esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Case Studies
It is also depicted in the figures that the values assumed are slightly higher (in absolute value) when referred to the upper surface in the Al-WC auxetic material in opposition to the Al-SiC one, which shows significantly lower values in the upper surface of the composite (Figure 5b). Figure 4: Schematic representation of a simply supported beam submitted to a uniformly distributed transverse loading. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Material** & **Designation** & **E [GPa]** & **v** \\ \hline Tungsten Carbide (WC) & WC & 550 & 0.31 \\ Silicon Carbide (SiC) & SiC & 440 & 0.17 \\ Aluminum Oxide (Al\({}_{2}\)O\({}_{3}\)) & Al\({}_{2}\)O\({}_{3}\) & 370 & 0.26 \\ Zirconia (ZrO\({}_{2}\)) & ZrO\({}_{2}\) & 200 & 0.31 \\ Aluminum (Al) & Al & 70.3 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Materials’ properties. If one looks now at [PERSON]'s ratio \(\
u_{21}\) for the same two FGM, the results are observed in Figure 6, again for the bottom and top surfaces of the beam. As previously, now Figure 6a shows that, in the case of the Al-WC auxetic material, the values assumed for each specific value of \(\eta 1\) at the outer surfaces of the beam are not very different. However, for the Al-SiC auxetic material, the differences between the values observed on the bottom and top surfaces are again greater. In opposition to the effect produced by the ratio \(\eta 1\), in the Poisson's ratio \(\
u_{12}\), in this case, when \(\eta 1\) increases, it produces a decreasing effect in \(\
u_{21}\). In Tables 4 and 5, [PERSON]'s ratios \(\
u_{21}\) for the Al-WC and Al-SiC auxetic material, with \(\eta 3=0.01\), are presented for illustrative purposes. Figure 5: [PERSON]’s ratio \(\
u_{12}\) function of angle \(\theta\) [\({}^{\circ}\)] and \(\eta 1\), for \(\eta 3=0.01\), (**a**) Al-WC, (**b**) Al-SiC. Considering the nature of the loadings that will be considered in the present work, namely transverse loadings, it is worth illustrating the bending stiffness coefficients and the transverse shear coefficients associated with the first-order shear deformation theory (without considering the shear correction factor). Figures 7-10 depict those evolutions for illustrative purposes, considering that the relation between the thickness and the length of the unit cell horizontal segment (\(\eta\)3) remains constant and equal to 0.01. The stiffness coefficient \(\mathrm{D}_{11}\) for the selected FGM auxetic materials presents different profiles, as depicted in Figure 7. However, it is possible to see that the higher absolute values are obtained for \(-75^{\circ}\). In both cases, when the ratio between the horizontal and the inclined segments of the cell, \(\eta 1\), increases, this bending stiffness also increases (in absolute value). Opposite trend profiles are visible for the bending stiffness coefficient \(\mathrm{D}_{12}\) (Figure 8), for the Al-WC, and for the Al-SiC FGM auxetic materials. As in the previous coefficient, this trend is related to the Poisson ratio magnitudes of the constituent materials (the metal and the ceramic) involved in the two materials illustrated.
###### Abstract  
The increase in high-quality video consumption requires increasingly efficient video coding algorithms. Versatile video coding (VVC) is the current state-of-the-art video coding standard. Compared to the previous video standard, high efficiency video coding (HEVC), VVC demands approximately 50% higher video compression while maintaining the same quality and significantly increasing the computational complexity. In this study, coarse-grain profiling of a VVC decoder over two different platforms was performed: One platform was based on a high-performance general purpose processor (HGPP), and the other platform was based on an embedded general purpose processor (EGPP). For the most intensive computational modules, fine-grain profiling was also performed. The results allowed the identification of the most intensive computational modules necessary to carry out subsequent acceleration processes. Additionally, the correlation between the performance of each module on both platforms was determined to identify the influence of the hardware architecture.  
