esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Microstructural Characterization  
SEM analysis of PCL, PCL_45S5, PCL_BG10, and PCL_BGZn (Figure 2a-d) showed a homogeneous fiber diameter distribution. Some bioactive glass particles embedded in PCL matrix are observed in the insets in Figure 2b-d. The average fiber diameter was calculated by ImageJ [54] to verify the influence of bioactive glass particles on fiber diameter. The average fiber diameter was \(1.3\pm 0.4\)\(\upmu\)m for neat PCL; while \(1.0\pm 0.7\)\(\upmu\)m, \(1.2\pm 0.6\)\(\upmu\)m, and \(1.1\pm 0.7\)\(\upmu\)m were the average values for PCL_45S5, PCL_BG10, and PCL_BGZn fibers, respectively. These results demonstrate that bioactive glass particles did not influence the morphology of fibers in terms of average fiber diameter.  
Furthermore, to verify the presence of bioactive glass particles in the fibers, the composite electrospun mats were characterized by EDX (Figure 3b-d). Although EDX analysis is not a quantitative analysis, elements present in bioactive glasses such as Si, Ca, Na were detected confirming the incorporation of bioactive glass particles in PCL matrix. C element from the PCL matrix was also detected.  
Figure 2: SEM analysis of electrospun mats: PCL (**a**), PCL_45S5 (**b**), PCL_BG10 (**c**), and PCL_BGZn (**d**).  
Figure 3: EDX mapping of electrospun PCL (**a**) and composites PCL_45S55 (**b**), PCL_BG10 (**c**), PCL_BGZn (**d**) at d0 (before immersion in simulated body fluid (SBF)).  
FTIR analysis was performed to detect the characteristic bands of bioactive glass particles embedded in PCL fibers. Figure 4 showed the characteristic bands of PCL: (i) two bands at 2942 and 2867 cm\({}^{-1}\) ascribed to CH\({}_{2}\) asymmetric and symmetric stretching [65]; (ii) a band at 1722 cm\({}^{-1}\) correlated to carbonyl stretching [66]; (iii) a band at 1294 cm\({}^{-1}\) related to the C-O and C-C stretching [67]; and (iv) two bands at 1241 and 1164 cm\({}^{-1}\) ascribed to the asymmetric and symmetric C-O-C stretching, respectively [61; 65]. Unfortunately, the bands ascribed to stretching and bending of functional groups (i.e., Si-O-Si) of bioactive glass particles were not detected, since they overlap with the above mentioned bands. Such overlapping is highlighted in Figure 4; the different shape of the characteristic peak at 1164 cm\({}^{-1}\) of PCL in PCL/bioactive glass composites can be ascribed to the overlapping of Si-O-Si group of bioactive glasses [68]. This could also be related to the low amount of bioactive glass particles added to PCL starting solution (5%wt with respect to PCL).
# Research on Effective Design Methods of Core Beam of Full Bridge Aeroelastic Model
## 5 Practical Application  
Taking a suspension bridge with a main span of 920 m as a prototype, the whole optimization process of the core beams in the full bridge reduced-scale model is represented. The overall layout of the suspension bridge is shown in Figure 15, and the cross-section of the steel box girder is shown in Figure 16.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Moment** & **Target** & **Optimized** & & & & \\
**of Inertia** & **Values (mm4)** & **(mm4)** & **Error (\%)** & & & \\ \hline \(l_{z}\) & 79.05 & 79.34 & 0.37 & **geometric sizes** & & \\ \(l_{y}\) & 4518.74 & 4518.70 & 0.09 & & **(mm)** & 3.09 \\ \(l_{x}\) & 241.44 & 241.42 & 0.01 & & & 19.57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization results of genetic algorithm.  
Figure 14: Genetic algorithm optimization process.  
Figure 15: Layout of bridge span.  
Considering the limiting dimensions of the wind tunnel (length \(\times\) width \(\times\) height: 15 \(\times\) 8.5 \(\times\) 2 m), the geometric scale of the scaled model to the prototype is 1:121. In addition to the geometric similarity, the aeroelastic model and the real bridge should meet the similarity of mass distribution, stiffness and damping characteristics that determine the similarity of dynamic characteristics. Based on these similarity requirements, the similarity ratio of the bending and torsion stiffness with the prototype is 1:121\({}^{5}\), shown in Table 5. The main cable is designed based on the principles of similar stiffness, mass, and quasi-steady wind load. The hangers are tensile components, and the diameters of the hangers are determined by the principle of similar tensile stiffness. The full-bridge reduced-scale model is shown in Figure 17. The aeroelastic model adopts a combination of core beams enwrapped in \"clothing\", which simulate aerodynamic configurations. The core beams provide the required rigidity, and the clothing simulates the aerodynamic configurations.
