENG is that the coupled effect of the triboelectric effect and electrostatic induction converts biomechanical energy into electrical energy [53]. In essence, the triboelectric effect refers to the phenomenon that when materials with different electronegativities come into contact, electron transfer occurs, resulting in both materials carrying equal amounts of opposite charges [54]. Electrostatic induction occurs when a charged object approaches a conductor. The attractive and repulsive electrostatic forces  
Figure 1: Applications of self-powered sensors based on TENGs in the fields of gait monitoring include basic gait information monitoring, abnormal gait detection and fall monitoring, rehabilitation assessment and assisted training, as well as identity recognition.  
among charges induce the redistribution of charges inside the conductor [55]. During the contact process of two materials with different electronegativities, electron transfer takes place due to the triboelectric effect. After the two materials are separated, electrostatic induction generates an induced potential difference between the materials and the electrodes, which in turn prompts the flow of electrons between the external circuit and the electrodes [56; 57; 58; 59; 60]. Therefore, during the periodic contact and separation of materials with different electronegativities, the TENG produces corresponding alternating current signals.
# Key Distribution Scheme for Optical Fiber Channel Based on SNR Feature Measurement
###### Abstract  
With the increase in the popularity of cloud computing and big data applications, the amount of sensitive data transmitted through optical networks has increased dramatically. Furthermore, optical transmission systems face various security risks at the physical level. We propose a novel key distribution scheme based on signal-to-noise ratio (SNR) measurements to extract the fingerprint of the fiber channel and improve the physical level of security. The SNR varies with time because the fiber channel is affected by many physical characteristics, such as dispersion, polarization, scattering, and amplifier noise. The extracted SNR of the optical fiber channel can be used as the basis of key generation. [PERSON] and [PERSON] can obtain channel characteristics by measuring the SNR of the optical fiber channel and generate the consistent key by quantization coding. The security and consistency of the key are guaranteed by the randomness and reciprocity of the channel. The simulation results show that the key generation rate (KGR) can reach 25 kbps, the key consistency rate (KCR) can reach 98% after key post-processing, and the error probability of [PERSON]'s key is \(\sim\)50%. In the proposed scheme, the equipment used is simple and compatible with existing optic fiber links.  
key distribution; signal-to-noise ratio; reciprocity; key consistency rate +
Footnote †: journal: _photonics_  
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Footnote †: journal: _photonics_  
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Footnote 0: _E-mail:_
## 6 The Stability Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Figure** & \(\omega_{1}\) & \(\omega_{2}\) & **Stable Area** & **Unstable Area** \\ \hline Figures 26–28 & 3.316 & 3.131 & \(-0.5<\sigma_{2}<-0.06\) & \(-0.06<\sigma_{2}<0.5\) \\ \hline Figures 26–28 & 3.354 & 3.131 & \(-0.5<\sigma_{2}<-0.04\) & \(-0.04<\sigma_{2}<0.5\) \\ \hline Figures 26–28 & 3.391 & 3.131 & \(-0.5<\sigma_{2}<-0.03\) & \(-0.03<\sigma_{2}<0.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stability and instability area when \(\omega_{1}(=\)3.316, 3.354, 3.391) at \(\omega_{2}=\) 3.131. Figure 32: Time histories of \(u_{j}\) (\(j=1,2,3\)), \(v_{j}\) and the projection of phase planes tracks \(u_{j}v_{j}\) when \(c_{2}=0.02\), \(c_{3}=0.2\), \(\omega_{1}=3.354\), and \(\omega_{2}=3.131\): (**–**i**) at \(c_{1}\)(=\(0.01,0.015,0.02\)). Figure 33: Time histories of \(u_{j}\) (\(j=1,2,3\)), \(v_{j}\), and the projection of phase planes tracks \(u_{j}v_{j}\) when \(c_{1}=0.01\), \(c_{3}=0.2\), \(\omega_{1}=3.354\), and \(\omega_{2}=3.131\): (**a–i**) at \(c_{2}\)(=\(0.018,0.021,0.024\)). Figure 35: Time histories of \(u_{j}\ (j=1,2,3)\), \(v_{j}\), and the projection of phase planes tracks \(u_{j}v_{j}\) when \(\omega_{2}=3.131\), \(c_{1}=0.01\), \(c_{2}=0.02\), and \(c_{3}=0.2\): **(a–i)** at \(\omega_{1}(=3.316,3.354,3.391)\). The good effectiveness of the change of the frequency parameters \(\omega_{1}\) and \(\omega_{2}\) on the dynamical behavior of considered system (33) has been shown in parts of Figures 35 and 36. Time histories curves of the new parameters \(u_{j}\ (j=1,2,3)\) and \(v_{j}\) are plotted in parts (a), (d), and (g) and (b), (e), and (h), respectively. Whereas the plane curves \(u_{j}v_{j}\) have been drawn in parts (c), (f), and (i) of Figures 35 and 36. It is noted that these waves have been impacted with the various values of the frequency parameters, in which decay curves of the time histories are obtained and spiral ones of the phase plane toward one single point are plotted, indicating that the motion is smooth, steady and without disorder.
