esa-sceva/satcom-chunk-collection huggingface dataset:
# A Decision Tree for Rockburst Conditions Prediction
## 3 Methods
### Modelling Approach  
#### 3.1.1 Decision Tree Approach  
The decision tree is a popular ML algorithm used for both classification and regression tasks. It is a tree-like model, where each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a numeric value. The algorithm works by recursively splitting the data based on the most important attribute at each level of the tree, thus forming a decision path from the root of the leaf node.  
Two approaches based on decision trees are described: the DT-RT approach predicts rockburst conditions for each rock type i.e., Igneous (103 cases), Metamorphic (58 cases), and Sedimentary (49 cases) using DT (J48), while the Unique-DT approach uses all datasets (210 cases) to predict rockburst conditions, reduce analysis work, and improve accuracy. Both approaches aim to improve the effectiveness of the algorithm in predicting rockburst conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Approach** & **Model** & **Hyperparameters** & **Dataset Used** \\ \hline \multirow{3}{*}{DT-RT} & \multirow{3}{*}{J48 (Default)} & Confidence Factor: 0.25 & \multirow{3}{*}{Individual Rock types} \\  & & reduced Error Pruning: T & \\  & & minNumObj: 2 & \\ \hline \multirow{3}{*}{Unique-DT} & \multirow{3}{*}{J48 (Default)} & Confidence Factor: 0.25 & \multirow{3}{*}{All Rock types} \\  & & reduced Error Pruning: T & \\  & & minNumObj: 3 & \\ \hline \multirow{6}{*}{Multiple ML} & \multirow{3}{*}{RF (Default)} & I = 200 (Number of trees) & \multirow{3}{*}{All Rock types} \\  & & K = 10 (Number of features) & \\  & & depth = 10 (Max depth of each tree) & \\ \cline{2-4}  & KNN (Default) & K = 1 (number of neighbors to use) & \\ \cline{2-4}  & AdaBoostM1 & Default base learner: J48 & \multirow{3}{*}{All Rock types} \\  & & P = 10 (number of iterations) & \\  & & L = 0.1 (learning rate) & \\ \cline{2-4}  & & Kernel: RBF & \\ \cline{2-4}  & SVM (Default) & C = 1 (complexity constant) & \\ \cline{2-4}  & & L = 1.0e-12 (tolerance parameter) & \\ \cline{2-4}  & & P = 1.0e-10 (epsilon) & \\ \hline \multirow{6}{*}{ANN} & \multirow{3}{*}{L = 0.2 (Learning rate)} & \multirow{3}{*}{M = 0.3 (Momentum)} & \multirow{3}{*}{All Rock types} \\  & & N = 500 (Number of epochs) & \\ \cline{1-1} \cline{3-4}  & & H = 1 (Number of hidden layers) & \\ \cline{1-1} \cline{3-4}  & & Sigmoid (Activation function) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modelling Approach with Hyperparameters for the ML Algorithms.
## 5 Security Analysis
### Reliability Analysis
The main data distribution of \(\Delta V_{comp}\) along the interquartile range (IQR) remains highly stable at \(\approx 13\) mV over the full measured temperature range. This shows the PUF's good resilience over temperature, thus achieving high reliability on the analog signal level. To gain insight into unstable responses, we plot \(\Delta V_{comp}\) of PUF core 20 over all response bits, as shown in Figure 4(b). The bit positions \(r_{0}\), \(r_{6}\), \(r_{12}\), and \(r_{25}\) show small absolute values for \(\Delta V_{comp}\) of less than \(200\,\mathrm{\SIUnitSymbolMicro V}\), directly degrading reliability. As there is a chance that the output bits are biased towards an output direction (logical 0 or 1) due to the comparator's input offset voltage and, being kept stable, thus generating a false output, or if both comparator input voltages are inflicted due to noise, the comparator output is susceptible to showing flipping behavior. On the digital level, the evaluation of the binary responses by means of Equation (4) results in an average reliability of \(99.20\,\mathrm{\char 37}\). Figure 6 shows the reliability distribution for ten measured PUF cores, after performing test cases \(P_{1}\) to \(P_{15}\). All cores show reliabilities close to the ideal value of \(100\%\). Figure 5: PUF comparator input voltage differences \(\Delta V_{comp}\). (**a**) \(\Delta V_{comp}\) of ten PUF cores over ambient temperature range, (**b**) \(\Delta V_{comp}\) per response bit of one selected PUF core (no. 20).