versatile video coding; complexity analysis; H.266; codec; multicore; heterogeneous, GPU; inter prediction; deblocking filter; adaptive loop filter +
Footnote †: journal: Sensors
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 5 Security Analysis
### Real-World Context for Attribute and Policy Complexity with Advanced Access Constraints  
To underscore the importance of evaluating CP-ABE performance under increasing attribute cardinality and complex policy structures, as shown in Figure 11, we introduce two real-world cyber threat intelligence (CTI) sharing scenarios. These cases incorporate our proposed enforcement mechanisms: _time rivaling, time-bombing_, and _location interlocking_, which extend traditional CP-ABE capabilities with contextual awareness. Basic CP-ABE attributes are defined at the time of key issuance and do not change based on real-time conditions. This is what differentiates basic CP-ABE from context-aware or dynamic CP-ABE (which includes constraints like \"Access only valid before 24 h\" or \"User must be in EU region\"). **Scenario 1: Healthcare Threat Sharing Consortium:** In this use case, hospitals and certified diagnostic laboratories collaborate to exchange time-sensitive indicators of compromise (IoCs) related to ransomware campaigns. Access to shared data is governed by a multifaceted policy: \begin{tabular}{l l} Role: & Analyst \(\wedge\) Org: & Certified Lab \(\wedge\) Clearance: & Level-2 \(\wedge\) Access Time \(<\) \\  & & 48 h \(\wedge\) Location: & US \\ \end{tabular}   
Figure 11: Performance of CP-ABE under complex policies incorporating _time rivaling, time-bombing_, and _location interlocking_ across varying attribute counts and policy sizes. Here, **time-bombing** ensures that decryption keys expire 48 h after issuance, mitigating the risk of outdated intelligence usage. **Time rivaling** prioritizes and promotes newer threat reports, automatically suppressing older data. Meanwhile, **location interlocking** enforces decryption exclusively on authorized devices physically located in the United States, aligning with health data localization policies. **Scenario 2: Financial Sector Threat Intelligence Network:** This network enables secure threat data exchange among banks and financial entities. For example, access to phishing and fraud-related intelligence is subject to the following access control policy:  
\[ \begin{array}{ll}\text{Entity Type:}&\text{Bank}\land\text{Compliance Tier: Tier-1}\land\text{Subscription Level:}&\text{Premium}\\ &\land\text{Access Time}<24\text{ h}\land\text{Location: EU}&\end{array} \]  
In this scenario, **time-bombing** enforces ephemeral decryption keys with a 24 h lifespan. **Time rivaling** enables systems to give precedence to up-to-date threat data, and **location interlocking** ensures compliance with EU data residency requirements under the GDPR by restricting decryption to EU-based endpoints. These scenarios highlight the applicability of expressive attribute-rich access policies in operational CTI environments. Our implementation demonstrates that CP-ABE can remain computationally feasible even when extended with contextual constraints, such as time and location. The complete encryption and decryption flow for this extended CP-ABE scheme is outlined in Algorithm 1.
## 3 Results and Discussion
### Stability and Anti-Interference Ability  
The reliability of the GCE/ERGO-NiNPs is evaluated from repetitive DPV measurements (\(n\) = 10) in acetate buffer pH 4 + 0.5 uM SY (see Electronic Supporting Information Figure S9A). The Relative Standard Deviation (RSD) value, calculated at 1.08%, indicates the excellent repeatability of the modified electrodes. This electrochemical sensor was maintained in pH 4 buffer for stability measurements. The DPV measurements show almost no change in the SY response after 24 h. The signal current was decreased at 70% after 48 h (see Electronic Supporting Information Figure S9B). The result shows that modified electrodes exhibit acceptable stability within 2 days. Nevertheless, this sensor could be efficiently reused after the cleaning process by a simple cycling potential in 0.5 M H\({}_{2}\)SO\({}_{4}\) (five cycles) solution. The response of the sensor exhibits no significant change below 5% after five repetitive detection/cleaning cycles, suggesting high reusability (see Electronic Supporting Information Figure S9C). Selectivity is also an important characteristic for a sensor; the anti-interference ability of the electrochemical sensor has been evaluated for 0.5 uM SY acetate buffer with the addition of some potential conventional additives in soft drinks. The result demonstrates that 100-fold concentrations of glucose, sodium citrate and acid ascorbic, KCl, NaCl, CaCl\({}_{2}\), do not result in a significant change to SY response (below 5%), indicating a good selectivity of this sensor toward these interferents (see Electronic Supporting Information Figure S9D).