# Western Mediterranean Precipitation Extremes, the Result of Quasi-Resonant Sea-Atmosphere Feedbacks
## 2 Materials and Methods
### Coherence and Phase of Rainfall and SST Anomalies According to Harmonic Modes  
The region most frequently affected by the western Mediterranean EREs is located in the southeast of France (region in blue in Figure 1). It results from upper-level flow anomalies in the form of troughs and cut-offs and low-level flows that are subject to orographic uplift, that is the Cevennes to the north, the Pyrenees to the west, and the Alps to the East [29].  
The precipitation \(P_{m}\) averaged over the region representative of the most frequent EREs is used as a temporal reference from 2000 to 2021 in the expression of the coherence  
Figure 1: Precipitation height is averaged over the blue region covering southeastern France and the Mediterranean off the coast, while SST anomalies are averaged over the red regions 1, 2, 4, 6 extending from west to east Mediterranean, 3 (Adriatic Sea), 5 (Aegean Sea), 7, 8 (west and east Black Sea), 9 (Baltic Sea), 10 (North Sea), 11, 12 (Atlantic facing France with English Channel and Atlantic facing Spain and North Africa).  
and phase of the pairs \((P_{m},\text{SSTA}_{i})\), where \(\text{SSTA}_{i}\) is the average SST anomalies over the region \(i\). The possible causal relationships between the average precipitation observed in this region of interest and the average SST anomalies in the 12 marine regions are established from the coherence and the phase of the pairs considered one by one (Figure 2). We can then consider that the concomitance of SST and precipitation anomalies reveals a causal relationship with a confidence level of the coherence higher than 95% and the phase included between 0 and 2 days (Figure 2c). In the opposite case, any phase shift reflects the independence of the phenomena.
## 3 Numerical Applications
### Numerical Results  
In this section, the main outcomes obtained by applying the method to the responses of the nonlinear numerical models are presented. In Appendix A, Tables A1-A5 present the values of fundamental mode shapes, related mode curvatures and curvature differences among floors evaluated in the reference time instant immediately before the earthquake (A) and in the time instant of minimum fundamental frequency (B). Furthermore, the values of curvature differences between the two-time instants and Inter-Story Drift retrieved from the nonlinear numerical analyses performed in SAP2000 are shown.  
The curvature difference between the two time-instants (B) and (A), and the inter-story drift, are plotted from Figures 9-13 for all the numerical models.  
Figure 8: Response spectra related to the two artificial accelerograms (A2 and A4, representative of soil class D) used within the numerical analyses.  
Figure 9: Structural damage localization for the numerical models. Results of the 3-story regular in plan structure: (**a**) curvature difference between the selected time-instants B and A; (**b**) inter-story drift and damage threshold equal to 0.5% (for non-structural components) valid for reinforced concrete framed structures.  
Figure 11: Structural damage localization on numerical models. Results of the 5-story regular in plan structure with infill panels: (**a**) curvature difference between the selected time-instants B and A; (**b**) inter-story drift and damage threshold equal to 0.5% (for non-structural components) valid for reinforced concrete framed structures.  
Figure 12: Structural damage localization for the numerical models. Results of the 8-story regular in plan structure: (**a**) curvature difference between the selected time-instants B and A; (**b**) inter-story drift and damage threshold equal to 0.5% (for non-structural components) valid for reinforced concrete framed structures.  
