esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 5 Conclusions  
This paper presents an in-depth investigation into early-warning methods for wind turbine gearboxes, proposing a novel M-IALO-GRU prediction model. By integrating wind turbine SCADA data, the model predicts the gearbox oil temperature and determines the alarm threshold range using the \(3-\sigma\) principle. An alarm signal is triggered when the residual between the actual oil temperature and the predicted value exceeds the threshold, enabling effective fault early warning for the gearbox. To enhance model performance, the Pearson correlation coefficient is employed to filter SCADA time-series data, aligning it with the characteristics of the GRU network. Furthermore, the improved ant lion optimizer (IALO) algorithm is utilized to optimize the GRU hyperparameters, addressing issues of poor model fitting caused by suboptimal parameter selection. Validation is conducted  
Figure 12: (**a-e**) are the prediction results of different model exception samples, respectively.  
using one year of operational data and fault logs from a wind turbine at the Dabancheng Wind Farm, Xinjiang. Comparative experiments with five distinct early-warning models demonstrate that the M-IALO-GRU method outperforms existing approaches under the tested conditions. Specifically, it achieves a narrower alarm threshold range, higher sensitivity, and improved early-warning accuracy, significantly reducing the risk of severe accidents caused by delayed alarms. This advancement contributes to the safe and stable operation of wind turbines by enabling timely fault detection and intervention.  
Conceptualization, Y.W. and H.L.; methodology, Y.W.; software, Y.W.; validation, Y.W. and H.L.; formal analysis, Y.W.; investigation, Y.W.; resources, W.S.; data curation, Y.W. and S.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W.; visualization, Y.W. and H.L.; supervision, W.S.; project administration, Y.W. and Q.Z.; funding acquisition, W.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by research on the trend prediction method of operation stability deterioration of large-scale wind energy generating units (Grant number: 51565055) and research on multimodal intelligent perception and the migration method of spindle bearings of wind turbines under the Collaborative Framework (Grant number: 2022B01049).  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors would like to thank the Xinjiang Wind Energy Group in Xinjiang, China, for their support throughout the research project.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Data Analysis  
#### 2.4.1 Accuracy and Complexity Evaluation  
To identify the best model, we calculated the overall accuracy (OA), mean intersection over union (mIoU), kappa, F1-score, precision (Pre), and recall (Rec) metrics and evaluated classification accuracy using a confusion matrix. In this matrix, true positives (TPs) are correctly classified pixels, false positives (FPs) are incorrectly classified pixels, true negatives (TNs) are correctly predicted failures, and false negatives (FNs) are incorrectly predicted failures. Pre is the ratio of TPs to the sum of the TPs and FPs and indicates the accuracy within the class. Rec is the ratio of TPs to the sum of TPs and FNs; this is a measure of class confusion. The inclusion of Rec in the F1-score mitigates the imbalance between different types. The OA and kappa value were calculated as percentages of correctly classified pixels [97]. Kappa is more accurate and objective when there is an imbalance in dataset types. Kappa denotes the correlation between human-based validation and that of the machine learning classifier. The kappa value [98] ranges from \(-1\) to \(1\), where values below 0 indicate no agreement, 0-0.2 indicate slight agreement, 0.2-0.4 indicate fair agreement, 0.4-0.6 indicate agreement, 0.6-0.8 indicate substantial agreement, and 0.8-1 indicate near-perfect or perfect agreement. IoU is the ratio of the overlapping area of the sum of the ground truth and predicted area to the total area and is widely used to assess semantic segmentation [2]. We compared the number of floating-point operations per second (FLOPs) and number of parameters [99] between the MSG-GCN and other SOTA models.
