esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, a high-resolution monitoring method is proposed for the depth development diagnosis of pitting corrosion in rod-like waveguide. According to the multimode and multipath natures of guided waves propagation, two modes with a complementary radial distribution of the NAPFD are excited simultaneously to identify the defect development. The energy of the defect scattered signal is focused by TRM, which effectively overcomes the problem that the damage is inconclusively evaluated rooting in the hard interpreted signal originating from multimode, multipath and dispersion of guided waves propagation.  
The method is verified to evaluate the 1 mm to 5 mm artificial pitting corrosion in the middle positions of galvanized steel wires. Mode \(L(0,2)\), with the NAPFD profile concentrating on the surface of a wire, is suitable for seeking the tiny superficial defect in early time. Mode \(L(0,3)\) whose radial distribution of the NAPFD is centered in the core area of a wire is turned out to track the defect deep inside instead of Mode \(L(0,2)\). The time-frequency representation of the swept signals at 790 kHz shows that modes \(L(0,2)\) and \(L(0,3)\) can be excited simultaneously and they are of approximately the same energy. The defect scattered signals were extracted by a truncation window determined by the proposed method to eliminate the direct signals that may cause erroneous judgment towards defect. In consideration of the inevitable discrepancies due to the different coupling conditions between the specimen and transducers, the amplitudes of the reconstructed signals are normalized by the RMS of the signals captured before the reflected wave packet of mode \(L(0,2)\) from the boundary. It is demonstrated that the amplitudes of the normalized reconstructed signals focused at the trigger point exhibit almost linearly increasing trend in line with the growing depth of pitting corrosion.  
The conventional method of straight forward detection through the differential amplitudes of single mode echo from different defects is also applied to corrosion assessment. By contrast, the proposed approach integrating multimode excitation with TRM is proved to be able to potentially supply more definite entire historical information of pitting corrosion.  
The authors are grateful for the financial support from National Natural Science Foundation of China (NSFC) under Grant Nos. 51478079 the Fundamental Research Funds for the Central Universities (Project No. DUT15 LAB11) and Science and technology project of Fujian Province (Grant Nos. 2012Y0021).  
Figure 12: Normalized defect echoes of mode \(L(0,2)\) at 700 kHz versus damage size from 1 mm to 4 mm.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] contributed analysis and checked the paper; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Detection of Received Signal Data Processing and ISI/BER Analysis
When the dual-mode (variable step constant modulus algorithm (CMA) and direct decision least mean square (DDLMS)) blind equalization scheme is selected, the size of the error function value can be used as the judgment criterion for mode conversion and step transformation: when the error function value is large, it can be converted into the CMA blind equalization scheme with fast convergence speed (or increase the iteration step of the blind equalizer), and when the error function value is small, it can be converted into the DDLMS blind equalization scheme (or decrease the iteration step of the blind equalizer), which is characterized by a small steady-state error. When the error function value is small, it can be converted to the DDLMS blind equalization scheme  
Figure 12: Phase distortion and amplitude distortion of received time-domain pulse waveforms after transmission over the airborne air-to-air channel. (**a**) Phase distortion and (**b**) amplitude distortion. with a small steady-state error (or reduce the iteration step size of the blind equalizer). The specific dual-mode blind equalization design scheme should also be based on the design principles of the overall scheme of the communication system and the actual application of the channel to determine the relevant content, described in another paper.
Previous studies have mainly focused on the theoretical and experimental research of thermal insulation materials, formulation processes, exploration of different material properties, as well as discussions on the advantages and disadvantages of specific insulation techniques. However, there are still shortcomings in the efficiency and methods of calculating the thermal conductivity of insulation materials. The lack of an efficient and accurate method for calculating the thermal conductivity of insulation materials restricts the further optimization, improvement, and efficient development of new products in insulation material design. Based on this, in order to improve the efficiency of calculating the TC of insulation materials and to make up for the shortcomings of existing research, this paper takes the graphite composite insulation material (GCIM) as the research object and establishes a theoretical calculation model of the TC of composite materials based on its structural characteristics. By utilizing theoretical calculations to quickly predict the TC of the GCIM, the efficiency of TC calculations is enhanced. Furthermore, a numerical model of the GCIM is established. Through the calculation analysis of the theoretical model and numerical model, the influence of changes in material composition on the TC of the GCIMis further explored. Compared to the majority of previous studies that focused on a single perspective or conducted shallow analyses of material performance, a more systematic and in-depth approach can provide a more comprehensive and valuable reference basis for the development of new insulation materials. This study aims to focus on graphite composite insulation materials as the core research object, addressing key issues in the thermal conductivity research of insulation materials. By utilizing a numerical model of graphite composite insulation boards and combining it with the aforementioned theoretical calculation models, this research intends to provide a comprehensive and highly valuable reference basis for the development of new insulation materials. This, in turn, will drive further advancements in the field of insulation materials in terms of thermal conductivity calculations and material development applications, contributing to alleviating building energy consumption pressures, addressing climate change, and tackling other real-world challenges. The research results have important theoretical significance for improving the efficiency of calculating the TC of composite materials and the development of new insulation materials.
