esa-sceva/satcom-chunk-collection huggingface dataset:
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
### Research Gap and Novelty of the Proposed FRA6\(\sigma\) Framework  
While FRA is widely recognized as a non-invasive and highly sensitive technique for diagnosing transformer mechanical faults, its diagnostic accuracy is limited by the subjectivity of interpretation. Conventional approaches rely heavily on the visual comparison of response signatures or the expertise of trained personnel, which introduce inconsistency and limit scalability across utilities.  
In recent years, several studies have introduced artificial intelligence (AI)-based methods--such as support vector machines, decision trees, and neural networks--to automate FRA interpretation. Although these techniques show promising results, they often suffer from low transparency, dependence on extensive training datasets, and limited adaptability across transformer designs and operating conditions. Furthermore, these methods do not provide insight into the statistical behavior of the fault signature deviations, which is essential for robust and interpretable fault classification. Six Sigma, on the other hand, has been extensively used in manufacturing and process quality control, but its application in the field of transformer diagnostics is largely unexplored. No prior studies have integrated Six Sigma principles--such as control charts and process capability indices--with FRA data to detect early-stage winding deformities or dielectric anomalies. This presents a critical research gap. The proposed FRA66 framework is the first to systematically integrate Six Sigma methodology with FRA-based transformer diagnostics. By applying \(\overline{X}\) and \(\overline{R}\) control charts and evaluating the process capability index (\(C_{p}\)) and process capability performance index (\(C_{pk}\)), the method introduces quantifiable, repeatable thresholds for anomaly detection. This provides a structured alternative to heuristic or AI-based models, enabling objective diagnostics and early fault detection. Additionally, the framework links diagnostic deviations to frequency-specific fault categories, enhancing interpretability and facilitating predictive maintenance planning.  
This novel contribution bridges a gap between quality control engineering and power system diagnostics, offering a new paradigm for transformer health monitoring that is both statistically rigorous and practically implementable.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 3 A Data and Knowledge-Driven Digital Twin Framework for Smart Bridge O&M  
This study proposed a BDT model driven by both data and expert knowledge to address the limitations of traditional models concerning accuracy and intelligence. The model comprises three main components: the spatial model, temporal model, and expert knowledge model. These models work in unison to facilitate real-time health monitoring and generate early warnings for bridges, such as alerts for structural damage, deformation, or other critical anomalies.  
The spatial and temporal models utilize high-resolution cameras, light detection and ranging (LiDAR), and various sensors to collect multi-source data from the bridge structure, including 2D image data, 3D point cloud data, and sensor time series data. The spatial model primarily describes the bridge's spatial characteristics through the modeling of 2D image and 3D point cloud data, providing an accurate spatial representation of the bridge structure. The temporal model focuses on the bridge's physical characteristics, capturing changes in internal physical states and long-term developmental trends under load.  
The expert knowledge model consolidates insights from expert interviews, workshops, industry standards, and historical case analyses. This aggregated knowledge is then standardized and structured into an expert knowledge base, which integrates extensive expertise in bridge maintenance and inspection to offer theoretical support and practical guidance for evaluating and assessing bridge conditions. The spatial and temporal models together form the data-driven module, which is responsible for collecting and processing real-time information and developmental trends of the bridge structure. These data are then transmitted to the expert knowledge-driven evaluation module. The evaluation module automatically analyzes and assesses the data by incorporating knowledge from the expert knowledge base, providing a comprehensive assessment of the bridge's condition and predicting its health status. Thus, the DTM, combining data-driven and knowledge-driven approaches, forms a powerful bridge health monitoring and early warning system. The DK-DTM framework shown in Figure 1.  
Figure 1: The data and knowledge-driven digital twin modeling.
## 2 Materials and Methods
### Methods
#### 2.5.2 Time-Series Phenological Modelling
This procedure was conducted using the R model Phenopix mentioned in Section 2.5[68]. The spline model employed In this study is the smoothing spline function. This function is capable of fitting the seasonal phenological curve while also effectively removing outliers and off-season signals. It achieves this by fitting piecewise polynomials to temporal segments of the pre-processed phenological curve and subsequently joining these polynomials into one continuous curve. The spline model not only models the seasonal trajectories but also demands the continuity of both the modeled curve and its derivative [38]. As a data-driven function, the spline model does not impose constraints on the shape of the modeled curve, unlike double logistic functions. This characteristic makes it an ideal alternative to the two double logistic functions previously introduced, especially when dealing with data of lower quality, as it can effectively capture the complex patterns exhibited by crops.
