esa-sceva/satcom-chunk-collection huggingface dataset:
# Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review
## 2 Conceptualization of Partial Discharge in Transformers
Internal discharges are created due to voids or cavities inside solid insulation material. These voids are generally formed during the manufacturing process and are impossible to eliminate. Electrical treeing is a form of defect propagation which occurs within solid  
Figure 1: Partial discharge equivalent circuit. insulation. These generally originate as insulation defects such as voids and spread through the material under high electric fields. The degradation of transformer cellulose insulation under the chemical processes of pyrolysis, hydrolysis and oxidation further results in the breakdown of compounds which are trapped in the transformer oil and thus tend to fast track other failure modes due to tracking. The mechanisms used for detecting and monitoring PD are premised on the properties and physical characteristics that PD exhibit. Various detection methods and sensors have been developed with the aim of identifying the sounds, electromagnetic waves, optical waves and electrical pulses that are generated by the onset of PD. Some of the PD detection methods which are widely used include acoustic emission, electrical, optical, electromagnetic and ultra-high frequency. Acoustic emission entails the conversion of pressure waves--which can or may not include sound and are created by the mechanical force at the location of the PD pulse [16]--into electrical signals which can be detected via a dedicated acquisition system. Benefits of the acoustic emission method are that it can be used for PD detection both inside and outside a transformer by means of multiple acoustic sensors placed at strategic locations. It is capable of detecting multiple PD sources, as well as detecting the location of the PD by the use of Time Delay of Arrival difference between the sensors. Electrical detection method is the most widely used method and involves the detection of the current pulse generated by the PD. This method employs either a direct probing or RF emission. Direct probing requires capacitive couplers to be connected to the phase terminals of the transformer, while RF uses antennas for the detection. Both these detection methods are highly susceptible to noise, and require careful setup and calibration in industrial use. The optical detection method relies on the presence of light created due to a spark. This method works well for detecting corona discharge but falls short when it comes to internal discharge. The method utilizes optical sensors, with advancement in these sensors allowing for PD detection both in air and oil [16].
## 2 Related Work  
### Rolling Production Mode  
The emergence of the rolling production mode stems from enterprises' dual needs to maximize equipment utilization and dynamically adapt to continuous orders. Firstly,manufacturers need to avoid idle equipment through continuous production task allocation. Secondly, in real-time order-receiving scenarios, new orders and corresponding workpieces continuously enter the production system. Unlike traditional FJSP, the rolling production mode does not create a scheduling plan for the entire production cycle at once. Instead, it divides the production plan into multiple cycles. At the end of each cycle, rescheduling is carried out based on the current production status, order demand, and resource availability. By continuously rolling adjustments and optimizing scheduling, the dynamic adaptability of the plan is maintained, and equipment utilization is fully maximized. Although some literature [9] mentions the rolling production mode, there is currently no authoritative study that formally defines this concept.  
In order to improve equipment utilization, some studies have adopted a workpiece batching strategy to reduce the risk of equipment vacancy. For example, [PERSON] et al. [10] constructed a batch scheduling model under dual constraints of set time and transportation resources. [PERSON] et al. [11] studied the scheduling problem of batch flow shop. A competitive and cooperative bird optimization algorithm was proposed in [12] for solving the batch scheduling problem in flexible job shops. [PERSON] et al. [13] conducted research on batch processing and scheduling problems in mixed flow workshops. They minimize total job delays and improve machine utilization by determining the number and composition of batches. [PERSON] et al. [14] proposed a branch and bound parallel batch scheduling algorithm that performs batch scheduling for jobs with different date attributes to improve device utilization. There are also methods that use rolling production planning to improve equipment utilization. For example, [PERSON] et al. [9] proposed an improved genetic algorithm to solve the flexible job shop scheduling problem in rolling production mode. In summary, there is currently relatively little research on scenarios based on the rolling production mode.
## 5 Analysis and Discussion of Calculation Results
### Distribution Law of Stratum Frost Heave Displacement  
The size of the frozen soil curtain grows larger with time, causing vertical and horizontal displacement of the stratum due to frost heave. Figure 8 depicts the distribution features of the vertical frost heave displacement field. The highest vertical frost heave displacement at 32 d is 26.08 mm. The formation of the frozen soil curtain is relatively slow during the first 32 days because only the circular freezing tube inside the concrete pipe is opened for freezing; after 32 days, the formation rate accelerates due to the opening of the profiled freezing tube to strengthen the freezing. The maximum displacement is 75.5 mm after 45 days and 133.69 mm after 70 days. Following that, the size of the frozen soil curtain changes insignificantly, with a maximum displacement of 167.49 mm at 90 days.  
