esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The early detection of defects as cracks, micro-cracks, and finger failures in solar cells is important for the production of PV modules. Analyzing EL images to locate and identify these failures is typically a time-consuming manual process and requires expert knowledge.  
In this paper, a machine learning-based failure identification method was presented. The technique uses EL images to classify three classes of faults using a feature vector based on statistical parameters. The feature vector has a significant advantage over the standard feature vector that uses all the main output metrics. The developed feature vector achieves an accuracy and F1-score similar to state-of-the-art reported results despite a smaller dataset. As the proposed method requires less computation power and time, it will be valuable for outdoor EL inspection using intelligent unmanned aerial vehicles or drone-mounted systems.  
Conceptualization: H.R.P.; Investigation: H.R.P., Y.B., and Z.H.; Methodology: H.R.P. and F.V.; Project administration: D.S.; Resources: S.W.; Software: H.R.P., Y.B.; Supervision: Z.H., and S.S.; Validation: Z.H., Y.B.; Writing--original draft, H.R.P.; Writing--review & editing: Z.H., Y.B., S.S., F.V., G.A.D.R.B., P.B.P., T.K., and D.S. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Innovation Fund Denmark, grant number: ID 6154-00012B and Australian Renewable Energy Agency (ARENA), grant number: ID 2020/RNND016.  
The Innovation Fund Denmark partially supported this study within the research project DronEL-\"Fast and accurate inspection of large PV plants using aerial drone imaging\" (ID 6154-00012B). The Australian Renewable Energy Agency (ARENA) funded a part of the project (Grant ID 2020/RNND016). The authors also would like to express their gratitude to the PI Berlin Group for providing the EL images.  
The authors declare no conflict of interest. Moreover, the funders had no role in the study's design; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
The solar cells in the test modules used in this study were analyzed individually by automatically extracting the cell-level EL images. From each solar cell image, the cell-level EL intensity distribution, \(p_{EL-Cell}(k,i)\), is calculated [49], where \(k\) is the solar cell number, and \(i\) is the intensity level (gray level occurrences) at a particular pixel position in a solar cell image. \(L\) is the maximum intensity level (256), while \(N_{c}\) is the number of solar cells in a module. \(n_{i}^{k}\) is the number of occurrences of gray level \(i\) in the cell \(k\), while \(n^{k}\) is the total number of pixels in the image of the cell \(k\).  
From the cell-level image, distribution parameters are calculated for each solar cell, such as STD, mean, median, skewness, kurtosis, as defined in Table 11.
# Generative Adversarial Network for Image Super-Resolution Combining Texture Loss
## 5 Conclusions  
Based on the generative adversarial network framework, we have described a super-resolution model TSRGAN. We have designed the method of removing BN layers and introducing residual dense blocks to deepen the structure of generator network. In addition, we have used WGAN-GP to improve adversarial loss to provide stronger and more effective supervision for model training. Moreover, we have enhanced perceptual loss by using the features before activation layer, which offer stronger supervision and thus restore more accurate brightness and realistic textures. Finally, we have cited texture loss which encourages to match local texture details to achieve better outcomes. The experimental results show that our method makes the average PSNR of reconstructed images reach 27.99 dB and the average SSIM reach 0.778 without losing too much speed, which is superior to other comparison algorithms in objective evaluation index. TSRGAN has significantly improved subjective visual evaluations such as brightness information and texture details, this further proves that our algorithm can reconstruct more realistic images. In future research work, we will consider super-resolution reconstruction of images in specific fields or scenes to improve the quality of image generation.  
Writing--original draft, Y.J.; Writing--review & editing, Y.J. and J.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Key Research and Development Plan - Major Scientific and Technological Innovation Projects of ShanDong Province (2019 JZZY020101). This study is undertaken within the framework of SRGAN. Furthermore, the authors wish to thank [PERSON] and [PERSON] for their helpful comments and encouragement for many aspects of the paper, and we wish to thank [PERSON] for helping with the ISRR experimental environment support. We declare that we have no conflict of interest.  
Figure 6: Reconstruction effects from the selected algorithms on Set14 dataset.