# Design and Implementation of a Tether-Powered Hexacopter for Long Endurance Missions
## 3 Experimental Results
### Endurance Test  
Log of Endurance test is shown in Figure 7. The hexacopter reached average altitude of 48 m and maximum altitude of 59 m. The PSM provides average 17.9 A current, 23.5 V voltage, and is equivalent to 421 W power. The power requirement in the endurance test is more than in the functionality test because the length is much longer. The tether generates significant heat while conducting power from the ground station to the hexacopter. The side wind is also stronger at higher altitudes; therefore, the hexacopter needs more power to stabilize itself. The mission of 240 min has been completed successfully and thus the \"near infinite\" flight time can be demonstrated with a time-lapse video [26]. Among our surveyed research works so far, no battery powered UAV can fly for longer than 4 h.  
Averaged measurement data during the endurance test are listed in Table 5 along with those derived from the model in the previous section. In the endurance test, total tether length in air is 60 m and data are derived based on this length.  
Figure 6: Functional test: (**a**) altitude/current and (**b**) altitude/voltage during 50-min flight.  
Figure 7: Endurance test: (**a**) altitude/current and (**b**) altitude/voltage during 240-min flight.
# Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios
## 3 Localization System  
This section describes the reference indoor localization system as well as the anchor device modeling. Afterwards, we introduce the dual-band DoA estimation approach that is implemented in the proposed analysis environment based on the one presented in [16].
## 4 Discussion
### Effect on Coefficient of Thermal Expansion  
Table 5 displays the computed CTE values for the composition series (6P55-0B to 6P55-15B) via computational analysis from temperatures between 300 \({}^{\circ}\)C and 650 \({}^{\circ}\)C. As expected, the computational CTE values appear to be slightly higher than the experimentally recorded CTE for the base composition 6P55-0B (11.0 \(\times\) 10\({}^{-6}\) /\({}^{\circ}\)C); however, the values fall within the range. A general downward trend in CTE was observed with the substitution of boron with the exception of the 6P55-10B composition that deviates above the trend. The introduction of boron generally impacts thermal expansion properties. The increased CTE in 6P55-10B suggests that a specific balance of boron enhances the flexibility of the network, allowing for greater expansion when temperature changes. This optimal structure was determined previously in Section 4.5 of this paper. CTE is influenced by network connectivity, as described in the previous section. The increase in non-bridging oxygens and changes in bond types with higher boron content may lead to more space within the glass network, contributing to increased thermal expansion.  
Previous studies by [PERSON] et al. [22] indicated that computational methods, including quantitative structure-property relationships (QSPRs) and structural descriptors like \(F_{net}\) can accurately predict CTE (R\({}^{2}\)\(\geq\) 0.943). In coating applications, aligning the CTE of coatings with that of substrates is essential to avoid thermal and mechanical stress, which can cause delamination or cracking. For instance, Ti6 Al4V has a CTE of 9.40 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C. In dental applications like crowns, matching the coating's CTE with that of the substrate ensures consistent expansion and contraction. Mismatched CTEs can lead to tensile or compressive stress, impacting durability and performance. A ceramic with a higher CTEthan a metal alloy may chip due to expansion, while a lower CTE can enhance compressive strength and reduce cracking, improving material stability.
# Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation
## 6 Conclusions  
This paper proposes an optimal model of spinning reserve capacity with wind speed correlation, in which the load forecast error, wind power output prediction error, and unit forced outage rate of the uncertainty factors are considered. At the same time, the model is solved by the QPSO algorithm based on stochastic simulation. The main conclusions of this paper are as follows:  
* With an increase of wind speed correlation, the fluctuation of the wind farm output is greater, so the spinning reserve capacity should be increased. However, when the wind speed correlation is negative, the outputs of wind farms can be complementary, so the spinning reserve capacity should be reduced in comparison to non-correlation;
* With the increase of wind power installed capacity, the wind speed correlation has a greater effect on the spinning reserve capacity;
* The relationship between the total cost and confidence level of the test system was analyzed so that the results can provide decision support for dispatchers in the balance between reliability and economy.  
This paper is a collaborative effort among the authors. [PERSON] conceived the main ideas presented in the paper and conducted the main analysis; [PERSON] performed simulations and wrote part of the first draft. [PERSON] and [PERSON] provided an in-depth review and revised the final version thoroughly together.  
This research was funded by Department of Science and Technology of Sichuan Province, China (2017 GZ0263) and Department of Science and Technology of Cheng Du Province, China (2016-HM01-00275-SF).  
The authors declare no conflict of interest.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 8 Discussion and Limitations  
### Practical Use Cases  
The usability of ErrorExplainer has been evaluated in the most common on-chain scenario: ERC-20, ERC-721, and ERC-1155 contracts. Those standards dominate wallet interactions, token bridges, and marketplace front ends. With an average latency of only 0.64 s (Section 7.3), a provider can pre-index verified token contracts once, cache the results, and return human-readable explanations to users in real time.  
The same pipeline applies to DeFi protocols, where business logic is more complex (e.g., multi-step liquidity provision, leveraged vaults, or automated liquidations). In such settings, a failed transaction can incur direct financial risk. By analyzing every revert path, ErrorExplainer reveals a clear revert reason, such as \"Health factor below 1.0\" and \"Pool  
Figure 8: Matching fitness vs. lines of code. Bars give the mean matching fitness for each LOC bin. The flat trends of function-level matching fitness show that the accuracy of ErrorExplainer is independent of contract length, indicating the approach is suitable for a wide range of smart contracts.  
Figure 7: Matching fitness of error context by Solidity version. Matching fitness measures the proportion of unique error contexts that ErrorExplainer can distinguish by error outputs. For function-level matching, performance stays above 90% for versions 0.5–0.8, indicating that ErrorExplainer generalizes well to evolving language features. The fitness of trace-level matching is lower, but still above 60% in average, suggesting that ErrorExplainer can still identify the correct error context even under complex call chains.  
cap reached\". This enables users to adjust parameters or strategy in a single iteration rather than through trial and error.  
For instance, DeFi staking products have multiple lock-up conditions. Unstaking logic may check the minimum lock period, a cool-down window, and delegation status. When a withdrawal attempt reverts, the crypto wallet can only display \"execution reverted\", leaving the user to guess which condition failed. However, ErrorExplainer maps the runtime information to the code-level error context and can provide a detailed error message drawn from the source code, such as \"Withdrawal blocked: cool-down period ends in 2 days\". The user can learn what must change to unblock the withdrawal.