Because of the model's symmetry, the vertical frost heave displacement field is horizontally symmetrical in general, and its value increases with the time of freezing. The maximum positive value of vertical displacement always appears near the top of the pipe curtain, related to the tunnel's shallow buried depth, the thin covering soil layer, and the comparatively large volume of frozen soil. The vertical frost heave displacement decreases as soil depth increases, indicating that the larger effective stress of deep soil partially offsets the frost heave force, which has a limiting effect on frost heave deformation until the vertical displacement at the bottom of the pipe curtain shows a small negative value. The vertical frost heave displacement in the tunnel excavation segment is quite minor due to the overall stiffness of the pipe curtain.  
As shown in Figure 9, the horizontal frost heave displacement field, like the vertical frost heave displacement field, changes slightly within 0-32 days, with a maximum value of 20.64 mm at 32 d. It increased rapidly from 32 d to 70 d, with maximum values of 39.9 mmand 56.8 mm at 45 d and 70 d, respectively. The development then slows in 70 to 90 days, reaching a maximum of 72.3 mm at 90 days.  
Figure 8: Vertical frost heave displacement distribution of stratum: (**a**) 32 days; (**b**) 45 days; (**c**) 90 days.  
Figure 9: Horizontal frost heave displacement distribution of stratum: (**a**) 32 days; (**b**) 45 days; (**c**) 90 days.  
Overall, the stratum's horizontal frost heave displacement field has a horizontally antisymmetric distribution, and its absolute magnitude increases with the duration of continuous freezing. In addition to the ground surface, areas with high displacement values are distributed beyond the pipe curtain's waist, where there is primarily a silty clay layer with high water content and a high frost heave rate, causing significant deformation as the thickness of the frozen soil curtain increases during the freezing process. The horizontal frost heave displacement is quite minor in the tunnel excavation segment.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 3 Methods  
### Framework Overview  
To address the challenges of low-light image enhancement, our framework integrates a patch-wise regularization strategy guided by uncertainty estimation. This method combines local feature refinement and global structural consistency by processing multi-scale patches, ensuring the model captures both detailed information and overall image structure effectively.  
As Figure 1 shows, our framework begins with a low-light input image \(X_{0}\), where a specific region is selected for patch-wise regularization to maintain local structural features. In the top section, the diffusion process iteratively refines the input image by adding noise \(\epsilon\), then uses a U-Net to predict and remove noise, resulting in a progressively enhanced image \(X_{t-1}\). This process is guided by the target image \(Y\) and a learned weighting parameter \(\tilde{\alpha}_{t}\), ensuring stepwise improvement. Simultaneously, the bottom section applies patch-wise structure regularization, extracting identical patches from both the input and intermediate enhanced images. A matrix structure feature supervises this process with uncertainty guidance, ensuring the model follows a stable optimization path and preservesglobal structural consistency. The combination of these two methods enables the model to enhance the image while retaining critical structural details for improved visual quality.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework
### Plan Executor Module Technical Implementation
#### 3.1.1 Dynamic Execution Process  
The Plan Executor manages the complete scheduling lifecycle through a sequence of coordinated operations: (1) triggering reschedules based on system feedback, time periods, or operator intervention, (2) coordinating with the simulator to obtain updated contact windows, (3) interfacing with the Task Manager to generate initial task lists incorporating failed tasks and remaining assignments from previous schedules, (4) updating satellite resource states using telemetry feedback, (5) invoking the scheduler optimization process, and (6) distributing the resulting plans to respective system agents. The execution diagram is illustrated in Figure 2.
# A Comparative Study of Signal Processing Methods for Contactless Geodetic Monitoring
###### Abstract  
Building structures are subject to various deformations caused by external and internal factors. Deformations are determined by various methods in the form of monitoring. It is very important to monitor the dynamic vibration response on bridge structures since these measurements allow us to identify any possible damage over time and take appropriate action. Our experiment, described in this article, is based on the use of non-contact methods, among which we used a geodetic instrument RTS (Robotic Total Station) and a seismograph to measure vibrations. The purpose and novelty of our work are reflected in the use of geodetic instruments to determine the dynamic response and synchronization of the obtained results. When using RTS technology, we increased data acquisition from 9 to 26 measurements per second. Comparative analysis of the measured signals was performed using FFT (Fast Fourier Transformation) and LSP (Lomb-Scargle Periodogram), based on LSSA (Least-Squares Spectral Analysis). The results showed us that when using the RTS geodetic instrument, it is possible to achieve frequency spectra comparable to those measured with a seismograph instrument. By increasing the number of measurements, the RTS method can be used to obtain more continuous data, which are essential for dynamic analyses.  