# Advances in the Noninvasive Diagnosis of Dry Eye Disease
## 11 Image Modality Based Computerized Detection Techniques  
Through recent advances in AI and the rapid progression of analytic techniques, researchers are trying to overcome the main pitfalls of clinical testing, such as the long time required for acquisition and the need for skillful maneuvers [105]. The aim of the computerization of clinical tests is to help clinicians to increase accuracy and reduce the time taken to form a diagnosis.  
In the last decade, several computerized DED detection techniques have been proposed, each based on different technologies. The first attempt to automatically detect DED was undertaken by [PERSON] et al., who used a multi-step algorithm to evaluate BUT from an eye video that was captured using an Eyescan device [106]. [PERSON] et al. proposed an automatic methodology for characterizing tear film dynamics over the exposed corneal surface from the emergence of the first break-up in the tear film until the subsequent blink. To this end, the BUT measurement was computed, and the break-up areas were segmented in each video frame to analyze other break-up parameters, such as the size or spatial extension of the BUT [107]. [PERSON] et al. proposed an automatic method to detect the BUT area using a deep learning convolutional neural network (CNN) model, a hierarchical multilayered neural network that can learn visual patterns directly from image pixels [108; 109]. The proposed CNN model detected break-up with an accuracy rate of 98% [110].  
Two techniques based on the interference pattern of the lipid layer have been proposed. The first is a computer-aided system to support DED diagnosis based on tear film maps (CASDES), and the second is called iDEAS. The former system, which analyzes the images acquired by Tearscope, allows the recognition of dry area regions in agreement with the region annotated manually by the ophthalmologist [111]. The latter is a web-based system for DED assessment, in which, once the patient's interference pattern images are uploaded, a support vector machine classifier based on statistical learning theory allocates the images into five categories based on Guillon classification [112; 113].  
[PERSON] et al. developed a technique based on thermal infrared images. The result produced by this technique is binary; that is, input images of either normal eye or dry eye are converted into 1D data and then fed into different classifiers, such as k-Nearest Neighbor (KNN), decision tree, Probabilistic Neural Network (PNN), SVM and Naive Bayes (NB). The authors obtained an average sensitivity of 99.8%, specificity of 99.8% and classification accuracy of 99.8% using PNN and KNN classifier [114].
# Actuating Performance Analysis of a New Smart Aggregate Using Piezoceramic Stack
## 3 Comparison and Discussion
### Discussion  
In this section, the electromechanical and displacement output performance of SAPSs is discussed by using the proposed one-dimensional model, and an experimental setup was designed to verify the proposed model. The analyses indicate that the structural parameters, including the thickness and number of the PZT disc, the diameter of the protective shell, and thickness of the copper lids, have an influence on the SAPS's electromechanical performance, while only the thickness and number of the PZT discs and the thickness of the protective shell have an obvious effect on the displacement output of SAPSs. Moreover, the elastic modulus of the epoxy has a very limited influence on both the electromechanical and displacement output performance of SAPSs and it will be helpful in the design of SAPSs.
## 3 Proposed Energy Management Strategy
### Operational Mode Control  
After ECMS idea formulation, the different operational modes of PEMFC and battery output power classifications into 0.5 kW intervals are used to reduce FC and battery power fluctuations. The proposed OMC is based on changes of the battery SOC, UC VDR, vehicle demanded power, and VSM, which tries to be used as follow:  
Figure 8: Operational modes of PEMFC based on HCR.  
Figure 7: Hydrogen consumption ratio curve.  
The main priority of EMS is the PEMFC operation at a minimum hydrogen consumption ratio.
* The battery charging mode has been done for modes in which the PEMFC operates at the minimum HCR, and the equivalent hydrogen consumption at the present mode is lower than the HCR at the next mode (\(EHCR_{Bat}^{(i)}<HCR_{i}^{(i+1)}\)).
* The battery discharging mode has been done in cases that the demanded power is higher than PEMFC output power, or the UC VDR is very low.
* During idling state, the FC with regarding SOC and VDR states can operate at 0.4 pu, and charge the battery and UC.
* If the demand power is higher than FC power, then the proposed OMC distributes demand power between HESS using results of the FLC method.
* When the vehicle is in the regenerative braking state, the FC is OFF. The battery is charged based on FLC output, and the UC captures regenerative braking energy as possible.  