## 3 Simulation Results and Sensitivity Analysis
### Influence of Backfill Compactness on the Pipe Under Traffic Load  
In these simulations, the magnitude and speed of the traffic load were set to 0.7 MPa and 60 km/h, respectively, as base criteria. The diameter and the hoop stiffness of the pipe were 600 mm and 4 kPa, respectively. The time history curves of the Von-Mises stresses of the crown, the springline, and the invert are given in Figure 10. When the compaction degree of the pipe bed and lateral backfill decreased, the Von-Mises stress of the pipe was less sensitive to the traffic load than to the earth pressure. Figure 10a illustrates that the crown Von-Mises stress decreased as the compaction degree in L1/L2 decreased or as the compaction degree in L3 increased. The time history curve of the springline Von-Mises stress was similar to that of the crown, but the springline stress value was higher than that of the crown (Figure 10b). In addition, the invert Von-Mises stress decreased significantly when the filling media at L1 was loose, while the invert stress increased as the compaction degree of L2 decreased or the compaction degree of L3 increased (Figure 10c).  
The reason for the above results might be that the energy generated by the earth pressure was absorbed because of the looseness of the base (L1); therefore, the earth pressure on the pipe was smaller, and the Von-Mises stress decreased. However, this does not mean that the loose base is  
Figure 10: Time history curves of Von-Mises stresses under traffic load: (**a**) the crown; (**b**) the springline; (**c**) the invert.  
Figure 9: Time history curves of the Von-Mises stresses of the different buried depth of pipe under traffic load.  
beneficial for the operation of the pipe. Poorly compacted bases have negative effects on flexible pipes in many aspects, such as bending [57], differential settlements [58], failures in the pipe joints, and, consequently, poor hydraulic performance [59]. These aspects lead to the leakage of sewage, which pollutes groundwater, shortens the service life of the pipe, and increases the operating costs. Therefore, the compaction degree of the base is fundamental in both the design phase and in the operating conditions of any sewer network and should be highlighted.
## 4 Security Analysis
### Security Requirements  
We analyze the registration phase as a key exchange protocol. The typical security requirements for mutual entity authentication schemes and key-related requirements are considered. We prove the following four properties:  
1. Authentication of parties. 1. Authentication of a new drone: it should not be possible for an adversary to act like a legitimate drone. 2. Authentication of the base station: adversaries should not be able to impersonate a legal base station. 3. Authentication of the other drones: adversaries should not be able to impersonate the other legal drones. 2. Secrecy of the key: during the key exchange, the newly generated key remains confidential, and an adversary should not have any information about the new key.  
Key freshness: a new, randomly chosen key should be exchanged during a protocol runs.
* Parties must confirm that the other party knows the new symmetric key and their ability to use it.
## 3 Results and Discussion  
A brown product was observed immediately upon mixing the aqueous copper(II) and AMT solutions. The electronic spectrum of the free ligand solution (\(1.2\times 10^{-4}\) mol L\({}^{-1}\)) recorded against deionized water as a blank showed two peaks at 254, and 376 nm assigned to \(\pi\)-\(\pi^{*}\) and \(n\rightarrow\pi^{*}\) transitions, respectively (Figure 1a). The peak at 376 nm shifted to 434 nm after reaction of the AMT reagent with copper(II) (Figure 1b-d). Figure 1 also demonstrates the increase in absorbance with increasing Cu\({}^{2+}\) concentration. Moreover, the electronic spectrum of Cu(II)-AMT complex recorded in solid state using the Nujol mulls method and shown in Supplementary Materials (Figure S3) exhibited a broad band at 630 nm corresponding to T\({}_{2\text{g}}\)(\({}^{2}\)D) \(\leftarrow\) E\({}_{\text{g}}\) in octahedral environment [30]. The effective magnetic moment (\(\mu_{\text{eff}}\)) of copper complex with AMT calculated from the Equation (1) was 1.82 B.M confirming the distorted octahedral symmetry of this complex.  
\[\mu_{\text{eff}}=2.828\ (\chi_{\text{M}}.\text{T})^{1/2} \tag{1}\]  
where, \(\chi_{\text{M}}\) is molar susceptibility modified using [PERSON]'s constants of all atoms diamagnetism, while T is absolute temperature.  