###### Abstract  
High-performance cross-linked polyethylene (XLPE) is currently employed in ultra-high-voltage direct current (UHVDC) cables, with the electrical tree being an important cause of DC cable breakdown. The comparison of XLPE samples under different manufacturing processes can provide a reference for the progress of cable production processes. This paper compares laboratory-prepared XLPE samples (DC-XLPE) with XLPE samples extracted from actual cables (Cable-XLPE) through electrical tree experiments, X-ray diffraction (XRD), and gel permeation chromatography (GPC). The experimental findings indicate that the breakdown time of DC-XLPE increased by nearly 50% compared to Cable-XLPE, with slower electrical tree growth and lower average discharge magnitude observed. Overall, DC-XLPE exhibited superior resistance to DC electrical tree and partial discharge. XRD and GPC analyses revealed minimal differences in crystallinity and grain size between the two types, with the primary distinction being DC-XLPE's notably higher molecular weight and more concentrated molecular weight distribution. The differences in physicochemical properties may be attributed to more precise and uniform temperature control during the crosslinking process in laboratory settings, as well as a higher removal rate of crosslinking byproducts, ultimately leading to enhanced resistance to electrical tree and partial discharge in DC-XLPE.  
H  
VDC cable; XLPE insulation; electrical tree; partial discharge; physicochemical properties
## 3 Proposed Architecture
### Merlin
#### 3.2.1 MERLIN Core  
At its core, MERLIN has a FSM capable of interacting with ROSPlan. As it is shown in Figure 2, this FSM is represented by the class MerlinSM. This class is based on class StateMachine, which is the class in green, and the class MerlinROSPlanAssistant. Thus, StateMachine belongs to SMACH and provides the state machine engine. On the other hand, MerlinROSPlanAssistant has a pool of functions to interact with the knowledge base of ROSPlan. Using these functions, the knowledge that the robot has from the scenario can be created, updated or deleted.  
Finally, a factory has been created to manage the creation of states. This factory is represented in this class diagram by the class MerlinStateFactory, which has been developed using the factory software design pattern.  
Figure 1: Proposed architecture.  
Figure 2: Class Diagram of MERLIN Finite-State Machine.  
FSMs are used especially to create the Executive layer. In addition, they have been used in the Planning layer and the Mission layer. The FSM of the Executive layer and Planning layer has been created using the factory pattern, which is based on using a factory class to create the states of the FSM. The factory pattern will help us to arrange and create new states associated with robot basic capabilities such as navigation, object recognition. Besides, it provides the states for the planning layer.
## 4 Experimental Results and Discussion
### Quantitative Metrics Comparison  
Based on all the mentioned combinations, a quantitative comparison of performance metrics--including mAP_0.5:0.95, mAP_0.5, F1-Score, Recall, Precision, and Accuracy--is presented in Tables 8 and 9 and shows the percentage increase in performance metrics for all combinations and the final algorithm compared to the YOLOv5s6 Default model with augmentation.
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 4 Experimental Method
### Experimental Setup  
Figure 9 illustrates the operation of the complete test setup. Here, the TTL camera captures a JPEG image of the mangrove trees. After this, the Arduino MEGA microprocessor saves the photo data in the SD memory and coverts this data into a hexadecimal format. The hexadecimal data is then transferred to the receiver node via the LoRa physical layer. This data is split into 84-character packets to enable transmission. Once all these packets have been received, they are sent by the Arduino Uno processor to a personal computer, where they are reassembled and displayed using MATLAB code.
## 3 Friction-Induced Vibration Behavior under Low-Speed Operating Conditions
### Effect of Young's Modulus on Frictional Vibration Behavior  
In engineering practice, rubber with a different hardness is usually used as the bearing lining. Rubber with a different hardness has a different Young's modulus, and the frictional vibration behavior also changes with the change in the Young's modulus. So this section analyzes the influence of the Young's modulus on the frictional vibration behavior under low-speed operating conditions. The initial Young's modulus is set as E\({}_{0}\) (E\({}_{0}\) = 13.71 MPa), and E\({}_{0}\) is multiplied by different coefficients to obtain five Young's moduli: 0.5 E\({}_{0}\), 0.75 E\({}_{0}\), E\({}_{0}\), 1.25 E\({}_{0}\), and 1.5 E\({}_{0}\). The obtained Young's modulus is substituted into finite element software for analysis.  