## 5 Modeling of Hybrid Fiber-Reinforced Concrete Elements
### Materials Constitutive Models  
As a brittle material, concrete is susceptible to two types of failure mechanisms: crushing under compression stress and cracking under tension stress, which often occurs in the plastic range. To a certain extent, however, concrete is considered an isotropic material in the elastic range. The elastic-isotropic function in the ABAQUS software was used to identify the concrete model's modulus of elasticity (\(E_{c}\)) and Poisson's ratio (0.2). Meanwhile, to implementing the actual concrete performance at the plastic range for these FE models, two different stress-strain constitutive models were adopted for the concrete under compression and tension stresses using the concrete damage plasticity (\(CDP\)) option [50; 51; 52; 53; 54].  
In general, for implementing the concrete model at the plastic range, the values of dilation angle, eccentricity, stress ratio, shape factor (\(K\)), and viscosity parameters have been taken equal to 40, 0.1, 1.16, 0.667, and 0.0, respectively. The newly developed analytical expression (Equation (12)) was used for the concrete compressive behaviour to estimate the stress-strain relationships for the hybrid fiber-reinforced concrete. While, for the concrete tension behavior, the same constitutive model adopted earlier by [PERSON] et al. [50] for estimating the concrete stress-strain relationships were used, which are expressed as follows:  
\[\varepsilon_{cr}=f_{to}/E_{c} \tag{14}\]  
Figure 15: Typical boundary conditions of the suggested \(3D\)\(FE\) models. (**a**) Cube model. (**b**) \(RC\) column model.  
\[f_{t}=E_{c}\cdot\epsilon_{t}\text{ for }\epsilon_{t}\leq\epsilon_{cr} \tag{15}\]  
\[f_{t}=f_{t0}\cdot\left(\epsilon_{cr}/\epsilon_{t}\right)^{0.8}\text{ for }\epsilon_{t}<\epsilon_{cr} \tag{16}\]  
where \(E_{c}\) is the elastic modulus of concrete; \(f_{t}\) and \(f_{t0}\) are the concrete tensile strength, and the cracking failure (\(0.31\left(0.8\,f_{cu}\right)^{1/2}\)), respectively. The \(f_{c}\) and \(f_{cu}\) are the cubic compressive strength and the ultimate cubic compressive strength of concrete, respectively. The \(\epsilon_{t}\) and \(\epsilon_{cr}\) are the strains of concrete at relevant tensile stress (\(f_{t}\)) and cracking strain at \(f_{t0}\), respectively. The \(CDP\) of the concrete model under compression and tension stresses are illustrated in Figure 16, where their damage parameters (\(\hat{d}_{c}\) and \(d_{t}\)) and the related inelastic and cracking stains (\(\epsilon^{in}\) and \(\epsilon^{ck}\)) are estimated as follows:  
\[d_{c} =1-f_{c}/f_{cu} \tag{17}\] \[d_{t} =1-f_{t}/f_{t0}\] (18) \[\epsilon^{in} =\epsilon_{c}-f_{c}/E_{c}\] (19) \[\epsilon^{ck} =\epsilon_{t}-f_{t}/E_{c} \tag{20}\]  
Both the steel bars' elastic modulus and Poisson's ratio as well as their yield strengths were determined in the elastic-isotropic option, whereas their plastic-isotropic (related plastic strain) were identified. Stress-strain models similar to those used in [49] were employed in this work.
# A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach
###### Abstract  
In numerous electrical power distribution systems and other engineering contexts, single-line diagrams (SLDs) are frequently used. The importance of digitizing these images is growing. This is primarily because better engineering practices are required in areas such as equipment maintenance, asset management, safety, and others. Processing and analyzing these drawings, however, is a difficult job. With enough annotated training data, deep neural networks perform better in many object detection applications. Based on deep-learning techniques, a dataset can be used to assess the overall quality of a visual system. Unfortunately, there are no such datasets for single-line diagrams available to the general research community. To augment real image datasets, generative adversarial networks (GANs) can be used to create a variety of more realistic training images. The goal of this study was to explain how deep-convolutional-GAN- (DCGAN) and least-squares-GAN- (LSCAN) generated images are evaluated for quality. In order to improve the datasets and confirm the effectiveness of synthetic datasets, our work blended synthetic images with actual images. Additionally, we added synthetic images to the original picture collection to prepare an augmented dataset for symbol detection. In this scenario, we employed You Look Only Once (YOLO) V5, one of the versions of YOLO. The recognition performance was improved, reaching an accuracy of 95% with YOLO V5, after combining the actual images with the synthetic images created by the DCGAN and LSGAN. By incorporating synthetic samples into the dataset, the overall quality of the training data was improved, and the learning process for the model became simpler. Furthermore, the proposed method significantly improved symbol detection in SLDs, according to the findings of the experiments.  