Therefore, the output modes of the proposed OMC are adjusted according to Table 2, in which \(u\) is the step function, and, \(P_{FC,opt}\), \(P_{Bat,opt}\), and \(P_{UC,opt}\) is the optimal output power of the proposed EMS for FC, battery, and UC, respectively.
## 3 Experimental Apparatus and Procedure
To avoid the above problems, in the present experiment, an 18-m-long PE pipeline with two layers of anechoic tips of different lengths installed at the end was used, which completely eliminated echo and prevented inverse superposition of the sound field in the axial direction. As the source, a piston transducer was mounted on the front of the pipe through a flange, and vibration isolation material was interposed between the flange and the pipeline, thereby preventing direct excitation of the pipe wall. The wall parameters were the same as those used in the theoretical calculations (see Table 1). Five slots, each of length 1 m, in the axial direction were cut in the pipe wall at a distance of 1 m from the transducer, with a 1 m gap between them (see Figure 4). Four 8103 hydrophones were mounted on a bracket, and so the sound pressure at four different radial positions could be measured at the same time, as shown in Figure 2. The hydrophone bracket was made from polyvinyl chloride (PVC), which has characteristic impedance similar to that of water, greatly reducing the scattering of sound waves as they passed through the bracket. Three other hydrophones measured the near-field signal at 0, 0.05, 0.1, and 0.15 m from the axial center of the transducer. After the pipeline was filled with water, it was allowed to stand for more than 30 h to eliminate the effect of bubbles on the experiment. After the fluid column (water) was excited by the transducer, the hydrophones were moved along the axial slots away from the source, and recordings were taken at 10 cm intervals; thus, each slot had 9 or 10 recording points. The use of the hydrophone bracket ensured that the radial positions of the hydrophones remained unchanged when the axial position was changed. In this way, the sound pressure distributions of the fluid column along both the axial and radial directions were measured. The analysis bandwidth of the collector was set to 0-25 600 Hz, in accordance with the theoretical normal frequencies, and the corresponding sampling frequency was set to 65,536 Hz by the Nyquist law, with a sampling time of 10 s. The power spectrum of the corresponding working condition was obtained through fast Fourier transform (FFT) processing of the time-domain signal. To determine the propagation path of the sound in the experimental system and compare it with the acoustic signal of the fluid in the pipeline at the same time, the vibration of the wall was also measured in this experiment. There were three rows of vibration sensors encircling the outside of the wall, each with four sensors, with a separation of 0.33 m between each row. The deployment of the hydrophones and vibration sensors and the corresponding labels are shown in Figure 5.  
Figure 4: Slots of the pipeline.
## 5 Results and Discussion
### Structural Assessment and Proposals for Improvement  
Different alternatives are evaluated to obtain a better structural behavior of the mast, with a generic standard wind load, applied perpendicularly and uniformly along the mast axis, with a maximum displacement of 31.68 mm and a maximum stress of 4.02-10\({}^{7}\) Pa (Figure 12).  
It is decided to place two prestressed cables at the height of the midpoint of the mast, 180\({}^{\circ}\) out of phase with each other. The cables are simulated in the numerical model restricting the displacement and allowing the rotation at that point. Structural behavior varies depending on whether these cables are oriented along the x- or z-axis. Figure 13 shows the maximum displacement, for the standard wind load, of the two cable configurations, x-axis (31.68 mm) and z-axis (3.7 mm).  
It is observed that the prestressed cables in the direction of the wind load decrease the maximum displacement by 88.3%, with a negligible effect when the location of the  
Figure 12: Mast under wind loading. (**a**) Force representation, (**b**) maximum displacement (m), (**c**) maximum stress.  
Figure 13: Maximum displacement (in meters) of the mast under wind loading with cables. (**a**) In the \(x\)-direction, (**b**) in the \(z\)-direction, (**c**) drawing of the mast with two cables at 180\({}^{\circ}\).  
prestressed cables is perpendicular to the load direction. Therefore, it is recommended to use prestressed cables with a 90\({}^{\circ}\) offset in order to cover all possible load directions. On the other hand, the influence of the arrangement of brackets at the base of the mast, which are welded between it and the bridge top structure, is studied. The objective is the same as with the prestressed cables, but by means of a welding between elements that favors the transit of forces and moments on the local reinforcement. Figure 14 shows displacement for the standard wind load with four triangular brackets' (200 \(\times\) 200 mm) influence, 90\({}^{\circ}\) out of phase.  