## 5 Discussion
It can be observed from the time series of individual points that correlation of LOS displacements with seasonal variation of water surface area increases towards the south of the region. The effect of seasonal change on different regions can also be examined by looking at the local standard deviation of the mean deformation velocity. Statistics provided in Figure 7 include the whole area for each geological unit. In order to observe how seasonal variation affects different regions, additionally, the standard deviations for local regions alone are also calculated. The average standard deviation for the deformation rate increases for both dataset from 0.3 mm/year to 1.7 mm/year as from north to the south. The increase of standard deviation is a direct effect of seasonal variation. Although the water source of Eber Lake has no direct relation with groundwater, both Eber Lake water surface and ground water level are affected by climate and fed by precipitation. Moreover, LOS displacement time series for both datasets are compared with groundwater level changes. There is quite a good correlation between groundwater level changes and  
Figure 10: Mean velocities along the profiles AA’-DD’ shown in Figure 5. (**a**) Vertical mean velocity. (**b**) Horizontal (E-W) mean velocity. LOS deformation trends both in long term and short term as shown in Figure 9a,b. As a result, the relation of groundwater level and lake water surface area with displacement time series show effects of hydrological changes to deformation. Correlation of the high deformation rate with soft lithological units of the Bolvadin region, the similarity between deformation rate and groundwater level changes and the high correlation of deformation rate change with seasonal variations in water surface of Eber Lake suggest that subsidence phenomenon might be a direct result of hydrological effects in the region. Besides, mean deformation velocities over the profile lines AA'-DD' suggest that there is no clear spatial evidence about the effects of the Bolvadin fault on deformation. Furthermore, the surface deformations in the a, c regions such as fracturing and fissuring are almost in the south-west north-east direction which is not parallel to the Bolvadin fault. As a result, it is very likely that those surface deformations observed in the region are the consequences of generic deformation trend affecting the whole graben system which is mainly caused by overexploitation of ground water and hydrological changes in the region.
# Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors
## 1 Introduction
Additionally, the model maintained a real-time detection speed that aligns with industrial standards, underscoring the practicality and effectiveness of our approach in identifying defects in miniature capacitors. Our work's contributions are multifaceted:  
1. Integrated algorithmic enhancement and performance efficiency: The deployment of YOLOv8 for detecting defects in micro-capacitors was notably advanced by integrating the SimAM attention mechanism with the BiFPN architecture. This combination significantly improved the model's precision in identifying small defects amidst complex visual backgrounds. The SimAM mechanism sharpened feature discernment, while the BiFPN architecture enhanced multi-scale feature fusion, leading to increased detection accuracy. Furthermore, the adoption of the WISE-IOU loss function marked a crucial progression in boosting the model's generalization and robustness. This adjustment is vital for ensuring real-time detection efficiency, effectively addressing challenges of sample variability and outlier impacts, and maintaining a balance between computational demand and operational effectiveness. 2. Dataset compilation and application relevance: The development of a high-quality dataset, derived from real industrial scenarios, underpins this paper. This dataset not only serves as a benchmark for model evaluation but also enriches the pool of data available for future research in this field. The way it is structured plays a crucial role in pushing forward the use of deep learning to identify defects in micro-capacitors, particularly in settings in which data are scarce. We start with a review of existing literature in Section 2, summarizing key knowledge and relevant studies to lay the groundwork for our work. In Section 3, we explore the architecture and essential elements of our proposed method, giving a detailed view of its design and execution. Section 4 provides a comprehensive evaluation of our technique, including an ablation study to highlight the effectiveness of various components and a performance comparison. Finally, Section 5 wraps up our study, reflecting on what our findings mean and suggesting future research avenues in this area.
# Pothole Classification Model Using Edge Detection in Road Image
###### Abstract  
Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2-0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71-0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.  
pothole; road damage; edge detection; real-time; classification; YOLO +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### The APP Feedback Case
#### 3.3.2 The PIC Output Variance with APP Feedback  
We now investigate the PIC output variance for the APP feedback case, using the model for residual feedback. We have,  
\[\mathcal{E}\left[(\hat{x}_{k,a}^{(n)})^{2}\right]=\mathcal{E}\left[\left(x_{k }+\sum_{j\
eq k}c_{jk}(x_{j}-\hat{x}_{j,a}^{(n-1)})+z_{k}\right)^{2}|_{x_{k}=1 },\hat{x}_{j,e}^{(n-1)},\hat{x}_{j,a}^{(n-1)},\hat{x}_{j,p}^{(n-1)}\forall j \right]. \tag{38}\]  
For the sake of notational elegance, we remove the conditional information in the following.  
