esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Thus, runtime should not be seen merely as a technical metric but as a strategic factor in choosing the most suitable algorithm for a specific case. Future research could investigate hybrid approaches that merge the speed of classical methods with the perceptual accuracy of deep learning models, thereby optimizing both quality and efficiency in edge detection for conservation purposes. From a conservation perspective, these findings suggest that automated edge detection can significantly streamline the process of damage mapping, reducing the time and effort required for manual tracing. However, the choice of algorithm should be made according to the specific visual and material characteristics of the artwork. While classical methods may offer better metric performance in some cases, deep learning models provide more flexible and visually interpretable outputs, especially when trained on domain-specific data. Although this study focused on easel paintings, the methodology explored here could be extended to other art forms such as murals and sculptures. In the case of murals, edge detection could assist in mapping surface deterioration or graffiti over large wall surfaces, especially when combined with photogrammetry or panoramic imaging. For sculptures and other three-dimensional objects, edge detection could be applied to surface textures and cracks, particularly when integrated with 3D scanning or structured light data. However, these applications would require adaptations to account for lighting variability, surface curvature, and material properties. Future research should explore the development of deep learning models trained specifically on annotated cultural heritage datasets, including spectral and multiband imaging. Additionally, integrating edge detection with 3D modeling could enhance the spatial understanding of surface deformation and material textures. Combining the strengths of classical and AI methods may also offer a more robust solution for diverse conservation scenarios. Finally, the creation of user-friendly tools that support expert conservation and non-specialist users would align with the ChemiNova project's people-centered mission. While this study emphasizes the potential of automation, it is important to acknowledge the practical challenges associated with deploying such methods in real-world con servation workflows. One of the main obstacles is the computational cost of certain steps, particularly during preprocessing. Techniques such as histogram equalization, adaptive thresholds, and Fourier-based filtering can be resource-intensive, especially with the high-resolution images typical in heritage documentation. These steps may require significant processing time and memory, which could limit their use in field conditions or on devices with limited hardware capabilities.
## 2 Materials and Methods
### Remotely Sensed Data  
We used the MCD43A4 Version 6 dataset obtained from the MODIS instrumentation aboard the Terra and Aqua satellites available from 2000 to 2014 in the different sites. This data set contains daily reflectance at a 500 m spatial resolution for the MODIS bands 1 to 7. The reflectance was corrected for atmospheric effects and was nadir bidirectional reflectance distribution function (BRDF)-adjusted to simulate the observations collected from a nadir view. Data were provided by NASA's LP DAAC portal ([[https://doi.org/10.5067/MODIS/MCD43A4.006](https://doi.org/10.5067/MODIS/MCD43A4.006)]([https://doi.org/10.5067/MODIS/MCD43A4.006](https://doi.org/10.5067/MODIS/MCD43A4.006))).  
The near-infrared reflectance of the vegetation (NIRv) was calculated as the product of the NIR nadir normalized top-of-the-canopy reflectance (\(\rho\)NIR) and the normalized difference vegetation index (NDVI) [7]:  
\[\text{NIRv}=\rho\text{NIR}\times\text{NDVI}\]  
where  
\[\text{NDVI}=(\rho\text{NIR}-\rho\text{red})/(\rho\text{NIR}+\rho\text{red})\]  
and pred and \(\rho\)NIR are the reflectance values for MODIS band 1 (620-670 nm) and band 2 (841-871 nm), respectively.
## 4 Experiment and Discuss
### Comparative Analysis and Experimental Results  
#### 4.3.1 Sparseness Analysis of SBELM  
The size of classification models based on SVM and KELM depends on the training set size, and the training time also gradually increases as the training set size gradually rises. However, the size of ELM-based and SBELM-based classification models depends on  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Failure Type** & **Failure No.** & **Description** \\ \hline \multirow{6}{*}{Single fault} & C1 & Normal state \\  & C2 & Gear hard point \\  & C3 & Gear crack \\  & C4 & Gear tooth broken \\  & C5 & Gear burr \\  & C6 & Gear error \\  & C7 & Misalignment \\ \hline \multirow{6}{*}{Simultaneous fault} & C2, C3 & Gear hard point and gear crack \\  & C3, C6 & Gear crack and gear error \\ \cline{1-1}  & C5, C7 & Gear burr and misalignment \\ \cline{1-1}  & C2, C3, C6 & Gear hard point, gear crack and gear error \\ \cline{1-1}  & C3, C5, C7 & Gear crack, gear burr and misalignment \\ \hline \hline \end{tabular}
\end{table}
Table 1: Failure modes description.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Single Failure Samples** & **Simultaneous Failure Samples** & **Total** \\ \hline Training set & \(350\times 7\) & None & 2450 \\ \hline Validation set & \(100\times 7\) & \(400\times 5\) & 2700 \\ \hline Testing set & \(50\times 7\) & \(100\times 5\) & 850 \\ \hline Total & \(3500\) & \(2500\) & 6000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assignment of the experimental dataset.  