The stoichiometry of Cu(II)-AMT complex was estimated using the mole ratio method, wherein the absorbance at 434 nm was plotted versus the [AMT]/[Cu\({}^{2+}\)] ratio as shown in Figure 2. The results indicate that the AMT ligand and Cu\({}^{2+}\) reacted in a 1:1 ratio.  
Figure 1: Electronic spectra recorded in aqueous media of (**a**) 4-amino-3-mercapto-6-[2-(2-thienyl)vinyl]-1,2,4-triazin-5(4H)-one (AMT) ligand at concentration of \(1.2\times 10^{-4}\) mol L\({}^{-1}\) and following addition of the Cu(II)-AMT complex at Cu\({}^{2+}\) concentrations of (**b**) 0.70 \(\mu\)g/mL, (**c**) 3.50 \(\mu\)g/mL, and (**d**) 5.50 \(\mu\)g/mL.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 3 Experiments and Results
### Tracking Accuracy Analysis in Different Conditions
#### 3.3.2 Look-Ahead Distance  
From the process of pure pursuit model derivation, it can be seen that the look-ahead distance has a relatively large impact on trajectory tracking accuracy. For a more accurate evaluation of its impact, comparison experiments were conducted for look-ahead distances of 3 m, 4.5 m, and 6 m, the results of which can be seen in Figures 13 and 14. Figure 13 shows that the larger the look-ahead distance, the larger the error between the vehicle turning and the reference path, while a smaller look-ahead distance results in a smaller error. Figure 14 shows the magnitude of the error of the distance, including the maximum error of approximately 0.7 m at a look-ahead distance of 6 m, and the maximum error of just 0.3 m at a look-ahead distance of 3 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Speed (km/h)** & **X Error (m)** & **Y Error (m)** & **Total Error (m)** & **Mean Error (m)** \\ \hline \multirow{3}{*}{5} & \(-0.32\) & \(0.060\) & \(0.326\) & \multirow{3}{*}{0.260} \\  & \(0.145\) & \(-0.049\) & \(0.153\) & \\  & \(0.30\) & \(-0.027\) & \(0.301\) & \\ \hline \multirow{3}{*}{10} & \(0.15\) & \(-0.104\) & \(0.183\) & \multirow{3}{*}{0.383} \\  & \(-0.24\) & \(-0.088\) & \(0.256\) & \\  & \(-0.71\) & \(-0.043\) & \(0.711\) & \\ \hline \multirow{3}{*}{15} & \(0.70\) & \(-0.373\) & \(0.793\) & \multirow{3}{*}{0.505} \\  & \(-0.14\) & \(-0.229\) & \(0.269\) & \\ \cline{1-1}  & \(0.45\) & \(-0.040\) & \(0.452\) & \\ \hline \end{tabular}
\end{table}
Table 2: Stop position error.  
Figure 12: Tracking position error at different speeds.  
However, a smaller look-ahead distance is not necessarily better, and when the look-ahead distance is small to a certain value, vehicle control will appear unstable, with the specific performance of the phenomenon being that the steering wheel will quickly swing left and right until the system is out of control. With the experimental vehicle used in this study, the minimum look-ahead distance without the loss of control was found to be 2.45 m, which was obtained after repeated testing.
## 7 Robustness
### Evaluation Metrics and Baseline Performance
#### 7.2.1 COCO2017 Dataset: Resilience to JPEG Compression Attacks  
JPEG compression is one of the most ubiquitous transformations applied to digital images. To evaluate robustness, the proposed watermarking algorithm was tested on a range of compression levels using the COCO2017 dataset. As shown in Tables 5 and 6, which separately report the Normalised Correlation (NC) and bit error rate (BER), the watermark was consistently and perfectly recovered (NC = 1.0, BER = 0) at quality factors (QF) of 80, 70, and 60 across all tested image dimensions.  