An improvement, in terms of displacement, of 5.55% is observed after the arrangement of four brackets at the base of the mast, a smaller improvement than the cable arrangement but sufficient depending on the structural requirements, with a minimum impact on the bridge top and improving the transmission of loads on the local reinforcement.  
As a summary of this section, the different improvements tested that are applicable to other type of masts, i.e., radar masts, compass decks, or guyed masts in other vessels, are:  
- Mass distribution (tapered sections).  
- Cables at 180\({}^{\circ}\).  
- Brackets at the base.  
- The structural behavior, in terms of obtaining representative eigenmodes, for the constant cross-section (up to the sixth natural frequency) and the variable cross-section.  
- Structural design tools, as regression models between eigenfrequencies for each of the mast geometries, that relate the behavior of different masts.
## 3 Results
### Binary Classifiers and Hyperparameter Tuning  
As mentioned in Section 2.3, we constructed a binary classifier for each pair of labels considered here, namely, (Green, Yellow), (Yellow, Red) and (Red, Black). Each of these classifiers was tuned separately, and we optimized the following hyperparameters:  
* max_depth: This is the maximum allowed depth of each individual Decision Tree; too large or too small values can lead to overfitting or underfitting, respectively [48].
* n_estimators: This is the number of individual Decision Trees used in Gradient Boosting.
* min_samples_leaf: This is the minimum number of samples that must remain in an end node (leaf) of each individual tree.
* learning_rate: This controls the contribution of each individual tree, as shown in Algorithm 1. If the value is too large, the algorithm might overfit; however, a lower learning rate has the trade-off that more trees are required to reach the desired accuracy.  
Table 2 presents the tuning range of each hyperparameter as well as the optimal value for each of the three classifiers considered here.  
Having obtained the optimal hyperparameter configuration, we trained and tested our three models using five-fold cross-validation [49]. In this framework, the dataset is split into five parts and each part is iteratively used as test set, while the remaining parts are used for training. This ensures that the model's predictions are always on unseen data and reduces the sensitivity/variability of the obtained performance metrics. The performance was measured using the well known classification metrics of Precision, _Recall_, F1-score, Accuracy, and Area Under the Curve (AUC) [50], with the results shown in Table 3. The results clearly show that the classifiers achieved high performance, especially for the most critical pairs, i.e., (Red, Black) and (Yellow, Red). The accuracy with which the model was able to distinguish between these two categories increases the reliability of the feature importance analysis, which is the main focus of the study.
# An Empirical Performance Analysis of the Speak Correct Computerized Interface
## 3 Speak Correct Computerized Interface
### The Speak Correct Tool Architecture  
The Speak Correct system is depicted in Figure 2 as a block diagram. It detects pronunciation problems in users' speech using a state-of-the-art speech detection method based on the Hidden Markov Model (HMM). The following Table 1 explains the main components:  
Figure 2: The Speak Correct system architecture.  
The tool has two main functions. It firstly analyzes a mispronounced statement, even if it is spoken incorrectly, and then it uncovers the speaker's pronunciation faults at the phoneme stage. The Automatic Speech Recognizer (ASR) as well as the Pronunciation Analyzer (PA) are two modules of the system that perform these two functions. The ASR's job is to record the user's words in the system, whereas the pronunciation analyzer analyzes the ASR's output to determine if the pronunciation is correct or not, as well as to identify prototypically aberrant phonemes (i.e., finding on what part of the utterance the feedback should be focused).  
Only the possible pronunciation alternatives that encompass frequent forms of pronunciation problems are analyzed to improve the interface's effectiveness. A method for automatically generating pronunciation hypotheses is employed [28; 29; 30]. The pronunciation assumptions are reached using this method, which employs matching criteria to recognize pronunciation patterns and generate potential pronunciation mistakes. The Speak Correct interface adaptor and the confidence score module are thoroughly described in the following sections.