the hidden layer scale. Therefore, the running efficiency of ELM-based and SBELM-based models is superior to SVM-based and KELM-based models. By using the sparse Bayesian framework to prune the initial hidden layer, SBELM is able to retain a small hidden layer scale with non-zero output weights to express the hidden layer sparsely. Accordingly, the SBELM-based classification model is more compact and concise.  
The training set which contains 2450 single-fault samples is utilized to train the SBELM-based classification model. The hidden layer scale is increased with an increment of 10 from 20 to 200 to study the sparse representation rate of the hidden layer nodes.  
As shown in Figure 7, the SBELM-based classification model finally retains 53 hidden layer nodes with non-zero weights from 200 initial hidden nodes. The sparse representation rate is nearly 1:4. Meanwhile, when the initially hidden layer scale is greater than 120, the non-zero hidden layer nodes remain on a similar order of magnitude. The results show that in the training phase of the SBELM-based model, the model with 200 initial hidden layer nodes can further realize the sparse representation of the hidden layer.
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 3 System and Network Model
### Handover Mechanism
The last improvement in the handover mechanism is related to the CSMA/CA default parameters of IEEE 802.15.4. In order to speed up message transmissions, this proposal uses alternative values for the _macMinBE_ and _macMaxBE_ parameters, as originally proposed in [33].
# RoseCliff Algorithm: Making Passwords Dynamic
## 4 Implementation
### Dynamic Password/Ciphertext Generation  
To ensure a dynamically changing encrypted password, the RoseCliff algorithm employs a randomizing value to alter the user-entered password before encrypting and transmitting it over a network. The algorithm suggests the use of public-private keys, where the one-time number and a segment of the password serve as private keys. A value is generated to randomize the user-entered password, and this value is intended to be combined with common language characters before being encrypted and sent over the network. Notably, the algorithm prioritizes the nontransmission of the one-time number over the internet to prevent man-in-the-middle attacks. Additionally, it infuses common characters to thwart dictionary attacks.  
In our implementation, we use the extracted password segment as the client-side private key and the one-time number as the server or authentication-side private key. Employing the Diffie-Hellman key exchange, we generate a value, convert it to base 16, hash it, and use the indexing locations of the numbers from the original password (NumIndex) to infuse the ten most commonly used English characters. Subsequently, we merge the other segment of the password with the newly generated value using the index locations, and all other values are stored at the end to create a temporary password. Finally, we employ the AES-256 encryption technique to encrypt the temporary password, where AES-256 stands for the Advanced Encryption Standard with a 256-bit key length, is renowned for its robustness and ability to withstand contemporary hacking challenges. In today's dynamic and ever-evolving digital landscape, where cyber threats continue to grow in complexity and sophistication, AES-256 serves as a stalwart guardian against malicious intrusions and data breaches. Its 256-bit key length provides an exceptionally large number of possible combinations, thus rendering brute force attacks nearly infeasible. Moreover, it benefits from extensive scrutiny by the cryptographic community, which has further validated its resilience against known vulnerabilities.  
Algorithm 2 outlined below enforces this security measure.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 6 Conclusions  
In this article, we propose to transform traditional satellites into space edge computing nodes. The software and hardware architecture of iSat is illustrated. In terms of hardware, the original satellite hardware modules are broken up and reorganized to form various resource networks, which provides a basis for the unified management and flexible scheduling. In terms of software, edge computing technology is combined with traditional satellite software architecture to realize the virtualization of satellite resources, the flexible orchestration of satellite missions, the on-demand allocation of on-board resources and the coordinated service of satellite and ground. Furthermore, we combine the mobile edge computing and satellite orbit to establish a system model of the application scenario and analyze the execution time and energy consumption of the task. Through the simulation experiments, the space edge computing system takes less time and energy than traditional satellite constellation. The quality of service is related to the number of satellites, the performance of the satellite itself, and the satellite mission offloading strategy. In the future, we will optimize the design of the system in response to the above three aspects.  
Figure 8: (**a**) the impact of task offloading strategy on the total time spent; (**b**) the impact of task offloading strategy on total energy consumption.  
Figure 7: (**a**) the total time spent in terms of the number of VMs on satellite; (**b**) the total energy consumption in terms of the number of VMs on satellite.  