Figure 10: Structural damage localization on numerical models. Results of the regular in plan \(5\)-story structure: (**a**) curvature difference between the selected time-instants B and A; (**b**) inter-story drift and damage threshold equal to 0.5% (for non-structural components) valid for reinforced concrete framed structures.  
To verify the presence of damage along the height of the structures the inter-story drift has been considered as damage indicators in order to validate the proposed method. Table 2 shows the values of drift related to the different performance levels [45]. Figures 8-12 show the inter-story drift equal to 0.5%, representing a threshold for non-structural components (such as the infilled panels). Above this threshold, the monitored structure changes its damage mechanism with a change in the maximum curvature difference and maximum inter-story drift relationship.  
Analyzing the results obtained from the numerical campaign, it can be observed that the mode curvature difference B-A is able to localize structural damage and identify the most damaged floors after strong motion earthquakes.
# Pressure-Reducing Design of 3D-Printed Diabetic Shoe Midsole Utilizing Auxetic Lattice Structure
## 5 Conclusions  
This study investigated the feasibility of utilizing auxetic structures in shoe midsoles fabricated via 3D printing technology to reduce plantar pressure in diabetic patients. The outcomes from plantar pressure testing systems and FEA indicate that in both walking and running scenarios, auxetic midsoles exhibit superior pressure reduction and conformability characteristics compared to non-auxetic midsoles, with the auxetic 60\({}^{\circ}\) configuration displaying the most effective plantar pressure reduction. Thus, this study suggests that the auxetic midsole (A60) could potentially mitigate plantar pressure loads and reduce ulceration risk in diabetic patients. However, long-term empirical experiments are necessary for further validation and to offer more insights into applying auxetic structures in footwear design. Furthermore, future research will investigate the efficacy of therapeutic shoes that combine customized insoles with auxetic midsoles to address diabetic foot issues.  
Figure 9: Differences in the lattice structures of the pressure centers in the (**a**) A60 shoe midsole and (**b**) N90 shoe midsole.  
**Author Contributions:** Conceptualization, J.Z. and Y.L.; Methodology, J.Z. and Y.L.; Software, J.Z., Y.W., X.Y. and W.F.; Validation, Y.L., Y.W., X.Y. and W.F.; Formal analysis, J.Z. and W.F.; Investigation, Y.L. and W.F.; Resources, Y.W. and X.Y.; Data curation, Y.W. and W.F.; Writing--original draft, J.Z. and Y.L.; Writing--review & editing, S.L. and J.Z.; Visualization, X.Y. and W.F.; Supervision, S.L. and J.Z.; Project administration, S.L. and J.Z.; Funding acquisition, S.L. and J.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is funded by 2022 Educational Science Planning Project (grant number: 2022 GXJK184).  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Scientific Research Management Department at Guangdong University of Technology.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors gratefully acknowledge the support related to material analysis techniques and 3D printing equipment provided by the Instrumental Analysis Center and the Experimental Center of the School of Art and Design at Guangdong University of Technology.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could appear to have influenced the work reported in this paper.
## 3 Results and Discussion
### Modeling Study  
#### 3.2.1 The Kinetic Model  
The results of the kinetic model in Figure 3 revealed an increase in \(k_{a}\) and \(k_{d}\), along with a decrease in \(\sigma_{m}\), with increasing filtration velocity. Moreover, a good fit to the measured results was obtained for each filtration velocity, with a correlation coefficient (\(r^{2}\)) of 0.98\(-\)0.99, thus suggesting that the kinetic model is useful for describing the overall performance of the system. In detail, the \(k_{a}\) values are seen to be 3.16 \(\times\) 10\({}^{-5}\), 4.09 \(\times\) 10\({}^{-5}\), and 7.93 \(\times\) 10\({}^{-5}\) h\({}^{-1}\), with corresponding \(k_{d}\) values of 0.07 \(\times\) 10\({}^{-5}\), 6.03 \(\times\) 10\({}^{-5}\), and 29.18 \(\times\) 10\({}^{-5}\) h\({}^{-1}\), and \(\sigma_{max}\) values of 901.20, 428.95, and 306.81 kg/m\({}^{3}\), at filtration velocities of 20, 30, and 40 m/h, respectively. The decrease in \(\sigma_{m}\) is attributed to an increased probability of detachment of the accumulated SS from the media with an increasing amount of accumulated SS and with increasing filtration velocity, the latter leading to higher shear stress and hydraulic gradient [37]. This conclusion is supported by the significantly greater increase in \(k_{d}\) relative to \(k_{a}\), with increasing filtration velocity.  