Performance degradation becomes evident at QF 50 and lower, particularly in smaller images such as 335 \(\times\) 500 pixels, where both NC and BER begin to show significant deterioration. In contrast, images with dimensions near the standard 512 \(\times\) 512 benchmark, such as 500 \(\times\) 500 pixels, exhibited substantially greater resilience and maintained high NC values and low BER, even at reduced quality levels (QF 50 and 40).  
To improve clarity and reduce visual density, the results are divided into two separate tables. Table 5 presents the NC values, while Table 6 presents the corresponding BER values. This separation facilitates targeted performance interpretation across metrics.
# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 5 Conclusions  
In this paper, a method was developed to address the issue relating to the accurate registration of spectral bands in Zhuhai-1 satellite hyperspectral imagery. A full-spectrum registration method was proposed that combines the transference strategy for the affine transformation relationship between adjacent bands with the differential correction from dense Delaunay triangulation. This method uses the affine transformation relationship between adjacent bands to establish the same relationship between non-adjacent bands. This method was used to conduct registration of non-adjacent bands with large gray variations. Moreover, dense Delaunay triangulation was constructed to establish multiple affine transformation relations for triangular differential correction and to eliminate the influence of local distortion on registration accuracy caused by terrain undulation. Finally, an experiment was conducted using three Zhuhai-1 satellite hyperspectral images covering different topographies.  
Figure 13: Similarity measures for the registered spectral bands. (**a**) OHS–Arizona, USA; (**b**) OHS–Guangxi, China; (**c**) OHS–Xinjiang, China.  
The experiment showed that the proposed method could obtain accurate and robust registration results. The registration accuracy between adjacent spectral bands ranged from 0.2 to 0.6 pixels. The registration accuracy for non-adjacent bands reached a sub-pixel level, and the resulting structural and cosine similarity measures were both greater than 0.80.  
Conceptualization, J.Y.; methodology, J.W.; software, J.M.; validation, S.F. and Q.Z.; formal analysis, J.M.; investigation, Q.L.; resources, J.M.; data curation, J.M.; writing--original draft preparation, J.M.; writing--review and editing, L.L.; visualization, S.F.; supervision, L.L.; project administration, J.W.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA19090107); the National Key Research and Development Program of China (No. 2017 YFE0100800); the Local Innovative and Research Teams Project of Guangdong Pearl River Talent Program (No. 2017 BT01G115); and the Zhuhai Innovation and Entrepreneurship Team Introduction Program (No. ZH0405-1900-01 PWC).  
The authors appreciate the comments and suggestions from the editors and reviewers.  
The authors declare no conflict of interest.  
The codes of the proposed registration method are available from the corresponding author upon reasonable request.
## 7 Performance Evaluation
### Performance Evaluation of SeFLOOD
It follows that _NoM_ is \(\approx 330\%\) in the dense scenario and \(\approx 200\%\) in the sparse scenario. As to _NoB_, we have first to observe that the size of the CKDP messages is fixed whereas the size of FLOOD is variable. As argued above, the CKDP messages are 64 and 192 bit in size. In contrast, a FLOOD discovery message has a minimum size of 200-400 bits [31]. Furthermore, as it carries both list of the nodes through which the message has propagated and the link quality report, the message length increases with the message propagation (Section 6.1.1). By simulations, we have evaluated that the average size of a FLOOD discovery message is about 1 Kbyte in both scenarios. It follows that CKDP messages tend to be much smaller than FLOOD discovery messages. From these considerations, it turns out that although the CKDP protocol causes additional messages, the resulting overall additional traffic may be negligible with respect to the FLOOD discovery traffic. In details, FLOOD sends 144,580 bits in the dense scenario and 88,856 bits in the sparse one, while CKDP sends 9,792 bits in the dense scenario and 4,032 bits in the sparse one. Therefore, _NoB_ is \(\approx 6\%\) in the dense scenario and \(\approx 4\%\) in the sparse one. It turns out that while the additional round of messages required by CKDP causes the discovery phase in SeFLOOD to get longer than FLOOD, the additional SeFLOOD traffic results instead practically negligible with respect to the overall FLOOD traffic. Such a negligible traffic also makes the additional SeFLOOD energy overhead practically negligible with respect to the overall FLOOD energy overhead. As to reconfiguration phase, SeFLOOD does not introduce any additional message with respect to FLOOD. SeFLOOD introduces some overhead in terms of _NoB_ because they convey authenticators for integrity and proof of origin. Such an overhead is at most \(1\%\) (see Figure 14). Figure 14: Discovery Bits, Add Bits and Del Bits in Sparse and Dense Scenarios. Figure 13: Discovery Messages in Sparse and Dense Scenarios. However, it is worthwhile to notice that CKDP assumes a random deployment according to which a node cannot know its neighbourhood until deployment is completed. However, and underwater acoustic network is often employed in mission-oriented systems such as the surveillance system considered in the UAN project. In such systems, nodes are initially located in predefined locations. Therefore, a node knows in advance its _potential_ neighbours. Of course, depending on the sea conditions, a potential neighbour becomes an _actual_ one if it can actually communicate with the node. If a node knows its _potential_ neighbourhood in advance, then the first phase of CKDP, namely the discovery phase, can be completely avoided. Then, during the key distribution phase, the node is able to recognize its _effective_ neighbors. This allows SeFLOOD to reduce the number of sent discovery messages and bits and makes the protocol faster.