Figure 12 shows the time-domain and frequency-domain diagrams of the frictional vibration under low-speed operating conditions with five Young's moduli. From 0.5 E\({}_{0}\) to 1.5 E\({}_{0}\), the peak frequency increases from 1443.8 Hz to 1928.4 Hz, respectively, an increase of approximately 33.6%. The downward displacement of the steel block decreased from 0.762 mm to 0.346 mm, a reduction of 52.3%, as shown in Figure 13.  
The modulus of the materials directly affects the frictional vibration behavior of objects. The modulus also affects the displacement of the block, thereby indirectly affecting the frictional vibration behavior by affecting the contact area between the ring and the block. The modulus can also affect the bearing capacity, with a large modulus indicating a strong bearing capacity, and vice versa. In engineering design, selecting low-modulus materials to reduce the vibration frequency can also lead to a decrease in the load-bearing capacity. Therefore, comprehensive consideration should be given, between the vibration behavior and the load-bearing capacity, to select the most suitable material.  
Figure 12: Time-domain and frequency-domain diagrams of low-speed operating conditions under different Young’s moduli.  
Figure 13: Peak frequency and downforce displacement of low-speed operating conditions under different Young’s moduli.
## 5 Discussion
### The Performance of the Rough Detection Network Using Different Outputs  
We only used 'output1' as the final result, although there are five 'output' for the rough detection network. Therefore, we compared the impact of different 'output' on the performance of the rough detection network. For 'output2' to 'output5', we enlarged the image size to 512 \(\times\) 512 by bilinear interpolation. The mean IoU values of 'output1' to 'output5' are shown in Table 9. The results of 'output1', 'output2', and 'output3' are similar. In order to improve the detection speed, the results of 'output3' to 'output5' were calculated alone while discarding the subsequent network structure. Furthermore, due to the reduction in the network size, we stack the cropped patch images together and a rescaled image with the shape of 1536 \(\times\) 1024 could be detected by the rough detection network immediately.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Information** & **Video3** & **Video4** & **Video5** & **Video6** \\ \hline Two adjacent frames (6 channels) & 71.52\% & 66.51\% & 67.49\% & 68.18\% \\ Two adjacent frames with optical flow (8 channels) & 70.94\% & 67.87\% & 69.51\% & 67.92\% \\ Three consecutive frames with optical flow & 71.50\% & 67.75\% & 70.60\% & 69.76\% \\ (13 channels) & & & & \\ Five consecutive frames with optical flow & 71.86\% & 66.86\% & 67.86\% & 67.12\% \\ (23 channels) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The influence of different input information on the performance of the rough detection network.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Video1** & **Video2** & **Video3** & **Video4** & **Video5** & **Video6** \\ \hline Three frame difference [4] & 34.60\% & 28.07\% & 27.39\% & 32.33\% & 33.34\% & 39.23\% \\ GMM model [5] & 45.51\% & 35.26\% & 34.87\% & 39.97\% & 44.99\% & 36.76\% \\ Improved Kalman [1] & 44.52\% & 39.87\% & 39.48\% & 42.95\% & 47.93\% & 51.15\% \\ Improved VIBE [2] & 55.51\% & 51.74\% & 53.29\% & 56.82\% & 58.71\% & 57.73\% \\ This paper & 80.19\% & 76.09\% & 73.12\% & 70.13\% & 73.34\% & 72.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The comparison between our method and traditional video object detection methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Output** & **Video1** & **Video2** & **Video3** & **Video4** & **Video5** & **Video6** \\ \hline output1 & 80.04\% & 77.91\% & 72.08\% & 67.94\% & 71.32\% & 71.51\% \\ output2 & 80.02\% & 77.95\% & 72.00\% & 67.85\% & 71.28\% & 71.25\% \\ output3 & 79.41\% & 77.40\% & 71.64\% & 67.53\% & 71.05\% & 70.83\% \\ output4 & 76.19\% & 74.77\% & 70.76\% & 65.78\% & 68.84\% & 68.15\% \\ output5 & 75.96\% & 73.40\% & 70.42\% & 65.30\% & 68.40\% & 68.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: The mean IoU values of ‘output1’ to ‘output5’ for rough detection network.
## 5 Conclusions  
This article reports a GPU-based path-independent video extensometer with two major novelties: First, it achieves sampling rates of 1.2 kHz, which is close to mechanical extensometers, and the analogue strain signal is generated with a latency of 2 milliseconds. Therefore, it is able to replace mechanical extensometers in standard LCF testing machines fulfilling the recommendations of ASTM E606 for standard strain-controlled fatigue testing with cycle frequencies up to 3 Hz. According to our knowledge, this is one of the first GPU-based image processing systems for real-time closed-loop applications. Second, it enables marker-free DIC measurements on polished hourglass specimen without loss of accuracy. As it is also able to acquire full-field strain images, this sensor combines most advantages of state-of-the-art optical and mechanical extensometers in terms of sampling rate, non-tactile and slip-free measurement, and full-field strain acquisition. Therefore, it simplifies LCF testing and it might trigger many new applications, in particular, if strain-fields need to be acquired synchronously to the load. It might also trigger industrial applications because the marker-free operation eliminates the need for sample preparation.  