###### Abstract  
Botanical biopesticides have emerged as sustainable and eco-friendly alternatives to synthetic pesticides, whose indiscriminate use leads to several drawbacks to human and environmental health. To the best of our knowledge, there have been no reports on _M. pubscens_' bioactivity on phytopathogens affecting crops as a potential fungicide or antifedant. This has encouraged us to investigate the potential of the roots of this plant as a source of biopesticides. The present study reports on the evaluation of the roots extract from _Magonia pubescens_ A. St.[PERSON], a species from the Cerrado (Brazilian savannah), on the phytopathogenic fungi _Botrits etarea_, _Fusarium oxysporum_, and _Alternaria alternata_. In addition, its insect antifedant effect was assayed against _Chrysadeixis chalicites_. Thus, an in vitro test-assay was used to determine the fungicide potential (percentage growth inhibition, % GI) of the ethanolic extract of this plant species, whereas a leaf-disk bioassay on the 5 th instar larvae of _C. chalcites_ was performed to evaluate its insecticidal potential. The ethanolic extract was further fractionated by liquid-liquid partition using solvents of increasing polarity. The hexane/dichloromethane fraction exhibited a moderated potency and was similar to the ethanolic extract on the three assayed fungi (around % GI 30 at 1 mg/mL), whereas the _n_-butanol fraction showed a slight improvement of the fungicide effect against _B. cinerea_ (% GI 39.18 at 1 mg/mL). Moreover, the ethanolic extract exhibited a strong antifeedant activity, with a refusal rate (FR) higher than 90% in both choice and non-choice assays against _C. chalcites_, while the ethyl acetate and _n_-butanol fractions behaved as appetite suppressors. These results highlight _M. pubscens_ as a promising source of biopesticides and deserve further investigations to optimize extraction procedures.  
Keywords:_Magonia pubescens_; biopesticides; phytopathogenic fungi; _Chrysodeixis chalicites
# Droop Control Strategy of Utility-Scale Photovoltaic Systems Using Adaptive Dead Band
## 2 Reactive Power-Voltage Droop Control Method
### Conventional Reactive Power-Voltage Droop Control Method
The voltage magnitude at both ends of the dead band is included in the formula, excluding the part where the reactive power output is zero due to the dead band range. \[\textit{Droop slope}=\frac{Q_{max,i}}{V_{dd,low,i}-V_{L}}=\ -\frac{Q_{min,i}}{V_{dd,high,i}-V_{H}}\, \forall i\in T \tag{5}\]  
In the droop equation, the terms related to voltage and reactive change can be explained through an example. The reactive power output change is set to 2.00 p.u with full leading to full lagging. Figure 1: Characteristic curve of existing reactive power-voltage droop control method. The ratio of the scheduled operating voltage to the allowable voltage magnitude range is used in the voltage change term of the droop equation, and if the operating voltage is 1.00 p.u and the allowable range is 0.95-1.05 p.u, it can be confirmed by Equation (6) that the droop is 20%. \[\textit{Droop}\ (\%)=\frac{2.00\ p.u}{\frac{1.05\ p.u-0.95\ p.u}{1.0\ p.u}}=20\ p.u= \frac{1}{20\%(\textit{droop})} \tag{6}\]  
According to the characteristic curve of the reactive power-voltage droop control, the dead band value is set for the voltage. If the dead band is not set, there may be a chattering phenomenon in which the switch of specific equipment in the system is opened and closed repeatedly. Appropriate range setting of the dead band is essential for maintaining stability according to the voltage deviation in systems containing volatile resources, such as RESs. In the conventional reactive power-voltage droop control method, voltage control is performed using a fixed dead band value. However, using fixed values for the variable output of renewable energy and load variation can lead to the following problems. * If the voltage drops significantly when the system strength is weak, it is difficult to recover the voltage because sufficient capacitive reactive power is not supplied owing to a fixed dead band. * If an overvoltage occurs when the system strength is strong, an overshoot appears because sufficient inductive reactive power is not produced because of the use of a fixed dead band. * An unnecessary converter operation may occur frequently because of the voltage fluctuations. * Frequent switching for multiple converters to deal with power quality issues may even cause resonance and transient overvoltage.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 3 Scenario Description  
We investigated an ad-hoc network of bicycles. More specifically, we considered a smart bike sharing systems for data collection application. Each bicycle has embedded sensor and a 802.11p communication device. The bikes read their sensors, generate packets every second and store them in their buffers. All bicycle sharing stations are equipped with base stations that are connected to the Internet. They have a 802.11p interface and act as fixed sinks. In our scenario, a packet is relayed until it reaches one of the sinks. Only when a node is in communication range with base stations, it forwards all data stored in its buffer to them.  