## 2 Materials and Methods
### The Required Equations for Creating the Excel Spreadsheet  
Using the measured data, the ANN model was trained. Working field criterion (WFC) (dimensionless), soil texture norm (STN) (dimensionless), initial soil moisture content (MC) (%db), and horizontal force were determined by Equation (1) using the new--Fi-parameter (DD) (kN), which acted as inputs. The output was the modified horizontal force (MHF) for a tillage implement like chisel plow, moldboard plow, or disk plow. The three network layers, four input nodes, one output node, one hidden layer with twenty nodes, sigmoid transfer function, learn rate of 0.002854, momentum of 0.8, and iteration of 600,000 were some of the characteristics of the ANN used in the current study. For calculating the MHF, each input and equation, which were used to compute the normalized value of each input,  
Figure 2: The best ANN structure (4-20-1) for predicting the modified horizontal force (MHF) of tillage implements. were normalized. The maximum and minimum values for the inputs are shown in Table 4. However, the normalized values of the inputs are as follows:  
\[\text{WFC}_{\text{N}}=\frac{(\text{WFC}_{\text{A}}-\text{WFC}_{\text{min}}) \times 0.7}{(\text{WFC}_{\text{max}}-\text{WFC}_{\text{min}})}+0.15 \tag{12}\]  
\[\text{STN}_{\text{N}}=\frac{(\text{STN}_{\text{A}}-\text{STN}_{\text{min}}) \times 0.7}{(\text{STN}_{\text{max}}-\text{STN}_{\text{min}})}+0.15 \tag{13}\]  
\[\text{MC}_{\text{N}}=\frac{(\text{MC}_{\text{A}}-\text{MC}_{\text{min}})\times 0.7}{(\text{MC}_{\text{max}}-\text{MC}_{\text{min}})}+0.15 \tag{14}\]  
\[\text{DD}_{\text{N}}=\frac{(\text{DD}_{\text{A}}-\text{DD}_{\text{min}})\times 0.7}{(\text{DD}_{\text{max}}-\text{DD}_{\text{min}})}+0.15 \tag{15}\]  
\(\text{WFC}_{\text{N}}\), \(\text{WFC}_{\text{max}}\), \(\text{WFC}_{\text{min}}\), and \(\text{WFC}_{\text{A}}\) are the normalized, maximum, minimum, and measured values of working field criterion, respectively. \(\text{STN}_{\text{N}}\), \(\text{STN}_{\text{max}}\), \(\text{STN}_{\text{min}}\), and \(\text{STN}_{\text{A}}\) are the normalized, maximum, minimum, and measured values of the soil moisture norm, respectively. \(\text{MC}_{\text{N}}\), \(\text{MC}_{\text{max}}\), \(\text{MC}_{\text{min}}\), and \(\text{MC}_{\text{A}}\) are the normalized, maximum, minimum, and measured values of the initial soil moisture content, respectively. \(\text{DD}_{\text{N}}\), \(\text{DD}_{\text{max}}\), \(\text{DD}_{\text{min}}\), \(\text{DD}_{\text{A}}\) are the normalized, maximum, minimum, and measured values of the horizontal force, as determined by Equation (1) when using the new--Fi -parameter, respectively. Next, the summation equations as indicated in Equation (7) were computed for the modified horizontal force with the connection weight values obtained from the trained ANN model (Table 5).
## 2 Data Description and Preprocessing
### Arching Characteristics  
The inspection data of arched and non-arched segments are contrastively analyzed combined with the maintenance information. It can be found that the arching characteristics are mainly reflected in the longitudinal level irregularity (LLI). Figure 6 shows several arching segments of 15 July 2017, taking the data of 8 January 2017 as reference. \(A\) is the semi-crest amplitude, \(\delta\) is the amplitude difference between different months, and \(\lambda\) is the wavelength. We can observe that different arched segments present various waveforms. Although the waveforms of different months in the specific segment are roughly similar, \(\delta\) in the arched segment increases compared to the non-arched segment. By statistics, in the arched segments, the maximum value of \(A\) exceeds 3 mm, and the value of \(\delta\) is generally greater than 1 mm. Most values of \(\lambda\) are close to the length of the track slab (6.45 m), and the maximum is less than 10 m.