\[\mathcal{E}\left[(\hat{x}_{k,a}^{(n)})^{2}\right]=\mathcal{E}\left[\left(x_{k }+\sum_{j\
eq k}c_{jk}(x_{j}-\hat{x}_{j,e}^{(n-1)})+\sum_{j\
eq k}c_{jk}\hat{x} _{j,R}^{(n-1)}+z_{k}\right)^{2}\right]. \tag{39}\]  
After some simplifications, i.e., \(\mathcal{E}[z_{k}]=0\), \(x_{k}^{2}=1\) and \(\mathcal{E}[z_{k}^{2}]=\sigma_{n}^{2}\), we get,  
\[(\tilde{\sigma}_{k,a}^{(n)})^{2}=1+\frac{1}{N}\sum_{j\
eq k}c_{jk} \Big{(}1-(\hat{x}_{j,e}^{(n)})^{2}\Big{)}+\mathcal{E}\left[\left(\sum_{j\
eq k }c_{jk}\hat{x}_{j,R}^{(n-1)}\right)^{2}\right] \tag{40}\] \[-2\mathcal{E}\left[x_{k}\sum_{j\
eq k}c_{jk}\hat{x}_{j,R}^{(n-1)}\right]\] \[-2\mathcal{E}\left[\left(\sum_{j\
eq k}c_{jk}(x_{j}-\hat{x}_{j,e }^{(n-1)})\right)\left(\sum_{j\
eq k}c_{jk}\hat{x}_{j,R}^{(n-1)}\right)\right] -(\hat{x}_{k,a}^{(n)})^{2}.\]  
Similarly to the PIC conditional mean for APP feedback, calculation of the expectations for \((\hat{\sigma}_{k,a}^{(n)})^{2}\) is very difficult. Specifically, determining expectations of \(\hat{x}_{j,R}^{(n)}\) conditioned on \(x_{k}=1\), given that \(\hat{x}_{j,R}^{(n)}\) has terms in the denominator that are correlated with \(x_{k}\), remains a challenge.
## 2 Materials and Methods
### Data
#### 2.2.2 DEM Data  
The elevation information is essential for the surface water detection models to generate logical results that surface water usually accumulates at the lowest relative points within a local drainage system. The elevation data from the digital elevation model (DEM) are also required as the surface water mapping model input. In this research, we use six different DEM or digital surface model (DSM) datasets to compare the associated water surface products, including the (1) Japan Aerospace Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) DSM V2.2 product [49], (2) World Wide Fund for Nature (WWF) HydroSHEDS Void-Filled DEM [50], (3) WWF HydroSHEDS Hydrologically Conditioned DEM [50], (4) Multierror Removed Improved-Terrain (MERIT) DEM [51], (5) Global Multiresolution Terrain Elevation Data 2010 (GMTED2010) [52], and (6) NASA/ USGS Shuttle Radar Topography Mission (SRTM) V3 product [53]. Figure 3 shows the terrain maps based on the selected DEM datasets. For more details, Table 2 provides additional information on their spatial resolution and references.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Satellite** & **Provider** & **Sensor** & **Polarization/Band** & **Resolution** & **Wavelength** \\ \hline Sentinel-1 & ESA & C-band SAR & VV & 10 m & \(\sim\)5.55 cm \\ Sentinel-2 & ESA & Multispectral Instrument (MSI) & Green & 10 m & \(\sim\)0.56 \(\upmu\)m \\  & & SWIR & 20 m & -1.61 \(\upmu\)m \\ Landsat-7 & NASA & Enhanced Thematic Mapper Plus (ETM+) & Green & 30 m & 0.52–0.60 \(\upmu\)m \\  & & SWIR & 30 m & 1.55–1.75 \(\upmu\)m \\ Landsat-8 & NASA & Operational Land Imager (OLI) & Green & 30 m & 0.53–0.59 \(\upmu\)m \\  & & SWIR & 30 m & 1.57–1.65 \(\upmu\)m \\ Landsat-9 & NASA & Operational Land Imager 2 (OLI 2) & Green & 30 m & 0.53–0.59 \(\upmu\)m \\  & & SWIR & 30 m & 1.57–1.65 \(\upmu\)m \\ \hline \end{tabular}
\end{table}
Table 1: Characters of the satellite images used in this study.  
Figure 3: DEM products in the study area.