# Transmission Lines Small-Target Detection Algorithm Research Based on YOLOv5
## 4 Experimental Results and Analysis
### Ablation Experiment  
The enhancement effect of each new or improved module on the overall model was explored through an ablation experiment using the transmission line small-target data set. Starting with the original YOLOv5, each module was successively added for experiments until the final model, P2E-YOLOv5, was obtained. The experimental results are presented in Table 3.  
As shown in Table 3, the addition of a small-target detection head result in a slight decrease in the model's accuracy (P), but it significantly improved the recall rate (R) and mean average accuracy (mAP) by 3.5% and 2.2%, respectively. It indicates that the inclusion of the P2 layer small-target detection head greatly enhances the model's performance, effectively improving its ability to detect small targets. Subsequently, the ECA attention module was added on top of the small-target detection heads. While the mean average accuracy remained relatively unchanged, the accuracy (P) and recall rate (R) of the model increased by 0.4% and 0.3%, respectively. This demonstrates that the ECA attention module effectively enhances the network's focus on small target objects and improves the model's feature extraction capability for small targets. Finally, the EIOU_Loss function was added on top of the small-target detection head and ECA attention module. Although the recall rate (R) of the model decreased, both the accuracy (P) and mean average accuracy (mAP) were improved. This indicates that the EIOU_Loss effectively enhances the model's detection accuracy for small targets.  
Furthermore, in terms of detection aging, the inference detection speed of the model decreased due to the increased computational load caused by the large detection layer scale.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **mAP\_0.5 (\%)** & **FPS (Frames)** \\ \hline YOLOv5s & 95.7 & 91.2 & 94.7 & 101.010 \\ YOLOv5s + P2 & 95.5 & 94.7 & 96.9 & 86.207 \\ YOLOv5s + P2 + ECA & 95.9 & 95.0 & 96.9 & 88.495 \\ YOLOv5s + P2 + ECA + EIOU & 96.0 & 94.7 & 97.0 & 87.719 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influences of different new modules on the model.  
However, after the addition of the ECA attention module and EIOU_Loss function, there was a noticeable increase in the detection speed.  
The data analysis results mentioned above align well with the theoretical analysis, providing further validation of the algorithm's rationality and effectiveness proposed in this paper. The approach not only meets the requirements of real-time detection but also significantly improves the detection accuracy of small targets.
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 4 Case Study
### Clustering  
Through the matrix points extracted from the PCA, a grouping was made to identify the groups that most identify themselves. Each group that identifies itself is seen as an observation of the equipment; in other words, an observation that matches a normal data equipment behavior will be grouped into a cluster that groups observations with that behavior and malfunction characteristics, that will be grouped into another cluster, and so on. The algorithm used to perform Clustering is K-means, therefore, it is necessary to define, a priori, the number of clusters that will be performed. This criterion will be done through a silhouette analysis where the silhouette criterion is used to assess the ideal number of clusters; for these data, it was found that the ideal would be three clusters (Figure 6).  
Each cluster will then be classified as the observable state in the study period. A descending ordering of clusters was also done. In this way, we guarantee that cluster 1 is the one with the most points, cluster 2 is the second cluster with the most points, and so on. Based on this, cluster three corresponds to the observation with the fewest points and is what happens less frequently and, therefore, the rarest to happen, which leads to deduce that an observation occurs when the equipment is not in good condition (this will be demonstrated in the HMM section). In Figure 7, we can see how each cluster looks over time. As each cluster is in an observable state of the equipment, we can look at the graph and see the development of the observable states of the equipment over the study period.  
Figure 6: Identification of the ideal number of clusters for the sample under study through silhouette analysis.  
In order to verify if the groups chosen by [PERSON] make sense, we can compare the cluster development with the direct observations of the reading equipment. As we saw above, cluster 3 will be the most unlikely to happen, and it should be represented when there are observations in the less common data, as we can see in the Figure 7, that is what is happening. This leads to the inference that [PERSON] did a good data grouping.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Vehicular Communications  
#### 7.1.1 Dedicated Short Range Communications  
Dedicated Short Range Communications is a wireless communication technology designed to operate within the 5.9 GHz frequency band to facilitate short-range communication between vehicles and infrastructure [94]. V2X communication enables a dynamic and interconnected environment among different ITS applications through the support of DSRC [95]. However, this technology is dominant for safety applications, as it enables communication between vehicles, such as location, speed, and trajectory. DSRC's short-range capabilities are critical in traffic management and allow vehicles to share data with other vehicles and the roadside infrastructure to optimize traffic flow. DSRC's versatility is the reason for building the foundation for the connected future of transportation.