Conceptualization, D.C. and C.E.; Funding acquisition, A.B., and M.S; Investigation, D.J.R., S.E., and M.S.; Methodology, A.B., S.E., and M.S.; Project administration, A.B., M.S., and S.E.; Software, A.B. and D.J.R.; Supervision, A.B.; Writing--original draft, A.B. and S.E.; Writing--review and editing, D.J.R., M.S., A.B. and D.C. This research was funded by the Fraunhofer-Gesellschaft in the project 'Schnelle und beruhrungslose optische Dehnungsmessung zur Echtraeit-Regelung bei der Materialprufung >>StrainControl>>'.  
The authors declare no conflict of interest.
## 3 Results and Discussion
All of the presented simulations were carried out at 1 s acquisition time per source-detector pair over the number of hardware gates of the detector. Overall, considering the 64 combinations of the source-detector pair, the measurement time would be around 1 minute. That measurement time was estimated as an acceptable trade-off between the collected signal and measurement duration. Furthermore, different source-detector implementations may allow shorter times. For example, efficient choosing of the most meaningful source-detector pair could reduce the measurement time, at least by a factor of 2.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods
### Method Overview  
To compare the traditional ML approach with TL in the context of knowledge transfer from laboratory-recorded data (source domain) to an emulated workshop environment (target domain), a structured workflow was implemented. This workflow consists of four parts: raw data preprocessing, domain data preparation and splitting, scenario parameter variation, and feature extraction and modeling, as illustrated in Figure 2. Detailed explanations for each stage are provided in the corresponding subsections.  
Binary classification was implemented to distinguish between balanced and unbalanced cases, with all unbalance levels grouped into a single class. In the modeling step, an ML model was developed and evaluated on the source-to-source transfer task, where both training and testing were performed using the source domain data. A TL model was then executed for the same task, and its classification accuracy was compared with that of ML models.  
To assess model generalization, the ML and TL models were further evaluated on the source-to-target task, where training was conducted on the source domain data, whereas testing was performed on the target domain data. Model accuracy, sensitivity, and specificity were analyzed under different experimental conditions by varying three key parameters as follows:  
* Segmentation step--influences the amount of training and testing data (Section 2.3.2);
* Type of added noise--represents ambient noise in a workshop environment, affecting the signal characteristics (Section 2.4.2);
* Severity of added noise--defines noise severity, simulating different levels of signal disruption under workshop conditions (Section 2.4.2).  
These parameter variations resulted in 24 distinct preprocessing configurations, referred to as scenarios. The ML and TL models were executed for each scenario, with a detailed description of the parameters used in each scenario provided in Section 2.5.  
Figure 2: The methodology workflow.
## 4 Study on the Influence of Blast Vibrations on Tunnels
### Displacement Variation of Measurement Points Due to Blast Vibrations  
During the blasting process, the displacement changes at each measurement point are minimal. We only extracted and analyzed the extreme values and abrupt variations in the simulated results. Subsequently, the displacement histories of each measurement point under in situ stress conditions of 1 MPa and 5 MPa were plotted. For detailed results, please refer to Figure 11.  
Figure 10: The stress distribution at different measurement points under in situ stress of 1 MPa and 5 MPa.  
Due to the use of a plastic dynamic material model (\"MAT_PLASTIC_KINEMATIC) in some parts of the rock mass, and because the vibration speeds are all less than 9 cm/s, according to the \"Safety regulations for blasting GB6722-2014 [30]\", the safe allowable standard for blasting vibration particles is 18-25 cm/s. Under in situ stress conditions of 1 MPa and 5 MPa, the vibration speeds at each measuring point are all below 18 cm/s, which complies with safety standards. Furthermore, the maximum displacement values at each measuring point are less than 1.2 \(\times\) 10\({}^{-3}\) m, indicating that the displacement generated by blasting is very small. According to Section 3.2 and Figure 8, it can be observed that within the time interval of 0 to 2.5 s, although there is significant variation in the displacement values among the measurement points, the fluctuation of displacement at each point over time is relatively small. Furthermore, after 0.025 s following the detonation of the explosives, the displacement values at all measurement points generally stabilize. Under the in situ stress condition of 1 MPa, the displacement values at each measuring point fluctuate within 0-11 \(\times\) 10\({}^{-4}\) m, whereas under the 5 MPa in situ stress condition, the displacement values at each measuring point fluctuate within 0-1.23 \(\times\) 10\({}^{-3}\) m.