A simple scenario with eight bikes and six base stations is depicted in Figure 1. Bike 1 leaves Base Station 2 and starts generating data. After a certain period of time defined in seconds and when Bikes 1 and 2 are within communication range, they exchange data stored. Each bike stores the data in their buffers until it encounters a base station. Bike 4 lies in the area of Base Station 5, hence it forwards all data stored in its buffer. In this way, data are stored in the buffers of neighboring nodes until they reach the destination.
## 2 Results and Discussion
### Vapor-Phase Minimum Inhibitory Concentration (VP-MIC)
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Vapor-Phase Minimum Inhibitory Concentration (VP-MIC) mg/mL**} \\ \hline \multirow{3}{*}{Mix} & _Enterococcus faecalis_ & _Staphylococcus aureus_ & _MRSA_ & _Klebsiella pneumoniae_ & _Acinetobacter_ & _Pseudomonas aeruginosa_ & _Enterobacter_ & _Escherichia coli_ & _Salmonella typhi ATCC_ \\  & _ATCC19433_ & _Newman_ & _USA300_ & _ATCC13883_ & _AB5075_ & _PAO1_ & _Cloace_ & _ATCC87_ & _35664_ \\ \hline Th & * & \(1.8\pm 1\) & \(2.4\pm 0\) & \(2\pm 0.7\) & * & \(1.2\pm 1\) & \(4.7\pm 0\) & \(2\pm 0.7\) & \(4.7\pm 0\) \\ \hline Su & * & \(2.4\pm 0\) & \(2.4\pm 0\) & \(1.6\pm 0.7\) & \(4.7\pm 0\) & \(2.4\pm 2\) & * & 3.1 \(\pm\) 1.4 & \(2\pm 0.7\) \\ \hline Cr & * & \(1.2\pm 0\) & \(1.2\pm 0\) & \(1.2\pm 0\) & \(2.4\pm 2\) & \(1.2\pm 0\) & \(1.6\pm 0.7\) & \(2\pm 0.7\) & \(1\pm 0.3\) \\ \hline Cp & * & \(3.5\pm 2\) & * & \(3.5\pm 2\) & * & * & \(4.7\pm 0\) & \(4.7\pm 0\) \\ \hline EDT & * & \(2.4\pm 0\) & \(2.4\pm 0\) & \(2.4\pm 0\) & \(2.4\pm 0\) & \(2.4\pm 0\) & \(3.1\pm 1.4\) & \(4.7\pm 0\) & \(2.4\pm 0\) \\ \hline EDN & \(3.1\pm 1.4\) & \(0.8\pm 0.3\) & \(0.8\pm 0.3\) & \(3.1\pm 1.4\) & \(2.4\pm 0\) & \(1.2\pm 0\) & \(2.0\pm 0.7\) & \(0.8\pm 0.3\) & \(0.8\pm 0.3\) \\ \hline SZT & * & \(2.4\pm 0\) & \(2.4\pm 0\) & \(2.4\pm 0\) & \(2.4\pm 0\) & * & \(2.4\pm 0\) & \(2.4\pm 0\) \\ \hline SZS & * & \(4.7\pm 0\) & \(4.7\pm 0\) & \(4.7\pm 0\) & \(4.7\pm 0\) & * & \(4.7\pm 0\) & \(4.7\pm 0\) \\ \hline \hline \end{tabular}  
*—Indicates that there was no antibacterial activity detected within the tested concentrations.  
\end{table} 
Table 3: Vapor-phase antibacterial activity of the tested herbal mixtures and their major individuals.