# An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery
## 3 Methods
### Accuracy Assessment Method  
Visual interpretation and the quantitative accuracy index are adopted to conduct accuracy verification. The first method is to judge visual effects such as the degree of continuousness and the smoothness of the boundary, which can quickly assess the performance of extraction water bodies. Regarding the detailed differences in water body extraction, the quantitative accuracy index is a widely used method that uses random sample points and high-resolution images. The flow contains three steps: (1) 200 random water sample points and 200 random nonwater sample points are generated for each study area; (2) high-resolution images are used as the reference data to verify and adjust the random sample points and the ground reference points are then generated; (3) ground reference points are superimposed on the water extraction results to verify the accuracy of each random sample point; (4) four widely used accuracy evaluation indices, including producer accuracy, user accuracy, overall accuracy, and Kappa coefficient, are employed to quantitatively assess the water body extraction accuracy.
## 5 Conclusions and Future Work  
This study proposes a method for multi-satellite and multi-ground station downlink scheduling problem considering waiting time. Firstly, the simulated annealing algorithm with a tabu table is used to solve the selection problem of multiple satellites and multiple ground stations. Then, two precise algorithms, the downlink scheduling algorithm based on arrival time and the downlink scheduling algorithm based on requirement time, are used to solve the specific start time decision of multiple time conflict downlink tasks in a single ground station.  
Calculation and simulation analysis shows this method is a balanced algorithm between profit and efficiency that can efficiently perform ground station selection and mission start time decision. In addition, the constraints considered in this work are mainly geometric visibility and temporal conflicts, without considering uncertain factors, such as communication interruption, and more specific constraints, such as ground station antenna maneuvering, which can be studied in future work. The downlink start time determination method reduces the waiting time of the task, and has practical value for the planning and scheduling of downlink tasks of multi-satellite and multi-ground stations.  
Conceptualization: Y.L. and S.Z.; methodology: Y.L. and S.Z.; supervision: H.H.; writing--review and editing: Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 4 Proposed Method
### Adaptive Gamma Correction  
Low-valued transmission maps may lead to distortions in dehazed images, potentially causing darkening. For example, in Figure 2d, shallow pixel values indicate a risk of the dehazed image appearing similarly shallow and darker if not corrected. Adjusting the transmission map is necessary to address this issue and achieve proper brightness correction.  
Gamma correction is a remedy that involves adjusting pixel values in an image to compensate for the non-linear way human eyes perceive light through a \(\gamma\) parameter [19]. As shown in Figures 3 and 4, when \(\gamma\) is increased, images become darker, gradually reducing fog, whereas decreasing \(\gamma\) retains more fog. An improper selection of \(\gamma\) may result in insufficient darkening or ineffective fog removal. Therefore, choosing an appropriate \(\gamma\) should consider the characteristics of the fog image. Instead of manual adjustment, we propose an adaptive approach to determine the \(\gamma\) value based on the maximum and minimum values of the enhanced transmission map \(t^{eh}(x)\):  
\[\gamma=\sqrt{\frac{1-(\alpha+\min(t^{eh}(x)))}{\max(t^{eh}(x))+\max(t^{eh}(x)) \cdot(1-(\alpha+\min(t^{eh}(x))))}} \tag{10}\]  
To prevent the maximum and minimum values from being saturated, \(\alpha\) is set to 0.1.  
Figure 3: The distribution of the histogram changes with \(\gamma\). As \(\gamma\) increases, pixel values decrease, and pixel values intensify as \(\gamma\) decreases.  
Figure 2: The illustrations of transmission maps. (**a**) A product of \(t^{ero}(x)\) and \(t^{e}(x)\) preserves foreground features while improving image contrast. (**b**) \(t^{fa}(x)\) addresses the darkening issue of (**a**) by stretching. (**c**) An average of \(t^{fa}(x)\) and \(t^{or}(x)\). (**d**) Enhanced transmission map wherein taking an average of \(t^{e}(x)\) and \(t^{avg}(x)\) aims to remove sky distortion and restore structural details.