DCGAN; LSGAN; synthetic images; single-line diagrams; YOLO V5 2023
## 2 Measurement Principles of Four-Sensor Probe  
### Electrical Circuit of Four-Sensor Probe  
As shown in Figure 1, an electrical circuit can be adopted in the four-sensor probe measurement. Since signal filtering and noise reduction can be easily realized by the appropriate signal pre-processing MATLAB codes (MATLAB 2017b, MathWorks, Inc., Natic, MA, USA), the circuit elements responsible for these functions were not necessary and thereby only four electrical resistances were employed in the circuit. The probe contains four sensors denoted \(s_{0}\), \(s_{1}\), \(s_{2}\) and \(s_{3}\), which connect to the negative electrode of the DC power supply through their respective resistance. The four sensors are covered by a rigid stainless-steel shell, which connects to the positive electrode of the DC power and functions as the common high voltage pole.  
The shell of the probe made of metal was not insulated and thus always in contact with water. The four sensors are well insulated except at their very ends where the sensors can contact the water and thus get through to the positive pole of DC supplier. The signals from \(s_{0}\) to \(s_{3}\) are either high level or low level, depending on whether the sensor tips are submerged into water or exposed to the bubble air. A high voltage level indicates that a particular sensor is submerged in water and a low voltage level indicates that a particular sensor is exposed to air. Finally, the signals of high or low voltage from four sensors were transmitted to data collection system composed by a data acquisition unit (Art Technology, Beijing, China) and a PC. It is worth noting that the sensors have to be connected to the negative electrode of the DC power to avoid electrochemical corrosion and expand the probe's lifespan.  
For the data acquisition system, it is suggested in the literature that a sample frequency higher than 10 kHz is required to guarantee resolution. Therefore, a sample frequency of 10 kHz for each of the four sensors was employed in this study.
## 4 Validation
### Validation Using VH-IVUS Images  
For this experiment, 76 VH-IVUS images were selected from five patients. The VH-IVUS images were classified into TCFA ('1') and non-TCFA ('0') by SVM with RBF kernel. A cardiologist's decision was also applied, and those images were classified [109]. Based on the assigned labels, a confusion matrix was obtained and TP, TN, FP, FN, accuracy, sensitivity, specificity, precision, and F-score are presented in Table 9.  
Based on the above table, TP and TN demonstrate that VH-IVUS correctly identified 41 non-TCFA and 33 TCFA plaques. In addition, FP indicates that all non-TCFA plaques are correctly classified, whereas FN depicts that VH-IVUS misclassified two TCFA plaques. Furthermore, VH-IVUS demonstrates acceptable results in terms of accuracy, sensitivity, specificity, precision, and F-score in identifying TCFA plaque. The accuracy of the cardiologist's decision and the SVM classifier for 76 VH-IVUS images achieved the best results, with 97.36% in terms of accuracy. These results demonstrate that the proposed approach has the potential to identify high-risk lesions.