## 6 Blockage-Free Scenario
### Blockage-Free End-to-End Performances  
Using the same rationale, we can obtain blockage-free end-to-end performance as a baseline. From Equations (20), (25), and (26), the average end-to-end hop counts \(\eta\) for the blockage-free case for FN and NN are obtained as  
\[\eta_{FN}^{*}= \lim_{\beta\to 0}\frac{L\Phi}{2\sin\left(\frac{\Phi}{2}\right) \mathbb{E}\{D_{FN}|K\geq 1\}}\] \[= \sqrt{-\frac{\lambda\Phi}{2}}\frac{L\Phi\left(e^{\frac{\lambda \Phi R^{2}}{2}}-1\right)}{\sin\left(\frac{\Phi}{2}\right)\left[2\Gamma \left(\frac{3}{2},-\frac{1}{2}\lambda\Phi R^{2}\right)-\sqrt{\pi}\right]}, \tag{27}\]  
\[\eta_{NN}^{*}= \lim_{\beta\to 0}\frac{L\Phi}{2\sin\left(\frac{\Phi}{2}\right) \mathbb{E}\{D_{NN}|K\geq 1\}}\] \[= \sqrt{\frac{\lambda\Phi}{2}}\frac{L\Phi\left(1-e^{-\frac{ \lambda\Phi R^{2}}{2}}\right)}{\sin\left(\frac{\Phi}{2}\right)\left[\sqrt{\pi }-2\Gamma\left(\frac{3}{2},\frac{1}{2}\lambda\Phi R^{2}\right)\right]}, \tag{28}\]respectively. Hence, the corresponding blockage-free end-to-end outage rate, which can serve as the lower bounds of \(P_{out:EE}\) in (21), is given by  
\[P_{out:EE}^{*}=\lim_{\beta\to 0}1-(1-P_{out})^{\eta}\] \[=1-\left(1-\lim_{\beta\to 0}e^{-\frac{\Phi_{0}}{\beta^{2}}\left[1-(1+ \beta R)e^{-\beta R}\right]}\right)^{\eta^{*}}\] \[=1-\left(1-e^{-\frac{1}{2}\pi\lambda R^{2}}\right)^{\eta^{*}}, \tag{29}\]  
where \(\eta^{*}\in\{\eta_{FN}^{*},\eta_{NN}^{*}\}\) in Equations (27) and (28). Furthermore, assuming \(\alpha=2\), the normalized transmit energies of the FN and NN for the end-to-end transmission are  
\[\mathrm{Y}_{FN}^{*}=\frac{\sqrt{-\frac{2\Phi}{\lambda}}}{L^{\alpha-1}\sin \left(\frac{\Phi}{2}\right)}\frac{1-\Gamma\left(2,-\frac{1}{2}\lambda\Phi R^{2 }\right)}{2\Gamma\left(\frac{3}{2},-\frac{1}{2}\lambda\Phi R^{2}\right)-\sqrt {\pi}}, \tag{30}\]  
\[\mathrm{Y}_{NN}^{*}=\frac{\sqrt{\frac{2\Phi}{\lambda}}}{L^{\alpha-1}\sin \left(\frac{\Phi}{2}\right)}\frac{1-\Gamma\left(2,\frac{1}{2}\lambda\Phi R^{2 }\right)}{\sqrt{\pi}-2\Gamma\left(\frac{3}{2},\frac{1}{2}\lambda\Phi R^{2} \right)}. \tag{31}\]  
respectively.
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 4 Experiments
### Experiments and Analysis
#### 4.2.4 Ablation Experiments  
The ablation experiment was conducted to verify the important role of the two key technologies, SDM and multi-scale adaptive edge detector, in the proposed method. The experimental control groups were set up as follows: (1) using the gradient map instead of SDM to compare and (2) selecting edge-detection algorithms, such as Canny [26], Edge Drawing [27], and SBED [28], to replace the multi-scale adaptive edge detector.  
Table 2 shows the statistics of the average errors of the equivalent diameter, elliptical major diameter, and elliptical short diameter of our method and the comparison methods for all test samples. Through observation, it can be seen that the mean errors of the equivalent diameter, elliptical major diameter, and elliptical short diameter all increased to varying degrees after replacing SDM with the gradient map. Among them, the mean error of the equivalent diameter increased by 0.43 cm, the mean error of the elliptical long diameter increased by 1.45 cm, and the mean error of the elliptical short diameter increased by 1.25 cm. The experiments demonstrate that SDM can improve the accuracy of ore rock fragmentation calculation.  
It also can be seen that the mean errors of the equivalent diameter, elliptical major diameter, and elliptical short diameter increased to varying degrees after replacing the multi-scale adaptive edge detector with Canny, Edge Drawing, and SBED. Among them, Canny had a significant increase in error, with a mean error of 0.31 cm in the equivalent diameter, 1.11 cm in the elliptical long diameter, and 0.88 cm in the elliptical short diameter. The experiments demonstrate that multi-scale adaptive edge detector can improve the accuracy of the ore rock fragmentation calculation.