vibration monitoring; dynamic bridge movements; robotic total station; geodesy +
Footnote †: journal: Applied Sciences  
**2021**, _11_, 11276. [[https://doi.org/10.3390/app112311276](https://doi.org/10.3390/app112311276)]([https://doi.org/10.3390/app112311276](https://doi.org/10.3390/app112311276))
# Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles
## 5 Conclusions and Future Directions  
In this paper, we thoroughly studied the minimum safe driving separation distance or the Assured Clear Distance Ahead (ACDA) that must be used to avoid forward collisions. We have presented the braking and the stopping distance as well as the braking dynamics that are essential to understand the minimum required separation gap to avoid the collision. We have discussed all factors that affect the ACDA, namely, the speed of both vehicles, the initial separation distance, the tire state, the weather condition, the road condition, and the vehicle's mass. Furthermore, we have presented the necessary equations required to calculate that distance. In addition, we have devised and analyzed the maximum speed of the following vehicle to avoid the collision given the speed of the followed vehicle and the current separation distance for all studied factors that affect the stopping distance.  
Moreover, both ACDA and the maximum safe driving speed equations and analysis were presented in the case when the followed vehicle suddenly stops. The study was complemented by the case when the following vehicle is equipped with Autonomous Emergency Braking (AEB) system. Finally, we have presented the required braking force as a function of the vehicle mass for different speeds and braking distances. We have presented the traditional and smart distance measurement techniques between the followed and the following vehicles that can be used in connected and autonomous vehicles. The presented analytical results are essential for connected vehicles and self-driving vehicles to avoid collisions and maximize road utilization, and hence reduce traffic problems. One important advantage of this study is using vehicle-to-vehicle communication of IoV; both vehicles know the factors affecting the ACDA and the maximum allowed safe speeds, and they exchange them; thus, each can calculate them in real-time.  
The future directions of this study are to use the proposed calculations of ACDA and the safe following driving speed to detect automatically and autonomously tailgating violations using IoV. We are currently exploring this research direction.  
Conceptualization, S.A.E.M.; methodology, S.A.E.M. and M.A.A.-H.; software, M.T.B.O. and S.A.E.M. validation, S.A.E.M. and K.A.A.; formal analysis, S.A.E.M. and M.T.B.O.; investigation, S.A.E.M.; resources, K.A.A. and M.T.B.O.; writing--original draft preparation, S.A.E.M.; writing--review and editing, S.A.E.M. and M.A.-H.; visualization, S.A.E.M. project administration, K.A.A.; funding acquisition, K.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Plan for Science, Technology, and Innovation (MAARIFAH)--King Abdulaziz City for Science and Technology (KACST), Kingdom of Saudi Arabia. Award number: 13-INF2456-08-R.  
Figure 24: Required braking force as a function of the vehicle mass for different speeds and braking distances.
# Interface Behavior Analysis of Different Geomembrane Liner Systems Based on PIV Techniques
## 3 Results and Discussion
### Sand Displacements Analysis of Different Geomembranes  
The photos of the smooth geomembrane and the ribbed geomembrane during the loading process were intercepted and calibrated, respectively, and then the cross-correlation calculation was carried out with the initial time to obtain the displacement nephogram at the corresponding time. To study the effect of ribs on the sand displacements, the horizontal and vertical displacement nephograms of the smooth geomembrane conditions were compared with the strip rib geomembrane conditions, as shown in Figures 12 and 13. The shadows in the middle of the figures were caused by the test equipment blocking 3 the model container in the shooting direction. Its influence on the calculation result was eliminated by drawing masks in the PIV image settings. Figure 12 shows that the absolute value of the horizontal displacement of sand above the smooth geomembrane interface was 0.0825 mm. The absolute value of the horizontal displacement of sand above the ribbed geomembrane interface was 0.0063. As shown in Figure 13, the absolute value of the horizontal displacement of sand above the smooth geomembrane interface was 0.2275 mm, and the absolute value of the horizontal displacement of sand above the ribbed geomembrane interface was 0.0588 mm. This showed that the ribbed geomembrane reduced the horizontal and vertical displacements effectively.  
Figure 12: Displacement nephograms of the smooth geomembrane condition. (**a**) Horizontal displacement. (**b**) Vertical displacement.  
Figure 13: Displacement nephograms of the ribbed geomembrane condition (\(d=60\) mm, \(h=4.5\) mm). (**a**) Horizontal displacement. (**b**) Vertical displacement.  
To analyze the displacements of the sand particles above the geomembrane interface, the data in the loading process were extracted and plotted, as shown in Figures 14 and 15. It was shown that the ribs directly resisted the sand particles, formed a more comprehensive range of indirectly affected areas, and combined the sand closely. This area prevented the interface of the geomembrane liner system from slipping and constrained the deformation of the liner system. This was a kind of quasi-cohesive force, which improved the strength of the slope surface. With the increasing normal stress of the ribs, the bearing resistance of the ribs enhanced the interface shear strength significantly. At the same time, the friction resistance of the ribs increasing with the incremental upper load. Therefore, compared with the smooth geomembrane conditions, the ribbed geomembrane conditions could effectively improve the stability of the liner system, which was in accordance with the interaction mechanism between the sand and the denti-strip [11].