# An Experimental Study of Glycerol Carbonate Synthesis over g-C\({}_{3}\)N\({}_{4}\) Catalysts
## 1 Introduction
The main goal of our work was to compare the catalytic performance of a series of catalysts obtained by doping g-C\({}_{3}\)N\({}_{4}\) with phosphorus (P), sulfur (S), magnesium (Mg), and lithium (Li) in the glycerol carbonate synthesis. To the best of our knowledge, there is no study in the literature comparing all these promoters in the reaction of glycerol carbonylation reaction. Also, the multi-element doping strategy represents a distinct and innovative approach to enhancing both the structural and electronic properties of g-C\({}_{3}\)N\({}_{4}\), aiming to improve its catalytic activity in the direct coupling reaction of glycerol with CO\({}_{2}\). The synthesized catalysts were characterized using a variety of techniques to thoroughly evaluate their properties. Additionally, an experimental design approach was employed for identifying the best operating conditions to maximize the yield in glycerol carbonate. This comprehensive characterization and systematic evaluation aimed to enhance the efficiency and effectiveness of the glycerol carbonate production process.
# Evaluating the Effect of Intensity Standardisation
## 2 Materials and Methods
### Evaluation Analysis and Measures  
#### 2.4.1 Quality of Intensity Standardisation  
The Kullback-Leibler (KL) divergence measures the difference between two probability distributions, expressed as intensity histograms in this work. As the standardisation process seeks to map intensity distributions to a similar range, we use this metric as a measure of the standardisation quality. Given two probability density functions, \(p\) and \(q\), the KL divergence is computed as follows,  
\[KL(p,q)=\sum_{i=1}^{N}p(x_{i})\log\frac{p(x_{i})}{q(x_{i})}. \tag{6}\]  
The more similar the distributions, the lower the KL divergence value. We compare the KL divergence between baseline and follow-up scans before and after standardisation using the Wilcoxon signed-rank test. We expect the KL divergence value to decrease after standardising intensities.
## 4 Discussion
### Influencing Factors
#### 4.4.5 Aligner Thickness  
Material thickness emerged as a key variable influencing aligner performance, particularly in relation to stiffness, force delivery, and response to thermoforming. [PERSON] et al. [20] and [PERSON] et al. [19] reported that thicker, double-layer samples exhibited lower stiffness and yield strength compared to thinner, single-layer counterparts, potentially due to structural layering or reduced material homogeneity. [PERSON] et al. [26] observed that the thinner (0.55 mm) Naturaligner produced a significantly lower initial force and stress than the 0.75 mm variant, highlighting how small differences in thickness can influence force levels.  
[PERSON] et al. [24] found that thermoforming led to greater reductions in elastic modulus in the thicker Essix PLUS (0.9 mm) compared to thinner materials, suggesting that thicker sheets may be more prone to performance loss after processing. [PERSON] et al. [22] similarly reported that thermoforming affected flexural modulus differently depending on thickness--it decreased in 1 mm samples but increased in 0.5 mm ones--concluding that thinner materials may provide more controlled force delivery in stepwise aligner activation.  
These results emphasize that aligner thickness is not simply a geometric dimension but a variable that interacts with material composition and manufacturing processes, ultimately shaping the mechanical and clinical performance of clear aligners.