## 4 Numerical Examples
### The Case for an FGM Ring Reinforced in a Homogeneous Plate
Hence, it can be concluded that the increase of the width of the ring is beneficial to relieve the bending stress concentration, but the effect will not be obvious after the width reaches a certain value.
## 3 Proposed Approach  
The end-to-end sequential pipeline, illustrated in Figure 1, consists of five primary steps: (i) dataset preprocessing, (ii) model training, (iii) transfer learning, (iv) knowledge distillation, and (v) evaluation. These steps are briefly described as follows, with further details provided in the relevant subsequent sections:  
1. The dataset preprocessing step involves window-based segmentation, signal quality assessment, peak detection, and HRV/PRV feature extraction.
2. The model training step involves the implementation of multiple neural network architectures and hyperparameter selection.
3. The transfer learning step involves adapting the learned representation of the ECG signals to fine-tune the PPG-based model.
4. The knowledge distillation step involves the utilization of the _teacher_-_student_ learning paradigm to compress the fine-tuned PPG-based model for lower resource consumption.
5. Finally, in the evaluation step, we assess the performance of the implemented models using standard measures and interpretability techniques.  
Figure 1: High-level end-to-end overview of the proposed approach. Each of the models separately undergoes preprocessing and model evaluation steps. The PPG (student) model development is based on the distillation of the ECG (teacher) model. The PPG model is then further fine-tuned on the PPG dataset.  
While ECGs and PPG represent different physiological processes, a reliable sensor captures heart rhythm patterns similarly, even if their magnitudes differ. A peak in an ECG correlates with a peak in PPG, and rhythmic abnormalities manifest in both modalities. The underlying patterns, i.e., an irregular rhythm and the absence of distinct P-waves in ECGs, translate to comparable irregularities in pulse intervals detected by PPG. In atrial fibrillation specifically, the fundamental irregularity in cardiac rhythm manifests in both signals through a common underlying pathophysiology. ECGs directly measure the electrical activity of the heart, while PPG captures blood volume changes in peripheral vessels. The electrical impulses detected by ECGs trigger mechanical contraction of the heart, which then generates pressure waves that propagate through the arterial system and are detected by PPG with a small, predictable time delay. Our transfer learning approach focuses on these shared pattern characteristics rather than intricate modality-specific details. This physiological correspondence provides the foundation for our teacher-student model's effectiveness, as the network learns to recognize these rhythm patterns regardless of the specific signal characteristics.
## 2 Methodology
### General Framework  
To solve the problem of missing local details in classification maps due to superpixel segmentation, we proposed a feature fusion of the CNN and GCN, as shown in Figure 1. The proposed network framework contained a spectral dimension reduction module (see Section 2.2 for details), a graph convolutional branch (see Section 2.3 for details), a convolutional branch (see Section 2.4 for details), a feature fusion module, and a Softmax classifier. It should be noted that the features extracted from convolutional neural networks were different from those of graph convolutional networks. Feature fusion methods can utilize different features of an image to complement each other's strengths, thus obtaining more robust and accurate results. Because of that, it is possible to obtain better classification results than a single branch by fusing features from two branches.  
The original HSI was handled by the spectral dimensionality reduction module first, which was used for spectral information transformation and feature dimensionality reduction. Then, we used convolutional neural networks to extract the detailed features in a local fine region. Considering the problem that the CNN-based method may induce overfitting with too many parameters and an insufficient number of training samples, we used a depth-wise separable convolution to reduce the parameters and enhance the robustness. To further improve the model, we added attention modules to the convolution branch. We used the SE attention module to optimize the proposed network [30]. The SE module can obtain the weight matrix of different channels. Then, the weight values of each channel calculated by the SE module were multiplied with the two-dimensional matrix of the corresponding channel of the original feature map. We used graph convolutional networks to extract the superpixel-level contextual features. In this branch, we applied a graph encoder and a graph decoder to implement the transformation of pixel features and superpixel-level features (see Section 2.5 for details). Next, the different features acquired by the two branches were fused to obtain richer image features by complementing their strengths. Finally, after the processing of the Softmax classifier, we obtained the label of each pixel. The role of Softmax is to assign a probability value to each output classification, indicating the probability of belonging to each class.