## 4 Results and Discussion
The power produced from PV is only available between 08:00-18:00 h daily due to the intermittent nature of solar irradiance. In this case study, two diesel generators operated at minimal capacity. The cost of operation of this configuration is NGN 10,250/kWh and it is slightly lower when compared to a system mainly run by diesel generators. The environmental assessment shows a further reduction in emission production at 2387 lb/h as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time (h)**} & **Load demand** & **Generator 1** & **Generator 2** & **PV Generator** \\  & **(kWh)** & **(kWh)** & **(kWh)** & **(kWh)** \\ \hline
01:00 & 12.58 & 10 & 2.58 & - \\ \hline
02:00 & 12.34 & 10 & 2.4 & - \\ \hline
03:00 & 11.17 & 10 & 1.17 & - \\ \hline
04:00 & 11  
**Case 4**: The configuration of this case study utilized a diesel generator, wind turbine, solar PV and a battery storage system to meet the load demand of the clinic and the power produced by these generating units can be seen in Table 7. There is an indication that the power generated by the diesel generator drastically reduced when compared to other case studies as shown in Figure 9. It is further observed that the integration of the renewable energy sources with a battery storage system into the system enhance the performance of the hybrid energy system in meeting the load demand. This configuration shows an improvement in the economic and emission performance as presented in Figure 1.0 The result provides the lowest optimized cost of NGN 9312/kWh and emission performance at 2349 lb/h. This is an indication that the diesel generator is only operating at the least possible working capacity. Figure 1.1 Case 3: Performance evaluation of economics and emissions. Figure 1.2 Case 3: power output variation from diesel generators and PV generator. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time (h)** & **Load Demand** & **Generator 1** & **PV Generator** & **WTG (kWh)** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 7: Optimal power output for Case 4. Figure 9: Case 4: Power output variation from diesel, PV and wind turbine generators.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ TCXO & Temperature Compensated Crystal Oscillator \\ PCO & Phase Center Offset \\ PCV & Phase Center Variations \\ SNR & Signal to Noise Ratio \\ SBAS & Satellite-Based Augmentation System \\ PPP & Precise Point Positioning \\ RTCM & Radio Technical Commission for Maritime Service \\ QZSS & Quasi-Zenith Satellite System \\ EUREF & European Reference Frame \\ RINEX & Receiver Independent Exchange format \\ LGO & Leica Geo Office \\ BSW & Bernese GPS Software \\ RMSE & Root Mean Square Error \\ \end{tabular}
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 1 Introduction
### Current Methods  
Numerous DL techniques for kidney segmentation have been created utilizing (CNNs). In reference [56], The proposed 3D fully convolutional network uses a pyramid combining unit and progressively improved feature unit, evaluated using a specific dataset with an average Dice coefficient of 0.931%, by [PERSON] et al. [PERSON] et al. [57] AlexNet was utilized as a classifier to distinguish non-kidney CT slices from kidney slices to prevent segment discontinuity, and a phase of recovery was implemented. Later, in initial segmentation by a 2D U-Net architecture, the CCL method decreased the number of false-positive predictions. A local test dataset produced a Dice coefficient degree of 96.33% for the (KiTS19) dataset. [PERSON] et al. [PERSON] et al. [58] Instead of aggregating operations, dilated convolution blocks were utilized in a three-stage 3D U-Net. Due to the three phases within the 3D U-Net, input images comprised low-resolution, high-resolution, and trimmed images. A hybrid reduction function comprised of (DC) and a weighted cross-entropy was employed. On the KiTS19 dataset, the technique attained an average Dice score(DS) of 0.9674. [PERSON] et al. [PERSON] et al. [59] A hybrid 3D V-Net model using ResNet++ output layer architecture outperformed the original V-Net with a 0.97 Dice coefficient. Training is challenging due to time and computing resources, and lightweight 2D models are preferred for kidney segmentation assignments.
# Study on the Leakage and Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
## 4 Validation Work on the Numerical Method
### Comparison between Experimental Results and Numerical Results  
In order to better verify the approach based on the FEM/CFD coupling iterative algorithm, the static room temperature experimental results are used to compare with the numerical results. Corresponding to the operating conditions shown in Table 2, Figure 11 presents the comparison between the experimental results and the numerical results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Points** & **1** & **2** & **3** & **4** & **5** \\ \hline Pressure in high-pressure cavity/MPa & 0.350 & 0.445 & 0.530 & 0.618 & 0.698 \\ Pressure in low-pressure cavity/MPa & 0.150 & 0.195 & 0.230 & 0.268 & 0.298 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental conditions.  