**Author Contributions:** Conceptualization: [PERSON] and [PERSON]; methodology: [PERSON] and [PERSON]; validation: [PERSON], [PERSON] and [PERSON]; formal analysis: [PERSON]; investigation: [PERSON] and [PERSON]; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON], [PERSON], and [PERSON]; visualization: [PERSON]; supervision: [PERSON], [PERSON], and [PERSON]; project administration: [PERSON].  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Result Analysis
#### 4.1.2 Experiment 2 Results
On the ABIDE test dataset, the  
Figure 10: Mean ASD classification accuracy of the seven models on features sets in Experiment 2: (**a**) with recursive feature elimination with cross-validation; (**b**) with Boruta algorithm; (**c**) with GWO-based FS algorithm. Within each subplot, callouts are used to highlight the models with the lowest accuracy (indicated by a red frame), highest accuracy (indicated by a green frame), and models that achieved an accuracy of 65% or higher (indicated by a yellow frame). DT model achieved 61.2% accuracy, while on the KAU data it achieved 45.5%. The lowest classification accuracy was obtained by NB with the F3 set. In the Boruta sub-experiment, CatBoost with the F5 set achieved the highest accuracy (67.5%), while DT with the F2 set had the lowest accuracy (57.65%). CatBoost selected only four left-brain features as the most important, with a learning rate of 0.03, 100 iterations, and a maximum depth of 10. On the ABIDE and KAU test datasets, CatBoost achieved accuracies of 62.7% and 72.7%, respectively. In the GWO-based sub-experiment, the mean validation accuracies ranged from 57.06% to 70.20%. The highest accuracy was achieved by SVM with 11 features from the F5 set (70%) and 62 features from the F6 set. The lowest accuracies were obtained by NB with 14 features from the F3 set and NB with 19 features from the F4 set. On the ABIDE test set, SVM achieved an accuracy of 65.8% with the F6 set and 63.5% with the F5 set. On the KAU test set, SVM achieved an accuracy of 57.6% with the F6 set and 69.7% with the F5 set. Table 7 displays the results of the best-performing model, determined by mean accuracy, for each feature set in the optimization experiments. Across these three experiments, the SVM model consistently demonstrated good performance, followed by the ensemble models CatBoost and XGB, particularly when using the F5 and F6 feature sets. On the other hand, the F3 feature set consistently resulted in the worst performance. Notably, NB and DT classifiers consistently had the lowest mean validation accuracy, which may be attributed to their assumption of equal statistical relevance among features.
## 2 Materials and Methods  
### Integrated System Architecture and Workflow  
The architecture of the proposed drone control system integrates multiple hardware and software components to enable intuitive, gesture-based drone navigation with obstacle avoidance and 3D mapping. The user initiates control by performing predefined hand gestures in front of a webcam. These gestures are captured and processed using Google's MediaPipe Hands framework. The landmark data are analyzed using custom Python classification functions that determine the type of gesture being shown. Using a command-mapping module, each recognized gesture is mapped to a specific flight command.  
The mapped flight commands are wirelessly transmitted to the Bitcraze Crazyflie 2.1 drone via the Crazyflie Python library. Once airborne, the drone operates with three key expansion decks: the Multi-Ranger Deck for obstacle detection, the Flow Deck V2 for position estimation, and the AI-Deck for First-Person View (FPV) video streaming. The Multi-Ranger Deck uses infrared sensors to monitor proximity to objects in the front, back, left, and right directions.  
Meanwhile, the Flow Deck V2 provides position data through a Kalman filter, which is logged every 0.1 s by a flight data logger. These coordinates generate a 3D map of the drone's path using Python's Plotly (Version 6.0.0) visualization library. Obstacle encounters are also marked on the map as red rectangular prisms. The AI-Deck streams live grayscale or color video to the user's screen using socket communication, enabling remote navigation beyond the user's direct line of sight. The FPV video feed and the interactive 3D map are displayed to the user, offering system feedback. This modular system functions in parallel, creating a responsive platform for safe, beginner-friendly drone piloting and for advanced mission-based piloting. This workflow and architecture can be visualized in Figure 1 below.