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
###### Abstract  
Path hopping serves as an active defense mechanism in network security, yet it encounters challenges like a restricted path switching space, the recurrent use of similar paths and vital nodes, a singular triggering mechanism for path switching, and fixed hopping intervals. This paper introduces an active defense method employing multiple constraints and strategies for path hopping. A depth-first search (DFS) traversal is utilized to compute all possible paths between nodes, thereby broadening the path switching space while simplifying path generation complexity. Subsequently, constraints are imposed on residual bandwidth, selection periods, path similitude, and critical nodes to reduce the likelihood of reusing similar paths and crucial nodes. Moreover, two path switching strategies are formulated based on the weights of residual bandwidth and critical nodes, along with the calculation of path switching periods. This facilitates adaptive switching of path hopping paths and intervals, contingent on the network's residual bandwidth threshold, in response to diverse attack scenarios. Simulation outcomes illustrate that this method, while maintaining normal communication performance, expands the path switching space effectively, safeguards against eavesdropping and link-flooding attacks, enhances path switching diversity and unpredictability, and fortifies the network's resilience against malicious attacks.  
active defense; path hopping; multi-constraint; multi-strategy Article
## 4 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Type** & **Model** & **Epoch** & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{3-5}  & & & **Training** & **Validation** \\ \hline \multirow{4}{*}{MFCC} & VGGNet-19 & 5 & 2.17 & 2.17 \\ \cline{2-5}  & Xception & 5 & 2.17 & 5.43 \\ \cline{2-5}  & ResNet-101 & 5 & 18.12 & 22.83 \\ \cline{2-5}  & DenseNet-201 & 5 & 66.67 & 48.91 \\ \hline \multirow{4}{*}{Spectrogram} & VGGNet-19 & 5 & 2.9 & 2.17 \\ \cline{2-5}  & Xception & 5 & 14.49 & 8.7 \\ \cline{2-5}  & ResNet-101 & 5 & 36.96 & 38.04 \\ \cline{2-5}  & DenseNet-201 & 5 & 75.36 & 63.04 \\ \hline \multirow{4}{*}{Log spectrogram} & VGGNet-19 & 5 & 2.17 & 2.17 \\ \cline{2-5}  & Xception & 5 & 94.06 & 70.65 \\ \cline{2-5}  & ResNet-101 & 5 & 86.96 & 72.83 \\ \cline{2-5}  & DenseNet-201 & 5 & 97.10 & 84.78 \\ \hline \multirow{4}{*}{Mel spectrogram} & VGGNet-19 & 5 & 2.17 & 2.17 \\ \cline{2-5}  & Xception & 5 & 2.9 & 5.43 \\ \cline{2-5}  & ResNet-101 & 5 & 24.64 & 32.61 \\ \cline{2-5}  & DenseNet-201 & 5 & 50.72 & 38.04 \\ \hline \multirow{4}{*}{Scalogram} & VGGNet-19 & 5 & 3.62 & 4.35 \\ \cline{2-5}  & Xception & 5 & 96.38 & 67.39 \\ \cline{1-1} \cline{2-5}  & ResNet-101 & 5 & 96.38 & 67.39 \\ \cline{1-1} \cline{2-5}  & DenseNet-201 & 5 & 99.28 & 75.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracies achieved by various models based on the CNN using different time-frequency representations on MIT-BIH-arrhythmia-ECG. The mean accuracies of the different models in PTB-ECG were 94.43% for VGGNet-19, 97.82% for Xception, 97.48% for ResNet-101, and 96.87% for DenseNet-201. The mean accuracies of the different models in CU-ECG were 87.70% for VGGNet-19, 92.13% for Xception, 90.78% for ResNet-101, and 90.80% for DenseNet-201. The mean accuracies of PTB-ECG and CU-ECG for the different models were 91.06% for VGGNet-19, 94.97% for Xception, 94.13% for ResNet-201, and 93.84% for DenseNet-201. The Xception accuracies were 3.91%, 0.84%, and 1.14% higher than the VGGNet-19, ResNet-101, and DenseNet-201 accuracies, respectively. Figure 16 shows a comparison between the accuracies of various models based on the CNN. The mean accuracies of PCA-L2 and PCANet were 97.81 and 98.54 in PTB-ECG, 88.75 and 90.42 in CU-ECG, and 2.4 and 84.78 in MIT-BIH-arrhythmia-ECG, respectively. The Xception in PTB-ECG was 0.1% higher than PCA-L2 and 0.72% lower than PCANet. The Xception in CU-ECG was 3.38% and 1.71% higher than PCA-L2 and PCANet, respectively.