Meanwhile, the values of \(k_{a}\) and \(k_{d}\) were 1.423-3.983 and 2.88 h\({}^{-1}\), respectively, for the fibrous media with the fiber diameters of 1-26 um and with the porosity of 0.67-0.85 when the SS of 5 um was introduced [37]. This indicates that the SS accumulated on the medium in this study was more subjected to detachment, probably due to higher filtration velocity.
# COMS Visible Channel Calibration Using Moon Observation Data
## Appendix A  
### Sensitivity Test  
The lunar reflectance phase curve is generally known to be a smooth function with any wavelength. Small variations were seen in the model results, which were a result of the effects of lunar librations; a corresponding structure was expected and observed in measured reflectance. However, measurement results varied far more than what can be accounted for by libration contribution. Therefore, the observed scatter must have originated with the irradiance measurements, as calculated using (1). Several potential sources of this scatter have been identified and are described here.  
A sensitivity test was conducted to determine on-Moon pixels and to determine the threshold for Moon pixel selection for use in calculating Moon-observed irradiance (I\({}_{\text{Instrument}}\)) for the visible channel using COMS MI Moon observation data. It was also used to determine the optimum Moon image size and the pixels used in lunar calibration from those in the entire observation area.
## 4 Optimization Method
### Case Study Descriptions and Simulation Results
#### 5.1.3 Case C: Probabilistic Multi-Objective RPP Considering the Load Demand Uncertainty

Table 9: The specifications of the system loading for probabilistic multi-objective RPP considering the load demand uncertainty. The optimal values of the control variables for the BSC among the different load scenarios are given in Table 11. Having checked this table closely, it is noticed that some VAR sources have a value of zero. Consequently, the variable cost of those VAR sources is equal to zero. However, those VAR sources are allocated, and their fixed VAR installation costs are considered for the planning studies in this paper. It is clear from Figure 2 that the voltage magnitude of the load buses remains in the range of 0.95 p.u. and 1.05 p.u. for all three load scenarios. Although the loadability factor is improved, the voltage stability index of the system is ensured from the voltage magnitude point of view. Therefore, by making an allowance for the load demand uncertainty, the obtained total VAR cost seems to be more realistic. In the same way, the voltage stability index and the loadability factor are more reliable due to including more scenarios for the planning horizon. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control Variable** & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & **Expected Value** \\ \hline \(V_{g1}\) (p.u.) & 1.0621 & 1.0632 & 1.0640 & 1.0632 \\ \(V_{g2}\) (p.u.) & 1.0530 & 1.0532 & 1.0533 & 1.0532 \\ \(V_{g5}\) (p.u.) & 1.0749 & 1.0783 & 1.0794 & 1.0780 \\ \(V_{g8}\) (p.u.) & 1.0550 & 1.0522 & 1.0514 & 1.0524 \\ \(V_{g11}\) (p.u.) & 1.1000 & 1.1000 & 1.1000 & 1.1000 \\ \(V_{g13}\) (p.u.) & 1.0828 & 1.0903 & 1.0927 & 1.0898 \\ \(t_{6-9}\) (p.u.) & 1.0285 & 1.0249 & 1.0234 & 1.0251 \\ \(t_{6-10}\) (p.u.) & 0.9000 & 0.9000 & 0.9000 & 0.9000 \\ \(t_{4-12}\) (p.u.) & 1.0036 & 1.0143 & 1.0171 & 1.0135 \\ \(t_{28-27}\) (p.u.) & 0.9721 & 0.9500 & 0.9492 & 0.9521 \\ \(Q_{224}\) (MVAR) & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ \(Q_{225}\) (MVAR) & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ \(Q_{226}\) (MVAR) & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ \(Q_{29}\) (MVAR) & 1.8307 & 0.0000 & 0.0000 & 0.1831 \\ \(Q_{230}\) (MVAR) & 3.1448 & 0.0000 & 0.1993 & 0.3344 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The optimal values of the control variables among the load scenarios for probabilistic multi-objective RPP considering the load demand uncertainty. Figure 2: Voltage profile of the load buses for probabilistic multi-objective RPP considering the load demand uncertainty.