## 4 Optimization
### Optimization of the Expander
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cycle** & **Design Parameters** & **Metamodel** \\ \hline \multirow{3}{*}{1} & blade & \(U=0.872630-0.000602\)\(\beta_{H3}-0.001696\)\(\beta_{H4}+0.000415\)\(\beta_{S3}+0.000553\)\(\beta_{S4}+\) \\  & \(\beta\) angle & \(0.000022\)\(\beta_{H3}\)\(\beta_{H3}+0.000134\)\(\beta_{H4}\)\(\beta_{H4}+0.000015\)\(\beta_{S3}\)\(\beta_{S3}+0.000084\)\(\beta_{S4}\)\(\beta_{S4}\)\(-0.000050\)\(\beta_{H3}\)\(\beta_{H4}-\) \\  & \(0.000017\)\(\beta_{H3}\)\(\beta_{S3}-0.000003\)\(\beta_{H3}\)\(\beta_{S4}-0.000040\)\(\beta_{H4}\)\(\beta_{S3}-0.000188\)\(\beta_{H4}\)\(\beta_{S4}+0.000112\)\(\beta_{S3}\)\(\beta_{S4}\) \\ \hline \multirow{3}{*}{2} & meridional & \(U=0.875270-0.003063\)\(Z_{H2}-0.001480\)\(R_{H3}+0.003210\)\(Z_{S2}+0.001197\)\(R_{S3}-\) \\  & meridional & \(0.000112\)\(Z_{H2}\)\(Z_{H2}-0.000010\)\(R_{H3}\)\(R_{H3}-0.000145\)\(Z_{S2}\)\(Z_{S2}-0.000180\)\(R_{S3}\)\(R_{S3}+\) \\  & contours & \(0.000207\)\(Z_{H2}\)\(R_{H3}+0.000153\)\(Z_{H2}\)\(Z_{S2}-0.000100\)\(Z_{H2}\)\(R_{S3}-0.000222\)\(R_{H3}\)\(Z_{S2}-\) \\  & & \(0.000020\)\(R_{H3}\)\(R_{S3}+0.000170\)\(Z_{S2}\)\(R_{S3}\) \\ \hline \multirow{3}{*}{3} & blade & \(U=0.884060-0.000268\)\(\beta_{H3}-0.001175\)\(\beta_{H4}+0.000102\)\(\beta_{S3}+0.000020\)\(\beta_{S4}-\) \\  & \(\beta\) angle & \(0.000064\)\(\beta_{H3}\)\(R_{H3}-0.000192\)\(\beta_{H4}\)\(\beta_{H4}-0.000044\)\(\beta_{S3}\)\(\beta_{S3}-0.000162\)\(\beta_{S4}\)\(\beta_{S4}-0.000140\)\(\beta_{H3}\)\(\beta_{H4}+\) \\  & \(0.000080\)\(\beta_{H3}\)\(\beta_{S3}+0.000095\)\(\beta_{H3}\)\(\beta_{S4}+0.000055\)\(\beta_{H4}\)\(\beta_{S3}+0.000190\)\(\beta_{H4}\)\(\beta_{S4}-0.000060\)\(\beta_{S3}\)\(\beta_{S4}\) \\ \hline \multirow{3}{*}{4} & meridional & \(U=0.885210-0.002642\)\(Z_{H2}-0.001767\)\(R_{H3}+0.002832\)\(Z_{S2}+0.001001\)\(R_{S3}-\) \\  & meridional & \(0.000214\)\(Z_{H2}\)\(Z_{H2}+0.000050\)\(R_{H3}\)\(R_{H3}-0.000225\)\(Z_{S2}-0.000099\)\(R_{S3}\)\(R_{S3}+\) \\  & contours & \(0.000005\)\(Z_{H2}\)\(R_{H3}+0.000242\)\(Z_{H2}\)\(Z_{S2}+0.000095\)\(Z_{H2}\)\(R_{S3}-0.000023\)\(R_{HFigure 11: Isentropic total-to-total efficiency of expander by shape optimization without assuming the extra mechanical losses. Figure 12: Comparison of blade \(\beta\) angle distributions obtained by shape optimization. Figure 13: Comparison of hub and shroud meridional contours obtained by shape optimization.
## 2 Materials and Methods
### Models Development and Assessment  
The computational experiment was designed to develop multiple input single output (MISO) models. ED and FPF models were developed separately without any interaction of these two variables. Model performance was evaluated according to the 10-fold cross validation and expressed by three goodness of fit metrics: root-mean-square error (RMSE, Equation (1)), normalized root-mean-square error (NRMSE, Equation (2)), and coefficient of determination (\(R^{2}\), Equation (3)). NRMSE was calculated according to experimental FPF and ED ranges.  
\[\text{RMSE}=\sqrt{\frac{\sum_{\text{i=1}}^{\text{n}}\left(\text{pred}_{\text{ i}}-\text{obs}_{\text{i}}\right)^{2}}{\text{n}}} \tag{1}\]  
where: \(\text{obs}_{\text{i}}\), \(\text{pred}_{\text{i}}\) = observed and predicted values of i-th record, n = total number of records.  