Figure 10: Test 2 SFS.  
Figure 9: Partial schematic diagram of the test cavity.  
It can be seen from Figure 11a that the numerical leakage of 2 SFS is in good agreement with the tested leakage in general. The results also show that the method built in this paper overestimates the leakage at high axial pressure difference, and this is mainly because the pressure closing effect of finger seal is not considered [11]. When the axial pressure difference is relatively large, the finger/rotor radial clearance decreases due to the pressure closing effect of finger seal, so the tested leakage is lower than the numerical leakage. In Figure 11b, it is clear to see that the numerical predicted pressure in the middle-pressure cavity is less than the experimental results, but the deviation is not obvious. According to the experimental results, the 2 nd finger seal bears more axial pressure difference, so the pressure closing effect is more significant, which leads to smaller finger/rotor radial clearance than that of the 1 st finger seal. However, the numerical method does not consider the pressure closing effect. As a result, the predicted pressure in middle-pressure cavity is smaller than the experimental results. It can be draw from the above analysis that the method proposed in this paper can predict the leakage performance and pressure drop characteristics of 2 SFS with reasonable accuracy.
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 2 Anonymous Vehicle Authentication  
The OBU's identity can be kept perfectly anonymous in this protocol, since the real ID of OBU is not known to the RSU and other vehicles except the TA.  
* When the OBU requests for a short-time anonymous key, it sends to RSU the pseudo-id \(PID_{i}=Enc_{u}(rnd||ID_{i})\) which is a random identity mark, and RSU does not know which it is.
* When OBUs communicate each other, OBU uses a random pseudo-id \(PID_{i}^{\prime}=Enc_{j_{i}}(T_{i},PID_{i})\) to denote the identity, it is different with time going by and it has no means to other OBUs.
## 5 Conclusions  
Based on [PERSON] et al.'s [PERSON] et al. (2019) identification of the clamping phenomenon, we sought to investigate whether the concept's applicability could be extended to S0 Lamb wave modes. Two approaches for designing a resonator were used based on the concepts discussed by [PERSON] et al. These approaches resulted in the frequency-matched resonators and the clamping resonators (based on the clamping concept). The clamping resonators performed well by decreasing the amplitude of the transmitted  
Figure 8: Finite-element-predicted wavenumber spectrum of the guided waves that were allowed to transmit past the resonator array. This spectrum is expected to only contain forward (\(+x\)) traveling waves.  
Figure 7: Finite-element predictions of the \(x\)-component displacement of an S0 Lamb wave at 50 kHz plotted along the \(x\)-axis showing that the clamping resonators block wave propagation, while the frequency-matched resonators do not.  
wave by about 75%. When measuring the reflection from the clamping resonators, 62% of the incident wave was found to be reflected. The reason these two percentages do not equal each other is provisionally attributed to scattering from the resonator array and beam spread. In comparison, the frequency-matched resonators did not reduce the S0 Lamb wave amplitude. While the results for the clamping resonators used in these experiments are limited to the S0 Lamb mode at 50 kHz, these results and those of [PERSON] et al. suggest that by designing a resonator based on the concept of clamping the waveguide, a metamaterial capable of prohibiting the propagation of a low-frequency A0 or S0 Lamb wave is possible. This also suggests that these resonators could be mode-specific by using different resonator designs for each mode. Moreover, a more detailed description of the basis for the concepts used to design the clamping resonators will be provided along with the associated theoretical modeling effort, which is still ongoing. Future work will focus on the frequency stop-bandwidth.  
Conceptualization, P.S., C.L. and C.H.; investigation, C.H.; writing--original draft preparation, C.H.; writing--review and editing, C.H., C.L. and P.S.; supervision, C.L.; project administration, P.S.; funding acquisition, P.S. This research was funded by a seed grant from the College of Engineering at The Pennsylvania State University, \"Engineering a giant metamaterial: a band-stop seismic/blast filter to shield critical civil infrastructure\". The authors declare no conflict of interest.