## 3 Results and Discussion
### XRD Analysis  
Based on XRD measurements, the amorphous nature of the as-prepared glasses was confirmed. Similarly, glasses annealed at temperatures of 450 \({}^{\circ}\)C, 480 \({}^{\circ}\)C and 500 \({}^{\circ}\)C, proposed on the basis of DTA studies, are characterized by an amorphous structure. The exemplary diffraction patterns for unannealed samples and the BBO(50:50) + Ag, BBO(60:40) + Ag and BBO(70:30) + Ag samples annealed at 480 \({}^{\circ}\)C for 5 h are shown in Figure 3. Although bismuth-borate glass is very susceptible to crystallization, at least five stable crystalline phases can be observed [15; 19; 25; 26], the broad maxima present in the 20-35 (20) and 40-55 (20) ranges indicate short-range ordering present in the samples. It was not possible to observe Ag nanoparticles in the glassy matrix due to the fact that only 1 mol% of silver was introduced into the samples.  
Figure 2: DTA curves of precursive glass compositions: BBO(50:50), BBO(60:40) and BBO(70:30). The characteristic Tg and Tc temperatures are marked with arrows.
## 3 Results
### Geometric Consistency
#### 3.1.2 Assessment of the Distance between Overlapping Blocks
This is not a surprise for the authors, nor should it be for any aware user. However, in times of widespread use of photogrammetry [54], we thought it was worth noting. Ig/Ir and Ig/Ir scenarios both adopt GCPs within different adjustment strategies. The RMSE values of the residuals for the single components \(x\), \(y\), and \(z\) are range between 6 and 11 cm. Considering the already mentioned granularity of the analyzed datasets, the reported figures highlight that the overlapping point clouds are optimally aligned and consistent. Maps of the 3D distances between the overlapping point clouds are meaningful. Figure 6 shows them for all the three scenarios assessed. Remarkably, the three sub-figures shown adopt the same color map, even if Figure 6b,c only shows a small part of it. We also remark that values shown in Table 3 and plots reported in Figure 6 are related to the original point clouds, as they were generated by the photogrammetric procedure. ICP was only used to properly couple points belonging to different clouds in order to conveniently evaluate their distance. Figure 6a highlights that in scenario DG, the clouds are quite far. Moreover, the map of the distances is a sort of a ramp, meaning that the two clouds are not simply displaced but are also affected by a significant rotation. The other two sub-figures are related to Ig/Ir and Ig/Ir scenarios and confirm that the distances are limited in size and are substantially constant. Moreover, Ig/Ir scenario shows lower values above the fields, where the terrain is flat, while distances are slightly greater beside dirt roads and ditches, where low vegetation is present. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **delta E [m]** & **delta N [m]** & **delta H [m]** & **delta 3D [m]** \\ \hline \multirow{4}{*}{DG\#} & Min. & \(-\)2.210 & \(-\)0.164 & \(-\)4.874 & 1.625 \\  & Max. & \(-\)1.352 & 1.050 & \(-\)0.368 & 5.254 \\  & Mean & \(-\)1.736 & 1.214 & \(-\)2.609 & 3.236 \\
367982 & STD & 0.098 & 0.193 & 1.092 & 0.877 \\  & RMSE & 1.739 & 0.464 & 2.828 & 3.353 \\ \hline \multirow{4}{*}{Ig/Ir\#} & Min. & \(-\)0.241 & \(-\)0.294 & \(-\)0.480 & 0.003 \\  & Max. & 0.361 & 0.377 & 0.362 & 0.493 \\ \cline{1-1}  & Mean & 0.061 & 0.051 & \(-\)0.043 & 0.167 \\ \cline{1-1}  & STD & 0.080 & 0.089 & 0.098 & 0.066 \\ \cline{1-1}  & RMSE & 0.100 & 0.103 & 0.107 & 0.179 \\ \hline \multirow{4}{*}{Jg/Ir\#} & Min. & \(-\)0.253 & \(-\)0.265 & \(-\)0.291 & 0.001 \\ \cline{1-1}  & Max. & 0.272 & 0.350 & 0.250 & 0.369 \\ \cline{1-1}  & Mean & 0.006 & 0.038 & \(-\)0.022 & 0.132 \\ \cline{1-1}  & STD & 0.079 & 0.093 & 0.058 & 0.053 \\ \cline{1-1}  & RMSE & 0.079 & 0.101 & 0.062 & 0.143 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary statistics of the 3D distance between overlapping point clouds.