\[\text{NRMSE}=\frac{\text{RMSE}}{\text{X}_{\text{max}}-\text{X}_{\text{min}}} \cdot 100\% \tag{2}\]  
where: \(\text{RMSE}\) = root-mean-square error calculated for the model, \(\text{X}_{\text{max}}\) = maximum value of the observed results, \(\text{X}_{\text{min}}\) = minimum value of the observed results.  
\[R^{2}=1-\frac{\sum_{\text{i=1}}^{\text{n}}\left(\text{pred}_{\text{i}}-\text{ obs}_{\text{i}}\right)^{2}}{\sum_{\text{i=1}}^{\text{n}}\left(\text{obs}_{\text{i}}- \text{obs}_{\text{mean}}\right)^{2}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**API Content** & **Sol. Conc.** & **Ethanol** & **Inlet Air** & **Airflow** & **Pressure** & **FPF [\%]** & **ED [\%]** \\
**[m/m\%]** & **[m/V\%]** & **[V/V\%]** & **Temperature** [\({}^{\circ}\)**C] & **[L/min]** & **[mbar]** & **FPF [\%]** & **ED [\%]** \\ \hline
50 & 2 & 40 & 85 & 95 & 50 & 64.88 & 77.00 \\
50 & 1 & 30 & 85 & 95 & 50 & 85.80 & 98.55 \\
50 & 2 & 30 & 85  
where: \(R^{2}\) = coefficient of determination, \(\text{obs}_{i}\), \(\text{pred}_{i}\) = observed and predicted values, respectively, \(\text{obs}_{\text{mean}}\) = arithmetic mean of observed values.  
Models were built and assessed using in-house software developed in R environment [16]. Additional packages delivering evolutionary algorithms, fuzzy logic, and genetic programming methods were used. Computations were executed using 29 workstations (542 threads) working under Linux openSUSE Tumbleweed operating system. All tools applied in the study including source code and examples are available on SourceForge website [17; 18; 19].
# Repair Performance of Self-Healing Microcapsule/Epoxy Resin Insulating Composite to Physical Damage
## Materials and Methods  
### Materials and Preparation  
In the existing research, the microcapsule system of urea-formaldehyde (UF) resin coated epoxy resin is mostly selected to achieve the self-healing behavior in epoxy resin matrix [2; 17; 20]. However, previous studies did not consider the application requirements of epoxy resin insulating material. Although the compatibility of the epoxy repairing agent and the epoxy matrix is good, the curing rate of epoxy repairing agent is slow. It is not suitable for the timely requirement of repair behavior in insulating material. Especially, in high electric field, the epoxy repairing agent cannot suppress the partial discharge in time. The reaction rate of dicyclopentadiene (DCPD) is faster. And the dielectric constant of its reaction product (i.e., polydicyclopentadiene (PDCPD)) are close to those of epoxy resin, which can effectively homogenize the local high field of the defect, thus reducing the impact of structural damage [23]. Therefore, the UF/DCPD microcapsule was selected in this paper.
###### Abstract  
Cobalt-doped graphene-coupled hypercrosslinked polymers (Co-GHCP) have been successfully prepared on a large scale, using an efficient RAFT (Reversible Addition-Fragmentation Chain Transfer Polymerization) emulsion polymerization and nucleophilic substitution reaction with Co (II) porphyrin. The Co-GHCP could be transformed into cobalt-doped porous carbon nanosheets (Co-GPC) through direct pyrolysis treatment. Such a Co-GPC possesses a typical 2D morphology with a high specific surface area of 257.8 m\({}^{2}\) g\({}^{-1}\). These intriguing properties of transition metal-doping, high conductivity, and porous structure endow the Co-GPC with great potential applications in energy storage and conversion. Utilized as an electrode material in a supercapacitor, the Co-GPC exhibited a high electrochemical capacitance of 455 F g\({}^{-1}\) at a specific current of 0.5 A g\({}^{-1}\). After 2000 charge/discharge cycles, at a current density of 1 A g\({}^{-1}\), the specific capacitance increased by almost 6.45%, indicating the excellent capacitance and durability of Co-GPC. These results demonstrated that incorporation of metal porphyrin into the framework of a hypercrosslinked polymer is a facile strategy to prepare transition metal-doped porous carbon for energy storage applications.  
transition metal-doping; porous carbon; hypercrosslinked polymer; two-dimensional nanosheet; supercapacitor +
Footnote †: journal: Journal of Alloys and Compounds